Empirical single-cell tracking and cell-fate simulation reveal dual roles of p53 in tumor suppression
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Evaluation Summary:
This paper examines the role of p53 in cell division by using a combination of live-cell imaging, cell tracking, and simulations. Overall, the results are extensively and transparently documented, and are of interest to cell biologists studying cell division, cell death, and p53.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1, Reviewer #2 and Reviewer #3 agreed to share their name with the authors.)
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Abstract
The tumor suppressor p53 regulates various stress responses via increasing its cellular levels. The lowest p53 levels occur in unstressed cells; however, the functions of these low levels remain unclear. To investigate the functions, we used empirical single-cell tracking of p53-expressing (Control) cells and cells in which p53 expression was silenced by RNA interference (p53 RNAi). Here, we show that p53 RNAi cells underwent more frequent cell death and cell fusion, which further induced multipolar cell division to generate aneuploid progeny. Those results suggest that the low levels of p53 in unstressed cells indeed have a role in suppressing the induction of cell death and the formation of aneuploid cells. We further investigated the impact of p53 silencing by developing an algorithm to simulate the fates of individual cells. Simulation of the fate of aneuploid cells revealed that these cells could propagate to create an aneuploid cell population. In addition, the simulation also revealed that more frequent induction of cell death in p53 RNAi cells under unstressed conditions conferred a disadvantage in terms of population expansion compared with Control cells, resulting in faster expansion of Control cells compared with p53 RNAi cells, leading to Control cells predominating in mixed cell populations. In contrast, the expansion of Control cells, but not p53 RNAi cells, was suppressed when the damage response was induced, allowing p53 RNAi cells to expand their population compared with the Control cells. These results suggest that, although p53 could suppress the formation of aneuploid cells, which could have a role in tumorigenesis, it could also allow the expansion of cells lacking p53 expression when the damage response is induced. p53 may thus play a role in both the suppression and the promotion of malignant cell formation during tumorigenesis.
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Author Response:
Reviewer #1
1: “A major weakness was that the simulation algorithm was both highly complex, but insufficiently explained. As a consequence, it was not clear what the underlying assumptions of the simulations were and how these assumptions were based on and/or constrained by the experiments.”
We have revised the section related to the simulation algorithm. This reviewer also raised a similar issue and suggested adding pseudocode or explaining it in plain language. We have therefore included two sections, “Cell-fate simulation algorithm” and “Cell-fate simulation options with Operation data”, as well as Figure 7, Figure 8 and Supplementary Figure 9.
In our previous version of the manuscript, we named the data used for the simulation as “Source data”. However, we realize that this journal uses this term for other …
Author Response:
Reviewer #1
1: “A major weakness was that the simulation algorithm was both highly complex, but insufficiently explained. As a consequence, it was not clear what the underlying assumptions of the simulations were and how these assumptions were based on and/or constrained by the experiments.”
We have revised the section related to the simulation algorithm. This reviewer also raised a similar issue and suggested adding pseudocode or explaining it in plain language. We have therefore included two sections, “Cell-fate simulation algorithm” and “Cell-fate simulation options with Operation data”, as well as Figure 7, Figure 8 and Supplementary Figure 9.
In our previous version of the manuscript, we named the data used for the simulation as “Source data”. However, we realize that this journal uses this term for other purposes. We have therefore changed “Source data” to “Operation data” to avoid confusion.
- “The single-cell analysis, including measuring lineages, by itself is not cutting-edge and has been done before, and so the novelty should be in the analysis.”
We agree that single-cell tracking per se is not a new technology, and was carried out as early as 1989 using 16 mm film. However, it has not been used frequently in the field of cell biology because of its extremely laborious nature. Our focus was thus on the development of a single-cell tracking technique that could be used routinely in cell biological research. We therefore computerized the analysis (preprint, BioRxiv 508705; doi: https://doi.org/10.1101/508705 (2018)) to allow the generation of large amounts of single-cell tracking data for bioinformatics analysis. We have mentioned this in the Results (“System to investigate the functional implications of maintaining low levels of p53 in unstressed cells”).
- “However, in many cases, the resulting data is presented in a manner that does not rely on the single-cell tracking (e.g. total cell number vs time in Fig. 2, average frequency of events in Fig. 4).”
We realize that we did not adequately explain the data relating to Figure 2. Counting experiments were performed to validate the results of single-cell tracking data, because such verification has not previously been performed. We therefore intended to produce a figure including both the actual counting data and single-cell tracking data together, to allow the readers to compare the results obtained by the different approaches. Although this reviewer commented that some data did “not rely on the single-cell tracking”, we would like to stress that the counting data were only used for the purpose of comparison. We have thus rewritten the “Effect of silencing the low levels of p53 on cell population expansion” in the Results, to clarify this.
- “The impact of p53 was only assessed on level of differences between experimental conditions (p53 siRNA or not), but p53 levels themselves were not measured and therefore not incorporated in the single-cell analysis.”
To the best of our knowledge, there are currently no techniques that allow the expression levels of proteins or genes of interest to be determined in individual live cells that are being tracked, and which could thus be used to generate data for bioinformatics analysis. It may be possible to use cells expressing a fluorescence-tagged protein, but as noted by this reviewer, frequent excitement of fluorophores in cells could affect cell growth (phototoxicity). We have thus been searching for a suitable technique that could be combined with single-cell tracking since 2012. If it becomes possible to perform an experiment similar to that suggested by this reviewer, it could potentially reveal many unknown cellular characteristics. We have revised the Discussion to consider this matter.
- “In general, differences between wild-type and p53 siRNA data were small, while cell-to-cell variability in p53 knock-down appears high (as judged by Supplementary Fig. 4). This leaves open whether the relatively minor difference between wild-type and p53 siRNA cells reflects variability in p53 knockdown between cells, which is currently not directly assessed.”
With regard to the “differences between wild-type and p53 siRNA data were small”, we would like to make a comment related to the small difference. In a typical study of p53, a lethal dose of an agent that could kill a majority of growing cells within e.g. 24-48 hrs has been used to detect a difference with control cells. A reason to use the lethal dose of agents is to make the status of cells homogeneous to detect any alteration of interest using average-based techniques, which represent the alteration that occurred in a majority of cells. On the other hand, when lower doses of agents are used, cell-to-cell heterogeneity has to be talking into account, as only a certain group of cells in a cell population may respond to the agents. In this case, only a small or no difference may be able to detect by the average-based analyses, if only a small number of cells in a cell population respond. Distance from the average-based analysis, single-cell tracking is a technique that allows quantitative analysis of alteration that occurred in individual cells in a cell population. By Western blotting, which is an average-based assay, (Supplementary Fig. 4), the level of p53 in unstressed cells was reduced to 30%. As the levels of p53 in unstressed cells are already low, a 70% reduction of the amount of p53 may be considered to be small. However, at the individual cell levels, it was sufficient to increase cell death, multipolar cell division, and cell fusion (Fig. 4). Thus, analysis of cells at the single-cell level could allow obtaining information that is difficult to find by the average-based analysis.
The comment related to “reflects variability”, however, made an important point. It is currently technically difficult to determine the expression levels of p53 or other proteins in individual live cells that are being tracked by long-term live-cell imaging. We therefore assumed that silencing reduced the levels of p53 in all the tracked cells. However, it is reasonable to expect variations in the silencing levels of p53 among individual cells, and it may be possible that cells in which p53 levels were reduced, e.g. to 0%, underwent cell death, while cells in which expression was only reduced to 50% underwent cell fusion, etc. Information on the levels of silencing in each cell would allow us to evaluate the relationship between p53 levels and the type of induced events. However, this analysis is currently technically difficult, as explained above. Nevertheless, the fact that silencing induced changes in cell fate suggested that the low background levels of p53 may have some functions. We have revised “Silencing of p53 and single-cell tracking” in the Results.
Reviewer #2
“The study's main weakness is the lack of empirical evidence from the simulation predictions of biology, and that the cellular consequences of p53 function were predictable and mostly confirmatory.”
We appreciate these interesting comments regarding the similarities and differences of the empirical and simulation approaches. In empirical studies, a model or hypothesis is often based on the results of an analysis that aims to reveal characteristics of interest e.g. of cells. However, such a model or hypothesis generally needs to be confirmed or tested independently. We therefore considered simulation as a tool to build a model or hypothesis, which also needed to be confirmed or tested.
Simulation could thus be considered as an additional tool, e.g. in addition to western blotting and DNA sequencing, which could generate different types of data than other existing techniques. We therefore think that such simulations could provide new options for cell biological studies. Regarding its “confirmatory” use, we think that simulation can be used to confirm existing models, but may also be used as a discovery tool. For example, p53-knockout cells are known to produce tetraploid cells, but how such cells are formed remains unclear. Single-cell tracking analysis can be used to fill the gap between the loss of p53 and tetraploid cell formation, and simulation can then be used to simulate the fate of cells generated by this loss.
Although we focused on describing our approach using single-cell tracking and cell-fate simulation in our manuscript, we believe these methods could be used in combination with empirical studies, to widen the cell biological research options.
We have discussed these issues in “Cell fate simulation and its applications” in the Discussion.
Reviewer #3
"Yet it is unclear how these results can be generalized because the authors only studied one cell line."
The current work focused on addressing a biological question using single-cell tracking and cellfate simulation; however, it will also be interesting to see if the proposed models can be generalized. Given that HeLa cells, in which p53 function is neutralized by papillomavirus E6 protein, also frequently undergo cell fusion followed by multipolar cell division and cell death (Sato, Rancourt, Sato and Satoh Sci Rep (2916) 6:23328), we believe that the low levels of p53 may also play a similar role in suppressing those events in many other types of cells.
"The results are not compared to other cell lines or primary cells, in terms of baseline expression of p53. "
We agree that it will be interesting to apply the methods in various types of cells and primary cell lines. However, there are significant variations in growth profiles among cell types. We have created live-cell imaging videos for > 30 cell lines, and found that each cell type showed unique characteristics in terms of growth patterns, frequencies of cell death, cell fusion, and multipolar cell division, and in the degree of cell-to-cell heterogeneity, implying that each cell type must be characterized using single-cell tracking analysis before moving on to studies using those cells, given that no such data are currently available. We believe that establishing a public data archive of single-cell tracking data will be useful for cell biological research, as well as for testing the current model.
"In addition, it is unclear how this model is superior to testing homeostatic p53 compares to models that use mutated p53.”
Most cancer cells carrying p53 gene mutations still express mutant p53 in the cytoplasm, and mutant p53 is suggested to confer gain-of-function in cancer cells. The characteristics of the cells used in the current study were related to the p53 null phenotype, but it will be interesting to determine if cancer cells carrying mutant p53 have a null+gain-of-function phenotype, or if gainof-function alters the null phenotype, in order to further understand the role of p53 in tumorigenesis. Such a study will require a large amount of work, but is probably feasible.
In addition to our responses, we would like to take this opportunity to discuss the cell biological meaning of “generality”. For example, if a response is detected in cell types A, B, and C by e.g. enzymatic assay, quantitation of protein expression levels, and staining of cells, it is often concluded that the response is commonly induced in those cells (generalized). However, as noted by this reviewer, the levels of responses may vary among cells, and commonly induced responses may thus only occur in a specific group of cells in the A, B, and C cell populations. In this case, such responses may not be generally induced in cell types A, B, and C, but only in certain subpopulations of these cell populations. In the current study, cell death etc. were induced in the A549 cell population following p53 silencing, but not in the majority of A549 cells, indicating that this might not be “general” for A549 cells, according to the definition of “generality” used for classical experimental approaches. We have thus been considering the meaning of the term “general”. Each cell in a cell population may have a different status, and without knowing the context affecting the status of each cell, it is not possible to establish “generality”. Information regarding the context of each cell in various types of cell populations is currently lacking, and we do not know how many contexts exist. In the current study, we described one context related to A549 cells, but there will be many other contexts, which may be similar to or distinct from A549 cells. We therefore consider that we are still at the stage of revealing such contexts, e.g. contexts for cancer cells carrying p53 mutation and for metastatic cells, and some commonality may begin to emerge after more contexts have been revealed. However, revealing these contexts will require extensive work, and we hope that other groups will also show an interest in this type of study.
We have addressed some these points in the revised Discussion.
“The tools described, including the DIC tracking software and the simulation algorithms would be useful additions to the biologist's toolkit. The direct visualization of siRNA transfection agents through DIC, and its integration with western blotting is novel, and the authors may consider preparing a protocol or methods paper that describes this in more detail, as it may be useful for trouble-shooting when encountering difficulties with siRNA transfections. ”
We appreciate the encouraging comments and would be happy to publish a protocol.
“The use of white-light imaging is refreshing, as many of us in the field default to fluorescence imaging, which has the potential to interfere with cell proliferation. Overall, the approach is innovative by extracting the most information possible from optical imaging data sets, in the less invasive way possible.”
We have been working on live-cell imaging since 2000 and had difficulty maintaining cell viability using fluorescent imaging. We therefore tried various light sources and found that nearinfrared light (not white light) was less toxic to the cells, allowing us to maintain cell cultures for at least a month on a microscope stage. We mentioned that near-infrared was used in the current study (“System to investigate the functional implications of maintaining low levels of p53 in unstressed cells” in the Results.
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Evaluation Summary:
This paper examines the role of p53 in cell division by using a combination of live-cell imaging, cell tracking, and simulations. Overall, the results are extensively and transparently documented, and are of interest to cell biologists studying cell division, cell death, and p53.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1, Reviewer #2 and Reviewer #3 agreed to share their name with the authors.)
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Reviewer #1 (Public Review):
The manuscript addresses the question whether low P53 activation has a functional role in controlling normal cell function, with a focus here on correctly executing the cell cycle. To do so, the authors study cells either with stress (exposure to MNNG) or without, and compare the response to both cases for wild-type cells (which presumably have low P53 activation) and p53 siRNA cells (which presumably lack P53 activation altogether). On the technological side, the authors use automated cell tracking to measure individual cell lineages, manual annotation of specific cell division defects, and use complex simulations that generate virtual data from their measured single-cell data, to study the impact of their observations on scenarios that were not or could not be studied experimentally.
Major strengths:
- The …
Reviewer #1 (Public Review):
The manuscript addresses the question whether low P53 activation has a functional role in controlling normal cell function, with a focus here on correctly executing the cell cycle. To do so, the authors study cells either with stress (exposure to MNNG) or without, and compare the response to both cases for wild-type cells (which presumably have low P53 activation) and p53 siRNA cells (which presumably lack P53 activation altogether). On the technological side, the authors use automated cell tracking to measure individual cell lineages, manual annotation of specific cell division defects, and use complex simulations that generate virtual data from their measured single-cell data, to study the impact of their observations on scenarios that were not or could not be studied experimentally.
Major strengths:
- The experimental dataset gives a very detailed view on cell cycle dynamics and its dependence on P53, as it includes both cell lineage data and data on the occurrence of relevant, but infrequent events, such as cell death or cell fusion, that impact population dynamics.
- This data set is used to reveal patterns in such defects that would not be visible without such single-cell analysis. For instance, it reveals a link between cell fusion, which occurs preferentially between siblings, and subsequent multipolar division.Major weaknesses:
- The measure lineage data is subsequently used to generate simulation data, including for scenarios that were not measured experimentally. A major weakness was that the simulation algorithm was both highly complex, but insufficiently explained. As a consequence, it was not clear what the underlying assumptions of the simulations were and how these assumptions were based on and/or constrained by the experiments.
- The single-cell analysis, including measuring lineages, by itself is not cutting-edge and has been done before, and so the novelty should be in the analysis. However, in many cases, the resulting data is presented in a manner that does not rely on the single-cell tracking (e.g. total cell number vs time in Fig. 2, average frequency of events in Fig. 4).
- The impact of p53 was only assessed on level of differences between experimental conditions (p53 siRNA or not), but p53 levels themselves were not measured and therefore not incorporated in the single-cell analysis. In general, differences between wild-type and p53 siRNA data were small, while cell-to-cell variability in p53 knock-down appears high (as judged by Supplementary Fig. 4). This leaves open whether the relatively minor difference between wild-type and p53 siRNA cells reflects variability in p53 knockdown between cells, which is currently not directly assessed. -
Reviewer #2 (Public Review):
In the manuscript, "Empirical single-cell tracking and cell-fate simulation reveal dual roles of p53 in tumor suppression," Rancourt et al examine how the tumor suppressor gene p53 can protect a cell in normal conditions and how its failure can lead to tumor proliferation. They use single-cell tracking of thousands of cancer cells imaged over several days in vitro with and without carcinogenic drug application, as well as extrapolated simulations of different scenarios and outcomes.
Overall, the study is interesting and its strengths lie in the identification of the cellular consequences of p53 function and malfunction in cell proliferation, in the presence and absence of a carcinogenic insult. The paper makes good use of single-cell analysis and simulations. The study's main weakness is the lack of …
Reviewer #2 (Public Review):
In the manuscript, "Empirical single-cell tracking and cell-fate simulation reveal dual roles of p53 in tumor suppression," Rancourt et al examine how the tumor suppressor gene p53 can protect a cell in normal conditions and how its failure can lead to tumor proliferation. They use single-cell tracking of thousands of cancer cells imaged over several days in vitro with and without carcinogenic drug application, as well as extrapolated simulations of different scenarios and outcomes.
Overall, the study is interesting and its strengths lie in the identification of the cellular consequences of p53 function and malfunction in cell proliferation, in the presence and absence of a carcinogenic insult. The paper makes good use of single-cell analysis and simulations. The study's main weakness is the lack of empirical evidence from the simulation predictions of biology, and that the cellular consequences of p53 function were predictable and mostly confirmatory. The study uses novel methods and excellent use of single-cell tracking and imaging of cell fate, and the simulations quantify specific conditions and make interesting and testable predictions of biological function.
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Reviewer #3 (Public Review):
The authors use a combination of cell lineage tracking and knockdown approaches to compare cell proliferation in cells expressing wild-type p53 (wt p53) compared to cells where p53 is knocked down using siRNA (siRNA p53). The imaging is based on differential interference contrast (DIC), a white light technique that avoids the use of fluorescent probes. Cellular levels of the p53 protein were lowered using siRNA, and notably, the the internalization of the siRNA particles in the cell was directly observed. These results were correlated with p53 expression in cells biochemically. Control and knockdown cells were then studied under basal and genotoxic conditions. Through a detailed analysis of cell fate and simulations, the authors conclude that cells expressing less p53 under baseline conditions can initiate …
Reviewer #3 (Public Review):
The authors use a combination of cell lineage tracking and knockdown approaches to compare cell proliferation in cells expressing wild-type p53 (wt p53) compared to cells where p53 is knocked down using siRNA (siRNA p53). The imaging is based on differential interference contrast (DIC), a white light technique that avoids the use of fluorescent probes. Cellular levels of the p53 protein were lowered using siRNA, and notably, the the internalization of the siRNA particles in the cell was directly observed. These results were correlated with p53 expression in cells biochemically. Control and knockdown cells were then studied under basal and genotoxic conditions. Through a detailed analysis of cell fate and simulations, the authors conclude that cells expressing less p53 under baseline conditions can initiate tumour expression when tissue that contains a mixture of cells containing both normal and low levels of p53 are exposed to genotoxic stress.
Through direct visualization and tracking of individual cell fate, including cell division, cell growth and cell death, the authors observe track the lineages and events during proliferation. Through simulations, they can analyze the data in a way that decouples the effects of cell division on proliferation from cell death, and they use this approach in different combinations as they test and simulate other conditions. For example, although the siRNA cells yield more cells per division on average, their proliferation is about the same as that observed for wt p53 cells. Through simulations, the authors demonstrate that this arises because that siRNA p53 cells undergo cell death more frequently. When exposed to genotoxic stress, the situation is reversed, with siRNA p53 cells proliferating faster due to increased cell death in the wt p53 cells. The authors conclude that the results demonstrate that homeostatic levels of p53 are relevant in cancer progression, with lower levels being a risk. Yet it is unclear how these results can be generalized because the authors only studied one cell line. The results are not compared to other cell lines or primary cells, in terms of baseline expression of p53. In addition, it is unclear how this model is superior to testing homeostatic p53 compares to models that use mutated p53.
Strengths
Generally, it is easier to track cell lineages automatically from using fluorescence micrographs because these data are easier to segment from the background. Yet fluorescence probes have the potential to create off-target effects that may affect biological functions. For this reason, the authors' methodology would be of interest to anyone studying cell proliferation at the single cell level. The tools described, including the DIC tracking software and the simulation algorithms would be useful additions to the biologist's toolkit. The direct visualization of siRNA transfection agents through DIC, and its integration with western blotting is novel, and the authors may consider preparing a protocol or methods paper that describes this in more detail, as it may be useful for trouble-shooting when encountering difficulties with siRNA transfections.
The level of dedication and attention to detail in reporting the full cell lineage results and the details of the simulation algorithm is outstanding. Together, the manual and corresponding automatically tracked data are invaluable ground truth data sets for any researcher interested in modelling and simulating cell proliferation near the point of confluency. The use of white-light imaging is refreshing, as many of us in the field default to fluorescence imaging, which has the potential to interfere with cell proliferation. Overall, the approach is innovative by extracting the most information possible from optical imaging data sets, in the less invasive way possible.
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