Analysis of alternative polyadenylation from single-cell RNA-seq using scDaPars reveals cell subpopulations invisible to gene expression
- 1Graduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, Texas 77030, USA;
- 2Department of Medicine, Baylor College of Medicine, Houston, Texas 77030, USA;
- 3Division of Computational Biomedicine, Department of Biological Chemistry, School of Medicine, University of California at Irvine, Irvine, California 92697, USA
Abstract
Alternative polyadenylation (APA) is a major mechanism of post-transcriptional regulation in various cellular processes including cell proliferation and differentiation, but the APA heterogeneity among single cells remains largely unknown. Single-cell RNA sequencing (scRNA-seq) has been extensively used to define cell subpopulations at the transcription level. Yet, most scRNA-seq data have not been analyzed in an “APA-aware” manner. Here, we introduce dynamic analysis of APA from single-cell RNA-seq (scDaPars), a bioinformatics algorithm to accurately quantify APA events at both single-cell and single-gene resolution using either 3′-end (10x Chromium) or full-length (Smart-seq2) scRNA-seq data. Validations in both real and simulated data indicate that scDaPars can robustly recover missing APA events caused by the low amounts of mRNA sequenced in single cells. When applied to cancer and human endoderm differentiation data, scDaPars not only revealed cell-type-specific APA regulation but also identified cell subpopulations that are otherwise invisible to conventional gene expression analysis. Thus, scDaPars will enable us to understand cellular heterogeneity at the post-transcriptional APA level.
Footnotes
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[Supplemental material is available for this article.]
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Article published online before print. Article, supplemental material, and publication date are at https://www.genome.org/cgi/doi/10.1101/gr.271346.120.
- Received September 23, 2020.
- Accepted April 20, 2021.
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