A common approach to understanding neurodegenerative disease is comparing gene expression in diseased versus healthy tissues. We illustrate that expression profiles derived from whole tissue RNA highly reflect the degenerating tissues’ altered cellular composition, not necessarily transcriptional regulation. To accurately understand transcriptional changes that accompany neuropathology, we acutely purify neurons, astrocytes and microglia from single adult mouse brains and analyse their transcriptomes by RNA sequencing. Using peripheral endotoxemia to establish the method, we reveal highly specific transcriptional responses and altered RNA processing in each cell type, with Tnfr1 required for the astrocytic response. Extending the method to an Alzheimer’s disease model, we confirm that transcriptomic changes observed in whole tissue are driven primarily by cell type composition, not transcriptional regulation, and identify hundreds of cell type-specific changes undetected in whole tissue RNA. Applying similar methods to additional models and patient tissues will transform our understanding of aberrant gene expression in neurological disease. Whole tissue RNA profiling can help identify altered molecular pathways underlying neurodegenerative disease, but often masks cell type-specific transcriptional changes. Here, the authors compare transcriptomes of neurons, astrocytes, and microglia from Alzheimer's disease model brains and identify hundreds of cell-type specific changes.
Untangling the brain's neuroinflammatory and neurodegenerative transcriptional responses
Karpagam Srinivasan,Brad A. Friedman,Jessica L. Larson,Benjamin E. L. Lauffer,Leonard D. Goldstein,Laurie L Appling,Jovencio B Borneo,Ch Poon,T. Ho,Fang Cai,P. Steiner,M. P. van der Brug,Z. Modrusan,J. Kaminker,David V. Hansen
Published 2016 in Nature Communications
ABSTRACT
PUBLICATION RECORD
- Publication year
2016
- Venue
Nature Communications
- Publication date
2016-04-21
- Fields of study
Biology, Medicine
- Identifiers
- External record
- Source metadata
Semantic Scholar, PubMed
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