Evidence map›Paper›PMID 41702898›Full record

ArticleNature communications2026

Characterising processing conditions that artifactually bias human brain tissue transcriptomes.

Moein Yaqubi, Michael Thomas, Jonathan Talbot-Martin, Abdulshakour Mohammadnia, Alexis Allot, Adam M R Groh, Nurun Fancy, Marianna Papageorgopoulou, Xiaowen Zhang, Aisling McGarry and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Moein YaqubiNeuroimmunology Unit, Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada.ORCID http://orcid.org/0000-0001-7299-5930
Michael ThomasUK Dementia Research Institute at Imperial College London, London, UK.
Jonathan Talbot-MartinUK Dementia Research Institute at Imperial College London, London, UK.
Abdulshakour MohammadniaNeuroimmunology Unit, Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada.
Alexis AllotNeuroimmunology Unit, Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada.
Adam M R GrohNeuroimmunology Unit, Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada.
Nurun FancyUK Dementia Research Institute at Imperial College London, London, UK.ORCID http://orcid.org/0000-0002-6481-6266
Marianna PapageorgopoulouUK Dementia Research Institute at Imperial College London, London, UK.
Xiaowen ZhangUK Dementia Research Institute at Imperial College London, London, UK.
Aisling McGarryUK Dementia Research Institute at Imperial College London, London, UK.
Paul M MatthewsUK Dementia Research Institute at Imperial College London, London, UK.ORCID http://orcid.org/0000-0002-1619-8328
Kevin PetreccaDepartment of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada.ORCID http://orcid.org/0000-0002-8140-6402
Johanna S JacksonUK Dementia Research Institute at Imperial College London, London, UK. johanna.jackson@imperial.ac.uk.ORCID http://orcid.org/0000-0002-8459-0591
Jo Anne StrattonNeuroimmunology Unit, Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada. jo.stratton@mcgill.ca.ORCID http://orcid.org/0000-0002-1205-1353

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

ArtifactsBrainTranscriptomeAdultAutopsyFemaleGene Expression ProfilingHumansNeuronsOligodendrogliaRNA-SeqTime Factors

Identifiers

PMID41702898
PMCPMC13021938

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.