Cross-species cell type annotation of single cell RNA-Seq data

Abstract

Single cell RNA sequencing (scRNA-Seq) has enabled the parallel cell-specific transcriptional profiling revealing new insights about cell types within tissues. Clustering of scRNA-Seq data enables identification of cell types in heterogeneous cell populations, which can be used in downstream analyses of scRNA-Seq data. Dimension reduction and clustering of scRNA-Seq data is followed by cell type annotation and involves examination of known cell markers, which is a labour intensive task. Recently, manually curated database of cell markers for human and mouse was published (Zhang et al., 2018) representing a valuable resource for cell type classification using single cell transcriptomic data. In the current study, we used cell-marker database and scRNA-Seq data from published studies on rat (pineal gland) (Mays et al., 2018), bovine (embryos) (Lavagi et al., 2018), chimpanzee and bonobo (neural cells) (Marchetto et al., 2019). Unsupervised clustering and semi-supervised learning approaches were applied to classify cell types based on conserved orthologous gene expression across species. We classified cell types in different species and developed an approach for automated annotation of cell types in non model organisms. The authors would like to acknowledge the contribution of the COST Action CA15112 - Functional Annotation of Animal Genomes European network (FAANG-Europe).