experimental control biology

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Biology archive

Course: biology archive > unit 1.

The scientific method

Controlled experiments

The scientific method and experimental design

Introduction

How are hypotheses tested.

One pot of seeds gets watered every afternoon.
The other pot of seeds doesn't get any water at all.

Control and experimental groups

Independent and dependent variables, independent variables, dependent variables, variability and repetition, controlled experiment case study: co 2 ‍ and coral bleaching.

What your control and experimental groups would be
What your independent and dependent variables would be
What results you would predict in each group

Experimental setup

Some corals were grown in tanks of normal seawater, which is not very acidic ( pH ‍ around 8.2 ‍ ). The corals in these tanks served as the control group .
Other corals were grown in tanks of seawater that were more acidic than usual due to addition of CO 2 ‍ . One set of tanks was medium-acidity ( pH ‍ about 7.9 ‍ ), while another set was high-acidity ( pH ‍ about 7.65 ‍ ). Both the medium-acidity and high-acidity groups were experimental groups .
In this experiment, the independent variable was the acidity ( pH ‍ ) of the seawater. The dependent variable was the degree of bleaching of the corals.
The researchers used a large sample size and repeated their experiment. Each tank held 5 ‍ fragments of coral, and there were 5 ‍ identical tanks for each group (control, medium-acidity, and high-acidity). Note: None of these tanks was "acidic" on an absolute scale. That is, the pH ‍ values were all above the neutral pH ‍ of 7.0 ‍ . However, the two groups of experimental tanks were moderately and highly acidic to the corals , that is, relative to their natural habitat of plain seawater.

Analyzing the results

Non-experimental hypothesis tests, case study: coral bleaching and temperature, attribution:, works cited:.

Hoegh-Guldberg, O. (1999). Climate change, coral bleaching, and the future of the world's coral reefs. Mar. Freshwater Res. , 50 , 839-866. Retrieved from www.reef.edu.au/climate/Hoegh-Guldberg%201999.pdf.
Anthony, K. R. N., Kline, D. I., Diaz-Pulido, G., Dove, S., and Hoegh-Guldberg, O. (2008). Ocean acidification causes bleaching and productivity loss in coral reef builders. PNAS , 105 (45), 17442-17446. http://dx.doi.org/10.1073/pnas.0804478105 .
University of California Museum of Paleontology. (2016). Misconceptions about science. In Understanding science . Retrieved from http://undsci.berkeley.edu/teaching/misconceptions.php .
Hoegh-Guldberg, O. and Smith, G. J. (1989). The effect of sudden changes in temperature, light and salinity on the density and export of zooxanthellae from the reef corals Stylophora pistillata (Esper, 1797) and Seriatopora hystrix (Dana, 1846). J. Exp. Mar. Biol. Ecol. , 129 , 279-303. Retrieved from http://www.reef.edu.au/ohg/res-pic/HG%20papers/HG%20and%20Smith%201989%20BLEACH.pdf .

Additional references:

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v.20(10); 2019 Oct 4

Why control an experiment?

John s torday.

1 Department of Pediatrics, Harbor‐UCLA Medical Center, Torrance, CA, USA

František Baluška

2 IZMB, University of Bonn, Bonn, Germany

Empirical research is based on observation and experimentation. Yet, experimental controls are essential for overcoming our sensory limits and generating reliable, unbiased and objective results.

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Object name is EMBR-20-e49110-g001.jpg

We made a deliberate decision to become scientists and not philosophers, because science offers the opportunity to test ideas using the scientific method. And once we began our formal training as scientists, the greatest challenge beyond formulating a testable or refutable hypothesis was designing appropriate controls for an experiment. In theory, this seems trivial, but in practice, it is often difficult. But where and when did this concept of controlling an experiment start? It is largely attributed to Roger Bacon, who emphasized the use of artificial experiments to provide additional evidence for observations in his Novum Organum Scientiarum in 1620. Other philosophers took up the concept of empirical research: in 1877, Charles Peirce redefined the scientific method in The Fixation of Belief as the most efficient and reliable way to prove a hypothesis. In the 1930s, Karl Popper emphasized the necessity of refuting hypotheses in The Logic of Scientific Discoveries . While these influential works do not explicitly discuss controls as an integral part of experiments, their importance for generating solid and reliable results is nonetheless implicit.

… once we began our formal training as scientists, the greatest challenge beyond formulating a testable or refutable hypothesis was designing appropriate controls for an experiment.

But the scientific method based on experimentation and observation has come under criticism of late in light of the ever more complex problems faced in physics and biology. Chris Anderson, the editor of Wired Magazine, proposed that we should turn to statistical analysis, machine learning, and pattern recognition instead of creating and testing hypotheses, based on the Informatics credo that if you cannot answer the question, you need more data. However, this attitude subsumes that we already have enough data and that we just cannot make sense of it. This assumption is in direct conflict with David Bohm's thesis that there are two “Orders”, the Explicate and Implicate 1 . The Explicate Order is the way in which our subjective sensory systems perceive the world 2 . In contrast, Bohm's Implicate Order would represent the objective reality beyond our perception. This view—that we have only a subjective understanding of reality—dates back to Galileo Galilei who, in 1623, criticized the Aristotelian concept of absolute and objective qualities of our sensory perceptions 3 and to Plato's cave allegory that reality is only what our senses allow us to see.

The only way for systematically overcoming the limits of our sensory apparatus and to get a glimpse of the Implicate Order is through the scientific method, through hypothesis‐testing, controlled experimentation. Beyond the methodology, controlling an experiment is critically important to ensure that the observed results are not just random events; they help scientists to distinguish between the “signal” and the background “noise” that are inherent in natural and living systems. For example, the detection method for the recent discovery of gravitational waves used four‐dimensional reference points to factor out the background noise of the Cosmos. Controls also help to account for errors and variability in the experimental setup and measuring tools: The negative control of an enzyme assay, for instance, tests for any unrelated background signals from the assay or measurement. In short, controls are essential for the unbiased, objective observation and measurement of the dependent variable in response to the experimental setup.

The only way for systematically overcoming the limits of our sensory apparatus […] is through the Scientific Method, through hypothesis‐testing, controlled experimentation.

Nominally, both positive and negative controls are material and procedural; that is, they control for variability of the experimental materials and the procedure itself. But beyond the practical issues to avoid procedural and material artifacts, there is an underlying philosophical question. The need for experimental controls is a subliminal recognition of the relative and subjective nature of the Explicate Order. It requires controls as “reference points” in order to transcend it, and to approximate the Implicate Order.

This is similar to Peter Rowlands’ 4 dictum that everything in the Universe adds up to zero, the universal attractor in mathematics. Prior to the introduction of zero, mathematics lacked an absolute reference point similar to a negative or positive control in an experiment. The same is true of biology, where the cell is the reference point owing to its negative entropy: It appears as an attractor for the energy of its environment. Hence, there is a need for careful controls in biology: The homeostatic balance that is inherent to life varies during the course of an experiment and therefore must be precisely controlled to distinguish noise from signal and approximate the Implicate Order of life.

P < 0.05 tacitly acknowledges the explicate order

Another example of the “subjectivity” of our perception is the level of accuracy we accept for differences between groups. For example, when we use statistical methods to determine if an observed difference between control and experimental groups is a random occurrence or a specific effect, we conventionally consider a p value of less than or equal to 5% as statistically significant; that is, there is a less than 0.05 probability that the effect is random. The efficacy of this arbitrary convention has been debated for decades; suffice to say that despite questioning the validity of that convention, a P value of < 0.05 reflects our acceptance of the subjectivity of our perception of reality.

… controls are essential for the unbiased, objective observation and measurement of the dependent variable in response to the experimental setup.

Thus, if we do away with hypothesis‐testing science in favor of informatics based on data and statistics—referring to Anderson's suggestion—it reflects our acceptance of the noise in the system. However, mere data analysis without any underlying hypothesis is tantamount to “garbage in‐garbage out”, in contrast to well‐controlled imaginative experiments to separate the wheat from the chaff. Albert Einstein was quoted as saying that imagination was more important than knowledge.

The ultimate purpose of the scientific method is to understand ourselves and our place in Nature. Conventionally, we subscribe to the Anthropic Principle, that we are “in” this Universe, whereas the Endosymbiosis Theory, advocated by Lynn Margulis, stipulates that we are “of” this Universe as a result of the assimilation of the physical environment. According to this theory, the organism endogenizes external factors to make them physiologically “useful”, such as iron as the core of the hemoglobin molecule, or ancient bacteria as mitochondria.

… there is a fundamental difference between knowing via believing and knowing based on empirical research.

By applying the developmental mechanism of cell–cell communication to phylogeny, we have revealed the interrelationships between cells and explained evolution from its origin as the unicellular state to multicellularity via cell–cell communication. The ultimate outcome of this research is that consciousness is the product of cellular processes and cell–cell communication in order to react to the environment and better anticipate future events 5 , 6 . Consciousness is an essential prerequisite for transcending the Explicate Order toward the Implicate Order via cellular sensory and cognitive systems that feed an ever‐expanding organismal knowledge about both the environment and itself.

It is here where the empirical approach to understanding nature comes in with its emphasis that knowledge comes only from sensual experience rather than innate ideas or traditions. In the context of the cell or higher systems, knowledge about the environment can only be gained by sensing and analyzing the environment. Empiricism is similar to an equation in which the variables and terms form a product, or a chemical reaction, or a biological process where the substrates, aka sensory data, form products, that is, knowledge. However, it requires another step—imagination, according to Albert Einstein—to transcend the Explicate Order in order to gain insight into the Implicate Order. Take for instance, Dmitri Ivanovich Mendeleev's Periodic Table of Elements: his brilliant insight was not just to use Atomic Number to organize it, but also to consider the chemical reactivities of the Elements by sorting them into columns. By introducing chemical reactivity to the Periodic Table, Mendeleev provided something like the “fourth wall” in Drama, which gives the audience an omniscient, god‐like perspective on what is happening on stage.

The capacity to transcend the subjective Explicate Order to approximate the objective Implicate Order is not unlike Eastern philosophies like Buddhism or Taoism, which were practiced long before the scientific method. An Indian philosopher once pointed out that the Hindus have known for 30,000 years that the Earth revolves around the sun, while the Europeans only realized this a few hundred years ago based on the work of Copernicus, Brahe, and Galileo. However, there is a fundamental difference between knowing via believing and knowing based on empirical research. A similar example is Aristotle's refusal to test whether a large stone would fall faster than a small one, as he knew the answer already 7 . Galileo eventually performed the experiment from the Leaning Tower in Pisa to demonstrate that the fall time of two objects is independent of their mass—which disproved Aristotle's theory of gravity that stipulated that objects fall at a speed proportional to their mass. Again, it demonstrates the power of empiricism and experimentation as formulated by Francis Bacon, John Locke, and others, over intuition and rationalizing.

Even if our scientific instruments provide us with objective data, we still need to apply our consciousness to evaluate and interpret such data.

Following the evolution from the unicellular state to multicellular organisms—and reverse‐engineering it to a minimal‐cell state—reveals that biologic diversity is an artifact of the Explicate Order. Indeed, the unicell seems to be the primary level of selection in the Implicate Order, as it remains proximate to the First Principles of Physiology, namely negative entropy (negentropy), chemiosmosis, and homeostasis. The first two principles are necessary for growth and proliferation, whereas the last reflects Newton's Third Law of Motion that every action has an equal and opposite reaction so as to maintain homeostasis.

All organisms interact with their surroundings and assimilate their experience as epigenetic marks. Such marks extend to the DNA of germ cells and thus change the phenotypic expression of the offspring. The offspring, in turn, interacts with the environment in response to such epigenetic modifications, giving rise to the concept of the phenotype as an agent that actively and purposefully interacts with its environment in order to adapt and survive. This concept of phenotype based on agency linked to the Explicate Order fundamentally differs from its conventional description as a mere set of biologic characteristics. Organisms’ capacities to anticipate future stress situations from past memories are obvious in simple animals such as nematodes, as well as in plants and bacteria 8 , suggesting that the subjective Explicate Order controls both organismal behavior and trans‐generational evolution.

That perspective offers insight to the nature of consciousness: not as a “mind” that is separate from a “body”, but as an endogenization of physical matter, which complies with the Laws of Nature. In other words, consciousness is the physiologic manifestation of endogenized physical surroundings, compartmentalized, and made essential for all organisms by forming the basis for their physiology. Endocytosis and endocytic/synaptic vesicles contribute to endogenization of cellular surroundings, allowing eukaryotic organisms to gain knowledge about the environment. This is true not only for neurons in brains, but also for all eukaryotic cells 5 .

Such a view of consciousness offers insight to our awareness of our physical surroundings as the basis for self‐referential self‐organization. But this is predicated on our capacity to “experiment” with our environment. The burgeoning idea that we are entering the Anthropocene, a man‐made world founded on subjective senses instead of Natural Laws, is a dangerous step away from our innate evolutionary arc. Relying on just our senses and emotions, without experimentation and controls to understand the Implicate Order behind reality, is not just an abandonment of the principles of the Enlightenment, but also endangers the planet and its diversity of life.

Acknowledgements

John Torday has been a recipient of NIH Grant HL055268. František Baluška is thankful to numerous colleagues for very stimulating discussions on topics analyzed in this article.

EMBO Reports (2019) 20 : e49110 [ Google Scholar ]

Contributor Information

John S Torday, Email: ude.alcu@yadrotj .

František Baluška, Email: ed.nnob-inu@aksulab .

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Knowledge Base

Methodology

What Is a Controlled Experiment? | Definitions & Examples

What Is a Controlled Experiment? | Definitions & Examples

Published on April 19, 2021 by Pritha Bhandari . Revised on June 22, 2023.

In experiments , researchers manipulate independent variables to test their effects on dependent variables. In a controlled experiment , all variables other than the independent variable are controlled or held constant so they don’t influence the dependent variable.

Controlling variables can involve:

holding variables at a constant or restricted level (e.g., keeping room temperature fixed).
measuring variables to statistically control for them in your analyses.
balancing variables across your experiment through randomization (e.g., using a random order of tasks).

Why does control matter in experiments, methods of control, problems with controlled experiments, other interesting articles, frequently asked questions about controlled experiments.

Control in experiments is critical for internal validity , which allows you to establish a cause-and-effect relationship between variables. Strong validity also helps you avoid research biases , particularly ones related to issues with generalizability (like sampling bias and selection bias .)

Your independent variable is the color used in advertising.
Your dependent variable is the price that participants are willing to pay for a standard fast food meal.

Extraneous variables are factors that you’re not interested in studying, but that can still influence the dependent variable. For strong internal validity, you need to remove their effects from your experiment.

Design and description of the meal,
Study environment (e.g., temperature or lighting),
Participant’s frequency of buying fast food,
Participant’s familiarity with the specific fast food brand,
Participant’s socioeconomic status.

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You can control some variables by standardizing your data collection procedures. All participants should be tested in the same environment with identical materials. Only the independent variable (e.g., ad color) should be systematically changed between groups.

Other extraneous variables can be controlled through your sampling procedures . Ideally, you’ll select a sample that’s representative of your target population by using relevant inclusion and exclusion criteria (e.g., including participants from a specific income bracket, and not including participants with color blindness).

By measuring extraneous participant variables (e.g., age or gender) that may affect your experimental results, you can also include them in later analyses.

After gathering your participants, you’ll need to place them into groups to test different independent variable treatments. The types of groups and method of assigning participants to groups will help you implement control in your experiment.

Control groups

Controlled experiments require control groups . Control groups allow you to test a comparable treatment, no treatment, or a fake treatment (e.g., a placebo to control for a placebo effect ), and compare the outcome with your experimental treatment.

You can assess whether it’s your treatment specifically that caused the outcomes, or whether time or any other treatment might have resulted in the same effects.

To test the effect of colors in advertising, each participant is placed in one of two groups:

A control group that’s presented with red advertisements for a fast food meal.
An experimental group that’s presented with green advertisements for the same fast food meal.

Random assignment

To avoid systematic differences and selection bias between the participants in your control and treatment groups, you should use random assignment .

This helps ensure that any extraneous participant variables are evenly distributed, allowing for a valid comparison between groups .

Random assignment is a hallmark of a “true experiment”—it differentiates true experiments from quasi-experiments .

Masking (blinding)

Masking in experiments means hiding condition assignment from participants or researchers—or, in a double-blind study , from both. It’s often used in clinical studies that test new treatments or drugs and is critical for avoiding several types of research bias .

Sometimes, researchers may unintentionally encourage participants to behave in ways that support their hypotheses , leading to observer bias . In other cases, cues in the study environment may signal the goal of the experiment to participants and influence their responses. These are called demand characteristics . If participants behave a particular way due to awareness of being observed (called a Hawthorne effect ), your results could be invalidated.

Using masking means that participants don’t know whether they’re in the control group or the experimental group. This helps you control biases from participants or researchers that could influence your study results.

You use an online survey form to present the advertisements to participants, and you leave the room while each participant completes the survey on the computer so that you can’t tell which condition each participant was in.

Although controlled experiments are the strongest way to test causal relationships, they also involve some challenges.

Difficult to control all variables

Especially in research with human participants, it’s impossible to hold all extraneous variables constant, because every individual has different experiences that may influence their perception, attitudes, or behaviors.

But measuring or restricting extraneous variables allows you to limit their influence or statistically control for them in your study.

Risk of low external validity

Controlled experiments have disadvantages when it comes to external validity —the extent to which your results can be generalized to broad populations and settings.

The more controlled your experiment is, the less it resembles real world contexts. That makes it harder to apply your findings outside of a controlled setting.

There’s always a tradeoff between internal and external validity . It’s important to consider your research aims when deciding whether to prioritize control or generalizability in your experiment.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

Student’s t -distribution
Normal distribution
Null and Alternative Hypotheses
Chi square tests
Confidence interval
Quartiles & Quantiles
Cluster sampling
Stratified sampling
Data cleansing
Reproducibility vs Replicability
Peer review
Prospective cohort study

Research bias

Implicit bias
Cognitive bias
Placebo effect
Hawthorne effect
Hindsight bias
Affect heuristic
Social desirability bias

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In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

A control group that receives a standard treatment, a fake treatment, or no treatment.
Random assignment of participants to ensure the groups are equivalent.

Depending on your study topic, there are various other methods of controlling variables .

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

A testable hypothesis
At least one independent variable that can be precisely manipulated
At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

How you will manipulate the variable(s)
How you will control for any potential confounding variables
How many subjects or samples will be included in the study
How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

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Microbe Notes

Controlled Experiments: Definition, Steps, Results, Uses

Controlled experiments ensure valid and reliable results by minimizing biases and controlling variables effectively.

Rigorous planning, ethical considerations, and precise data analysis are vital for successful experiment execution and meaningful conclusions.

Real-world applications demonstrate the practical impact of controlled experiments, guiding informed decision-making in diverse domains.

Controlled experiments are the systematic research method where variables are intentionally manipulated and controlled to observe the effects of a particular phenomenon. It aims to isolate and measure the impact of specific variables, ensuring a more accurate causality assessment.

Table of Contents

Interesting Science Videos

Importance of controlled experiments in various fields

Controlled experiments are significant across diverse fields, including science, psychology, economics, healthcare, and technology.

They provide a systematic approach to test hypotheses, establish cause-and-effect relationships, and validate the effectiveness of interventions or solutions.

Why Controlled Experiments Matter?

Validity and reliability of results.

Controlled experiments uphold the gold standard for scientific validity and reliability. By meticulously controlling variables and conditions, researchers can attribute observed outcomes accurately to the independent variable being tested. This precision ensures that the findings can be replicated and are trustworthy.

Minimizing Biases and Confounding Variables

One of the core benefits of controlled experiments lies in their ability to minimize biases and confounding variables. Extraneous factors that could distort results are mitigated through careful control and randomization. This enables researchers to isolate the effects of the independent variable, leading to a more accurate understanding of causality.

Achieving Causal Inference

Controlled experiments provide a strong foundation for establishing causal relationships between variables. Researchers can confidently infer causation by manipulating specific variables and observing resulting changes. The capability informs decision-making, policy formulation, and advancements across various fields.

Planning a Controlled Experiment

Formulating research questions and hypotheses.

Formulating clear research questions and hypotheses is paramount at the outset of a controlled experiment. These inquiries guide the direction of the study, defining the variables of interest and setting the stage for structured experimentation.

Well-defined questions and hypotheses contribute to focused research and facilitate meaningful data collection.

Identifying Variables and Control Groups

Identifying and defining independent, dependent, and control variables is fundamental to experimental planning.

Precise identification ensures that the experiment is designed to isolate the effect of the independent variable while controlling for other influential factors. Establishing control groups allows for meaningful comparisons and robust analysis of the experimental outcomes.

Designing Experimental Procedures and Protocols

Careful design of experimental procedures and protocols is essential for a successful controlled experiment. The step involves outlining the methodology, data collection techniques, and the sequence of activities in the experiment.

A well-designed experiment is structured to maintain consistency, control, and accuracy throughout the study, thereby enhancing the validity and credibility of the results.

Conducting a Controlled Experiment

Randomization and participant selection.

Randomization is a critical step in ensuring the fairness and validity of a controlled experiment. It involves assigning participants to different experimental conditions in a random and unbiased manner.

The selection of participants should accurately represent the target population, enhancing the results’ generalizability.

Data Collection Methods and Instruments

Selecting appropriate data collection methods and instruments is pivotal in gathering accurate and relevant data. Researchers often employ surveys, observations, interviews, or specialized tools to record and measure the variables of interest.

The chosen methods should align with the experiment’s objectives and provide reliable data for analysis.

Monitoring and Maintaining Experimental Conditions

Maintaining consistent and controlled experimental conditions throughout the study is essential. Regular monitoring helps ensure that variables remain constant and uncontaminated, reducing the risk of confounding factors.

Rigorous monitoring protocols and timely adjustments are crucial for the accuracy and reliability of the experiment.

Analysing Results and Drawing Conclusions

Data analysis techniques.

Data analysis involves employing appropriate statistical and analytical techniques to process the collected data. This step helps derive meaningful insights, identify patterns, and draw valid conclusions.

Common techniques include regression analysis, t-tests , ANOVA , and more, tailored to the research design and data type .

Interpretation of Results

Interpreting the results entails understanding the statistical outcomes and their implications for the research objectives.

Researchers analyze patterns, trends, and relationships revealed by the data analysis to infer the experiment’s impact on the variables under study. Clear and accurate interpretation is crucial for deriving actionable insights.

Implications and Potential Applications

Identifying the broader implications and potential applications of the experiment’s results is fundamental. Researchers consider how the findings can inform decision-making, policy development, or further research.

Understanding the practical implications helps bridge the gap between theoretical insights and real-world application.

Common Challenges and Solutions

Addressing ethical considerations.

Ethical challenges in controlled experiments include ensuring informed consent, protecting participants’ privacy, and minimizing harm.

Solutions involve thorough ethics reviews, transparent communication with participants, and implementing safeguards to uphold ethical standards throughout the experiment.

Dealing with Sample Size and Statistical Power

The sample size is crucial for achieving statistically significant results. Adequate sample sizes enhance the experiment’s power to detect meaningful effects accurately.

Statistical power analysis guides researchers in determining the optimal sample size for the experiment, minimizing the risk of type I and II errors .

Mitigating Unforeseen Variables

Unforeseen variables can introduce bias and affect the experiment’s validity. Researchers employ meticulous planning and robust control measures to minimize the impact of unforeseen variables.

Pre-testing and pilot studies help identify potential confounders, allowing researchers to adapt the experiment accordingly.

A controlled experiment involves meticulous planning, precise execution, and insightful analysis. Adhering to ethical standards, optimizing sample size, and adapting to unforeseen variables are key challenges that require thoughtful solutions.

Real-world applications showcase the transformative potential of controlled experiments across varied domains, emphasizing their indispensable role in evidence-based decision-making and progress.

https://www.khanacademy.org/science/biology/intro-to-biology/science-of-biology/a/experiments-and-observations
https://www.scribbr.com/methodology/controlled-experiment/
https://link.springer.com/10.1007/978-1-4899-7687-1_891
http://ai.stanford.edu/~ronnyk/GuideControlledExperiments.pdf
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6776925/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4017459/
https://www.merriam-webster.com/dictionary/controlled%20experiment

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Krisha Karki

What An Experimental Control Is And Why It’s So Important

Daniel Nelson

An experimental control is used in scientific experiments to minimize the effect of variables which are not the interest of the study. The control can be an object, population, or any other variable which a scientist would like to “control.”

You may have heard of experimental control, but what is it? Why is an experimental control important? The function of an experimental control is to hold constant the variables that an experimenter isn’t interested in measuring.

This helps scientists ensure that there have been no deviations in the environment of the experiment that could end up influencing the outcome of the experiment, besides the variable they are investigating. Let’s take a closer look at what this means.

You may have ended up here to understand why a control is important in an experiment. A control is important for an experiment because it allows the experiment to minimize the changes in all other variables except the one being tested.

To start with, it is important to define some terminology.

Terminology Of A Scientific Experiment


Negative	The negative control variable is a variable or group where no response is expected
Positive	A positive control is a group or variable that receives a treatment with a known positive result
Randomization	A randomized controlled seeks to reduce bias when testing a new treatment
Blind experiments	In blind experiments, the variable or group does not know the full amount of information about the trial to not skew results
Double-blind experiments	A double-blind group is where all parties do not know which individual is receiving the experimental treatment

Randomization is important as it allows for more non-biased results in experiments. Random numbers generators are often used both in scientific studies as well as on 지노 사이트 to make outcomes fairer.

Scientists use the scientific method to ask questions and come to conclusions about the nature of the world. After making an observation about some sort of phenomena they would like to investigate, a scientist asks what the cause of that phenomena could be. The scientist creates a hypothesis, a proposed explanation that answers the question they asked. A hypothesis doesn’t need to be correct, it just has to be testable.

The hypothesis is a prediction about what will happen during the experiment, and if the hypothesis is correct then the results of the experiment should align with the scientist’s prediction. If the results of the experiment do not align with the hypothesis, then a good scientist will take this data into consideration and form a new hypothesis that can better explain the phenomenon in question.

Independent and Dependent Variables

In order to form an effective hypothesis and do meaningful research, the researcher must define the experiment’s independent and dependent variables . The independent variable is the variable which the experimenter either manipulates or controls in an experiment to test the effects of this manipulation on the dependent variable. A dependent variable is a variable being measured to see if the manipulation has any effect.

Photo: frolicsomepl via Pixabay, CC0

For instance, if a researcher wanted to see how temperature impacts the behavior of a certain gas, the temperature they adjust would be the independent variable and the behavior of the gas the dependent variable.

Control Groups and Experimental Groups

There will frequently be two groups under observation in an experiment, the experimental group, and the control group . The control group is used to establish a baseline that the behavior of the experimental group can be compared to. If two groups of people were receiving an experimental treatment for a medical condition, one would be given the actual treatment (the experimental group) and one would typically be given a placebo or sugar pill (the control group).

Without an experimental control group, it is difficult to determine the effects of the independent variable on the dependent variable in an experiment. This is because there can always be outside factors that are influencing the behavior of the experimental group. The function of a control group is to act as a point of comparison, by attempting to ensure that the variable under examination (the impact of the medicine) is the thing responsible for creating the results of an experiment. The control group is holding other possible variables constant, such as the act of seeing a doctor and taking a pill, so only the medicine itself is being tested.

Why Are Experimental Controls So Important?

Experimental controls allow scientists to eliminate varying amounts of uncertainty in their experiments. Whenever a researcher does an experiment and wants to ensure that only the variable they are interested in changing is changing, they need to utilize experimental controls.

Experimental controls have been dubbed “controls” precisely because they allow researchers to control the variables they think might have an impact on the results of the study. If a researcher believes that some outside variables could influence the results of their research, they’ll use a control group to try and hold that thing constant and measure any possible influence it has on the results. It is important to note that there may be many different controls for an experiment, and the more complex a phenomenon under investigation is, the more controls it is likely to have.

Not only do controls establish a baseline that the results of an experiment can be compared to, they also allow researchers to correct for possible errors. If something goes wrong in the experiment, a scientist can check on the controls of the experiment to see if the error had to do with the controls. If so, they can correct this next time the experiment is done.

A Practical Example

Let’s take a look at a concrete example of experimental control. If an experimenter wanted to determine how different soil types impacted the germination period of seeds , they could set up four different pots. Each pot would be filled with a different soil type, planted with seeds, then watered and exposed to sunlight. Measurements would be taken regarding how long it took for the seeds to sprout in the different soil types.

Photo: Kaz via Pixabay, CC0

A control for this experiment might be to fill more pots with just the different types of soil and no seeds or to set aside some seeds in a pot with no soil. The goal is to try and determine that it isn’t something else other than the soil, like the nature of the seeds themselves, the amount of sun they were exposed to, or how much water they are given, that affected how quickly the seeds sprouted. The more variables a researcher controlled for, the surer they could be that it was the type of soil having an impact on the germination period.

Not All Experiments Are Controlled

“It doesn’t matter how beautiful your theory is, it doesn’t matter how smart you are. If it doesn’t agree with experiment, it’s wrong.” — Richard P. Feynman

While experimental controls are important , it is also important to remember that not all experiments are controlled. In the real world, there are going to be limitations on what variables a researcher can control for, and scientists often try to record as much data as they can during an experiment so they can compare factors and variables with one another to see if any variables they didn’t control for might have influenced the outcome. It’s still possible to draw useful data from experiments that don’t have controls, but it is much more difficult to draw meaningful conclusions based on uncontrolled data.

Though it is often impossible in the real world to control for every possible variable, experimental controls are an invaluable part of the scientific process and the more controls an experiment has the better off it is.

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The structure and function of experimental control in the life sciences.

Published online by Cambridge University Press: 01 January 2022

This article presents a new framework for the analysis of experimental control. The framework highlights different functions for experimental controls in the realization of an experiment: experimental controls that serve as tests and experimental controls that serve as probes. The approach to experimental control proposed here can illuminate the constitutive role of controls in knowledge production, and it sheds new light on the notion of exploratory experimentation. It also clarifies what can and what cannot be expected from reviewers of scientific journal articles giving feedback on experimental controls.

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I wrote this article while I was a member of the School of Historical Studies at the Institute for Advanced Study (Princeton, NJ). I am most grateful for the institute’s support of my work. Discussions with Ann-Sophie Barwich, Stuart Firestein, Stephan Güttinger, Miriam Solomon, and students and researchers at the Center for Science and Society at Columbia University (New York) and at the Consortium for the History of Science/Greater Philadelphia Philosophy Consortium (Philadelphia) helped me to develop my ideas on experimental control. I also wish to thank two anonymous referees for Philosophy of Science for their helpful comments on the penultimate version of this article.

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Volume 86, Issue 2
Jutta Schickore
DOI: https://doi.org/10.1086/701952

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Primers provide a concise introduction into an important aspect of biology highlighted by a current PLOS Biology research article.

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Controlling control—A primer in open-source experimental control systems

* E-mail: [email protected]

Affiliation Department of Chemistry, Northwestern University, Evanston, Illinois, United States of America

Christopher James Forman

Published: September 10, 2020

https://doi.org/10.1371/journal.pbio.3000858
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Biological systems are composed of countless interlocking feedback loops. Reactor control systems—such as Chi-Bio ( https://chi.bio/ ), recently published in PLOS Biology —enable biologists to drive multiple processes within living biological samples, using a single experimental framework. Consequently, the dynamic relationships between many biological variables can be explored simultaneously in situ. Similar multivariable experimental reactors are employed beyond biology in the study of active matter and non-equilibrium chemical reactions, in which physical systems are maintained far from equilibrium through the continuous introduction of energy or matter. Inexpensive state-of-the-art components enable open-source implementation of such multiparameter architectures, which represent a move away from expensive systems optimised for single measurements, towards affordable and reconfigurable multi-measurement systems. The transfer of well-understood engineering knowledge into the hands of biological and chemical specialists via open-source channels allows rapid cycles of experimental development and heralds a change in experimental capability that is driving increased theoretical and practical understanding of out-of-equilibrium systems across a wide range of scientific fields.

Citation: Forman CJ (2020) Controlling control—A primer in open-source experimental control systems. PLoS Biol 18(9): e3000858. https://doi.org/10.1371/journal.pbio.3000858

Copyright: © 2020 Christopher James Forman. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Abbreviations: APEX, Automated Parametric Explorer; DC, direct current; GFP, green fluorescent protein; I 2 C, inter-integrated circuit; LED, light emitting diode; PID, proportional-integral-derivative; SPI, serial peripheral interface; USB, universal serial bus; UV-Vis, ultraviolet to visible

Multiparameter measurement and feedback systems

Soyuz and Space X capsules allow mission specialists to reach orbit. Along similar lines, well-conceived open-source control systems and high-quality state-of-the-art components give specialists the ability to construct sophisticated experimental configurations with little to no engineering expertise, bringing domain-specific knowledge closer to the laboratory equipment design process. The demand for such capabilities has resulted in the development of core control systems into which a range of high-quality state of the art sensors, actuators, and control algorithms can be inserted with minimal effort. Instead of many separate machines optimised for single measurements—such as ultraviolet to visible (UV-Vis) light spectrometers, or microscopes—these generalised frameworks allow for simultaneous measurement and control of multiple experimental parameters, under the complete direction of the end-user. Such systems are made affordable by the diverse range of extremely cheap—yet high-quality—components available on the consumer market, from supply companies such as Mouser, RS, or Digikey.

Multiparameter experimental arrangements introduce the possibility of sophisticated feedback loops to investigate coupling between several measurable quantities. By modifying an input perturbation in response to observed system behaviour, it is possible to drive the system out of equilibrium to specific steady states and actively keep it there—like balancing a pencil on its tip. The amount of energy or matter needed to maintain such a state can be tracked, allowing characterisation of the interaction between perturbed quantities and other co-monitored quantities. Although each measurement may not be as accurate as a measurement on a dedicated system, such loss of fidelity is more than compensated for by enhanced understanding of the interactions between parameters. For example, with the Chi-Bio bioreactor, Steel and colleagues [ 1 ] made use of the sensor histidine kinase—CcaS—and its cognate response receptor—CcaR—to form an optogenetic system coupled to green fluorescent protein (GFP) expression. They were able to drive the quantity of GFP in a culture of cells so it tracked an externally defined profile. The impact of the expression of GFP on other gene circuits—coupled via cellular burden—could then be monitored at distinct wavelengths with more than sufficient accuracy and precision to understand the behaviour.

Experimental feedback loops are becoming increasingly popular in other fields such as physics and chemistry.

A beautiful example of a feedback loop experiment [ 2 ]—with relevance to biologists—contributed evidence to support the topical and beguiling idea of dissipative adaptation [ 3 , 4 ] from the field of non-equilibrium physics, in which time-varying energy input into a system can be used to select assembly processes. Control over the assembly of a wide range of objects—including living cells—was established far from equilibrium by regulating the flow of energy supplied by a laser. Such observations are clearly of direct relevance in the behaviour of biological cells which exist in external flows of matter or energy.

In an intriguing chemical example, a feedback loop enabled external selection of the morphology of gold nanoparticles, directing their evolution [ 5 ]. A pedagogical example is shown in Fig 1 , using the author’s own setup in which a dye (fluorescein) is combined with a continuous flow of water and the resulting emission is tracked to ensure the concentration stays the same.

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(A) Engineered feedback loops are extremely common and well understood for driving and monitoring physical processes. A sensor and actuator work in tandem to drive an arbitrary physical process—typically using an error-driven algorithm. (B) An example feedback loop uses spectral intensity as an input into a PID algorithm to regulate the concentration of a dye (fluorescein) in a continuous flow of water. (C) Only a narrow band of the spectrum is monitored in this setup. In principle many such bands can be monitored, allowing tracking of multiple species. (D) The emission intensity oscillates about the set-point, allowing the system to respond to impulses such as the rapid manual addition of water directly into the solution. Data acquisition was intermittent to simulate lost data. The time taken to return to the steady state behaviour after a transient impulse is known as the response time. Em, Emission; Ex, Excitation; I 2 C, inter-integrated circuit; LED, light emitting diode; PID, Proportional-Integral-Derivative.

https://doi.org/10.1371/journal.pbio.3000858.g001

The concept of a feedback loop is universal ( Fig 1A ) and could be applied at any time and length scale. The properties of the physical system dictate the specification of the necessary hardware that drives the cost of the system. Biological and chemical processes are generally, but not always, quite slow and respond on the order of seconds to hours. For a modern processor, such timescales are easy to cope with, so simple computers such as Arduinos (Arduino, Somerville, MA), Raspberry Pis (Raspberry Pi Foundation, Cambridge, UK), or Beaglebones ( Beagleboard.org Foundation, Oakland, MI) have the capacity to drive biological and chemical systems with reasonably sophisticated algorithms.

Exploring picosecond chemical process—such as photoexcitation—needs femtosecond laser pulses and substantially more expensive electronics. Consequently, re-fitting engineered bioreactors, such as Chi-Bio, for use as chemical reactors is probably more likely to work for larger macromolecular polymerisations and self-assembly reactions, which can take hours or days. Such an observation is unsurprising when you consider a cell to be, in part, a macromolecular polymerization reaction network.

To go about designing a specific reactor such as Chi-Bio, or the author’s system ( Fig 1B ), a range of concepts need to be understood, which are explained in Box 1 . Perhaps the most enticing aspect of these kinds of system is the requirement to include software as a key part of the experiment, which often incorporates a model—such as the digital twinning process described in Box 1 . Indeed, such is the scientific goal of building feedback systems: If you can understand how a physical system responds to arbitrary input, and drive it to any particular state from any other state, and you know what all its states are—both numerically and in real life—is there anything left that is learnable about that system, besides pushing it into new conditions?

Box 1. Universal control principles: A brief definition of key concepts associated with control systems

Open or closed.

Control systems operate in open or closed configuration. In closed loops, the perturbing value is adjusted in response to observed behaviour, enabling stabilisation of physical quantities—such as the temperature. Open loops passively record behaviour in response to predetermined or uncontrolled input, e.g., observing turbidity as a cell culture matures or reaction colour as reagents are introduced at unregulated rates.

Response time

Every physical system consists of a set of processes that operate at different rates. Such a system will take time to settle to a new state in response to an input perturbation. Whether open or closed loop, the time taken for the system to respond depends on which processes are perturbed.

Sampling rate

The rate at which information about the physical system is captured determines the fastest process that the control system can respond to. The sampling rate should be high enough to capture information about the fastest process of interest.

The bandwidth of the control system refers to the range of rates of processes that a control system can monitor and respond to. The wider the bandwidth, the higher the sampling rate and the faster a process that the control system can monitor and control.

Sensitivity

The more sensitive the control system, the smaller the detectable change in the physical system. Sensitivity is generally governed by the quality of the sensor and the amount of noise.

Signals that are dominated by noise can be detected by summing multiple measurements. The random noise cancels out, but the signal does not. However, control decisions must be made within a finite time, so long integration times are not always possible. Continuous accumulation of historical data is therefore extremely helpful but creates memory and processing requirements. As each new measurement arrives, it is weighed against previously collected data. Does a change in a measured quantity correspond to noise? Or is it genuinely a true change in the measured quantity of the system?

Processing gain

Additional signal processing can make up for poor signal-to-noise ratio. Some examples follow.

Proportional-integral-derivative controller

The proportional-integral-derivative (PID) controller used in Fig 1 is one example of how to maintain a steady state in a closed-loop configuration using a relatively simple algorithm. The value to apply to the actuator is calculated from the error between the desired value (the set-point) and the current sensor value. “P,” “I,” and “D” refer to 3 terms of the equation used in the calculation, which are values proportional to the error (P), the integrated sum of the error (I), and the derivative of the error (D). Often only P and I are necessary.

Advanced algorithms

In complex systems like spacecraft or automated factories, many sensors and actuators are monitored and adjusted continuously, so covariant effects must be considered in the model used to make control decisions. A process called digital twinning compares experimental and theoretical versions of a system to check performance. The better characterised a system becomes, the more likely a model can correctly predict system behaviour, yielding excellent knowledge of how to control the system.

Comparison of systems

The Chi-Bio [ 1 ] is a great example of an open-source multiparameter control system, in which a team of engineers and specialists has taken care of a host of power, control, and mechanical engineering problems, enabling biologists (and chemists) to put together a wide range of experiments involving combinations of a broad variety of components. A comparison of Chi-Bio with the author’s own home-built feedback control system for polymer chemistry, dubbed Automated Parametric Explorer (APEX), reveals some key similarities and differences that will help provide a baseline of examples at different price points for anyone thinking about entering the space of multiparameter control systems.

Chi-Bio consists of a series of predesigned circuit boards that assemble to form a mechanical infrastructure that creates a cavity to host a monitored reaction chamber in standard reaction vessels. The control and power system service a range of sensors (spectrometer, thermometer) and actuators (light emitting diodes [LEDs], laser, fluidic pumps, stirrer) enabling interactions to occur within the reaction chamber volume. Up to 8 bioreactors can be operated in parallel from the same controlling computer (a Beaglebone).

APEX is part off-the-shelf and part homemade. It is mainly a software framework for linking together a wide variety of devices that are controlled via universal serial bus (USB) and a network of Arduinos. Each Arduino controls a specific laboratory actuator (such as an LED, a motor, pneumatic solenoids) which can be distributed around an opto-fluidic setup and hooked into a single control and power framework. The assembly in Fig 1 took less than a day to set up and begin acquiring data. It is trivial to upload complex functions to each Arduino allowing low-level commands to trigger complex actuation behaviour. Sensing is performed via standard devices—such as cameras and spectrometers—which employ conventional optics. A simple potentiostat has also been integrated. Such a setup transfers laboratory control into user-written software allowing all these devices to be incorporated into a single data-driven application.

Power supply

Chi-Bio uses a standard 12 V power supply that is down-regulated to 6 V and 3.3 V. Such direct current (DC) to DC conversion is achieved with buck convertors, which are inexpensive circuits. In APEX, power supply is a standard 450 W computer power supply delivering 5 V and 12 V power rails. APEX also employs a 24 V rail that enables control over higher-power components, such as pump motors, through H-Bridge circuits. The Chi-Bio also employs a watch circuit that monitors for short circuits caused by splashes and automatically cuts power in that eventuality.

Optical system

The Chi-Bio optical input consists of a 7-LED board that generates narrow-band light signals across the visible spectrum as well as a UV LED and a 650 nm diode laser for optical density measurements. The output is monitored by an 11-channel spectrometer chip that consists of a single light-sensitive array with a checker-board pattern of 11 filters. Thus, each region of the sensor chip monitors a slightly different frequency, trading resolution for number of channels. Chi-Bio monitors laser brightness through the sample and—since the LED optical path is perpendicular to the spectrometer’s optical axis—Chi-Bio can monitor fluorescence emissions caused by one of the 8 excitation LEDs without the need for expensive dichroic mirrors and filters.

In contrast, APEX employs individual LEDs mounted on threaded 1-inch discs (SM1CP2M, Thorlabs, Newton, NJ) enabling their simplified incorporation into a standard 1-inch optical setup. A high-power broadband LED can be filtered using narrow-band filters to achieve a wide range of input signals, which can be focused using normal optics as shown in Fig 1B . Two bandpass filters and a dichroic are used to isolate emission and excitation using the same objective lens. This design helps to keep optics and fluids well separated. The signals across a range of wavelengths and integration times can be detected by a spectrometer (CCS250 from Thorlabs, Newton, NJ). To put the full-blown optical solution into contrast with Chi-Bio, the precision milled 1-inch disc alone costs around $20, a basic optical filter set starts at $1,000, and the spectrometer $2,000+. While the optical arrangements and quality of signals are no doubt superior for APEX, the cost difference is enormous. The spectrometer in Chi-Bio is a single chip that costs $9 but still provides good enough results to make excellent scientific progress. The value is in the flexibility and number of simultaneous channels. The entire Chi-Bio kit costs around $800 and measures temperature, absorbance, and fluorescence. APEX can be a fluorimeter, a microscope, a spectrometer, a potentiostat, a camera, a fluidic control system, or a machine-learning and data management centre, and it probably comes in at around $20,000 for parts—but not design labour—which is about the price of a high-end UV-Vis instrument.

Control protocols

In both cases, the same universal protocol enables communications across the devices in the system. Several low-level standards exist such as inter-integrated circuit (I 2 C) and serial peripheral interfaces (SPIs), in which one wire provides a clock to synchronize data transfer and the other wires provide one or more data channels and secondary circuit activation selection mechanisms. The I 2 C system used in Chi-Bio and APEX is an industry standard that is used to link together microchips with just 2 wires. Typically, these are primary-secondary configurations in which a single primary can control many secondary circuits.

As open-source systems employ ever more sophisticated architectures, the gaps between the knowledge of biologists, engineers, physicists, and chemists dwindle to occupy a similar realm, in which inert matter is pushed out of equilibrium to bring it one step closer to living material.

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The Difference Between Control Group and Experimental Group

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In an experiment , data from an experimental group is compared with data from a control group. These two groups should be identical in every respect except one: the difference between a control group and an experimental group is that the independent variable is changed for the experimental group, but is held constant in the control group.

Key Takeaways: Control vs. Experimental Group

The control group and experimental group are compared against each other in an experiment. The only difference between the two groups is that the independent variable is changed in the experimental group. The independent variable is "controlled", or held constant, in the control group.
A single experiment may include multiple experimental groups, which may all be compared against the control group.
The purpose of having a control is to rule out other factors which may influence the results of an experiment. Not all experiments include a control group, but those that do are called "controlled experiments."
A placebo may also be used in an experiment. A placebo isn't a substitute for a control group because subjects exposed to a placebo may experience effects from the belief they are being tested; this itself is known as the placebo effect.

What Are Is an Experimental Group in Experiment Design?

An experimental group is a test sample or the group that receives an experimental procedure. This group is exposed to changes in the independent variable being tested. The values of the independent variable and the impact on the dependent variable are recorded. An experiment may include multiple experimental groups at one time.

A control group is a group separated from the rest of the experiment such that the independent variable being tested cannot influence the results. This isolates the independent variable's effects on the experiment and can help rule out alternative explanations of the experimental results.

While all experiments have an experimental group, not all experiments require a control group. Controls are extremely useful where the experimental conditions are complex and difficult to isolate. Experiments that use control groups are called controlled experiments .

A Simple Example of a Controlled Experiment

A simple example of a controlled experiment may be used to determine whether or not plants need to be watered to live. The control group would be plants that are not watered. The experimental group would consist of plants that receive water. A clever scientist would wonder whether too much watering might kill the plants and would set up several experimental groups, each receiving a different amount of water.

Sometimes setting up a controlled experiment can be confusing. For example, a scientist may wonder whether or not a species of bacteria needs oxygen in order to live. To test this, cultures of bacteria may be left in the air, while other cultures are placed in a sealed container of nitrogen (the most common component of air) or deoxygenated air (which likely contained extra carbon dioxide). Which container is the control? Which is the experimental group?

Control Groups and Placebos

The most common type of control group is one held at ordinary conditions so it doesn't experience a changing variable. For example, If you want to explore the effect of salt on plant growth, the control group would be a set of plants not exposed to salt, while the experimental group would receive the salt treatment. If you want to test whether the duration of light exposure affects fish reproduction, the control group would be exposed to a "normal" number of hours of light, while the duration would change for the experimental group.

Experiments involving human subjects can be much more complex. If you're testing whether a drug is effective or not, for example, members of a control group may expect they will not be unaffected. To prevent skewing the results, a placebo may be used. A placebo is a substance that doesn't contain an active therapeutic agent. If a control group takes a placebo, participants don't know whether they are being treated or not, so they have the same expectations as members of the experimental group.

However, there is also the placebo effect to consider. Here, the recipient of the placebo experiences an effect or improvement because she believes there should be an effect. Another concern with a placebo is that it's not always easy to formulate one that truly free of active ingredients. For example, if a sugar pill is given as a placebo, there's a chance the sugar will affect the outcome of the experiment.

Positive and Negative Controls

Positive and negative controls are two other types of control groups:

Positive control groups are control groups in which the conditions guarantee a positive result. Positive control groups are effective to show the experiment is functioning as planned.
Negative control groups are control groups in which conditions produce a negative outcome. Negative control groups help identify outside influences which may be present that were not unaccounted for, such as contaminants.
Bailey, R. A. (2008). Design of Comparative Experiments . Cambridge University Press. ISBN 978-0-521-68357-9.
Chaplin, S. (2006). "The placebo response: an important part of treatment". Prescriber : 16–22. doi: 10.1002/psb.344
Hinkelmann, Klaus; Kempthorne, Oscar (2008). Design and Analysis of Experiments, Volume I: Introduction to Experimental Design (2nd ed.). Wiley. ISBN 978-0-471-72756-9.
Difference Between Independent and Dependent Variables
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What Is the Difference Between a Control Variable and Control Group?
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What Is an Experiment? Definition and Design
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What Is a Double Blind Experiment?
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Control Group Definition and Examples

The control group is the set of subjects that does not receive the treatment in a study. In other words, it is the group where the independent variable is held constant. This is important because the control group is a baseline for measuring the effects of a treatment in an experiment or study. A controlled experiment is one which includes one or more control groups.

The experimental group experiences a treatment or change in the independent variable. In contrast, the independent variable is constant in the control group.
A control group is important because it allows meaningful comparison. The researcher compares the experimental group to it to assess whether or not there is a relationship between the independent and dependent variable and the magnitude of the effect.
There are different types of control groups. A controlled experiment has one more control group.

Control Group vs Experimental Group

The only difference between the control group and experimental group is that subjects in the experimental group receive the treatment being studied, while participants in the control group do not. Otherwise, all other variables between the two groups are the same.

Control Group vs Control Variable

A control group is not the same thing as a control variable. A control variable or controlled variable is any factor that is held constant during an experiment. Examples of common control variables include temperature, duration, and sample size. The control variables are the same for both the control and experimental groups.

Types of Control Groups

There are different types of control groups:

Placebo group : A placebo group receives a placebo , which is a fake treatment that resembles the treatment in every respect except for the active ingredient. Both the placebo and treatment may contain inactive ingredients that produce side effects. Without a placebo group, these effects might be attributed to the treatment.
Positive control group : A positive control group has conditions that guarantee a positive test result. The positive control group demonstrates an experiment is capable of producing a positive result. Positive controls help researchers identify problems with an experiment.
Negative control group : A negative control group consists of subjects that are not exposed to a treatment. For example, in an experiment looking at the effect of fertilizer on plant growth, the negative control group receives no fertilizer.
Natural control group : A natural control group usually is a set of subjects who naturally differ from the experimental group. For example, if you compare the effects of a treatment on women who have had children, the natural control group includes women who have not had children. Non-smokers are a natural control group in comparison to smokers.
Randomized control group : The subjects in a randomized control group are randomly selected from a larger pool of subjects. Often, subjects are randomly assigned to either the control or experimental group. Randomization reduces bias in an experiment. There are different methods of randomly assigning test subjects.

Control Group Examples

Here are some examples of different control groups in action:

Negative Control and Placebo Group

For example, consider a study of a new cancer drug. The experimental group receives the drug. The placebo group receives a placebo, which contains the same ingredients as the drug formulation, minus the active ingredient. The negative control group receives no treatment. The reason for including the negative group is because the placebo group experiences some level of placebo effect, which is a response to experiencing some form of false treatment.

Positive and Negative Controls

For example, consider an experiment looking at whether a new drug kills bacteria. The experimental group exposes bacterial cultures to the drug. If the group survives, the drug is ineffective. If the group dies, the drug is effective.

The positive control group has a culture of bacteria that carry a drug resistance gene. If the bacteria survive drug exposure (as intended), then it shows the growth medium and conditions allow bacterial growth. If the positive control group dies, it indicates a problem with the experimental conditions. A negative control group of bacteria lacking drug resistance should die. If the negative control group survives, something is wrong with the experimental conditions.

Bailey, R. A. (2008). Design of Comparative Experiments . Cambridge University Press. ISBN 978-0-521-68357-9.
Chaplin, S. (2006). “The placebo response: an important part of treatment”. Prescriber . 17 (5): 16–22. doi: 10.1002/psb.344
Hinkelmann, Klaus; Kempthorne, Oscar (2008). Design and Analysis of Experiments, Volume I: Introduction to Experimental Design (2nd ed.). Wiley. ISBN 978-0-471-72756-9.
Pithon, M.M. (2013). “Importance of the control group in scientific research.” Dental Press J Orthod . 18 (6):13-14. doi: 10.1590/s2176-94512013000600003
Stigler, Stephen M. (1992). “A Historical View of Statistical Concepts in Psychology and Educational Research”. American Journal of Education . 101 (1): 60–70. doi: 10.1086/444032

Positive Control vs Negative Control: Differences & Examples

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Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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positive control vs negative control, explained below

A positive control is designed to confirm a known response in an experimental design , while a negative control ensures there’s no effect, serving as a baseline for comparison.

The two terms are defined as below:

Positive control refers to a group in an experiment that receives a procedure or treatment known to produce a positive result. It serves the purpose of affirming the experiment’s capability to produce a positive outcome.
Negative control refers to a group that does not receive the procedure or treatment and is expected not to yield a positive result. Its role is to ensure that a positive result in the experiment is due to the treatment or procedure.

The experimental group is then compared to these control groups, which can help demonstrate efficacy of the experimental treatment in comparison to the positive and negative controls.

Positive Control vs Negative Control: Key Terms

Control groups.

A control group serves as a benchmark in an experiment. Typically, it is a subset of participants, subjects, or samples that do not receive the experimental treatment (as in negative control).

This could mean assigning a placebo to a human subject or leaving a sample unaltered in chemical experiments. By comparing the results obtained from the experimental group to the control, you can ascertain whether any differences are due to the treatment or random variability.

A well-configured experimental control is critical for drawing valid conclusions from an experiment. Correct use of control groups permits specificity of findings, ensuring the integrity of experimental data.

See More: Control Variables Examples

The Negative Control

Negative control is a group or condition in an experiment that ought to show no effect from the treatment.

It is useful in ensuring that the outcome isn’t accidental or influenced by an external cause. Imagine a medical test, for instance. You use distilled water, anticipating no reaction, as a negative control.

If a significant result occurs, it warns you of a possible contamination or malfunction during the testing. Failure of negative controls to stay ‘negative’ risks misinterpretation of the experiment’s result, and could undermine the validity of the findings.

The Positive Control

A positive control, on the other hand, affirms an experiment’s functionality by demonstrating a known reaction.

This might be a group or condition where the expected output is known to occur, which you include to ensure that the experiment can produce positive results when they are present. For instance, in testing an antibiotic, a well-known pathogen, susceptible to the medicine, could be the positive control.

Positive controls affirm that under appropriate conditions your experiment can produce a result. Without this reference, experiments could fail to detect true positive results, leading to false negatives. These two controls, used judiciously, are backbones of effective experimental practice.

Experimental Groups

Experimental groups are primarily characterized by their exposure to the examined variable.

That is, these are the test subjects that receive the treatment or intervention under investigation. The performance of the experimental group is then compared against the well-established markers – our positive and negative controls.

For example, an experimental group may consist of rats undergoing a pharmaceutical testing regime, or students learning under a new educational method. Fundamentally, this unit bears the brunt of the investigation and their response powers the outcomes.

However, without positive and negative controls, gauging the results of the experimental group could become erratic. Both control groups exist to highlight what outcomes are expected with and without the application of the variable in question. By comparing results, a clearer connection between the experiment variables and the observed changes surfaces, creating robust and indicative scientific conclusions.

Positive and Negative Control Examples

1. a comparative study of old and new pesticides’ effectiveness.

This hypothetical study aims to evaluate the effectiveness of a new pesticide by comparing its pest-killing potential with old pesticides and an untreated set. The investigation involves three groups: an untouched space (negative control), another treated with an established pesticide believed to kill pests (positive control), and a third area sprayed with the new pesticide (experimental group).

Negative Control: This group consists of a plot of land infested by pests and not subjected to any pesticide treatment. It acts as the negative control. You expect no decline in pest populations in this area. Any unexpected decrease could signal external influences (i.e. confounding variables ) on the pests unrelated to pesticides, affecting the experiment’s validity.
Positive Control: Another similar plot, this time treated with a well-established pesticide known to reduce pest populations, constitutes the positive control. A significant reduction in pests in this area would affirm that the experimental conditions are conducive to detect pest-killing effects when a pesticide is applied.
Experimental Group: This group consists of the third plot impregnated with the new pesticide. Carefully monitoring the pest level in this research area against the backdrop of the control groups will reveal whether the new pesticide is effective or not. Through comparison with the other groups, any difference observed can be attributed to the new pesticide.

2. Evaluating the Effectiveness of a Newly Developed Weight Loss Pill

In this hypothetical study, the effectiveness of a newly formulated weight loss pill is scrutinized. The study involves three groups: a negative control group given a placebo with no weight-reducing effect, a positive control group provided with an approved weight loss pill known to cause a decrease in weight, and an experimental group given the newly developed pill.

Negative Control: The negative control is comprised of participants who receive a placebo with no known weight loss effect. A significant reduction in weight in this group would indicate confounding factors such as dietary changes or increased physical activity, which may invalidate the study’s results.
Positive Control: Participants in the positive control group receive an FDA-approved weight loss pill, anticipated to induce weight loss. The success of this control would prove that the experiment conditions are apt to detect the effects of weight loss pills.
Experimental Group: This group contains individuals receiving the newly developed weight loss pill. Comparing the weight change in this group against both the positive and negative control, any difference observed would offer evidence about the effectiveness of the new pill.

3. Testing the Efficiency of a New Solar Panel Design

This hypothetical study focuses on assessing the efficiency of a new solar panel design. The study involves three sets of panels: a set that is shaded to yield no solar energy (negative control), a set with traditional solar panels that are known to produce an expected level of solar energy (positive control), and a set fitted with the new solar panel design (experimental group).

Negative Control: The negative control involves a set of solar panels that are deliberately shaded, thus expecting no solar energy output. Any unexpected energy output from this group could point towards measurement errors, needed to be rectified for a valid experiment.
Positive Control: The positive control set up involves traditional solar panels known to produce a specific amount of energy. If these panels produce the expected energy, it validates that the experiment conditions are capable of measuring solar energy effectively.
Experimental Group: The experimental group features the new solar panel design. By comparing the energy output from this group against both the controls, any significant output variation would indicate the efficiency of the new design.

4. Investigating the Efficacy of a New Fertilizer on Plant Growth

This hypothetical study investigates the efficacy of a newly formulated fertilizer on plant growth. The study involves three sets of plants: a set without any fertilizer (negative control), a set treated with an established fertilizer known to promote plant growth (positive control), and a third set fed with the new fertilizer (experimental group).

Negative Control: The negative control involves a set of plants not receiving any fertilizer. Lack of significant growth in this group will confirm that any observed growth in other groups is due to the applied fertilizer rather than other uncontrolled factors.
Positive Control: The positive control involves another set of plants treated with a well-known fertilizer, expected to promote plant growth. Adequate growth in these plants will validate that the experimental conditions are suitable to detect the influence of a good fertilizer on plant growth.
Experimental Group: The experimental group consists of the plants subjected to the newly formulated fertilizer. Investigating the growth in this group against the growth in the control groups will provide ascertained evidence whether the new fertilizer is efficient or not.

5. Evaluating the Impact of a New Teaching Method on Student Performance

This hypothetical study aims to evaluate the impact of a new teaching method on students’ performance. This study involves three groups, a group of students taught through traditional methods (negative control), another group taught through an established effective teaching strategy (positive control), and one more group of students taught through the new teaching method (experimental group).

Negative Control: The negative control comprises students taught by standard teaching methods, where you expect satisfactory but not top-performing results. Any unexpected high results in this group could signal external factors such as private tutoring or independent study, which in turn may distort the experimental outcome.
Positive Control: The positive control consists of students taught by a known efficient teaching strategy. High performance in this group would prove that the experimental conditions are competent to detect the efficiency of a teaching method.
Experimental Group: This group consists of students receiving instruction via the new teaching method. By analyzing their performance against both control groups, any difference in results could be attributed to the new teaching method, determining its efficacy.

Table Summary

Aspect	Positive Control	Negative Control
	To confirm that the experiment is working properly and that results can be detected.	To ensure that there is no effect when there shouldn’t be, and to provide a baseline for comparison.
	A known effect or change.	No effect or change.
	Used to demonstrate that the experimental setup can produce a positive result.	Used to demonstrate that any observed effects are due to the experimental treatment and not other factors.
	Plants given known amounts of sunlight to ensure they grow.	Plants given no sunlight to ensure they don’t grow.
	A substrate known to be acted upon by the enzyme.	A substrate that the enzyme doesn’t act upon.
	A medium known to support bacterial growth.	A medium that doesn’t support bacterial growth (sterile medium).
	Validates that the experimental system is sensitive and can detect changes if they occur.	Validates that observed effects are due to the variable being tested and not due to external or unknown factors.
	If the positive control doesn’t produce the expected result, the experimental setup or procedure may be flawed.	If the negative control shows an effect, there may be contamination or other unexpected variables influencing the results.

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Verywell Mind - What Is a Control Group?
National Center for Biotechnology Information - PubMed Central - Control Group Design: Enhancing Rigor in Research of Mind-Body Therapies for Depression

control group , the standard to which comparisons are made in an experiment. Many experiments are designed to include a control group and one or more experimental groups; in fact, some scholars reserve the term experiment for study designs that include a control group. Ideally, the control group and the experimental groups are identical in every way except that the experimental groups are subjected to treatments or interventions believed to have an effect on the outcome of interest while the control group is not. Inclusion of a control group greatly strengthens researchers’ ability to draw conclusions from a study. Indeed, only in the presence of a control group can a researcher determine whether a treatment under investigation truly has a significant effect on an experimental group, and the possibility of making an erroneous conclusion is reduced. See also scientific method .

A typical use of a control group is in an experiment in which the effect of a treatment is unknown and comparisons between the control group and the experimental group are used to measure the effect of the treatment. For instance, in a pharmaceutical study to determine the effectiveness of a new drug on the treatment of migraines , the experimental group will be administered the new drug and the control group will be administered a placebo (a drug that is inert, or assumed to have no effect). Each group is then given the same questionnaire and asked to rate the effectiveness of the drug in relieving symptoms . If the new drug is effective, the experimental group is expected to have a significantly better response to it than the control group. Another possible design is to include several experimental groups, each of which is given a different dosage of the new drug, plus one control group. In this design, the analyst will compare results from each of the experimental groups to the control group. This type of experiment allows the researcher to determine not only if the drug is effective but also the effectiveness of different dosages. In the absence of a control group, the researcher’s ability to draw conclusions about the new drug is greatly weakened, due to the placebo effect and other threats to validity. Comparisons between the experimental groups with different dosages can be made without including a control group, but there is no way to know if any of the dosages of the new drug are more or less effective than the placebo.

It is important that every aspect of the experimental environment be as alike as possible for all subjects in the experiment. If conditions are different for the experimental and control groups, it is impossible to know whether differences between groups are actually due to the difference in treatments or to the difference in environment. For example, in the new migraine drug study, it would be a poor study design to administer the questionnaire to the experimental group in a hospital setting while asking the control group to complete it at home. Such a study could lead to a misleading conclusion, because differences in responses between the experimental and control groups could have been due to the effect of the drug or could have been due to the conditions under which the data were collected. For instance, perhaps the experimental group received better instructions or was more motivated by being in the hospital setting to give accurate responses than the control group.

In non-laboratory and nonclinical experiments, such as field experiments in ecology or economics , even well-designed experiments are subject to numerous and complex variables that cannot always be managed across the control group and experimental groups. Randomization, in which individuals or groups of individuals are randomly assigned to the treatment and control groups, is an important tool to eliminate selection bias and can aid in disentangling the effects of the experimental treatment from other confounding factors. Appropriate sample sizes are also important.

A control group study can be managed in two different ways. In a single-blind study, the researcher will know whether a particular subject is in the control group, but the subject will not know. In a double-blind study , neither the subject nor the researcher will know which treatment the subject is receiving. In many cases, a double-blind study is preferable to a single-blind study, since the researcher cannot inadvertently affect the results or their interpretation by treating a control subject differently from an experimental subject.

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Experimental Group

Reviewed by: BD Editors

Experimental Group Definition

In a comparative experiment, the experimental group (aka the treatment group) is the group being tested for a reaction to a change in the variable. There may be experimental groups in a study, each testing a different level or amount of the variable. The other type of group, the control group , can show the effects of the variable by having a set amount, or none, of the variable. The experimental groups vary in the level of variable they are exposed to, which shows the effects of various levels of a variable on similar organisms.

In biological experiments, the subjects being studied are often living organisms. In such cases, it is desirable that all the subjects be closely related, in order to reduce the amount of genetic variation present in the experiment. The complicated interactions between genetics and the environment can cause very peculiar results when exposed to the same variable. If the organisms being tested are not related, the results could be the effects of the genetics and not the variable. This is why new human drugs must be rigorously tested in a variety of animals before they can be tested on humans. These different experimental groups allow researchers to see the effects of their drug on different genetics. By using animals that are closer and closer in their relation to humans, eventually human trials can take place without severe risks for the first people to try the drug.

Examples of Experimental Group

A simple experiment.

A student is conducting an experiment on the effects music has on growing plants. The student wants to know if music can help plants grow and, if so, which type of music the plants prefer. The students divide a group of plants in to two main groups, the control group and the experimental group. The control group will be kept in a room with no music, while the experimental group will be further divided into smaller experimental groups. Each of the experimental groups is placed in a separate room, with a different type of music.

Ideally, each room would have many plants in it, and all the plants used in the experiment would be clones of the same plant. Even more ideally, the plant would breed true, or would be homozygous for all genes. This would introduce the smallest amount of genetic variation into the experiment. By limiting all other variables, such as the temperature and humidity, the experiment can determine with validity that the effects produced in each room are attributable to the music, and nothing else.

Bugs in the River

To study the effects of variable on many organisms at once, scientist sometimes study ecosystems as a whole. The productivity of these ecosystems is often determined by the amount of oxygen they produce, which is an indication of how much algae is present. Ecologists sometimes study the interactions of organisms on these environments by excluding or adding organisms to an experimental group of ecosystems, and test the effects of their variable against ecosystems with no tampering. This method can sometimes show the drastic effects that various organisms have on an ecosystem.

Many experiments of this kind take place, and a common theme is to separate a single ecosystem into parts, with artificial divisions. Thus, a river could be separated by netting it into areas with and without bugs. The area with no nets allows bugs into the water. The bugs not only eat algae, but die and provide nutrients for the algae to grow. Without the bugs, various effects can be seen on the experimental portion of the river, covered by netting. The levels of oxygen in the water in each system can be measured, as well as other indicators of water quality. By comparing these groups, ecologists can begin to discern the complex relationships between populations of organisms in the environment.

Related Biology Terms

Control Group – The group that remains unchanged during the experiment, to provide comparison.
Scientific Method – The process scientists use to obtain valid, repeatable results.
Comparative Experiment – An experiment in which two groups, the control and experiment groups, are compared.
Validity – A measure of whether an experiment was caused by the changes in the variable, or simply the forces of chance.

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Independent/Dependent Variables

Definitions of Control, Constant, Independent and Dependent Variables in a Science Experiment

Why Should You Only Test for One Variable at a Time in an Experiment?

The point of an experiment is to help define the cause and effect relationships between components of a natural process or reaction. The factors that can change value during an experiment or between experiments, such as water temperature, are called scientific variables, while those that stay the same, such as acceleration due to gravity at a certain location, are called constants.

The scientific method includes three main types of variables: constants, independent, and dependent variables. In a science experiment, each of these variables define a different measured or constrained aspect of the system.

Constant Variables

Experimental constants are values that should not change either during or between experiments. Many natural forces and properties, such as the speed of light and the atomic weight of gold, are experimental constants. In some cases, a property can be considered constant for the purposes of an experiment even though it technically could change under certain circumstances. The boiling point of water changes with altitude and acceleration due to gravity decreases with distance from the earth, but for experiments in one location these can also be considered constants.

Sometimes also called a controlled variable. A constant is a variable that could change, but that the experimenter intentionally keeps constant in order to more clearly isolate the relationship between the independent variable and the dependent variable.

If extraneous variables are not properly constrained, they are referred to as confounding variables, as they interfere with the interpretation of the results of the experiment.

Some examples of control variables might be found with an experiment examining the relationship between the amount of sunlight plants receive (independent variable) and subsequent plant growth (dependent variable). The experiment should control the amount of water the plants receive and when, what type of soil they are planted in, the type of plant, and as many other different variables as possible. This way, only the amount of light is being changed between trials, and the outcome of the experiment can be directly applied to understanding only this relationship.

Independent Variable

The independent variable in an experiment is the variable whose value the scientist systematically changes in order to see what effect the changes have. A well-designed experiment has only one independent variable in order to maintain a fair test. If the experimenter were to change two or more variables, it would be harder to explain what caused the changes in the experimental results. For example, someone trying to find how quickly water boils could alter the volume of water or the heating temperature, but not both.

Dependent Variable

A dependent variable – sometimes called a responding variable – is what the experimenter observes to find the effect of systematically varying the independent variable. While an experiment may have multiple dependent variables, it is often wisest to focus the experiment on one dependent variable so that the relationship between it and the independent variable can be clearly isolated. For example, an experiment could examine how much sugar can dissolve in a set volume of water at various temperatures. The experimenter systematically alters temperature (independent variable) to see its effect on the quantity of dissolved sugar (dependent variable).

Control Groups

In some experiment designs, there might be one effect or manipulated variable that is being measured. Sometimes there might be one collection of measurements or subjects completely separated from this variable called the control group. These control groups are held as a standard to measure the results of a scientific experiment.

An example of such a situation might be a study regarding the effectiveness of a certain medication. There might be multiple experimental groups that receive the medication in varying doses and applications, and there would likely be a control group that does not receive the medication at all.

Representing Results

Identifying which variables are independent, dependent, and controlled helps to collect data, perform useful experiments, and accurately communicate results. When graphing or displaying data, it is crucial to represent data accurately and understandably. Typically, the independent variable goes on the x-axis, and the dependent variable goes on the y-axis.

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Controlled Experiment

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This is when a hypothesis is scientifically tested.

In a controlled experiment, an independent variable (the cause) is systematically manipulated, and the dependent variable (the effect) is measured; any extraneous variables are controlled.

The researcher can operationalize (i.e., define) the studied variables so they can be objectively measured. The quantitative data can be analyzed to see if there is a difference between the experimental and control groups.

What is the control group?

In experiments scientists compare a control group and an experimental group that are identical in all respects, except for one difference – experimental manipulation.

Unlike the experimental group, the control group is not exposed to the independent variable under investigation and so provides a baseline against which any changes in the experimental group can be compared.

Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to experimental manipulation rather than chance.

Randomly allocating participants to independent variable groups means that all participants should have an equal chance of participating in each condition.

The principle of random allocation is to avoid bias in how the experiment is carried out and limit the effects of participant variables.

What are extraneous variables?

The researcher wants to ensure that the manipulation of the independent variable has changed the changes in the dependent variable.

Hence, all the other variables that could affect the dependent variable to change must be controlled. These other variables are called extraneous or confounding variables.

Extraneous variables should be controlled were possible, as they might be important enough to provide alternative explanations for the effects.

controlled experiment extraneous variables

In practice, it would be difficult to control all the variables in a child’s educational achievement. For example, it would be difficult to control variables that have happened in the past.

A researcher can only control the current environment of participants, such as time of day and noise levels.

Why conduct controlled experiments?

Scientists use controlled experiments because they allow for precise control of extraneous and independent variables. This allows a cause-and-effect relationship to be established.

Controlled experiments also follow a standardized step-by-step procedure. This makes it easy for another researcher to replicate the study.

Key Terminology

Experimental group.

The group being treated or otherwise manipulated for the sake of the experiment.

Control Group

They receive no treatment and are used as a comparison group.

Ecological validity

The degree to which an investigation represents real-life experiences.

Experimenter effects

These are the ways that the experimenter can accidentally influence the participant through their appearance or behavior.

Demand characteristics

The clues in an experiment lead the participants to think they know what the researcher is looking for (e.g., the experimenter’s body language).

Independent variable (IV)

The variable the experimenter manipulates (i.e., changes) – is assumed to have a direct effect on the dependent variable.

Dependent variable (DV)

Variable the experimenter measures. This is the outcome (i.e., the result) of a study.

Extraneous variables (EV)

All variables that are not independent variables but could affect the results (DV) of the experiment. Extraneous variables should be controlled where possible.

Confounding variables

Variable(s) that have affected the results (DV), apart from the IV. A confounding variable could be an extraneous variable that has not been controlled.

Random Allocation

Randomly allocating participants to independent variable conditions means that all participants should have an equal chance of participating in each condition.

Order effects

Changes in participants’ performance due to their repeating the same or similar test more than once. Examples of order effects include:

(i) practice effect: an improvement in performance on a task due to repetition, for example, because of familiarity with the task;

(ii) fatigue effect: a decrease in performance of a task due to repetition, for example, because of boredom or tiredness.

What is the control in an experiment?

In an experiment , the control is a standard or baseline group not exposed to the experimental treatment or manipulation. It serves as a comparison group to the experimental group, which does receive the treatment or manipulation.

The control group helps to account for other variables that might influence the outcome, allowing researchers to attribute differences in results more confidently to the experimental treatment.

Establishing a cause-and-effect relationship between the manipulated variable (independent variable) and the outcome (dependent variable) is critical in establishing a cause-and-effect relationship between the manipulated variable.

What is the purpose of controlling the environment when testing a hypothesis?

Controlling the environment when testing a hypothesis aims to eliminate or minimize the influence of extraneous variables. These variables other than the independent variable might affect the dependent variable, potentially confounding the results.

By controlling the environment, researchers can ensure that any observed changes in the dependent variable are likely due to the manipulation of the independent variable, not other factors.

This enhances the experiment’s validity, allowing for more accurate conclusions about cause-and-effect relationships.

It also improves the experiment’s replicability, meaning other researchers can repeat the experiment under the same conditions to verify the results.

Why are hypotheses important to controlled experiments?

Hypotheses are crucial to controlled experiments because they provide a clear focus and direction for the research. A hypothesis is a testable prediction about the relationship between variables.

It guides the design of the experiment, including what variables to manipulate (independent variables) and what outcomes to measure (dependent variables).

The experiment is then conducted to test the validity of the hypothesis. If the results align with the hypothesis, they provide evidence supporting it.

The hypothesis may be revised or rejected if the results do not align. Thus, hypotheses are central to the scientific method, driving the iterative inquiry, experimentation, and knowledge advancement process.

What is the experimental method?

The experimental method is a systematic approach in scientific research where an independent variable is manipulated to observe its effect on a dependent variable, under controlled conditions.

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control experiment

Definition of control experiment

Examples of control experiment in a sentence.

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'control experiment.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

1848, in the meaning defined above

Dictionary Entries Near control experiment

control electrode

control freak

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“Control experiment.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/control%20experiment. Accessed 20 Jun. 2024.

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Published: 17 June 2024

Catalytic Glycosylation for Minimally Protected Donors and Acceptors

Qiu-Di Dang 1 na1 ,
Yi-Hui Deng 2 , 3 na1 ,
Tian-Yu Sun 2 , 3 ,
Yao Zhang 1 ,
Xia Zhang ORCID: orcid.org/0000-0002-5073-0269 1 ,
Yun-Dong Wu ORCID: orcid.org/0000-0003-4477-7332 2 , 3 &
Dawen Niu ORCID: orcid.org/0000-0002-5114-4413 1

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Carbohydrate chemistry
Organocatalysis
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Oligosaccharides have myriad functions throughout biology. 1,2 To investigate these functions requires multi-step chemical synthesis of these structurally complex molecules. With a dense concentration of stereocentres and hydroxyl groups, oligosaccharide assembly through O -glycosylation requires simultaneous control of site-, stereo-, and chemoselectivities 3,4 . Chemists have traditionally relied on protecting group manipulations for this purpose, 5–8 adding a lot of synthetic work. Here, we report a glycosylation platform that enables selective coupling between unprotected or minimally protected donor and acceptor sugars, producing 1,2- cis - O -glycosides in a catalyst-controlled, site-selective manner. Radical-based activation 9 of allyl glycosyl sulfones forms glycosyl bromides. A designed aminoboronic acid catalysts bring this reactive intermediate close to an acceptor through a network of noncovalent hydrogen bonding and reversible covalent B–O bonding interactions, allowing precise glycosyl transfer. The site of glycosylation can be switched with different aminoboronic acid catalysts by affecting their interaction modes with substrates. The method accommodates a wide range of sugar types, amenable to preparing naturally occurring sugar chains and pentasaccharides containing 11 free hydroxyls. Experimental and computational studies provide insights into the origin of selectivity outcomes.

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Site-selective, stereocontrolled glycosylation of minimally protected sugars

Efficient and versatile formation of glycosidic bonds via catalytic strain-release glycosylation with glycosyl ortho−2,2-dimethoxycarbonylcyclopropylbenzoate donors

Direct radical functionalization of native sugars

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These authors contributed equally: Qiu-Di Dang, Yi-Hui Deng

Authors and Affiliations

State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, and School of Chemical Engineering, Sichuan University, Chengdu, China

Qiu-Di Dang, Yao Zhang, Jun Li, Xia Zhang & Dawen Niu

The Key Laboratory of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomic, School of Chemical Biology and Biotechnology, Peking University Shenzhen Graduate School, Shenzhen, China

Yi-Hui Deng, Tian-Yu Sun & Yun-Dong Wu

Institute of Molecular Chemical Biology, Shenzhen Bay Laboratory, Shenzhen, China

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Correspondence to Yun-Dong Wu or Dawen Niu .

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The file contains additional optimization results, detailed experimental procedures, compound characterization data, experimental and theoretical mechanistic results, as well as nuclear magnetic resonance spectra.

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CIF file of compound 16A (CCDC no. 2303249).

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CIF file of compound 17c-OH (CCDC no. 2303235).

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Dang, QD., Deng, YH., Sun, TY. et al. Catalytic Glycosylation for Minimally Protected Donors and Acceptors. Nature (2024). https://doi.org/10.1038/s41586-024-07695-4

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Published: 20 June 2024

Alternative splicing coupled to nonsense-mediated decay coordinates downregulation of non-neuronal genes in developing mouse neurons

Anna Zhuravskaya ORCID: orcid.org/0000-0001-9312-1212 1 ,
Karen Yap ORCID: orcid.org/0000-0002-0182-4795 1 ,
Fursham Hamid ORCID: orcid.org/0000-0002-6846-7382 1 &
Eugene V. Makeyev ORCID: orcid.org/0000-0001-6034-6896 1

Genome Biology volume 25 , Article number: 162 ( 2024 ) Cite this article

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The functional coupling between alternative pre-mRNA splicing (AS) and the mRNA quality control mechanism called nonsense-mediated decay (NMD) can modulate transcript abundance. Previous studies have identified several examples of such a regulation in developing neurons. However, the systems-level effects of AS-NMD in this context are poorly understood.

We developed an R package, factR2, which offers a comprehensive suite of AS-NMD analysis functions. Using this tool, we conducted a longitudinal analysis of gene expression in pluripotent stem cells undergoing induced neuronal differentiation. Our analysis uncovers hundreds of AS-NMD events with significant potential to regulate gene expression. Notably, this regulation is significantly overrepresented in specific functional groups of developmentally downregulated genes. Particularly strong association with gene downregulation is detected for alternative cassette exons stimulating NMD upon their inclusion into mature mRNA. By combining bioinformatic analyses with CRISPR/Cas9 genome editing and other experimental approaches we show that NMD-stimulating cassette exons regulated by the RNA-binding protein PTBP1 dampen the expression of their genes in developing neurons. We also provided evidence that the inclusion of NMD-stimulating cassette exons into mature mRNAs is temporally coordinated with NMD-independent gene repression mechanisms.

Conclusions

Our study provides an accessible workflow for the discovery and prioritization of AS-NMD targets. It further argues that the AS-NMD pathway plays a widespread role in developing neurons by facilitating the downregulation of functionally related non-neuronal genes.

Many mammalian transcripts can undergo alternative splicing (AS), incorporating distinct exons and, occasionally, introns into mature mRNAs depending on the circumstances [ 1 , 2 ]. AS plays a crucial role in the production of tissue- and cell type-specific protein isoforms [ 3 , 4 ] with numerous functionally important and evolutionarily conserved examples of this regulation identified in the nervous system [ 5 , 6 , 7 , 8 ].

Besides diversifying the proteome, AS can regulate gene expression by changing the ratio between productively spliced mRNAs that encode functional proteins and unproductively spliced isoforms eliminated by RNA quality control mechanisms [ 9 , 10 , 11 ]. mRNAs containing premature translation termination codons > 50–55 nucleotides (nt) upstream of the last exon-exon splice junction typically undergo nonsense-mediated decay (NMD) in the cytoplasm following a pioneer round of translation [ 12 , 13 , 14 ]. NMD is well known for its role in destabilizing aberrant transcripts produced because of splicing errors or mutations in cis -elements or trans -acting factors involved in splicing regulation [ 15 , 16 ].

Additionally, the scheduled production of alternatively spliced transcripts sensitive to NMD (AS-NMD) can facilitate the normal control of gene expression. For instance, several RNA-binding proteins use AS-NMD to auto- or cross-regulate their expression levels [ 17 , 18 , 19 , 20 , 21 ]. These post-transcriptional circuitries often rely on two types of alternative cassette exons (CEs) that trigger NMD when included into or skipped from mature mRNAs. We will refer to the former group as NMD-stimulating (NS-CEs; also known as “poison exons”), and the latter, NMD-repressing (NR-CEs).

AS-NMD is also known to contribute to the gene expression dynamics in development. A striking example of this regulation is provided by the large-scale upregulation of retained introns (RIs) in differentiating granulocytes and possibly other cell types, triggering NMD-mediated downregulation of functionally related genes [ 22 , 23 , 24 , 25 ]. Notably, bioinformatics analyses of tissue- and cell type-specific transcriptomes have identified NMD-regulating CEs in diverse groups of genes [ 26 , 27 ]. Yet, it is currently unknown whether specific groups of CEs, or other exonic events such as alternative donors and acceptors (ADs and AAs), might coordinate gene expression dynamics on a scale similar to that observed for RIs.

Several lines of evidence point to the possibility of such systems-level regulation in developing neurons. For example, the RNA-binding protein PTBP1 controls NMD-repressing CEs in transcripts encoding the neuron-enriched PTBP1 paralog PTBP2 and the post-synaptic proteins GABBR1 and DLG4/PSD-95 [ 28 , 29 , 30 , 31 ]. These exons are predominantly skipped in embryonic stem cells (ESCs) and neural progenitor cells (NPCs)—where PTBP1 is abundant—giving rise to NMD-sensitive splice forms. The downregulation of PTBP1 in developing neurons by the neuron-enriched microRNA miR-124 facilitates the CE inclusion, thus protecting Ptbp2, Gabbr1, and Dlg4 mRNAs from NMD [ 29 ]. PTBP1 also represses NMD-stimulating CEs and ADs in several non-neuronal genes including Bak1 , Flna , and Hps1 , limiting their expression to ESCs and NPCs [ 32 , 33 , 34 ]. The decline in PTBP1 levels in neurons promotes the inclusion of these events, rendering the mature mRNAs sensitive to NMD.

Mutations in key NMD factors are known to lead to neurological and psychiatric defects [ 35 , 36 , 37 , 38 ]. NMD is also known to control neural cell identity, fine-tune the expression of important axonal and dendritic components, and regulate the excitation/inhibition balance in mature neurons [ 39 , 40 , 41 , 42 , 43 ]. It is tempting to speculate that these diverse functions involve coordinated regulation of different groups of AS-NMD events in a differentiation stage-specific manner.

However, understanding the developmental functions of AS-NMD at a systems level is a challenging task due to the lack of straightforward approaches for shortlisting biologically meaningful events. Moreover, NMD targets are inherently unstable and tend to be underrepresented in reference transcriptome annotations. The quantitative aspect of AS-NMD is also poorly understood. In most cases, it remains unclear whether this pathway drives developmental changes in gene expression or simply fine-tunes the abundance of productively spliced transcripts.

To systematically identify AS-NMD events with a strong potential to regulate genes in developing neurons, we developed a user-friendly computer package factR2 that annotates custom transcriptomes and prioritizes targets with significant correlation between NMD-protective splicing patterns and gene expression levels. By combining factR2 with longitudinal analysis of neuronal differentiation and acute inhibition of the NMD pathway, we uncovered a widespread role of NMD-stimulating CEs in downregulating non-neuronal genes in developing neurons. We validate our findings and demonstrate their generality using ex vivo neural cultures, subcellular fractionation, minigene and CRISPR-Cas9 experiments, and single-cell data analyses.

An inducible system for longitudinal analysis of AS-NMD in developing neurons

We began by establishing a single-dish protocol for neuronal differentiation in vitro that avoids cell dissociation and replating steps (Fig. 1 A). Inducible expression of the proneural transcription factor neurogenin 2 (NGN2; encoded by the Ngn2 / Neurog2 gene) has been shown to promote efficient differentiation of mouse ESCs into glutamatergic neurons [ 44 ]. In that study, Ngn2 was expressed from a constitutive promoter following the 4-hydroxytamoxifen-induced Cre recombination [ 44 ]. Yet, in mice, Ngn2 upregulation in NPCs is transient, followed by a decrease in its expression at later stages of differentiation [ 45 ]. To better recapitulate the endogenous regulation, we knocked in a mouse Ngn2 transgene into the A2Lox ESC line [ 46 ] under the doxycycline (Dox) inducible promoter TRE (Fig. 1 A).

Systematic analysis of the AS-NMD program in developing neurons. A Doxycycline (Dox) inducible differentiation of mouse ESCs into neurons. Top , RT-qPCR analysis of differentiation stage-specific markers in Dox-induced TRE-Ngn2 samples. Data are averaged from 3 experiments with SD shown by the shaded areas. Bottom , Dox-induced TRE-Ngn2 cells were treated for 6 h with DMSO or CHX and analyzed by RNA-seq. B factR2 streamlines the discovery of functional AS-NMD events and provides additional transcriptome annotation functions. C Genes containing Whippet-shortlisted AS-NMD events (see the “ Results ” section for details) show a stronger upregulation by CHX compared to non-AS-NMD genes. This is particularly prominent for genes containing NMD-stimulating cassette exons (NS-CEs). The -log10-transformed FDR values for CHX-upregulated genes are presented as box plots and compared by a two-tailed Wilcoxon rank sum test. Dotted line, FDR = 0.001 cutoff used throughout this study. D Genes containing Whippet-shortlisted factR2 AS-NMD events are more likely to be significantly upregulated (FDR < 0.001) by CHX than genes lacking such events. E Distribution of AS-NMD events responding to CHX at both the splicing and gene expression levels at different stages of neuronal differentiation. F Wild-type (WT) mouse ESCs (IB10) and NPCs and neurons from mouse embryonic cortex were treated with CHX or DMSO and analyzed by RNA-seq as in ( A ). CHX-responsive AS-NMD events detected in these samples were intersected with three groups of the TRE-Ngn2 factR2 events: not regulated by CHX, regulated by CHX at the splicing level, and regulated by CHX at both the splicing and gene expression levels. The ESC and neural samples were analyzed separately. Note particularly strong enrichment for the third TRE-Ngn2 group. G Nuclear and cytoplasmic fractions of untreated samples introduced in ( F ) were analyzed by RNA-seq, and the nuclear enrichment of putative AS-NMD events in the three TRE-Ngn2 groups mentioned in ( F ) was calculated by Whippet

The resultant TRE-Ngn2 line maintained its ESC properties in the 2i + LIF medium [ 47 ] without Dox, producing dome-shaped colonies positive for the POU5F1/OCT4 marker (Additional file 1 : Fig. S1A). When treated with Dox (see the “ Materials and methods ” section for details), most cells lost OCT4, acquired spindle-shaped morphology typical for NPCs, and increased the expression of the NPC marker vimentin (VIM) by differentiation day 3 (Additional file 1 : Fig. S1B). To mimic transient upregulation of Ngn2 in neurogenesis, we progressively reduced Dox concentration beginning from day 2 (see the “ Materials and methods ” section). By differentiation day 6, most cells stained positive for the early neuronal marker tubulin βIII (TUBB3/TUJ1) and developed characteristic neuronal morphology with round somas and readily detectable neurites (Additional file 1 : Fig. S1C).

The differentiating cultures were viable for at least 24 days and showed an increased expression of the late neuronal marker MAP2 by days 12–24 (Additional file 1 : Fig. S1C). By day 24, cells displayed signs of neuronal maturation, including the presence of ankyrin G (ANK3/ANKG) at the axon initial segment and punctate distribution of the postsynaptic density protein-95 (DLG4/PSD-95), as well as the presynaptic marker Synaptophysin (SYP) in neurites (Additional file 1 : Fig. S1D–F). Reverse transcriptase-quantitative PCR (RT-qPCR) assays confirmed stage-specific expression of corresponding mRNA markers (Fig. 1 A). Consistent with the transient Dox treatment used in our protocol, Ngn2 expression peaked at day 3 and declined at the later stages of differentiation (Additional file 1 : Fig. S1G).

To understand the role of AS-NMD in neurodevelopment, we treated differentiating TRE-Ngn2 cultures for 6 h prior to RNA extraction with cycloheximide (CHX), to repress NMD, or DMSO, as a control (Fig. 1 A). The samples collected on days 0, 3, 6, 12, and 24 were then analyzed by RNA-seq. Principal component analysis (PCA) of gene expression in this experiment showed a tight clustering of biological replicates and a clear separation of samples according to the differentiation stage and the treatment (Additional file 1 : Fig. S2A). Further inspection of the DMSO samples confirmed stage-specific expression of pluripotency markers on day 0, NPC markers on day 3, and progressive upregulation of mature neuronal and glutamatergic markers between days 6 and 24 (Additional file 1 : Fig. S2B). This developmental trajectory was further confirmed by deconvolving cell types in the DMSO samples using MuSiC [ 48 ] (Additional file 1 : Fig. S2C, D).

We concluded that transient expression of transgenic NGN2 in mouse ESCs provides a suitable system for time-resolved analyses of AS-NMD targets in developing neurons.

A bioinformatics tool for annotating AS-NMD events in custom transcriptomes

To enable in-depth analysis of AS-NMD targets, we updated our prototype R tool factR ( f unctional a nnotation of c ustom tr anscriptomes; [ 49 ]) with a suite of relevant functions. The new package, factR2 (Fig. 1 B; [ 50 ]), allocates transcripts identified by RNA-seq to their genes of origin and identifies NMD-sensitive isoforms. It further detects splicing events underlying the NMD sensitivity and analyzes their regulation potential. factR2 can also deduce the domain structure of productively spliced protein-coding sequences and plot RNA and protein isoform structures for data exploration. Overall, factR2 provides a rich set of functions for the functional annotation of custom transcriptomes and AS-NMD analyses (Additional file 2 : Table S1).

As a proof of principle, we searched for NMD-sensitive transcripts among the 90,159 mRNAs present in the GENCODE M26 reference transcriptome using factR2’s predictNMD function. The workflow was kept blind to the GENCODE transcript biotype annotation. This returned 13,042 putative NMD targets containing premature termination codons > 50 nt upstream of the last exon-exon junction. The set included 7161 out of the 7201 transcripts (i.e. 99.4%) with the “nonsense_mediated_decay” biotype, as well as 5881 additional transcripts that had not been previously flagged as possible NMD targets (Additional file 1 : Fig. S3A).

Transcript-level analysis of our DMSO- and CHX-treated TRE-Ngn2 RNA-seq data (Fig. 1 A) by HISAT2-StringTie [ 51 ] predicted 57,535 novel mRNA isoforms absent from the GENCODE annotation. Of these, 30,228 transcripts were classified by factR2 as NMD-sensitive. The analysis of the combined NMD-sensitive transcriptome using factR2’s testASNMDevents function revealed 19,198 putative AS-NMD events. These events were classified (Additional file 1 : Fig. S3B, C) as cassette exons (CEs), alternative donors (ADs), alternative acceptors (AAs), and retained introns (RIs) that either stimulate or repress NMD when included into mature mRNA (NS and NR events, respectively).

These data demonstrate the utility of factR2 and suggest that the extent of AS-NMD regulation in developing neurons is substantially greater than currently thought.

factR2 identifies AS-NMD events with a strong regulation potential

To shortlist high-quality AS-NMD targets, we first examined changes in their splicing patterns and gene expression in response to CHX. We used Whippet [ 52 ] for the splicing-level analysis and selected CEs, ADs, AAs, and RIs consistently changing their percent spliced in (PSI) values in response to CHX at least at one differentiation stage (NS events, ΔPSI > 0.1; NR events, ΔPSI < − 0.1; probability > 0.9). We additionally selected NS events with a PSI average of > 0.9 for DMSO- and CHX-treated samples, and NR events with a PSI average of < 0.1. This was done because these groups have a limited capacity to change splicing in response to CHX but may still be regulated at the gene expression level.

Genes with Whippet-shortlisted events were more frequently upregulated in response to CHX compared to genes lacking AS-NMD events (Fig. 1 C). In many cases, the false discovery rate (FDR) of this effect, calculated by the DESeq2 Wald test [ 53 ], was < 0.001. Cassette exons stimulating NMD upon their inclusion (NS-CEs) were associated with particularly robust regulation in neural cells (days 3–24; Fig. 1 C). The fractions of Whippet-shortlisted entries passing the FDR < 0.001 gene-level regulation cutoff were significantly larger compared to genes lacking factR2-predicted AS-NMD events (Fig. 1 D). Overall, we identified 1432 non-redundant AS-NMD events responding to CHX at both splicing and gene levels (Additional file 3 : Table S2). The number of such events peaked at the intermediate stages of differentiation (days 3–12), with CEs dominating the distributions throughout the entire time course (Fig. 1 E). Notably, 49.2% of the CHX-responsive events were not previously annotated in the GENCODE transcriptome. Similar results were obtained when we shortlisted CHX-responsive splicing events by rMATS [ 54 ] instead of Whippet (Additional file 1 : Fig. S3D).

This analysis suggests that the AS-NMD pathway controls hundreds of genes expressed at different stages of neuronal differentiation.

Many AS-NMD events identified in TRE-Ngn2 cells are regulated in natural samples

To validate our approach, we performed RNA-seq analyses of wild-type (WT) mouse ESCs (IB10 line) and primary cortical NPCs and neurons treated with CHX or DMSO (Fig. 1 F and Additional file 1 : Fig. S4A–D). Splicing events responding to CHX in these “natural” samples (with the Whippet cutoffs described above) were significantly overrepresented among the CHX-responsive TRE-Ngn2 factR2 events compared to the non-responsive TRE-Ngn2 factR2 control (Fig. 1 F).

Since NMD occurs in the cytoplasm [ 12 , 13 , 14 ], transcripts including NMD-stimulating and excluding NMD-repressing events are expected to be enriched in the nucleus. We reasoned that such nuclear enrichment could provide a CHX-independent validation method for AS-NMD targets. With this in mind, we sequenced nuclear and cytoplasmic transcriptomes from untreated WT ESCs and cortical NPCs and neurons and compared the two compartments using Whippet (Fig. 1 G). Nucleus-enriched NR and nucleus-depleted NS events were overrepresented in the CHX-responsive TRE-Ngn2 factR2 sets (Fig. 1 G). Notably, the sequence context of events responding to CHX in both the TRE-Ngn2 and natural cells was more conserved across vertebrates compared to a non-responsive control (Additional file 1 : Fig. S4E, F).

These data argue that many AS-NMD events identified by factR2 are regulated in vivo. Since the targets responding to CHX at both the splicing and gene expression levels showed the largest overlap with the natural regulation (Fig. 1 F, G) and the highest median conservation scores (Additional file 1 : Fig. S4E, F), we prioritized this set in our subsequent analyses.

AS-NMD is widespread among genes downregulated in developing neurons

So far, we focused on the impact of AS-NMD on individual stage-specific transcriptomes. To explore the potential effects of this pathway on gene expression dynamics in development, we analyzed the correlation between splicing patterns protecting mRNA from NMD (i.e. inclusion of NR events and skipping of NS events) and gene expression levels. This was done by applying factR2’s function testGeneCorr to genes differentially expressed (DESeq2 likelihood-ratio test; FDR < 0.001) in the control-treated longitudinal TRE-Ngn2 data.

Compared to the non-responsive control, CHX-responsive AS-NMD events were enriched for positively correlated entries (Fig. 2 A). The enrichment was predominantly due to CEs rather than other types of AS-NMD events (Fig. 2 A, B). The largest fraction of significantly correlated events was detected for NS-CEs, with the magnitude of this effect increasing for more stringent correlation P -values and Whippet cutoffs (Fig. 2 A, B and Additional file 1 : Fig. S5). The enrichment of correlated events among NS-CEs ranged from ~ 2.4-fold (ΔPSI > 0.1 and probability > 0.9 Whippet cutoffs and P < 0.05 correlation cutoff; Fig. 2 A, B) to ~ 5.8-fold (ΔPSI > 0.25 and probability > 0.95 Whippet cutoffs and P < 0.001 correlation cutoff; Additional file 1 : Fig. S5). Hereafter, we will use the term “facilitating” to refer to CHX-responsive events showing significant positive correlation ( P < 0.05; FDR < 0.1) between NMD-protective splicing patterns and gene expression.

Neurodevelopmentally downregulated genes are often controlled by AS-NMD. A , B factR2 events responding to CHX at both the splicing and gene levels show a stronger correlation with developmental changes in gene expression compared to non-responsive events. A Percentages of events correlating with gene expression dynamics in differentiating TRE-Ngn2 cells for different P -value cutoffs. Ctrl are factR2 events not responding to CHX. AS-NMD, Other, NR-CE, and NS-CE are, respectively, all AS-NMD events, AD, AA, and RI AS-NMD events, NMD-repressing cassette exons, and NMD-stimulating cassette exons responsive to CHX. B Fold enrichments of the correlated events in the shortlisted categories in ( A ) plotted as a function of the correlation P -value cutoff. NS-CEs show the strongest enrichment for correlated entries, with the fold enrichment increasing for tighter cutoffs. C One-sided Fisher’s exact test showing that the monotonic downregulation trend (Kendall’s τ < − 0.75, P < 0.05) is significantly overrepresented among genes with CHX-responsive facilitating CEs, particularly NS-CEs, correlating with gene expression dynamics ( P < 0.05). Genes with non-responsive factR2 events are used as a control. D No significant difference is detected for the monotonic upregulation trend (Kendall’s τ > 0.75, P < 0.05). E , F PhastCons [ 55 ] analysis shows strong conservation of the intronic sequence context in NS-CEs in general and NS-CEs residing in monotonically downregulated genes in particular

Since the above results pointed to a possible role of AS-NMD in gene expression dynamics, we wondered if such a mechanism might preferentially feed into specific types of temporal regulation. We clustered differentially expressed genes (DESeq2 likelihood-ratio test; FDR < 0.001) according to their TRE-Ngn2 time-course trajectories using DP_GP_cluster [ 56 ] or k-means methods (Additional file 1 : Fig. S6, Additional file 1 : Fig. S7A–H, and Additional file 4 : Table S3).

Notably, facilitating AS-NMD events were often associated with neurodevelopmentally downregulated gene clusters (Additional file 1 : Fig. S7C–F and Additional file 5 : Table S4). The enrichment was especially robust for genes with facilitating NS-CEs (Additional file 1 : Fig. S7F–H). Similar trends were detected when we analyzed the trajectories of individual genes without clustering (Additional file 1 : Fig. S7I and Fig. 2 C, D).

Consistent with the opposite effects of NR-CE and NS-CE inclusion on mRNA stability, Kendall’s analysis of the PSI dynamics revealed a skew towards negative τ values in NR-CEs and positive τ values in NS-CEs (Additional file 1 : Fig. S7J). Events with significant positive PSI trends ( P < 0.05) were significantly overrepresented in the NS-CE group (Additional file 1 : Fig. S7K). Moreover, the intronic sequence context of NS-CEs encoded in monotonically downregulated genes was often conserved across species (Fig. 2 E, F).

Overall, our pipeline identified 87 facilitating NS-CEs associated with monotonic gene downregulation (Kendall’s τ < − 0.75, P < 0.05; the first tab in Additional file 5 : Table S4). Metascape [ 57 ] analysis of this set revealed significant enrichment for RNA metabolism and localization as well as cell cycle regulation functions (Additional file 1 : Fig. S8A and the first tab in Additional file 6 : Table S5). Other examples of the overrepresented functional categories included RHO GTPase effectors and actin cytoskeleton organization (Additional file 1 : Fig. S8A).

To validate our bioinformatics predictions, we examined four monotonically downregulated genes ( τ < − 0.75, P < 0.05; the first tab in Additional file 5 : Table S4) with evolutionarily conserved NS-CEs (Additional file 1 : Fig. S9). These included three examples encoding RNA metabolism factors, Fbl , Srsf9 , and Xpo1 (the first tab in Additional file 6 : Table S5), and the Ctnnal1 gene predicted to contribute to actin filament binding and RHO signaling ( https://www.ncbi.nlm.nih.gov/gene/8727 ). All four NS-CEs were readily detectable in CHX-treated samples showing progressively stronger inclusion at later stages of differentiation.

Thus, AS-NMD in general and NMD-stimulating cassette exons in particular may play a role in downregulation of functionally related groups of genes in developing neurons.

Comprehensive identification of AS-NMD events regulated by PTBP1

Previous studies have identified several AS-NMD events controlled by PTBP1, an RNA-binding protein expressed at high levels in ESCs and NPCs and downregulated in neurons [ 28 , 29 , 30 , 31 , 34 ]. We therefore used PTBP1-regulated events to benchmark the performance of the factR2-based workflow. Ptbp1 was monotonically downregulated in differentiating TRE-Ngn2 cells, and the shortlist of facilitating AS-NMD events included previously characterized PTBP1 targets (e.g., NR-CEs maintaining open reading frames in Ptbp2 , Gabbr1 , and Dlg4 ; Additional file 1 : Fig. S10A and Additional file 5 : Table S4).

To identify PTBP1-dependent AS-NMD targets systematically, we examined factR2 events correlating with Ptbp1 expression in DMSO-treated TRE-Ngn2 cells and changing their splicing in ESCs or NPCs in response to PTBP1 knockdown alone or together with its functionally similar paralog, PTBP2 (Fig. 3 A; RNA-seq data from [ 58 ]; Whippet |ΔPSI|> 0.1 and probability > 0.9 cutoffs; see the “ Materials and methods ” section for further details).

Role of PTBP1 in AS-NMD regulation in developing neurons. A The workflow used to shortlist PTBP1-controlled events. B AS-NMD events with strong regulatory potential are enriched for PTBP1-controlled entries. C – E Uninduced TRE-Ngn2 ESCs were treated for 48 h with a PTBP1-specific (siPtbp1) or a non-targeting control siRNA (siCtrl) and post-treated with CHX (+ CHX) or DMSO (− CHX) for an additional 6 h. C The samples were analyzed by RT-PCR with primers flanking the Fmnl3 , Iqgap1 , and Ripk1 NS-CEs. PTBP1 represses NS-CE inclusion and CHX stabilizes NS-CE-containing isoforms. D PSI values in ( C ) were quantified from 3 experiments and shown as box plots. E RT-qPCR analyses with primers against constitutively spliced parts of Fmnl3 , Iqgap1 , and Ripk1 show that siPtbp1 downregulates these genes in the presence of DMSO but not CHX. Ptbp1 -specific primers were used to estimate its knockdown efficiency. The data were averaged from three experiments ± SD. F Fmnl3 minigenes. Pyrimidine-rich motifs (Py; YUCUYY and YYUCUY) predicted to recruit PTBP1 within the NS-CE or at intronic positions overlapping with a PTBP1 iCLIP cluster [ 58 ] (blush highlight) were mutated (“m”) in the corresponding minigenes. G TRE-Ngn2 ESCs pretreated with siPTBP1 or siCtrl were transfected with the wild-type ( WT ) Fmnl3 minigene and analyzed by RT-PCR with minigene-specific primers. The minigene recapitulates the repressive effect of PTBP1 on the Fmnl3 NS-CE. H Quantification of the NS-CE PSI values in ( G ). I Untreated TRE-Ngn2 ESCs were transfected with the Fmnl3 minigenes introduced in ( F ) and analyzed by RT-PCR. J Quantification of the PSI values in ( I ) showing that the mutation of the upstream exonic Py ( mut1 ) promotes the NS-CE inclusion, especially when combined with downstream exonic Py ( mut2 ) and iCLIP Py ( mut3 ) mutations. Box plots in H , J show data from 3 experiments compared by a two-tailed t -test assuming unequal variances

Notably, the PTBP1-dependent behavior was significantly overrepresented among the high-quality TRE-Ngn2 factR2 events (Fig. 3 B). Of the 46 facilitating AS-NMD events regulated by PTBP1, 29 were NS-CEs (Additional file 5 : Table S4). Similar to the trend observed for all facilitating NS-CEs (Additional file 1 : Fig. S7E, F), the PTBP1-regulated NS-CEs were strongly enriched in monotonically downregulated ( τ < − 0.75, P < 0.05) but not upregulated ( τ > 0.75, P < 0.05) genes (Additional file 1 : Fig. S10B, C).

Metascape analysis showed that monotonically downregulated genes with PTBP1-controlled NS-CEs were enriched for the RHO GTPase effector and actin cytoskeleton organization functions (Additional file 1 : Fig. S8B and the second tab in Additional file 6 : Table S5). Another enriched category related to the programmed cell death (apoptosis modulation by HSP70; Additional file 1 : Fig. S8B). Interestingly, the overrepresentation of the RHO GTPase effector and actin cytoskeleton organization terms among all downregulated genes with NS-CEs (Additional file 1 : Fig. S8A) was largely due to the PTBP1-regulated targets. Indeed, these terms were missing in the Metascape output when we analyzed monotonically downregulated genes with PTBP1-independent NS-CEs (Additional file 1 : Fig. S8C and the third tab in Additional file 6 : Table S5).

These data suggest that PTBP1 controls a wider range of NMD-stimulating cassette exons than previously thought, and that many genes containing these events belong to specific functional categories.

PTBP1-mediated repression of NS-CEs promotes the expression of their genes in ESCs

We selected three neurodevelopmentally downregulated genes with PTBP1-repressed NS-CEs for further analyses: Fmnl3 and Iqgap1 encoding RHO GTPase effectors involved in actin cytoskeleton organization and Ripk1 , a regulator of programmed cell death (the second tab in Additional file 6 : Table S5). The three genes were strongly downregulated in developing neurons and their expression was rescued by CHX (Additional file 1 : Fig. S10D-F). Their NS-CEs were evolutionarily conserved and neighbored or overlapped with PTBP1 interaction motifs (YUCUYY and YYUCUY) and PTBP1 binding sites identified by iCLIP [ 58 ] (Additional file 1 : Fig. S11). PTBP1 was proposed to control Ripk1 and Iqgap1 NS-CE splicing [ 27 , 59 , 60 , 61 ], but the role of these exons in the regulation of their host genes during neuronal differentiation was not addressed experimentally. To the best of our knowledge, the Fmnl3 NS-CE has not been previously identified.

To validate the role of PTBP1 in the expression of these targets, we transfected TRE-Ngn2 ESCs with Ptbp1-specific or control siRNAs (siPtbp1 or siCtrl, respectively) and post-treated the cells with either CHX or DMSO. Subsequent RT-PCR analyses showed that siPTBP1 stimulated the inclusion of all three NS-CEs, which was further enhanced by CHX (Fig. 3 C, D). PTBP1 knockdown dampened the total mRNA abundance for the three genes in the DMSO- but not CHX-treated samples (Fig. 3 E). Importantly, the downregulation of Fmnl3 , Iqgap1 , and Ripk1 by siPtbp1 was also significantly rescued by an siRNA against the key NMD factor UPF1 (Additional file 1 : Fig. S12).

To test if PTBP1 might regulate NS-CE splicing directly, we cloned the Fmnl3 NS-CE in its natural sequence context into a mammalian expression vector (Fig. 3 F). Transcripts produced from this minigene lacked an in-frame translation initiation codon and were not predicted to undergo NMD. The wild-type minigene ( WT ) recapitulated the endogenous splicing regulation in TRE-Ngn2 ESCs (Fig. 3 G, H). There are two PTBP1 binding sites inside the Fmnl3 NS-CE and two such positions in the downstream intron that overlap with a PTBP1 iCLIP cluster (Additional file 1 : Fig. S11A). Mutation of the upstream exonic site ( mut1 ; Fig. 3 F) increased the NS-CE inclusion in the presence of PTBP1 (Fig. 3 I, J). Mutations of the downstream exonic site ( mut2 ) or the two iCLIP positions ( mut3 ) did not alter the minigene splicing pattern individually, but enhanced the NS-CE stimulation effect of mut1 in the mut12 and mut123 minigenes (Fig. 3 F, I–J).

We concluded that PTBP1 promotes the expression of several non-neuronal genes in ESCs by repressing NMD-stimulating cassette exons.

Ptbp1 downregulation in developing neurons may act as a switch for NS-CE inclusion

The TRE-Ngn2 RNA-seq data showed a substantial decrease in Ptbp1 expression between differentiation days 3 (NPCs) and 6 (young neurons), which coincided with the expression trajectories of Fmnl3 , Iqgap1 , and Ripk1 (Additional file 1 : Fig. S10A). To find out whether the Ptbp1 dynamics could promote the NS-CE inclusion in these targets, we analyzed the TRE-Ngn2 differentiation time course collecting DMSO- or CHX-treated samples daily between days 0 and 6 (Fig. 4 ).

PTBP1-repressed NS-CEs are regulated in a switch-like manner during neuronal differentiation. A RT-PCR analyses of Dox-induced TRE-Ngn2 ESCs treated with DMSO or CHX on days 0–6 show that the inclusion of NS-CEs into the Fmnl3, Iqgap1, and Ripk1 mRNAs increases during neuronal differentiation. B NS-CE PSI values for the DMSO and CHX samples in ( A ) averaged from three experiments ± SD and compared by two-tailed t -test assuming unequal variances. Note that the CHX curves are S-shaped, consistent with a switch-like regulation. Dotted lines, PSI median of the CHX-treated data for the three genes used to estimate the onset of the rapid increase phase. C RT-qPCR analysis Ptbp1 expression dynamics during the day 0–6 differentiation period. Note that the abundance of the Ptbp1 mRNA decreases monotonically beginning from day 2, consistent with the onset of the rapid increase phase in the CHX curves in ( B ). D – F NS-CE inclusion in ( D ) Fmnl3, ( E ) Iqgap1, and ( F ) Ripk1 shows a strong negative correlation with Ptbp1 expression, as expected. Correlations in ( C – F) were analyzed using the Kendall’s method

RT-PCR analysis of DMSO-treated samples (Fig. 4 A) did not detect the NS-CE-containing isoforms of Fmnl3, Iqgap1, and Ripk1 between days 0 and 2. Small amounts of these isoforms appeared on days 3–4 and accumulated at the subsequent stages of differentiation. Treating the cells with CHX tended to increase the relative abundance of the NS-CE-containing isoforms with a robust difference between the CHX and the DMSO PSI values in newly developed neurons (Fig. 4 B).

Since CHX protects NS-CE-containing isoforms from NMD, we used the CHX-treated data to follow developmental changes in pre-mRNA splicing patterns (Fig. 4 B). In all three cases, the time-course curves were S-shaped with little or no changes in the NS-CE inclusion between days 0 and 2, a rapid increase between days 2 and 4, and a plateau between days 4 and 6. Using the median PSI value for the three genes as a threshold (43.8%; dotted line in Fig. 4 B) placed the onset of the rapid increase phase at day 2 for Fmnl3 and day 3 for Iqgap1 and Ripk1 . RT-qPCR analysis of Ptbp1 expression showed that it was relatively stable between days 0 and 2, and progressively declined beginning from day 2 (Fig. 4 C). Notably, NS-CE-inclusion values showed a strong negative correlation with Ptbp1 for all three targets (Fig. 4 D–F).

Taken together, these results suggest that reduced Ptbp1 expression may facilitate a switch towards the inclusion of NMD-stimulating cassette exons during the transition from NPCs to neurons.

NS-CEs work in coordination with NMD-independent gene downregulation mechanisms

Increased inclusion of NS-CEs may contribute to gene downregulation in developing neurons in four different ways. (1) It may act as the sole downregulation mechanism. Alternatively, it may work alongside AS-NMD-independent repression mechanisms by (2) preempting, (3) coinciding with, or (4) following them in development. We modeled these four scenarios assuming possible cooperation with transcriptional repression (Additional file 1 : Fig. S13 and Materials and Methods). The key predictions from this simple model were that AS-NMD should consistently diminish gene expression later in development in all four scenarios and accelerate the onset of downregulation in scenarios (1) and (2), but not (3) and (4).

To find out which of the above mechanisms could be used in developing neurons, we targeted NS-CE sequences in Fmnl3 , Iqgap1 , and Ripk1 using appropriate CRISPR gRNAs and Cas9 (Additional file 1 : Fig. S14). For each gene, we selected two TRE-Ngn2 ESC clones with biallelic NS-CE disruption (Additional file 1 : Fig. S14). RT-PCR analyses of undifferentiated (day-0 ESCs) and Dox-differentiated (day-6 neurons) samples treated with DMSO or CHX showed that the mutant clones lost or had a severely impaired ability to splice in NMD-stimulating exons (Additional file 1 : Fig. S15).

We then analyzed the expression dynamics of the three genes in the wild-type (WT) and mutant cells undergoing neuronal differentiation (Fig. 5 A–C). As expected, the three WT genes were downregulated in day-6 neurons compared to day-0 ESCs. Iqgap1 and Fmnl3 additionally showed transient upregulation peaks on days 1–2, which could not be detected in our day 0, 3, 6, 12, and 24 RNA-seq time series. The mutants generally followed the WT trends (Fig. 5 A–C), with two key differences revealed by normalizing the data by the wild-type values (Fig. 5 D–F).

NS-CEs facilitate neurodevelopmental downregulation of non-neuronal genes. A – C TRE-Ngn2 ESCs with wild-type (WT) or biallelically mutated NS-CEs ( Fmnl3 , clones A15, and D47; Iqgap1 , clones B2 and E2; and Ripk1 , clones A38 and B41) were differentiated into neurons and the gene expression dynamics was analyzed by RT-qPCR. Note that the mutant ( A ) Fmnl3 , ( B ) Iqgap1 , and ( C ) Ripk1 have a higher residual expression in neurons compared to their WT counterparts. Mutant Fmnl3 is also expressed somewhat higher than the WT at earlier stages of differentiation. D – F Normalizing the data in ( A – C ) to the WT values confirms that ( D ) Fmnl3 , ( E ) Iqgap1 , and ( F ) Ripk1 with mutated NS-CEs have a substantially reduced ability to undergo downregulation at later stages of development. We defined WT-mutant dichotomy points as differentiation stages preceding the increase of normalized expression values averaged for the two mutants above the median of all normalized mutant values calculated for the three genes (1.73; dotted line). This places the dichotomy points at day 2 for Fmnl3 and day 3 for Iqgap1 and Ripk1 (arrowheads). G – I Normalizing CHX-treated time-course to the corresponding DMSO-treated controls for ( G ) Fmnl3 , ( H ) Iqgap1 , and ( I ) Ripk1 confirms that the AS-NMD regulation is lost or markedly diminished in the NS-CE mutants, and that days 2–3 correspond to the onset of facilitating AS-NMD in the WT

First , NS-CE inactivation led to progressive upregulation of gene expression at later developmental stages (Fig. 5 D–F). For all 3 genes, the mutant trajectories diverged from their WT counterparts after an initial lag period. By using the median of the normalized mutant values for all three genes as a cutoff (1.73; dotted lines in Fig. 5 D–F), we estimated that the WT-mutant dichotomy point was around day 2 for Fmnl3 and day 3 for Iqgap1 and Ripk1. According to Additional file 1 : Fig. S13, these points mark the onset of facilitating AS-NMD in the WT, and they indeed coincided with the corresponding parts of the NS-CE inclusion curves in CHX-treated WT samples (Fig. 4 B).

Second , the temporal relationship between the AS-NMD dichotomy points in Fig. 5 D–F and the monotonically downregulated parts of the mutant trajectories in Fig. 5 A–C varied depending on the gene. The estimated onset of AS-NMD preceded the monotonic downregulation for the Fmnl3 (day 2 vs. day 3), while following it for Iqgap1 and Ripk1 (day 3 vs. day 2). These behaviors matched models (2) and (4) in Additional file 1 : Fig. S13, with an apparent 1-day difference between the onset of NMD-dependent and NMD-independent downregulation mechanisms. Confirming the role of the NS-CEs in the AS-NMD regulation, CHX strongly upregulated the WT but not the mutant genes at the corresponding differentiation stages (Fig. 5 G–I).

Thus, in the three examples tested, NS-CEs are essential for full-scale downregulation of non-neuronal genes in developing neurons, working in coordination with NMD-independent repression mechanisms. Our additional bioinformatics analysis showed that genes containing functional NMD-stimulated cassette exons generally maintained declining neurodevelopmental trends in the presence of CHX, even though their downregulation was more robust in DMSO- compared to CHX-treated samples (Additional file 1 : Fig. S16). This points to a wider crosstalk between AS-NMD and other gene regulation mechanisms.

Our study offers valuable insights into the gene regulation functions of the AS-NMD pathway. Firstly, it introduces a straightforward workflow for shortlisting biologically relevant AS-NMD targets. Secondly, it reveals the extensive involvement of AS-NMD in the downregulation of non-neuronal genes in developing neurons. Thirdly, it argues that the activity of NMD-stimulating cassette exons (NS-CEs) is often coordinated with other downregulation mechanisms to ensure full-scale repression of their target genes in mature neurons (Fig. 6 ).

Model for the role of NMD-stimulating cassette exons in gene downregulation in developing neurons

Additionally, our optimized inducible differentiation system provides a useful resource for time-resolved analyses of gene expression dynamics in developing neurons (Fig. 1 A, Additional file 1 : Fig. S1 and Additional file 1 : Fig. S2). We also believe that users working with custom transcriptomes deduced from either short- or long-read sequencing data will appreciate the ability of factR2 (Fig. 1 B and Additional file 2 : Table S1) to quickly allocate sample-specific transcripts to their original genes (done when creating the factR2 object), predict open reading frames (buildCDS), identify transcripts containing NMD-promoting features (predictNMD), and pinpoint AS events underlying NMD sensitivity (testASNMDevents). Functions testGeneCorr and getAScons are helpful for assessing the regulatory potential and evolutionary conservation of splicing events, while plotTranscripts provides rich data visualization capabilities. Although not showcased in this study, factR2 can be also used to predict the domain structure of proteins encoded by productively spliced isoforms (predictDomain).

In our evaluation, factR2 provides a wider range of functions for the functional analysis of custom transcriptomes and shortlisting AS-NMD with strong regulation potential compared to similar currently available tools [ 62 , 63 , 64 , 65 , 66 ] (Additional file 2 : Table S1). The efficiency of factR2 as an AS-NMD discovery tool is supported by its ability to identify the vast majority of the GENCODE-annotated NMD targets (99.4%). Moreover, when applied to a custom transcriptome containing both reference and novel isoforms, factR2 expanded the NMD biotype annotation by ~ 6-fold (43,270 vs. 7201).

factR2 analysis of TRE-Ngn2 neuronal differentiation time series identified hundreds of AS-NMD targets responding to the NMD inhibitor CHX at specific time points (Additional file 3 : Table S2 and Fig. 1 E). Confirming their biological relevance and authenticity, many of these events were also detected in genetically unperturbed ESCs and ex vivo neural cells (Fig. 1 F, G). Interestingly, the number of CHX-responsive targets increased transiently in NPCs and developing neurons (days 3–12; Fig. 1 E), suggesting that AS-NMD might be particularly important at differentiation stages associated with a large-scale rewiring of the transcriptome. The relative paucity of AS-NMD targets in mature neurons (day 24) is consistent with previously reported downregulation of the NMD factors, UPF1 and MLN51 by the neuronal microRNAs [ 39 , 40 ].

Our data corroborate earlier findings that a considerable fraction of mammalian genes generate NMD-sensitive isoforms [ 14 , 26 , 63 , 67 , 68 ]. A key advancement of the present study is the identification of events with a strong potential to modulate gene expression during development. By focusing on this high-quality subset, we show that AS-NMD events, and their NS-CE subset in particular, are enriched in genes downregulated during neuronal differentiation (Fig. 2 , Additional file 1 : Fig. S7 and Additional file 5 : Table S4). Notably, our reanalysis of an RNA-seq dataset for a functionally distinct type of neurons, dentate gyrus granule cells [ 69 , 70 ], argues for a broader association between NS-CEs and gene downregulation in developing brain (Additional file 7 : Supplementary Results, Additional file 1 : Fig. S17, and Additional file 8 : Table S6).

The enrichment of neurodevelopmentally downregulated genes is an important finding since previously characterized examples of exonic AS-NMD events have shown varied effects on their host genes [ 28 , 29 , 30 , 31 , 32 , 33 , 34 ]. Whether specific regulation scenarios are favored at the systems level has been an open question. In contrast, a post-transcriptional circuitry operating in developing granulocytes involves a coordinated increase in intron retention in 86 functionally related genes, dampening their expression in an NMD-dependent manner [ 22 ].

We detected 87 NS-CEs in genes monotonically downregulated in differentiating TRE-Ngn2 cells and enriched for specific biological functions (Additional file 1 : Fig. S8A, Additional file 5 : Table S4 and Additional file 6 : Table S5). Thus, despite the obvious difference in the type of regulated splicing events, AS-NMD provides a transcriptome-wide mechanism for repressing functionally related genes in both neuronal and granulocyte development. The sequence context of the facilitating NS-CEs identified by our pipeline showed a remarkable degree of evolutionary conservation (Fig. 2 E–F and Additional file 1 : Fig. S17C, D). This points to the functional relevance of these events and distinguishes them, e.g., from the less conserved “cryptic” exons identified by examining transcriptome-wide effects of PTBP1/PTBP2 knockdown [ 61 ].

It should be noted that we utilized relatively lenient cutoffs (Whippet ΔPSI > 0.1, probability > 0.9, and correlation P < 0.05) to shortlist AS-NMD events potentially influencing gene expression in neurodevelopment (the first tab in Additional file 5 : Table S4). factR2 users may consider adjusting these parameters, based on the data in Fig. 2 A–B, Additional file 1 : Fig. S5, and the second tab of Additional file 5 : Table S4, to strike the optimal balance between capturing all regulatory events and events with the strongest regulation potential.

Our data suggest that PTBP1-controlled events are highly enriched for NS-CEs residing in neurodevelopmentally downregulated genes and that its NR-CE-containing targets upregulated in neurons (Ptbp2, Gabbr1, and Dlg4; [ 28 , 29 , 30 , 31 ]) are more of an exception rather than the rule (Additional file 1 : Fig. S10). Interestingly, PTBP1-controlled NS-CEs are strongly enriched for the RHO GTPase effectors and actin cytoskeleton organization functions (Additional file 1 : Fig. S8B and Additional file 6 : Table S5). Another category over-represented in this set of genes relates to programmed cell death. These functions are known to play important roles in brain development and disease [ 71 , 72 , 73 , 74 ].

Conversely, neurodevelopmentally downregulated genes with PTBP1-independent NS-CEs are enriched for functions related to pre-mRNA splicing and other aspects of cellular RNA metabolism (Additional file 1 : Fig. S8C and Additional file 6 : Table S5). This is surprising, considering the previously established role of conserved NS-CEs in maintaining RNA-binding protein homeostasis through negative feedback loops [ 18 , 20 ]. Understanding the mechanisms that allow such exons to downregulate RNA-associated factors in developing neurons is an intriguing question for future studies. It is possible that cross-regulation between functionally related proteins or developmental changes in protein modification or cellular localization patterns contributes to this process [ 19 , 75 ].

An integral aspect of our work is the quantification of the extent to which NS-CEs modulate gene expression in developing neurons. Biallelic inactivation of the PTBP1-repressed NS-CEs in Fmnl3 , Iqgap1 , and Ripk1 suggested that these cis -elements facilitate downregulation of their host genes during neuronal differentiation (Fig. 5 ). Indeed, Fmnl3 , Iqgap1 , and Ripk1 lacking functional NS-CEs were expressed 4-6-fold higher compared to the wild type on differentiation days 4–6. It is also evident that NS-CEs do not function alone since some downregulation is observed even in their absence. Notably, the NMD-dependent and NMD-independent downregulation mechanisms appear to be coordinated in time with a ± 1-day precision (Fig. 5 ).

Further studies will be required to understand the nature of the NMD-independent repression mechanism(s) partnered with NS-CEs. We assumed transcriptional repression in our model (Additional file 1 : Fig. S13). However, the wild-type and mutant trajectories would be expected to diverge in a similar manner if AS-NMD cooperated with NMD-independent post-transcriptional repression, e.g., dampening mRNAs abundance via microRNAs [ 76 ], RNA binding proteins [ 77 ], or nuclear retention of incompletely spliced mRNAs [ 78 ]. Exploring potential crosstalk between AS-NMD and these, as well as other processes regulating mRNA stability, nucleocytoplasmic distribution, and translational activity [ 79 , 80 , 81 ] in different developmental and physiological contexts may unveil novel mechanisms regulating eukaryotic gene expression.

We systematically elucidated the role of AS-NMD during neuronal differentiation using the newly developed bioinformatics suite factR2 and relevant experimental approaches. This revealed that NMD-stimulating cassette exons play a widespread role in promoting the downregulation of non-neuronal genes in neurons. With the increasing availability of RNA sequencing data, particularly from single-cell and long-read studies, we anticipate that the systematic use of factR2 as the transcriptome annotation tool will unveil the global impacts of AS-NMD in a variety of biological contexts.

Materials and methods

Dna constructs.

Plasmids p2lox and pX330-U6-Chimeric_BB-CBh-hSpCas9 were kindly provided by Michael Kyba (Addgene plasmid #34,635; [ 46 ]) and Feng Zhang (Addgene plasmid #42,230; [ 82 ]), respectively. pEGFP-N3 was from Clontech and pGEM-T Easy was from Promega (cat# A137A). New constructs were generated as described in Additional file 9 : Table S7 using routine molecular cloning techniques and enzymes from New England Biolabs. We used a Quikchange site-directed mutagenesis protocol with the KAPA HiFi DNA polymerase (Kapa Biosystems, cat# KK2101) to modify the Fmnl3 minigene plasmid. Primers used for cloning, mutagenesis, and other purposes are listed in Additional file 10 : Table S8. All constructs were verified by Sanger sequencing.

ESC culture

A2lox [ 46 ], IB10 [ 83 ], and TRE-Ngn2 (this study) mouse ESCs were cultured in a humidified incubator at 37 °C, 5% CO 2 , in tissue-culture plates or dishes coated with gelatin (Millipore, cat# ES-006-B). The 2i + LIF medium [ 84 ] used for ESC maintenance contained a 1:1 mixture of Neurobasal (Thermo Fisher Scientific, cat# 21,103,049) and DMEM/F12 (Sigma, cat# D6421) media supplemented with 100 units/ml PenStrep (Thermo Fisher Scientific, cat# 15,140,122), 1 μM PD03259010 (Cambridge Bioscience, cat# SM26-2), 3 μM CHIR99021 (Cambridge Bioscience, cat# SM13-1), 0.5 mM L-Glutamine (Thermo Fisher Scientific, cat# 25,030,024), 0.1 mM β-mercaptoethanol (Sigma, cat# M3148), 1000 units/ml ESGRO LIF (Millipore, cat# ESG1107), 0.5 × B-27 supplement without vitamin A (Thermo Fisher Scientific, cat# 12,587,010) and 0.5 × N2 supplement. N2 100 × stock was prepared using DMEM/F12 medium as a base and contained 5 mg/ml BSA (Thermo Fisher Scientific, 15,260,037), 2 µg/ml progesterone (Sigma, P8783-1G), 1.6 mg/ml putrescine (Sigma, P5780), 3 µM sodium selenite solution (Sigma, S5261), 10 mg/ml apo-transferrin (Sigma, T1147), and 1 mg/ml insulin (Sigma, I0516) and stored in single-use aliquots at – 80 °C. Cells were typically passaged every 2–3 days by treating the cultures with 0.05% Trypsin–EDTA (Thermo Fisher Scientific, cat# 15,400,054) for 8–10 min at 37 °C. After quenching trypsin with FBS (Thermo Fisher Scientific, cat# SH30070.03E), cells were washed once with Neurobasal medium, spun down at 260 × g for 4 min and plated at 1:3 to 1:6 dilution. The identity of all ESC lines was confirmed by their ability to form OCT4-positive, dome-shaped colonies in 2i + LIF medium (see the “ Immunofluorescence analyses ” section). A2lox cells were additionally validated by performing inducible cassette exchange reactions, and TRE-Ngn2 cells were validated by their growth in the presence of G418/geneticin (see the section below).

Production of Dox-inducible Ngn2 knock-in ESCs

A2lox cells were pre-treated overnight with 2 μg/ml doxycycline (Dox; Sigma, cat# D9891) to activate Cre expression, trypsinized, FBS-quenched, and transfected in suspension with 1 μg of p2Lox-based plasmid pML156 (Additional file 9 : Table S7) mixed with 3 µl of Lipofectamine 2000 (Thermo Fisher Scientific, cat# 11,668,019) and 100 µl of Opti-MEM I (Thermo Fisher Scientific, cat# 31,985,070). The transfection was done in 4 ml of 2i + LIF medium at ~ 2.5 × 10 5 cells/ml in 6-cm bacterial dishes. Cells were pelleted 2 h post transfection, resuspended in 2 ml of fresh 2i + LIF medium, serially diluted, and replated into a gelatinized tissue-culture 6-well plate. 350 μg/ml of G418/geneticin (Sigma, cat# 10,131,019) was added 36 h post transfection and the incubation was continued for an additional 10 days with regular medium changes to allow G418-resistant cells to form colonies. These were picked, expanded, and analyzed for Dox-inducible expression of Ngn2 by reverse-transcriptase quantitative PCR (RT-qPCR).

Genetic inactivation of NMD-stimulating cassette exons

NS-CEs were knocked out by co-transfecting single-cell suspensions of TRE-Ngn2 cells with four plasmids encoding Cas9 and distinct gene-specific CRISPR gRNAs, as well as the pEM584 plasmid [ 85 ] encoding a blasticidin resistance gene. 1.2 µg of a mixture containing equal amounts of each of these plasmids was combined with 3 µl of Lipofectamine 2000 and 100 µl of Opti-MEM I. TRE-Ngn2 cells were transfected with the mixture in 4 ml of 2i + LIF at ~ 2.5 × 10 5 cells/ml in 6-cm bacterial dishes. Cells were collected 2 h post transfection, spun down, and the cell pellets were serially diluted in fresh 2i + LIF prior to plating in 6-well format. Antibiotic selection with 8 µg/ml blasticidin (Sigma, cat# 15,205) was started 24 h post transfection and lasted for 3 days. Cells were then cultured in 2i + LIF medium for another 6–10 days. Surviving colonies were picked, expanded for 3–4 days, and PCR-genotyped (see below). In cases where heterogeneity was suspected, the clones were dissociated to single-cell suspensions and allowed to form colonies (without blasticidin). These were picked, expanded, genotyped, and used for subsequent analyses.

Genomic DNA was extracted and analyzed using the PCRBIO Rapid Extract PCR Kit (PCR Biosystems; cat# PB10.24–08) following the manufacturer’s protocol. Amplified DNA fragments were resolved by electrophoresis in 1% agarose gels alongside GeneRuler 1 kb Plus DNA Ladder (Thermo Fisher Scientific, cat# SM1331). CRISPR/Cas9-induced mutations in AS-NMD-regulated genes were characterized with primers designed to amplify either short (~ 0.5 kb) or long (~ 2 kb) fragments centered on the targeted exon. Gel-purified PCR fragments were cloned into the pGEM-T Easy vector (Promega, cat# A137A) and sequenced using Sanger’s method.

Transient transfection

Cells were counted using a hemocytometer, and ~ 2 × 10 5 cells in 1 ml of 2i + LIF medium were seeded per well of a gelatinized 12-well plate and immediately transfected with 50 pmol of an appropriate siRNA (Horizon Discovery/Dharmacon; Additional file 11 : Table S9) premixed with 2.5 µl of Lipofectamine 2000 and 100 µl of Opti-MEM I, as recommended. In experiments involving simultaneous knockdown of two genes, 100 pmol of mixed siRNAs was combined with 4 µl of Lipofectamine 2000 and 100 µl Opti-MEM I. Cells were exposed to siRNA-containing complexes overnight, followed by a medium change to fresh 2i + LIF next morning. RNAs were extracted 48 h post transfection. In experiments requiring CHX treatment, cells grown for 48 h following siRNA transfection were supplemented with 100 μg/ml CHX (Sigma, cat# C4859) or an equal volume of DMSO (Sigma, cat# D2650) and incubated for an additional 6 h prior to RNA extraction. In minigene experiments, cells were transfected with 1 µg of a corresponding plasmid mixed with 3 µl of Lipofectamine 2000 and 100 µl of Opti-MEM I and incubated for 24 h. Alternatively, cells were pre-treated with siRNAs for 24 h and then transfected with 1 µg of minigene plasmid, as above, and incubated for another 24 h prior to RNA extraction.

Inducible neuronal differentiation

We have adapted previously published protocols for Ngn2 -induced glutamatergic neuronal differentiation [ 44 , 45 ] (Additional file 12 : Table S10). On the day of Ngn2 induction (day 0), cell culture plates (12-well plates were Corning™ Costar™ cat# 3513 and 6-well plates were Thermo Fisher Scientific Nunc™ cat# 140675) were coated with Geltrex™ (Gibco, cat# A1413302), diluted 1:50 in cold DMED/F12, for 2 h in a humidified incubator at 37 °C. Geltrex was aspirated and TRE-Ngn2 ESCs were immediately plated into wells, without washing, in the iN-D0 medium containing a 1:1 mixture of Neurobasal (Thermo Fisher Scientific, cat# 21103049) and DMEM/F12 (Sigma, cat# D6421) supplemented with 100 units/ml PenStrep (Thermo Fisher Scientific, cat# 15140122), 1 × N2 (Gibco, cat# 17502048), 1 × B27 with retinoic acid (Gibco, cat# 17504044), 1 µg/ml laminin (Sigma, cat# L2020), 20 µg/ml insulin (Sigma, cat# I0516), 500 µM L-glutamine (Gibco, cat# 25030–024), 2 mM db-cAMP (Sigma, cat# D0627), 10 ng/ml NT-3 (Miltenyi Biotec, cat# 130–093-973), 100 µM β-mercaptoethanol and 2 μg/ml Dox. If not stated otherwise, we seeded 2 × 10 4 ESCs per well of a Geltrex-coated 12-well plate for subsequent RT-PCR and RT-qPCR analyses and 1–3 × 10 3 cells per well of a 12-well plate containing a Geltrex-coated 18-mm glass coverslip (VWR, cat# 631–1580) for immunostaining. The cells attached to the wells were then cultured in a humidified incubator at 37 °C, 5% CO 2 .

On day 2, the medium was changed completely to the iN-D2 medium, similar to iN-D0 except containing 200 µM L-ascorbic acid (Sigma, A4403) instead of β-mercaptoethanol and 1 μg/ml instead of 2 μg/ml Dox. Starting from day 4, we replaced only ~ 50% of conditioned medium with fresh Dox-free medium. The iN-D4 medium used for this purpose on day 4 had the same composition as iN-D2 but lacked Dox. In short-term differentiation experiments, samples were collected on day 6 without further medium changes. In experiments where induced neurons were maintained longer than 6 days, the half-volume changes on day 6 onwards were done using the iN-D6 medium containing Neurobasal supplemented with 1 × B27 with retinoic acid, 1 × CultureOne supplement (Gibco, cat# 15674028), 500 µM L-glutamine, 1 µg/ml laminin, 100 units/ml PenStrep, 200 µM L-ascorbic acid, 10 ng/ml NT-3, 10 ng/ml BDNF (Miltenyi Biotec, cat# 130–093-811), and 2 mM db-cAMP.

RT-qPCR and RT-PCR analyses

Total RNAs were purified using the EZ-10 DNAaway RNA Miniprep Kit (BioBasic, cat# BS88136), as recommended. Reverse transcription (RT) was performed at 50 °C for 40 min using SuperScript IV reagents (Thermo Fisher Scientific, cat# 18090200), 5 µM of random decamer (N10) primers, and 2 units/μl of murine RNase inhibitor (New England Biolabs, M0314L). In minigene experiments, purified RNAs were additionally incubated with 2 units of RQ1-DNAse (Promega, cat# M6101) per 1 µg of RNA at 37 °C for 30 min to eliminate plasmid DNA contamination. RQ1-DNAse was inactivated by adding the Stop Solution. The RNAs were then immediately reverse-transcribed as described above.

cDNA samples were analyzed by qPCR using a Light Cycler®96 Real-Time PCR System (Roche) and the qPCR BIO SyGreen Master Mix (PCR Biosystems; cat# PB20.16). All RT-(q)PCR primers are listed in Additional file 10 : Table S8. RT-qPCR signals were normalized to expression levels of housekeeping genes selected based on our RNA-seq data. Cnot4 was used for longitudinal differential expression analyses. Gars was used to compare between CHX- and DMSO-treated samples.

To analyze NS-CE inclusion, cDNA samples were amplified using DreamTaq DNA Polymerase (Thermo Fisher Scientific cat# EP0702) or ROCHE Taq DNA polymerase (Sigma-Aldrich, cat# 11596594001). The RT-PCR products were then resolved by electrophoresis in 2% agarose gels alongside GeneRuler 1 kb Plus DNA Ladder. Band intensities were quantified using Image Studio Lite (Version 5.2) (LI-COR Biosciences). Percent spliced in (PSI) values were calculated by normalizing the intensity of the NS-CE inclusion product by combined intensity of the inclusion and skipping products and multiplying the quotient by 100.

TRE-Ngn2 samples for RNA-sequencing

For RNA-Seq, TRE-Ngn2 cells were differentiated into neurons in a 6-well plate format as described above. We plated 6–8 × 10 4 cells per well to produce day 3, 6, 12, and 24 differentiated samples. Day 0 non-induced samples were generated by plating TRE-Ngn2 ESCs in 2i + LIF at 1 × 10 5 per well and then lysing the culture 3 days later. All time points were lysed using TRIzol reagent (Thermo Fisher Scientific, cat# 15596026). The lysates were immediately frozen, stored at − 80 °C, and processed simultaneously using the TRIzol Plus RNA Purification Kit (Thermo Fisher Scientific cat# 12183555). RNAs were eluted in nuclease-free water (Invitrogen, cat# AM9939).

Natural samples for RNA sequencing

To identify natural AS-NMD targets, cultures of wild-type (IB10) ESCs, NPC neurospheres, and primary neurons were treated with either DMSO or 100 µg/ml CHX for 6 h at 37 °C. Alternatively, we prepared nuclear and cytoplasmic fractions from untreated cells using a PARIS™ kit (Thermo Fisher Scientific, cat# AM1921).

Primary NPC cultures were obtained by dissecting embryonic day 14.5 (E14.5) mouse embryonic cortices in Hank’s Balanced Salt Solution (1 × HBSS, Thermo Fisher Scientific; cat# 14025092) and dissociating them by trituration. The NPCs were then cultured as neurospheres in reconstituted NeuroCult® Proliferation Kit medium (STEMCELL Technologies, cat# 05702) supplemented with 20 ng/ml recombinant human EGF (STEMCELL Technologies, cat# 78006). For passaging, neurospheres were dissociated with NeuroCult® Chemical Dissociation Kit (STEMCELL Technologies, cat# 05707), as recommended. We also prepared adherent NPC cultures for immunofluorescence experiments by plating dissociated neurospheres to polyornithine (Sigma; cat# P3655-10MG) and fibronectin (STEMCELL Technologies, cat# 07159) coated dishes.

Primary cortical neurons were isolated from E15.5 mouse embryos and cultured as described [ 86 ]. Briefly, cortices were dissociated with 2.5% trypsin (Thermo Fisher Scientific, cat# 15090046) and plated on poly-L-lysine (Sigma, cat# P2636) coated 6-well plates or 18-mm round coverslips in Minimum Essential Media (MEM) with L-glutamine (Thermo Fisher Scientific, cat# 11095080), 0.6% glucose (Fisher Scientific, cat# 10335850) and 10% horse serum (GIBCO/Life Technologies) at 1 × 10 6 neurons per well and 2.5 × 10 4 neurons per coverslip.

Neurons in 6-well plates were cultured for 5 days without glial feeders, whereas neurons attached to coverslips were transferred to wells containing a monolayer of newborn rat astrocytes [ 86 ], and cultured in neuronal maintenance medium (MEM with L-glutamine, 0.6% glucose and 1 × Neurocult SM1 neuronal supplement (STEMCELL Technologies, cat# 05711)) for 21 days replacing half of the medium every 3–4 days.

Astrocytes for long-term neuronal cultures were prepared by dissociating newborn rat cortices with 2.5% trypsin and 1 mg/ml DNase (Merck, cat# 10104159001) and plating the cells in MEM with L-glutamine supplemented with 0.6% glucose, 10% FBS (GE Hyclone, cat# HYC85), 100 units/ml penicillin and 100 µg/ml streptomycin (Thermo Fisher Scientific, cat# 15140122) at 2 × 10 6 cells per 25-cm 2 flask [ 86 ]. The medium was changed once after 3 days, and the cultures were maintained for a total of 7 days to allow astrocytes to expand to ~ 90% confluence.

We isolated RNAs from the whole-cell and fractionated material using either the Purelink™ RNA mini kit (Thermo Fisher Scientific, cat# 12183025; IB10 ESCs and NPCs) or the RNeasy micro kit (Qiagen, cat# 74004; primary neurons).

RNA sequencing

Total RNA concentration was estimated using Nanodrop and measured with the Qubit RNA BR Assay Kit (Fisher Scientific, cat# Q10211). Additionally, RNA quality was evaluated using the Agilent Technologies RNA ScreenTape Assay (5067–5576 and 5067–5577).

For the TRE-Ngn2 time series, intact poly(A) RNA was purified from total RNA samples (100–500 ng) with oligo(dT) magnetic beads. Stranded mRNA sequencing libraries were then prepared as described using the Illumina TruSeq Stranded mRNA Library Prep kit (20020595) and TruSeq RNA UD Indexes (20022371). Purified libraries were qualified on an Agilent Technologies 2200 TapeStation using a D1000 ScreenTape assay (cat# 5067–5582 and 5067–5583). The molarity of adapter-modified molecules was defined by quantitative PCR using the Kapa Biosystems Kapa Library Quant Kit (cat# KK4824). Individual libraries were normalized to 1.30 nM in preparation for Illumina sequence analysis. Sequencing libraries were chemically denatured and applied to an Illumina NovaSeq flow cell using the NovaSeq XP chemistry workflow (20021664). Following the transfer of the flowcell to an Illumina NovaSeq instrument, a 2 × 51 cycle paired end sequence run was performed using the NovaSeq S1 reagent Kit (20027465).

For the natural samples, intact poly(A) RNA was purified from total RNA (100–500 ng) with oligo(dT) magnetic beads and stranded mRNA sequencing libraries were prepared as described using the Illumina TruSeq Stranded mRNA Library Preparation Kit (RS-122–2101, RS-122–2102). Purified libraries were qualified on an Agilent Technologies 2200 TapeStation using a D1000 ScreenTape assay (cat# 5067–5582 and cat# 5067–5583). The molarity of adapter-modified molecules was defined by quantitative PCR using the Kapa Biosystems Kapa Library Quant Kit (cat# KK4824). Individual libraries were normalized to 5 nM and equal volumes were pooled in preparation for Illumina sequence analysis. Sequencing libraries (25 pM) were chemically denatured and applied to an Illumina HiSeq v4 single-read flow cell using an Illumina cBot. Hybridized molecules were clonally amplified and annealed to sequencing primers with reagents from an Illumina HiSeq SR Cluster Kit v4-cBot (GD-401–4001). The flowcell was transferred to an Illumina HiSeq 2500 instrument (HCSv2.2.38 and RTA v1.18.61) and a 50-cycle single-read sequence run was performed using HiSeq SBS Kit v4 sequencing reagents (FC-401–4002). The library preparation and sequencing steps were performed by the High-Throughput Genomics facility at the Huntsman Cancer Institute, University of Utah, USA.

Immunofluorescence analyses

Non-induced TRE-Ngn2 ESCs were plated in 2i + LIF medium onto 18-mm gelatin-coated coverslips (VWR, cat# 631–1580) at 6 × 10 4 cells per well of a 12-well plate. Dox-induced TRE-Ngn2 cells were plated onto 18 mm coverslips coated with Geltrex, as described above. Cells were fixed with 4% PFA in 1 × PBS for 15 min at room temperature. After 3 washes with 1 × PBS, cells were permeabilized with 0.1% Triton X-100 in 1 × PBS for 5 min, followed by another 3 washes with 1 × PBS. The coverslips were then incubated in the blocking buffer (5% horse serum, 5% goat serum, 1% BSA in 1 × PBS) for 1 h at room temperature. Blocked samples were incubated with primary antibodies (Additional file 13 : Table S11) diluted in the blocking buffer at 4 °C in a humidified chamber overnight. The next day, coverslips were washed 3 times with 1 × PBS and incubated with Alexa Fluor-conjugated secondary antibodies diluted 1:400 in the blocking buffer at room temperature for 1 h. Coverslips were then washed 3 times with 1 × PBS and stained with 0.5 µg/ml DAPI (Thermo Fisher Scientific, cat# D1306) in 1 × PBS for 5 min. Coverslips were mounted on microscope slides in a drop of ProLong Gold Antifade Mountant solution (Thermo Fisher Scientific, cat# P36934). Images were taken using a ZEISS Axio Observer Z1 Inverted Microscope with alpha Plan-Apochromat 100x/1.46 oil immersion objective.

Bioinformatics

RNA-seq data were quality-controlled by FastQC ( http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ ) and trimmed by Trimmomatic [ 87 ]. HISAT2 [ 51 ] index and splice site files were prepared using the GRCm39 mouse genome (GRCm39.primary_assembly.genome.fa) and M26 transcriptome (gencode.vM26.primary_assembly.annotation.gtf) files from GENCODE ( ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_mouse/release_M26/ ):

hisat2-build -p [n_threads] GRCm39.primary_assembly.genome.fa GRCm39_genome

hisat2_extract_splice_sites.py gencode.vM26.primary_assembly.annotation.gtf > M26.ss.txt

We then aligned our longitudinal TRE-Ngn2 differentiation data using HISAT2:

hisat2 -p [n_threads] -k 50 -fr -rna-strandness RF -dta-cufflinks -no-unal \

--known-splicesite-infile M26.ss.txt \

-x GRCm39_genome \

-1 [read_1.fastq] \

-2 [read_2.fastq] \

-S [aligned.sam]

SAM files were converted to the BAM format and optionally concatenated, sorted, and indexed using SAMtools [ 88 ]. To produce CPM-normalized strand-specific RNA-seq coverage plots, sorted and indexed [aligned.bam] files were converted into the bedGraph format using deepTools [ 89 ]:

bamCoverage -p [n_threads] -bs 1 -of bedgraph \

--filterRNAstrand forward \

--effectiveGenomeSize 2654621837 --normalizeUsing CPM \

-b [aligned.bam] -o [forward.bedGraph]

--filterRNAstrand reverse \

-b [aligned.bam] -o [reverse.bedGraph]

The resultant [forward.bedGraph] and [reverse.bedGraph] files were then visualized in IGV [ 90 ].

To identify novel transcripts enriched for AS-NMD events, we first analyzed individual stage-specific DMSO- and CHX-treated samples by StringTie [ 51 ]:

stringtie -p [n_threads] --rf -M 1 -u -f 0.05 \

-G gencode.vM26.primary_assembly.annotation.gtf \

-o [sample_specific.gtf] \

[sample_specific.bam]

Different sample_specific.gtf files were then merged by cuffmerge [ 91 ]:

cuffmerge -p [n_threads] --min-isoform-fraction 0.05 \

-g gencode.vM26.primary_assembly.annotation.gtf \

[list_of_sample_specific_gtfs_to_merge.txt]

and novel transcripts in the resultant merged.gtf file were shortlisted by removing entries with intron chains identical to those in gencode.vM26.primary_assembly.annotation.gtf (class code " = "):

grep -v -P "class_code \" = \";" merged.gtf > novel.gtf

The remaining transcripts were analyzed by GffCompare [ 92 ] to allocate them to known genes where possible:

gffcompare novel.gtf -r gencode.vM26.primary_assembly.annotation.gtf

We then used R [ 93 ] and factR2 [ 50 ] to annotate the resultant gffcmp.merged.gtf file, combine it with the gencode.vM26.primary_assembly.annotation.gtf reference and extract AS-NMD events from thus produced extended_M26.gtf:

library(factR2).

library(rtracklayer)

custom.gtf <-import("gffcmp.merged.gtf")

ref.gtf <-import("gencode.vM26.primary_assembly.annotation.gtf")

seqlevels(ref.gtf) <-unique(c(seqlevels(custom.gtf), seqlevels(ref.gtf)))

extended_M26.gtf <-c(ref.gtf, custom.gtf)

factRobj <-createfactRObject( extended_M26.gtf, "vM26")

factRobj <-buildCDS(factRobj)

factRobj <-predictNMD(factRobj)

factRobj <-testASNMDevents(factRobj)

factRobj <-getAScons(factRobj, type = "flanks", padding = 100)

exportGTF(factRobj, “extendend_M26.gtf”)

exportTable(factRobj, “extended_M26_NMD_exons.txt”, “exons”)

Transcript abundance in our TRE-Ngn2 differentiation experiment was quantified using Kallisto [ 94 ] with an extended_M26.gtf-based index:

kallisto quant -t [n_threads] -rf-stranded \

-i extended_M26.index -o [out_dir] \

[read_1.fastq] [read_2.fastq]

Kallisto-deduced transcript-specific counts for individual biological replicates were imported into R using the tximport package [ 95 ] and analyzed by DESeq2 [ 53 ]. Genes not represented by ≥ 5 sequencing reads in ≥ 2 samples were considered poorly expressed and excluded from subsequent analyses. DMSO and CHX data for specific differentiation time points were compared pairwise using the Wald test:

dds <-DESeq(dds, fitType = 'local')

Differentially expressed genes in DMSO time-course data were analyzed by the likelihood-ratio test:

dds_lrt <-DESeq(dds_lrt, test = "LRT", reduced = ~ 1)

Unless indicated otherwise, genes with FDR (calculated as Benjamini-Hochberg-adjusted P -value) < 0.001 were considered differentially regulated. The PCA plot in Additional file 1 : Fig. S2A was produced by DESeq2’s plotPCA function using VST-transformed expression values ( ${\text{VST}}_{\text{gene}}$ ), which were calculated for all detectably expressed genes by the vst(dds, blind = FALSE) function of DESeq2. ${\text{VST}}_{\text{gene}}$ values were also used for correlation and developmental trend analyses. Heatmaps in Additional file 1 : Fig. S2B and Additional file 1 : Fig. S10A were produced using the ComplexHeatmap R package and centered and scaled ${\text{VST}}_{\text{gene}}$ values for manually selected marker genes.

Alternative splicing patterns were analyzed by Whippet with an extended_M26.gtf-based index:

julia whippet-quant.jl [read_1.fastq] [read_2.fastq] -biascorrect \

-x extended_M26.whippet.graph.jls -o [out_dir]

Condition-specific psi.gz files produced by the above code were then compared for each differentiation stage as follows:

julia whippet-delta.jl -s 1 -a condition_A.psi.gz -b condition_B.psi.gz \

-o [output_prefix]

The difference (Δ) between condition-specific percent spliced in values (PSI; defined in Whippet as a fraction rather than %) for CHX-treatment and nucleus-cytoplasm fractionation experiments was calculated as:

respectively.

We used the following filters to shortlist regulated NMD-stimulating (NS) and NMD-repressing (NS) events. NS events, ΔPSI > 0.1 and probability > 0.9; NR events, ΔPSI < − 0.1 and probability > 0.9. To retain events that control mRNA sensitivity to NMD but are nearly completely included or excluded at the splicing level, we also shortlisted NS events where PSI averaged across conditions was > 0.9, and NR events where the average was < 0.1. In some cases, the stringency of Whippet cutoffs was increased to |ΔPSI|> 0.25 and probability > 0.95. Additionally, regulated alternative splicing (AS) events were occasionally shortlisted by rMATS v4.1.2 [ 54 ] with |ΔPSI|> 0.1 and FDR < 0.05 cutoffs. These instances are explicitly indicated in the text, figures, and tables. Shortlisted events from the cassette/core exon (CE), alternative donor/5′ splice site (AD), alternative acceptor/3′ splice site (AA), and retained intron (RI) groups were used for further analyses.

For correlation and trend analyses we used facR2’s function testGeneCorr subjecting PSI values to arcsine-square root variance-stabilizing transformation [ 96 ] and adding the 2/π coefficient to maintain transformed PSI within the $[0, 1]$ interval:

The correlation between the AS-NMD splicing pattern and gene expression was then calculated as Pearson’s product-moment correlation between ${\text{VST}}_{\text{gene}}$ and ${\text{VST}}_{\text{PSI}}$ for NMD-repressing events and between ${\text{VST}}_{\text{gene}}$ and $1-{\text{VST}}_{\text{PSI}}$ for NMD-stimulating events.

Evolutionary conservation of the intronic sequence context of AS-NMD events was analyzed using factR2’s function getASCons and the PhastCons [ 55 ] mm39 35-vertebrate conservation track from the UCSC genome browser ( http://hgdownload.soe.ucsc.edu/goldenPath/mm39/phastCons35way/ ). For each event type, we examined a 200-nt sequence, defining the context as 100-nt intronic sequences preceding and following CEs, the first and last 100 nt of RIs, 200-nt intronic sequences following ADs, and 200-nt intronic sequences preceding AAs.

PTBP1-regulated events were shortlisted by Whippet analyses of ESCs and NPCs, where PTBP1 was knocked down alone or together with its functionally similar paralog, PTBP2 (|ΔPSI|> 0.1; probability > 0.9 cutoffs). If the PSI values changed significantly in response to multiple treatments, individual PTBP1 knockdown experiments were given priority over combined PTBP1 and PTBP2 knockdowns. Moreover, ESCs were prioritized over NPCs. We additionally requested that the event’s PSI correlates with Ptbp1 expression levels (Pearson’s P < 0.05) in our TRE-Ngn2 RNA-seq differentiation dataset and that the sign of the correlation coefficient (Pearson’s r ) is opposite to the sign of ΔPSI in response to PTBP1 knockdown in ESCs or NPCs.

We estimated the proportions of different cell types in differentiating TRE-Ngn2 cultures using the R package MuSiC [ 48 ]. Raw gene expression counts from our bulk RNA-seq dataset were compared with mouse single-cell RNA-seq data for ESCs (GSM2098554), NPCs (GSE67833; [ 97 ]), and adult cortices (GSE185862; [ 98 ]). We first converted count matrices to the ExpressionSet object format:

library(Biobase)

metadata <-data.frame(row.names = colnames(counts), group = colnames(counts))

metalabels <-data.frame(labelDescription = c("group"))

metadata <-AnnotatedDataFrame(data = metadata, varMetadata = metalabels)

bulk.eset <-ExpressionSet(counts, phenoData = metadata)

and then performed cellular deconvolution using MuSiC’s music_prop function. The output data frame was processed and visualized in ggplot2 [ 99 ].

Genes were clustered according to their neurodevelopmental expression trajectories using DP_GP_cluster [ 56 ] as follows:

DP_GP_cluster.py -i [expression.txt] -o [output_prefix] \

-fast -n 1000 –max_iters 1000 \

-check_burnin_convergence –check_convergence \

-cluster_uncertainty_estimate –plot -p pdf

where expression.txt was a table with gene IDs and averages of ${\text{VST}}_{\text{gene}}$ values for each differentiation time point. The optimal_clustering.txt output file was used for further analyses.

To estimate AS-NMD contributions to gene downregulation in development, we modeled changes in mRNA abundance as a function of time ( $R(t)$ ) depending on mRNA synthesis rate ( ${v}_{sr}$ ) and the decay constant ( ${k}_{dr}$ ) related to mRNA half-life ( ${t}_{1/2}$ ) [ 100 , 101 ] as follows:

For simplicity, we assumed that the rate at which productively spliced mRNA is synthesized can decrease through the onset of transcriptional repression ( ${r}_{t}$ ) or/and an increase in AS-NMD repression ( ${r}_{n}$ ) at specific time points within the day 2–4 period, whereas the ${k}_{dr}$ remains constant. We also assumed that NMD-sensitive isoforms are much less stable than productively spliced mRNA, and therefore, ignored their contribution to $R(t)$ . This produced the following equation, which was solved using the ode45 function or the R package pracma:

In the model described in Additional file 1 : Fig. S13, we used published transcriptome-wide estimates for ${v}_{sr}$ (1.76 mRNA molecules per cell per hour; the median rate measured for mouse NIH 3T3 cells; [ 101 ]) and ${t}_{1/2}$ (7.08 h; the median half life for proliferating and differentiating mouse ESCs; [ 102 ]). However, similar results were obtained when we estimated the ${v}_{sr}$ and ${t}_{1/2}$ parameters by averaging the Fmnl3- , Iqgap1- , and Ripk1- specific values ( ${v}_{sr}$ =2.63 mRNA molecules per cell per hour in NIH 3T3 cells and ${t}_{1/2}$ = 5.11 h in proliferating (LIF +) and differentiating (RA +) mouse ESCs). ${r}_{t}$ was set to 1 in early development. To estimate ${r}_{t}$ following the onset of repression in developing neurons, we averaged the ratios between expression levels on days 5–6 and days 0–1 for the Fmnl3 , Iqgap1 , and Ripk1 mutants in Fig. 5 A–C. This resulted in ${r}_{t}=0.39$ . We estimated the initial (stem cell-specific) and the final (neuron-specific) ${r}_{n}$ values from the ${\text{PSI}}_{\text{CHX}}$ data (Fig. 4 B):

by averaging day-0–1 and day-5–6 Fmnl3 , Iqgap1 , and Ripk1 ${\text{PSI}}_{\text{CHX}}$ values, respectively. This yielded the initial ${r}_{n}=0.78$ and the final ${r}_{n}=0.12$ .

Single-cell RNA-sequencing data from developing mouse dentate gyrus were downloaded from https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE95753 . A total of 2000 FASTQ files from the granule cell lineage were sampled and combined into four groups to represent the transcriptomes of the four distinct cell subpopulations: radial glia-like cells, neuronal intermediate progenitors, neuroblasts, and granule cells. Pooled FASTQ files were aligned to the mm39 genome by HISAT2, and custom transcriptomes were constructed using StringTie2, as described above. Group-specific GTF files were then merged:

stringtie --merge -F 0.5 -T 0.5 -G gencode.vM26.primary_assembly.annotation.gtf -o stringtie.merged.gtf [*.gtf]

The stringtie.merged.gtf file was processed by factR2 to annotate AS-NMD events and score the interspecies conservation of intronic regions flanking NS-CEs. Transcript abundances and splicing patterns in individual cells were analyzed by Kallisto and Whippet, respectively, with stringtie.merged.gtf-based indexes. In this analysis, high-quality NS-CEs were identified as events predicted by factR2 to stimulate NMD and showing a correlation (Pearson’s r > 0.2 and P < 0.05) between NMD-protective splicing and gene expression after applying appropriate variance-stabilizing transformations. Cassette exons that were not predicted to be NMD-inducing by factR2 were used as a control group.

Statistical analyses

Unless stated otherwise, all statistical procedures were performed in R . We repeated experiments at least three times and showed the results as averages ± SD. Data obtained from RT-(q)PCR quantifications were typically analyzed using a two-tailed Student’s t -test assuming unequal variances. Other types of data were compared using a two-tailed Wilcoxon rank sum test, one-sided Kolmogorov–Smirnov (KS) test, or Fisher’s exact test. Where necessary, p -values were adjusted for multiple testing using Benjamini–Hochberg correction (FDR). Correlation analyses were done using Pearson’s product-moment and Kendall’s rank correlation methods, as specified in the text. Numbers of experimental replicates, P -values, and the tests used are indicated in the Figures and/or Figure legends.

Availability of data and materials

The RNA-seq data generated and analyzed in this study are available from BioStudies under the accession number E-MTAB-13134 [ 103 ]. The source code and a detailed user manual for factR2 are available on GitHub [ 50 ]. Details of the key bioinformatics procedures used in our work are also described on GitHub [ 104 ]. Uncropped versions of our gel and microscopy images have been published on Figshare ( https://doi.org/10.6084/m9.figshare.25943524.v1 ).

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Acknowledgements

We thank Snezhka Oliferenko for valuable discussions, Brian Dalley for the help with RNA sequencing, and Michael Kyba and Feng Zhang, for reagents.

Review history

The review history is available as Additional file 14 .

Peer review information

Tim Sands was the primary editor of this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

This work was supported by the Biotechnology and Biological Sciences Research Council (BB/M001199/1, BB/M007103/1, and BB/V006258/1).

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Anna Zhuravskaya, Karen Yap, Fursham Hamid & Eugene V. Makeyev

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Contributions

AZ performed the TRE-Ngn2 experiments, analyzed and interpreted the data, and contributed to writing the manuscript; KY performed the experiments with wild-type ESCs and neural cortical cells and analyzed and interpreted the data; FH developed factR2 and performed bioinformatics analyses; EVM obtained funding for this project, performed bioinformatics analyses and was a major contributor to the writing of the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Fursham Hamid or Eugene V. Makeyev .

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All animal procedures were approved by the Institutional Animal Care and Use Committee and the Home Office (Project Licence PP5451667).

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Supplementary Information

Additional file 1. supplementary figures., additional file 2. table s1. key functions of currently available mrna isoform/as-nmd analysis programs., additional file 3. table s2. chx-responsive as-nmd events., additional file 4. table s3. ranked gene trajectory clusters., 13059_2024_3305_moesm5_esm.xlsx.

Additional file 5. Table S4. Facilitating AS-NMD events in the TRE-Ngn2 differentiation dataset showing significant positive correlation between NMD-protective splicing patterns and gene expression with P < 0.05 and FDR < 0.1.

Additional file 6. Table S5. Metascape results.

Additional file 7. supplementary results., additional file 8. table s6. facilitating as-nmd events in developing dentate gyrus granule cells., additional file 9. table s7. plasmids generated in this study., additional file 10. table s8. dna oligonucleotides used in this study., additional file 11. table s9. sirnas used in this work., additional file 12. table s10. media for induced neuronal differentiation of tre-ngn2 cells., additional file 13. table s11. antibodies used in this work., additional file 14. peer review history., rights and permissions.

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Zhuravskaya, A., Yap, K., Hamid, F. et al. Alternative splicing coupled to nonsense-mediated decay coordinates downregulation of non-neuronal genes in developing mouse neurons. Genome Biol 25 , 162 (2024). https://doi.org/10.1186/s13059-024-03305-8

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Alternative splicing
Nonsense-mediated decay
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Downregulation of non-neuronal genes
Custom transcriptome annotation

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Controlled experiments

Introduction

Control and experimental groups

Experimental setup

Analyzing the results

Additional references:

Why control an experiment?

František Baluška

P < 0.05 tacitly acknowledges the explicate order

Further reading

Acknowledgements

Contributor Information

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What Is a Controlled Experiment? | Definitions & Examples

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Control groups

Random assignment

Masking (blinding)

Difficult to control all variables

Risk of low external validity

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Pritha Bhandari

Controlled Experiments: Definition, Steps, Results, Uses

Importance of controlled experiments in various fields

Why Controlled Experiments Matter?

Minimizing Biases and Confounding Variables

Achieving Causal Inference

Planning a Controlled Experiment

Identifying Variables and Control Groups

Designing Experimental Procedures and Protocols

Conducting a Controlled Experiment

Data Collection Methods and Instruments

Monitoring and Maintaining Experimental Conditions

Analysing Results and Drawing Conclusions

Interpretation of Results

Implications and Potential Applications

Common Challenges and Solutions

Dealing with Sample Size and Statistical Power

Mitigating Unforeseen Variables

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What An Experimental Control Is And Why It’s So Important

Daniel Nelson

Terminology Of A Scientific Experiment

Independent and Dependent Variables

Control Groups and Experimental Groups

Why Are Experimental Controls So Important?

A Practical Example

Not All Experiments Are Controlled

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Response time

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