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Related Concept Videos

Overview Of Cell Separation And Isolation01:20

Overview Of Cell Separation And Isolation

Cell separation was first achieved in 1964 by S. H. Seal, who separated large tumor cells from the smaller blood cells using filtration. Two years later, Pohl and Hawk performed experiments on how cells respond differently to a nonuniform electric field based on the cell type. Such observations were the inception of cell separation methods, which allow isolating a single cell type from a heterogeneous sample.
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The cell cycle refers to the sequence of events occurring throughout a typical cell’s life. In eukaryotic cells, the somatic cell cycle has two stages: interphase and the mitotic phase. During interphase, the cell grows, performs its basic metabolic functions, copies its DNA, and prepares for mitotic cell division. Then, during mitosis and cytokinesis, the cell divides its nuclear and cytoplasmic materials, respectively. This generates two daughter cells that are identical to the original...
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To consistently produce healthy cells, the cell cycle—the process that generates daughter cells—must be precisely regulated.

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Related Experiment Video

Updated: Jun 25, 2026

Generation and Isolation of Cell Cycle-arrested Cells with Complex Karyotypes
05:22

Generation and Isolation of Cell Cycle-arrested Cells with Complex Karyotypes

Published on: April 13, 2018

CycleVI: isolating cell cycle variation with an interpretable deep generative model.

Pia Mozdzanowski1,2, Marcel Tarbier3, Gustavo S Jeuken1

  • 1Systems Biology Lab, AIMMS/A-LIFE, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

Bioinformatics (Oxford, England)
|June 23, 2026
PubMed
Summary

CycleVI, a novel deep generative model, accurately disentangles cell cycle variation in single-cell RNA sequencing data. This method improves the analysis of biological processes intertwined with cell proliferation.

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Last Updated: Jun 25, 2026

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Area of Science:

  • Computational Biology
  • Genomics
  • Single-cell analysis

Background:

  • Cell cycle progression is a major source of noise in single-cell RNA sequencing (scRNA-seq) data.
  • Existing methods for cell cycle phase inference are often unstable when combined with other biological or technical variations.

Purpose of the Study:

  • To develop a robust method for disentangling cell cycle variation from other biological signals in scRNA-seq data.
  • To improve the analysis of cellular heterogeneity in proliferating cell populations.

Main Methods:

  • Developed CycleVI, a deep generative model utilizing a partitioned latent representation with a circular subspace.
  • The model disentangles cell cycle variation from other transcriptional signals.

Main Results:

  • CycleVI accurately infers continuous cell cycle phase, validated by protein-level measurements.
  • The residual latent space is free of cell cycle artifacts, improving analysis of hematopoietic differentiation and drug response.
  • CycleVI isolates rather than removes cell cycle variation, providing a principled analytical framework.

Conclusions:

  • CycleVI offers a powerful new approach for analyzing scRNA-seq data from proliferating systems.
  • The method enhances the resolution of biological processes obscured by cell cycle heterogeneity.