TL;DR
An adult synaptic ASD-associated layer and a mid-prenatal developmental-regulatory layer within predefined ASD risk-gene sets are defined and size-matched gene-level resampling indicated that the signal was not explained by gene-set size alone.
Autism spectrum disorder (ASD) arises from highly heterogeneous genetic and developmental liabilities, raising the question of whether this heterogeneity converges on shared molecular programmes in the human cerebral cortex. This structured review, based on systematic database searching and narrative synthesis, examined that question specifically in human post‐mortem cortical transcriptomic studies. PubMed, Scopus and Europe PMC were searched from 1 January 2009 to 6 May 2026, and 43 studies met the final eligibility criteria. Across the available literature, the evidence does not support a single invariant cortical transcriptomic signature in ASD. Rather, the most consistent signal indicates non‐uniform convergence on reduced neuronal and synaptic expression together with increased immune‐glial programmes. A substantial additional body of evidence implicates dysregulation of transcript‐regulatory processes, particularly in studies interrogating alternative splicing and related RNA‐processing mechanisms. Cell‐resolved datasets further suggest that these abnormalities are concentrated within defined neuronal and glial populations rather than being distributed uniformly across the cortex. By contrast, mitochondrial and broader metabolic alterations are supported less consistently and are better interpreted as conditional or secondary features of cortical pathology than as equally well‐established core axes. Interpretation of these findings is constrained by the structure of the evidence base itself. Only 9 of the 43 included studies were judged to provide direct support for the central convergence question, and only 13 were based on primary independent cohorts; much of the literature relies on dataset reuse, regionally restricted sampling and heterogeneous analytical platforms. Collectively, human cortical transcriptomic studies in ASD support a model of partial, context‐dependent convergence on a limited set of biological programmes, rather than a single stable molecular lesion.
NRXN1 haploinsufficiency is associated with coordinated downregulation of RNA-processing genes in cortical organoids, and the convergence on mRNA nuclear export and RNA-processing genes should be interpreted cautiously and verified by direct experimental perturbation.
Autism spectrum disorder (ASD) involves heterogeneous genetic and transcriptomic alterations, but how these changes are organized across hierarchical co-expression scales and linked to long noncoding RNAs (lncRNAs) remains incompletely understood. Here, we present Minimum Span Clustering Network (MSCN), an unsupervised, deterministic framework that constructs traceable multilevel mRNA co-expression hierarchies without requiring soft-thresholding powers, fixed module numbers, cut heights, or stochastic initialization. Applied to two independent ASD brain transcriptome cohorts, MSCN reveals hierarchical gene modules across four resolution levels, enabling the detection of transcriptional patterns ranging from low-level, specific signals to high-level, broader biological pathways. We uncovered modules enriched in neuronal/axonal, developmental, and immune-related pathways, reflecting interconnected neurodevelopmental and immune-dysregulation programs in ASD. Cross-method comparisons showed that MSCN complements WGCNA and MEGENA by preserving biologically concordant modules while providing an explicit parent-child hierarchy; simulation and benchmark analyses supported comparable module recovery, preservation, and enrichment performance. By mapping lncRNAs to MSCN-derived mRNA modules, we identified lncRNAs associated with ASD-related mRNA modules and evaluated them as candidate statistical mediators in downstream transcription factor (TF)-lncRNA-mRNA analyses. This analysis identified over 11,000 candidate TF-lncRNA-mRNA axes, including 644 axes involving 46 SFARI score 1 or 2 genes, four of which were syndromic genes, and 12 named lncRNA mediators. External transcriptomic evaluation further supported 789 axes, including representative HIF1A-STXBP5-AS1-CADPS, E2F1-PART1-SCN2A, and RELA-RFPL1S-GRIN2A relationships. Together, these findings establish MSCN as a scalable framework for decomposing ASD mRNA co-expression architecture, provide a hypothesis-generating resource linking coding and noncoding transcriptomic alterations, and help prioritize these relationships for future validation.
Autism gene discovery is constrained by the rarity and heterogeneity of damaging variants, requiring large cohorts to identify susceptibility genes. Neural organoids and single-cell foundation models enable perturbation modeling in neurodevelopmental contexts. Here, we show that perturbation-informed foundation modeling of neural organoids can provide functional context for prioritizing candidate genes with genomic and clinical support. We constructed a 3.6-million-cell organoid atlas and trained models to predict genome-wide perturbation responses. Benchmarking 17 models identified a telencephalic neuron-specific model best preserving autism-relevant perturbation structure. Genome-wide profiling revealed two clusters associated with mid-fetal synaptic neuronal processes and early radial glia ubiquitin signaling. These clusters were supported by damaging-variant enrichment and clinical phenotypes across 89,916 family-based samples. Logistic-regression prioritization identified 343 candidates, including 167 in the key clusters, with convergence across TADA signals and recurrent evidence for NBEA and KLHDC10. This framework integrates predicted perturbation effects with genomic evidence to support autism candidate prioritization.
FMRI data from 162 ASD and 175 TD adolescents are analyzed to demonstrate statistical associations among altered spatial-functional properties, clinical severity, and transcriptomic profiles related to synaptic signaling, mitochondrial processes, and glial-related functions in ASD, providing a complementary spatial perspective on large-scale functional organization.
Schizophrenia (SCZ) is a highly heritable and complex neuropsychiatric disorder. Emerging evidence implicates dysregulated brain iron, particularly in subcortical regions, in SCZ pathophysiology. Here, we systematically dissected the shared genetic architecture between SCZ and subcortical brain susceptibility phenotypes by integrating large-scale genome-wide association study (GWAS) data with quantitative susceptibility mapping (QSM) phenotypes across 16 subcortical regions. Using the MiXeR framework, we quantified the extent and pattern of shared genetic overlap between SCZ and each QSM phenotype, revealing the strongest overlap in the left and right nucleus accumbens. Conditional and conjunction false discovery rate analyses identified 666 unique shared SNPs. Functional annotation highlighted enrichment in pathways related to synaptic function and neuronal development, with pronounced expression in neurons. Additionally, we characterized their spatio-temporal expression patterns of shared genes and identified 89 significant expression-trait associations linked to SCZ-related phenotypes. Furthermore, the virtual drug screen identified 691 potential protein-drug pairs that may contribute to both iron regulation and SCZ. Our findings provide new insights into the complex interaction between subcortical brain susceptibility phenotypes and SCZ, highlighting pathways that may offer novel therapeutic strategies for SCZ and related brain susceptibility-associated conditions.