Characterizing cell type composition in human samples by integrating mouse and human single cell RNA sequencing data

Single-cell RNA sequencing (scRNA-seq) resolves heterogenous cell populations in tissues and helps to reveal single-cell level function and dynamics. In neuroscience, the rarity of brain tissue is the bottleneck for such study. Evidence shows that, mouse and human share similar cell type gene markers.

A team of researchers from The Ohio State University, Indiana University School of Medicine and Purdue University hypothesized that the scRNA-seq data of mouse brain tissue can be used to complete human data to infer cell type composition in human samples. The research team supplemented cell type information of human scRNA-seq data, with mouse. The resulted data were used to infer the spatial cellular composition of 3702 human brain samples from Allen Human Brain Atlas. The team then mapped the cell types back to corresponding brain regions. Most cell types were localized to the correct regions. They also compared the mapping results to those derived from neuronal nuclei locations. They were consistent after accounting for changes in neural connectivity between regions. Furthermore, the research team applied this approach on Alzheimer’s brain data and successfully captured cell pattern changes in AD brains. They believe this integrative approach can solve the sample rarity issue in the neuroscience.

The classic characterization of cell types from RNA-Seq data

(A-1) and (A-2) are conventional workflows for cell type specific expression acquisition before the advent of scRNA-Seq. (A-3) is the current state of the art workflow, and was chosen as the basis for cell type characterization in this study. (B) The workflow in this study. MusNG mouse neuron and glia cell scRNA-seq dataset, HumN human neuronal cell scRNA-seq dataset, HumNG human neuronal and glia cell scRNA-seq dataset.