Scholarlink

<p>Autopsy-derived brain tissue analysis is crucial for understanding neurobiology, but <i>post-mortem</i> handling can introduce artifacts. We studied adult human brain transcriptomic signatures from tissue immediately extracted from brains (&lt; 0 hours) and compared to autopsy brain tissue with short (~6 hours) and long (~36 hours) <i>post-mortem</i> intervals&#xa0;(PMIs). Significant deviations in gene signatures were observed in both short and long PMIs compared to&#xa0;immediately extracted tissue, which we defined as <b>B</b>rain <b>A</b>rtifact <b>G</b>enes (BAGs). By subjecting brain samples to processing variables that are unavoidable in autopsy programs (<i>post-mortem</i> time and temperature), we characterized&#xa0;a set of artifact-responsive genes and mapped this signature&#xa0;onto matched&#xa0;single-nucleus RNA-seq data,&#xa0;revealing that it was predominantly glutamatergic neurons that exhibited the earliest induction of artifact genes followed by oligodendrocytes later.&#xa0;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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Characterising processing conditions that artifactually bias human brain tissue transcriptomes

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月17日

查看全文
  • Moein Yaqubi,
  • Michael Thomas,
  • Jonathan Talbot-Martin,
  • Abdulshakour Mohammadnia,
  • Alexis Allot,
  • Adam M. R. Groh,
  • Nurun Fancy,
  • Marianna Papageorgopoulou,
  • Xiaowen Zhang,
  • Aisling McGarry,
  • Paul M. Matthews,
  • Kevin Petrecca,
  • Johanna S. Jackson,
  • Jo Anne Stratton

摘要

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.