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 singlecell
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 immunecell
heterogeneity usually found in vivo.