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Life sciences · Preprint
arXiv · September 8, 2026
Early or partial results. Treat as a signal, not a conclusion.
This preprint reports a transformer-based encoder for psilocybin-induced differential gene expression at single-nucleus resolution, achieving 69.4% weighted accuracy and identifying cell-type-specific and individual-level heterogeneity in transcriptional response. Three novel findings are reported (stereotypy of downregulation, cortical depth gradient, unsupervised module recovery), and one published hypothesis about HTR2A-gating is contradicted; however, the work is neither peer-reviewed nor validated in an independent cohort, and lacks clinical outcome data.
Machine learning model development and retrospective validation study (computational). Single-nucleus RNA-sequencing data from psilocybin-treated tissue (Liao et al. 2025 dataset); 18 cell types including L2/3 IT excitatory neurons (primary HTR2A target), endothelial cells, and other CNS compartments; 2 drug conditions and 6 timepoints.. Intervention: Psilocybin (inferred from context; drug conditions not explicitly named). Compared with: Untreated or vehicle control (inferred; not explicitly stated). n = 623.
Overall weighted classification accuracy 69.4% for upregulated/downregulated/neutral status across 623 pseudobulk examples Per-cell-type accuracy ranges from 28.3% (L2/3 IT neurons) to 99.6% (endothelial cells), aligning with known psilocybin target expression Psilocybin-induced transcriptional downregulation significantly more stereotyped across individuals than upregulation (Mann-Whitney U=18615.0, p<0.0001)
Classification accuracy does not correlate to clinical phenotypes (efficacy, adverse events, or individual drug response)
If validated in independent cohorts with clinical phenotyping, this approach could stratify individuals by cell-type-specific transcriptional response to psilocybin and improve mechanistic understanding of variable drug efficacy. Currently, the lack of peer review, single-dataset training, and absence of clinical outcome linkage prevent clinical translation.
First-in-human application of a transformer model to psilocybin transcriptomics in a single dataset; computational validation without independent replication or clinical outcome correlation; preprint status.
As stated by the source record.
Quoted from the source exactly as published.
If validated in independent cohorts with clinical phenotyping, this approach could stratify individuals by cell-type-specific transcriptional response to psilocybin and improve mechanistic understanding of variable drug efficacy. Currently, the lack of peer review, single-dataset training, and absence of clinical outcome linkage prevent clinical translation.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
Understanding why individuals respond differently to psilocybin requires modeling the drug's transcriptional perturbation signature at the cell-type level. I present a Transformer-based delta expression encoder that learns to classify differential gene expression status - upregulated, downregulated, or neutral - from single-nucleus RNA-sequencing data, without supervision from pathway annotations or prior biological knowledge. The model is trained on pseudobulk profiles from 623 examples spanning 18 cell types, 2 drug conditions, and 6 timepoints derived from the Liao et al. 2025 dataset, and achieves 69.4% weighted classification accuracy. Three principal findings are reported, alongside one direct test of a published hypothesis that returned a result inconsistent with that hypothesis. First, per-cell-type classification accuracy ranges from 28.3% (L2/3 IT, a primary HTR2A-expressing psilocybin target) to 99.6% (endothelial cells), consistent with known psilocybin response biology. Second, psilocybin-induced transcriptional downregulation is significantly more stereotyped across individuals than upregulation (Mann-Whitney U=18615.0, p<0.0001), a novel finding with a cortical depth gradient across excitatory subtypes. Third, attention-guided gene co-regulation analysis recovers drug-specific modules without pathway supervision. Separately, a direct test of whether baseline HTR2A expression predicts drug-response separability across cell types found a significant negative correlation (Spearman r = -0.7088, p = 0.0021), the opposite of what a simple HTR2A-gating account would predict.
Taken from the source record, never inferred. Follow any of these and new work involving them reaches your briefing.