Unknown

Dataset Information

0

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


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.

SUBMITTER: Jolly A 

PROVIDER: S-EPMC9606136 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

altmetric image

Publications

CycleFlow simultaneously quantifies cell-cycle phase lengths and quiescence <i>in vivo</i>.

Jolly Adrien A   Fanti Ann-Kathrin AK   Kongsaysak-Lengyel Csilla C   Claudino Nina N   Gräßer Ines I   Becker Nils B NB   Höfer Thomas T  

Cell reports methods 20221006 10


Populations of stem, progenitor, or cancer cells show proliferative heterogeneity <i>in vivo</i>, 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 mathemati  ...[more]

Similar Datasets

| S-EPMC4868629 | biostudies-literature
| S-EPMC3386279 | biostudies-literature
| S-EPMC10592697 | biostudies-literature
| S-EPMC11219882 | biostudies-literature
| S-EPMC6605788 | biostudies-literature
| S-EPMC7028488 | biostudies-literature
| S-EPMC3688750 | biostudies-literature
| S-EPMC8555441 | biostudies-literature
| S-EPMC1849950 | biostudies-literature
| S-EPMC6538531 | biostudies-literature