Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysis
- Chiaowen Joyce Hsiao1,5,
- PoYuan Tung2,5,
- John D. Blischak1,
- Jonathan E. Burnett1,
- Kenneth A. Barr2,
- Kushal K. Dey3,
- Matthew Stephens1,4 and
- Yoav Gilad1,2
- 1Department of Human Genetics, University of Chicago, Chicago, Illinois 60637, USA;
- 2Department of Medicine, University of Chicago, Chicago, Illinois 60637, USA;
- 3Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts 02115, USA;
- 4Department of Statistics, University of Chicago, Chicago, Illinois 60637, USA
-
↵5 These authors contributed equally to this work.
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). By using these data, we developed a novel approach to characterize cell cycle progression. Although 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.
Footnotes
-
[Supplemental material is available for this article.]
-
Article published online before print. Article, supplemental material, and publication date are at http://www.genome.org/cgi/doi/10.1101/gr.247759.118.
-
Freely available online through the Genome Research Open Access option.
- Received February 3, 2019.
- Accepted April 2, 2020.
This article, published in Genome Research, is available under a Creative Commons License (Attribution 4.0 International), as described at http://creativecommons.org/licenses/by/4.0/.
