Modification of Seurat v4 for the development of a phase assignment tool able to distinguish between G2 and Mitotic cells
Steven Watson, Harry Porter, Ian M. SudberyiD, Ruth H. ThompsoniD
Abstract
Single cell RNA sequencing (scRNAseq) is a rapidly advancing field which allows for the characterization of the cellular heterogeneity of gene expression profiles within a population. Cell cycle phase is a major contributor to gene expression variance between cells and computational analysis tools have been developed to assign cell cycle phase to scRNAseq datasets. Whilst these tools can be extremely useful, all have the drawback that they classify cells as G1, S or G2/M. Discrete cell phase assignment tool are unable to differentiate between G2 and M and continuous phase assignments tools are unable to identify a region corresponding specifically to mitosis in a pseudo-timeline for continuous assignment along the cell cycle. Bulk RNA sequencing was used to identify differentially expressed genes between mitotic and interphase cells isolated based on phospho-histone H3 expression using fluorescence activated cell sorting. The gene lists were used to develop a Modified Seurat Mitotic Sort (MoSMiS) methodology which can distinguish G2 and M phase cells in single cell RNA sequencing data. The phase assignment tools present in Seurat were modified to allow for cell cycle phase assignment of all stages of the cell cycle identifying a mitotic specific cell population.
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 6
- FWCI
- 0.81
- Field percentile
- top 33%
- Organism
- Assay
- Research Focus
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Computational cell cycle analysis of single cell RNA-Seq data
Marmar R. Moussa, Ion Măndoiu·bioRxiv·Nov 21, 2020
- Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysis
Chiaowen Joyce Hsiao, Po‐Yuan Tung, … Yoav Gilad·Genome ResearchPeer-reviewed·Apr 1, 2020
- CycleVI: Isolating cell cycle variation with an interpretable deep generative model
Pia Mozdzanowski, Marcel Tarbier, Gustavo S. Jeuken·BioinformaticsPeer-reviewed·Jun 1, 2026
- CycleMix: Gaussian Mixture Modeling of the Cell Cycle
Jack E. Peplinski, Tallulah S. Andrews·Bioinformatics AdvancesPeer-reviewed·Jan 1, 2026
- Cell cycle gene regulation dynamics revealed by RNA velocity and deep-learning
Andrea Riba, Attila Oravecz, … Nacho Molina·Nature CommunicationsPeer-reviewed·May 23, 2022
- Beyond comparisons of means: understanding changes in gene expression at the single-cell level
Catalina A Vallejos, Sylvia Richardson, John C Marioni·bioRxiv·Jan 5, 2016