Superloaded Multiplexed scRNA-seq Data Preserves Primary Immune Cell Heterogeneity but Necessitates Stringent Doublet Removal.

Abstract

Background: Single-cell RNA sequencing (scRNA-seq) has improved our ability to characterize rare cell populations. In practice, cells from different tissues or donors are simultaneously loaded onto the instrument (multiplexed) at the recommended (standard loading) or higher (superloading) numbers to save time and money. Although cost-effective, superloading can stymie computational analyses owing to high multiplet rates and sample complexity.

Methods: We compared the effects of superloading on multiplexed single-cell gene expression and T cell receptor (TCR) data generated from human thymus and blood samples from different donors.

Results: Minimal transcriptomic differences were observed between the data generated by either standard or superloading. Irrespective of the loading cell number, we found that over 50% of the T cells expressing multiple TCR chains were doublets.

Conclusion: Multiple samples can be run simultaneously without compromising data quality and subsequent analyses. However, an additional doublet removal step based on TCR configuration may improve the accuracy of T cell analysis.