ReviewMolecular psychiatry2026
Single-cell transcriptomics of human brain disorders.
Review in Molecular psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
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.
Who cites it
0 citing papers in PubMed.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain disease is ultimately a disease of its constituent cells, and single-cell genomics has recently begun to illuminate the cellular pathologies underlying human brain disorders. In this review, we synthesize the key biological insights from recent single-cell studies of post-mortem human brain tissue across a range of neurological and psychiatric conditions. Organized by major central nervous system cell type, we detail the depletion of vulnerable cellular populations, the emergence of disease-associated states, the dysregulation of gene expression programs, and the interplay between these changes and genetic risk factors. Collectively, these studies mark a turning point in brain disease research, providing the first comprehensive maps of how neurological and psychiatric disorders manifest across the cellular landscape of the human brain.
Indexed as
Identifiers
42009984What Socratic holds
Registered trials
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 Socratic graph.