The molecular architecture of cell cycle arrest
2022; Springer Nature; Volume: 18; Issue: 9 Linguagem: Inglês
10.15252/msb.202211087
ISSN1744-4292
AutoresWayne Stallaert, Sovanny R. Taylor, Katarzyna M. Kedziora, Colin D. Taylor, Holly K. Sobon, Catherine L. Young, Juanita C. Limas, Jonah Varblow Holloway, Martha S. Johnson, Jeanette Gowen Cook, Jeremy E. Purvis,
Tópico(s)Single-cell and spatial transcriptomics
ResumoArticle26 September 2022Open Access Transparent process The molecular architecture of cell cycle arrest Wayne Stallaert Wayne Stallaert orcid.org/0000-0001-8064-6503 Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Contribution: Conceptualization, Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review & editing Search for more papers by this author Sovanny R Taylor Sovanny R Taylor Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Contribution: Investigation Search for more papers by this author Katarzyna M Kedziora Katarzyna M Kedziora Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Bioinformatics and Analytics Research Collaborative (BARC), University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Contribution: Data curation, Software, Formal analysis, Methodology, Writing - review & editing Search for more papers by this author Colin D Taylor Colin D Taylor orcid.org/0000-0002-1693-5359 Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Contribution: Software, Formal analysis Search for more papers by this author Holly K Sobon Holly K Sobon Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Contribution: Investigation Search for more papers by this author Catherine L Young Catherine L Young Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Contribution: Investigation Search for more papers by this author Juanita C Limas Juanita C Limas Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Contribution: Resources Search for more papers by this author Jonah Varblow Holloway Jonah Varblow Holloway Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Contribution: Data curation Search for more papers by this author Martha S Johnson Martha S Johnson orcid.org/0000-0002-0476-5567 Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Contribution: Resources, Investigation Search for more papers by this author Jeanette Gowen Cook Jeanette Gowen Cook orcid.org/0000-0003-0849-7405 Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Contribution: Supervision, Funding acquisition, Writing - review & editing Search for more papers by this author Jeremy E Purvis Corresponding Author Jeremy E Purvis [email protected] orcid.org/0000-0002-6963-0524 Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Contribution: Conceptualization, Resources, Supervision, Funding acquisition, Writing - review & editing Search for more papers by this author Wayne Stallaert Wayne Stallaert orcid.org/0000-0001-8064-6503 Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Contribution: Conceptualization, Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review & editing Search for more papers by this author Sovanny R Taylor Sovanny R Taylor Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Contribution: Investigation Search for more papers by this author Katarzyna M Kedziora Katarzyna M Kedziora Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Bioinformatics and Analytics Research Collaborative (BARC), University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Contribution: Data curation, Software, Formal analysis, Methodology, Writing - review & editing Search for more papers by this author Colin D Taylor Colin D Taylor orcid.org/0000-0002-1693-5359 Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Contribution: Software, Formal analysis Search for more papers by this author Holly K Sobon Holly K Sobon Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Contribution: Investigation Search for more papers by this author Catherine L Young Catherine L Young Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Contribution: Investigation Search for more papers by this author Juanita C Limas Juanita C Limas Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Contribution: Resources Search for more papers by this author Jonah Varblow Holloway Jonah Varblow Holloway Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Contribution: Data curation Search for more papers by this author Martha S Johnson Martha S Johnson orcid.org/0000-0002-0476-5567 Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Contribution: Resources, Investigation Search for more papers by this author Jeanette Gowen Cook Jeanette Gowen Cook orcid.org/0000-0003-0849-7405 Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Contribution: Supervision, Funding acquisition, Writing - review & editing Search for more papers by this author Jeremy E Purvis Corresponding Author Jeremy E Purvis [email protected] orcid.org/0000-0002-6963-0524 Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Contribution: Conceptualization, Resources, Supervision, Funding acquisition, Writing - review & editing Search for more papers by this author Author Information Wayne Stallaert1,2,6, Sovanny R Taylor1,2, Katarzyna M Kedziora1,3, Colin D Taylor1,2, Holly K Sobon1,2, Catherine L Young1,2, Juanita C Limas4, Jonah Varblow Holloway1,2, Martha S Johnson4, Jeanette Gowen Cook4,5 and Jeremy E Purvis *,1,2 1Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA 2Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA 3Bioinformatics and Analytics Research Collaborative (BARC), University of North Carolina at Chapel Hill, Chapel Hill, NC, USA 4Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA 5Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA 6Present address: Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA *Corresponding author. Tel: +919 962 4923; E-mail: [email protected] Molecular Systems Biology (2022)18:e11087https://doi.org/10.15252/msb.202211087 PDFDownload PDF of article text and main figures.PDF PLUSDownload PDF of article text, main figures, expanded view figures and appendix. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract The cellular decision governing the transition between proliferative and arrested states is crucial to the development and function of every tissue. While the molecular mechanisms that regulate the proliferative cell cycle are well established, we know comparatively little about what happens to cells as they diverge into cell cycle arrest. We performed hyperplexed imaging of 47 cell cycle effectors to obtain a map of the molecular architecture that governs cell cycle exit and progression into reversible (“quiescent”) and irreversible (“senescent”) arrest states. Using this map, we found multiple points of divergence from the proliferative cell cycle; identified stress-specific states of arrest; and resolved the molecular mechanisms governing these fate decisions, which we validated by single-cell, time-lapse imaging. Notably, we found that cells can exit into senescence from either G1 or G2; however, both subpopulations converge onto a single senescent state with a G1-like molecular signature. Cells can escape from this “irreversible” arrest state through the upregulation of G1 cyclins. This map provides a more comprehensive understanding of the overall organization of cell proliferation and arrest. Synopsis Certain stresses can induce exit from the proliferative cell cycle into cell cycle arrest states. Mapping fate trajectories following hypomitogenic, replicative, and oxidative stress shows that cell cycle arrest is not a discrete state but a complex and continuous architecture of cell states. Cell cycle mapping is used to reveal how the cell cycle responds to hypomitogenic, replicative and oxidative stress. Cells can exit the proliferative cell cycle from different phases (G1 or G2) driven by distinct molecular mechanisms. Sustained replication stress can generate polyploid cells through mitotic skipping and endoreduplication. Regardless of the phase of cell cycle exit, cells converge on a single state of senescence with a G1-like molecular signature. Introduction The decision of when and where to trigger cell division is fundamental to nearly all aspects of development and physiology. At the level of the individual cell, the molecular basis of the proliferation/arrest decision is embedded within a highly interconnected and dynamic network of cell cycle regulators. Progression through the proliferative phases of the cell cycle (G1/S/G2/M) is governed by a series of biochemical reactions that are coordinated in time and space to ensure the successful replication of DNA and its division into two daughter cells. In addition to these four proliferative phases, cells may also “exit” the proliferative cell cycle into a state of cell cycle arrest, often referred to as G0. While arrested, cells still perform many essential cellular functions including metabolism, secretion, transcription, and translation. However, as long as they remain in the G0 state, arrested cells neither synthesize DNA nor undergo cell division. This five-state model has become the canonical cell cycle model found in most textbooks (Morgan, 2007) and the current literature (Spencer et al, 2013; Overton et al, 2014; Marescal & Cheeseman, 2020) and has shaped our thinking about the cell cycle for over 70 years (Howard & Pelc, 1951; Cameron & Greulich, 1963; Smith & Martin, 1973). While the mechanisms that govern progression through the proliferative cell cycle have been studied extensively, we know comparatively little about what happens to cells after they exit the proliferative cell cycle. We know that cells may exit the cell cycle in response to various biochemical (e.g., DNA damage and oxidative stress) or environmental insults (e.g., lack of mitogens and high local cell density) triggered by different molecular mechanisms (Sagot & Laporte, 2019; Marescal & Cheeseman, 2020). After exiting the cell cycle, cells may progress into deeper states of reversible (“quiescent”) cell cycle arrest (Owen et al, 1989; Kwon et al, 2017; Wang et al, 2017), and in some cases can transition into an irreversible (“senescent”) state of arrest (Marthandan et al, 2014; Sousa-Victor et al, 2014; Fujimaki et al, 2019; Fujimaki & Yao, 2020). Clearly, cell cycle arrest is far from a single, static molecular state (Coller et al, 2006; Klosinska et al, 2011; Sun & Buttitta, 2017), yet a systematic characterization of when and how cells arrest remains lacking. In this study, we used a combination of hyperplexed, single-cell imaging and manifold learning to map the molecular architecture of cell cycle arrest. Previously, we used this approach to map the structure of the proliferative cell cycle in unperturbed, nontransformed retinal pigment epithelial (RPE) cells (Stallaert et al, 2022). Building upon this work, here, we exposed asynchronous RPE cells to three distinct stressors—hypomitogenic, replication, and oxidative—known to induce cell cycle arrest. For each stress, we identify the points of exit from the proliferative cell cycle, the mechanism(s) that induced arrest, and the molecular signatures of cells as they transition through distinct arrest states. We reveal a complex architecture of molecular trajectories through arrest state space and identify states of arrest not observed in our previous mapping of the human cell cycle. We show that cells exit the cell cycle along two distinct arrest trajectories in response to replicative and oxidative stress and that these trajectories are distinct from the arrest state induced by hypomitogenic stress. We demonstrate how sustained replication stress can generate polyploid cells through mitotic skipping and endoreduplication. Finally, we identify the molecular trajectories that lead to “irreversible” arrest and reveal that cellular senescence is an obligate G1-like molecular state that can be reversed by increasing the expression of G1 cyclins. Results To map the molecular architecture of cell cycle arrest, we subjected an asynchronous population of RPE cells to a variety of natural stresses known to induce exit from the proliferative cell cycle. These stresses included hypomitogenic stress (induced by serum starvation), replication stress (using the topoisomerase inhibitor etoposide), and oxidative stress (by exogenous H2O2 addition). We performed iterative indirect immunofluorescence imaging (4i) (Gut et al, 2018) of 47 cell cycle effectors (Table EV1) and DNA. From these 48 images, we extracted 2,952 unique single-cell features, including the subcellular expression of each protein across different cellular compartments (i.e., nucleus, cytosol, plasma membrane, and perinuclear region) as well as cell morphological features, such as size and shape, for 23,605 individual cells (Fig 1A). After feature selection (to identify features that vary in a cell-cycle-dependent manner; Stallaert et al, 2022), we performed manifold learning using Potential of Heat-diffusion for Affinity-based Transition Embedding (PHATE; Moon et al, 2019). Manifold learning techniques such as PHATE are used to find the “surface” within this high-dimensional feature space that represents progression through the cell cycle. In other words, by placing cells with similar cell cycle signatures close to one another in a lower-dimensional (2-d) space, we can piece together the paths they take through the cell cycle and the molecular changes that accompany them. In this manuscript, we will use these lower-dimensional embeddings or cell cycle “maps,” to identify the points at which cells exit the proliferative cell cycle in response to each stress and the mechanisms governing these proliferation/arrest decisions. Figure 1. Mapping the architecture of cell cycle arrest A. Schematic of the experimental approach. B. Cell cycle map of unperturbed cells (N = 11,268 cells). Proliferative (G1/S/G2/M) and arrested (G0) cell cycle phases were predicted for each cell using a Gaussian-mixture model and labeled on the map. C–I. (C) Diffusion pseudotime values, (D) phospho/total RB, (E) DNA content, (F) cyclin D1, (G) cyclin A, (H) cyclin B1 and (I) p21 of unperturbed cells are plotted on the map. Median nuclear values are shown for (D–I). Download figure Download PowerPoint To identify the precise molecular states in which proliferating cells exit the cell cycle, we first resolved the unperturbed cell cycle as a reference map (Fig 1B–I). For each cell, phase annotations were obtained for the proliferative cell cycle (G1/S/G2/M; Fig 1B) using a Gaussian mixture model trained on cell cycle features previously shown to vary by phase (Stallaert et al, 2022). Arrested G0 cells were identified by thresholding on the phosphorylated fraction of RB (Figs 1B and EV1A), which distinguishes arrested from actively cycling cells. Using diffusion pseudotime, a nonlinear trajectory inference method that orders individual cells by their position in high-dimensional feature space and can resolve branching points in these trajectories (Haghverdi et al, 2016), we observed two principal trajectories: a cyclical proliferative trajectory and a single trajectory into cell cycle arrest (Fig 1C). This overall structure was reproducible across experimental replicates (Fig EV1B) and is consistent with an emerging model of the cell cycle in which cells bifurcate along two distinct trajectories following cell division (Spencer et al, 2013; Yang et al, 2017; Stallaert et al, 2022). Some cells maintain high RB phosphorylation (Fig 1D) and immediately reenter the proliferative cell cycle, through which we observed a doubling of DNA content (Fig 1E) and characteristic dynamics in cell cycle effectors, including cyclins D1, A and B1 (Fig 1F–H). Other cells diverge from the proliferative trajectory soon after cell division into a state of arrest that is accompanied by an abrupt dephosphorylation of RB (Fig 1D) and an increase in p21 (Fig 1I). This is the only state of arrest that we observed in unperturbed cells. Previous studies have shown that this “spontaneous” cell cycle arrest is driven by low levels of endogenous stress (including replication stress) during the mother cell cycle (Arora et al, 2017; Min & Spencer, 2019). Figure 2. The arrest architecture of hypomitogenic stress A. Unified cell cycle map of unperturbed (gray) and serum-starved cells (1 day: light blue, 7 days: dark blue, N = 3,007 cells). The proliferative cell cycle (dotted gray line) and the hypomitogenic arrest trajectory (black dotted line) are indicated on the map. Inset: Each treatment condition is shown individually on the unified map (other conditions are shown in lighter gray). B–F. (B) Diffusion pseudotime, (C) p21, (D) cyclin D1, (E) DNA content and (F) phospho/total RB of unperturbed (left panels) or serum-starved cells (right panels) are plotted on the map. G. Time-lapse imaging of cyclinD1-mVenus intensity in unperturbed (control, green) and serum-starved cells (blue). Cells were serum-starved for at least 8 h prior to imaging. The solid line represents the population median and the shaded area indicates the 95% confidence interval. N = 105 control cells and N = 111 starved cells. H. Heatmap of feature intensity along the hypomitogenic arrest trajectory. Features were ordered by hierarchical clustering according to their dynamics along the arrest trajectory. Diffusion pseudotime values were binned and pseudotime values with < 15 cells were excluded from the visualization. I. Median nuclear p27 abundance in serum-starved cells is plotted on the map. Download figure Download PowerPoint Click here to expand this figure. Figure EV1. The cell cycle map of unperturbed RPE cells Left: Distribution of nuclear intensity ratios of phospho/total RB in unperturbed RPE cells. A threshold value of 0.7 was used to label cells as arrested (low phospho/total RB) or actively cycling (high phospho/total RB). Right: Cycling and arrested labels are overlaid on the cell cycle map. Cells from three technical replicates are labeled on the cell cycle map. Download figure Download PowerPoint Hypomitogenic stress To induce hypomitogenic stress, cells were serum-starved for 1 or 7 days prior to fixation. To show how hypomitogenic stress disrupts the normal cycling of cells, we generated a new cell cycle map by performing manifold learning on the combined data from unperturbed (Fig 2A, dark gray) and serum-starved cells (Fig 2A, light and dark blue). This new embedding effectively “repositions” the unperturbed cell cycle (from Fig 1) relative to a new and distinct state of cell cycle arrest (“hypomitogenic G0”) that appears only in response to serum starvation. Trajectory inference by diffusion pseudotime revealed that serum-starved cells diverge from the proliferative cell cycle during G2 (Fig 2B). Unlike spontaneous arrest, this cell cycle exit was not accompanied by a large increase in p21 (Fig 2C). In the unperturbed cell cycle, cyclin D1 increased in late G2 and remained elevated during mitosis and after cell division (Fig 2D, left panel), as previously observed (Gookin et al, 2017; Stallaert et al, 2022). After serum starvation, cyclin D1 remained comparatively low during G2 (Fig 2D, right panel) and cells underwent mitosis (as indicated by a drop in DNA content; Fig 2E) directly into a state of arrest with low RB phosphorylation (Fig 2F). To validate this mechanism of cell cycle exit in individual living cells, we performed time-lapse imaging of RPE cells expressing cyclin D1 tagged with a fluorophore at its endogenous locus (cycD1-Venus), as well as a fluorescent biosensor of CDK2 activity (DHB-mCherry), which can be used to distinguish actively proliferative versus arrested cells (Spencer et al, 2013). While unperturbed cells exhibited a clear increase in cyclin D1 during G2, serum starvation significantly reduced the induction of cyclin D1 during G2 and in daughter cells following cell division (Fig 2G). This decrease in cyclin D1 protein in daughter cells following serum starvation was previously shown to result from a decrease in cyclin D1 mRNA during the mother cell cycle (Guo et al, 2005; Yang et al, 2017). After exiting the cell cycle, progression further along the hypomitogenic arrest trajectory was accompanied by a decrease in the abundance of nearly every protein measured, including key proliferative effectors such as CDK2, CDK4, CDK6, CDH1, CDT1, PCNA, SKP2, FRA1, and cJUN, as well as decreased nuclear YAP and mTOR signaling (S6 phosphorylation) (Fig 2H). The only proteins not downregulated following serum starvation were the CDK inhibitor proteins p27 (Fig 2H–I) and, to a much lesser extent, p21 (Fig 2C and H). In fact, the abundance of p27 gradually increased as cells progressed further along the arrest trajectory, consistent with a previous study showing an increase in p27 in murine fibroblasts following serum starvation (Coats et al, 1996). Replication stress To induce replication stress, cells were treated with etoposide (1 μM), an inhibitor of DNA topoisomerase II that interferes with DNA religation step during replication, for 1–4 days prior to fixation. Once again, we constructed a new cell cycle map by performing manifold learning on the combined data from unperturbed (Fig 3A, dark gray) and etoposide-treated cells (Fig 3A, green) to show how replicative stress interferes with cell cycle progression. Within a single population of cells treated with etoposide, individual cells diverged from the proliferative cell cycle along two distinct arrest trajectories. One subpopulation exited from G2 after DNA replication was complete (DNA content = 4C). A second subpopulation exited the cell cycle in the subsequent G1 phase of daughter cells immediately following mitosis (DNA content = 2C) (Fig 3B and C). Both subpopulations entered arrest states characterized by a loss of RB phosphorylation (Fig 3D). Cell cycle exit along the 4C trajectory was accompanied by activation of the DNA damage checkpoint in G2 as indicated by an increase in markers of DNA damage signaling, including phospho-H2AX, phospho-CHK1, phospho-p65, p53 (Fig EV2A–E), and p21 (Fig 3E). By contrast, daughter cells that exited the cell cycle following mitosis along the 2C trajectory did not express early markers of DNA damage signaling (phospho-H2AX, phospho-CHK1) (Fig EV2B and C), but possessed sustained elevation of phospho-p65, p53 (Fig EV2D and E) and p21 (Fig 3E), consistent with replication stress inherited from the previous cell cycle (Arora et al, 2017). We also observed two states of cell cycle arrest, corresponding to cell cycle exit from G1 (DNA content = 2C) or G2 (DNA content = 4C) in breast epithelial cells (MCF10A) following sustained replication stress (Fig EV2H). Figure 3. The arrest architecture of replication stress A. Unified cell cycle map of unperturbed (gray) and etoposide-treated cells (1 μM; 1 day: light green, 2 days: green, 3 days: dark green, 4 days: darker green – see inset, N = 4,315 cells). The unperturbed cell cycle trajectory (dotted gray line) and two arrest trajectories (into G02C and G04C; black dotted lines) are indicated on the map. Inset: Each condition is shown individually on the map (other conditions are shown in lighter gray). B–E. (B) Diffusion pseudotime, (C) DNA content, (D) phospho/total RB and (E) p21 of unperturbed (left panels) or etoposide-treated cells (right panels) are plotted on the arrest architecture. Median nuclear values are shown. F. Time-lapse imaging of CDK2 activity (DHB-mCherry, gray) and p21-YPet (green) intensity in etoposide-treated cells. Schematic shows the two arrest trajectories observed following etoposide treatment. Cells that successfully complete G2 (“Mothers,” N = 32 cells) but arrest following cell division (“Daughters,” N = 45 cells) are shown in the two leftmost panels. Cells that arrest in G2 (N = 40 cells) are shown in the rightmost panel. The solid lines represent population medians and the shaded area indicates the 95% confidence interval. Download figure Download PowerPoint Click here to expand this figure. Figure EV2. Arrest trajectories following replication stress A. Cell cycle map arrest of unperturbed (gray) and etoposide-treated cells (1 μM; 1 day: light green, 2 days: green, 3 days: dark green, 4 days: darker green). The unperturbed cell cycle (dotted gray line) and two arrest trajectories (into 2C and 4C, pink and purple, respectively) are indicated on the map. B–E. (B) Phospho-H2AX, (C) phospho-CHK1, (D) phospho-p65 and (E) p53 of unperturbed (left panels) or etoposide-treated cells (right panels) are plotted on the arrest architecture. Median nuclear values are shown. F, G. Heatmap of feature intensity along the (F) 2C and (G) 4C arrest trajectories. Features were ordered by hierarchical clustering according to their dynamics along each arrest trajectory. Diffusion pseudotime values were binned and pseudotime values with < 15 cells were excluded from the visualization. H. Top: Nuclear phospho-RB intensities of control and etoposide-treated MCF10A cells (7 days, 1 μM). Dashed-red box indicates arrested etoposide-treated cells (phospho-RB < 500 au). Bottom: Distribution of DNA content in arrested etoposide-treated cells. Arrested MCF10A cells were observed with both 2C and 4C DNA content. Download figure Download PowerPoint To validate the observation that individual cells exit the cell cycle along two distinct arrest trajectories in response to replication stress and to investigate the mechanisms that govern this decision, we performed single-cell time-lapse imaging of RPE cells expressing a cell cycle sensor (PCNA-mTq2), a CDK2 activity sensor to detect cell cycle arrest (DHB-mCherry), and endogenous p21 fused to a fluorophore (p21-YPet) for 4 days after etoposide treatment. We observed a similar bifurcation of cell fate in live cells in response to replication stress, with 56% of cells exiting the cell cycle in G2 (along the 4C trajectory) and 44% proceeding through to mitosis following etoposide treatment (Fig 3F). The loss of CDK2 activity that accompanied cell cycle arrest in G2 occurred simultaneously with an increase in p21 expression (Fig 3F, last panel). For each of the cells that successfully progressed through to mitosis, however, no detectable p21 induction was observed in G2 (Fig 3F, first panel). Instead, their daughter cells arrested immediately following cell division (as indicated by a sustained decrease in CDK2 activity) accompanied by an increase in p21 expression shortly after cell division (Fig 3F, second panel). Over several days of etoposide treatment cells proc
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