Abstract
Autopsy-derived brain tissue analysis is crucial for understanding neurobiology, but post-mortem handling can introduce artifacts. We studied adult human brain transcriptomic signatures from tissue immediately extracted from brains (< 0 hours) and compared to autopsy brain tissue with short (~6 hours) and long (~36 hours) post-mortem intervals (PMIs). Significant deviations in gene signatures were observed in both short and long PMIs compared to immediately extracted tissue, which we defined as Brain Artifact Genes (BAGs). By subjecting brain samples to processing variables that are unavoidable in autopsy programs (post-mortem time and temperature), we characterized a set of artifact-responsive genes and mapped this signature onto matched single-nucleus RNA-seq data, revealing that it was predominantly glutamatergic neurons that exhibited the earliest induction of artifact genes followed by oligodendrocytes later. Using deep learning, we distilled this broader processing-response program into a predictive signature, called Time and Temperature Response genes Underlying Transcriptional Heterogeneity (TTRUTH) and provide an Open Science tool for assigning TTRUTH scores to brain RNA-seq data. Together, this work will help better standardize datasets, enable additional sample stratification, and enhance data interpretation.
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The raw data of this study uploaded under this project (https://www.synapse.org/Synapse:syn52658339), Source data supporting the findings of this study are provided with this paper. Source data are provided with this paper.
Code availability
The custom code used for this paper is available on GitHub at: https://github.com/myaqubi/TTRUTH_project.git.
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Acknowledgements
We thank HBHL funding (3a-08, McGill), and the donors and their families for the use of human brain tissue in this study and the staff of the contributing brain banks for making these samples available. Tissue samples were provided by the London Neurodegenerative Diseases Brain Bank (King’s College London), the Newcastle Brain Tissue Resource, the Queen’s Square Brain Bank (UCL), the Manchester Brain Bank, the Oxford Brain Bank, the Parkinson’s UK Brain Bank at Imperial, the South West Dementia Brain Bank, the Edinburgh Brain Bank, and the Netherlands Brain Bank (NIN, Amsterdam). These brain banks are supported by the UK Medical Research Council, the National Institute for Health and Care Research (NIHR) Biomedical Research Centres, and the Brains for Dementia Research programme, jointly funded by Alzheimer’s Research UK and Alzheimer’s Society. The Parkinson’s UK Brain Bank is funded by Parkinson’s UK. Infrastructure support, including the Imperial BRC Genomics Facility, was provided by the NIHR Biomedical Research Centre. P.M.M. acknowledges support from the Edmond J. Safra Foundation, Lily Safra, an NIHR Senior Investigator Award, and the UK Dementia Research Institute, funded by the UK Medical Research Council, Alzheimer’s Society, and Alzheimer’s Research UK. J.S.J. was supported by Alzheimer’s Society (grant 599; AS-DRL-22-008). We thank Andrew Leduc for suggesting the TTRUTH acronym. We are grateful to Diana P. Benitez for her support in the human tissue management.
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M.Y.: analysis of the transcriptomic data (bulk and single nucleus), making all figures, writing the paper, M.T.: bulk RNA-seq data analysis, J.T. and N.F.: single-nuc RNA-seq data analysis, A.M.: machine-learning analysis, A.A.: Open Website developer, A.G.: surgery-derived sample processing, M.P., X.Z., and A.M. autopsy sample processing, P.M.: conceive the study and edit the paper, K.P.: provide surgery samples and write the paper, J.S.J. and J.S.: conceive the study, write the paper, make the figures.
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Yaqubi, M., Thomas, M., Talbot-Martin, J. et al. Characterising processing conditions that artifactually bias human brain tissue transcriptomes. Nat Commun (2026). https://doi.org/10.1038/s41467-026-68872-9
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DOI: https://doi.org/10.1038/s41467-026-68872-9
