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 evaluate 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 drop-out 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.
Competing Interest Statement
The authors have read the journal's policy and have the following conflicts: Tracy M. Yamawaki, Daniel Lu, Daniel C. Ellwanger, Hong Zhou, Oliver Homann, Songli Wang, and Chi-Ming Li are employees at Amgen Inc. Oh-Kyu Yoon employed by Amgen Inc. while working on the study. All authors owned Amgen shares when the experiments were carried out. However, these do not alter the authors' adherence to all the journal policies on sharing data and material.
Footnotes
Tracy M. Yamawaki: tyamawak{at}amgen.com
Daniel Lu: dlu{at}amgen.com
Daniel C. Ellwanger: dellwang{at}amgen.com
Dev Bhatt: dbhatt01{at}amgen.com
Paolo Manzanillo: pmanzani{at}amgen.com
Hong Zhou: hzhou{at}amgen.com
Oh-Kyu Yoon: Fyzkem{at}gmail.com
Oliver Homann: ohomann{at}amgen.com
Songli Wang: songliw{at}amgen.com
List of abbreviations
- RNA-seq
- RNA sequencing
- PBMC
- Peripheral blood mononuclear cell
- DE
- Differentially-expressed
- UMI
- Unique Molecular Identifier
- CID
- Cell identifier
- cDNA
- Complementary DNA
- mRNA
- Messenger RNA
- PCR
- Polymerization chain reaction
- UTR
- Untranslated region
- GD50
- Gene detection 50
- FPKM
- Fragments per kilobase of transcript per million mapped reads
- TCR
- T-cell receptor
- BCR
- B-cell receptor