Comparative analysis of single-cell RNA sequencing methods with and without sample multiplexing
Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful technique for investigating biological heterogeneity at the single-cell level in human systems and model organisms. Recent advances in scRNA-seq have enabled the pooling of cells from multiple samples into single libraries, thereby increasing sample throughput while reducing technical batch effects, library preparation time, and the overall cost. However, a comparative analysis of scRNA-seq methods with and without sample multiplexing is lacking. In this study, we benchmarked methods from two representative platforms: Parse Biosciences (Parse; with sample multiplexing) and 10X Genomics (10x; without sample multiplexing). By using peripheral blood mononuclear cells (PBMCs) obtained from two healthy individuals, we demonstrate that demultiplexed scRNA-seq data obtained from Parse showed similar cell type frequencies compared to 10X data where samples are not multiplexed. Despite a relatively lower library and cell capture efficiencies, Parse can detect rare cell types (e.g., plasmablasts and dendritic cells) which is likely due to its relatively higher sensitivity in gene detection. Moreover, comparative analysis of transcript quantification between the two platforms revealed platform-specific distributions of gene length and GC content. These results offer guidance for researchers in designing high-throughput scRNA-seq studies. Overall design: We benchmarked protocols (Parse Evercode WT v2 and 10x 3' v3.1) from Parse and 10x platforms using PBMCs from two healthy donors. Human PBMCs from two healthy donors (designated here as H1 and H2) were prepared in a single batch and distributed into two aliquots. Aliquot 1 was used for 10x library preparation without multiplexing H1 and H2 samples. Aliquot 2 was used for Parse library preparation by multiplexing H1 and H2 with nine other samples in a single library. All libraries were sequenced together to minimize differences in sequencing depth. **Notes for data downloading GSE246624_H03_DGE_filtered.tar.gz  corresponds to Parse_H1 in the paper GSE246624_H04_DGE_filtered.tar.gz corresponds to Parse_H2 in the paper GSE246624_Raw.tar corresponds to 10x_H1, 10x_H2 in the paper