why do we need replicates in an experiment

Why is Replication in Research Important?

Replication in research is important because it allows for the verification and validation of study findings, building confidence in their reliability and generalizability. It also fosters scientific progress by promoting the discovery of new evidence, expanding understanding, and challenging existing theories or claims.

Updated on June 30, 2023

Often viewed as a cornerstone of science , replication builds confidence in the scientific merit of a study’s results. The philosopher Karl Popper argued that, “we do not take even our own observations quite seriously, or accept them as scientific observations, until we have repeated and tested them.”

As such, creating the potential for replication is a common goal for researchers. The methods section of scientific manuscripts is vital to this process as it details exactly how the study was conducted. From this information, other researchers can replicate the study and evaluate its quality.

This article discusses replication as a rational concept integral to the philosophy of science and as a process validating the continuous loop of the scientific method. By considering both the ethical and practical implications, we may better understand why replication is important in research.

What is replication in research?

As a fundamental tool for building confidence in the value of a study’s results, replication has power. Some would say it has the power to make or break a scientific claim when, in reality, it is simply part of the scientific process, neither good nor bad.

When Nosek and Errington propose that replication is a study for which any outcome would be considered diagnostic evidence about a claim from prior research, they revive its neutrality. The true purpose of replication, therefore, is to advance scientific discovery and theory by introducing new evidence that broadens the current understanding of a given question.

Why is replication important in research?

The great philosopher and scientist, Aristotle , asserted that a science is possible if and only if there are knowable objects involved. There cannot be a science of unicorns, for example, because unicorns do not exist. Therefore, a ‘science’ of unicorns lacks knowable objects and is not a ‘science’.

This philosophical foundation of science perfectly illustrates why replication is important in research. Basically, when an outcome is not replicable, it is not knowable and does not truly exist. Which means that each time replication of a study or a result is possible, its credibility and validity expands.

The lack of replicability is just as vital to the scientific process. It pushes researchers in new and creative directions, compelling them to continue asking questions and to never become complacent. Replication is as much a part of the scientific method as formulating a hypothesis or making observations.

Types of replication

Historically, replication has been divided into two broad categories: 

• Direct replication : performing a new study that follows a previous study’s original methods and then comparing the results. While direct replication follows the protocols from the original study, the samples and conditions, time of day or year, lab space, research team, etc. are necessarily different. In this way, a direct replication uses empirical testing to reflect the prevailing beliefs about what is needed to produce a particular finding.
• Conceptual replication : performing a study that employs different methodologies to test the same hypothesis as an existing study. By applying diverse manipulations and measures, conceptual replication aims to operationalize a study’s underlying theoretical variables. In doing so, conceptual replication promotes collaborative research and explanations that are not based on a single methodology.

Though these general divisions provide a helpful starting point for both conducting and understanding replication studies, they are not polar opposites. There are nuances that produce countless subcategories such as:

• Internal replication : when the same research team conducts the same study while taking negative and positive factors into account
• Microreplication : conducting partial replications of the findings of other research groups
• Constructive replication : both manipulations and measures are varied
• Participant replication : changes only the participants

Many researchers agree these labels should be confined to study design, as direction for the research team, not a preconceived notion. In fact, Nosek and Errington conclude that distinctions between “direct” and “conceptual” are at least irrelevant and possibly counterproductive for understanding replication and its role in advancing knowledge.

How do researchers replicate a study?

Like all research studies, replication studies require careful planning. The Open Science Framework (OSF) offers a practical guide which details the following steps:

• Identify a study that is feasible to replicate given the time, expertise, and resources available to the research team.
• Determine and obtain the materials used in the original study.
• Develop a plan that details the type of replication study and research design intended.
• Outline and implement the study’s best practices.
• Conduct the replication study, analyze the data, and share the results.

These broad guidelines are expanded in Brown’s and Wood’s article , “Which tests not witch hunts: a diagnostic approach for conducting replication research.” Their findings are further condensed by Brown into a blog outlining four main procedural categories:

• Assumptions : identifying the contextual assumptions of the original study and research team
• Data transformations : using the study data to answer questions about data transformation choices by the original team
• Estimation : determining if the most appropriate estimation methods were used in the original study and if the replication can benefit from additional methods
• Heterogeneous outcomes : establishing whether the data from an original study lends itself to exploring separate heterogeneous outcomes

At the suggestion of peer reviewers from the e-journal Economics, Brown elaborates with a discussion of what not to do when conducting a replication study that includes:

• Do not use critiques of the original study’s design as  a basis for replication findings.
• Do not perform robustness testing before completing a direct replication study.
• Do not omit communicating with the original authors, before, during, and after the replication.
• Do not label the original findings as errors solely based on different outcomes in the replication.

Again, replication studies are full blown, legitimate research endeavors that acutely contribute to scientific knowledge. They require the same levels of planning and dedication as any other study.

What happens when replication fails?

There are some obvious and agreed upon contextual factors that can result in the failure of a replication study such as: 

• The detection of unknown effects
• Inconsistencies in the system
• The inherent nature of complex variables
• Substandard research practices
• Pure chance

While these variables affect all research studies, they have particular impact on replication as the outcomes in question are not novel but predetermined.

The constant flux of contexts and variables makes assessing replicability, determining success or failure, very tricky. A publication from the National Academy of Sciences points out that replicability is obtaining consistent , not identical, results across studies aimed at answering the same scientific question. They further provide eight core principles that are applicable to all disciplines.

While there is no straightforward criteria for determining if a replication is a failure or a success, the National Library of Science and the Open Science Collaboration suggest asking some key questions, such as:

• Does the replication produce a statistically significant effect in the same direction as the original?
• Is the effect size in the replication similar to the effect size in the original?
• Does the original effect size fall within the confidence or prediction interval of the replication?
• Does a meta-analytic combination of results from the original experiment and the replication yield a statistically significant effect?
• Do the results of the original experiment and the replication appear to be consistent?

While many clearly have an opinion about how and why replication fails, it is at best a null statement and at worst an unfair accusation. It misses the point, sidesteps the role of replication as a mechanism to further scientific endeavor by presenting new evidence to an existing question.

Can the replication process be improved?

The need to both restructure the definition of replication to account for variations in scientific fields and to recognize the degrees of potential outcomes when comparing the original data, comes in response to the replication crisis . Listen to this Hidden Brain podcast from NPR for an intriguing case study on this phenomenon.

Considered academia’s self-made disaster, the replication crisis is spurring other improvements in the replication process. Most broadly, it has prompted the resurgence and expansion of metascience , a field with roots in both philosophy and science that is widely referred to as "research on research" and "the science of science." By holding a mirror up to the scientific method, metascience is not only elucidating the purpose of replication but also guiding the rigors of its techniques.

Further efforts to improve replication are threaded throughout the industry, from updated research practices and study design to revised publication practices and oversight organizations, such as:

• Requiring full transparency of the materials and methods used in a study
• Pushing for statistical reform , including redefining the significance of the p-value
• Using pre registration reports that present the study’s plan for methods and analysis
• Adopting result-blind peer review allowing journals to accept a study based on its methodological design and justifications, not its results
• Founding organizations like the EQUATOR Network that promotes transparent and accurate reporting

Final thoughts

In the realm of scientific research, replication is a form of checks and balances. Neither the probability of a finding nor prominence of a scientist makes a study immune to the process.

And, while a single replication does not validate or nullify the original study’s outcomes, accumulating evidence from multiple replications does boost the credibility of its claims. At the very least, the findings offer insight to other researchers and enhance the pool of scientific knowledge.

After exploring the philosophy and the mechanisms behind replication, it is clear that the process is not perfect, but evolving. Its value lies within the irreplaceable role it plays in the scientific method. Replication is no more or less important than the other parts, simply necessary to perpetuate the infinite loop of scientific discovery.

Charla Viera, MS

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• v.13(4); 2012 Apr

Replicates and repeats—what is the difference and is it significant?

David l vaux.

1 The Walter and Eliza Hall Institute, and the Department of Experimental Biology, University of Melbourne, Melbourne, Australia.

Fiona Fidler

2 La Trobe University School of Psychological Science, Melbourne, Australia.

Geoff Cumming

Science is knowledge gained through repeated experiment or observation. To be convincing, a scientific paper needs to provide evidence that the results are reproducible. This evidence might come from repeating the whole experiment independently several times, or from performing the experiment in such a way that independent data are obtained and a formal procedure of statistical inference can be applied—usually confidence intervals (CIs) or statistical significance testing. Over the past few years, many journals have strengthened their guidelines to authors and their editorial practices to ensure that error bars are described in figure legends—if error bars appear in the figures—and to set standards for the use of image-processing software. This has helped to improve the quality of images and reduce the number of papers with figures that show error bars but do not describe them. However, problems remain with how replicate and independently repeated data are described and interpreted. As biological experiments can be complicated, replicate measurements are often taken to monitor the performance of the experiment, but such replicates are not independent tests of the hypothesis, and so they cannot provide evidence of the reproducibility of the main results. In this article, we put forward our view to explain why data from replicates cannot be used to draw inferences about the validity of a hypothesis, and therefore should not be used to calculate CIs or P values, and should not be shown in figures.

…replicates are not independent tests of the hypothesis, and so they cannot provide evidence of the reproducibility of the main results

Let us suppose we are testing the hypothesis that the protein Biddelonin (BDL), encoded by the Bdl gene, is required for bone marrow colonies to grow in response to the cytokine HH-CSF. Luckily, we have wild-type (WT) and homozygous Bdl gene-deleted mice at our disposal, and a vial of recombinant HH-CSF. We prepare suspensions of bone marrow cells from a single WT and a single Bdl −/− mouse (same sex littermates from a Bdl +/− heterozygous cross) and count the cell suspensions by using a haemocytometer, adjusting them so that there are 1 × 10 5 cells per millilitre in the final solution of soft agar growth medium. We add 1 ml aliquots of the suspension to sets of ten 35 × 10 mm Petri dishes that each contain 10 μl of either saline or purified recombinant mouse HH-CSF.

We therefore put in the incubator four sets of ten soft agar cultures: one set of ten plates has WT bone marrow cells with saline; the second has Bdl −/− cells with saline; the third has WT cells with HH-CSF, and the fourth has Bdl −/− cells with HH-CSF. After a week, we remove the plates from the incubator and count the number of colonies (groups of >50 cells) in each plate by using a dissecting microscope. The number of colonies counted is shown in Table 1 .

1 × 10 5 WT or Bdl −/− bone marrow cells were plated in 1 ml soft agar cultures in the presence or absence of 1 μM HH-CSF. Colonies per plate were counted after 1 week. WT, wild type.

We could plot the counts of the plates on a graph. If we plotted just the colony counts of only one plate of each type ( Fig 1A shows the data for plate 1), it seems clear that HH-CSF is necessary for many colonies to form, but it is not immediately apparent whether the response of the Bdl −/− cells is significantly different to that of the WT cells. Furthermore, the graph does not look ‘sciency’ enough; there are no error bars or P -values. Besides, by showing the data for only one plate we are breaking the fundamental rule of science that all relevant data should be reported and subjected to analysis, unless good reasons can be given why some data should be omitted.

Displaying data from replicates—what not to do. ( A ) Data for plate 1 only (shown in Table 1 ). ( B ) Means ± SE for replicate plates 1–3 (in Table 1 ), * P > 0.05. ( C ) Means ± SE for replicate plates 1–10 (in Table 1 ), * P < 0.0001. ( D ) Means ± SE for HH-CSF-treated replicate plates 1–10 (in Table 1 ). Statistics should not be shown for replicates because they merely indicate the fidelity with which the replicates were made, and have no bearing on the hypothesis being tested. In each of these figures, n = 1 and the size of the error bars in ( B ), ( C ) and ( D ) reflect sampling variation of the replicates. The SDs of the replicates would be expected to be roughly the square root of the mean number of colonies. Also, axes should commence at 0, other than in exceptional circumstances, such as for log scales. SD, standard deviation; SE, standard error.

To make it look better, we could add the mean numbers of colonies in the first three plates of each type to the graph ( Fig 1B ), with error bars that report the standard error (SE) of the three values of each type. Now it is looking more like a figure in a high-profile journal, but when we use the data from the three replicate plates of each type to assess the statistical significance of the difference in the responses of the WT and Bdl −/− cells to HH-CSF, we find P > 0.05, indicating they are not significantly different.

As we have another seven plates from each group, we can plot the means and SEs of all ten plates and re-calculate P ( Fig 1C ). Now we are delighted to find that there is a highly significant difference between the Bdl −/− and WT cells, with P < 0.0001.

However, although the differences are highly statistically significant, the heights of the columns are not dramatically different, and it is hard to see the error bars. To remedy this, we could simply start the y -axis at 40 rather than zero ( Fig 1D ), to emphasize the differences in the response to HH-CSF. Although this necessitates removing the saline controls, these are not as important as visual impact for high-profile journals.

With a small amount of effort, and no additional experiments, we have transformed an unimpressive result ( Fig 1A,B ) into one that gives strong support to our hypothesis that BDL is required for a response to HH-CSF, with a highly significant P -value, and a figure ( Fig 1D ) that looks like it could belong in one of the top journals.

So, what is wrong? The first problem is that our data do not confirm the hypothesis that BDL is required for bone marrow colonies to grow in response to HH-CSF, they actually refute it. Clearly, bone marrow colonies are growing in the absence of BDL, even if the number is not as great as when the Bdl genes are intact. Terms such as ‘required’, ‘essential’ and ‘obligatory’ are not relative, yet are still often incorrectly used when partial effects are seen. At the very least, we should reformulate our hypothesis, perhaps to “BDL is needed for a full response of bone marrow colony-forming cells to the cytokine HH-CSF”.

…by showing the data for only one plate we are breaking the fundamental rule of science that all relevant data should be reported and subjected to analysis…

The second major problem is that the calculations of P and statistical significance are based on the SE of replicates, but the ten replicates in any of the four conditions were each made from a single suspension of bone marrow cells from just one mouse. As such, we can at best infer a statistically significant difference between the concentration of colony-forming cells in the bone marrow cell suspension from that particular WT mouse and the bone marrow suspension from that particular gene-deleted mouse. We have made just one comparison, so n = 1, no matter how many replicate plates we count. To make an inference that can be generalized to all WT mice and Bdl −/− mice, we need to repeat our experiments a number of times, making several independent comparisons using several mice of each type.

Rather than providing independent data, the results from the replicate plates are linked because they all came from the same suspension of bone marrow cells. For example, if we made any error in determining the concentration of bone marrow cells, this error would be systematically applied to all of the plates. In this case, we determined the initial number of bone marrow cells by performing a cell count using a haemocytometer, a method that typically only gives an accuracy of ±10%. Therefore, no matter how many plates are counted, or how small the error bars are in Fig 1 , it is not valid to conclude that there is a difference between the WT and Bdl −/− cells. Moreover, even if we had used a flow cytometer to sort exactly the same number of bone marrow cells into each of the plates, we would still have only tested cells from a single Bdl −/− mouse, so n would still equal 1 (see Fundamental principle 1 in Sidebar A ).

Sidebar A | Fundamental principles of statistical design

Fundamental principle 1

Science is knowledge obtained by repeated experiment or observation: if n = 1, it is not science, as it has not been shown to be reproducible. You need a random sample of independent measurements.

Fundamental principle 2

Experimental design, at its simplest, is the art of varying one factor at a time while controlling others: an observed difference between two conditions can only be attributed to Factor A if that is the only factor differing between the two conditions. We always need to consider plausible alternative interpretations of an observed result. The differences observed in Fig 1 might only reflect differences between the two suspensions, or be due to some other (of the many) differences between the two individual mice, besides the particular genotypes of interest.

Fundamental principle 3

A conclusion can only apply to the population from which you took the random sample of independent measurements: so if we have multiple measures on a single suspension from one individual mouse, we can only draw a conclusion about that particular suspension from that particular mouse. If we have multiple measures of the activity of a single vial of cytokine, then we can only generalize our conclusion to that vial.

Fundamental principle 4

Although replicates cannot support inference on the main experimental questions, they do provide important quality controls of the conduct of experiments. Values from an outlying replicate can be omitted if a convincing explanation is found, although repeating part or all of the experiment is a safer strategy. Results from an independent sample, however, can only be left out in exceptional circumstances, and only if there are especially compelling reasons to justify doing so.

To be convincing, a scientific paper describing a new finding needs to provide evidence that the results are reproducible. While it might be argued that a hypothetical talking dog would represent an important scientific discovery even if n = 1, few people would be convinced if someone claimed to have a talking dog that had been observed on one occasion to speak a single word. Most people would require several words to be spoken, with a number of independent observers, on several occasions. The cloning of Dolly the sheep represented a scientific breakthrough, but she was one of five cloned sheep described by Campbell et al [ 1 ]. Eight fetuses and sheep were typed by microsatellite analysis and shown to be identical to the cell line used to provide the donor nuclei.

To be convincing, a scientific paper needs to provide evidence that the results are reproducible

Inferences can only be made about the population from which the independent samples were drawn. In our original experiment, we took individual replicate aliquots from the suspensions of bone marrow cells ( Fig 2A ). We can therefore only generalize our conclusions to the ‘population’ from which our sample aliquots came: in this case the population is that particular suspension of bone marrow cells. To test our hypothesis, it is necessary to carry out an experiment similar to that shown in Fig 2B . Here, bone marrow has been independently isolated from a random sample of WT mice and another random sample of Bdl −/− mice. In this case, we can draw conclusions about Bdl −/− mice in general, and compare them withWT mice (in general). In Fig 2A , the number of Bdl −/− mice that have been compared with WT mice (which is the comparison relevant to our hypothesis) is one, so n = 1, regardless of how many replicate plates are counted. Conversely, in Fig 2B we are comparing three Bdl −/− mice with WT controls, so n = 3, whether we plate three replicate plates of each type or 30. Note, however, that it is highly desirable for statistical reasons to have samples larger than n = 3, and/or to test the hypothesis by some other approach, for example, by using antibodies that block HH-CSF or BDL, or by re-expressing a Bdl cDNA in the Bdl −/− cells (see Fundamental principle 2 in Sidebar A ).

Sample variation. Variation between samples can be used to make inferences about the population from which the independent samples were drawn (red arrows). For replicates, as in ( A ), inferences can only be made about the bone marrow suspensions from which the aliquots were taken. In ( A ), we might be able to infer that the plates on the left and the right contained cells from different suspensions, and possibly that the bone marrow cells came from two different mice, but we cannot make any conclusions about the effects of the different genotypes of the mice. In ( B ), three independent mice were chosen from each genotype, so we can make inferences about all mice of that genotype. Note that in the experiments in ( B ), n = 3, no matter how many replicate plates are created.

One of the most commonly used methods to determine the abundance of mRNA is real-time quantitative reverse transcription PCR (qRT-PCR; although the following example applies equally well to an ELISA or similar). Typically, multi-well plates are used so that many samples can be simultaneously read in a PCR machine. Let us suppose we are going to use qRT-PCR to compare levels of Boojum mRNA ( Bjm ) in control bone marrow cells (treated with medium alone) with Bjm levels in bone marrow cells treated with HH-CSF, in order to test the hypothesis that HH-CSF induces expression of the Bjm gene.

We isolate bone marrow cells from a normal mouse, and dispense equal aliquots containing a million cells into each of two wells of a six-well plate. For the moment we use only two of the six wells. We then add 4 ml of plain medium to one of the wells (the control), and 4 ml of a mixture of medium supplemented with HH-CSF to the other well (the experimental well). We incubate the plate for 24 h and then transfer the cells into two tubes, in which we extract the RNA using TRizol. We then suspend the RNA in 50 μl TRIS-buffered RNAse-free water.

We put 10 μl from each tube into each of two fresh tubes, so that both Actin (as a control) and Bjm message can be determined in each sample. We now have four tubes, each with 10 μl of mRNA solution. We make two sets of ‘reaction mix’ with the only difference being that one contains Actin PCR primers and the other Bjm primers. We add 40 μl of one or the other ‘reaction mix’ to each of the four tubes, so we now have 50 μl in each tube. After mixing, we take three aliquots of 10 μl from each of the four tubes and put them into three wells of a 384-well plate, so that 12 wells in total contain the RT-PCR mix. We then put the plate into the thermocycler. After an hour, we get an Excel spreadsheet of results.

…should we dispense with replicates altogether? The answer, of course, is ‘no’. Replicates serve as internal quality checks on how the experiment was performed

We then calculate the ratio of the Bjm signal to the Actin signal for each of the three pairs of reactions that contained RNA from the HH-CSF-treated cells, and for each of the three pairs of control reactions. In this case, the variation among the three replicates will not be affected by sampling error (which was what caused most of the variation in colony number in the earlier bone marrow colony-forming assay), but will only reflect the fidelity with which the replicates were made, and perhaps some variation in the heating of the separate wells in the PCR machine. The three 10 μl aliquots each came from the same, single, mRNA preparation, so we can only make inferences about the contents of that particular tube. As in the previous example, in this case n still equals 1, and no inferences about the main experimental hypothesis can be made. The same would be true if each RNA sample were analysed in 10 or 100 wells; we are only comparing one control sample to one experimental sample, so n = 1 ( Fig 3A ). To draw a general inference about the effect of HH-CSF on Bjm expression, we would have to perform the experiment on several independent samples derived from independent cultures of HH-CSF-stimulated bone marrow cells ( Fig 3B ).

Means of replicates compared with means of independent samples. ( A ) The ratios of the three-replicate Bjm PCR reactions to the three-replicate Actin PCR reactions from the six aliquots of RNA from one culture of HH-CSF-stimulated cells and one culture of unstimulated cells are shown (filled squares). The means of the ratios are shown as columns. The close correlation of the three replicate values (blue lines) indicates that the replicates were created with high fidelity and the pipetting was consistent, but is not relevant to the hypothesis being tested. It is not appropriate to show P -values here, because n = 1. ( B ) The ratios of the replicate PCR reactions using mRNA from the other cultures (two unstimulated, and two treated with HH-CSF) are shown as triangles and circles. Note how the correlation between the replicates (that is, the groups of three shapes) is much greater than the correlation between the mean values for the three independent untreated cultures and the three independent HH-CSF-treated cultures (green lines). Error bars indicate SE of the ratios from the three independent cultures, not the replicates for any single culture. P > 0.05. SE, standard error.

For example, we could have put the bone marrow cells in all six wells of the tissue culture plate, and performed three independent cultures with HH-CSF, and three independent control cultures in medium without HH-CSF. mRNA could then have been extracted from the six cultures, and each split into six wells to measure Actin and Bjm mRNA levels by using qRT-PCR. In this case, 36 wells would have been read by the machine. If the experiment were performed this way, then n = 3, as there were three independent control cultures, and three independent HH-CSF-dependent cultures, that were testing our hypothesis that HH-CSF induces Bjm expression. We then might be able to generalize our conclusions about the effect of that vial of recombinant HH-CSF on expression of Bjm mRNA. However, in this case ( Fig 3B ) P > 0.05, so we cannot exclude the possibility that the differences observed were just due to chance, and that HH-CSF has no effect on Bjm mRNA expression. Note that we also cannot conclude that it has no effect; if P > 0.05, the only conclusion we can make is that we cannot make any conclusions. Had we calculated and shown errors and P -values for replicates in Fig 3A , we might have incorrectly concluded, and perhaps misled the readers to conclude that there was a statistically significant effect of HH-CSF in stimulating Bjm transcription (see Fundamental principle 3 in Sidebar A ).

Why bother with replicates at all? In the previous sections we have seen that replicates do not allow inferences to be made, or allow us to draw conclusions relevant to the hypothesis we are testing. So should we dispense with replicates altogether? The answer, of course, is ‘no’. Replicates serve as internal quality checks on how the experiment was performed. If, for example, in the experiment described in Table 1 and Fig 1 , one of the replicate plates with saline-treated WT bone marrow contained 100 colonies, you would immediately suspect that something was wrong. You could check the plate to see if it had been mislabelled. You might look at the colonies using a microscope and discover that they are actually contaminating colonies of yeast. Had you not made any replicates, it is possible you would not have realized that a mistake had occurred.

Replicates […] cannot be used to infer conclusions

Fig 4 shows the results of the same qRT-PCR experiment as in Fig 3 , but in this case, for one of the sets of triplicate PCR ratios there is much more variation than in the others. Furthermore, this large variation can be accounted for by just one value of the three replicates—that is, the uppermost circle in the graph. If you had results such as those in Fig 4A , you would look at the individual values for the Actin PCR and Bjm PCR for the replicate that had the strange result. If the Bjm PCR sample was unusually high, you could check the corresponding well in the PCR plate to see if it had the same volume as the other wells. Conversely, if the Actin PCR value was much lower than those for the other two replicates, on checking the well in the plate you might find that the volume was too low. Alternatively, the unusual results might have been due to accidentally adding two aliquots of RNA, or two of PCR primer-reaction mix. Or perhaps the pipette tip came loose, or there were crystals obscuring the optics, or the pipette had been blocked by some debris, etc., etc., etc. Replicates can thus alert you to aberrant results, so that you know when to look further and when to repeat the experiment. Replicates can act as an internal check of the fidelity with which the experiment was performed. They can alert you to problems with plumbing, leaks, optics, contamination, suspensions, mixing or mix-ups. But they cannot be used to infer conclusions.

Interpreting data from replicates. ( A ) Mean ± SE of three independent cultures each with ratios from triplicate PCR measurements. P > 0.05. This experiment is much like the one in Fig 3B . However, notice in this case, for one of the sets of replicates (the circles from one of the HH-CSF-treated replicate values), there is a much greater range than for the other five sets of triplicate values. Because replicates are carefully designed to be as similar to each other as possible, finding unexpected variation should prompt an investigation into what went wrong during the conduct of the experiment. Note how in this case, an increase in variation among one set of replicates causes a decrease in the SEs for the values for the independent HH-CSF results: the SE bars for the HH-CSF condition are shorter in Fig 4A than in Fig 3B . Failure to take note of abnormal variation in replicates can lead to incorrect statistical inferences. ( B ) Bjm mRNA levels (relative to Actin ) for three independent cultures each with ratios from triplicate PCR measurements. Means are shown by a horizontal line. The data here are the same as those for Fig 3B or Fig 4A with the aberrant value deleted. When n is as small as 3, it is better to just plot the data points, rather than showing statistics. SE, standard error.

Because replicate values are not relevant to the hypothesis being tested, they—and statistics derived from them—should not be shown in figures. In Fig 4B , the large dots show the means of the replicate values in Fig 4A , after the aberrant replicate value has been excluded. While in this figure you could plot the means and SEs of the mRNA results from the three independent medium- and HH-CSF-treated cultures, in this case, the independent values are plotted and no error bars are shown. When the number of independent data points is low, and they can easily be seen when plotted on the graph, we recommend simply doing this, rather than showing means and error bars.

What should we look for when reading papers? Although replicates can be a valuable internal control to monitor the performance of your experiments, there is no point in showing them in the figures in publications because the statistics from replicates are not relevant to the hypothesis being tested. Indeed, if statistics, error bars and P -values for replicates are shown, they can mislead the readers of a paper who assume that they are relevant to the paper's conclusions. The corollary of this is that if you are reading a paper and see a figure in which the error bars—whether standard deviation, SE or CI—are unusually small, it might alert you that they come from replicates rather than independent samples. You should carefully scrutinize the figure legend to determine whether the statistics come from replicates or independent experiments. If the legend does not state what the error bars are, what n is, or whether the results come from replicates or independent samples, ask yourself whether these omissions undermine the paper, or whether some knowledge can still be gained from reading it.

…if statistics, error bars and P -values for replicates are shown, they can mislead the readers of a paper who assume that they are relevant to the paper’s conclusions

You should also be sceptical if the figure contains data from only a single experiment with statistics for replicates, because in this case, n = 1, and no valid conclusions can be made, even if the authors state that the results were ‘representative’—if the authors had more data, they should have included them in the published results (see Sidebar B for a checklist of what to look for). If you wish to see more examples of what not to do, search the Internet for the phrases ‘SD of one representative’, ‘SE of one representative’, ‘SEM of one representative’, ‘SD of replicates’ or ‘SEM of replicates’.

Sidebar B | Error checklist when reading papers

• If error bars are shown, are they described in the legend?
• If statistics or error bars are shown, is n stated?
• If the standard deviations (SDs) are less than 10%, do the results come from replicates?
• If the SDs of a binomial distribution are consistently less than √( np (1 – p ))—where n is sample size and P is the probability—are the data too good to be true?
• If the SDs of a Poisson distribution are consistently less than √(mean), are the data too good to be true?
• If the statistics come from replicates, or from a single ‘representative’ experiment, consider whether the experiments offer strong support for the conclusions.
• If P -values are shown for replicates or a single ‘representative’ experiment, consider whether the experiments offer strong support for the conclusions.

David L. Vaux

Acknowledgments

This work was made possible through Victorian State Government Operational Infrastructure Support, and Australian Government NHMRC IRIISS and NHMRC grants 461221and 433063.

The authors declare that they have no conflict of interest.

• Campbell KH, McWhir J, Ritchie WA, Wilmut I (1996) Sheep cloned by nuclear transfer from a cultured cell line . Nature 380 : 64–66 [ PubMed ] [ Google Scholar ]

Replicates and repeats in designed experiments

In this topic, what is a replicate, what is the difference between replicates and repeats, example of replicates and repeats.

Replicates are multiple experimental runs with the same factor settings (levels). Replicates are subject to the same sources of variability, independently of each other. You can replicate combinations of factor levels, groups of factor level combinations, or entire designs.

For example, if you have three factors with two levels each and you test all combinations of factor levels (full factorial design), one replicate of the entire design would have 8 runs (2 3 ). You can choose to do the design one time or have multiple replicates.

• Screening designs to reduce a large set of factors usually don't use multiple replicates.
• If you are trying to create a prediction model, multiple replicates can increase the precision of your model.
• If you have more data, you might be able to detect smaller effects or have greater power to detect an effect of fixed size.
• Your resources can dictate the number of replicates you can run. For example, if your experiment is extremely costly, you might be able to run it only one time.

Repeat and replicate measurements are both multiple response measurements taken at the same combination of factor settings; but repeat measurements are taken during the same experimental run or consecutive runs, while replicate measurements are taken during identical but different experimental runs, which are often randomized.

It is important to understand the differences between repeat and replicate response measurements. These differences affect the structure of the worksheet and the columns in which you enter the response data, which in turn affects how Minitab interprets the data. You enter repeats across rows of multiple columns, while you enter replicates down a single column.

Whether you use repeats or replicates depends on the sources of variability you want to explore and your resource constraints. Because replicates are from different experimental runs, usually spread along a longer period of time, they can include sources of variability that are not included in repeat measurements. For example, replicates can include variability from changing equipment settings between runs or variability from other environmental factors that may change over time. Replicate measurements can be more expensive and time-consuming to collect. You can create a design with both repeats and replicates, which enables you to examine multiple sources of variability.

A manufacturing company has a production line with a number of settings that can be modified by operators. Quality engineers design two experiments, one with repeats and one with replicates, to evaluate the effect of the settings on quality.

• The first experiment uses repeats. The operators set the factors at predetermined levels, run production, and measure the quality of five products. They reset the equipment to new levels, run production, and measure the quality of five products. They continue until production is run one time at each combination of factor settings and five quality measurements are taken at each run.
• The second experiment uses replicates. The operators set the factors at predetermined levels, run production, and take one quality measurement. They reset the equipment, run production, and take one quality measurement. In random order, the operators run each combination of factor settings five times, taking one measurement at each run.

In each experiment, five measurements are taken at each combination of factor settings. In the first experiment, the five measurements are taken during the same run; in the second experiment, the five measurements are taken in different runs. The variability between measurements taken at the same factor settings tends to be greater for replicates than for repeats because the machines are reset before each run, adding more variability to the process.

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What are Replicates? A Complete Guide

Updated: February 6, 2023 by Ken Feldman

When doing Design of Experiments (DOE), replicates become an important consideration for improving the accuracy of your experiment. Let’s learn a little more about replicates.

In Design of Experiments (DOE) , a replicate is a single repetition or run of an experiment, with all conditions and factors kept constant except for the treatment being studied. The purpose of replicates is to increase the precision and accuracy of the results by accounting for variability within the experiment.

Overview: What are replicates? 

The purpose of Design of Experiments (DOE) is to efficiently determine which set of factors and levels will optimize a response variable. A DOE will conduct a series of runs consisting of the relevant factors and levels. The number of runs is determined by the formula levels^factor .

The resulting prediction equation will seek to optimize the combination of factors and levels. Because of experimental variation, there will be some error in the prediction. By replicating the combinations using additional runs, you will add degrees of freedom which help make the prediction more precise and accurate. The combinations are subsequently replicated in additional runs during the DOE. 

Replicates are different from repeats. While repeats are also multiple runs of the same combinations, they are generally done sequentially and do not account for the variability or error as do replicates. Multiple repeats are generally averaged and may also include the standard deviation. Repeats do not add additional degrees of freedom.

The number of replicates needed in an experiment will depend on the level of precision required, the expected variability of the response, and the resources available. In general, increasing the number of replicates will increase the accuracy of the results, but also increase the cost and time required to conduct the experiment.

An industry example of replicates 

For example, if you are studying the effect of a new fertilizer on crop yield, you would conduct multiple replicates of the experiment, each with the same conditions (e.g., soil type, planting density, weather conditions) except for the type of fertilizer applied. The average of the replicates provides a more accurate estimate of the effect of the fertilizer on yield than a single run or replicate.

Frequently Asked Questions (FAQ) about replicates

What is a replicate in doe .

A replicate in DOE is a single repetition or run of an experiment, with all conditions and factors kept constant except for the treatment being studied.

Why are replicates important in DOE? 

Replicates are important in DOE because they increase the precision and accuracy of the      results by accounting for variability within the experiment.

How many replicates are needed in an experiment? 

What are the advantages and disadvantages of using replicates in doe .

The advantages of using replicates in DOE include increased precision and accuracy of the results, the ability to detect variability within the experiment, and the ability to make more robust conclusions about the treatment effect. The disadvantages include increased cost and time required to conduct the experiment, and the need for larger sample sizes.

What is the difference between a replicate and a repeat in DOE?

Repeat and replicate measurements are both additional measurements taken in the same combination of factor settings. The difference is that repeats are taken during the same experimental run or consecutive runs, while replicates are taken during identical but different experimental runs. These are generally done when the runs have been randomized.

About the Author

Ken Feldman

Why Is Replication Important to Consider When Designing an Experiment?

Author Ella Bos

Posted Aug 25, 2022

Replication is important for many reasons. When designing an experiment, replication allows for greater control over variables, which can produce more reliable and valid results. It also allows for more statistical power, which can be useful when testing for small effects. Additionally, replication can help to identify sources of error and improve the overall design of the experiment. Finally, replication can provide a check on the data and results, which can help to ensure the quality of the research.

What are the benefits of replication in an experiment?

How does replication help to ensure the validity of an experiment, what are the potential problems that can occur when replication is not considered in an experiment, how does replication help to control for extraneous variables, what is the difference between replication and replication with randomization, why is replication important when using statistical analysis, what are the benefits of replication when using control groups, how does replication help to increase the power of an experiment, what are the potential problems that can occur when replication is not used in an experiment, frequently asked questions, what is the purpose of experimental design.

The purpose of experimental design is to maximize the information gained in a sequence of tests by making a effective tradeoff between replication and reducing the effect of possibly hidden factors spoiling the results.

Why is replication so important in psychology?

When studies are replicated and achieve the same or similar results as the original study, it gives greater validity to the findings. If a researcher can replicate a study’s results, it means that it is more likely that those results can be generalized to the larger population. This is important because having replicable findings allows psychologists to build reliable knowledge about their field.

Why would a scientist want to replicate an experiment?

Scientists generally replicate experiments to verify the results and to ensure that the findings can be generalized to other populations. Replication also allows scientists to build on previous research by confirming and extending findings.

What is the importance of repetition in science?

Repetition and replication are two most important principles of scientific research. Repeating an experiment with the same results strengthens the evidence that what was found in the original experiment is a real result.

Why do replication studies stand for years without retraction?

Replication studies are typically less costly and time-consuming than validation studies, so they may be skipped if the original study yields results that are deemed acceptable. Furthermore, although replication studies may produce different results than the original study, these findings are typically considered preliminary until they are formally validated. This process can take years, which is why controversial scientific studies may stand for years without retraction – even though they have been met with multiple failed validation studies.

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Ella Bos is an experienced freelance article author who has written for a variety of publications on topics ranging from business to lifestyle. She loves researching and learning new things, especially when they are related to her writing. Her most notable works have been featured in Forbes Magazine and The Huffington Post.

What are replicates in a biology experiment?

Biological replicates are parallel measurements of biologically distinct samples that capture random biological variation, which can be a subject of study or a source of noise itself. [3] Biological replicates are important because they address how widely your experimental results can be generalized.

Table of Contents

What does replicates mean in science?

: to repeat or duplicate (as an experiment) intransitive verb. : to undergo replication : produce a replica of itself virus particles replicating in cells.

What are replicates used for?

Replicates can be used to measure variation in the experiment so that statistical tests can be applied to evaluate differences. Averaging across replicates increases the precision of gene expression measurements and allows smaller changes to be detected.

What is replicate and examples?

1. Replication is the act of reproducing or copying something, or is a copy of something. When an experiment is repeated and the results from the original are reproduced, this is an example of a replication of the original study. A copy of a Monet painting is an example of a replication. noun.

What is a replicate in an experiment example?

What is a replicate? Replicates are multiple experimental runs with the same factor settings (levels). Replicates are subject to the same sources of variability, independently of each other. You can replicate combinations of factor levels, groups of factor level combinations, or entire designs.

Why are replicates needed in an experiment?

Replication lets you see patterns and trends in your results. This is affirmative for your work, making it stronger and better able to support your claims. This helps maintain integrity of data. On the other hand, repeating experiments allows you to identify mistakes, flukes, and falsifications.

What is replication in simple words?

1 : the action or process of reproducing or duplicating replication of DNA. 2 : performance of an experiment or procedure more than once.

What is DNA replication explain?

DNA replication is the process by which the genome’s DNA is copied in cells. Before a cell divides, it must first copy (or replicate) its entire genome so that each resulting daughter cell ends up with its own complete genome.

How do cells replicate?

During mitosis, a cell duplicates all of its contents, including its chromosomes, and splits to form two identical daughter cells. Because this process is so critical, the steps of mitosis are carefully controlled by certain genes. When mitosis is not regulated correctly, health problems such as cancer can result.

How many replicates does an experiment have?

You can determine the number of experiments you would do by multiplying 3X4X n, where n is the number of replications. Please note that replications should be at least 2. The more you do replications, the more precise results you get. Best of luck!

How many biological replicates are there?

For future RNA-seq experiments, these results suggest that at least six biological replicates should be used, rising to at least 12 when it is important to identify SDE genes for all fold changes.

Does replicate mean reproduce?

to repeat, duplicate, or reproduce, especially for experimental purposes: We were unable to replicate the same results in the field. Genetics. (of a cell) to make a copy of (its DNA): The cell replicates its DNA to begin the process of cell division. verb (used without object), rep·li·cat·ed, rep·li·cat·ing.

Where does DNA replication occur?

DNA replication occurs in the interphase nuclei of eukaryotic cells . DNA replication occurs before mitosis at the S-stage (synthesis) of the cell cycle.

What does replicate mean in terms of mitosis?

In genetics, to replicate means to reproduce an exact copy of the genetic material prior to mitosis (or meiosis) in eukaryotic cells.

What is the difference between sample size and replicates?

Replicate: A replicate is one experimental unit in one treatment. The number of replicates is the number of experimental units in a treatment. Total sample size: My guess is that this is a count of the number of experimental units in all treatments.

What is DNA replication called?

The process of DNA duplication is called DNA replication . Replication follows several steps that involve multiple proteins called replication enzymes and RNA. In eukaryotic cells, such as animal cells and plant cells, DNA replication occurs in the S phase of interphase during the cell cycle.

Why do we replicate DNA?

DNA replication needs to occur because existing cells divide to produce new cells. Each cell needs a full instruction manual to operate properly. So the DNA needs to be copied before cell division so that each new cell receives a full set of instructions!

What is DNA replication example?

As a consequence, it is telomeres that are shortened with each round of DNA replication instead of genes. For example, in humans, a six base-pair sequence, TTAGGG, is repeated 100 to 1000 times. The discovery of the enzyme telomerase (Figure 9.2. 4) helped in the understanding of how chromosome ends are maintained.

What are the 3 types of DNA replication?

There were three models for how organisms might replicate their DNA: semi-conservative, conservative, and dispersive.

When and where does replication occur?

DNA replication occurs during the S phase (the Synthesis phase) of the cell cycle, before mitosis and cell division. The base pairing rules are crucial for the process of replication. DNA replication occurs when DNA is copied to form an identical molecule of DNA.

What are the 4 steps of replication?

• H bonds break between the two strands.
• 2 strands of DNA molecules separate (enzyme)
• bases of free nucleotides in nucleus of cell fasten onto complementary bases on each exposed strand (enzymes to proofread)
• nucleotides join together making complete complementary strands.

What is a biological replicate in cell culture?

Biological replicates, on the other hand, are independently repeated experiments performed on cells of the same cell line but derived from a biologically distinct source or of a different passage.

What is the difference between technical and biological replicates?

Generally, biological replicates are defined as measurements of biologically distinct samples that show biological variation (21). In contrast, technical replicates are repeated measurements of the same sample that show independent measures of the noise associated with the equipment and the protocols.

Are replicates independent?

As biological experiments can be complicated, replicate measurements are often taken to monitor the performance of the experiment, but such replicates are not independent tests of the hypothesis, and so they cannot provide evidence of the reproducibility of the main results.

What is the difference between replicate and reproduce?

So, according to Peng (2011), with replication, an independent group of researchers conduct a replication of a previously conducted study, including collecting and analyzing their own data, to see if they get the same results; with reproduction, an independent group of researchers analyze the data from a previously …

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In general, authors of scientific reports must state the number of replicate experiments and replicate samples. Samples are run in replicates to measure variation in an experiment. This also allows statistical tests to be performed on the data evaluate differences.

Replicate samples from identical experimental conditions can be thought of in terms of samples from different individuals or independent samples ( biological replicates ). They can also be thought of in terms of repeated measurements of the same sample ( technical replicates ) that support measurement of variation within the protocol, user, or equipment.

Study designers thinking about running ELISAs should consider both biological as well as technical replicates. Whereas biological replicates will show variation within a population, technical replicates demonstrate variability within the protocol. This variation within a single plate or assay can be studied and reported as  intra-assay variation.

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At least, how many times an experiment should be replicated?

Is there a recommendation on the number of times that an experiment should be replicated? As many of you know, is not always possible to make many replicas. What would be the recommended minimum? Is there some references to support it?

In my particular case (animal reproduction), for reasons of seasonality, I can only replicate experiments 3 times and I have sometimes been criticized for the low number of replicates performed. Could be considered appropriate to assess the effect that a parameter measured 3 times in the same individuals have on the performance of these individuals?

• sample-size
• experiment-design

• $\begingroup$ Do you have a three measurements on one individual or three measurements on three individuals? $\endgroup$ –  csgillespie Commented Nov 8, 2010 at 12:08
• $\begingroup$ I have three measurements in 20 individuals belonging to a small population of about 100 individuals. But my question seeks a more general rule. Csgillespie and you pointed out that the sample size is important when deciding whether or not the experiment should be replicated and how many times. However, it is not always possible to have a sufficiently large sample size, especially if you work with animals. $\endgroup$ –  Manuel Ramón Commented Nov 8, 2010 at 13:06

There is no such thing as a minimum (or maximum) sample size rule. It depends on the size of the effect you are trying to measure. Your description of the experiment is slightly unclear, but consider this example, if you measured blood pressure in three different people, what could you conclude about blood pressure in the population?

Likewise, if you are conducting a clinical trial and it's clear (using statistical arguments) that one of the treatments is harmful, should you continue?

Another comment. In experiments concerning animals/people I would consider it unethical to conduct an experiment that has no chance of success due to low sample sizes. If in doubt, find a local friendly statistician. Most institutions have them somewhere.

• $\begingroup$ The size of population is important, for sure. Thus, for the blood pressure example three individuals may be unrepresentative. My case is different, there is no a problem of sample size. Imagine you have a sample of fixed size (it is not possible to include more individuals) and you want to assess the effects of a drug on blood pressure. Would you realize the experiment only once or several times? I think that it should be replicated at least two (or three) times in order to consider the individual variability of each subject. $\endgroup$ –  Manuel Ramón Commented Nov 8, 2010 at 12:56
• 3 $\begingroup$ @Manuel You are correct; you need to assess individual variability. The number of repeated measurements needed depends on the size of that variability, how the variability translates to uncertainty in the inferences about the population, the cost of repeating the measurements, practical constraints such as the time needed for replication, and (perhaps) technical issues like the possibility of positive temporal correlation among the replicate measurements. $\endgroup$ –  whuber ♦ Commented Nov 8, 2010 at 13:30
• $\begingroup$ So, the idea could be to replicate the experiment two or three times, evaluate the variability of the estimates and based on this variability decide whether more replicates will be necesary. $\endgroup$ –  Manuel Ramón Commented Nov 8, 2010 at 13:39
• 1 $\begingroup$ @Manuel: This is a very dangerous strategy and you need to be careful. You can't just carry out a few experiments and stop when you like. Basically you can't change the sample size midway through your experiment unless you are very careful. $\endgroup$ –  csgillespie Commented Nov 9, 2010 at 21:25
• $\begingroup$ I totally agree with you. We can not decide to stop an experiment because the results are what we wanted or otherwise continue. In my work usually replicate experiments 3 to 5 times depending on the availability of time. I think 3 replicas are sufficient. Also, if the estimates are accompanied by a measure of variability (standard errors and confidence intervals) the reader will have enough information to decide whether these estimates are more or less "reliable". $\endgroup$ –  Manuel Ramón Commented Nov 12, 2010 at 12:32

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Why it's important to have replicates in an experiment ?

Universal source of knowledge

Why is replication important in an experiment?

Table of Contents

• 1 Why is replication important in an experiment?
• 2 What is replication in experimental design?
• 3 Why is replication important when designing an experiment chegg?
• 4 Why is it important to repeat and replicate an experiment?
• 5 Why is replication important in quantitative research?
• 6 Why is replication of a study so important quizlet?
• 7 Does replication introduce systematic variation?
• 8 Why do we need replicates in biology?
• 9 Why is it important that experiments be replicable?
• 10 Why is repeating the experiment important?
• 11 Why is replication in scientific experiments important?

In statistics, replication is repetition of an experiment or observation in the same or similar conditions. Replication is important because it adds information about the reliability of the conclusions or estimates to be drawn from the data.

What is replication in experimental design?

In experimental design, replication is where each treatment is assigned to many participants. Or, the entire experiment is repeated on a large group of subjects. The process: Improves the significance of your experimental results. Reduces variability .

What is replication in research and why is it important?

Replication is one of the most important tools for the verification of facts within the empirical sciences. Any piece of research must be repeated by other investigators before its findings can be considered as reasonably well established. [Replicability] gives credibility to the conclusions of scientific research.

Why is replication important when designing an experiment chegg?

Why is replication important to consider when designing an experiment? Replication is necessary to introduce systematic variation into an experiment. Replication increases the chances that a rare result leads you to an erroneous conclusion.

Why is it important to repeat and replicate an experiment?

Getting the same result when an experiment is repeated is called replication. Replication is important in science so scientists can “check their work.” The result of an investigation is not likely to be well accepted unless the investigation is repeated many times and the same result is always obtained.

What is the purpose of a replicate in biology?

Replication is an essential process because, whenever a cell divides, the two new daughter cells must contain the same genetic information, or DNA, as the parent cell. The replication process relies on the fact that each strand of DNA can serve as a template for duplication.

Why is replication important in quantitative research?

Replication is a cornerstone of quantitative research because it detects fraud or findings that lack internal validity. If a study cannot be replicated, then it is said to be an outlier or a fluke or to contain methodological flaws. Without replication, a study’s findings can never be certain.

Why is replication of a study so important quizlet?

replication is important because the results of a study can vary considerably depending on experimental conditions and the research method used.

What is replication in an experiment Why is replication important choose the correct answer below?

Replication is repetition of an experiment under the same or similar conditions. Replication is important because it enhances the validity of the results.

Does replication introduce systematic variation?

Replication is necessary to introduce systematic variation into an experiment. Replication increases the chances that a rare result leads one to an erroneous conclusion.

Why do we need replicates in biology?

[3] Biological replicates are important because they address how widely your experimental results can be generalized. They indicate if an experimental effect is sustainable under a different set of biological variables.

Why do you need replicates?

Replicates can be used to measure variation in the experiment so that statistical tests can be applied to evaluate differences. Averaging across replicates increases the precision of gene expression measurements and allows smaller changes to be detected.

Why is it important that experiments be replicable?

Why is repeating the experiment important.

• Scientists repeat others’ experiments to verify the accuracy of the findings (peer review)
• Repeating an experiment allows a person to refine the results or simplify the methodology

Why does a plasmid need an origin of replication?

Why is replication in scientific experiments important?

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Why do we need at least 3 biological replicates in qPCR analysis or other biological experiments?

1. The technical replicates.

A technical replicate is when you test the same sample multiple times - it's used to test the variability in the testing protocol itself.

The reason you do technical replicates is to make sure they are almost identical. Triplicate samples and standards are necessary fpr qPCR as there can be pipetting errors when you are adding such a small amount of cDNA that could lead to large differences in your delta cT values. For example, if you get 3 Ct values out of your replicates: 27,3; 27,9; 35. This was pipetted from the same cDNA. So obviously, something wrong happened to the 35 Ct value. You can probably confirm it with the melting curve. And in any case, it is fair (not perfect!) to eliminate your 35 Ct value from your average. Now imagine you did a 2-plicate. Your values are 27 and 35. What is the good one? You average them? No, the only option would be to set this sample aside from analysis.

2. The biological replicates.

A biological replicate is where you perform the same test on multiple samples of the same material / type of cells / tissue. The samples are different, but are expected to be very similar (if not identical) with regard to the test.

Biological replicates are used to test the variability between samples that were selected on the basis of being otherwise identical. According to qPCR analysis model, you will need at least 3 biological replicates. No statistics can be done on less than 3 samples in any case. For example, you want to compare IL6 expression in macrophages stimulated or not with LPS. Then you need at least 3 untreated cultures of macrophages and 3 LPS-treated cultures. But very likely, you will need more for subtler effects where the gene induction is less than 10-fold. Plan 6-10 biological replicates for something that is not very strong.

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Why Should We Make Multiple Trials of an Experiment?

10 Characteristics of a Science Experiment

When you have an idea, and you want to know if it is true, a simple experiment can give you a quick result. But how do you know for sure that your idea will hold up based on one experiment? A multitude of tests can narrow the chance that your original idea simply doesn’t hold water.

Scientific Method

Asking questions about the natural world is a human trait that has propelled the species into space and the deepest depths of the ocean. The scientific method is used by biologists and other scientists to explore the world, and it begins with an observation. The original observation turns into a multitude of questions, which leads to a hypothesis. The hypothesis part is where the true test of the original observation yields facts and findings of the truth of the original thought. The experiments completed to prove the hypothesis can open new ideas, explore previously undiscovered expanses and lead the observer in new directions. The experiments are the heart of the hypothesis. The outcomes can either uphold or undo the hypothesis.

Experiments Matter

When the conditions of an experiment are under control the scientist is able to better understand the outcome of the test. It’s not always possible to control all of the conditions of a test, particularly when first starting out in proving the hypothesis. If a controlled experiment is impractical or can’t be done due to ethical reasons, a hypothesis may be tested by making predictions about patterns that should arise if in fact the hypothesis is true. The scientist collects data from as many patterns they can test or push to be tested within reason. The more experiments completed by the scientist the stronger the principle is for the hypothesis.

Variables and Variation

There are two types of variables when running tests: independent and dependent. An experiment with two groups, such as using water on one set of plants and nothing on a second set, has independent and dependent variables. The group that receives water, in this example, is the independent variable because it does not depend on happenstance. The scientist applies the water by choice. The dependent variable is the response that is measured in an experiment to show if the treatment had any affect. The lack of water on the set of plants shows whether the application by the scientist changes the outcome so therefore it depends on the independent variable.

This experiment needs to be done more than once due to the potential for variation, meaning some of the plants could have had disease or other outside variable that spoiled the experiment unbeknownst to the scientist conducting the experiment. The more samples presented at each test the better chance the scientist has of coming to a solid conclusion with little room for error.

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About the Author

Kimberley McGee has written for national and regional publications, including People magazine, the New York Times, Los Angeles Times, Las Vegas Review-Journal and more. The award-winning journalist has covered home decor, celebrity renovations, and sat down with reality HGTV stars to discuss the latest trends.

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Why use a minimum of 3 technical replicates?

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• Published: 28 August 2024

Embryonic genome instability upon DNA replication timing program emergence

• Saori Takahashi   ORCID: orcid.org/0000-0001-9195-3656 1   na1 ,
• Hirohisa Kyogoku   ORCID: orcid.org/0000-0002-0364-3971 2 , 3   na1 ,
• Takuya Hayakawa 4 ,
• Hisashi Miura 1 ,
• Asami Oji   ORCID: orcid.org/0000-0002-4919-0875 1 ,
• Yoshiko Kondo 1 ,
• Shin-ichiro Takebayashi 4 ,
• Tomoya S. Kitajima   ORCID: orcid.org/0000-0002-6486-7143 2 &
• Ichiro Hiratani   ORCID: orcid.org/0000-0003-3710-3540 1  

Nature ( 2024 ) Cite this article

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• Embryogenesis
• Genomic instability
• Next-generation sequencing

Faithful DNA replication is essential for genome integrity 1 , 2 , 3 , 4 . Under-replicated DNA leads to defects in chromosome segregation, which are common during embryogenesis 5 , 6 , 7 , 8 . However, the regulation of DNA replication remains poorly understood in early mammalian embryos. Here we constructed a single-cell genome-wide DNA replication atlas of pre-implantation mouse embryos and identified an abrupt replication program switch accompanied by a transient period of genomic instability. In 1- and 2-cell embryos, we observed the complete absence of a replication timing program, and the entire genome replicated gradually and uniformly using extremely slow-moving replication forks. In 4-cell embryos, a somatic-cell-like replication timing program commenced abruptly. However, the fork speed was still slow, S phase was extended, and markers of replication stress, DNA damage and repair increased. This was followed by an increase in break-type chromosome segregation errors specifically during the 4-to-8-cell division with breakpoints enriched in late-replicating regions. These errors were rescued by nucleoside supplementation, which accelerated fork speed and reduced the replication stress. By the 8-cell stage, forks gained speed, S phase was no longer extended and chromosome aberrations decreased. Thus, a transient period of genomic instability exists during normal mouse development, preceded by an S phase lacking coordination between replisome-level regulation and megabase-scale replication timing regulation, implicating a link between their coordination and genome stability.

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Emergence of replication timing during early mammalian development

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Sex-specific DNA-replication in the early mammalian embryo

Aneuploidy is relatively common during early mammalian embryogenesis, although the exact frequency is unclear 5 , 6 , 7 , 8 . Factors contributing to this include the lack of stringent cell-cycle checkpoints and problems during chromosome segregation 5 . Moreover, unreplicated DNA due to replication stress could add another layer to this complexity 1 , 2 , 3 , 4 , as reported in human cleavage-stage embryos 9 . However, understanding of the regulation of DNA replication during early mammalian embryogenesis is lacking owing to the lack of reliable methodology.

Recently, we and others developed a single-cell DNA replication sequencing technology—scRepli-seq 10 , 11 . This technology, which enables high-resolution genome-wide profiling of the replication state in single S-phase cells, confirmed that replication is regulated at the level of megabase (Mb)-sized domains even in single cells, replicated in a defined temporal order in a cell-type-specific manner 12 , 13 . Early and late replication timing (RT) domains correspond well with the euchromatic (A compartment) and heterochromatic (B compartment) compartments defined by Hi-C, respectively 14 , 15 . scRepli-seq can also detect aneuploidy at a high resolution 13 .

Thus, scRepli-seq is an excellent method for studying replication regulation, inferring the three-dimensional (3D) genome organization dynamics and assessing the aneuploidy rate during early mouse embryogenesis. With this in mind, we constructed a single-cell genome-wide DNA replication atlas of pre-implantation mouse embryos.

RT emerges abruptly at the 4-cell stage

We generated binarized scRepli-seq data 16 of BDF1 (F 1 hybrid between C57BL/6 and DBA2 mice) embryos at the 4-, 8- and 16-cell and blastocyst stages (Fig. 1a ), during which the 3D genome gradually becomes somatic-cell like 17 , 18 , 19 . We generated whole-S RT profiles 10 , 16 with single cells covering the entire S phase (Fig. 1b ). We used 80 kb bins—our current resolution limit for reliable scRepli-seq binarization (Supplementary Note  1 and Extended Data Fig. 1a,b ). The 4-cell RT profile was already similar to that of mouse embryonic stem cells (mESCs) and was largely maintained thereafter with only small local differences (Fig. 1b and Extended Data Fig. 1c ). t -Distributed stochastic neighbour embedding ( t -SNE) analysis of mid-S-phase cells confirmed gradual RT changes after the 4- and 8-cell stages (Fig. 1c ). RT profiles of 8-cell embryos and inner cell mass (ICM) were similar, with only around 4% RT changes throughout the genome, which coincided with regions subject to developmental RT regulation 20 (Extended Data Fig. 1d,e ). Notably, RT profiles of ICM and trophectoderm (TE) cells were highly similar (Fig. 1b,c and Extended Data Fig. 1c,f ), suggesting that the A/B compartments do not change significantly at the onset of embryonic/extraembryonic lineage segregation.

a , Schematic of scRepli-seq profiling. WGA, whole-genome amplification. b , Binarized whole-S scRepli-seq profiles of 4-, 8- and 16-cell and blastocyst (ICM and TE) BDF1 embryos, along with BrdU immunoprecipitation (IP) population RT profiles of mESCs and averaged scRepli-seq profiles calculated from mid-S-phase cells with 30–70% replication scores. Each row represents a single-cell replication profile (80 kb bins). Cells throughout the S phase were analysed and ordered by their percentage replication score. Four-cell embryos were sampled at hourly intervals (1–11 h after 2-to-4-cell division) on embryonic day 2. Eight- and 16-cell embryos were sampled every 2 h on embryonic days 2.5 and 3, respectively. The durations that cover the entire S phase of each cell cycle were predetermined based on EdU-incorporation competency. c , t -SNE analysis of mid-S-phase cells (30–70% replication score, excluding chromosome X) in 4-, 8- and 16-cell embryos, ICM and TE, using the log 2 [median] RT scores. d , Binarized scRepli-seq profiles of 1-, 2- and 4-cell B6MSM embryos, along with the BrdU-IP population RT profiles of mESCs. One-cell embryos were sampled at hourly intervals (4–10 h after fertilization); 2- and 4-cell embryos were sampled hourly during 3–8 h and 5–13 h after their latest cell division, respectively. Most bins were called ‘replicated’ in 1- and 2-cell embryos (asterisks), reflecting the lack of regional copy-number differences due to gradual and uniform replication (Extended Data Fig. 2c ). e , The MAD score distribution 16 of 1-, 2- and 4-cell blastomeres and mESCs was analysed using scRepli-seq. Cell-cycle phases of each cell (or sampling timepoint) were estimated by EdU staining as described in Fig. 2a–c . Each dot represents a single cell. f , Overview of the EdU staining experiment in g . g , Representative EdU staining patterns of metaphase chromosomes derived from MC12 cells and 1-, 2- and 4-cell embryos. The line plots show the EdU (green) and DAPI (magenta) signal intensities along the white lines. Identical magnification was used for all images (chromosome size variations are probably a batch effect of colcemid treatment). T and C, the telomeric and centromeric ends, respectively. Scale bar, 5 μm ( g ).

In prezygotic genome activation (pre-ZGA) embryos, the paternal and maternal genomes undergo epigenetic reprogramming with distinct kinetics 21 , 22 . We analysed pre-ZGA embryos of B6MSM mice (F 1 hybrid between C57BL/6 and MSM mice) to distinguish the haplotypes. Notably, 1- and 2-cell embryos had no RT domains that were detectable throughout S phase by scRepli-seq, whereas 4-cell embryos clearly exhibited RT domains (Extended Data Fig. 1g and Supplementary Note  1 , haplotype-resolved at 400 kb resolution; Fig. 1d , non-haplotype-resolved at 80 kb). In 1- and 2-cell embryos, almost all genomic bins were categorized as replicated (blue), giving the impression that these cells have completed genome replication abruptly in early S phase (Fig. 1d and Extended Data Fig. 1g ). However, this cannot be true, as our sampling covered the entire S phase at hourly intervals and the 5-ethynyl-2′-deoxyuridine (EdU)-positive S phase lasted 4–5 h. Rather, it most likely reflected the lack of regional differences in copy number during S phase; scRepli-seq seems unable to detect replicated and unreplicated segments.

Consistently, tag or read density profiles of scRepli-seq data from individual mid-S-phase cells of 1- and 2-cell embryos did not exhibit bimodal distribution but showed a single peak using 200, 80 or 40 kb bins (Extended Data Fig. 2a,b (left) and Supplementary Note  1 ; although 40 kb is reaching or slightly exceeding the scRepli-seq resolution limit), and binary calling could hardly separate unreplicated and replicated bins (Extended Data Fig. 2a,b (right)). This resulted in a seemingly completely replicated or completely unreplicated output depending on the binarization mode chosen (Extended Data Fig. 2c ). Thus, scRepli-seq is unable to resolve replicated and unreplicated segments in 1- and 2-cell S phase, suggesting uniform replication.

This view was supported by the scRepli-seq tag density profile transition during S-phase progression. In 1- and 2-cell embryos, cells showed a single peak throughout S phase, indicating the lack of copy-number differences among genomic bins (Extended Data Fig. 2d,e ). However, the 4-cell S-phase profiles were bimodal with the larger peak height gradually increasing, reflecting the gradual increase of replicated bins as S phase progressed (Extended Data Fig. 2d,e ). Furthermore, log 2 [(mappability-corrected S phase reads)/median] (log 2 [median]) value profiles (close to raw data) lacked the high/low contrast along the length of chromosomes in 1- and 2-cell embryos, in contrast to in 4-cell embryos (Extended Data Fig. 1g ). These data are consistent with gradual and uniform replication in 1- and 2-cell embryos at 40–80 kb resolution.

Furthermore, the median absolute deviation (MAD) score, a measure of bin-to-bin data variability, was constant during S-phase progression in 1- and 2-cell embryos (Fig. 1e ), regardless of the bin-size settings or haplotype (Extended Data Fig. 2f,g ). This was in sharp contrast to mESCs and 4-cell embryos, which exhibited inverted V-shape patterns with the MAD score peaking at mid-S phase (Fig. 1e ), reflecting the presence of equal numbers of fully replicated and fully unreplicated bins at mid-S phase and minimum heterogeneity at the earliest and latest S phase.

Lastly, we prepared metaphase chromosome spreads after 1 h EdU labelling at the earliest S-phase period (Fig. 1f ). While cultured (somatic) cells and 4-cell embryos exhibited stripe patterns of early-RT domains, 1- and 2-cell embryos showed uniform staining, indicating a lack of RT domains (except for the centromeres; discussed later) (Fig. 1g ). Taken together, we conclude that, in 1- and 2-cell mouse embryos, an RT program is completely absent and the chromosomes are replicated in a gradual and uniform manner. Then, a somatic-cell-like RT program abruptly commences at the 4-cell stage.

The sole exception on the otherwise uniformly replicated chromosomes of 1- and 2-cell embryos was the heterogeneously late-replicating domains on the paternal chromosomes in 1-cell embryos (Extended Data Fig. 1g ). While this could be a feature of the MSM chromosomes, we speculate that this represents the remnants of strong heterochromatin structures (late-replicating B-compartment domains) derived from the sperm 19 that become reprogrammed by the 2-cell stage (Extended Data Fig. 2h–k ).

Nuclear compartments strengthen in 4-cell embryos

As RT correlates with A/B compartments, the somatic-cell-like RT emergence might reflect nuclear compartment emergence. Thus, we analysed the spatiotemporal patterns of replication, that is, replication foci, which change in somatic cells from early to late S as follows: (1) throughout the nuclear interior; (2) in the nuclear periphery and nucleoli; and (3) in internal heterochromatic foci (Extended Data Fig. 3a ; mESCs). We labelled 1- and 2-cell embryos with EdU at 1 h intervals and 4-cell embryos at 2 h intervals and observed replication foci patterns (Fig. 2a ). As replication foci patterns in early embryos were slightly different from in somatic cells, we classified them into a total of eight patterns (Fig. 2b,c ). In 1-cell embryos, S phase was short (around 5 h) and the entire maternal pronuclei were uniformly EdU stained throughout S phase, followed by a brief period of nucleolar precursor body (NPB) staining (Fig. 2d ). In the paternal pronuclei, the nuclear periphery and NPB were EdU stained during mid-S phase (Fig. 2d ), which may correspond to the heterogeneously late-RT domains (Extended Data Fig. 1g ). As centromeres accumulate around the NPBs 23 and were not replicated in early-S phase (Fig. 1g ), they are presumably replicated during late-S phase in both pronuclei (Fig. 2d ). In 2-cell embryos, S phase was still short (approximately 4 h), but the EdU pattern changed slightly, starting from the entire nuclei in early-S phase to the nuclear membrane and NPB, and to the internal foci (Fig. 2d ). However, the typical mid-S phase pattern (nuclear periphery/nucleoli) was still absent (Fig. 2d ). These results are consistent with the nuclear compartments being absent or still incomplete in 1- and 2-cell embryos 17 , 18 , 19 .

a , The EdU-labelling scheme. b , Representative nuclear EdU staining (replication foci) patterns ( Methods ). c , Exemplary replication foci images. The colour code represents the EdU staining patterns in b . Green, EdU; magenta, histone H3. Scale bar, 10 μm. d , Replication foci pattern dynamics. The colour code is as in b . The numbers ( n ) of nuclei and embryos are shown. e , scRepli-seq analysis of SCNT embryos, collected at 1 h intervals covering the S phase. f , Binarized whole-S scRepli-seq profiles of SCNT embryos (80 kb bins, 2-somy mode 59 ). Each row represents a single cell, ordered by their sampling order or percentage replication score. Averaged scRepli-seq RT (avg scRT) was calculated by averaging S-phase cells. Heterogeneously late-RT regions (hetero late) were regions with an average scRepli-seq RT of <0.50. RT class definitions have been described previously 20 ; CE, constitutively early; CL, constitutively late; D, developmentally regulated. PC1 is the A/B compartment profile based on cumulus cell Hi-C 60 . Embryos were sampled hourly (1 cell, 3–10 h after strontium activation; 2 cell, 1–8 h after cell division). SCNT S-phase lengths were predetermined by EdU. Asterisks indicate data reflecting gradual and uniform replication (Fig. 1d ). g , DNA fibre spreading assay. Embryos (early S) or mESCs (control, asynchronous) were labelled with IdU/CldU in vivo followed by fibre extension and immunostaining. h , Representative images of three fork categories. IdU and CldU (green), CldU (magenta) and ssDNA (blue) signals on DNA fibres. Scale bar, 4.55 μm (20 kb). i , DNA fibre classification based on fork categories. j , The IOD between the immobile single-dot forks and IOD on mobile 8-cell fibres (Extended Data Fig. 5b ). k , The mobile fork speed. l , G1-to-S-phase lengths revealed by PCNA live-cell imaging. The numbers ( n ) of cells from a total of 26 embryos are shown. Error bars represent the mean ± s.d. ( j – l ).

In 4-cell embryos, S phase was clearly longer (around 11 h; Fig. 2d ), and the nuclear periphery/internal foci pattern emerged during mid/late-S phase as in mESCs (Fig. 2d ), suggesting nuclear compartment strengthening. To test whether the nuclear periphery/internal foci staining pattern indeed emerged at 4-cell mid-S phase, such cells were analysed using scRepli-seq after imaging (Extended Data Fig. 3a ), which validated their mid-S phase identity (Extended Data Fig. 3b ). Likewise, cells with uniform or internal foci patterns were at early-S or late-S phase, respectively (Extended Data Fig. 3b ). These somatic-cell-like spatiotemporal replication foci patterns were also observed in 8-cell embryos (Extended Data Fig. 3c ). Consistent with the emergence of the heterochromatic replication foci pattern, histone H3 lysine 9 dimethylation (H3K9me2) showed strong discrete nuclear foci in 4-cell but not 1- and 2-cell embryos (Extended Data Fig. 3d–f ). These data suggest that the emergence of the somatic-cell-like RT program in 4-cell embryos is accompanied by a considerable strengthening of nuclear compartments, which may be accompanied by chromatin tethering to the nuclear lamina.

The near coincidence of nuclear compartment strengthening and the emergence of the somatic-cell-like RT program led us to examine whether the former could trigger the latter. Although we have no means to manipulate the nuclear compartment strength, we reasoned that we could use somatic cell nuclear transfer (SCNT) into enucleated oocytes using cumulus cells as donors, followed by scRepli-seq (Fig. 2e ). As the cumulus cell nuclei maintain compartment strength to some extent even after SCNT and show stronger A/B compartmentalization compared with control fertilized embryos 24 , we could instead examine whether precocious emergence of somatic-cell-like RT could be observed in SCNT embryos.

Somatic-cell-like RT was non-existent in SCNT 1-cell embryos as in control embryos (Fig. 2f and Extended Data Fig. 4a ), indicating that partial presence of A/B compartments is insufficient for somatic-cell-like RT emergence (although rigorous testing will ultimately require us to perform simultaneous Hi-C). However, in SCNT 2-cell embryos, large late-replicating domains appeared in some but not all cells (Fig. 2f (hetero late)), which corresponded well with constitutively late-replicating, B-compartment regions in somatic cells (Fig. 2f and Extended Data Fig. 4a–c ). They also corresponded well with the heterogeneously late-RT domains on the paternal chromosomes in 1-cell embryos (Extended Data Figs. 2a,b and 4d,e ), suggesting that they are the most preferential late-replicating domains on the mouse genome. Accordingly, the SCNT 2-cell embryos showed some ‘somatic’ signatures such as small deviations from the flat MAD score plots of the control embryos (Extended Data Fig. 4f ). However, overall, SCNT 2-cell embryos were also similar to 2-cell control embryos without a somatic-cell-like RT program (Fig. 2f and Extended Data Figs. 2e and 4a ), placing the SCNT 2-cell profile somewhere in between the control 2-cell and 4-cell profiles.

Discordance between 4-cell RT and fork speed

While the abrupt RT emergence was surprising, the coordinated strengthening of nuclear compartments is consistent with the known close relationship between them 2 , 12 , 13 . However, there is one major paradox with the ‘embryonic’ DNA replication program regarding how one can achieve gradual and uniform replication of all 80 kb bins across the genome in an approximately 5 h S phase if the replication forks travel at 1–2 kb per min as stated in the literature 12 , 25 , 26 . The logical conclusion is that replication forks must be extremely slow to achieve an approximately 5 h S phase with high and uniform origin density. To test this, we analysed the fork speed in early embryos using a standard DNA fibre spreading assay, in which early-S-phase cells were labelled sequentially with nucleoside analogues iododeoxyuridine (IdU) and chlorodeoxyuridine (CldU) for 30 min each (Fig. 2g ). As a control, we used mESCs and confirmed a fork speed of 1–2 kb per min as expected 26 (Fig. 2k ).

Notably, we found that 63–82% of all DNA fibres with IdU/CldU signals at the 1-, 2- and 4-cell stages were categorized as immobile forks (Fig. 2h,i and Extended Data Fig. 5a ). That is, IdU + CldU or CldU-only labels formed single dots that were around 12–22-kb apart from each other (Fig. 2j ). It was challenging to measure the fork speed of these tiny dots. However, we used an anti-single-stranded DNA (ssDNA) antibody that generated ‘dotty’ signal patterns, and these dots were also preferential sites of detection by our anti-IdU/CldU antibodies when IdU and CldU were incorporated. This enabled us to estimate the immobile dot size in base pairs and, in turn, their fork speed, which was <22–147 bp per min assuming bidirectional forks (or <43–147 bp per min assuming unidirectional forks) (Supplementary Note  2 ). Among all of the fibres, 11–24% contained mobile forks in 1-, 2- and 4-cell embryos with an average speed of 0.3–0.4 kb per min (median mobile fork speeds at the 1-, 2- and 4-cell stages of 0.38, 0.29 and 0.35 kb per min, respectively; Fig. 2i,k ). The remaining 7–15% of fibres contained intermediate-speed forks (Fig. 2i ).

Upon transition to the 8-cell stage, fork types changed substantially; the immobile forks almost disappeared, and the majority became mobile with an average speed of approximately 0.76 kb per min, which is less than, albeit comparable to, that of mESCs at around 1.2 kb per min (Fig. 2i,k ). Consistently, the inter-origin distance (IOD) was also greater at 56.1 kb (median) in 8-cell embryos (Fig. 2j and Extended Data Fig. 5b ).

We also used orthogonal measurements. First, the early-S-phase median IODs were around 21.9, 13.7 and 12.3 kb at the 1-, 2- and 4-cell stages, respectively (Fig. 2j and Extended Data Fig. 5b ). Next, the S-phase length (EdU-incorporation period) was approximately 5, 4 and 11 h at the 1-, 2- and 4-cell stages, respectively (Fig. 2d ). Moreover, the binarized scRepli-seq data showed almost no bin-to-bin difference throughout S phase in 1- and 2-cell embryos at 80 kb resolution (Fig. 1d ). Taken together, the 1-, 2- and 4-cell immobile fork speed estimate was <33 bp per min assuming bidirectional forks (<66 bp per min assuming unidirectional forks) (Supplementary Note  2 ).

Combining the two independent estimates, the immobile forks (63–82% of all forks) should be <33 bp per min assuming bidirectional forks (<66 bp per min assuming unidirectional forks) in 1-, 2- and 4-cell embryos. The fastest forks (mobile; 11–24% of all forks) have a speed of around 300–400 bp per min, and the remaining intermediate forks (7–15% of all forks) must be in between at 33–300 bp per min assuming bidirectional forks (66–300 bp per min assuming unidirectional forks). Thus, the majority of forks in 1-, 2- and 4-cell embryos are approximately 4–10 times slower than previously reported 27 , with an average IOD of 12–22 kb (Supplementary Note  2 ). Upon transition to the 8-cell stage, forks abruptly accelerated. As somatic-cell-like RT emerged one cell cycle earlier (Fig. 1b,d ), we identified a transient discordance between RT and fork regulation at the 4-cell stage.

scRepli-seq data support slow 4-cell fork speed

In the 4-cell S phase, the immobile bidirectional forks travelled at <33 bp per min and the median IOD was around 12 kb. Without additional origin firing, a given immobile bidirectional fork must travel around 6.0 kb at <33 bp per min, which takes approximately 3 h. This is much longer than the current consensus of about 1 h to replicate Mb-sized replication domains using forks travelling at 1–2 kb per min with an IOD of 100–200 kb (refs. 2 , 12 , 28 ). This raises the question of why the 4-cell RT profile is somatic-cell like (Fig. 1b ).

Revisiting the whole-S scRepli-seq data, we found that tag-density bimodality was less prominent in 4-cell than in 8-cell embryos or mESCs at 30–40% S phase (that is, cells with 30–40% of all genomic bins replicated based on binarized scRepli-seq data) (Extended Data Fig. 5c ). Such a feature disappeared at 50–60% S phase. Thus, while the forced binarization of 4-cell scRepli-seq data generated a somatic-cell-like whole-S-phase RT profile (Fig. 1d ), it reflected a state containing ‘relatively replicated’ and ‘completely unreplicated’ bins during the first half of S phase. Thus, 4-cell S phase is compartmentalized into early and late halves, and replication completion of individual Mb-sized replication domains takes longer (>4 h) than in 8-cell embryos and mESCs (Supplementary Note  2 ).

Slower forks could also explain the longer 4-cell S phase. As S-phase length estimation is challenging after the 8-cell stage owing to a loss of synchrony among blastomeres, we measured the G1-to-S-phase length using proliferating cell nuclear antigen (PCNA) live imaging, which confirmed a longer G1-to-S-phase length in 4-cell compared with in 8-cell and 2-cell embryos (Fig. 2l ).

In summary, while the 4-cell RT domain organization appeared somatic-cell like, the fork speed and IOD inside individual replication domains were almost identical to in 1- and 2-cell embryos. This suggests that RT regulation and replisome regulation are temporally uncoordinated during the 4-cell stage, giving rise to a transitional S-phase in between embryonic (1 and 2 cell) and somatic (after the 8-cell stage) S-phase types. Even during this transitional 4-cell S phase, the intra-S-phase checkpoint was functional, as in somatic cells 29 (Extended Data Fig. 5d–i ).

Transitional 4-cell S phase is error prone

The transitional 4-cell S phase exhibits both embryonic (slow forks) and somatic (somatic-cell-like RT) properties. We wondered whether this uncoordinated state is linked to cellular stress. As scRepli-seq is capable of high-resolution karyotyping, we examined the chromosome aberration frequency in G1- and S-phase cells.

Chromosome aberrations can be either whole-chromosome loss or gain (whole), or chromosome breaks (break). We detected both using scRepli-seq analysis of 4-, 8- and 16-cell embryos derived from in vitro development (Fig. 3a ). We collected all cells of an embryo, which were annotated with the embryo-of-origin information, enabling us to identify pairs of sister cells with complementary errors (Fig. 3a ). We determined at which cell division the segregation error had occurred, in a manner similar to lineage barcoding 30 , except that the barcoding is done endogenously by the spontaneous errors. Notably, more than 13% of cells exhibited de novo abnormalities during 4-to-8-cell division in vitro, the majority of which were of the break type (Fig. 3b , Extended Data Fig. 6a,b and Supplementary Table 1 ). Abnormalities were not observed during 2-to-4-cell and 8-to-16-cell divisions in vitro. Similar error rates were observed in embryos collected in vivo from the oviduct, with high error rates observed only during the 4-to-8-cell division (Fig. 3b ). Live-cell imaging confirmed that chromosome bridges formed and eventually broke into unbalanced chromosome mass or fragments more frequently during 4-to-8-cell division (Fig. 3c , Extended Data Fig. 6c and Supplementary Video  1 ). Consistent with the low frequency of whole-type errors, we never observed anaphase with merotelic centromeres remaining in the middle of the spindle in our live-cell imaging data (Fig. 3c and Supplementary Note  3 ).

a , Detection of de novo chromosome aberrations using scRepli-seq. Sister cells with reciprocal chromosome gain/loss are shown. b , Increased de novo errors during the 4-to-8-cell division as revealed by scRepli-seq. Errors were gain or loss of whole chromosomes (whole) or chromosome fragments (break). The number ( n ) of cell divisions is shown. c , Early-anaphase chromosome bridges were detected using live-cell imaging (major-satellite–mClover, centromeres, green; H2B–mCherry, chromosomes, magenta) and categorized by their late-anaphase fates: breakage into unbalanced chromosome mass (unbalanced) or breakage leaving an acentric fragment. Representative images are shown (min:s, time after anaphase onset). Merotelic (chromosome with centromere left in the middle) was not observed. The number ( n ) of cell divisions observed live are shown. The arrowheads indicate chromosome bridges; asterisks indicate unbalanced chromosome mass and acentric fragments. d , Representative early/mid/late-S breakpoints revealed by AneuFinder 59 (Extended Data Fig. 6d ). e , Breakpoints were predominantly late replicating in vitro and in vivo (4-to-8-cell division). The number ( n ) of breaks is shown. f , Live-cell imaging of mEGFP–SLX4 (DNA repair foci, green) and H2B–mCherry (chromosomes, magenta) until the 16-to-32-cell division. The arrowheads indicate SLX4 foci; the asterisk indicates chromosome fragments. g , The number and duration of mEGFP–SLX4 foci. The number ( n ) of nuclei observed live is shown. h , Replication stress is increased during 4-cell late-S phase. The numbers ( n ) of nuclei from 8 (2-cell), 15 (4-cell) and 10 (8-cell) embryos are shown. p-CHK1, replication stress marker, green; histone H3, magenta. i , DNA damage is increased during 4-cell late-S phase. The numbers ( n ) of nuclei from 26 (2-cell), 20 (4-cell) and 19 (8-cell) embryos are shown. The 4-to-8-cell M-phase image (bottom) shows γH2AX (DNA damage marker, green) enrichment on a chromosome arm (arrowhead). Histone H3, magenta. Scale bars, 5 μm ( c , f , h and i ). Error bars represent the mean ± s.d. ( e , g – i ).

Clues about the underlying molecular mechanism came from the breakpoints (Fig. 3d and Extended Data Fig. 6d ). During the 4-to-8-cell division in vitro, around 90% of the breakpoints were located in mid/late-RT regions, while 100% were in late-RT regions in vivo (Fig. 3e and Extended Data Fig. 6b ), suggesting under-replication of late-RT regions as a potential cause of breaks. We therefore analysed the localization of MUS81–EME1–SLX4 complex foci, which are involved in unreplicated DNA repair in G2/M phase 31 . The monomeric enhanced green fluorescent protein (mEGFP)-tagged SLX4 foci and SLX4-positive duration clearly increased at the 4-to-8-cell division but not at the other cell divisions during G2/M (Fig. 3f,g ).

Consistently, phosphorylated CHK1 and γH2AX, markers of replication stress and DNA damage, respectively, were significantly increased in 4-cell late-S phase (Fig. 3h,i ). Furthermore, low-dose aphidicolin treatment, which induces replication stress, increased severe chromosome aberrations specifically during the 4-to-8-cell division (Extended Data Fig. 7a ; with 30 ng ml −1 aphidicolin, 2-to-4-cell and 8-to-16-cell divisions were accompanied by mild (orange) but not severe (dark grey) chromosome aberrations, suggesting a higher basal replication stress level in 4-cell embryos). Furthermore, severe breakpoints were observed predominantly in late-RT domains (Extended Data Fig. 7b ), consistent with transitional 4-cell S phase being under replication stress and error prone.

To obtain more direct evidence suggesting a link between replication stress and chromosome aberrations, we recorded cell-cycle progression and chromosome segregation of developing embryos using live imaging of mEGFP–PCNA and histone H2B–mCherry (Extended Data Fig. 7c and Supplementary Video  2 ). PCNA foci not only mark S phase, but their spatial patterns also reflect S-phase time, with internal replication foci patterns marking late-S phase 32 . These PCNA features enabled us to measure the durations of G1-to-late-S, late-S, G2 and M phases (Extended Data Fig. 7d–f ). We identified error cells exhibiting chromosome bridges during the 4-to-8-cell division (Fig. 4a ) and tracked these cells back in time to assess their cell-cycle lengths (Fig. 4b ), which demonstrated that the 4-cell G1-to-late-S period was significantly extended (Fig. 4b–d and Extended Data Fig. 7g ). Thus, chromosome aberrations are more likely to occur in cells that exhibit S-phase extension, suggesting replication stress and under-replication as potential sources of the errors.

a , Live imaging of mEGFP–PCNA (S-phase marker, green) and H2B–mCherry (chromosomes, magenta) in mouse embryos. Magnified z -projection snapshots of 4-cell sister blastomeres are shown, along with 3D-reconstructed images of 2-to-4-cell and 4-to-8-cell divisions (h:min, time after 2-cell anaphase onset). The image frame colours reflect the cell-cycle phases in b . The asterisks show PCNA foci (late-S phase marker). b – d , Extended S phase often precedes chromosome bridge formation during 4-to-8-cell division (error). In b , each line represents a single cell, and the average cell-cycle durations are shown in c , based on durations plotted in  d . The number ( n ) of cells from 24 embryos is shown. e , Nucleoside-supplementation experiment. Embryos cultured for 6 h in +nucleoside medium from 4-cell G1 were sequentially labelled with IdU and CldU for 30 min each, then analysed using the DNA fibre spreading assay ( f and g ). Control embryos were sampled simultaneously at mid/late-S phase. f , DNA fibre classification of +nucleoside 4-cell embryos based on fork categories (Fig. 2h ). Control 4-cell embryos differ slightly from Fig. 2i , possibly reflecting differences between mid/late-S phase ( f ) and early-S phase (Fig. 2i ). g , Mobile fork speed. h , Nucleosides suppressed p-CHK1 at the 4-cell late-S phase. The numbers of nuclei from 30 (control) and 12 (+nucleosides) embryos are shown. i , Nucleosides suppressed γH2AX at the 4-cell late-S phase. Histone H3, magenta. The numbers ( n ) of nuclei from 23 (control) and 15 (+nucleosides) embryos are shown. j , Nucleosides suppressed chromosome segregation errors. The chromosome-aberration frequency at anaphase during 4-to-8-cell division was monitored using live-cell imaging. The numbers ( n ) of cell divisions analysed live are shown. Scale bars, 10 μm ( a (blue and grey boxes)) and 5 μm ( a (magenta boxes), h and i ). Error bars represent the mean ± s.d. ( d , g – i ).

Lastly, to test the causal relationship between replication stress and chromosome segregation errors, we used nucleoside supplementation (Fig. 4e ), which accelerates forks and reduces replication stress in human embryonic stem cells (hESCs) 33 , 34 . Nucleoside supplementation from 4-cell G1 led to a slight decrease and increase in the rate of immobile and intermediate forks, respectively (Fig. 4f ; although not statistically significant on the basis of a χ 2 test). Moreover, nucleoside supplementation modestly but significantly accelerated 4-cell mobile forks (Fig. 4g ; the average speed changed from 0.39 to 0.53 kb per min; n  = 26–32; P  = 0.0156), led to significant downregulation of phosphorylated CHK1 and γH2AX (Fig. 4h,i ) and significantly reduced errors during 4-to-8-cell division (Fig. 4j and Supplementary Note 3 ; from 21.1% to 4.26%, n  = 33–42; P  = 0.0377). Our results are consistent with a model in which replication stress involving slow forks contributes to chromosome segregation errors in 4-cell mitosis.

Notably, the proportion of immobile forks was slightly decreased in mid/late-S phase (Fig. 4f ) compared with in early-S phase (Fig. 2i ). Future studies are warranted to investigate whether there is a slight fork acceleration towards the later half of S phase.

Here we analysed the regulation of DNA replication during early mouse embryogenesis genome-wide in substantial detail. First, we found the complete absence of RT during the 1- and 2-cell stages, when embryonic DNA replication proceeds gradually and uniformly throughout the genome. Second, we demonstrated that the 2-to-4-cell transition is accompanied by an abrupt emergence of somatic-cell-like RT, coincident with nuclear compartment strengthening (Fig. 5 ). However, the abrupt RT emergence was uncoordinated with fork-speed regulation. We propose that 4-cell embryos undergo transient replication stress and show genomic instability, exhibiting frequent chromosome segregation errors due to under-replication during the transitional S phase (Fig. 5 ). These observations suggest a link between genome stability and coordination of replisome-level and RT-level regulation, and have implications for embryonic genome regulation from the standpoint of molecular, developmental and evolutionary biology. Moreover, they provide insights for future clinical applications in in vitro fertilization clinics regarding strategies to minimize chromosome aberrations common in early embryos 5 , 6 , 7 .

During the embryonic S phase in 1- and 2-cell embryos, DNA replication proceeds gradually and uniformly across the entire genome in the absence of Mb-sized RT domain structure. This is achieved by numerous replication forks with a median IOD of 12–22 kb that travel extremely slowly, with the majority estimated to move at <33 bp per min bidirectionally (or <66 bp per min assuming unidirectional forks). The only exceptions are the centromeres in 1- and 2-cell embryos and the sperm-derived heterochromatin in 1-cell embryos, which replicate later in S phase. At the 4-cell stage, a somatic-cell-like RT program commences abruptly, which is accompanied by a marked strengthening of nuclear compartments. Despite this abrupt switch, the forks were still extremely slow and the median IOD was still around 12 kb, giving rise to a transitional 4-cell S phase, in which the RT regulation mode is somatic, while the replisome-level regulation mode is still embryonic. This uncoordinated regulatory state led to S phase extension in 4-cell embryos, and the cells with the most extended S phase frequently showed chromosome breaks during the 4-to-8-cell division that probably arose due to under-replication of late-replicating domains after an S phase with increased replication stress, DNA damage and repair. After the 8-cell stage, forks accelerate (approximately 0.76 kb per min), IODs become larger (median, 56.1 kb) and DNA replication proceeds in a coordinated manner again, resulting in a reduction in genomic instability (somatic S phase).

In non-mammalian species, the cell cycle and replication in early embryos are unique 5 , 35 , 36 , 37 , 38 . In Drosophila and Xenopus embryos, multiple rapid S–M phase cycles are observed after fertilization, with an S phase of around 10 and 20 min, respectively 37 , 39 . In these organisms, it is believed that replication before ZGA is uniformly initiated from origins distributed throughout the genome 3 , 39 , 40 , 41 , 42 and, after ZGA, G1/G2 phases and a somatic cell-like RT program emerge coincident with the acquisition of somatic-cell-like 3D genome organization 43 , 44 , 45 , 46 , 47 .

By contrast, in mouse embryos, replication foci patterns have suggested the presence of some degree of 3D genome arrangement before ZGA 23 , 48 , 49 . Moreover, in contrast to in Drosophila , Xenopus and zebrafish pre-ZGA embryos, G1/G2 phases exist in mouse zygotes and 1- and 2-cell S phase is 4–5 h long 5 . Given these reports, uniform replication in pre-ZGA mouse embryos was completely unexpected. However, all our data are consistent with each other and Hi-C data showing unstructured genome before ZGA 17 , 18 , 19 . Thus, uniform replication and a lack of strong euchromatic/heterochromatic compartments before ZGA is evolutionarily conserved in metazoans, whereas the extremely slow fork movement is unique to mice and perhaps humans 9 , 27 .

Notably, while the relatively unstructured pre-ZGA 3D genome was conserved in zebrafish and medaka 50 , 51 , somatic-cell-like RT was reported in pre-ZGA zebrafish embryos, despite the extremely short S phase 52 . As with the controversy regarding the 3D genome state before ZGA 50 , 53 , further studies are needed to resolve this discrepancy.

Our data raise more questions than answers. First, the question of why forks are extremely slow only in mouse (and human) pre-ZGA embryos. While the mechanism is unclear, chromosome segregation errors were much fewer in 2-cell embryos than in 4-cell mouse embryos. The slower forks and longer S phase might have contributed to higher replication fidelity in mice and were selected during evolution. Notably, slow forks were conserved in 2-cell-like cells, while uniform RT was not 27 . In fact, the 2-cell-like cells exhibited somatic-cell-like RT 27 , resembling the 4-cell embryos from a replication regulation standpoint. Notably, a recent study revealed a lower frequency of chromosomal abnormalities in mouse versus human early-cleavage embryos, despite the similarly slow fork speed 9 . The difference between mice and humans is an important area of future investigation 54 , 55 , 56 .

Second, regarding what could trigger the abrupt emergence of somatic-cell-like RT, replication foci patterns suggest nuclear compartment strengthening at the 4-cell stage, when certain genomic regions start to interact with the nuclear lamina 57 . However, while the A/B compartment changes are candidate upstream events that might cause RT changes 15 , nuclear compartment strengthening may not be the decisive factor based on our SCNT experiments. Given its timing, 2-cell major ZGA may be involved, although suppressing transcription without inhibiting replication is technically challenging 17 , 18 .

Third, regarding why replisome and RT regulations are temporally uncoordinated, the presence of such a period suggests that they are controlled independently. Nevertheless, forks accelerate abruptly in 8-cell embryos, which becomes relatively error-free. Thus, there seems to be a strong constraint to coordinate replisome regulation with RT in developing mouse embryos, which may be important for genome integrity. Moreover, fork acceleration by nucleosides reduced chromosome segregation errors during 4-to-8-cell transition (Fig. 4j ). Given these observations, it is all the more surprising that the erroneous transitional 4-cell S phase was not eliminated during evolution.

Fourth, as chromosome abnormalities are detrimental to development, error cells must be excluded from the embryos. While error cells could simply be eliminated, they could, for example, go to the extraembryonic lineage. Nevertheless, the physiological importance of maintaining the peculiar 4-cell S phase remains a mystery. As cell divisions are synchronous within an embryo until the 4-to-8-cell division, there could be a trade-off between cell-cycle synchrony and replication fidelity.

Finally, while our manuscript was under revision, a separate study reported that the somatic-cell-like RT program is established gradually from the 1-cell stage in mice 58 . By contrast, our study shows an abrupt RT emergence at the 4-cell stage. Future studies are needed to clarify this difference.

All animal experiments conformed to the Guide for the Care and Use of Laboratory Animals and were approved by the Institutional Committee of Laboratory Animal Experimentation of the RIKEN Center for Biosystems Dynamics Research. B6D2F1 (C57BL/6 × DBA/2) and C57BL/6 mice, aged 8–10 weeks, were used to produce oocytes and sperm. For allele-specific analysis, C57BL/6 female mice and MSM/Ms male mice were used to produce oocytes and sperm, respectively. To eliminate the effect of individual differences between mice, multiple mice were used in each experiment as follows. Figure 1b , 2 (4-cell), 4 (8-cell), 2 (16-cell) and 4 (ICM and TE) mice; Fig. 1d , 10 mice each (1-cell, 2-cell and 4-cell); Fig. 2d , 4 (1-cell), 3 (2-cell) and 3 (4-cell) mice; Fig. 2f , 3 (1-cell), 3 (2-cell) and 4 (4-cell and 8-cell) mice; Fig. 3b , 4 in vitro and 2 in vivo (2-to-4-cell), 8 in vitro and 7 in vivo (4-to-8-cell), and 4 in vitro and 2 in vivo (8-to-16-cell) mice; Fig. 3c , 15 mice each (2-to-4-cell, 4-to-8-cell and 8-to-16-cell); Fig. 3g , 3 mice each (2-to-4-cell, 4-to-8-cell, 8-to-16-cell and 16-to-32-cell); Fig. 3h , 4 (2-cell) and 3 (4-cell and 8-cell) mice; Fig. 3i , 5 (2-cell, 4-cell, and 8-cell) mice; Fig. 4d , 4 mice; Fig. 4h–i , 6 mice; Fig. 4j , 16 mice; Extended Data Fig. 7a , 7 (2-to-4-cell DMSO), 6 (2-to-4-cell aphidicolin 15 ng ml −1 , 30 ng ml −1 , and 60 ng ml −1 ), 8 (2-to-4-cell aphidicolin 75 ng ml −1 ), 16 (4-to-8-cell DMSO), 6 (4-to-8-cell aphidicolin 15 ng ml −1 , 30 ng ml −1 and 60 ng ml −1 ), 6 (4-to-8-cell aphidicolin 75 ng ml −1 ), 12 (8-to-16-cell DMSO), 8 (8-to-16-cell aphidicolin 15 ng ml −1 , 30 ng ml −1 and 60 ng ml −1 ) and 6 (8-to-16-cell aphidicolin 75 ng ml −1 ) mice.

Oocyte and embryo collection

The temperature, humidity and light cycle of mouse cages were maintained at 20–24 °C, 45–65% and 12 h–12 h dark–light, respectively. Mature oocytes were collected from the oviducts of eight- to ten-week-old female mice that had been induced to superovulate with 5 IU of equine chorionic gonadotropin (eCG, ASKA Pharmaceutical) followed by 5 IU of human chorionic gonadotropin (hCG; ASKA Pharmaceutical) 48 h later. Cumulus-oocyte complexes were collected from the oviducts approximately 16 h after hCG injection. Cumulus-oocyte complexes were placed in M2 medium and treated with 0.1% (w/v) bovine testicular hyaluronidase. After several minutes, the cumulus-free oocytes were washed twice and then moved to Chatot, Ziomek and Bavister medium (CZB). Mature metaphase II (MII) oocytes were subjected to in vitro fertilization (IVF), intracytoplasmic sperm injection (ICSI) or SCNT. Developmentally arrested or delayed embryos at each embryonic day were excluded from further analysis. In vivo developed zygotes and embryos were collected from the oviducts of pregnant mice that had been induced to superovulate and mated with male mice. For in vitro development, in vivo developed zygotes were cultured in CZB. As we pooled embryos from multiple females before single-embryo sampling, our dataset is not suitable for female-to-female variability assessment. When indicated, embryos were cultured in the presence of nucleosides (Sigma-Aldrich, EmbryoMax Nucleosides, 100×; at a final concentration of 2×) or aphidicolin (FUJIFILM Wako). In Extended Data Fig. 7a,b , embryos were monitored under the microscope every 30 min, and embryos that had just completed cell division were judged to be in G1 and transferred to the medium containing aphidicolin. The subsequent M phase was monitored by live imaging as in Extended Data Fig. 7a . Embryos that had just completed the 4-to-8-cell division were collected for scRepli-seq as in Extended Data Fig. 7b .

Fertilization

IVF was performed according to the manufacturer’s instructions using CARD MEDIUM (Cosmo Bio). To limit fertilization, we set the insemination time to 1 h. Intracytoplasmic sperm injection with sperm heads was performed as described previously 61 . In brief, the sperm head was separated from the tail by applying several piezo pulses to the neck region, and the head was then injected into an oocyte. After 20 min of recovery at room temperature, injected oocytes were cultured in CZB.

Single-cell (blastomere) collection

Collection of single blastomeres was performed using micromanipulation. Embryos were transferred into M2 medium supplemented with 5 μg ml −1 cytochalasin B (Sigma-Aldrich) for 10 min. The zona pellucida was then cut using the LYKOS laser system (Hamilton Thorne) in a micromanipulation chamber, which was placed onto a warmed stage (37 °C) in an inverted microscope (Olympus). After cutting the zona pellucida, a fire-polished injection pipette (inner diameter, 30 µm) was inserted through the hole of the zona pellucida, and single blastomeres were isolated. For late-8-cell- and 16-cell-stage embryos, embryos were treated with TrypLE Express (Gibco) for 5 min before single-cell isolation. TE and ICM cells were isolated as previously described 62 . Microsurgical isolation of single cells was performed by micromanipulation. After single-cell isolation, the cell was washed twice with PBS. The cell in 0.5 µl PBS was transferred to a tube with 6 µl sampling buffer.

SCNT was performed as previously described 61 . In brief, groups of MII oocytes were transferred into droplets of M2 medium containing 500 µg ml −1 cytochalasin B on the microscope stage to collect the MII spindle. Oocytes undergoing microsurgery were held with a holding pipette. A hole was made in the zona pellucida through the application of several piezo-pulses using an enucleation pipette. The MII spindle was aspirated into the pipette with a minimal volume of ooplasm, and the resulting enucleated oocytes were transferred into CZB. For nuclear injection, donor cumulus cells were gently aspirated in and out of the injection pipette to broken plasma membranes. Each nucleus was injected into an enucleated oocyte, and these reconstructed oocytes were kept in the incubator until activation. Reconstructed oocytes were parthenogenetically stimulated by incubation in CZB supplemented with 10 mM SrCl 2 , 2 mM ethylene glycol tetraacetic acid (EGTA), and 5 µM latrunculin A for 10 h, and then cultured in CZB.

Single-cell DNA replication profiling (scRepli-seq) using mouse embryos

scRepli-seq experiments using mouse embryos were performed as previously reported with slight modifications. In brief, unfixed single blastomeres (derived from BDF1 or B6MSM strain embryos) were collected into 8-well PCR tubes with 6 μl cell lysis buffer (288 μl of H 2 O, 2 μl of 10 mg ml −1 proteinase K (Sigma-Aldrich, P4850), 32 μl of 10× single-cell lysis and fragmentation buffer (Sigma-Aldrich, L1043)), incubated at 55 °C for 1 h and then at 99 °C for 4 min for gDNA isolation and fragmentation. For scRepli-seq analysis, we analysed all blastomeres of an embryo unless there was accidental damage to the cell/sample. For scRepli-seq experiments after EdU staining (Extended Data Fig. 3b,c ), after taking the photographs of EdU-stained cells (according to the protocol in Extended Data Fig. 3a ), the cells were manually collected by a mouth pipette under the microscope into 12 μl of cell lysis buffer and incubated at 55 °C for 16 h (not 1 h). The remaining whole-genome amplification process (SeqPlex enhanced DNA amplification kit, Sigma-Aldrich, SEQXE) and next-generation sequencing (NGS) library construction (NGS LTP library preparation kit, KAPA, KK8232) were basically performed according to the manufacturer’s instructions. The samples were processed for NGS on the Illumina Hiseq 1500 or Hiseq X Ten system (80-bp-length single-read or 150-bp-length paired-end read sequencing).

Immunostaining

Embryos were fixed with 2% paraformaldehyde in PBS-polyvinyl alcohol (PVA) (pH 7.4) for 30 min. After blocking and permeabilization in PBS-PVA containing 1 mg ml −1 BSA (PBS-PVA-BSA) and 0.1% Triton X-100, the embryos were incubated with appropriate primary antibodies overnight at 4 °C, washed several times in PBS-PVA-BSA and incubated with secondary antibodies for 90 min at room temperature. DNA was counterstained with 40 µg ml −1 of Hoechst 33342. Finally, the embryos were washed and transferred to BSA-PVA for imaging with a Zeiss LSM780 confocal microscope. The following primary antibodies were used: mouse anti-γH2A.X (phosphorylated Ser139) (1:200, Abcam, ab22551); rabbit anti-histone H3 (1:200, Abcam, ab62706); mouse anti-histone H3 (1:200, Abcam, ab195277); mouse anti-histone H3K9me2 (1:200, Monoclonal Antibody Institute, Japan (MABI), MABI0317); rabbit anti-phosphorylated-CHK1 (Ser345) (133D3) (1:200, Cell Signaling Technology, 2348S) antibodies. The secondary antibodies were Alexa Fluor 488 goat anti-mouse IgG (H+L) (A11029); goat anti-rabbit IgG (H+L) (A11034); Alexa Fluor 555 goat anti-mouse IgG (H+L) (A21424) (1:400, Invitrogen).

Quantification of fluorescence signals

To quantify the levels of H3K9me2, p-Chk1 or γH2AX relative to the levels of histone H3, we obtained the mean signal intensity for H3K9me2, p-CHK1 or γH2AX within the nuclei ( I me_nuc ). We then subtracted the mean cytoplasmic signal intensity ( I me_cyto ), which was obtained from a region near the nuclei, from the I me_nuc value ( I me_nuc  −  I me_cyto ). Similarly, we determined the histone H3 level within the same nuclei ( I H3_nuc  −  I H3_cyto ). Finally, we calculated the ratio between the two values ( I me_nuc  −  I me_cyto )/( I H3_nuc  −  I H3_cyto ).

EdU staining

For each embryo, the timing of its fertilization or cleavage was recorded by observation using stereomicroscopy every 30 min. At each hour after fertilization or cleavage, embryos were collected, treated with 20 µM EdU for 30 min, and then fixed in 3.7% paraformaldehyde in PBS-PVA (pH 7.4) for 30 min. EdU staining was performed using Click-iT Plus EdU Alexa Fluor 555 or 594 Imaging Kit (Invitrogen). After EdU staining, samples were incubated with mouse anti-histone H3 (1:200, Abcam, ab195277) primary antibody at 4 °C overnight, washed several times in PBS-PVA-BSA and incubated with Alexa Fluor 488 goat anti-mouse IgG (H+L) (A11029) secondary antibody for 120 min at room temperature. The embryos were finally washed and transferred to BSA–PVA for imaging on the Zeiss LSM780 confocal microscope. The images were reconstructed into 3D with Imaris software. In Fig. 2b–d and Extended Data Fig. 3a–c , we categorized EdU staining patterns based on spatial distribution and intensity. Images of nuclei were subjected to auto-thresholding with the ‘default’ algorism in Fiji software, and the thresholded patterns were manually categorized into ‘uniform’, ‘nuclear periphery + NPBs’, ‘NPBs’, ‘nPBs + internal foci’, ‘nuclear periphery + internal foci’, and ‘internal foci’. The ‘uniform’ category was further divided into two groups: those with nuclear EdU intensity 1.5× higher relative to cytoplasmic intensity (strong) and the others (weak). If the nuclear EdU intensity was less than 1.1 times the cytoplasmic intensity, it was categorized as ‘no signal’. For the experiment shown in Extended Data Fig. 3a , EdU-stained cells after Hoechst 33342 treatment (20 μM, for 30 min at 37 °C) were analysed using fluorescence-activated cell sorting using the Sony SH800 cell sorter using the single-cell mode (SH800 v.2.1).

EdU staining of metaphase chromosome spreads

The MC12 cells (cultured in 10% FBS/DMEM medium) and embryos (1-cell, 2-cell and 4-cell; C57BL/6 strain) were labelled with 20 μM EdU for 60 min in the presumptive early-S phase. After several hours of cultivation without EdU, the cells and embryos were treated with 0.01 μg ml −1 colcemid for 120 min to synchronize to M phase. The cells and embryos, from which the zona pellucida was removed by acidic Tyrode’s solution, were exposed to hypotonic 1% FBS/PBS supplemented with 0.075 M KCl for 10–20 min. The cells and embryos were then fixed with 3:1 methanol:acetic acid solution at −20 °C for 30 min (cells) or room temperature for a few minutes (embryos), and the chromosome spreads were prepared on glass slides. EdU staining of metaphase chromosomes was performed using the Click-iT EdU Cell Proliferation Kit (Invitrogen), and EdU fluorescence signal intensity was analysed with ImageJ.

DNA fibre spreading assay

More than 30 embryos (C57BL/6 strain) at each developmental stage were collected. 1-, 2- and 4-cell embryos were collected at early S phase. Likewise, most 8-cell embryos were also collected at early-S phase (although some cells may not be at early S, as cells lose cell cycle synchrony after the 8-cell stage). Embryos were labelled with 100 μM 5-iodo-2′-deoxyuridine (IdU) for 30 min just before collection. After three quick washes with KSOM medium, the embryos were labelled with 100 μM 5-chloro-2′-deoxyuridine (CldU) for 30 min. The labelling reaction was stopped by washing the cells with ice-cold 1% FBS/PBS. The embryos, from which the zona pellucida was removed by acidic Tyrode’s solution, were transferred into 1% FBS/PBS under the microscope with a mouth pipette. The embryos were then placed onto an APS-coated glass slide (Matsunami) with less than 1 μl of 1% FBS/PBS. Then, 20 μl of spreading buffer (0.5% SDS, 200 mM Tris-HCl (pH 7.5), 50 mM EDTA, 100 mM NaCl) (NaCl was added for longer fibre recovery 63 ) was added onto the embryos on the glass slides, which were incubated at room temperature for 6 min (ref. 9 ). The slides were then gently tilted 20° from horizontal to stretch the DNA fibres. The DNA fibre slides were dried at room temperature for at least 1 h and the slides were fixed (methanol:acetic acid, 3:1) for 2 min. DNA fibres on the fixed slides were denatured with 1 M NaOH for 22 min, neutralized by five washes with PBS, and blocked with 1% BSA/PBST (0.05% Tween-20). Immunostaining of labelled DNA was performed with mouse anti-BrdU antibody (1:5; recognizes IdU and CldU 64 ; BD, 347580) and rat anti-BrdU antibody (1:25; recognizes CldU 64 ; Abcam, 6326) at 37 °C for 45 min followed by incubation with Alexa Fluor 488 donkey anti-mouse IgG (H+L) secondary antibody (1:200, Invitrogen, A21202) and Alexa Fluor 594 donkey anti-rat IgG (H+L) secondary antibody (1:200, Invitrogen, A21209) at 37 °C for 30 min. After these antibody incubation steps for IdU/CldU detection, the slides were further incubated with mouse monoclonal antibody against ssDNA (1:100, Millipore, MAB3034, 16-19) for 30 min at 37 °C and Alexa Fluor 647 goat anti-mouse IgG2a secondary antibody (1:50, Invitrogen, A21241) for 30 min at 37 °C to avoid cross-reaction 65 . Finally, the samples were mounted with ProLong Diamond (Invitrogen, P36970), and photographs were taken with the DeltaVision Elite microscope using a ×60 Plan/Apo NA1.42 oil-immersion objective at 2,048 × 2,048 pixels. The excitation and emission band-pass filter sets used were 542/27 and 594/45 nm, respectively (TRITC), or 632/22 and 679/34 nm, respectively (Cy5), to avoid signal overlap between Alexa Fluor 594 and 647. Using λDNA as a control, we estimated the extension rate to be around 4.4 kb per μm. As the length of the smallest individual dots (30 min labelled DNA) that we could observe by imaging using the DeltaVision microscope was approximately 0.3 μm (~1.3 kb), the maximum resolution of our microscopy analysis was about 44 bp per min (around 1.3 kb per 30 min).

Categorization of replicated DNA fibres and the measurement of IOD and fork speed

Images of DNA fibres were subjected to auto-thresholding with the ‘minimum’ or ‘default’ algorithm in Fiji software. Individual DNA fibres were identified as a series of linearly arranged Alexa Fluor 647 (ssDNA) ‘dot’ signals (Fig. 2h ). The thresholded fibres containing both Alexa Fluor 488 (IdU+CldU) and Alexa Fluor 594 (CldU) signals were manually categorized into those with ‘immobile’, ‘intermediate’ and ‘mobile’ class forks. The immobile class forks were defined as those with single dot signals of IdU + CldU or CldU with gaps between dots, reflecting extremely slow fork movement. Here, gaps were defined as those with at least one dot of Alexa Fluor 647 ssDNA signal (Fig. 2h (immobile)). The mobile class forks were defined as those with a series of dot signals (≥2 dots) of IdU + CldU that contain a series of dot signals (≥2 dots) of CldU on either side of the same DNA fibre (Fig. 2h (mobile)). The intermediate class forks were defined as those with an intermediate character between the two other classes; these fibres also contained dot signals of IdU + CldU and CldU with gaps between dots but also contained some IdU + CldU single colour signals (that is, IdU-only regions) in between these dot signals. The IOD measurement method on the mobile fork class fibres is provided in Extended Data Fig. 5b . The IOD between the immobile forks was calculated by measuring the distance between the brightest pixels in the centre of the dots using the Fiji software. To determine the fork speed of the mobile forks, CldU tracks flanked by IdU tracks were identified, their lengths were measured, and were divided by the duration of the second pulse (30 min). CldU tracks that had no ssDNA signals ahead of them were excluded from the fork speed measurement as these fibres may have been broken in the middle of the CldU track. For the immobile fork speed measurement, the details are provided in Supplementary Note  2 .

Intra-S-phase checkpoint analysis

For the experiments described in Extended Data Fig. 5d–i , MC12 cells were cultured on a glass-bottomed dish, treated with 0.5 μM nocodazole for 17 h to synchronize in prometaphase and subjected to 3 μg ml −1 aphidicolin treatment for 3 h. After synchronization at the G1/S-phase border, the cells were further cultured in the presence of aphidicolin for 15 h with or without 10 mM 2-aminopurine (2-AP; an intra-S-phase checkpoint inhibitor). After removing aphidicolin or 2-AP, the cells were labelled with 20 μM EdU for 60 min and stained with EdU. The C57BL/6 strain 4-cell and 8-cell embryos within 1–2 h after the 2-to-4-cell or 4-to-8-cell division (that is, in early S phase) were treated with 3 μg ml −1 aphidicolin with or without 2-AP for 5.5 h followed by reagent removal and EdU staining.

Live-cell imaging

After linearization of the template plasmid, mRNA was synthesized using the mMESSAGE mMACHINE KIT (Ambion). Synthesized RNAs were stored at −80 °C until use. In vitro-transcribed mRNAs (0.9 pl of 150 ng µl −1 mEGFP-SLX4, 0.9 pl of 150 ng µl −1 mEGFP-PCNA, 0.9 pl of 150 ng µl −1 Major-satellite-mClover and 0.9 pl of 35 ng µl −1 H2B-mCherry) were microinjected into 1-cell embryos. Live-cell imaging was performed as previously described 66 with some modifications. In brief, a Zeiss LSM710, LSM780 or LSM880 confocal microscope equipped with a 40× C-Apochromat 1.2NA water-immersion objective lens (Carl Zeiss) was controlled by a multi-position autofocus macro 67 for Zen Software (Carl Zeiss). For major-satellite imaging, 17 confocal z sections (every 1.5 µm) of 512 × 512 pixel xy images covering a total volume of 30.30 × 30.30 × 24.00 µm were acquired at 2 min 15 s intervals for at least 3 h just after nuclear envelope breakdown. For SLX4 imaging, 17 confocal z sections (every 2 µm) of 512 × 512 pixel xy images covering a total volume of 30.30 × 30.30 × 32.00 µm were acquired at 3 min intervals for at least 10 h. For PCNA imaging, 29 confocal z sections (every 3 µm) of 512 × 512 pixel xy images covering a total volume of 84.85 × 84.85 × 84.00 µm were acquired at 5 min intervals from the 1-cell to 16-cell stages. In Figs. 3c,g and 4j and Extended Data Fig. 7a , to achieve high-resolution live imaging while minimizing phototoxicity, we selected and imaged blastomeres (cells) that were just entering M phase and close to the objective lens, up to two blastomeres (cells) per embryo.

3D imaging analysis

To detect chromosome aberration using live imaging (Fig. 3c ), we analysed images of embryos expressing major-satellite-mClover and H2B-mCherry using Imaris software (Bitplane). Chromosome bridges were detected at anaphase timepoints. To analyse DNA repair foci at M phase (Fig. 3f ), we used images of embryos expressing mEGFP–SLX4 and H2B–mCherry. To detect DNA repair foci, images at 6 min after nuclear envelope breakdown were processed using the 3D Spots detection function in Imaris software with a threshold of 2.0 times the cytoplasmic signal intensity. The detected spots were manually checked for quality and tracked over time through prometaphase until they disappeared. At each timepoint, the number of SLX4 foci on chromosomes was counted. To measure the cell cycle progression from the 1- to 8-cell stage (Fig. 4 and Extended Data Fig. 7 ), we analysed images of embryos expressing mEGFP–PCNA and H2B–mCherry with Imaris. To determine the timing of late S, images were processed using the ‘3D Spots’ function in Imaris with a threshold of 6.0 times the cytoplasmic intensity. The detected spots were manually checked for quality. The timing when the first PCNA spot appeared was defined as the onset of late S, whereas the timing when the last spot disappeared was defined as the onset of G2. We tracked all cells in an embryo while detecting chromosome bridges at each cell division. In Fig. 4 , we defined error cells as those that exhibited chromosome bridges for the first time during the 4-to-8-cell division.

scRepli-seq data analysis

In brief, after NGS, the raw scRepli-seq FASTQ files were processed for adapter trimming of both Illumina and SEQXE adapters, mapped to the mm9 reference genome and we filtered out the duplicated reads and reads that overlapped with the mm9 blacklists as described previously 16 . For quality control of scRepli-seq data, we applied MAD-score-based screening to filter out cells with problematic data (MAD scores of 0 or >1.0). More than 90% of cells in each sample passed these criteria. To generate log 2 [median] single-cell RT profiles, we counted the reads in sliding windows of 200 kb at 40 kb intervals after normalizing S-phase data with AneuFinder’s correctMappability command based on G1 control without karyotype defects in each strain. The binarization using 80 kb or 400 kb (haplotype-resolved analysis) windows was performed using the findCNVs command in AneuFinder as described previously 16 . For 4-cell embryos, we applied the 1-somy mode for early-S-phase cells and the 2-somy mode for mid/late-S-phase cells. For 1- and 2-cell embryos, we used the 2-somy mode for binarization (that is, the default copy number is ‘replicated’). As such, the overall binary profile will become blue (replicated) if there is no copy-number variation; likewise, if we use the ‘1-somy’ mode for the analysis of 1- and 2-cell embryos, it will be all yellow (unreplicated) instead of blue (Extended Data Fig. 2c ). If we use a third colour to describe the peculiar replication regulation of the 1- and 2-cell S phase, we thought that it would be confusing. Thus, to highlight the binarization failure and the unconventional replication regulation in 1- and 2-cell-stage embryos, we decided to use the ‘2-somy’ mode and blue (replicated) to describe the copy-number state of the majority of bins (Figs. 1d and 2f and Extended Data Figs. 1g , 2e,h and 4a ). The percentage replication scores (that is, the percentages of all of the genomic bins that have completed their replication) were calculated from binarized scRepli-seq data (excluding chromosome X) as described previously 16 . Averaged scRepli-seq profiles shown in Fig. 1b and Extended Data Fig. 1d were calculated from cells with 30–70% replication scores (excluding chromosome X). To identify the heterogeneously late-replicating domains described in Fig. 2f and Extended Data Figs. 1g and 2h , scRepli-seq profiles obtained from cells throughout the S phase were used to calculate the averaged scRepli-seq profile. The tag-density profile was generated in sliding windows of 200 kb at 40 kb intervals using AneuFinder as described previously 16 . t -SNE clustering analysis of scRepli-seq data (excluding chromosome X) was performed using RtSNE. For RtSNE, log 2 [median] RT scores of mid-S cells (cells with 30–70% replication scores) were used that were obtained from 4-, 8-, 16-cell embryos, ICM and TE.

Hi-C data analysis

Principal component 1 (PC1; A/B compartment profile) of Hi-C data in 200 kb bins was computed from the .hic file using published mapped Hi-C datasets of sperm 68 and cumulus 60 cells as described previously 16 . The genomic coordinates of PC1 profiles were converted from mm10 to mm9 using the liftover tool (UCSC Genome Browser). In Extended Data Figs. 2k and 4c , we defined the four PC1 (A/B compartment) categories, A1, A2, B2 and B1, as those containing 25% of all PC1 values (200 kb bins) each from the highest (strongest A) to lowest (strongest B) without an overlap.

Chromosome aberration analysis by scRepli-seq

To analyse chromosome aberration, we used the findCNVs command in AneuFinder with a 500 kb bin size as described previously 16 (6-HMM options: method=“HMM”, max.iter=3000, states=c(“zero-inflation”, “0-somy”, “1-somy”, “2-somy”, “3-somy”, “4-somy”, “5-somy”, “6-somy”), eps=0.01). Using scRepli-seq data of 4-, 8- and 16-cell-stage embryos, we determined the cell division that produced de novo chromosome errors as follows. When a pair of cells within an embryo was found to exhibit a 3:1 copy-number ratio (3-somy:1-somy) in a complementary manner for a particular chromosome or a chromosomal region, these cells were judged to be a pair of sister cells that experienced a chromosome segregation error for the first time in the last cell division (shown in Fig. 3b as de novo chromosome aberrations). When identical chromosomal abnormalities were commonly found in multiple pairs of cells within an embryo, these cells were judged to have experienced errors in divisions preceding the last division. Details of all of the detected chromosome aberrations are shown in Extended Data Fig. 6b and Supplementary Table 1 . As we pooled embryos from multiple females before single-embryo sampling, our dataset is not suitable for female-to-female variability assessment.

Statistics and reproducibility

Statistical analyses were performed with GraphPad Prism v.7.02 using one-way ANOVA with Dunn’s multiple-comparison test (Figs. 2j–l , 3g–i and 4h,I ); two-tailed unpaired Student’s t -tests (Fig. 4d ); two-tailed unpaired χ 2 tests (Fig. 4f ); two-tailed Mann–Whitney U -tests (Fig. 4g ); and two-tailed Fisher’s exact tests (Fig. 4j ).

Experimental reproducibility was demonstrated as follows: Fig. 1g , two independent experiments; Fig. 2d , two (2-cell) and three (1-cell, 4-cell) independent experiments; Fig. 2l , two independent experiments; Fig. 3b , two independent experiments; Fig. 3c , four independent experiments; Fig. 3g , two (2-to-4-cell and 16-to-32-cell) and three (4-to-8-cell and 8-to-16-cell) independent experiments; Fig. 3h , two independent experiments; Fig. 3i , two independent experiments; Fig. 4a–d , two independent experiments; Fig. 4h , two independent experiments; Fig. 4i , two independent experiments; and Fig. 4j , four independent experiments.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

All RT datasets (BrdU-IP and scRepli-seq) have been deposited at GEO under the accession codes GSE108556 and GSE255458 . Previously published data were downloaded from GEO: sperm Hi-C data ( GSE119805 ) 68 ; cumulus cell Hi-C data ( GSE139430 ) 60 . The mm9 (UCSC) reference genome was used.

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Acknowledgements

We thank T. Ichinose, A. Tanigawa and Y. Wu for technical assistance; K. Inoue and H. Kiyonari for providing the frozen embryos; S. Wakana for providing the MSM mice; R. Nakamura for discussion on pre-ZGA zebrafish embryos; R. Cerbus, H. Hamada and S. Kuratani for comments on the manuscript; and J. Ellenberg for providing the imaging macro. This work was supported by RIKEN BDR intramural grants, RIKEN Pioneering Projects ‘Genome Building from TADs’ and ‘Long-timescale Molecular Chronobiology’, JST CREST grant number JPMJCR20S5, MEXT KAKENHI grant number 18H05530 and JSPS KAKENHI grant number 20K20582 to I.H.; JSPS KAKENHI grant numbers 23H04948, 21H02407 and 18H05549 to T.S.K.; JSPS KAKENHI grant number 18K14681 and the Mochida Memorial Foundation for Medical and Pharmaceutical Research to S.T.; and JST PRESTO grant number JPMJPR20K4 and JSPS KAKENHI grant number 22H04674 to H.K.

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These authors contributed equally: Saori Takahashi, Hirohisa Kyogoku

Authors and Affiliations

Laboratory for Developmental Epigenetics, RIKEN Center for Biosystems Dynamics Research (BDR), Kobe, Japan

Saori Takahashi, Hisashi Miura, Asami Oji, Yoshiko Kondo & Ichiro Hiratani

Laboratory for Chromosome Segregation, RIKEN Center for Biosystems Dynamics Research (BDR), Kobe, Japan

Hirohisa Kyogoku & Tomoya S. Kitajima

Graduate School of Agricultural Science, Kobe University, Kobe, Japan

Hirohisa Kyogoku

Laboratory of Molecular & Cellular Biology, Graduate School of Bioresources, Mie University, Tsu, Japan

Takuya Hayakawa & Shin-ichiro Takebayashi

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Contributions

S.T., H.K., T.S.K. and I.H. conceived the project. S.T. performed the metaphase spread and intra-S-phase checkpoint experiments. H.K. performed the preparation of all embryos and collection of cells for scRepli-seq, live-cell imaging and EdU staining. S.T. and H.K. performed scRepli-seq. S.T., H.K. and H.M. performed the bioinformatic analyses. S.T., A.O., Y.K., T.H. and S.-i.T. collected the embryos, performed DNA combing and analysed data. S.T., H.K., T.S.K. and I.H. wrote the manuscript with comments from H.M., A.O. and S.-i.T.

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Correspondence to Hirohisa Kyogoku , Tomoya S. Kitajima or Ichiro Hiratani .

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Extended data figures and tables

Extended data fig. 1 evaluation of screpli-seq resolution and the analysis of screpli-seq profiles of pre-implantation mouse embryos..

( a ) NGS tag density plots of mid-S mESC scRepli-seq data (three individual cells) with 4–6 million (M) (top panel) and ~20 M (bottom panel) total NGS reads per cell. Density was calculated using sliding windows of 200 kb at 40-kb intervals, 80 kb at 16-kb intervals, 40 kb at 8-kb intervals, or 20 kb at 4-kb intervals. While the bimodal distribution is evident when using 200-kb and 80-kb windows, it becomes obscure when using 40-kb windows. For the ~20 M read samples, bimodality is barely visible at 40-kb. ( b ) scRepli-seq NGS read density plots of representative mid-S mESC single-cell data (Extended Data Fig. 1a , MidS#1) with 4.6 M and 18.4 M reads with the indicated bin size settings. Binarization calling outputs are also shown. Note that the valid read count is not necessarily proportional to the total read count, owing to an increase in duplicate reads, suggesting the presence of redundancy due to the limit of the scRepli-seq library complexity. The dotted line shows the lower read count limit of 20 that we set for the unreplicated bins (see Supplementary Note  1 ). ( c ) Pearson correlation matrix of averaged mid-S cell scRepli-seq data (excluding chrX). ( d ) Average scRepli-seq RT (Ave. scRT) profiles calculated from mid-S cells with 30–70% replication scores derived from 8-cell embryos and ICM. Chr8 is shown (80-kb bins). RT class definitions are from Dileep et al. 20 (CE, constitutively early; CL, constitutively late; D, developmentally regulated). RT-switching regions were defined as regions with ∆ave. scRT (ICM–8-cell) values of >1 or <–1. ( e ) Types and frequency of RT switching (from the 8-cell stage to ICM) and their RT classes (defined in (c)). LtoE and EtoL represent RT changes from late-to-early and early-to-late S, respectively. ( f ) Pearson correlation matrix of averaged scRepli-seq data of ICM and TE derived from mid-S cells (30–70% replication score, excluding chrX). ( g ) Haplotype-resolved binarized and log2median scRepli-seq profiles of 1/2/4-cell B6MSM embryos along with BrdU-IP population RT profiles of mESCs. For binarization by AneuFinder 59 , 2-somy mode was applied to 1-, 2- and mid/late 4-cell blastomeres, and 1-somy mode to early 4-cell blastomeres. 1- and 2-cell scRepli-seq profiles are ordered based on their sampling order, as we were unable to calculate their percentage replication score values. The resolution of binarized RT profiles and log2median RT profiles of non-haplotype-resolved B6MSM data are 80 kb (non-overlapping windows) and 200 kb (sliding windows at 40-kb intervals), respectively. For B6 or MSM (haplotype-resolved) data, it was 400 kb (non-overlapping windows) and 1 Mb (sliding windows at 40-kb intervals), respectively. Heterogeneously late RT regions shown beneath the 1-cell scRepli-seq profiles were defined as regions with an average scRT of <0.75 on the MSM (paternal) chromosome. Asterisks, data reflecting gradual and uniform replication (see Fig. 1d legend for details).

Extended Data Fig. 2 Detailed analyses of scRepli-seq profiles of 1-, 2-, 4-cell embryos.

( a,b ) (left) NGS tag density plots of mid-S scRepli-seq data (three individual cells) from 1-cell (a) and 2-cell embryos (b) with >30 M total NGS reads per cell. Density was calculated using sliding windows of 200 kb at 40-kb intervals, 80 kb at 16-kb intervals, or 40 kb at 8-kb intervals. In all cases, bimodality is not visible. (right) scRepli-seq NGS read density plots of representative mid-S single-cell data from 1-cell (a) and 2-cell embryos (b) with 78.3 M and 45.9 M reads, respectively, with the indicated bin size settings. Binarization calling outputs are also shown, which revealed the lack of bimodal distribution regardless of the binarization modes chosen. The dotted line shows the lower read count limit of 20 that we set for the unreplicated bins (see Supplementary Note  1 ). ( c ) Binarized mid-S scRepli-seq profiles derived from mESCs, 1-cell embryos, and 2-cell embryos with high read counts (18.4–19.9, 30.7–78.3, 34.5–46.6 M reads respectively). The indicated bin size settings and binarization modes were chosen. Unlike mESCs, 1- and 2-cell embryos lacked bin-to-bin variation, showing either seemingly totally ‘unreplicated’ or totally ‘replicated’ profiles depending on the binarization mode (1-somy or 2-somy; see  Methods ). ( d ) NGS tag density plots of representative 1/2/4-cell and mESC scRepli-seq profiles during cell cycle at G1 and S (S1–S4), which are indicated in (e). Tag densities were calculated for 200-kb sliding windows at 40-kb intervals. For (d–k), we used scRepli-seq data with 4–6 M total reads (see Supplementary Note  1 ). ( e ) Binarized scRepli-seq profiles of 1/2/4-cell B6MSM embryos on chr15 (80-kb bins). For binarization by AneuFinder 59 , 2-somy mode was applied to 1-, 2- and mid/late 4-cell blastomeres, and 1-somy mode to early 4-cell blastomeres (see  Methods for more details). 1- and 2-cell scRepli-seq profiles are ordered based on their sampling order, as we were unable to calculate their percentage replication score. Asterisks, data reflecting gradual and uniform replication (see Fig. 1d legend for details). ( f ) MAD score distribution of 1/2/4-cell blastomeres analysed by scRepli-seq with 40-kb, 80-kb, and 500-kb bins. MAD scores were constant during S-phase in 1- and 2-cell embryos, in sharp contrast to 4-cell embryos, which exhibited inverted V-shape patterns with the MAD score peaking at mid-S regardless of bin size settings (40, 80, and 500-kb bins). ( g ) Haplotype-resolved MAD score distribution in 1- and 2-cell embryos. Relatively high scores were detected on the 1-cell paternal genome, although they were lower than somatic cells. ( h ) Binarized 1-cell S-phase scRepli-seq profiles of the MSM (paternal) chromosome, along with BrdU-IP population RT profiles of mESCs. The averaged scRepli-seq profile (Ave. scRT) was generated by calculating the mean of scRepli-seq data derived from S-phase cells on the MSM (paternal) chromosome. The sperm A/B compartment profile (Hi-C PC1) was calculated from published Hi-C data 68 . RT class definition is based on Dileep et al. 20 (CE, constitutively early; CL, constitutively late; D, developmentally regulated). Asterisks, data reflecting gradual and uniform replication (see Fig. 1d legend for details). ( i ) Percentages of heterogeneously late RT (Ave. scRT<0.75) regions (chrX excluded) on the paternal and maternal genome in 1- and 2-cell embryos. They were observed specifically on the 1-cell paternal genome. ( j ) RT class distribution of all genomic bins and heterogeneously late RT regions on the 1-cell paternal genome (chrX excluded from analysis). The majority of the heterogeneously late RT regions were classified as constitutively late-replicating (CL). ( k ) A/B compartment (Hi-C PC1) category distribution of all genomic bins and heterogeneously late RT regions on the 1-cell paternal genome. A1 (strongest A), A2, B2, and B1 (strongest B) each contain 25 % of all PC1 values (200-kb bins) based on a sperm Hi-C data 68 . The majority of the heterogeneously late RT regions were classified as B compartment (heterochromatin), B1 and B2.

Extended Data Fig. 3 Replication foci and H3K9me2 dynamics during early cleavage stages.

( a ) (top) Experimental scheme. (left) A cell-cycle fluorescence-activated cell sorting (FACS) profile of mESCs. Cells were stained with Hoechst 33342, and the six gates (S1 through S6) used to collect cells throughout S-phase are shown. (right) Replication foci pattern dynamics throughout S-phase (this figure is identical to Fig. 2d ). Replication foci patterns were categorized into the following classes: G1, no signal; I, uniform (weak signal); II, uniform (strong signal); III, nuclear periphery + internal foci; IV, internal foci. ( b, c ) scRepli-seq profiling after replication foci image analysis. (left) Replication foci pattern classification based on the definition provided in (a) of 4-cell (b) and 8-cell (c) embryos. M, mitosis. (right) Binarized scRepli-seq profiles of the 20 (b) or 10 (c) EdU-stained nuclei shown on the left. Our data provide direct, single-cell-level evidence that the spatial patterns of replication foci are a good indicator of S-phase time. Chr2 is shown. For experimental details, see  Methods under ‘Single-cell DNA replication profiling (scRepli-seq) using mouse embryos’. ( d ) Top panel shows the representative images of H3K9me2 (green) staining. Histone H3 was stained for reference (magenta). Bottom panel shows the heatmap images of the H3K9me2 signal. ( e ) H3K9me2 levels were increased after 4-cell stage. One-way ANOVA using Dunn’s multiple comparisons test was performed. Error bars represent the mean and SD. ( f ) The spatial pattern of H3K9me2 shifted at the transition from 2-cell to 4-cell stage. Number of nuclei from two independent experiments are shown in (e) and (f).

Extended Data Fig. 4 In-depth scRepli-seq data analysis of SCNT embryos.

( a ) Binarized and log2median scRepli-seq profiles of SCNT 1- and 2-cell embryos (80-kb bins), along with BrdU-IP population RT profiles of mESCs. 2-somy mode was applied for binarization. Chr3 is shown. Asterisks, data reflecting gradual and uniform replication (see Fig. 1d legend for details). ( b ) RT class distribution of all genomic bins and heterogeneously late RT regions (defined as regions with an average scRT of <0.50 from the analysis of S-phase cells) in SCNT 2-cell embryos. The majority of the heterogeneously late RT regions were classified as constitutively late-replicating (CL). RT class definitions are from Dileep et al. 20 (CE, constitutively early; CL, constitutively late; D, developmentally regulated). ( c ) A/B compartment (Hi-C PC1) category distribution of all genomic bins and heterogeneously late RT regions on the SCNT 2-cell genome. A1 (strongest A), A2, B2, and B1 (strongest B) each contain 25 % of all PC1 values (200-kb bins) based on a cumulus cell Hi-C data 60 . More than 80% of the heterogeneously late RT regions were classified as B (heterochromatin) compartment, B1 and B2. ( d ) Averaged S-phase scRepli-seq profiles and heterogeneously late RT regions on the 1-cell paternal genome and in SCNT 2-cell embryos, along with BrdU-IP population RT profiles of mESCs and RT class distribution (see also Fig. 2f and Extended Data Fig. 2h ). There is a substantial overlap between the heterogeneously late RT regions on the 1-cell paternal genome and in SCNT 2-cell embryos just by visual inspection. Chr12 and chr4 are shown. ( e ) A Venn diagram showing the degree of overlap between the heterogeneously late RT regions on the 1-cell paternal genome and the SCNT 2-cell genome (80-kb bins). ( f ) MAD score distribution 16 of SCNT 1- and 2-cell embryos analysed by scRepli-seq. SCNT 1-cell embryos showed constant MAD scores throughout S-phase as in control 1- and 2-cell embryos (Fig. 1e and Extended Data Fig. 2f,g ). In contrast, while the SCNT 2-cell embryos did not show inverted V-shape patterns observed in mESCs and control 4-cell embryos (Fig. 1e ), they clearly showed fluctuation during S-phase, indicative of the co-existence of ~2 (nearly replicated) and ~1 copy (under-replicated) genomic bins.

Extended Data Fig. 5 Analysis of replication forks in early cleavage stages and re-analysis of replication states in 4-cell embryos.

( a ) Representative replicated DNA fibres in 1/2/4/8-cell stage embryos. Scale = 4.55 μm (20 kb). ( b ) Schematic diagram of IOD measurement on the ‘immobile’ and ‘mobile’ class fibres (see also Fig. 2j ). Scale = 4.55 μm (20 kb). ( c ) Tag density plots of scRepli-seq data derived from individual cells in 4- and 8-cell embryos in comparison to mESCs at the indicated time points (% replication scores) during S-phase. During early S (30–40% S; in the middle of the first half of S, equivalent to 3.3–4.4 hr in S assuming a 11-h S-phase), the bimodal distribution of the 4-cell data was less prominent with an unclear ‘replicated’ peak compared to 8-cell embryos and mESCs (see arrowheads and highlighted areas), suggesting the presence of incompletely replicated genomic bins. Such features disappeared at mid-S. ( d–i ) Analysis of intra-S-phase checkpoint. ( d ) Scheme of the intra-S-phase checkpoint experiment using somatic cells (MC12 cells). Briefly, synchronized cells were released from G1/S border arrest into EdU-containing medium after 0-h or after 15-h in the presence of either aphidicolin (15-h Aph) or aphidicolin + 2-aminopurine (2-AP; an intra-S-phase checkpoint inhibitor) (15-h Aph+2AP), and their replication foci patterns were evaluated. ( e ) Replication foci patterns of MC12 cells. While the majority of 0-h and 15-h Aph samples exhibited the early-S pattern, the majority of 15-h Aph+2AP exhibited the mid-S pattern. This indicated that suppression of intra-S-phase checkpoint by 2-AP caused replication initiation of mid-S RT domains in the absence of early-S RT domain replication, as expected 29 . ( f, g ) Scheme of the intra-S-phase checkpoint experiment using 4-cell (f) and 8-cell (g) embryos. Briefly, cells were released from G1/Early-S into EdU-containing medium after 0-h, 5.5-h, 5.5-h in the presence of aphidicolin (5.5-h Aph), or 5.5-h in the presence of aphidicolin + 2-AP (5.5-h Aph+2AP), and their replication foci patterns were analysed. ( h, i ) Replication foci patterns of 4-cell (h) and 8-cell (i) embryos. While the majority of 0-h and 5.5-h Aph samples exhibited the early-S pattern, the majority of 5.5-h Aph+2AP exhibited the mid-S pattern as in 5.5-h samples. Thus, 2-AP treatment caused replication initiation of mid-S RT domains in the absence of early-S RT domain replication, indicating that intra-S-phase checkpoint was already functional in 4-cell embryos.

Extended Data Fig. 6 Chromosome aberrations detected by scRepli-seq and live cell imaging.

( a ) (left) Schematic diagram of chromosome aberration types (whole, whole chromosome gain/loss; break, chromosome breakage). (right) Shown are representative karyograms of three pairs of sister cells within an 8-cell mouse embryo generated by scRepli-seq. Aberrant chromosome pairs (partial (‘break’) or non-partial (‘whole’) trisomy/monosomy) are highlighted in red and by the red rectangles. ( b ) All chromosome breaks identified by scRepli-seq in in vitro and in vivo 8-cell embryos. Chromosomal abnormalities appear as large segments of high (blue) and low (yellow) copy numbers that are complementary between a pair of sister cells (red rectangles). The breakpoint position coordinates are shown on the right. As a reference, the averaged scRepli-seq profile of 4-cell embryos is shown at the top. ( c ) Live imaging of chromosome segregation at each cell division. Maximum z-projection images of Major-satellite-mClover (centromeres, green) and H2B-mCherry (chromosomes, magenta). The cell shown as “4-to-8-cell, Error” is identical to the one shown in Fig. 3c (acentric fragment). No chromosome alignment defects were observed at the late metaphase of 4-to-8-cell divisions (0/59 divisions), including those preceding chromosome bridges (0/11 divisions). Time after nuclear envelope breakdown is shown (mm:sec). ( d ) Three representative chromosome breakpoints in early, mid, and late-S RT regions are shown, along with the log2median scRepli-seq data of sister cell pairs with complementary errors. Note that the three regions are identical to those shown in Fig. 3d .

Extended Data Fig. 7 Aphidicolin-induced chromosome aberrations and single-cell tracking throughout early embryonic development by live imaging.

( a ) Chromosome aberrations after aphidicolin treatment. Embryos were treated with aphidicolin at G1 of each embryonic stage. Chromosome aberrations at the subsequent cell division were detected and categorized by live imaging with Major-satellite-mClover (centromeres, green) and H2B-mCherry (chromosomes, magenta). Anaphase bridges consisting of one or two chromosomes were categorized as ‘mild’, whereas those consisting of three or more chromosomes were categorized as ‘severe’ chromosome aberrations. Representative snapshots of ‘mild’ and ‘severe’ chromosome aberrations at the indicated time after anaphase onset (mm:sec) are shown. Arrowheads, chromosome bridges and fragments. Number (n) of cell division from at least two independent experiments analysed live. Each circle shows the data from one experiment. Fisher’s exact test was performed using the pooled data. ( b ) Late-replicating regions are prone to chromosome breaks during the 4-to-8-cell division after treatment with aphidicolin. Error bars represent the mean and SD. Number (n) of chromosome breaks is shown. ( c ) Live imaging of cell cycle progression from 1- to 8-cell stages. 3D-reconstructed images of mEGFP-PCNA (S-phase marker, green) and H2B-mCherry (chromosomes, magenta) with signal interpolation in z. The cell lineage that exhibited a chromosome segregation error at the 4-to-8-cell division is indicated by asterisks. The mEGFP-PCNA signals of this cell linage is magnified and shown in inverted greyscale in the lower panel. Note that PCNA foci mark late S-phase (arrows). Time after the first M-phase in shown (hr:mm). ( d ) Durations of each cell cycle phase were measured at 2-, 4-, and 8-cell stages. ( e ) Total cell cycle length of 2-, 4- and 8-cell stages. For (d) and (e), One-way ANOVA using Dunn’s multiple comparisons test was performed. Error bars represent the mean and SD. Number (n) of cells from 26 embryos in two independent experiments. ( f ) Average cell cycle profiles of 2-, 4- and 8-cell stages. ( g ) Cell lineage trees from 1- to 8-cell stages. All embryos that exhibited de novo chromosome segregation errors during the 4-to-8-cell division are shown. Grey lines indicate the cells that were located outside of the images.

Supplementary information

Supplementary information.

Supplementary Notes 1–3, Supplementary Table 1 and the legends for Supplementary Videos 1 and 2.

Reporting Summary

Supplementary video 1.

Live imaging of chromosome segregation at each cell.

Supplementary Video 2

Live imaging of cell cycle progression from 1 to 8 cell.

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Takahashi, S., Kyogoku, H., Hayakawa, T. et al. Embryonic genome instability upon DNA replication timing program emergence. Nature (2024). https://doi.org/10.1038/s41586-024-07841-y

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