Evaluation of genetic demultiplexing of single-cell sequencing data from model species

Single-cell sequencing (sc-seq) provides a species agnostic tool to study cellular processes. However, these technologies are expensive and require sufficient cell quantities and biological replicates to avoid artifactual results. An option to address these problems is pooling cells from multiple individuals into one sc-seq library. In humans, genotype-based computational separa-tion (i.e., demultiplexing) of pooled sc-seq samples is common. This approach would be instrumental for studying non-isogenic model organisms. We set out to determine whether genotype based demultiplexing could be more broadly applied among species ranging from zebrafish to non-human primates. Using such non-isogenic species, we benchmark genotype-based demultiplexing of pooled sc-seq datasets against various ground truths. We demonstrate that genotype-based demulti-plexing of pooled sc-seq samples can be used with confidence several non-isogenic model organisms and uncover limitations this method. Importantly, the only genomic resource required for this approach is sc-seq data and a de novo transcriptome. The incorporation of pooling into sc-seq study designs will decrease cost while simultaneously increasing the reproducibility and experimental options in non-isogenic model organisms.

Automated summary

Single-cell sequencing (sc-seq) is a useful tool for studying cellular processes, but its high cost and the need for sufficient cell quantities and replicates pose challenges. One option is pooling cells from multiple individuals into one sc-seq library. In this study, we validate the application of genotype-based demultiplexing for pooled sc-seq data across non-isogenic model organisms, identifying limitations and demonstrating its potential benefits.