PLoS Biology (Apr 2023)

A comparison of anatomic and cellular transcriptome structures across 40 human brain diseases

  • Yashar Zeighami,
  • Trygve E. Bakken,
  • Thomas Nickl-Jockschat,
  • Zeru Peterson,
  • Anil G. Jegga,
  • Jeremy A. Miller,
  • Jay Schulkin,
  • Alan C. Evans,
  • Ed S. Lein,
  • Michael Hawrylycz

Journal volume & issue
Vol. 21, no. 4

Abstract

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Genes associated with risk for brain disease exhibit characteristic expression patterns that reflect both anatomical and cell type relationships. Brain-wide transcriptomic patterns of disease risk genes provide a molecular-based signature, based on differential co-expression, that is often unique to that disease. Brain diseases can be compared and aggregated based on the similarity of their signatures which often associates diseases from diverse phenotypic classes. Analysis of 40 common human brain diseases identifies 5 major transcriptional patterns, representing tumor-related, neurodegenerative, psychiatric and substance abuse, and 2 mixed groups of diseases affecting basal ganglia and hypothalamus. Further, for diseases with enriched expression in cortex, single-nucleus data in the middle temporal gyrus (MTG) exhibits a cell type expression gradient separating neurodegenerative, psychiatric, and substance abuse diseases, with unique excitatory cell type expression differentiating psychiatric diseases. Through mapping of homologous cell types between mouse and human, most disease risk genes are found to act in common cell types, while having species-specific expression in those types and preserving similar phenotypic classification within species. These results describe structural and cellular transcriptomic relationships of disease risk genes in the adult brain and provide a molecular-based strategy for classifying and comparing diseases, potentially identifying novel disease relationships. Analysis of the transcription patterns of risk genes for human brain disease reveals characteristic expression signatures across brain anatomy; these can be used to compare and aggregate diseases, providing associations that often differ from conventional phenotypic classification.