Systematic Comparison of High-throughput Single-cell RNA-seq Methods for Immune Cell Profiling

Overview

Journal BMC Genomics

Publisher Biomed Central

Specialty Genetics

Date 2021 Jan 21

PMID 33472597

Citations 31

Authors

Tracy M Yamawaki

Daniel R Lu

Daniel C Ellwanger

Dev Bhatt

Paolo Manzanillo

Vanessa Arias

Hong Zhou

Oh Kyu Yoon

Oliver Homann

Songli Wang

Chi-Ming Li

Affiliations

Soon will be listed here.

Abstract

Background: Elucidation of immune populations with single-cell RNA-seq has greatly benefited the field of immunology by deepening the characterization of immune heterogeneity and leading to the discovery of new subtypes. However, single-cell methods inherently suffer from limitations in the recovery of complete transcriptomes due to the prevalence of cellular and transcriptional dropout events. This issue is often compounded by limited sample availability and limited prior knowledge of heterogeneity, which can confound data interpretation.

Results: Here, we systematically benchmarked seven high-throughput single-cell RNA-seq methods. We prepared 21 libraries under identical conditions of a defined mixture of two human and two murine lymphocyte cell lines, simulating heterogeneity across immune-cell types and cell sizes. We evaluated methods by their cell recovery rate, library efficiency, sensitivity, and ability to recover expression signatures for each cell type. We observed higher mRNA detection sensitivity with the 10x Genomics 5' v1 and 3' v3 methods. We demonstrate that these methods have fewer dropout events, which facilitates the identification of differentially-expressed genes and improves the concordance of single-cell profiles to immune bulk RNA-seq signatures.

Conclusion: Overall, our characterization of immune cell mixtures provides useful metrics, which can guide selection of a high-throughput single-cell RNA-seq method for profiling more complex immune-cell heterogeneity usually found in vivo.

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