Integration of Mouse and Human Single-cell RNA Sequencing Infers Spatial Cell-type Composition in Human Brains

Citations Over Time

Abstract

Technical advances have enabled the identification of high-resolution cell types within tissues based on single-cell transcriptomics. However, such analyses are restricted in human brain tissue due to the limited number of brain donors. In this study, we integrate mouse and human data to predict cell-type proportions in human brain tissue, and spatially map the resulting cellular composition. By applying feature selection and linear modeling, combinations of human and mouse brain single-cell transcriptomics profiles can be integrated to "fill in" missing information. These combined "in silico chimeric" datasets are used to model the composition of nine cell types in 3,702 human brain samples in six Allen Human Brain Atlas (AHBA) donors. Cell types were spatially consistent regardless of the scRNA-Seq dataset (91% significantly correlated) or AHBA donor (p-value = 4.43E-20 by t-test) used in the model. Importantly, neuron nuclei location and neuron mRNA location were correlated only after accounting for neural connectivity (p-value = 1.26E-10), which supports the notion that gene expression is a better indicator than nuclei location of cellular localization for cells with large and irregularly shaped cell bodies, such as neurons. These results advocate for the integration of mouse and human data in models of brain tissue heterogeneity.

Related Papers

• → Towards animal surrogates for characterising large strain dynamic mechanical properties of human brain tissue(2020)42 cited
• → Spatial cell type composition in normal and Alzheimers human brains is revealed using integrated mouse and human single cell RNA sequencing(2020)21 cited
• → The brain-mind quiddity: ethical issues in the use of human brain tissue for therapeutic and scientific purposes.(1998)10 cited
• Spatial cell type composition in normal and Alzheimers human brains is revealed using integrated mouse and human single cell RNA sequencing(2020)
• → LRcell: detecting the source of differential expression at the sub-cell type level from bulk RNA-seq data(2021)1 cited