Accurate genotype-based demultiplexing of single cell RNA sequencing samples from non-human animals

Single cell sequencing technologies (scRNA-seq, scATAC-seq, etc.) have revolutionized the study of complex tissues and unique organisms, providing researchers with a much needed species agnostic tool to study biological processes at the cellular level. To date, scRNA-seq technologies are expensive, require sufficient cell quantities, and need biological replicates to avoid batch effects or artifactual results. Pooling cells from multiple individuals into a single scRNA-seq library can address these problems. However, sample labeling protocols for facilitating the computational separation of pooled scRNA-seq samples, termed demultiplexing, have undesirable limitations, particularly in resource-limited organisms. One promising solution developed for use in humans exploits the genetic diversity between individuals (i.e., single nucleotide polymorphisms (SNP)) to demultiplex pooled scRNA-seq samples. The use of SNP-based demultiplexing methods has not been validated for use in non-human species, but the widespread use of SNP-based demuxers would greatly facilitate research in commonly used, emerging, and more obscure species. In this study we applied SNP-based demultiplexing algorithms to pooled scRNA-seq datasets from numerous species and applied diverse ground truth confirmation assays to validate genetic demultiplexing results. SNP-based demultiplexers were found to accurately demultiplex pooled scRNA-seq data from species including zebrafish, African green monkey, Xenopus laevis, axolotl, Pleurodeles waltl, and Notophthalmus viridescens. Our results demonstrate that SNP-based demultiplexing of unlabeled, pooled scRNA-seq samples can be used with confidence in all of the species studied in this work. Further, we show that the only genomic resource required for this approach is the single-cell sequencing data and a de novo transcriptome. The incorporation of pooling and SNP-demultiplexing into scRNA-seq study designs will greatly increase the reproducibility and experimental options for studying species previously limited by technical uncertainties, computational hurdles, or limited cell quantities.