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Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysis

View ORCID ProfileChiaowen Joyce Hsiao, PoYuan Tung, View ORCID ProfileJohn D. Blischak, Jonathan E. Burnett, View ORCID ProfileKenneth Barr, Kushal K. Dey, View ORCID ProfileMatthew Stephens, View ORCID ProfileYoav Gilad
doi: https://doi.org/10.1101/526848
Chiaowen Joyce Hsiao
Department of Human Genetics, University of Chicago
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PoYuan Tung
Department of Medicine, University of Chicago
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John D. Blischak
Department of Human Genetics, University of Chicago
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Jonathan E. Burnett
Department of Human Genetics, University of Chicago
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Kenneth Barr
Department of Medicine, University of Chicago
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Kushal K. Dey
Department of Statistics, University of Chicago
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Matthew Stephens
Department of Human Genetics, University of ChicagoDepartment of Statistics, University of Chicago
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Yoav Gilad
Department of Human Genetics, University of ChicagoDepartment of Medicine, University of Chicago
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Abstract

Cellular heterogeneity in gene expression is driven by cellular processes such as cell cycle and cell-type identity, and cellular environment such as spatial location. The cell cycle, in particular, is thought to be a key driver of cell-to-cell heterogeneity in gene expression, even in otherwise homogeneous cell populations. Recent advances in single-cell RNA-sequencing (scRNA-seq) facilitate detailed characterization of gene expression heterogeneity, and can thus shed new light on the processes driving heterogeneity. Here, we combined fluorescence imaging with scRNA-seq to measure cell cycle phase and gene expression levels in human induced pluripotent stem cells (iPSCs). Using these data, we developed a novel approach to characterize cell cycle progression. While standard methods assign cells to discrete cell cycle stages, our method goes beyond this, and quantifies cell cycle progression on a continuum. We found that, on average, scRNA-seq data from only five genes predicted a cell’s position on the cell cycle continuum to within 14% of the entire cycle, and that using more genes did not improve this accuracy. Our data and predictor of cell cycle phase can directly help future studies to account for cell-cycle-related heterogeneity in iPSCs. Our results and methods also provide a foundation for future work to characterize the effects of the cell cycle on expression heterogeneity in other cell types.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted March 05, 2019.
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Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysis
Chiaowen Joyce Hsiao, PoYuan Tung, John D. Blischak, Jonathan E. Burnett, Kenneth Barr, Kushal K. Dey, Matthew Stephens, Yoav Gilad
bioRxiv 526848; doi: https://doi.org/10.1101/526848
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Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysis
Chiaowen Joyce Hsiao, PoYuan Tung, John D. Blischak, Jonathan E. Burnett, Kenneth Barr, Kushal K. Dey, Matthew Stephens, Yoav Gilad
bioRxiv 526848; doi: https://doi.org/10.1101/526848

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