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The Cell Cycle Control System01:28

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The cell cycle regulation directs how a cell proceeds from one phase to the next and begins mitosis. The cell cycle control system includes intracellular regulatory molecules and external triggers. They provide "stop" or "advance" signals and operate at specific cell cycle stages termed checkpoints to ensure that a particular process is completed before the cell advances to the next phase.
Cyclins and cyclin-dependent kinases (Cdks) are the primary cell cycle regulators and...
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Related Experiment Video

Updated: May 7, 2026

Analysis of Cell Cycle Position in Mammalian Cells
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Classifying cell cycle states and a quiescent-like G0 state using single-cell transcriptomics.

Samantha A O'Connor1, Leonor Garcia2, Rori Hoover1

  • 1School of Biological and Health Systems Engineering, Arizona State University, Tempe AZ, USA.

Biorxiv : the Preprint Server for Biology
|April 25, 2024
PubMed
Summary

A new cell cycle classifier, ccAFv2, accurately identifies six cell cycle states and a quiescent state in single-cell RNA sequencing data. This tool enhances the analysis of cellular heterogeneity and biological signals across diverse cell types and experimental conditions.

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Area of Science:

  • Genomics
  • Cell Biology
  • Bioinformatics

Background:

  • Single-cell transcriptomics reveals significant cellular heterogeneity.
  • The cell cycle is a key driver of this heterogeneity.
  • Accurate cell cycle classification is crucial for understanding cellular states.

Purpose of the Study:

  • To develop a high-resolution cell cycle classifier (ccAFv2) for single-cell RNA sequencing data.
  • To improve the classification of cell cycle states, including quiescence.
  • To provide a versatile tool for analyzing cellular heterogeneity.

Main Methods:

  • Trained ccAFv2 on human neural stem cell single-cell RNA sequencing data.
  • Classified six cell cycle states (G1, Late G1, S, S/G2, G2/M, M/Early G1) and a Neural G0 state.
  • Incorporated a tunable parameter for filtering uncertain classifications.

Main Results:

  • ccAFv2 outperformed or matched existing state-of-the-art methods.
  • Demonstrated generalization of S, S/G2, G2/M, and M/Early G1 states across germ layers.
  • Showcased versatility across cell types, nuclei, and spatial transcriptomics data in humans and mice.
  • Provided methods to regress cell cycle effects for uncovering biological signals.

Conclusions:

  • ccAFv2 offers enhanced accuracy, flexibility, and adaptability for analyzing cell cycle and quiescence.
  • It is a powerful tool for dissecting complex biological systems and cellular heterogeneity.
  • Available as R and Python packages for broad integration into single-cell analysis workflows.