RT Journal Article
SR Electronic
T1 Integration of Mouse and Human Single-cell RNA Sequencing Infers Spatial Cell-type Composition in Human Brains
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 527499
DO 10.1101/527499
A1 Travis S Johnson
A1 Zachary B Abrams
A1 Bryan R Helm
A1 Peter Neidecker
A1 Raghu Machiraju
A1 Yan Zhang
A1 Kun Huang
A1 Jie Zhang
YR 2019
UL http://biorxiv.org/content/early/2019/01/22/527499.abstract
AB 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.