CycleFlow simultaneously quantifies cell-cycle phase lengths and quiescence in vivo

doi:10.1016/j.crmeth.2022.100315

. 2022 Oct 6;2(10):100315.

doi: 10.1016/j.crmeth.2022.100315. eCollection 2022 Oct 24.

CycleFlow simultaneously quantifies cell-cycle phase lengths and quiescence in vivo

Adrien Jolly¹, Ann-Kathrin Fanti², Csilla Kongsaysak-Lengyel², Nina Claudino¹, Ines Gräßer¹, Nils B Becker¹, Thomas Höfer¹

Affiliations

¹ Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany.
² Division of Cellular Immunology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany.

PMID: 36313807
PMCID: PMC9606136
DOI: 10.1016/j.crmeth.2022.100315

CycleFlow simultaneously quantifies cell-cycle phase lengths and quiescence in vivo

Adrien Jolly et al. Cell Rep Methods. 2022.

. 2022 Oct 6;2(10):100315.

doi: 10.1016/j.crmeth.2022.100315. eCollection 2022 Oct 24.

Authors

Adrien Jolly¹, Ann-Kathrin Fanti², Csilla Kongsaysak-Lengyel², Nina Claudino¹, Ines Gräßer¹, Nils B Becker¹, Thomas Höfer¹

Affiliations

¹ Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany.
² Division of Cellular Immunology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany.

PMID: 36313807
PMCID: PMC9606136
DOI: 10.1016/j.crmeth.2022.100315

Abstract

Populations of stem, progenitor, or cancer cells show proliferative heterogeneity in vivo, comprising proliferating and quiescent cells. Consistent quantification of the quiescent subpopulation and progression of the proliferating cells through the individual phases of the cell cycle has not been achieved. Here, we describe CycleFlow, a method that robustly infers this comprehensive information from standard pulse-chase experiments with thymidine analogs. Inference is based on a mathematical model of the cell cycle, with realistic waiting time distributions for the G₁, S, and G₂/M phases and a long-term quiescent G₀ state. We validate CycleFlow with an exponentially growing cancer cell line in vitro. Applying it to T cell progenitors in steady state in vivo, we uncover strong proliferative heterogeneity, with a minority of CD4⁺CD8⁺ T cell progenitors cycling very rapidly and then entering quiescence. CycleFlow is suitable as a routine method for quantitative cell-cycle analysis.

Keywords: BrdU labeling; EdU labeling; G0; cell cycle; cell proliferation; cell-cycle arrest; non-Markovian model; quiescence; statistical inference; thymocyte development.

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Conflict of interest statement

The authors declare no competing interests.

Figures

**Figure 1**
Outline of CycleFlow (A) Schematic of CycleFlow: the progression of EdU-labeled cells through the cell cycle is tracked over time, the mathematical model is fitted to the data, and cell-cycle parameters are estimated via Bayesian inference. (B) Schematic of TET21N cells’ exponential growth in culture (left) and T cell differentiation in the thymus (right). The number of TET21N cells grow exponentially, while the number of cells in DP remains constant. (C) Distribution of cells in the $\bar{G 01}$ , $\bar{S}$ , and $\bar{G 2 M}$ gates as determined by DNA content averaged over all time points. For TET21N (left barplot, n = 25); for DP thymocytes (right barplot, n = 31). Error bars indicate SEM. (D) EdU pulse-chase experiment for cells in culture or mouse. (E) Progression of EdU-labeled TET21N (top row) and DP thymocytes (bottom row) through the cell cycle, as defined by DNA content. In each case, four representative flow cytometry snapshots are shown.

**Figure 2**
Application of CycleFlow to determine quiescence and proliferation rate of TET21N cells and DP thymocytes *in vivo* (A and B) Time courses of EdU-labeled TET21N cells (A) and DP thymocytes (B) in $\bar{G 01}$ , $\bar{S}$ , and $\bar{G 2 M}$ gates compared between experimental data (error bars, pooled SEM; n = 3 to 6 per time point) and model fit. Population sizes are given as fractions of total cells. (C) Duration of total cell cycle, G₁, S, and G₂/M phases of TET21N cells (left panel), and quiescent fraction (right panel) inferred by CycleFlow. Violin plot indicates parameter distributions; white dots, median values; black bars, interquartile ranges. (D) Mean duration of cell-cycle and $G_{1}$ phase of TET21N, expressing the Cdt1 FUCCI degron, measured with time-lapse microscopy (data taken from Kuchen et al. [2020]). Error bars indicate standard deviation of the mean. (E) Duration of G₁, S, and G₂/M phases (left panel) and fraction of quiescent cells (right panel) of DP thymocytes inferred by CycleFlow. Violin plot indicates parameter distributions; white dots, median values; black bars, interquartile ranges. (F) Inferred time course of EdU-labeled G₀ and G₁ DP thymocytes in the $\bar{G 01}$ gate. (G) Samples of the posterior distribution of the EdU degradation time $τ_{E}$ for TET21N cells (red) and DP thymocytes (blue). Violin plot; white dots, median values; black bars, interquartile ranges.

**Figure 3**
Single-cell transcriptomic analysis of cycle progression (A) Distribution of cell-cycle phases in DP thymocytes scRNA-seq dataset as estimated by the Seurat package; n =1. (B) Principal-component analysis of DP scRNA-seq data performed on cell-cycle genes defined by the Seurat package; percentages on each axis denote the variability explained by the principal component. Cells are colored according to cell-cycle phase predicted by Seurat. Arrows indicate RNA velocities projected on the first and second principal components.

**Figure 4**
Parameters inferred by CycleFlow from synthetic data including cell-cycle arrest (A) G₁ length versus mean cell-cycle arrest time used in simulation. “NoReturn” indicates that cells arrest irreversibly. The coefficient of variation (CV) of cell-cycle arrest was set to 1.0 except for the points annotated with “LowCV,” where it was 0.13. Error bars indicate 90% credible intervals; the G₁ length used in the simulations is shown as a solid line. (B) Like (A) but for S (gray) and G₂/M (black) phase durations. (C) G₀ fraction inferred by CycleFlow (90% credible intervals; bars) and simulated arrested fractions (dots).

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References

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1. Arora M., Moser J., Phadke H., Basha A.A., Spencer S.L. Endogenous replication stress in mother cells leads to quiescence of daughter cells. Cell Rep. 2017;19:1351–1364. - PMC - PubMed
1. Barr A.R., Heldt F.S., Zhang T., Bakal C., Novák B. A dynamical framework for the all-or-none G1/S transition. Cell Syst. 2016;2:27–37. - PMC - PubMed
1. Busch K., Klapproth K., Barile M., Flossdorf M., Holland-Letz T., Schlenner S.M., Reth M., Höfer T., Rodewald H.R. Fundamental properties of unperturbed haematopoiesis from stem cells in vivo. Nature. 2015;518:542–546. - PubMed

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[1] Aguirre-Ghiso J.A. Models, mechanisms and clinical evidence for cancer dormancy. Nat. Rev. Cancer. 2007;7:834–846. - PMC - PubMed

[2] Aguirre-Ghiso J.A. Models, mechanisms and clinical evidence for cancer dormancy. Nat. Rev. Cancer. 2007;7:834–846. - PMC - PubMed

[3] Akinduro O., Weber T.S., Ang H., Haltalli M.L.R., Ruivo N., Duarte D., Rashidi N.M., Hawkins E.D., Duffy K.R., Lo Celso C. Proliferation dynamics of acute myeloid leukaemia and haematopoietic progenitors competing for bone marrow space. Nat. Commun. 2018;9:519. - PMC - PubMed

[4] Akinduro O., Weber T.S., Ang H., Haltalli M.L.R., Ruivo N., Duarte D., Rashidi N.M., Hawkins E.D., Duffy K.R., Lo Celso C. Proliferation dynamics of acute myeloid leukaemia and haematopoietic progenitors competing for bone marrow space. Nat. Commun. 2018;9:519. - PMC - PubMed

[5] Arora M., Moser J., Phadke H., Basha A.A., Spencer S.L. Endogenous replication stress in mother cells leads to quiescence of daughter cells. Cell Rep. 2017;19:1351–1364. - PMC - PubMed

[6] Arora M., Moser J., Phadke H., Basha A.A., Spencer S.L. Endogenous replication stress in mother cells leads to quiescence of daughter cells. Cell Rep. 2017;19:1351–1364. - PMC - PubMed

[7] Barr A.R., Heldt F.S., Zhang T., Bakal C., Novák B. A dynamical framework for the all-or-none G1/S transition. Cell Syst. 2016;2:27–37. - PMC - PubMed

[8] Barr A.R., Heldt F.S., Zhang T., Bakal C., Novák B. A dynamical framework for the all-or-none G1/S transition. Cell Syst. 2016;2:27–37. - PMC - PubMed

[9] Busch K., Klapproth K., Barile M., Flossdorf M., Holland-Letz T., Schlenner S.M., Reth M., Höfer T., Rodewald H.R. Fundamental properties of unperturbed haematopoiesis from stem cells in vivo. Nature. 2015;518:542–546. - PubMed

[10] Busch K., Klapproth K., Barile M., Flossdorf M., Holland-Letz T., Schlenner S.M., Reth M., Höfer T., Rodewald H.R. Fundamental properties of unperturbed haematopoiesis from stem cells in vivo. Nature. 2015;518:542–546. - PubMed

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CycleFlow simultaneously quantifies cell-cycle phase lengths and quiescence in vivo

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CycleFlow simultaneously quantifies cell-cycle phase lengths and quiescence in vivo

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Affiliations

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Conflict of interest statement

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