experiment with variables

9 Great Ways to Teach Variables in Science Experiments

by Katrina | Feb 17, 2024 | Pedagogy , Science | 1 comment

Science is a journey of exploration and discovery, and at the heart of every scientific experiment lies the concept of variables. Variables in science experiments are the building blocks of experimentation, allowing scientists to manipulate and measure different elements to draw meaningful conclusions.

Teaching students about variables is crucial for developing their scientific inquiry skills and fostering a deeper understanding of the scientific method.

In this blog post, we’ll explore the importance of teaching variables in science experiments, delve into the distinctions between independent, dependent, and controlled variables, and provide creative ideas on how to effectively teach these variable types.

So grab a coffee, find a comfy seat, and relax while we explore fun ways to teach variables in science experiments! 

ways to teach variables in science experiments

The Importance of Teaching Variables in Science Experiments:

Foundation of Scientific Inquiry: Variables form the bedrock of the scientific method. Teaching students about variables helps them grasp the fundamental principles of scientific inquiry, enabling them to formulate hypotheses, design experiments, and draw valid conclusions.

Critical Thinking Skills: Understanding variables cultivates critical thinking skills in students. It encourages them to analyze the relationships between different factors, question assumptions, and think systematically when designing and conducting experiments.

Real-world Application: Variables are not confined to the laboratory; they exist in everyday life. Teaching students about variables equips them with the skills to critically assess and interpret the multitude of factors influencing phenomena in the real world, fostering a scientific mindset beyond the classroom.

In addition to the above, understanding scientific variables is crucial for designing an experiment and collecting valid results because variables are the building blocks of the scientific method.

A well-designed experiment involves the careful manipulation and measurement of variables to test hypotheses and draw meaningful conclusions about the relationships between different factors. Here are several reasons why a clear understanding of scientific variables is essential for the experimental process:

1. Precision and Accuracy: By identifying and defining variables, researchers can design experiments with precision and accuracy. This clarity helps ensure that the measurements and observations made during the experiment are relevant to the research question, reducing the likelihood of errors or misinterpretations.

2. Hypothesis Testing: Variables in science experiments are central to hypothesis formulation and testing. A hypothesis typically involves predicting the relationship between an independent variable (the one manipulated) and a dependent variable (the one measured). Understanding these variables is essential for constructing a hypothesis that can be tested through experimentation.

3. Controlled Experiments: Variables, especially controlled variables, enable researchers to conduct controlled experiments. By keeping certain factors constant (controlled variables) while manipulating others (independent variable), scientists can isolate the impact of the independent variable on the dependent variable. This control is essential for drawing valid conclusions about cause-and-effect relationships.

4. Reproducibility: Clear identification and understanding of variables enhance the reproducibility of experiments. When other researchers attempt to replicate an experiment, a detailed understanding of the variables involved ensures that they can accurately reproduce the conditions and obtain similar results.

5. Data Interpretation: Knowing the variables in science experiments allows for a more accurate interpretation of the collected data. Researchers can attribute changes in the dependent variable to the manipulation of the independent variable and rule out alternative explanations. This is crucial for drawing reliable conclusions from the experimental results.

6. Elimination of Confounding Factors: Without a proper understanding of variables, experiments are susceptible to confounding factors—unintended variables that may influence the results. Through careful consideration of all relevant variables, researchers can minimize the impact of confounding factors and increase the internal validity of their experiments.

7. Optimization of Experimental Design: Understanding variables in science experiments helps researchers optimize the design of their experiments. They can choose the most relevant and influential variables to manipulate and measure, ensuring that the experiment is focused on addressing the specific research question.

8. Applicability to Real-world Situations: A thorough understanding of variables enhances the applicability of experimental results to real-world situations. It allows researchers to draw connections between laboratory findings and broader phenomena, contributing to the advancement of scientific knowledge and its practical applications.

The Different Types of Variables in Science Experiments:

There are 3 main types of variables in science experiments; independent, dependent, and controlled variables.

1. Independent Variable:

The independent variable is the factor that is deliberately manipulated or changed in an experiment. The independent variable affects the dependent variable (the one being measured).

Example : In a plant growth experiment, the amount of sunlight the plants receive can be the independent variable. Researchers might expose one group of plants to more sunlight than another group.

2. Dependent Variable:

The dependent variable is the outcome or response that is measured in an experiment. It depends on the changes made to the independent variable.

Example : In the same plant growth experiment, the height of the plants would be the dependent variable. This is what researchers would measure to determine the effect of sunlight on plant growth.

3. Controlled Variable:

Controlled variables, also called constant variables, are the factors in an experiment that are kept constant to ensure that any observed changes in the dependent variable are a result of the manipulation of the independent variable. These are not to be confused with control groups.

In a scientific experiment in chemistry, a control group is a crucial element that serves as a baseline for comparison. The control group is designed to remain unchanged or unaffected by the independent variable, which is the variable being manipulated in the experiment.

The purpose of including a control group is to provide a reference point against which the experimental results can be compared, helping scientists determine whether the observed effects are a result of the independent variable or other external factors.

Example : In the plant growth experiment, factors like soil type, amount of water, type of plant and temperature would be control variables. Keeping these constant ensures that any differences in plant height can be attributed to changes in sunlight.

Science variables in science experiments

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Best resources for reviewing variables in science experiments:

If you’re short on time and would rather buy your resources, then I’ve compiled a list of my favorite resources for teaching and reviewing variables in science experiments below. While there is nothing better than actually doing science experiments, this isn’t feasible every lesson and these resources are great for consolidation of learning:

1. FREE Science Variables Posters : These are perfect as a visual aide in your classroom while also providing lab decorations! Print in A4 or A3 size to make an impact.

2. Variable scenarios worksheet printable : Get your students thinking about variable with these train your pet dragon themed scenarios. Students identify the independent variable, dependent variable and controlled variables in each scenario.

3. Variable Valentines scenarios worksheet printable : Get your students thinking about variables with these cupid Valentine’s Day scenarios. Students identify the independent variable, dependent variable and controlled variables in each scenario.

4. Variable Halloween scenarios worksheet printable : Spook your students with these Halloween themed scenarios. Students identify the independent variable, dependent variable and controlled variables in each scenario.

5. Scientific Method Digital Escape Room : Review all parts of the scientific method with this fun (zero prep) digital escape room! 

6. Scientific Method Stations Printable or Sub Lesson : The worst part of being a teacher? Having to still work when you are sick! This science sub lesson plan includes a fully editable lesson plan designed for a substitute teacher to take, including differentiated student worksheets and full teacher answers. This lesson involves learning about all parts of the scientific method, including variables.

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9 teaching strategies for variables in science experiments.

To help engage students in learning about the different types of scientific variables, it is important to include a range of activities and teaching strategies. Here are some suggestions:

1. Hands-on Experiments: Conducting hands-on experiments is one of the most effective ways to teach students about variables. Provide students with the opportunity to design and conduct their experiments, manipulating and measuring variables to observe outcomes.

Easy science experiments you could include might relate to student heart rate (e.g. before and after exercise), type of ball vs height it bounces, amount of sunlight on the growth of a plant, the strength of an electromagnet (copper wire around a nail) vs the number of coils.

Change things up by sometimes having students identify the independent variable, dependent variable and controlled variables before the experiment, or sometimes afterwards.

Consolidate by graphing results and reinforcing that the independent variable goes alone the x-axis while the dependent variable goes on the y-axis.

2. Teacher Demonstrations:

Use demonstrations to illustrate the concepts of independent, dependent, and controlled variables. For instance, use a simple chemical reaction where the amount of reactant (independent variable) influences the amount of product formed (dependent variable), with temperature and pressure controlled.

3. Case Studies:

Introduce case studies that highlight real-world applications of variables in science experiments. Discuss famous experiments or breakthroughs in science where variables played a crucial role. This approach helps students connect theoretical knowledge to practical situations.

4. Imaginary Situations:

Spark student curiosity and test their understanding of the concept of variables in science experiments by providing imaginary situations or contexts for students to apply their knowledge. Some of my favorites to use are this train your pet dragon and Halloween themed variables in science worksheets.

5. Variable Sorting Activities:

Engage students with sorting activities where they categorize different variables in science experiments into independent, dependent, and controlled variables. This hands-on approach encourages active learning and reinforces their understanding of variable types.

6. Visual Aids:

Utilize visual aids such as charts, graphs, and diagrams to visually represent the relationships between variables. Visualizations can make abstract concepts more tangible and aid in the comprehension of complex ideas.

7. Technology Integration:

Leverage technology to enhance variable teaching. Virtual simulations and interactive apps can provide a dynamic platform for students to manipulate variables in a controlled environment, fostering a deeper understanding of the cause-and-effect relationships.

Websites such as   Phet   are a great tool to use to model these types of scientific experiments and to identify and manipulate the different variables

8. Group Discussions:

Encourage group discussions where students can share their insights and experiences related to variables in science experiments. This collaborative approach promotes peer learning and allows students to learn from each other’s perspectives.

9. Digital Escape Rooms:

Reinforce learning by using a fun interactive activity like this scientific method digital escape room.

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Teaching variables in science experiments is an essential component of science education, laying the groundwork for critical thinking, inquiry skills, and a lifelong appreciation for the scientific method.

By emphasizing the distinctions between independent, dependent, and controlled variables and employing creative teaching strategies, educators can inspire students to become curious, analytical, and scientifically literate individuals. 

What are your favorite ways to engage students in learning about the different types of variables in science experiments? Comment below!

Note: Always consult your school’s specific safety guidelines and policies, and seek guidance from experienced colleagues or administrators when in doubt about safety protocols. 

Teaching variables in science experiments

About the Author

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Katrina Harte is a multi-award winning educator from Sydney, Australia who specialises in creating resources that support teachers and engage students.

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Biology archive

Course: biology archive   >   unit 1.

  • The scientific method

Controlled experiments

  • The scientific method and experimental design

experiment with variables

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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What Is a Variable in Science?

Understanding Variables in a Science Experiment

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Variables are an important part of science projects and experiments. What is a variable? Basically, a variable is any factor that can be controlled, changed, or measured in an experiment. Scientific experiments have several types of variables. The independent and dependent variables are the ones usually plotted on a chart or graph, but there are other types of variables you may encounter.

Types of Variables

  • Independent Variable: The independent variable is the one condition that you change in an experiment. Example: In an experiment measuring the effect of temperature on solubility, the independent variable is temperature.
  • Dependent Variable: The dependent variable is the variable that you measure or observe. The dependent variable gets its name because it is the factor that is dependent on the state of the independent variable . Example: In the experiment measuring the effect of temperature on solubility, solubility would be the dependent variable.
  • Controlled Variable: A controlled variable or constant variable is a variable that does not change during an experiment. Example : In the experiment measuring the effect of temperature on solubility, controlled variable could include the source of water used in the experiment, the size and type of containers used to mix chemicals, and the amount of mixing time allowed for each solution.
  • Extraneous Variables: Extraneous variables are "extra" variables that may influence the outcome of an experiment but aren't taken into account during measurement. Ideally, these variables won't impact the final conclusion drawn by the experiment, but they may introduce error into scientific results. If you are aware of any extraneous variables, you should enter them in your lab notebook . Examples of extraneous variables include accidents, factors you either can't control or can't measure, and factors you consider unimportant. Every experiment has extraneous variables. Example : You are conducting an experiment to see which paper airplane design flies longest. You may consider the color of the paper to be an extraneous variable. You note in your lab book that different colors of papers were used. Ideally, this variable does not affect your outcome.

Using Variables in Science Experiment

In a science experiment , only one variable is changed at a time (the independent variable) to test how this changes the dependent variable. The researcher may measure other factors that either remain constant or change during the course of the experiment but are not believed to affect its outcome. These are controlled variables. Any other factors that might be changed if someone else conducted the experiment but seemed unimportant should also be noted. Also, any accidents that occur should be recorded. These are extraneous variables.

Variables and Attributes

In science, when a variable is studied, its attribute is recorded. A variable is a characteristic, while an attribute is its state. For example, if eye color is the variable, its attribute might be green, brown, or blue. If height is the variable, its attribute might be 5 m, 2.5 cm, or 1.22 km.

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What Is a Control Variable? Definition and Examples

A control variable is any factor that is controlled or held constant in an experiment.

A control variable is any factor that is controlled or held constant during an experiment . For this reason, it’s also known as a controlled variable or a constant variable. A single experiment may contain many control variables . Unlike the independent and dependent variables , control variables aren’t a part of the experiment, but they are important because they could affect the outcome. Take a look at the difference between a control variable and control group and see examples of control variables.

Importance of Control Variables

Remember, the independent variable is the one you change, the dependent variable is the one you measure in response to this change, and the control variables are any other factors you control or hold constant so that they can’t influence the experiment. Control variables are important because:

  • They make it easier to reproduce the experiment.
  • The increase confidence in the outcome of the experiment.

For example, if you conducted an experiment examining the effect of the color of light on plant growth, but you didn’t control temperature, it might affect the outcome. One light source might be hotter than the other, affecting plant growth. This could lead you to incorrectly accept or reject your hypothesis. As another example, say you did control the temperature. If you did not report this temperature in your “methods” section, another researcher might have trouble reproducing your results. What if you conducted your experiment at 15 °C. Would you expect the same results at 5 °C or 35 5 °C? Sometimes the potential effect of a control variable can lead to a new experiment!

Sometimes you think you have controlled everything except the independent variable, but still get strange results. This could be due to what is called a “ confounding variable .” Examples of confounding variables could be humidity, magnetism, and vibration. Sometimes you can identify a confounding variable and turn it into a control variable. Other times, confounding variables cannot be detected or controlled.

Control Variable vs Control Group

A control group is different from a control variable. You expose a control group to all the same conditions as the experimental group, except you change the independent variable in the experimental group. Both the control group and experimental group should have the same control variables.

Control Variable Examples

Anything you can measure or control that is not the independent variable or dependent variable has potential to be a control variable. Examples of common control variables include:

  • Duration of the experiment
  • Size and composition of containers
  • Temperature
  • Sample volume
  • Experimental technique
  • Chemical purity or manufacturer
  • Species (in biological experiments)

For example, consider an experiment testing whether a certain supplement affects cattle weight gain. The independent variable is the supplement, while the dependent variable is cattle weight. A typical control group would consist of cattle not given the supplement, while the cattle in the experimental group would receive the supplement. Examples of control variables in this experiment could include the age of the cattle, their breed, whether they are male or female, the amount of supplement, the way the supplement is administered, how often the supplement is administered, the type of feed given to the cattle, the temperature, the water supply, the time of year, and the method used to record weight. There may be other control variables, too. Sometimes you can’t actually control a control variable, but conditions should be the same for both the control and experimental groups. For example, if the cattle are free-range, weather might change from day to day, but both groups have the same experience. When you take data, be sure to record control variables along with the independent and dependent variable.

  • Box, George E.P.; Hunter, William G.; Hunter, J. Stuart (1978). Statistics for Experimenters : An Introduction to Design, Data Analysis, and Model Building . New York: Wiley. ISBN 978-0-471-09315-2.
  • Giri, Narayan C.; Das, M. N. (1979). Design and Analysis of Experiments . New York, N.Y: Wiley. ISBN 9780852269145.
  • Stigler, Stephen M. (November 1992). “A Historical View of Statistical Concepts in Psychology and Educational Research”. American Journal of Education . 101 (1): 60–70. doi: 10.1086/444032

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  • Identify Variables in a Scientific Investigation

Understanding the Difference Between Independent, Dependent and Control Variables is Crucial!

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EdPlace's Year 9 Home Learning Science Lesson: Identify Variables in an Investigation

Looking for short lessons to keep your child engaged and learning? Our experienced team of teachers have created English, maths and science lessons for the home, so your child can learn no matter where they are.  And, as all activities are self-marked, you really can encourage your child to be an independent learner.  

Get them started on the lesson below and then jump into our teacher-created activities to practice what they've learnt. We've recommended five to ensure they feel secure in their knowledge - 5-a-day helps keeps the learning loss at bay (or so we think!).

Are they keen to start practising straight away? Head to the bottom of the page to find the activities. 

Now...onto the lesson!

Are You Up to Speed with Variables?

Independent, dependent and control variables. Never heard of them? Well, grab yourself a cuppa, a biscuit or two, and prepare to feel confident enough to teach it to your young scientist standing on your head with a blindfold on! Working scientifically is a key area within the national science curriculum, from primary all the way up to A-level. This area of study concerns skills which relate to practical investigations, for example planning an experiment, carrying it out, writing up conclusions, as well as processing results mathematically. However, identifying variables in scientific investigations can be a cause of much confusion. 

By the end of this 5 step guide, your EdPlace team are confident that your child will be able to:

1)  Understand the different kinds of variables

2) Apply this knowledge to a practical investigation

3) Explain  this knowledge back to you (If they've really cracked it!)

Step 1: Learning the Lingo!

Before we get our hands dirty with practicals it's crucial that Year 9 students are clear on the following terminology. Below are three key terms and their definitions which we shall focus on in this topic.

Independent variable -  the one condition that is changed during a scientific experiment, by the scientist. The experimenter alters the independent variable in order to test the dependent variable.

Dependent variable - the one condition that is observed or measured during a scientific experiment. 

Control variable -  these are the elements that are kept the same during a scientific experiment. Any change to a controlled variable would invalidate the results.

Step 2: Why Must We Ensure Our Scientific Experiments are Fair?

Science experiments, or investigations, are the part of science lessons that students enjoy most! It gives them a chance to witness science at work beyond textbooks and worksheets, and really get stuck in. So much of science centres things you cannot see, so investigations enable teachers to bring the subject to life. 

Scientific investigations always have a purpose to them – they involve observations and measurements being taken. They involve conditions being tweaked, seeing how these changes impact the outcome. Then, from the results, we collect we can draw conclusions. These are the fundamentals of scientific study - investigations allow us to advance scientific knowledge and better our understanding of the world and its workings.

Children are taught as early as Year 1 that we must make sure any experiments are a fair test. For example, if we conduct an experiment looking at whether boys run faster than girls in a race, we must make the test fair. We must make sure the distance they run is the same, the conditions are the same (i.e. not make the girls run with only one shoe on) and the way we determine each participant's speed is the same (i.e. not count in our heads for the boys, but use a stopwatch to measure the girls' speed). This understanding of fairness is our foundation for learning about variables, which we shall look at now.

Step 3: Getting to Grips with Variables

The elements that change in an experiment are called  variables . A variable is any factor, trait, or condition that can exist in differing amounts or types. An experiment usually has three kinds of variables: independent , dependent , and controlled . Let's use a basic experiment as an example: A group of students want to find out whether temperature affects how quickly sugar dissolves . They set up an experiment with four beakers of water, each at a different temperature. They add a spoonful of sugar to each, sir each beaker once only, and timed how long it took for the sugar to disappear.

Let's quickly refresh our memory:

Independent variable -  the one condition that is changed during a scientific experiment, by the scientist. There is only ever one independent variable. 

Dependent variable - the one condition that is observed or measured during a scientific experiment. There is only one dependent variable. 

Control variable -  these are the elements that are kept the same during a scientific experiment. There can be multiple control variables. Any change to a controlled variable would invalidate the results, so it's really important that they are kept the same throughout. 

So, using our example, we now should be able to identify the variables ourselves...

Independent variable = the temperature of the water

Dependent variable = the time it takes for all the sugar to disappear/dissolve

Control variables = the volume of water in beakers, the size of the beaker, the amount of sugar, the number of times it is stirred, the type of sugar used.

An easy way to think of independent and dependent variables is, when you're conducting an experiment, the independent variable is what you change, and the dependent variable is what changes because of that. You can also think of the independent variable as the cause and the dependent variable as the effect.

Let’s attempt another example together. Imagine you want to see which type of fertiliser helps plants grow fastest, so you add a different brand of fertiliser to each plant and see how tall they grow.

cactus in pot

Independent variable = the type of fertiliser given to the plant

Dependent variable = plant height

Control variables = the type of plant used, the amount of fertiliser given, the time given to grow. And all other conditions kept the same between each plant e.g. the amount of water each plant receives, the temperature of the room, the amount of sunlight etc.

Why not try executing your own investigation? You could look at how the mass of a toy attached to a parachute affects how long it takes to fall. This will give you an opportunity to make a parachute (perhaps using a piece of scrap material and some string, tried to various toys such as a toy car, a Playmobil person, a cuddly toy). You will also need a set of scales to measure the mass of each toy. Remember to use the same parachute each time! As you’re doing the investigation you can identify what are the independent and dependent variables, and what elements are your control variables.

Step 4: Put Your Knowledge to the Test!

Ok, now its time to see whether all this information is sinking in. Answer the following questions to test your understanding of variables.

1. Sally is performing a test in which she is trying to see if plants can grow when given fizzy drinks instead of water. She gives one plant water and a second identical plant the same amount of fizzy drink for two weeks. What is the independent variable?

a) The plants

b) The amount of liquid

c) The type of liquid

2. Mark carried out an investigation to see how the strength of an electromagnet coil changes with the number of coils.  What is the dependent variable?

3. April and Harry wanted to find the best pen. They decided to put a few to the test and measure which pen type lasted the longest before running out. They each chose a pen, Harry a ballpoint pen and April a fountain pen. Both used their pen to write with at school from Monday morning, and by Wednesday, April’s had run out. They concluded that ballpoint pens were the best. 

a) What was the independent variable?

b) What was the dependent variable?

c) Why is the experiment not reliable enough to base a conclusion on? i.e.  What control variables should they have used?  

Ready for a trickier one that will really push you?

4.  When magnesium is added to hydrochloric acid, how does acid concentration affect temperature change? 

Variable

✔ if this is the independent variable 

✔ if this is the dependent variable

✔ if this is a control variable

Acid concentration

     

Volume of acid

     

Temperature change

     

Mass of magnesium

     

Step 5: Let's apply your knowledge

Now that we've moved through this lesson together and put this knowledge to the test with practice questions, why not have ago tackling some EdPlace activities? Assign your child the following five activities, in order, to really consolidate their understanding. This way, you will be able to identify potential areas of concern or, ideally, demonstrate your child's confidence and comprehension! All activities are created by teachers and automatically marked. Plus, with an EdPlace subscription, we can automatically progress your child at a level that's right for them. Sending you progress reports along the way so you can track and measure progress, together - brilliant! 

Activity 1 - Evaluate Scientific Investigations

Activity 2 - Measure Accurately

Activity 3 - Draw and Evaluate Conclusions

Activity 4 - Plan an Investigation: Hypothesis and Method

Activity 5 - End of Key Stage 3 Assessment: Biology

1) The type of liquid (c)

2) The strength of the electromagnet

3a) The type of pen 

3b) The time taken for each pen to run out

3c) The experiment is unreliable because so many variables were left uncontrolled. April and Harry should have controlled the amount of writing produced by each person, even the size of writing would have impacted how quickly each pen ran out. The amount of ink in each pen when they started should also have been controlled.

Variable

✔ if this is the independent variable 

✔ if this is the dependent variable

✔ if this is a control variable

Acid concentration

   

Volume of acid

   

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Mass of magnesium

   

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Understanding the definition and different types of variables is vital to properly conducting any science experiment. An independent variable is what you intentionally change in order to measure the effect of the dependent variable.To measure both of these, you must also have controlled variables: factors that remain consistent throughout every part of the experiment. Controlled variables ensure that the different elements of an experiment are similar enough that you know what is being changed or tested.

Direct vs. Indirect Sunlight

This experiment checks whether a particular kind of plant prefers direct or indirect sunlight. Get two specimens of the same small plant that can easily be checked on and moved. Put one in an area that receives lots of direct sunlight. Put the other in an area that gets only indirect sunlight. Water both plants the same measured amount and see which grows better. This experiment demonstrates how sunlight affects different kinds of plants, reinforcing the differing needs of different kinds of plant life. The controlled variables in this experiment are the type of plant used and the amount of water received. With different kinds of plants, or inconsistent watering, how well each plant grew might not be a factor of sunlight alone.

Which Surface Rolls Faster?

Experiment with what rolls faster by covering one of two planks with a smooth lining like shelf paper and thge other with a rough lining like carpet or astroturf. Place the planks at an angle and roll a tennis or golf ball down each plank simultaneously. Observe which ball reaches the ground first. In most cases, the ball will move fastest on the smooth surface, demonstrating the effect of friction on an object's acceleration. In this experiment, the controlled variable is the angle of the board and the type of ball you roll.

Does Sugar Dissolve Better in Hot Water?

Fill two identical containers with two cups of water each -- one hot and one cold. Add a teaspoon of sugar to each and stir the mixture the same number of times in each container. Use a stopwatch to record how long it takes for a teaspoon of sugar to dissolve in each. In most cases, the sugar will dissolve in the warm water faster, demonstrating that warm mediums -- in which the molecules are moving faster -- dissolve a solid faster than cold mediums. The ratios of water to sugar and the amount of stirring are both controlled variables in this experiment.

Does Water or Vinegar Clean a Penny Better?

Take two dirty pennies and place them in identical shallow containers that can hold liquid. Petri dishes are perfect for this, but a shallow bowl or empty yogurt cup would also work. Cover the penny in one container with 1/8 cup of water, and the other with 1/8 cup of vinegar. After a week, remove the pennies and see which has been cleaned more. The result will usually be that the vinegar cleans the pennies better, demonstrating that a lower-pH solution is better at cleaning discoloration due to oxidation. The volume of liquid and the material of the penny are both controlled variables in this experiment. This experiment is especially good for teaching the concept of a controlled variable if you include a third variable -- how dirty the pennies are.

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Methodology

  • Independent vs. Dependent Variables | Definition & Examples

Independent vs. Dependent Variables | Definition & Examples

Published on February 3, 2022 by Pritha Bhandari . Revised on June 22, 2023.

In research, variables are any characteristics that can take on different values, such as height, age, temperature, or test scores.

Researchers often manipulate or measure independent and dependent variables in studies to test cause-and-effect relationships.

  • The independent variable is the cause. Its value is independent of other variables in your study.
  • The dependent variable is the effect. Its value depends on changes in the independent variable.

Your independent variable is the temperature of the room. You vary the room temperature by making it cooler for half the participants, and warmer for the other half.

Table of contents

What is an independent variable, types of independent variables, what is a dependent variable, identifying independent vs. dependent variables, independent and dependent variables in research, visualizing independent and dependent variables, other interesting articles, frequently asked questions about independent and dependent variables.

An independent variable is the variable you manipulate or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.

Independent variables are also called:

  • Explanatory variables (they explain an event or outcome)
  • Predictor variables (they can be used to predict the value of a dependent variable)
  • Right-hand-side variables (they appear on the right-hand side of a regression equation).

These terms are especially used in statistics , where you estimate the extent to which an independent variable change can explain or predict changes in the dependent variable.

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There are two main types of independent variables.

  • Experimental independent variables can be directly manipulated by researchers.
  • Subject variables cannot be manipulated by researchers, but they can be used to group research subjects categorically.

Experimental variables

In experiments, you manipulate independent variables directly to see how they affect your dependent variable. The independent variable is usually applied at different levels to see how the outcomes differ.

You can apply just two levels in order to find out if an independent variable has an effect at all.

You can also apply multiple levels to find out how the independent variable affects the dependent variable.

You have three independent variable levels, and each group gets a different level of treatment.

You randomly assign your patients to one of the three groups:

  • A low-dose experimental group
  • A high-dose experimental group
  • A placebo group (to research a possible placebo effect )

Independent and dependent variables

A true experiment requires you to randomly assign different levels of an independent variable to your participants.

Random assignment helps you control participant characteristics, so that they don’t affect your experimental results. This helps you to have confidence that your dependent variable results come solely from the independent variable manipulation.

Subject variables

Subject variables are characteristics that vary across participants, and they can’t be manipulated by researchers. For example, gender identity, ethnicity, race, income, and education are all important subject variables that social researchers treat as independent variables.

It’s not possible to randomly assign these to participants, since these are characteristics of already existing groups. Instead, you can create a research design where you compare the outcomes of groups of participants with characteristics. This is a quasi-experimental design because there’s no random assignment. Note that any research methods that use non-random assignment are at risk for research biases like selection bias and sampling bias .

Your independent variable is a subject variable, namely the gender identity of the participants. You have three groups: men, women and other.

Your dependent variable is the brain activity response to hearing infant cries. You record brain activity with fMRI scans when participants hear infant cries without their awareness.

A dependent variable is the variable that changes as a result of the independent variable manipulation. It’s the outcome you’re interested in measuring, and it “depends” on your independent variable.

In statistics , dependent variables are also called:

  • Response variables (they respond to a change in another variable)
  • Outcome variables (they represent the outcome you want to measure)
  • Left-hand-side variables (they appear on the left-hand side of a regression equation)

The dependent variable is what you record after you’ve manipulated the independent variable. You use this measurement data to check whether and to what extent your independent variable influences the dependent variable by conducting statistical analyses.

Based on your findings, you can estimate the degree to which your independent variable variation drives changes in your dependent variable. You can also predict how much your dependent variable will change as a result of variation in the independent variable.

Distinguishing between independent and dependent variables can be tricky when designing a complex study or reading an academic research paper .

A dependent variable from one study can be the independent variable in another study, so it’s important to pay attention to research design .

Here are some tips for identifying each variable type.

Recognizing independent variables

Use this list of questions to check whether you’re dealing with an independent variable:

  • Is the variable manipulated, controlled, or used as a subject grouping method by the researcher?
  • Does this variable come before the other variable in time?
  • Is the researcher trying to understand whether or how this variable affects another variable?

Recognizing dependent variables

Check whether you’re dealing with a dependent variable:

  • Is this variable measured as an outcome of the study?
  • Is this variable dependent on another variable in the study?
  • Does this variable get measured only after other variables are altered?

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experiment with variables

Independent and dependent variables are generally used in experimental and quasi-experimental research.

Here are some examples of research questions and corresponding independent and dependent variables.

Research question Independent variable Dependent variable(s)
Do tomatoes grow fastest under fluorescent, incandescent, or natural light?
What is the effect of intermittent fasting on blood sugar levels?
Is medical marijuana effective for pain reduction in people with chronic pain?
To what extent does remote working increase job satisfaction?

For experimental data, you analyze your results by generating descriptive statistics and visualizing your findings. Then, you select an appropriate statistical test to test your hypothesis .

The type of test is determined by:

  • your variable types
  • level of measurement
  • number of independent variable levels.

You’ll often use t tests or ANOVAs to analyze your data and answer your research questions.

In quantitative research , it’s good practice to use charts or graphs to visualize the results of studies. Generally, the independent variable goes on the x -axis (horizontal) and the dependent variable on the y -axis (vertical).

The type of visualization you use depends on the variable types in your research questions:

  • A bar chart is ideal when you have a categorical independent variable.
  • A scatter plot or line graph is best when your independent and dependent variables are both quantitative.

To inspect your data, you place your independent variable of treatment level on the x -axis and the dependent variable of blood pressure on the y -axis.

You plot bars for each treatment group before and after the treatment to show the difference in blood pressure.

independent and dependent variables

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.

  • Normal distribution
  • Degrees of freedom
  • Null hypothesis
  • Discourse analysis
  • Control groups
  • Mixed methods research
  • Non-probability sampling
  • Quantitative research
  • Ecological validity

Research bias

  • Rosenthal effect
  • Implicit bias
  • Cognitive bias
  • Selection bias
  • Negativity bias
  • Status quo bias

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it “depends” on your independent variable.

In statistics, dependent variables are also called:

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment .

  • The type of soda – diet or regular – is the independent variable .
  • The level of blood sugar that you measure is the dependent variable – it changes depending on the type of soda.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both!

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

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Science Projects With Three Variables for Kids in Fifth Grade

Fifth graders can learn about the importance of variables in science experiments.

Ideas for Controlled Variable Science Projects

The concept of variables in a science experiment can be confusing for fifth graders. Think of the independent variable as what you change in an experiment, the dependent variable as the response you observe because of what you changed, and the controlled variable as the things you keep the same so they don’t interfere with your results. The independent variable must be something measurable that you can change in the experiment. The dependent variables must be able to be measured and caused by the independent variable. The controlled variable must not change during the experiment. Try some easy projects that use three variables to understand the importance of each variable in an experiment.

Do Seeds Germinate More Quickly in Fertilized Soil?

Plant seeds in identical seedling trays, using two trays of unfertilized soil and two seedling trays of fertilized soil, to see which soil helps the seeds germinate faster. Label the unfertilized seedling trays “A” and “B” and the fertilized seedling trays “C” and “D.” The controlled variables are: same kind of seed, same type of soil, same amount of water from the same source applied at the same frequency, same amount of exposure to the sun, same room temperature and same dew point. The fertilizer added to trays C and D is the independent variable. The time for germination to take place and the height of the seedlings are dependent variables.

Does More Sugar Dissolve in Heated Water?

Compare how much sugar dissolves in containers of one cup of water, each at different temperatures. When sugar dissolves in water, you cannot see any sugar crystals floating in the water or settling on the bottom of the cup when you stop stirring; you'll use these visual indicators to compare how much has dissolved in each cup. You will change the temperature of the water, so this is the independent variable. The dependent variable is the amount of sugar that dissolves in each cup of water. The controlled variables are stirring each container the same amount and using sugar from the same bag.

Does Changing the Mass on the End of a Pendulum Affect the Period?

Tie a weight to the end of a 3 1/2-foot string, leaving a 5-inch tail of string so you can add additional weights later in the experiment. Hang the string from a dowel rod taped to the top of a cabinet. Mark the angle from which you will swing the pendulum, then release the weight. Time how long it takes to swing back and forth five times. One swing is called a period. Divide the time by five to get the average period for the first trial. Conduct two more trials and average the period for the three trials. Repeat the procedure with two weights and three weights. The varying weights are the independent variable, while the number of swings, or periods, is the dependent variable. The length of the string and the angle of the swing are controlled variables.

Does the Type of Surface Affect the Speed of a Toy Car?

Make a ramp with sides to make sure the car stays on the ramp. The ramp can be as simple as a board with modeling clay guard rails. You will test different surfaces, such as sand paper, floor tile or bare wood, on top of the ramp and measure the time and distance that a toy car travels using at least three trials each. The various surfaces on the ramp are the independent variables. The speed of the car, measured in distance traveled over a length of time, is the dependent variable. The controlled variables are using the same car, using the same ramp at the same angle, and letting go of the car without pushing at the same starting point.

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  • Science Buddies: Variables in Your Science Fair Project

About the Author

Annette Strauch has been a writer for more than 30 years. She has been a radio news journalist and announcer, movie reviewer for Family Movie Reviews Online, chiropractic assistant and medical writer. Strauch holds a Master of Arts in speech/broadcast journalism from Bob Jones University, where she also served on the faculty of the radio/TV department.

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Skittles Experiment with Worksheets

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The popular Skittles experiment with worksheets offers lessons in diffusion, and in this version, we are also going to enjoy a lesson on polarity and nonpolarity in chemistry.

If your student isn’t familiar with independent, dependent, and control variables, let’s look at those terms first. (These are also covered in the printable pack. Scroll to the bottom to request your free worksheet pack.)

Chemistry 1

What are the Independent and Dependent Variables in the Skittles Experiment?

What is the independent variable in an experiment? The independent variable is the variable we manipulate or change.

Scientists design experiments using variables to understand the relationship between different factors. Variables include things like temperature, the amount of liquid, and location (e.g., a sunny window vs. inside a dark room). There are three types of variables: independent variables, dependent variables, and control variables.

The independent variable is the factor that we change or manipulate. We deliberately change this variable to see how it affects the dependent variable. In this experiment, one of the independent variables is the liquid we pour into the dish. We will use oil and room-temperature water.

The dependent variable is what we measure. In this experiment, we record how the dye in the candy dissolves and how long it takes for the colors to cover the entire dish.

The control variables help us ensure that any changes we observe in the dependent variable are due to the changes we make in the independent variable. They also help eliminate other explanations for the results, ensuring that the experiment tests only the effect of the independent variable. In this experiment the temperature of the room is the same, so it is one of the control variables.

How Do We Do the Skittles Experiment?

Before starting, we recommend you download and print the worksheets that accompany this Skittle experiment.

First, review the background information on the dependent, independent, and control variables in our version of the Skittles experiment. Then complete the pre-experiment pages.

Next, gather the materials and follow the instructions below.

Materials for the Skittles Experiment

  • Four plates
  • Several bags of Skittles
  • Room Temperature water – we used 1/2 cup per plate. But before starting determine how much water will completely cover the plate, without overflowing the sides. Use this same amount of water and oil for each plate.
  • Vegetable oil
  • Liquid measuring cup
  • Set of worksheets
  • Colored pencils or crayons

Step-by-Step Procedures for the Skittles Experiment

  • Complete the Think About It page in the printable packet.
  • Place several plates on a flat surface to easily observe the experiment.
  • Make five labels, one for each plate: Warm Water Only, Room Temperature Water Only, Cooking Oil, Warm Water & 2 T Sugar, and Warm Water &  2 Corn Syrup.
  • Fill the plates with water or oil in the following fashion. Carefully pour, ensuring that the water or oil covers the bottom of the plate but does not overflow:

skittlesall 1 1

  • One plate with cooking oil only
  • One plate with room temperature water only
  • Second plate with room temperature water only
  • Third plate with room temperature water only
  • Dump the 2 T of sugar in the middle of one of the plates with room-temperature water. Do not stir it.
  • On one of the plates, with room temperature water, pour 2 T of corn syrup into the center.
  • One plate with 1/2 cup of room temperature water only
  • Second plate with 1/2 cup of room temperature water only with 2 T sugar in the center
  • Third plate with 1/2 cup room temperature water only with 2 T corn syrup in the center
  • Next, line each plate around the edges with Skittles. You’ll need help to get this done quickly. But do NOT bump the plates as the candies are placed around each dish.
  • On each plate, arrange them in a circular pattern around the edge of the plate. You can organize them by color or mix them up for a rainbow effect!
  • Observe and record what happens to the Skittles as soon as the water or oil touches them.
  • Let the plates sit for 3 minutes, then 6, then 10 minutes. What happens with each plate over time? What does the dye look like after 20 minutes?

What Happened

skittlesroomtemp 1

Room-Temperature Water Only

skittlessugar2 2

With Sugar Added to the Dish

skittlescornsyrup 1

With Corn Syrup Added to the Dish

skittlesoil 1 1

In the dishes with room-temperature water only, the dye and sugar from the Skittles move toward the center, and we see an example of diffusion.

Let’s look at the dishes where we added either sugar or corn syrup in the center. When the dye and sugar from the candies dissolve, and the sugar or corn syrup in the center of the plate starts to dissolve too, there is a higher concentration of sugar around the rim of the dish and in the middle of the dish. However, there is a space between those two areas of lower concentration. So, the dye and sugar along the rim start to move toward the center. Plus, the sugar from the center begins to move outwards. This is why we see the dye start to curve as it moves closer to the center. The high concentration from the center is moving outward.

The concentration of sugar begins to equalize all over the plate, so the dye starts to spread more. What happened to the dye after 20 minutes?

This is a demonstration of diffusion. Diffusion is the movement of particles (atoms, molecules, ions) from an area of higher concentration to an area of lower concentration. In this experiment, the particles are sugar and dye moved from the higher concentration along the edge of the plate to the center of the plate, where there was an area that had a lower concentration of sugar. However, the dye started to curve as it moved closer to the enter because of the higher concentration of sugar right in the center of the dish.

However, once the corn syrup in the center of the plate starts to move, the two high concentrations of different solutes that meet in the middle collide, causing the dyes to spread throughout the dish.

When pouring the corn syrup onto the dish, did you notice how thicker it is than water or other liquids you’re familiar with, like soda or milk? Diffusion may still occur in a thicker liquid; however, because the corn syrup is denser than water, it will cause the dye molecules to disperse at a slower rate.

In the photos below, you can see how the dye started to move, then curved away as the high concentration in the center of the dish moved outward.

skittleswithsugarroomtemp 1

Water with sugar added.

Water with corn syrup added.

Polar vs Nonpolar

In chemistry, nonpolar and polar are descriptors that scientists use to differentiate how atoms share their electrons when they’re connected to other atoms. Sometimes, atoms are really good at sharing their electrons equally, so there are no differences between the charges of atoms in those molecules. However, some atoms are bigger and a bit more selfish than other atoms in a molecule, and as such do not share their electrons equally, leading to an imbalance of charge within the molecule.

FYI, you can learn more about polar and nonpolar molecules in our Testing the Properties of Water lesson.

Polar molecules are those cases where the sharing of electrons is not equal between the atoms. Think of this like as if you were playing tug of war with a lion, you’re probably not strong enough to win. A good example of a polar substance is water. Water molecules are made up of one oxygen and two hydrogen atoms. Compared to the negative charge of an oxygen atom, hydrogen atoms are really weak at their game of tug of war, so the oxygen atom always wins.

When you place Skittles in water, the colored coating dissolves because water is polar and can interact with the polar molecules in the Skittles. The polar nature of both the water, sugar, and dyes allows the water to break down the dye coating on the candies and spread the colors out, creating a colorful display in the water.

Now, nonpolar molecules are when the atoms are really good at sharing their electrons equally. Because of their shared charges, there is no imbalance of charge or power. Suppose you had superpowers, and you could replicate yourself and then played tug of war with your copy, you would be perfectly matched. Oil is nonpolar.

Because of their differences in how they share their electrons, polar and nonpolar molecules don’t like to play with each other. This is why we see that the sugars and dyes of the Skittles don’t dissolve in oil. The unequal charges of the Skittles’ molecules are repelled by the nonpolar molecules of the oil.

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I hold a master’s degree in child development and early education and am working on a post-baccalaureate in biology. I spent 15 years working for a biotechnology company developing IT systems in DNA testing laboratories across the US. I taught K4 in a private school, homeschooled my children, and have taught on the mission field in southern Asia. For 4 years, I served on our state’s FIRST Lego League tournament Board and served as the Judging Director.  I own thehomeschoolscientist and also write a regular science column for Homeschooling Today Magazine. You’ll also find my writings on the CTCMath blog. Through this site, I have authored over 50 math and science resources.

Identifying Variables

Three types of tomatoes

Three types of tomatoes (MOs810, Wikimedia Commons)

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Learn how scientists define independent, dependent and controlled variables in experimental inquiry.

As was mentioned in the  Asking Testable Questions  backgrounder, testable questions define the variables. In other words, what is being changed and what is to be kept constant, in an experimental inquiry.

What are variables in an experimental inquiry?

Scientists often use experimental inquiries to observe cause and effect relationships. In order to do so, scientists aim to make one change (the cause or  independent variable ) in order to determine if the variable is causing what is observed (the effect or  dependent variable ).

An experimental inquiry typically has three main types of variables: an independent variable, a dependent variable and controlled variables. We will look at each of these three types of variables and how they are related to experimental inquiries involving plants.

Independent Variables

The independent variable, also known as the experimental treatment , is the difference or change in the experimental conditions that is chosen by the scientist (the cause). To ensure a  fair test , a good experimental inquiry only has  one  independent variable and that variable should be something that can be measured quantitatively. For example, experimental inquiries about plants may include such independent variables as:

  • Volume of water given to plants
  • Nitrogen or phosphorus concentration in soil
  • Duration, intensity or wavelength of light plants are exposed to
  • Concentration or type of fertilizer

Dependent Variables

When a scientist chooses an independent variable (the cause), that person anticipates a certain response (the effect). This response is known as the dependent variable. The dependent variable should be something that is observable and measurable. Like the independent variable, an experimental inquiry should only have one dependent variable. For example, experimental inquiries about plants may include such dependent variables as:

  • Days to germination
  • Surface area of leaves
  • Days to flowering or fruiting
  • Dry mass (amount of plant material after all water has been removed)

Testable Question

How does the volume of water affect the number of days it takes for a tomato plant to flower?

Relationship between an independent and a dependent variable

Shown is a colour illustration explaining the relationship between an independent and a dependent variable. 

On the left is a blue oval with the word "Cause" inside it. This is labelled "Independent Variable" at the top, and "E.g., volume of water" below. On the right is a green rectangle with the word "Effect" inside. This is labelled "Dependent Variable" at the top, and "E.g., days to flowering" below. A red arrow points from cause on the left to the effect on the right.

Controlled Variables

In order for a scientist to ensure that only the independent variable is affecting the dependent variable, all the other factors acting upon the test situation (or test subjects) must be kept constant. The factors that must be kept the same are called the  controlled variables , or constant variables. In a given inquiry, there may be one or more variables that will need to be kept constant. For example, for an experimental inquiry in which you are interested in how the volume of water (independent variable) affects the days to flowering (dependent variable), you would want to keep constant:

  • The type of seeds
  • The type of soil
  • The light source
  • The humidity in the room
  • The type of container (e.g., plastic pots vs. clay pots)
  • The Temperature

Tomato plants in a greenhouse

Shown is a colour photograph of tomato plants in a greenhouse. 

Rows of tomato plants on both sides of the photograph stretch into the distance. Light comes in through a translucent ceiling. The plants are thick with green leaves. Tomato fruit is visible at the bottom of each plant. Most of the fruit is red and some is green.

A failure to control variables other than the independent variable will mean that you will not know which factor is actually causing the effects you see. In the example above, if some of the plants were sitting closer to the window than others, the differential exposure to light could be affecting the number of days to flowering, rather than the volume of water.

For more about designing experiments, see:  Setting Up a Fair Test

What are the variables in Tomatosphere™?

In the Seed Investigation, students investigate the germination rates of tomato seeds that have been to space (or exposed to space-like conditions) with seeds that have remained on Earth.

The  testable question  in the Seed Investigation is:

HOW DOES EXPOSURE TO THE SPACE ENVIRONMENT OR SPACE-LIKE CONDITIONS AFFECT THE GERMINATION RATE OF TOMATO SEEDS?

Independent variable:  type of seeds used - Earth seeds versus space seeds (sometimes seeds are treated to space-like conditions in years when seeds do not go to space)

Dependent variable:  number of seeds that germinate

Guided Practice

Have students read the following questions and determine the independent, dependent and potential controlled variables.

  • How does the duration of light exposure affect the surface area of tomato plant leaves?
  • How does the concentration of nitrogen fertilizer affect the days to flowering of tomato plants?
  • How does the volume of water (mL) affect the number of days to germination of tomato plants?

In their own words, have students define the terms “Independent variable,” “Dependent variable,” and “Controlled variable.”

Have students brainstorm the variables that should be controlled in the Seed Investigation (e.g., quantity of water, type of soil, type of planting container, temperature, etc.).

Have the students think about the Seed Investigation and brainstorm variables that may not be controllable (e.g., giving plants different amounts of water, some plants being closer to a heat vent than others, using different types of soil, etc.).

  • Independent variable:   duration of light (hours) Dependent variable:   surface area of plant leaves (Overall? Largest leaf? All leaves?) Controlled variable(s):   quantity of water, type of soil, depth of seeds, source of light, concentration/type of fertilizer (if any); temperature of the room, etc.
  • Independent variable:   Concentration of nitrogen fertilizer Dependent variable:   days to flowering (when first flower on plants open) Controlled variable(s):   Same type of seeds, same quantity of water, same type of soil, same source of light, same duration of light, etc.
  • Independent variable:   Volume of water in ml (per day) Dependent variable:   days to germination (when first seed germinates) Controlled variable(s):   Single type of seeds, same type of soil, same volume of soil, same type of pots, same source of light, same duration of light, temperature of the room, same time of day for watering, etc.

What are variables? How to use them in your science projects This page from Science Buddies explains different sorts of variables and how to use them to answer sample questions.

Controlled Variables This article by Explorable covers variables, control groups, and the value of consistency.

What are Independent and Dependent Variables?  (2019) This article by ThoughtCo explains how to tell the difference between independent and dependent variables, and how to plot variables on a graph.

Identifying and Controlling Variables in Scientific Investigations  (2015) This video (3:16 min.) from SciExperiment Basics explains how to identify and control variables in a scientific inquiry.

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50 Fun Kids Science Experiments

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Science doesn’t need to be complicated. These easy science experiments below are awesome for kids! They are visually stimulating, hands-on, and sensory-rich, making them fun to do and perfect for teaching simple science concepts at home or in the classroom.

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Top 10 Science Experiments

Click on the titles below for the full supplies list and easy step-by-step instructions. Have fun trying these experiments at home or in the classroom, or even use them for your next science fair project!

baking soda and vinegar balloon experiment

Baking Soda Balloon Experiment

Can you make a balloon inflate on its own? Grab a few basic kitchen ingredients and test them out! Try amazing chemistry for kids at your fingertips.

artificial rainbow

Rainbow In A Jar

Enjoy learning about the basics of color mixing up to the density of liquids with this simple water density experiment . There are even more ways to explore rainbows here with walking water, prisms, and more.

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This color-changing magic milk experiment will explode your dish with color. Add dish soap and food coloring to milk for cool chemistry!

experiment with variables

Seed Germination Experiment

Not all kids’ science experiments involve chemical reactions. Watch how a seed grows , which provides a window into the amazing field of biology .

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Egg Vinegar Experiment

One of our favorite science experiments is a naked egg or rubber egg experiment . Can you make your egg bounce? What happened to the shell?

experiment with variables

Dancing Corn

Find out how to make corn dance with this easy experiment. Also, check out our dancing raisins and dancing cranberries.

experiment with variables

Grow Crystals

Growing borax crystals is easy and a great way to learn about solutions. You could also grow sugar crystals , eggshell geodes , or salt crystals .

experiment with variables

Lava Lamp Experiment

It is great for learning about what happens when you mix oil and water. a homemade lava lamp is a cool science experiment kids will want to do repeatedly!

experiment with variables

Skittles Experiment

Who doesn’t like doing science with candy? Try this classic Skittles science experiment and explore why the colors don’t mix when added to water.

experiment with variables

Lemon Volcano

Watch your kids’ faces light up, and their eyes widen when you test out cool chemistry with a lemon volcano using common household items, baking soda, and vinegar.

DIY popsicle stick catapult Inexpensive STEM activity

Bonus! Popsicle Stick Catapult

Kid tested, STEM approved! Making a popsicle stick catapult is a fantastic way to dive into hands-on physics and engineering.

Grab the handy Top 10 Science Experiments list here!

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Free Science Ideas Guide

Grab this free science experiments challenge calendar and have fun with science right away. Use the clickable links to see how to set up each science project.

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Get Started With A Science Fair Project

💡Want to turn one of these fun and easy science experiments into a science fair project? Then, you will want to check out these helpful resources.

  • Easy Science Fair Projects
  • Science Project Tips From A Teacher
  • Science Fair Board Ideas

50 Easy Science Experiments For Kids

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Kids’ Science Experiments By Topic

Are you looking for a specific topic? Check out these additional resources below. Each topic includes easy-to-understand information, everyday examples, and additional hands-on activities and experiments.

  • Chemistry Experiments
  • Physics Experiments
  • Chemical Reaction Experiments
  • Candy Experiments
  • Plant Experiments
  • Kitchen Science
  • Water Experiments
  • Baking Soda Experiments
  • States Of Matter Experiments
  • Physical Change Experiments
  • Chemical Change Experiments
  • Surface Tension Experiments
  • Capillary Action Experiments
  • Weather Science Projects
  • Geology Science Projects
  • Space Activities
  • Simple Machines
  • Static Electricity
  • Potential and Kinetic Energy
  • Gravity Experiments

Science Experiments By Season

  • Spring Science
  • Summer Science Experiments
  • Fall Science Experiments
  • Winter Science Experiments

Science Experiments by Age Group

While many experiments can be performed by various age groups, the best science experiments for specific age groups are listed below.

  • Science Activities For Toddlers
  • Preschool Science Experiments
  • Kindergarten Science Experiments
  • First Grade Science Projects
  • Elementary Science Projects
  • Science Projects For 3rd Graders
  • Science Experiments For Middle Schoolers

experiment with variables

How To Teach Science

Kids are curious and always looking to explore, discover, check out, and experiment to discover why things do what they do, move as they move, or change as they change! My son is now 13, and we started with simple science activities around three years of age with simple baking soda science.

Here are great tips for making science experiments enjoyable at home or in the classroom.

Safety first: Always prioritize safety. Use kid-friendly materials, supervise the experiments, and handle potentially hazardous substances yourself.

Start with simple experiments: Begin with basic experiments (find tons below) that require minimal setup and materials, gradually increasing complexity as kids gain confidence.

Use everyday items: Utilize common household items like vinegar and baking soda , food coloring, or balloons to make the experiments accessible and cost-effective.

Hands-on approach: Encourage kids to actively participate in the experiments rather than just observing. Let them touch, mix, and check out reactions up close.

Make predictions: Ask kids to predict the outcome before starting an experiment. This stimulates critical thinking and introduces the concept of hypothesis and the scientific method.

Record observations: Have a science journal or notebook where kids can record their observations, draw pictures, and write down their thoughts. Learn more about observing in science. We also have many printable science worksheets .

Theme-based experiments: Organize experiments around a theme, such as water , air , magnets , or plants . Even holidays and seasons make fun themes!

Kitchen science : Perform experiments in the kitchen, such as making ice cream using salt and ice or learning about density by layering different liquids.

Create a science lab: Set up a dedicated space for science experiments, and let kids decorate it with science-themed posters and drawings.

Outdoor experiments: Take some experiments outside to explore nature, study bugs, or learn about plants and soil.

DIY science kits: Prepare science experiment kits with labeled containers and ingredients, making it easy for kids to conduct experiments independently. Check out our DIY science list and STEM kits.

Make it a group effort: Group experiments can be more fun, allowing kids to learn together and share their excitement. Most of our science activities are classroom friendly!

Science shows or documentaries: Watch age-appropriate science shows or documentaries to introduce kids to scientific concepts entertainingly. Hello Bill Nye and the Magic Schoolbus! You can also check out National Geographic, the Discovery Channel, and NASA!

Ask open-ended questions: Encourage critical thinking by asking open-ended questions that prompt kids to think deeper about what they are experiencing.

Celebrate successes: Praise kids for their efforts and discoveries, no matter how small, to foster a positive attitude towards science and learning.

What is the Scientific Method for Kids?

The scientific method is a way scientists figure out how things work. First, they ask a question about something they want to know. Then, they research to learn what’s already known about it. After that, they make a prediction called a hypothesis.

Next comes the fun part – they test their hypothesis by doing experiments. They carefully observe what happens during the experiments and write down all the details. Learn more about variables in experiments here.

Once they finish their experiments, they look at the results and decide if their hypothesis is right or wrong. If it’s wrong, they devise a new hypothesis and try again. If it’s right, they share their findings with others. That’s how scientists learn new things and make our world better!

Go ahead and introduce the scientific method and get kids started recording their observations and making conclusions. Read more about the scientific method for kids .

Engineering and STEM Projects For Kids

STEM activities include science, technology, engineering, and mathematics. In addition to our kids’ science experiments, we have lots of fun STEM activities for you to try. Check out these STEM ideas below.

  • Building Activities
  • Self-Propelling Car Projects
  • Engineering Projects For Kids
  • What Is Engineering For Kids?
  • Lego STEM Ideas
  • LEGO Engineering Activities
  • STEM Activities For Toddlers
  • STEM Worksheets
  • Easy STEM Activities For Elementary
  • Quick STEM Challenges
  • Easy STEM Activities With Paper  

Printable Science Projects For Kids

If you’re looking to grab all of our printable science projects in one convenient place plus exclusive worksheets and bonuses like a STEAM Project pack, our Science Project Pack is what you need! Over 300+ Pages!

  • 90+ classic science activities  with journal pages, supply lists, set up and process, and science information.  NEW! Activity-specific observation pages!
  • Best science practices posters  and our original science method process folders for extra alternatives!
  • Be a Collector activities pack  introduces kids to the world of making collections through the eyes of a scientist. What will they collect first?
  • Know the Words Science vocabulary pack  includes flashcards, crosswords, and word searches that illuminate keywords in the experiments!
  • My science journal writing prompts  explore what it means to be a scientist!!
  • Bonus STEAM Project Pack:  Art meets science with doable projects!
  • Bonus Quick Grab Packs for Biology, Earth Science, Chemistry, and Physics

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~ projects to try now ~.

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  • Open access
  • Published: 11 June 2024

Repositioning of antiarrhythmics for prostate cancer treatment: a novel strategy to reprogram cancer-associated fibroblasts towards a tumor-suppressive phenotype

  • Valentina Doldi 1 ,
  • Monica Tortoreto 1 ,
  • Maurizio Colecchia 2 ,
  • Massimo Maffezzini 3 ,
  • Stefano Percio 1 ,
  • Francesca Giammello 4 ,
  • Federico Brandalise 4 ,
  • Paolo Gandellini 4   na1 &
  • Nadia Zaffaroni 1   na1  

Journal of Experimental & Clinical Cancer Research volume  43 , Article number:  161 ( 2024 ) Cite this article

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Cancer-associated fibroblasts (CAFs) play a significant role in fueling prostate cancer (PCa) progression by interacting with tumor cells. A previous gene expression analysis revealed that CAFs up-regulate genes coding for voltage-gated cation channels, as compared to normal prostate fibroblasts (NPFs). In this study, we explored the impact of antiarrhythmic drugs, known cation channel inhibitors, on the activated state of CAFs and their interaction with PCa cells.

The effect of antiarrhythmic treatment on CAF activated phenotype was assessed in terms of cell morphology and fibroblast activation markers. CAF contractility and migration were evaluated by 3D gel collagen contraction and scratch assays, respectively. The ability of antiarrhythmics to impair CAF-PCa cell interplay was investigated in CAF-PCa cell co-cultures by assessing tumor cell growth and expression of epithelial-to-mesenchymal transition (EMT) markers. The effect on in vivo tumor growth was assessed by subcutaneously injecting PCa cells in SCID mice and intratumorally administering the medium of antiarrhythmic-treated CAFs or in co-injection experiments, where antiarrhythmic-treated CAFs were co-injected with PCa cells.

Activated fibroblasts show increased membrane conductance for potassium, sodium and calcium, consistently with the mRNA and protein content analysis. Antiarrhythmics modulate the expression of fibroblast activation markers. Although to a variable extent, these drugs also reduce CAF motility and hinder their ability to remodel the extracellular matrix, for example by reducing MMP-2 release. Furthermore, conditioned medium and co-culture experiments showed that antiarrhythmics can, at least in part, reverse the protumor effects exerted by CAFs on PCa cell growth and plasticity, both in androgen-sensitive and castration-resistant cell lines. Consistently, the transcriptome of antiarrhythmic-treated CAFs resembles that of tumor-suppressive NPFs. In vivo experiments confirmed that the conditioned medium or the direct coinjection of antiarrhythmic-treated CAFs reduced the tumor growth rate of PCa xenografts.

Conclusions

Collectively, such data suggest a new therapeutic strategy for PCa based on the repositioning of antiarrhythmic drugs with the aim of normalizing CAF phenotype and creating a less permissive tumor microenvironment.

Introduction

Favorable interactions between cancer cells and the surrounding microenvironment are crucial for sustaining tumor mass formation and progression. The acquisition of malignant features, including the development of resistance to standard therapies and the enhancement of metastatic potential, is also greatly promoted by a supportive tumor microenvironment [ 1 , 2 ]. Cancer-associated fibroblasts (CAFs) are the most representative non-tumorigenic cells within the tumor microenvironment. They are a highly heterogenous cellular population, in terms of phenotype and function, resulting from the cancer cell-mediated reprogramming of stroma, through the direct influence of tumor-derived cytokines and growth factors [ 3 , 4 ]. Little is known about the different tumor supporting-CAF subpopulations that can cohabitate within the tumor mass, which may include immune CAFs, desmoplastic and contractile CAFs, and aggressive and secretory CAFs [ 5 ]. They are typically identified by the overexpression of activation markers, such as α-smooth muscle actin (α-SMA), fibroblast activation protein (FAP), collagens or fibroblast specific protein 1 [ 6 ], and by an increased migratory and extracellular matrix (ECM) remodeling capability [ 7 ].

Since CAFs are critically involved in cancer biology, several efforts have been recently made in the attempt to develop therapeutic strategies able to eradicate cancer-promoting CAFs from the tumor mass (reviewed in [ 8 ]). However, most of them have failed to substantially improve in cancer treatment, mainly due to the extreme complexity of this population and the lacking of unequivocal markers. Therefore, the newest therapeutic approaches in this regard are focused on restoring a suppressive phenotype rather than depleting CAFs from the tumor microenvironment [ 8 ].

As for many solid tumors, prostate cancer (PCa) stroma is characterized by a heterogenous population of CAFs with tumor-supporting features [ 9 ]. CAFs can promote proliferation, migration and invasion of PCa cells. By overproducing ECM components, such as collagens, periostin and fibronectin, and by dysregulating matrix metalloproteases proteins (e.g., MMP-2 and MMP-9), CAFs can provide physical support to PCa cells and increase matrix stiffness and remodeling, thus ultimately promoting cancer cell migration and invasion [ 10 , 11 ]. CAFs can also sustain PCa progression by providing metabolic resources required for cancer cell growth. Driven by PCa cells, CAFs undergo aerobic glycolysis to release pyruvate and lactate that are taken up by cancer cells to activate mitochondrial oxidative metabolism and promote cell growth even in nutrient- or oxygen-deprived microenvironment [ 12 ]. In addition, several in vitro and in vivo studies have demonstrated that CAFs can promote the acquisition of the castration-resistant phenotype, an aggressive trait of PCa, and accelerate metastatic dissemination [ 13 , 14 , 15 , 16 , 17 ].

With the aim of elucidating the molecular mechanisms governing the activation of PCa stroma, we previously showed that tumor-derived IL-6 and TGF-β can both convert normal prostate fibroblasts (NPF) into CAFs with tumor-promoting features [ 18 ], confirming the complexity of tumor stroma composition in PCa [ 4 , 18 , 19 ]. Moreover, gene expression profiles of patient-derived CAFs and matched NPFs revealed the up-modulation of gene sets related to cardiomyopathy and cation channel activity, including genes that encode for sodium, calcium and potassium ion channels [ 18 ]. These proteins are crucial regulators of specific physiological processes, such as muscle contractions and nerve impulses, as well as the main biological processes that are frequently deregulated in cancer cells, and also in CAFs, including cell cycle progression and survival, cell migration and invasion [ 20 , 21 , 22 ]. Therefore, given this possible similarity between prostate CAFs and contractile cells, we set out to investigate the potential of cation channel blockers, such as antiarrhythmics, to revert CAF-activated state into a tumor suppressive phenotype. To that end, we tested class I (sodium-channel blocker, such as flecainide), class III (potassium-channel blocker, such as amiodarone) and class IV (calcium-channel blockers, such as verapamil and nifedipine) antiarrhythmics on prostate CAFs and investigated the impact of these treatments on CAF activated state and PCa-CAF interplay.

Cell culture

Primary CAFs and NPFs cultures were isolated from surgical specimens of three PCa-bearing patients (Gleason score 4 + 5) who underwent radical prostatectomy. Specimens were collected upon informed consent, and the study was approved by the Ethics committee of IRCCS Istituto Nazionale dei Tumori of Milano (INT n. 154/16). Intra-tumor areas or non-tumor regions of radical prostatectomy specimens were identified by an expert uro-pathologist (M.C.), selected and digested overnight at 37 °C and 5% CO 2 in DMEM medium (Lonza, Basel, Switzerland) supplemented with 300 units/ml collagenase and 100 units/ml hyaluronidase solution (Stemcell Technologies Vancouver, Canada), 1% penicillin–streptomycin (Lonza, Basel, Switzerland) and 2.5 μg/ml of Amphotericin B. The cell suspension was centrifuged at 1,500 × g for 5 min. The resulting fibroblast-rich pellet was suspended and plated in DMEM medium (Lonza) containing 10% FBS (Thermo Fisher Scientific Inc., Waltham, MA, US), 4 mM L-glutamine and 1% penicillin–streptomycin (Lonza). CAFs or NPFs were maintained in culture for 3 passages and the absence of epithelial markers expression was verified before being used in the experiments. All established primary cultures negative for epithelial markers and expressing fibroblast markers were used until the 15th passage and maintained in DMEM medium (Lonza) containing 10% FBS (Thermo Fisher Scientific Inc.) and 4 mM L-glutamine (Lonza). PCa cell lines (DU145, PC3 and LNCaP) and the prostate myofibroblast cell line WPMY-1 were purchased from American Type Tissue Culture Collection (ATCC, VA, USA). PCa cell lines were maintained in RPMI-1640 medium (Lonza) supplemented with 10% FBS (Thermo Fisher Scientific), at 37 °C and 5% CO 2 . WPMY-1 cells were maintained in DMEM (Lonza) supplemented with 10% FBS (Gibco, Thermo Fisher Scientific Inc.). All the cell lines were authenticated and periodically monitored by genetic profiling using short tandem repeat analysis AmpFISTR Identifier PCR amplification kit (Thermo Fisher Scientific Inc.).

Conditioned medium

For indirect co-culture and in vivo experiments, conditioned medium (CM) was collected from NPFs, antiarrhythmic-treated or untreated CAFs and DU145 cells. To obtain CM, a total of 7 × 10 5 cells were seeded in T-75 cm 2 culture flask. CAFs were treated with 2.5 μM of amiodarone (Hikma Pharmaceuticals, London, UK), 2.5 μM of verapamil (Abbott Laboratories, Chicago, US), 2.5 μM of nifedipine (Meda AB, Solna, Sweden) or 2.5 μM flecainide (Meda AB) for 24 h. Upon treatment, the culture medium was removed, cells were washed 2 times with PBS (Lonza) and 6 ml of serum-free medium was added for starvation. Twenty-four hours later, the CM was collected, clarified for 5 min at 1,500 × g and used freshly to treat PCa cells (DU145, PC-3 or LNCaP), or to activate NPFs or WPMY-1 or concentrated for western blotting analysis.

Electrophysiological recordings and analyses

Whole cell patch-clamp recordings were performed on activated and control WPMY-1 fibroblasts using an Axopatch 200B amplifier (Molecular Devices) and data were sampled with a Digidata -1440 (Molecular Devices) interface (sampling time = 250 ms for voltage clamp recordings). Patch pipettes were pulled from borosilicate capillaries (Hingelberg, Malsfeld, Germany) and had 3–5 MΩ resistance before a seal was formed. Cells were recorded in a bath solution containing (in mM): NaCl 140, KCl 5, Hepes 10, glucose 5, CaCl2 2, MgCl2 1, pH 7.4. The filling solution contained (in mM): KCl 135, NaCl 10, Hepes 10, MgCl2 1, EGTA 1, CaCl2 0.1 and GTP 0.1. The pH was adjusted to 7.2 with KOH. Membrane passive properties were recorded in voltage clamp configuration. A hyperpolarizing step from -70 mV to -80 mV, normally used for evaluating the passive properties of the recorded cell [ 23 ], elicited an inward transient current used to estimate the series resistance, input resistance and membrane capacitance. Series resistance and input resistance were monitored throughout each experiment. Cells were rejected if these parameters deviated by more than 20% from the beginning of the recording. Outward currents were measured in voltage clamp by applying subsequent voltage steps of + 20 mV from a holding potential of -60 mV up to + 140 mV. Sustained outward currents (K + steady) were recorded as an average of the last 15 ms of each voltage step. Transient outward currents (K + inactivated) were calculated by subtracting the sustained outward current from peak outward currents. The amplitude of the transient currents measured at the membrane potential of + 140 mV was used to report the current density. Transient inward currents (inward) were calculated as the peak outward current for each applied voltage step. For the depolarizing protocols, the PN leak subtraction of the Clampex program was used to eliminate the effects of the leakage current on the whole-cell responses [ 24 ]. The extracellular medium containing flecainide or nifedipine was bath perfused using a peristaltic pump. Pipette capacitance was compensated, and the bridge was balanced during each recording. All data were reported as the mean ± standard error of the mean (SEM). Each protocol was averaged digitally 5 times before being analyzed. All the recorded data were analyzed off-line with pCLAMP10.7 (Axon Instruments).

Cell growth assay

PCa cells (DU145, PC-3 or LNCaP) were seeded in 12-well plates (2 × 10 4 cells/well) and after 24 h were exposed to CM of NPFs, CM of CAFs or CM of CAF-treated with antiarrhythmics (as described above). Upon starvation with appropriate CM, cells were harvested with Trypsin–EDTA (Lonza) and counted with an automated cell counter (Beckman, Coulter, Brea, CA, US).

Migration assay

Cell were seeded at 4 × 10 4 cells/well into a 12-well culture plate. After 2 days, the monolayer of cells was wounded by manual scratching with a pipet tip, washed with PBS 1 × (Lonza), photographed (t0 point) and media were replaced with serum-free DMEM containing 2.5 μM of amiodarone (Hikma Pharmaceuticals), 2.5 μM of verapamil (Abbott Laboratories), 2.5 μM nifedipine (Meda AB), or 2.5 μM flecainide (Meda AB) for CAF migration experiments experiments or with CM of CAFs treated or not with antiarrhythmics (as described above) for DU145 cell migration experiments. Images of cell movement were captured at regular time intervals until 48 h by using EVOS XL – Core microscope system (Thermo Fischer Scientific Inc.).

3D gel collagen contraction assay

Type I collagen from rat-tail (Sigma-Aldrich, St. Louis, MO, US) was dissolved at 2 mg/ml in 0.1% acetic acid to create a stock solution. The collagen matrix was quickly prepared on ice by adding 6 ml of collagen stock solution to 3.6 ml of 0.1% acetic acid, 1.2 ml of 10 × concentrated DMEM, and 1.2 ml of sodium bicarbonate solution (11.76 mg/ml) for a final concentration of 1 mg/ml collagen. The pH was adjusted to 7.2–7.4 by adding 0.1 mol/l NaOH. CAFs or NPFs cells were then added to achieve a final concentration of 5 × 10 5 cells/ml; gel-cells suspension was aliquoted into each well of a 24-well culture plate. After polymerization for 30 min at 37 °C, the gel in each well was overlayed with 500 μl of complete growth medium. Twenty-four hours later, CAFs were treated with 2.5 μM amiodarone (Hikma Pharmaceuticals), 2.5 μM verapamil (Abbott Laboratories), 2.5 μM nifedipine (Meda AB) or 2.5 μM flecainide (Meda AB), in serum free medium. Then, the gels were mechanically released from the wall and bottom of the wells with a sterile spatula. Gel contraction was monitored for 48 h and scanned by standardized photography at time 0 and at sequential time points.

Gene expression profile

After RNA quality check, transcriptomic profiles of CAFs, treated or not with nifedipine (2.5 µM) or flecainide (2.5 µM), and NPFs were assessed using the Clariom™ S Human Microarray (Thermo Fisher Scientific Inc.). Raw data were normalized according to the Robust multiarray averaging (RMA) algorithm, implemented into the oligo package [ 25 ]. Normalized data were filtered removing probes with no associated official gene symbol; for probes mapping on the same gene symbol, the one with highest variance was selected. In addition, an empirical Bayes approach was applied to adjust gene expression for batch effect, using ComBat function implemented into the sva package [ 26 ]. To verify the efficacy of these analyses, the t-distributed Stochastic Neighbor Embedded (t-SNE) statistical method was employed to visualized data in a low-dimensional space since it adopts a non-linear reduction of high-dimensional transcriptomic data maintaining the similarity among samples. Differential expression analysis was performed applying a linear model implemented into the limma package [ 27 ]. A pre-ranked Gene Set Enrichment Analysis (GSEA) was performed on gene sets of the Molecular Signature Database (MSigDB), selecting Reactome pathway in the C2 collection [ 28 ]. Ranking was defined according to the t-statistic and normalized enrichment score (NES) was calculated using the functions implemented into the fgsea package [ 29 ].

Three different normalized datasets (GSE68164, GSE85606, and GSE86256) were retrieved from the Gene Expression Omnibus database [ 30 ] and for each, a pre-ranked GSEA analysis was conducted on custom selected gene sets of MSigDB, using “ion channel activity” as query in the C5 collection and using the t-statistics, obtained by linear model implemented into the limma package, as the measure for ranking gene expression in the comparison CAF vs NPF samples. A FDR threshold of 0.05 was applied to assess significant enrichments.

Cell viability

The cytotoxic effect of amiodarone (Hikma Pharmaceuticals), verapamil (Abbott Laboratories), nifedipine (Meda AB) or flecainide (Meda AB) was determined by the CellTiter96® AQueous One Solution Cell Proliferation Assay (MTS) (Promega Corporation, Madison Wisconsin, USA). Cells were plated for 24 h in 96-well flat-bottomed microtiter plates at a density of 1.5 × 10 3 /50 μl, and then treated with increasing concentrations of amiodarone (Hikma Pharmaceuticals), verapamil (Abbott Laboratories), nifedipine (Meda AB) or flecainide (Meda AB) (0–5 µM) for 72 h. At the end of the treatment, MTS solution was added to each well and the plate was incubated for 3 h in a 5% CO 2 incubator at 37 °C. The absorbance at 490 nm was recorded using the POLARstar optima plate-reader (VWR International, Radnor, Pennsylvania, USA).

Immunofluorescence

CAFs were seeded at 6 × 10 4 cell/well in a 6-well plate containing a coverslip suitable for microscopy. After 24 h, CAFs were treated with 2.5 μM amiodarone (Hikma Pharmaceuticals), 2.5 μM verapamil (Abbott Laboratories), 2.5 μM nifedipine (Meda AB) or 2.5 μM flecainide (Meda AB), in serum free medium. Twenty-four hours later cells were fixed in 4% formaldehyde dissolved in PBS for 10 min. Cells were permeabilized with cold 70% of ethanol and probed with primary antibodies for α-SMA (1:200, A2547 Sigma-Aldrich), Collagen I (1:200 ab34710; Abcam) and phospho-FAK (1:200 Y397 ab4803; Abcam) diluted in antibody diluent (S080983, Dako, Agilent Technologies) for 1 h at room temperature. Alexa Fluor594/488-labeled secondary antibody (Thermo Fisher Scientific Inc.) was used to incubate cells for 1 h at room temperature. Actin filaments were stained using phalloidin-conjugate-Fluor488 dye and nuclei were stained with DAPI (Invitrogen, Thermo Fisher Scientific Inc.). Images were acquired by Nikon Eclipse E600 microscope using ACT-1 software (Nikon, Minato City, Tokyo, Japan).

Proteome profiler array

Proteome profiling was performed using Proteome Profiler Human Cytokine Array Kit (ARY005B, R&D, Minneapolis, MN, US) according to the manufacturer’s instructions. The array was performed on 500 μl of fivefold concentrated CM from NPFs, CAFs treated or not with nifedipine or flecainide. Chemiluminescence signals were detected using Chemi Reagent Mix provided by the kit. Semi-quantitative analysis was performed using ImageJ software (National Institutes of Health, Bethesda, MD, USA).

In vivo experiments

Animal studies were performed in accordance with guidelines of animal care protocols approved by Ethical Committee for animal experimentation of IRCCS Istituto Nazionale dei Tumori of Milano and Italian Ministry of Health (approval code n. 350/2017-PR). Male SCID mice were purchased from Charles River Laboratories. PCa xenografts were generated by subcutaneous injection of 1 × 10 7 DU145 cells into the right flank of SCID mice. When tumor burden reached ~ 100 mm 3 , mice were randomly assigned to control or treatment groups ( n  = 6 mice per group). For conditioned medium experiments mice were intratumorally treated (5 consecutive days for 2 weeks) with 250 μl of CM of NPFs, CAFs exposed or not to 2.5 μM amiodarone (Hikma Pharmaceuticals), 2.5 μM verapamil (Abbott Laboratories), 2.5 μM nifedipine (Meda AB) or 2.5 μM flecainide (Meda AB). At two different time points (after 1 week and at the end of the treatments) tumors were harvested. For co-injection experiments, 1 × 10 7 DU145 cells were co-injected with NPFs or CAFs treated or not with flecainide at a ratio of 1:3 into in the right flank of SCID mice. Tumor size was measured twice a week with a Vernier caliper, and the volume was calculated using the standard modified formula: Volume (mm 3 ) = (length × height 2 )/2.

Ki-67 and CD31 staining

At the end of the treatment, mice were scarified and subcutaneous tumors were harvested, and formalix-fixed and paraffin-embedded. Tumor sections were then deparaffinised in xylene, rehydrated through graded alcohols to water, and subjected to immunohistochemical analysis using Ki-67 antibody (MIB-1, Dako; 1:200) or CD31 (MEC 13.3, sc-18916, Santa Cruz; 1:100, incubation over-night). Nuclei were counterstained with hematoxylin. Images were acquired by Nikon Eclipse E600 microscope using ACT-1 software (Nikon). At least 10 fields were scanned and the average number of Ki-67-positive or CD31-positive and negative cells was plotted.

Statistical analysis

Statistical analysis was performed with Mann–Whitney test and Student’s t-test, when appropriate, using GraphPad Prism software (version 9.4; GraphPad Prism Inc., San Diego, CA, USA). P  ≤ 0.05 was considered statistically significant.

Additional methods

RNA extraction, RT-qPCR, protein isolation and western blotting protocol are described in detail in Additional Methods.

Cation channels are up-modulated in CAFs and involved in fibroblast activation

Interrogation of independent gene expression data of prostate CAFs and matched NPFs confirmed our previous observation [ 18 ] showing the up-modulation of voltage-gated cation channel gene sets in CAFs (Fig.  1 a). Accordingly, in an additional set of three paired cultures of CAFs and NPFs established from radical prostatectomies in our laboratory, we appreciated the up-modulation of a selected panel of cation channels, including calcium ( CACNA1H, CACNB1, CACNB3 ), sodium ( SCAN2A and SCN1B ) and potassium channels ( KCNS3 ) in CAFs compared to NPFs at either the mRNA or protein levels, or both (Fig.  1 b and c, Table  1 ). Discrepancy observed between mRNA and protein levels of the KCNS3 /Kv9 channel may be attributed to various post-transcriptional [ 31 ] or post-translational [ 32 ] and compensatory mechanisms that may affect protein stability, localization, or activity. As an experimental validation, we induced the “ in-vitro activation” of patient-derived NPFs and of WPMY-1 cells (a normal prostate myofibroblast cell line) via direct exposition to conditioned medium (CM) of PCa cells (DU145). During activation, which was confirmed by the up-modulation of specific fibroblast activation markers (Fig.  1 d-f), we observed the concomitant up-modulation of cation channels, including CACNA1H , CACNB1 , CACNB3 , SCN2A , SCN1B , KCNS 3 and KCNH2 , at both mRNA and protein levels (Fig.  1 g and h). Taken together, these findings suggest the involvement of cation channels in CAF activation.

figure 1

Cation channels are up-modulated in CAFs and involved in fibroblast activation. a Heatmap (bottom) reporting normalized enrichment scores (NES) for gene sets related to voltage gated channels and bar plot (top) reporting mean + sd, as calculated by GSEA on three independent datasets of prostate cancer patient-derived CAFs vs. matched NPFs. b qRT-PCR showing CACNA1H , CACNB1 , CACNB3 , SCN2A , SCN1B , and KCNS3 expression levels in an independent setting of three CAF and matched NPF cultures. Data were reported as relative expression compared to NPF and were representative of three independent experiments. c Western blotting analysis showing selected ion channel protein levels in a pair of matched CAFs and NPFs. β-actin was used as endogenous control. d qRT-PCR indicating relative expression levels of α-SMA, FAP and COL1A1 in NPF#1, NPF#2 and WPMY-1 fibroblasts exposed to CM of DU145 cells with respect to control fibroblasts. e Western blotting showing α-SMA, FAP and COL1A1 expression levels in WPMY-1 fibroblasts exposed or not to CM of DU145 cells. β-tubulin was used as endogenous control. f Immunofluorescence microphotographs showing α-SMA (green) and Col1a1 (red) expression in NPF and NPF exposed to CM of DU145 cells. Nuclei counterstained with DAPI (blue). Scale bar, 50 µm. g Cation channel mRNA expression levels in NPF#1, NPF#2 and WPMY-1 fibroblasts exposed to CM of DU145 cells with respect to untreated fibroblasts. h Western blotting displaying cation channel protein levels in NPF#1 and WPMY-1 fibroblasts exposed or not to CM of DU145 cells. β-tubulin was used as endogenous control. Results reported in the figure represent the mean (+ SD) of three independent experiments. * p  < 0.05, ** p  < 0.01, *** p  < 0.005, Student’s t-test

Activated fibroblasts show increased membrane conductance for potassium, sodium and calcium

Electrophysiological recordings were performed on control and activated WPMY-1 fibroblasts. The two groups were not different in any of the passive properties like membrane capacitance (control: 31.4 ± 2.4 pF, n  = 11; activated: 32.1 ± 3.8 pF; n  = 12; p  = 0.96, Mann–Whitney test) or membrane resistance (control: 221. ± 27 MΩ, n  = 11; activated: 212 ± 28 MΩ; n  = 12; p  = 0.82, Mann–Whitney test). In control condition, upon progressively more depolarized potentials (see methods), fibroblasts showed a very consisted current pattern in which only the sustained potassium current (K + steady) was detected (Fig.  2 a). On the contrary, for activated fibroblasts we could also record an inward current (Fig.  2 a) that was sensitive to both flecainide, a voltage gated sodium channel inhibitor (untreated: 609.25 ± 115.16 pA, n  = 4; flecainide: 351.75 ± 62.16 pA; n  = 4; p  = 0.036, paired t-test, Fig.  2 b), and nifedipine, a voltage gated calcium channel inhibitor (untreated: 449.25 ± 90.82 pA, n  = 4; nifedipine: 164.00 ± 26.99 pA; n  = 4; p  = 0.037, paired t-test, Fig.  2 b), suggesting that this current was mediated by influx of sodium and calcium. While the potassium steady current was present in the majority of the recorded fibroblasts in both control and activated condition (control: 100%, n  = 11; activated: 91%, n  = 12), the inward current as well as a fast-inactivating potassium current was functionally detected only in some of the activated fibroblasts (inward: 67%, n  = 12; K + inactivating: 25%, n  = 12, Fig.  2 c). The current density at the peak value of the IV-plot for the K + steady current was significantly higher for the activated group compared to the control condition (control: 33.9 ± 2.8 pA/pF, n  = 11; activated: 50.2 ± 7.2 pA/pF; n  = 11; p  = 0.04, Multiple unpaired t-test, Fig.  2 d). The peak current density recorded for in the activated group for the K + inactivating current was 17.8 ± 7.1 pA/pF ( n  = 3) while for the inward current it was 7.6 ± 2.9 pA/pF ( n  = 8). Overall these data suggest that activated fibroblasts show increased membrane conductance for potassium, sodium and calcium, consistently with the mRNA and protein content analysis (Fig.  1 ).

figure 2

Activated fibroblasts show increased membrane conductance for potassium, sodium and calcium. a Representative Voltage-clamp recordings of inward and outward currents from activated and control WPMY-1 cells fibroblasts. A schematic of the applied protocol is shown in the insert. Upon activation, an inward current as well as a fast-inactivating outward current can be detected in fibroblasts. b The inward current shows significant sensitivity to both flecainide (2.5 μM) and nifedipine (2.5 μM) when applied to the bath solution. c Pie chart summarizing the percentage of cells expressing the main currents detected in A upon depolarization in both the control and the activated group. d The IV plot for the K + steady shows a significant increase in the current density in the activated group compared to the control condition. The I-V plot for the inward current is also displayed

Antiarrhythmics counteract the activated state of prostate CAFs

Aiming to assess the functional role of voltage-gated cation channels in supporting fibroblast activation, a panel of pharmacological blockers of sodium, calcium and potassium channels, commonly used as antiarrhythmics, were tested as potential agents to revert CAF activated state (Table  1 ). The treatment of CAFs with sub-toxic doses of antiarrhythmics (Additional Fig. 1) was sufficient to induce a variable reduction of fibroblast activation markers, such as α-SMA and Col1a1 protein levels as a function of the drug and concentration (Fig.  3 a). Among the several features and functions of CAFs, increased cell motility, ECM remodeling and deposition are definitely some of the main characteristics distinguishing them form NPFs [ 33 ]. The treatment with antiarrhythmics was sufficient to reduce CAF migratory ability, as indicated by the significantly reduced wound closure upon treatment (Fig.  3 b and Additional Fig. 2a). In addition, p-FAK, which is a crucial mediator of cell migration and spindle orientation, was reduced, although to a variable extend, upon treatment of CAFs with antiarrhythmics (Fig.  3 c), confirming that such drugs impaired CAF migratory capability by hindering focal adhesion formation. CAF-mediated ECM deposition and mechanical remodeling resulted to be affected by antiarrhythmics as well. Specifically, as depicted in Fig.  3 d, the degree of contraction of gels exerted by antiarrhythmics-treated CAFs was significantly reduced compared to that of untreated CAFs. In addition, the secretion of Col1a1 and fibronectin in the extracellular environment was largely abrogated upon the treatment of CAFs with antiarrhythmics (Additional Fig. 2b), indicating a reduction of CAF-induced ECM deposition. The reason behind the reduced ECM remodeling capability of treated CAFs was investigated by measuring MMP2 levels in the CM of CAFs exposed to antiarrhythmics. As shown by western blotting (Fig.  3 e), CAF-released MMP2 was significantly lower in CM of treated cells, as indicated by the reduced levels of secreted active- and pro-MMP2 in the CM of CAFs treated with antiarrhythmics compared to untreated CAFs, which was paralleled by an increased intracellular accumulation of pro-MMP2 in treated CAFs.

figure 3

Antiarrhythmics counteract the activated state of prostate CAFs. a Western blotting and relative quantification showing protein levels of fibroblast activation markers (α-SMA and COL1A1) in CAFs treated for 48 h with sub-toxic doses of antiarrhythmics. β-tubulin was used as endogenous control. b Bar plots showing the wound-healing rate assessed by scratch assay on CAFs exposed to antiarrhythmics. Data are reported as wound healing ratio at 24 h compared to 0 h point. c Representative immunofluorescence microphotographs (upper panel) showing the organization of β-actin cytoskeleton (green) and p-FAK (red) in CAFs treated with verapamil as compared to untreated. Scale bar, 50 μm. Western blotting analysis (lower panel) showing p-FAK, FAK protein levels in CAFs treated with sub-toxic doses of antiarrhythmics. β-tubulin was used as endogenous control. d Representative images (upper panel) showing 3D-collagen gel remodeling of CAFs exposed to sub-toxic doses of antiarrhythmics. NPFs were used as negative control. The dotted lines define gel areas. Bar plots (lower panel) showing ECM remodeling ratio of treated CAFs assessed by 3D-collagen gel assay. Data are reported as ECM remodeling ratio at 48 h compared to 0 h point. e Western blotting showing levels of pro-MMP2 and active-MMP2 in CM from CAFs exposed or not to sub-toxic doses of antiarrhythmics and, pro-MMP2 intracellular levels in treated cells. Gapdh was used as endogenous control for cell lysate. Results reported in the figure represent the mean (+ SD) of three independent experiments. * p  < 0.05, ** p  < 0.01, *** p  < 0.005, Student’s t-test

Antiarrhythmics affect PCa cell growth by impairing CAF function

It has been well established that CAFs induce PCa progression by supporting tumor cell growth as well as by enhancing tumor cell motility and the switch from an epithelial-like to a more mesenchymal-like phenotype [ 10 , 34 , 35 , 36 ]. In this regard, the impact of antiarrhythmics on CAF-PCa cross-talk was evaluated by performing indirect co-culture experiments using CM (Fig.  4 a). As shown in Fig.  4 b,c and Additional Fig. 3a, CM of CAFs induced a slight increase of PCa cell growth, which was more pronounced in DU145 and LNCaP compared to PC3 cells [ 37 ]. In this regard, controversial information has been reported regarding the ability of CAFs to promote cell growth of PCa cell lines. In fact, high-metastatic potential PCa cell lines, like PC3 cells, were reported to be less responsive to the proliferative effects induced by CAFs compared to low-metastatic potential cell lines, like LNCaP. However, such a moderate enhancement was significantly abolished in all the PCa cell models upon exposure to CM of antiarrhythmic-treated CAFs, especially with CM-CAF-nifedipine (calcium-channel blocker) and CM-CAF-flecainide (sodium channel-blocker), partially recapitulating the tumor cell growth suppression exerted by CM of NPFs (Fig.  4 b,c and Additional Fig. 3a). Consistent with cell growth findings, cell cycle analysis of DU145 cells reveled that exposure to CM of CAFs increased the S-phase cell fraction and reduced the G1-phase cell fraction compared to untreated cells, while an opposite trend was observed after exposure to CM of NPFs (Fig.  4 d). CM of CAFs treated with the different antiarrhythmics, with the exception of amiodarone, was sufficient to recapitulate the effects mediated by CM-NPF on DU145 cell cycle distribution, resulting in an accumulation of cells in G1-phase and a reduction of S-phase cell population (Fig.  4 d).

figure 4

Antiarrhythmics affect PCa cell growth by impairing CAF function. a Schematic representation of CM experiment work-flow (Created with Biorender.com). b Graph reporting the growth of DU145 cells cultured with CM from CAFs treated or not with antiarrhythmics, or CM from NPFs at different time points (24, 48, 72 h). c Graph reporting the growth of LNCaP cells cultured with CM from CAFs treated or not with antiarrhythmics, or CM from NPFs at different time points (24, 48, 72 h). d Cell cycle phase distribution of DU145 cells cultured with CM from CAFs treated or not with antiarrhythmics, or CM from NPFs at 72 h until treatment. Results reported in the figure represent the mean (+ SD or ± SD) of three independent experiments. * p  < 0.05, ** p  < 0.01, *** p  < 0.005, Student’s t-test

Antiarrhythmics affect PCa cell plasticity by impairing CAF function

As a typical CAF-induced aggressive trait, EMT markers were evaluated in the castration-resistant (DU145) and in the androgen-sensitive PCa cell model (LNCaP) exposed to CM of CAFs treated or not with antiarrhythmics. As expected, DU145 cell exposure to CM-CAF mediated a transition from a more epithelial-like toward a more mesenchymal-like phenotype, as highlighted by the down-regulation of epithelial markers ( CDH1 /E-cadherin and CTNNB1 /β-catenin) and the increased expression of mesenchymal ones ( VIM /vimentin and SNAI1 /Snail) at both the mRNA and protein levels (Fig.  5 a and Additional Fig. 3b). Conversely, the expression levels of VIM /vimentin and SNAI1 /Snail were generally reduced in DU145 cells exposed to CM of CAF-treated with antiarrhythmics (Fig.  5 b and Additional Fig. 3c), mimicking the EMT suppressive effect exerted by CM-NPF, although an enhancement of epithelial markers by CM of antiarrhythmic-treated CAFs was consistently observed only at the protein levels (Fig.  5 a,b). The ability of antiarrhythmics to affect CAF-induced PCa plasticity was particularly appreciable in the androgen-sensitive model, where the treatment generally reverted the expression of both epithelial ( CDH1 /E-cadherin and CTNNB1 /β-catenin) and mesenchymal markers ( VIM /vimentin and SNAI1 /Snail) in LNCaP cells, which was more appreciable at the protein levels (Fig.  5 c,d and Additional Fig. 3d,e). Focusing on those antiarrhythmics that showed a greater capability to impact on ECM remodeling process mediated by CAFs (Fig.  3 f) and to induce a partial reversal of EMT in co-culture experiments (Fig.  5 a, b), we evaluated whether the treatment could revert CAF-promoted migratory boost on DU145 cells. As indicated in the representative photomicrographs and bar graph, DU145 cells exposed to CM of CAFs displayed a high and rapid capacity to close the wound. Conversely, the CM of CAFs treated with nifedipine or flecainide significantly reduced DU145 cell migration capability, showing a wound-healing ratio to an extent approaching that observed with CM-NPF (Fig.  5 e). These findings suggest that nifedipine and flecainide decrease CAF-mediated pro-migratory boost on DU145 cells, thus confirming the repressive effects of antiarrhythmics on CAF pro-tumor spur. Since CAF-derived cytokines are master regulators of EMT, migration and invasion of cancer cells, we investigated the perturbation induced by antiarrhytmics on CAF secretome profile. Protein profiler analysis reveled an increase in the glycosylation-inhibiting factor (GIF), also known as macrophage migration inhibitory factor, in the CM form CAFs. Upon the treatment with nifedipine, GIF levels decreased in the CM of treated CAFs, bringing them closer to those observed in CM of NPFs. However, no difference in GIF levels was observed in CM of CAFs treated with flecainide compared to untreated CAFs. Additionally, IL-8, which is a well–known promoter of migration and EMT, was found to be increased in CM from CAFs [ 38 ]. In contrast, both nifedipine and flecainide completely abrogated IL-8 release from CAFs, resembling the IL-8 levels observed in the CM of NPFs (Fig.  5 f). These observations indicate that antiarrhythmics perturb CAF protumor effects by reducing tumor-stroma cross-talk, potentially inhibiting the initial phases of the metastatic process, such as EMT and migration. Another interesting role exerted by CAFs is the promotion of stemness in PCs cells [ 34 ]. Thus, we investigated whether the treatment could revert CAF-promoted stemness in DU145 cells. As showed in Fig.  5 g, DU145 cells exposed to CM from untreated CAF displayed a slight increase in the expression of both the stemness markers CD44 and CD133 compared to untreated cells. In contrast, CM from NPF reduced the expression of CD144 and CD133, suggesting a possible inhibition of stemness features in DU145 cells exerted by NPF. Interestingly, treatment with antiarrhythmics partially abolished the stemness-promoting effect of CM from CAF, which was particularly evident for CD133. However, this effect was observed for CD44 only when amiodarone was used (CM-CAF-amio).

figure 5

Antiarrhythmics affect PCa cell plasticity by impairing CAF function. a - d Western blotting analysis showing E-cadherin, β-catenin, Vimentin and Snail protein amount in DU145 cells (a-b) and LNCaP cells ( c - d ) exposed to CM from NPFs or CM from CAFs treated or not to antiarrhythmics. β-tubulin was used as endogenous control. e Representative bright-field microphotographs (left panel) showing migration rate of DU145 cells exposed to CM from NPFs or CM from CAFs treated or not with nifedipine or flecainide. Scale bar, 100 μm. The dotted lines define the areas lacking cells. Bar plots (right panel) showing the wound-healing rate of DU145 cells upon the indicated treatments, as from the scratch assay. Data are reported as wound healing ratio at 24 h compared to 0 h. f Cytokine and chemokine protein array blots (left panel) of CM from CAFs treated or not with nifedipine or flecainide, and CM from NPFs. Bar plot (right panel) depicts the pixel density of each cytokine or chemokine (mean). The signal intensity of each cytokine or chemokine was expressed relative to the mean of the intensity of the corresponding spots from vehicle control sample. g Western blotting and relative quantification showing the expression of stemness markers (CD133 and CD44) in DU145 cells exposed to CM from CAFs treated or not with antiarrhythmics, or CM from NPFs, with respect to untreated cells. β-tubulin was used as endogenous control. Results reported in the figure represent the mean (+ SD) of three independent experiments. * p  < 0.05, ** p  < 0.01, *** p  < 0.005, Student’s t-test., when calculated against untreated cells

Antiarrhythmics normalize the transcriptome of CAFs

To investigate the perturbation induced by antiarrhythmics at the transcriptome level, gene expression profiling analysis was performed on three independent patient-derived CAF cultures, treated or not treated with nifedipine or flecainide, and matched NPFs as controls. The t-distributed stochastic neighborhood embedding (t-sne) projection of all genes revealed that, even though at moderate physical distance, both nifedipine and flecainide-treated CAFs clustered in between the clearly separated NPFs and CAFs clusters (Fig.  6 a). This suggests that antiarrhythmics polarize CAF transcriptome into a more NPF-like one, which is in trend with the previously shown results suggesting that conversion from a tumor supporting to a tumor suppressing phenotype. Gene set enrichment analysis (GSEA) run using Reactome pathways showed commonalities between nifedipine- and flecainide-treated CAFs (Fig.  6 b and Additional Fig. 4), especially regarding the down-modulation of gene sets related to extracellular matrix organization, collagen formation, TGF-beta signaling, elastic fiber formation and glucose metabolism (Fig.  6 c, Additional Table 1), all processes known to be relevant for CAF activation and function [ 18 ]. Among genes up-regulated in antiarrhythmics-treated CAFs enrichment was observed for gene sets related to lipid metabolism, an aspect that might warrant investigation in future studies (Additional Table 2).

figure 6

Antiarrhythmics normalize the transcriptome of CAFs. a Scatter plot of t-SNE components showing similarity of transcriptomes of CAFs, NPFs and antiarrhythmics-treated CAFs. b Venn diagram showing overlap between Reactome gene sets enriched (GSEA, NES < 0, FDR p -val < 0.05) in genes down-regulated in CAFs upon nifedipine and flecainide treatments. c Bar plot showing NES of representative gene sets down-regulated in nifedipine- and flecainide-treated CAFs

Antiarrhythmics impair the capability of CAFs to sustain PCa cell growth in vivo

To evaluate the impact of antiarrhythmics on CAF-PCa cross-talk in vivo, PCa xenograft-bearing mice were treated with intra-tumorally administered CM derived from antiarrhythmic-treated CAFs, CAFs or NPFs as control (Fig.  7 a). In accord with the in vitro evidence, CM of nifedipine- or flecainide-treated CAFs significantly attenuated the in vivo growth of PCa tumors compared to CM-CAF, exerting a tumor suppressive effect resembling that of CM of NPF. In addition, CM-CAF-mediated EMT was evaluated in vivo, indicating that tumors exposed to antiarrhythmic-CM CAF showed a higher expression of E-cadherin, similarly to what observed in CM-NPF treated tumors (Fig.  7 b). Tumors explanted from mice exposed to CM of treated CAFs were also characterized by the lowest proliferative rate, as indicated by Ki-67 index, with a 20% reduction compared to that of tumors from CM-CAF group (Fig.  7 c).

figure 7

Antiarrhythmics impair the capability of CAFs to sustain PCa cell growth in vivo. a DU145 cells were subcutaneously injected into the right flanks of SCID mice. When tumors reached the volume of ~ 100 mm. 3 , mice were randomized into four groups and were intra-tumorally treated for 5 days per 2 weeks with CM from CAFs treated or not with nifedipine or flecainide, or CM from NPFs (see insert on the right for a schematic representation of the experiment). The graph reports tumor volumes along the experiment. Black rows indicate when treatment was administered. Schematic representation of the experimental workflow (created with Biorender.com) ( b ) Western blotting showing E-cadherin levels in PCa tumors excised at the end of the treatment with CM from CAFs treated or not with antiarrhythmics, or CM from NPFs. β-tubulin was used as endogenous control. c Representative bright-field microphotographs (upper panel) showing Ki-67 staining in PCa tumors excised at the end of the treatment with CM from CAFs treated or not with antiarrhythmics, or CM from NPFs. Bar plot (lower panel) showing Ki-67 positive cells in PCa tumors upon the relative treatment. Data were reported as percentage of Ki-67 positive cells with respect to total number of cells. Eight fields were evaluated for each condition. d Representative bright-field microphotographs (upper panel) showing CD31 staining in PCa tumors excised at the end of the treatment with CM from CAFs treated or not with antiarrhythmics, or CM from NPFs. Bar plot (lower panel) showing CD31 positive cells in PCa tumors upon the relative treatment. Data were reported as percentage of CD31 positive cells with respect to total number of cells. Eight fields were evaluated for each condition. e DU145 cells were subcutaneously co-injected with CAFs, CAFs pretreated with nifedipine or flecainide, or with NPFs, into the right flanks of SCID mice. The graph report tumor volumes along the experiment. f Timeline indicating tumor take (n. of tumors/n. of co-injected mice) in the different experimental groups, as from the experiment described in panel e . (Created with Biorender.com)

Given the well-established contribution of CAFs to promote tumor angiogenesis [ 3 ], we investigated whether CM from treated CAFs was able to impair the endothelial network formation in PCa tumors. As indicated in Fig.  7 d, tumors explanted from mice exposed to CM from antiarrhythmic-treated CAFs showed a lower expression of the angiogenesis marker CD31, resembling the anti-angiogenic role exerted by CM of NPF on DU145 cells. Co-injection of DU145 cells with CAFs pretreated with flecainide (DU145 + CAF-fleca) slightly reduced tumor growth compared to DU145 cells injected with untreated CAFs, although not affecting the overall tumor take (Fig.  7 e). However, DU145 cells injected with flecainide-treated CAFs required a longer interval of time to develop palpable tumors in all transplanted mice compared to DU145-CAF group (6/6 animals with palpable tumors at day 30 vs 5/5 animals with palpable tumors at day 20) (Fig.  7 f). In line with NPF tumor suppressive behavior, co-injection of NPFs with DU145 cells resulted in the highest tumor growth delay and lowest xenograft take (0/6 animals with palpable tumors at day 20 after co-injection).

Despite the numerous attempts, cancer-centric therapeutic strategies frequently fail to overcome the malignancy, also due to the presence of a tumor-supportive microenvironment that may promote therapeutic resistance and tumor relapse [ 8 ]. Cancer initiation, progression, metastatic dissemination and multi-drug resistance are processes extensively driven by CAF-cancer cell interactions [ 39 ]. Considering this crucial role of CAFs in cancer outcome, several preclinical studies have shown that blocking CAF function may be beneficial in different cancer types [ 8 ]. However, only a few clinical trials have been conducted so far using agents or strategies specifically designed to target CAFs in cancer patients, showing very limited results. In this regard, sibrotuzumab, a humanized anti-FAP monoclonal antibody was safely administrated in phase I and II clinical trials on advanced tumors with FAP + stroma, although no objective tumor response was observed [ 40 ].

The poor knowledge of the key processes governing CAF biology, together with the complexity in defining univocal markers for this heterogeneous population have impaired the translation of CAF-focused strategies into clinical practice. Therefore, further efforts are needed to fully understand CAF biology and define targetable vulnerabilities. We previously highlighted a possible involvement of cation channels in CAF activation and function, showing that gene sets encoding for calcium, sodium and potassium ion channels were up-modulated in prostate cancer-derived CAFs [ 18 ]. Here, these initial findings were confirmed both at the mRNA and protein level in independent sets of prostate CAFs established from PCa surgical samples or in NPFs experimentally activated in vitro, showing also concordance with CAF-expression profiles from publicly available data sets. Moreover, the electrophysiological recordings showed an increased membrane conductance for potassium, sodium, and calcium in activated fibroblasts. The role of cation channels has been widely studied in physiological and pathological processes, including carcinogenesis. Noteworthy, the carcinogenesis process has been recognized as a certain type of “channelopathy”, due to the functional involvement of cation channels in the main distinctive features acquired by cancer cells, including unlimited proliferation, uncontrolled differentiation and apoptosis, increased cellular motility and secretion [ 41 ].

Multiple lines of evidence pointed out that ion channels have considerable biological significance in PCa development, progression and response to therapy. For instance, the voltage-gated potassium (Kv) 2.1 was found to be upmodulated in PC3 cells and involved in cell migration. Targeting Kv2.1 with stromatoxin-1 or siRNA-mediated approaches significantly inhibited the migration of PCa cells [ 42 ]. Moreover, highly selective voltage-gated sodium channel inhibitors induced the suppression of metastasis from PCa models in vivo [ 43 ]. In addition, calcium channels were found overexpressed during androgen deprivation in PCa, suggesting their involvement in the acquisition of neuroendocrine features [ 44 ]. In this regard, we have to take into consideration that PCa presentation may include very-low risk and clinically indolent tumors, which never metastasize, or high-risk and aggressive tumors characterized by a high rate of metastatization and poor response to treatments [ 45 , 46 ]. The biological mechanisms underlying these different clinical behaviors are not fully understood. Interestingly, the comparison between PCa stroma from indolent and aggressive tumors revealed a prominent difference in terms of transcriptional profile. For instance, bone-remodeling and immune-suppressive signatures have been observed in high-risk PCa stroma, but not in the stroma of indolent PCa samples [ 47 ]. This piece of evidence, together with the experimental prove of the involvement of CAFs in inducing castration-resistance in PCa, suggested that the existence of aggressive traits within the stroma can promote PCa progression toward a more aggressive phenotype. On the other hand, a less tumor-permissive stroma might eventually repress aggressive features of PCa and promote indolent behaviors.

To the best of our knowledge, only a handful of reports showed the involvement of cation channels in CAF biology [ 48 , 49 , 50 ]. In PCa, Vancauwenberghe and colleagues recently described that the alteration of TRPA1-calcium channel in PCa stroma is sufficient to reduce resveratrol-induced apoptosis in PCa cells, highlighting how deregulated ion channels in CAFs can affect PCa response to treatments [ 51 ]. Our work illustrates that antiarrhythmics, used as cation channel blocker agents, are able to counteract the activated state of CAFs and potentially restore a tumor-suppressive phenotype. Although at a different extent as a function of the drug and concentration used, antiarrhythmics modulate the expression of CAF markers and hinder the main tumor-promoting features of reactive prostate CAFs, including motility and capability to remodel the ECM, by reducing focal adhesion formation and MMP-2 secretion. More importantly, the use of antiarrhythmics impaired the tumor-supportive role exerted by CAFs on androgen-sensitive and –castration-resistent PCa cells, reducing their ability to foster cancer cell proliferation, plasticity, stemness, and angiogenesis both in vitro and in vivo. Normalizing activated stroma or restoring a quiescent environment has recently emerged as a valid and attractive anti-cancer CAF-centered strategy [ 8 ]. In fact, instead of depleting the tumor stroma, which could also affect cellular and structural components exerting tumor suppressive functions, reprogramming the microenvironment towards to a more quiescent phenotype should create a less permissive milieu and reduce cancer growth. In this regard, we acknowledge the existence of controversial data regarding the opportunity to reprogram the tumor microenvironment in order to re-stabilize tumor-inhibiting signals. For instance, in preclinical models of pancreatic ductal adenocarcinoma (PDA), where the role of the abundant fibrotic tumor stroma has been largely investigated, it was shown that depleting CAFs by using monoclonal antibodies against CAF markers, such as FAP or α-SMA, conferred resistance to chemotherapy [ 52 ]. Conversely, inducing a transcriptional reprogramming of pancreatic stroma cells through administration of vitamin D receptor ligands was sufficient to re-establish a physiological stroma, thus reducing tumor volume and improving survival in PDA-bearing mice [ 53 ]. Similarly, our data indicated that targeting cation channels in prostate CAFs by antiarrhythmics resulted in a reduction of PCa cell growth in mice as a consequence of a transcriptional reprogramming leading to a shift from a tumor-promoting CAF-phenotype to a tumor-suppressive one. Of note, preclinical studies reported that direct exposure to antiarrhythmics is able to reduce PCa cell proliferation in vitro and in vivo [ 33 ]. Consistent with this, clinical reports suggest that long-term use of antiarrhythmics may confer a benefit by reducing the risk of developing high-grade PCa [ 54 ]. However, while there is currently no evidence indicating a reduction in PCa-specific mortality with the use of antiarrhythmics [ 55 ], an intriguing observation comes from recent research by Fairhurst C. and colleagues. Their study revealed a potential link between the treatment with antiarrhythmics against voltage-gated sodium channels and a modest improvement in cancer-specific survival. This association was identified in a retrospective cohort of cancer patients, encompassing individuals with breast, bowel, or prostate cancer [ 56 ]. The use of antiarrhythmics in the context of PCa may offer a dual benefit by concurrently targeting the tumor and its stromal counterpart, making them a promising strategy for a comprehensive therapeutic intervention in such disease. Furthermore, exploring a drug repositioning strategy has the advantage of potentially reducing the time and costs associated with developing new compounds.

Here, we speculate on the use of antiarrhythmics as a potential repositioning strategy to normalize PCa stroma through the inhibition of voltage-gated cation channels. Our results show that antiarrhythmics are indeed able to modulate CAF-activated phenotype and impair the CAF-mediated pro-tumor boost on PCa cells both in vitro and in vivo.

Availability of data and materials

All data generated or analyzed during this study are included in this published article (and its Additional files) and available from the corresponding author on reasonable request.

Abbreviations

α-Smooth muscle actin

  • Cancer-associated fibroblasts

Extracellular matrix

Epithelial-mesenchymal transition

Fibroblast activation protein

Fold change

Gene Set Enrichment Analysis

Matrix metalloproteases proteins

Normalized enrichment score

Normal prostate fibroblasts

  • Prostate cancer

Pancreatic ductal adenocarcinoma

Molecular Signature Database

Voltage-gated potassium

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Acknowledgements

This research was supported by Italian Association for Cancer Research (AIRC) grant: Special Program “Innovative Tools for Cancer Risk Assessment and Early Diagnosis”, 5 × 1000, (ED12162 to N.Z.) and by I. Monzino Foundation (to N.Z.).

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Paolo Gandellini and Nadia Zaffaroni contributed equally to this work.

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Molecular Pharmacology Unit, Department of Experimental Oncology, Fondazione IRCSS Istituto Nazionale Dei Tumori, Milan, 20133, Italy

Valentina Doldi, Monica Tortoreto, Stefano Percio & Nadia Zaffaroni

Vita-Salute San Raffaele University, IRCCS San Raffaele Hospital and Scientific Institute, Milan, 20132, Italy

Maurizio Colecchia

Department of Urology, Hospitals of Legnano and Magenta, Milan, 20013, Italy

Massimo Maffezzini

Department of Biosciences, University of Milan, Milan, 20133, Italy

Francesca Giammello, Federico Brandalise & Paolo Gandellini

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VD, MT, FB and FG performed experiments. MC and MM provided the clinical samples. SP performed the bioinformatics analysis. VD analyzed data. VD and PG designed the research. VD, PG and NZ wrote the manuscript. PG and NZ provided critical advice for the study and manuscript. NZ acquired the funds. All authors reviewed the manuscript.

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Correspondence to Valentina Doldi .

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For human specimens, the study protocol (INT n. 154/16) was approved by the Ethics Committee of IRCCS Istituto Nazionale dei Tumori of Milano in accordance with the declaration of Helsinki. Informed consents were obtained from patients before starting the study. Animal studies were performed in accordance with guidelines of animal care protocols approved by Ethics Committee for animal experimentation of IRCCS Istituto Nazionale dei Tumori of Milano and Italian Ministry of Health (approval code n. 350/2017-PR).

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13046_2024_3081_moesm1_esm.pdf.

Additional file 1: Additional Figure 1. Dose-response curves of CAFs exposed to antiarrhythmics for 72 h. Results reported in the figure represent the mean (+SD or ±SD) of three independent experiments.

13046_2024_3081_MOESM2_ESM.pdf

Additional file 2: Additional Figure 2. a. Representative bright-field microphotographs showing migration rate of CAFs exposed to antiarrhythmics Scale bar, 100 μm. The dotted lines define the areas lacking cells. b. Western blotting showing fibronectin and Col1a1 in CM from CAFs treated or not with antiarrhythmics.

13046_2024_3081_MOESM3_ESM.pdf

Additional file 3: Additional Figure 3. a. Graph reporting the growth of PC3 cells cultured with CM from CAFs treated or not with antiarrhythmics, or CM from NPFs at different time points (24, 48, 72 hours). b and d. qRT-PCR showing relative expression levels of epithelial markers ( CDH1 and CTNNB1 ) in DU145 cells (b) or LNCaP cells (d) exposed to CM from CAFs treated or not with antiarrhythmics, or CM from NPFs with respect to untreated cells. c and e. qRT-PCR showing relative expression levels of mesenchymal markers ( VIM and SNAI1 ) in DU145 cells (c) and LNCaP cells (e) exposed to CM from CAFs treated or not with antiarrhythmics, or CM from NPFs with respect to untreated cells.

13046_2024_3081_MOESM4_ESM.pdf

Additional file 4: Additional Figure 4. Venn diagram showing overlap between Reactome gene sets enriched (GSEA, NES<0, adjusted p-val<0.05) in genes up-regulated in CAFs upon nifedipine and flecainide treatments.

Additional file 5: Additional Table 1. Commonly down-regulated Reactome genesets in antiarrhythmics-treated CAFs.

Additional file 6: additional table 2. commonly up-regulated reactome genesets in antiarrhythmics-treated cafs., additional file 7., rights and permissions.

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Doldi, V., Tortoreto, M., Colecchia, M. et al. Repositioning of antiarrhythmics for prostate cancer treatment: a novel strategy to reprogram cancer-associated fibroblasts towards a tumor-suppressive phenotype. J Exp Clin Cancer Res 43 , 161 (2024). https://doi.org/10.1186/s13046-024-03081-0

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This page contains more complex installation instructions for the major operating systems. For a command-line quickstart installation, see Quick Command Line Install .

On Windows, macOS, and Linux, it is best to install Miniconda for the local user, which does not require administrator permissions and is the most robust type of installation. However, if you need to, you can install Miniconda system wide, which does require administrator permissions.

Download the .exe installer.

(Optional) Verify your installer’s SHA-256 checksum. This check proves that the installer you downloaded is the original one.

Open PowerShell version 4.0 or later. For instructions for using Windows PowerShell 3.0 or older, see the Cryptographic hash verification instructions in the conda project documentation.

Run the following command, replacing filename with the path to your installer.

Check the hash that appears against the hash listed next to the installer you downloaded. See all Miniconda installer hashes here .

Double-click the .exe file.

Follow the instructions on the screen. If you are unsure about any setting, accept the defaults. You can change them later.

When the installation finishes, from the Start menu, open Anaconda Prompt.

Test your installation by running conda list . If conda has been installed correctly, a list of installed packages appears.

More information on installing in silent mode on Windows is in the conda project documentation .

The graphical installer for MacOS installs Miniconda into /opt/miniconda3 in your file system. If you want to install Miniconda into your Home directory or if you have multiple users on a system and want to manage your installation more carefully, Anaconda recommends the shell (or command line) installer .

Download the .pkg installer.

Open a terminal application.

Double-click the .pkg file.

When the installation finishes, open your terminal application.

More information on installing in silent mode on macOS is in the conda project documentation .

Open your terminal.

In your terminal, run the following command, replacing filename with the path to your installer.

bash filename

Follow the prompts on the installer screens.

If you are unsure about any setting, accept the defaults. You can change them later.

To make the changes take effect, close and then re-open your terminal window.

For more information on installing in silent mode, see the macOS instructions in the conda project documentation .

COMMENTS

  1. Ideas for Controlled Variable Science Projects

    In two glass containers, place one cup of distilled water in one and white vinegar in the other. Carefully drop a dirty penny into each container of liquid and record the changes in the penny's appearance over the course of one week. The controlled variable is in the amount of liquid used to clean each penny. References.

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    The two key variables in science are the independent and dependent variable, but there are other types of variables that are important. In a science experiment, a variable is any factor, attribute, or value that describes an object or situation and is subject to change. An experiment uses the scientific method to test a hypothesis and establish whether or not there is a cause and effect ...

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    Here are several examples of independent and dependent variables in experiments: In a study to determine whether how long a student sleeps affects test scores, the independent variable is the length of time spent sleeping while the dependent variable is the test score. You want to know which brand of fertilizer is best for your plants.

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    These variables are crucial for defining the relationships between factors within an experiment or study and determining the cause-and-effect relationships that underpin scientific knowledge. Independent Variables: An independent variable is a factor or characteristic that the researcher manipulates or controls in an experiment or study. It is ...

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    Parts of the experiment: Independent vs dependent variables. Experiments are usually designed to find out what effect one variable has on another - in our example, the effect of salt addition on plant growth.. You manipulate the independent variable (the one you think might be the cause) and then measure the dependent variable (the one you think might be the effect) to find out what this ...

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    The second experiment does not require any more data collection, but it does require looking at the data from experiment #1 in a different way. For experiment #2, graph the data with the voltage on the y-axis and time on the x-axis for each type (low, medium, high) of current drain device. Variables Experiment #1:

  13. Variables

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    An experiment designed to determinate the effect of a fertilizer on plant growth has the following variables:Independent VariablesFertilizerDependent VariablesPlant height, plant weight, number of leavesExtraneous VariablesPlant type, sunlight, water, temperature, air quality, windSituational VariablesSunlight, water, temperature, air quality ...

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    All types of variables can affect your science experiment. Get information about independent, dependent, control, intervening, and extraneous variables.

  17. What Is a Control Variable? Definition and Examples

    Control Variable Examples. Anything you can measure or control that is not the independent variable or dependent variable has potential to be a control variable. Examples of common control variables include: Duration of the experiment. Size and composition of containers. Temperature.

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    The independent variable is the cause. Its value is independent of other variables in your study. The dependent variable is the effect. Its value depends on changes in the independent variable. Example: Independent and dependent variables. You design a study to test whether changes in room temperature have an effect on math test scores.

  21. Science Projects With Three Variables for Kids in Fifth Grade

    The concept of variables in a science experiment can be confusing for fifth graders. Think of the independent variable as what you change in an experiment, the dependent variable as the response you observe because of what you changed, and the controlled variable as the things you keep the same so they don't interfere with your results.

  22. What Are Variables In Science

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    The independent variable is the variable we manipulate or change. Scientists design experiments using variables to understand the relationship between different factors. Variables include things like temperature, the amount of liquid, and location (e.g., a sunny window vs. inside a dark room). There are three types of variables: independent ...

  25. Identifying Variables

    In their own words, have students define the terms "Independent variable," "Dependent variable," and "Controlled variable." Exercise 3 Have students brainstorm the variables that should be controlled in the Seed Investigation (e.g., quantity of water, type of soil, type of planting container, temperature, etc.).

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    Theme-based experiments: Organize experiments around a theme, such as water, air, magnets, or plants. Even holidays and seasons make fun themes! Even holidays and seasons make fun themes! Kitchen science : Perform experiments in the kitchen, such as making ice cream using salt and ice or learning about density by layering different liquids.

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    Although to a variable extent, these drugs also reduce CAF motility and hinder their ability to remodel the extracellular matrix, for example by reducing MMP-2 release. Furthermore, conditioned medium and co-culture experiments showed that antiarrhythmics can, at least in part, reverse the protumor effects exerted by CAFs on PCa cell growth and ...

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    View PDF HTML (experimental) Abstract: Identifying the active factors that have significant impacts on the output of the complex system is an important but challenging variable selection problem in computer experiments. In this paper, a Bayesian hierarchical Gaussian process model is developed and some latent indicator variables are embedded into this setting for the sake of labelling the ...

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  30. Installing Miniconda

    Check the hash that appears against the hash listed next to the installer you downloaded. See all Miniconda installer hashes here.. Double-click the .exe file.. Follow the instructions on the screen.