Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling
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...
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doaj-e6c3edf6b23b4041b41e0ead257ffb4d2021-01-24T12:20:45ZengBMCBMC Genomics1471-21642021-01-0122111810.1186/s12864-020-07358-4Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profilingTracy M. Yamawaki0Daniel R. Lu1Daniel C. Ellwanger2Dev Bhatt3Paolo Manzanillo4Vanessa Arias5Hong Zhou6Oh Kyu Yoon7Oliver Homann8Songli Wang9Chi-Ming Li10Genome Analysis Unit, Amgen ResearchGenome Analysis Unit, Amgen ResearchGenome Analysis Unit, Amgen ResearchOncology/Inflammation, Amgen ResearchOncology/Inflammation, Amgen ResearchGenome Analysis Unit, Amgen ResearchGenome Analysis Unit, Amgen ResearchGenome Analysis Unit, Amgen ResearchGenome Analysis Unit, Amgen ResearchGenome Analysis Unit, Amgen ResearchGenome Analysis Unit, Amgen ResearchAbstract 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.https://doi.org/10.1186/s12864-020-07358-4Single cellTranscriptomicsSingle-cell RNA-seqHigh throughput sequencingImmune-cell profiling |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
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 |
spellingShingle |
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 Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling BMC Genomics Single cell Transcriptomics Single-cell RNA-seq High throughput sequencing Immune-cell profiling |
author_facet |
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 |
author_sort |
Tracy M. Yamawaki |
title |
Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling |
title_short |
Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling |
title_full |
Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling |
title_fullStr |
Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling |
title_full_unstemmed |
Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling |
title_sort |
systematic comparison of high-throughput single-cell rna-seq methods for immune cell profiling |
publisher |
BMC |
series |
BMC Genomics |
issn |
1471-2164 |
publishDate |
2021-01-01 |
description |
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. |
topic |
Single cell Transcriptomics Single-cell RNA-seq High throughput sequencing Immune-cell profiling |
url |
https://doi.org/10.1186/s12864-020-07358-4 |
work_keys_str_mv |
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