Characterising processing conditions that artifactually bias human brain tissue transcriptomes
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Autopsy-derived brain tissue analysis is crucial for understanding neurobiology, but post 31 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. Significant deviations in transcriptomic signatures were observed in both short and long post-mortem intervals compared to 0h, which we defined as Brain Artifact Genes (BAGs). By subjecting brain samples to processing variables that are unavoidable in autopsy programs (post-mortem interval time and temperature), snRNAseq data revealed neuronal populations initially express these artifactual genes, followed by oligodendrocytes. Using deep learning, we discovered a precise set of artifact genes in the human brain that we defined as "Time and Temperature Responses genes Underlying Transcriptional Heterogeneity (TTRUTH)", and offer an Open Science tool for assigning TTRUTH scores to brain RNAseq data. Together, this work will help better standardise datasets, enable additional sample stratification and enhance data interpretation.
autopsy brain tissue with short (~6 hours) and long (~36 hours) post-mortem intervals. Significant deviations in transcriptomic signatures were observed in both short and long post-mortem intervals compared to 0h, which we defined as Brain Artifact Genes (BAGs). By subjecting brain samples to processing variables that are unavoidable in autopsy programs (post-mortem interval time and temperature), snRNAseq data revealed neuronal populations initially express these artifactual genes, followed by oligodendrocytes. Using deep learning, we discovered a precise set of artifact genes in the human brain that we defined as "Time and Temperature Responses genes Underlying Transcriptional Heterogeneity (TTRUTH)", and offer an Open Science tool for assigning TTRUTH scores to brain RNAseq data. Together, this work will help better standardise datasets, enable additional sample stratification and enhance data interpretation.
Date Issued
2026-03-26
Date Acceptance
2025-12-22
Citation
Nature Communications, 2026, 17
ISSN
2041-1723
Publisher
Nature Portfolio
Journal / Book Title
Nature Communications
Volume
17
Copyright Statement
This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Identifier
10.1038/s41467-026-68872-9
Publication Status
Published
Article Number
ARTN 2848
Date Publish Online
2026-02-17