Editor's evaluation: Empirical single-cell tracking and cell-fate simulation reveal dual roles of p53 in tumor suppression

Article Figures and data Abstract Editor's evaluation Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics 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. Editor's evaluation This article 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. https://doi.org/10.7554/eLife.72498.sa0 Decision letter Reviews on Sciety eLife's review process Introduction The p53 gene is mutated in >50% of cancers (Kandoth et al., 2013; Hollstein et al., 1991; Petitjean et al., 2007), and loss of p53 function is considered to be involved in tumor progression, thereby defining TP53 as a tumor suppressor gene (Boutelle and Attardi, 2021; Levine, 2019). The cellular functions mediated by p53 are mainly related to its cellular levels, which increase in response to stress, thus activating various mechanisms, for example, cell cycle arrest, cell senescence, cell death, and DNA damage responses (Levine, 2019; Kastenhuber and Lowe, 2017; Lavin and Gueven, 2006; Vogelstein et al., 2000; Hafner et al., 2019). Given that cell cycle arrest, cell senescence, and cell death could lead to the removal of the damaged cells, these processes could act to suppress tumor formation (Kastenhuber and Lowe, 2017; Livingstone et al., 1992; Kastan et al., 1991; Clarke et al., 1993; Lowe et al., 1993; Campisi, 2005; Rufini et al., 2013). p53 levels are regulated via a mechanism mediated by Mdm2, which constantly degrades p53 by ubiquitination to maintain a low level. p53 accumulation occurs when Mdm2 activity is suppressed by stress-induced phosphorylation, which inhibits its ubiquitination activity, resulting in inhibition of p53 degradation (Boutelle and Attardi, 2021; Kastenhuber and Lowe, 2017; Lavin and Gueven, 2006; Haupt et al., 1997; Honda et al., 1997; Kubbutat et al., 1997). These observations suggest that increased stress levels are required to elevate p53 levels sufficiently to allow it to exert its tumor suppressor function. Maintaining a background low level of p53 may thus have evolved as a means of allowing cells to respond quickly to stress. Alternatively, given that removing p53 by gene silencing increases the efficiency of induced pluripotent stem cell formation (Guo et al., 2014), maintaining low levels of p53 in unstressed cells per se may have functional implications. In this regard, it has been suggested that low levels of p53 activity may be required to respond to a broad spectrum of stress (Vousden and Lane, 2007) through suppression of apoptosis and regulation of metabolisms (Boutelle and Attardi, 2021; Kastenhuber and Lowe, 2017; Kruiswijk et al., 2015; Lassus et al., 1996). If low levels of p53 indeed play a role in regulating cellular functions, it is plausible that loss of these functions could be involved in tumorigenesis. However, the effects of loss of low levels of p53 on cell characteristics remain unclear. In this study, we silenced the expression of low levels of p53 using small interfering RNA (siRNA) and investigated the effects of this loss on the frequency of induction of cellular events using single-cell tracking to detect rare cellular events and creating a cell-lineage database revealing the fates of individual cells. Cell lineages can be generated by lineage-reconstruction approaches; however, these assume that all cell divisions are bipolar (Chow et al., 2021; Chapal-Ilani et al., 2013; Zafar et al., 2020), and thus cannot be used to analyze lineages, including cells that undergo cell death, multiple cell division, and cell fusion. To overcome this problem, we developed a single-cell-tracking method that can detect cell death, multipolar cell division, and cell fusion events in individual cells, thus allowing the generation of accurate cell-lineage data. In addition to silencing, we also used the alkylating agent, N-methyl -N′-nitroso-N-nitrosoguanidine (MNNG), which acts as a DNA-damaging agent by generating methylated bases (Wood, 1996; Jacobs and Schär, 2012; Lindahl, 1990; Satoh and Lindahl, 1992; Lindahl et al., 1995). Among MNNG-induced methylated bases, O6-methylguanine, which acts as a premutagenic DNA lesion that induces a G:C to A:T transversion mutation, is repaired by O6-methylguanine methyltransferase, and 7-methylguanine, which causes the induction of cell death through the formation of DNA breaks, is repaired by base excision repair (Wood, 1996; Jacobs and Schär, 2012; Lindahl, 1990; Satoh and Lindahl, 1992; Lindahl et al., 1995). It has been reported that MNNG can increase p53 levels in cells (Lindahl et al., 1988; Kim et al., 2005) and activate DNA repair processes that are not related to p53. We, therefore, studied how the fate trajectories of wild-type and p53-silenced cells were affected by p53-mediated and non-mediated processes at single-cell resolution. Furthermore, because single-cell tracking can reveal spatiotemporal information on individual cells, we used this information to simulate the fates of individual cells beyond the empirical limit of cell culture by developing a cell-fate simulation algorithm. Our results obtained by single-cell tracking and cell-fate simulation suggest that the low levels of p53 in unstressed cells play a role in suppressing the induction of cell death and cell fusion, which may lead to multipolar cell division and the production of aneuploid progeny. Cell-fate simulation analysis revealed that some aneuploid progeny derived from p53-silenced (p53 RNAi) cells could propagate in an environment dominated by p53-expressing (Control) cells, while p53 RNAi cells per se were unable to gain an advantage over Control cells in terms of population expansion. In contrast, the balance of expansion between Control and p53 RNAi cells was altered by induction of the damage response by MNNG, resulting in the relative expansion of p53 RNAi cells. Thus, although low levels of p53 could act as a tumor suppressor by inhibiting the formation of aneuploid cells, this role could largely depend on the status of the cell population harboring the cells with impaired p53 function, and the stress-damaged environment. Results System to investigate the functional implications of maintaining low levels of p53 in unstressed cells We developed a system to determine whether the low levels of p53 retained in unstressed cells had a functional role in suppressing changes in cell characteristics. We, therefore, performed concurrent video recordings of A549 p53 proficient lung carcinoma cells treated with scrambled siRNA or p53 siRNA (Control and p53 RNAi cells, respectively) and analyzed cells using single-cell tracking. Distinct from other single-cell analyses, for example, single-cell transcriptomics, which reveal the characteristics of individual cells at a certain moment in time, data obtained by single-cell tracking include information on spatiotemporal changes for individual cells, thus revealing the changes in cell population characteristics over time. Furthermore, because tracking of individual cells allows cellular events to be detected regardless of the frequency of their occurrence, the response of a cell population can be delineated at single-cell resolution with higher accuracy compared with analyses based on the average responses of a cell population. The first step in single-cell-tracking analyses is the generation of a long-term live-cell imaging video recording continuous cell movement. Images, typically 2300 × 2300 pixel area (1200 × 1200 µm), were acquired every 10 min (Figure 1). Phototoxicity, which can damage cells during long-term live-cell imaging, was minimized by using differential interference contrast (DIC) imaging with near-infrared light. The single-cell-tracking videos were then started by selecting cells referred to as progenitors in the video images at a tracking time of 0 min, and single-cell tracking of these progenitors and their progeny was performed using our previous computerized single-cell-tracking analysis system (Sato et al., 2018). This system created fully automated live-cell imaging videos (up to 16 videos concurrently) and carried out image segmentation, automated single-cell tracking, database creation, data analysis, and archiving. Because we excluded any data related to progenitors or progeny that moved out of the field of view, the resulting single-cell-tracking data contained complete information for progenies derived from progenitors produced during the single-cell-tracking period. The resulting single-cell-tracking data thus contained the position of each cell at every time point in an image, the type of events that occurred in a cell (bipolar cell division, tripolar cell division, tetrapolar cell division [tripolar and tetrapolar cell divisions were defined as multipolar cell division], cell death, and cell fusion [see Figure 1—figure supplement 1 for still images of bipolar cell division, multipolar cell division, cell death, and cell fusion; corresponding videos have been deposited in Dryad]), and the relationships of a cell to its parent and offspring cells. To organize single-cell-tracking data, each progenitor and its progeny was assigned a unique ID, that is, a cell-lineage number. In this study, a group of cells (a progenitor and its progeny cells) with the same ID is referred as to a cell lineage. A unique cell number was also assigned to each cell constituting a cell lineage, and a database composed of multiple cell-lineage data is referred as to a cell-lineage database. We then used the cell-lineage database to perform quantitative bioinformatics analysis (Figure 1) at the single-cell and cell-lineage levels. Cell population-level analysis could also be performed by collecting data for cells that belonged to different cell lineages. Figure 1 with 6 supplements see all Download asset Open asset System for investigating the functional role of maintaining low cell levels of p53. A549 cells were cultured on multi-well chamber slides and the areas of interest were scanned. Cells were treated with scrambled siRNA or p53 siRNA in the presence or absence of various doses of MNNG, and the cell responses were monitored to generate live-cell imaging videos. The videos were used for imaging and single-cell tracking, starting immediately after MNNG treatment. Progenitors were selected from the image designated as tracking time 0 min (T0), and the progenitors and their progeny were tracked empirically. Cell-lineage database comprised tracking results for one progenitor and their progeny, and any events that occurred in the cells. A cell-lineage map was generated from the database to represent the proliferation profile of a progenitor and its progeny through bipolar cell division (BD), multipolar cell division (MD), cell fusion (CF), and cell death (CD). If any progeny moved out from the areas of interest, data related to the lineage of that was not included in the cell-lineage database. Quantitative bioinformatics analysis was performed using the database, which was also used to develop a cell-fate simulation algorithm. Some information in the cell-lineage database could be visually represented as a cell-lineage map (Figure 1), containing multidimensional information, for example, number of progeny produced from a progenitor, type, and time of events that occurred in a cell or in all cells comprising the lineage, and the length of time between events. Quantitative bioinformatics analyses were performed to analyze specific aspects of the information. Furthermore, given that the information stored in a cell-lineage database can reveal a pattern of events that occurred in the cell, cell lineage, and cell population, we used this information to simulate the fates of cells by developing a cell-fate simulation algorithm. Quantitation of cell numbers, areas, and densities First, we prepared uniform cell cultures for siRNA treatment using plated cells occupying ~90% of the culture surface, where >99% of cells were attached to other cells. As shown in Figure 1—figure supplement 2a, the number of cells/µm2 increased linearly while the average cell surface area was reduced, confirming that the number of cells could increase continuously in a linear manner during video monitoring. To visualize this increase, density maps were also created at each imaging time point (Figure 1—figure supplement 2b for still images and Figure 1—video 1) by assigning a value of 1 to pixels within a 20-pixel diameter (10.43 µm) of the position of the cell (Figure 1—figure supplement 2c). Cell positions were determined using data recorded in the cell-lineage database, and the pixel was assigned the sum of the number of overlapped areas. When a cell was close to other cells, the number of overlaps (i.e., the sum) increases, representing the cell density in an area. These sums were converted into a heat map, which showed an increase in density during video monitoring (Figure 1—figure supplement 2b and Figure 1—video 1). Minimum number of cell lineages required to build a cell-lineage database We then determined the minimum number of cell lineages (i.e., number of progenitors to be tracked) needed per experiment to build a cell-lineage database. For this purpose, we first constructed 485 cell lineages by performing single-cell tracking using 485 progenitor Control cells and then randomly selected 50, 80, 120, 140, and 240 cell lineages from the 485 cell lineages to generate cell-lineage databases containing the selected number of lineages. This selection was repeated three times for triplicate analysis for each cell-lineage database. Variation in the rate of cell population expansion was then evaluated by calculating the total number of cells at each time point using the cell-lineage databases. The results for the 50, 80, 120, 140, and 240 cell lineages are shown in Figure 1—figure supplement 3a–e. For example, the variation in the number of cells at the 4000 min-determined cell-lineage database containing 50 cell lineages (Figure 1—figure supplement 3a, black bar) was 51 cells, and the variations became constant (7.5–6.0 cells for 150–240 cell lineages) when >240 cell lineages were used (Figure 1—figure supplement 3f). This suggests that 240 cell lineages are sufficient for reproducible single-cell-tracking analysis, and we therefore used 335 cell lineages to create a cell-lineage database. Determination of length of time for single-cell tracking following siRNA and/or MNNG treatment We determined the length of time required for single-cell tracking following siRNA transfection. Because transfection can be monitored by observing the accumulation of particles (transfection reagents) in cells (Figure 1—figure supplement 4a, DIC), we quantitated the number of particles in an area of the cell culture by extracting particles with a grayscale value >150/255 from the video image and counted the number of particles with a size of 5–30 pixels (Figure 1—figure supplement 4a, Particles, and Figure 1—video 2). The number of particles started to increase at about 1200 min (20 hr) after transfection and peaked at 3750 min (62.5 hr) (Figure 1—figure supplement 4b). Western blotting analysis performed 48 hr after transfection confirmed silencing of p53 (Figure 1—figure supplement 4c; Scrambled siRNA vs. p53 siRNA, ~70% reduction), the expression of low levels of p53 (Scrambled siRNA, 0 μM MNNG), and the occurrence of p53 stabilization by exposure of cells to 7 µM MNNG (Scrambled siRNA, 5.8-fold increase). Based on these parameters, we applied a 30 min pulse of MNNG treatment after the start of particle accumulation (Figure 1—figure supplement 4b; 1740 min [29 hr] after transfection). Single-cell tracking was started at the end of MNNG treatment and continued for 4000 min (66.7 hr), by which time the number of particles was reduced to 70% of that at the peak. Notably, the degree of p53 silencing may vary among cells, and it is possible that only certain groups of cells may respond to silencing. This technique cannot reveal the level of silencing within the individual cells, but the data for the population as a whole can reveal differences, providing a clue to the function of the low levels of p53. Analysis of rates of cell population expansion of Control and p53 RNAi cells, and cells treated with MNNG using cell-counting and single-cell-tracking approaches The lowest levels of p53 could occur in unstressed cells (Boutelle and Attardi, 2021; Kastenhuber and Lowe, 2017; Lavin and Gueven, 2006; Haupt et al., 1997; Honda et al., 1997; Kubbutat et al., 1997). To gain insights into the functions of such low levels of p53, we analyzed the expansion rate of a p53 RNAi cell population and their response to MNNG exposure. The rate of cell population expansion, often referred to as the cell growth curve, has generally been measured by counting cells at certain time intervals. We therefore initially determined the rate of expansion using a classical, cell-counting approach (count cell numbers every 1000 min) and compared the results with those obtained by single-cell-tracking analysis (every 10 min) to evaluate the accuracy of the analysis (Figure 2). For the cell-counting approach, cell population expansion curves were determined manually by counting cells present in images of live-cell imaging videos every 100 time points (1000 min). To this end, images corresponding to every 100 time points were divided into 25 (5 × 5) squares (~512 × 512 pixels per square, 266.24 × 266.24 µm per square), and the number of cells within a square was counted (Figure 2—figure supplement 1). We used three independently generated videos and randomly selected 15 squares. The results of counting of Control and p53 RNAi cells are summarized in Figure 2a and b, respectively. The rates of expansion of the Control and p53 RNAi cells and their responses to MNNG exposure were also compared (Figure 2c–f). There was no significant difference in the expansion rates of Control and p53 RNAi cells (Figure 2c). There was also no significant difference in the expansion rates of Control cells and Control cells exposed to 1 µM MNNG (MNNG1) (Figure 2a, Control vs. MNNG1), while the expansion rate of p53 RNAi cells exposed to 1 µM MNNG (p53 RNAi-MNNG1) was reduced relative to Control cells exposed to MNNG1 (MNNG1) and p53 RNAi cells (Figure 2b and d). When the dose of MNNG was increased to 3 µM (MNNG3), the expansion rate of MNNG3 cells was reduced relative to Control cells (Figure 2a) but was higher than that of p53 RNAi-MNNG3 cells (Figure 2e, MNNG3, and p53 RNAi-MNNG3). At the highest MNNG dose used in this study (7 µM, MNNG7), at which significant accumulation of p53 was observed (Figure 1—figure supplement 4c), the rates of expansion of both the MNNG7 and p53 RNAi-MNNG7 cell populations were significantly reduced compared with non-exposed Control and p53 RNAi cells, respectively, but p53 RNAi cells were significantly less to the MNNG dose than Control cells (Figure These results suggest that cells with different to MNNG compared with Control cells were generated by silencing the low levels of p53. Furthermore, the relative of Control and p53 RNAi cells to different doses of MNNG further that these p53 RNAi cells not respond linearly to the different doses of Figure with 1 supplement see all Download asset Open asset Cell population expansion rates determined by cell-counting and single-cell-tracking Control and p53 RNAi cells were exposed to and 7 µM MNNG (Control and p53 RNAi cells exposed to those doses of MNNG are referred to as MNNG3, p53 p53 and p53 were divided into a × squares and cell population expansion curves were determined by counting the number of cells in a square (~512 × 512 at 1000 min Figure 2—figure supplement 1). squares were selected from three independently generated videos. curves were compared as Control vs. p53 RNAi MNNG1 vs. p53 MNNG3 vs. p53 RNAi-MNNG3 and MNNG7 vs. p53 RNAi-MNNG7 cells. and were performed and are Cell population expansion curves of Control and p53 RNAi cells were determined using a cell-lineage database. Single-cell tracking was performed for 4000 min with videos acquired at 10 min The number of cells every 10 min was and Cell population expansion curves were compared as Control vs. p53 RNAi MNNG1 vs. p53 MNNG3 vs. p53 RNAi-MNNG3 and MNNG7 vs. p53 RNAi-MNNG7 cells. The data were analyzed by in the following The difference in cell numbers between and 10 min was the numbers of cells at 10 and min were 100 and the difference was Because the total number of imaging points was the in time points can be Those for Control and p53 RNAi cells and those exposed to MNNG were used for the Figure data 1 Cell population expansion rates determined by cell-counting and single-cell-tracking Data used in Figure Download These results revealed how cell population size was by p53 silencing and/or MNNG treatment. However, this approach could not information on how each cell to the treatment or how the response affected the size of the cell population. We then analyzed cell-lineage databases obtained by single-cell tracking, given that the database information on events such as cell death, fusion, and division, which the cell population expansion, at single-cell resolution. Because the cell-lineage database generated by tracking contained information on individual cells at each time point in the images (every 10 the total number of cells every 10 min was in Figure of the single-cell-tracking cell-lineage and cell-lineage data have been deposited in analysis and cell counting results (Figure and the relative in the rates of cell expansion between the Control and p53 RNAi cells, and those treated with MNNG, were between the counting and single-cell-tracking results (Figure the between Control and p53 RNAi cells, although counting every 1000 min showed no significant differences, single-cell tracking, which can the cell number every 10 min, revealed that the population expansion rate of p53 RNAi cells was significantly than that of Control cells, confirming that the single-cell-tracking analysis approach produced results to the cell-counting approach (Figure and However, in contrast to the counting approach, which only the number of cells at a certain time cell-lineage databases spatiotemporal information at single-cell which could allow a more analysis of how the cell population is or reduced, type of cellular events were induced, and how the of a cell population with reduced p53 expression or DNA damage stress. We therefore further analyses to gain insights into the Control and p53 RNAi cell populations and those exposed to MNNG using single-cell Analysis of of cells using cell-lineage database Cell populations are to be composed of cells with different et al., and the rate of cell population expansion (Figure is thus often by the relative of the cells within the population. Cell population expansion curves per se not any insights the of individual cells to allow the impact of such variations in the cell population to be We therefore further revealed the characteristics of p53 and cell populations by the of the different cells comprising each cell population. To this end, we each cell lineage into groups (Figure Given that the of a cell could be represented by the number of cells and its comprising a cell lineage, we the total number of cells comprising each cell lineage and to the number cell lineages shown in Figure are composed of and We cell lineages that produced and cells as groups and respectively. The lineages in Figure 3a, and were therefore as groups and respectively. Figure 3 Download asset Open asset of progeny produced from a cell lineages are If a cell lineage was composed of one progenitor and progenies the cell lineage was as a cell lineage was composed of one progenitor and progenies the lineage was as Cells that underwent multipolar cell division, cell death, and cell fusion were also included in this The total number of cells to each group was For example, cell lineages were composed of three cells the number of cells in group was × 3 which could represent the number of cells with the to three cells within 4000 min of culture in a cell population. Control cells and cells exposed to p53 RNAi cells and cells exposed to Figure data 1 of progeny produced from a Data used in Figure Download The total number of cells in each group was then (Figure and For example, cell lineages were composed of cells the total number shown in the group be cells. The in Figure and thus represent the of cells that could have different as In the of Control cells, cell lineages belonged to group which for of the total number of Control cells (Figure p53 RNAi cell lineages also belonged to group but the number of cells with higher that is, group was increased relative to Control cells cells vs. p53 which represent a induced by silencing the low levels of p53 (Figure The number of cell lineages in group was increased in the MNNG1 compared with the Control population cells vs. while the number of corresponding lineages in p53 cells was reduced to cells compared with p53 RNAi cells (Figure This difference may the difference in cell population expansion rates between MNNG1 and p53 cells (Figure Because MNNG is a agent, it could the number of cells with a higher and increase cells with a