BrainEnrich is introduced, an R package that integrates whole‐brain gene expression profiles from the Allen Human Brain Atlas with in vivo imaging‐derived phenotypes (IDPs) and provides a flexible framework for integrating macro‐level IDPs with micro‐level transcriptomic profiles for molecular contextualization of IDPs.
Abstract
While the field of imaging transcriptomics is evolving rapidly, several methodological challenges persist in functional enrichment analysis. Here, we introduce BrainEnrich, an R package that integrates whole‐brain gene expression profiles from the Allen Human Brain Atlas (AHBA) with in vivo imaging‐derived phenotypes (IDPs). By offering a suite of flexible association methods, aggregation strategies, comprehensive lists of predefined gene sets, and both competitive and self‐contained null models, the package enables researchers to examine the spatial coupling between molecular profiles and IDPs at both group and individual levels. A novel feature of BrainEnrich is its individual‐level enrichment analysis, which mapped individual IDPs onto a molecular coordinate framework, capturing the molecular signature of individual IDPs and enabling a deeper exploration of inter‐individual variability. Its statistical power was examined through extensive simulation studies based on linear regression with different combinations of test statistics and null models. The results suggest that with appropriate null models, this approach effectively controlled Type 1 error while retaining sensitivity to detect associations between molecular profiles and phenotypic data. Two case studies were performed to demonstrate the utility of the package. In the group‐level enrichment analysis, the effect size map of case–control comparison in cortical thickness of major depressive disorder patients was associated with molecular pathways such as synaptic signaling, lipid regulation, and steroid hormone balance, providing candidate molecular annotations for group‐level IDPs. A separate case study found that synaptic gene set scores showed nominal associations with multiple cognitive measures, demonstrating its utility for individual‐level molecular annotations to explore associations with phenotypic variables. Collectively, BrainEnrich provides a flexible framework for integrating macro‐level IDPs with micro‐level transcriptomic profiles for molecular contextualization of IDPs.
The human hippocampus is a unique cortical structure central to brain function, plasticity, and disease. Unravelling its complex organization requires the integration of multiscale data, linking molecular features to mesoscale anatomy and macroscale functional patterns. Gene expression is a fundamental microscale phenotype, and its profiling can provide a reference description of how molecular features are distributed across the brain. Capitalizing on recent imaging-transcriptomic analyses, we introduce HippoGenes, a repository of fine-grained gene expression patterns across human hippocampal subregions. We leveraged spatial statistical models and hippocampal surface mapping to reconstruct dense transcriptomic maps from sparse post-mortem tissue samples of the Allen Human Brain Atlas, generating continuous expression estimates for thousands of genes aligned to a common surface-based coordinate system. We illustrate the utility of HippoGenes to (i) map medial-lateral and anterior-posterior transcriptomic gradients that align with subfield and tripartite subdivisions of the hippocampal formation, (ii) examine associations between gene expression and canonical microstructural and functional features of the hippocampus, and (iii) perform a molecular decoding of subregional alterations in neurological patients with hippocampal pathology. HippoGenes provides a framework for exploring the molecular organization of the hippocampus, opening avenues for multiscale integration in health and disease, and is openly available on https://hippogenes.readthedocs.io/.
A. Ngo, Sara Larivière, J. Royer et al.· bioRxiv· 0 citations
Adaptive and adverse brain states are often assumed to lie on a shared molecular continuum, but this assumption has rarely been evaluated against explicit transcriptomic alternatives. This study aimed to compare two representations of cross-context brain transcriptomic organization: a transcriptome-wide global-axis model and a low-dimensional reciprocal model. We benchmarked these models across a curated cross-study brain cohort spanning exercise, alcohol-related adversity-like contexts, stress, aging, and neurodegeneration, using prespecified intervention-like and adversity-like directional contrast labels rather than assuming homogeneous biological states. We assessed the competing representations using signed-effect correlations, permutation analyses, non-linear fitting, and held-out reconstruction, and we then examined the resulting structure through region-specific human bulk evaluation and exploratory cellular, single-nucleus, spatial, and chromatin projection analyses. These downstream analyses were used to examine localization and biological interpretability and were not treated as independent evaluation of the module 1/module 2 (M1/M2) partition. The combined signed-effect statistics were interpreted as representation-level directional summaries rather than estimates of a homogeneous cross-study biological effect. The global-axis model received limited support: intervention-like and adversity-like signed-effect summaries were only weakly correlated, were not stronger than permutation null expectations, and were not improved by non-linear fitting. Within the selected reciprocal-gene space, a rank-1 latent profile reconstructed held-out genes more accurately than the hard M1/M2 partition, whereas the M1/M2 discretization provided a more interpretable but selection-conditioned directional summary. Human analyses yielded an asymmetric pattern: a significant M1 association was observed only in the hippocampal dataset, whereas M2, the reciprocal index, and the other examined brain regions showed no consistent corresponding effects; leave-one-stratum-out analyses indicated poor cross-stratum reproducibility of the exact gene-level partition. These findings motivate a low-dimensional reciprocal representation as an exploratory framework while emphasizing context dependence, cohort dependence, and heterogeneity.
Zongnan Lyu, Chunxue Shao, Qi Yu et al.· International Journal of Mol...· 0 citations
The mammalian brain's functional complexity arises from the sophisticated architecture of neurons and glia. This network is essentially defined by its dynamic proteome, which reveals the functional execution underlying neural computation and disease. This review integrates the technological leap in neuroproteomics. It has moved beyond bulk tissue proteome cataloguing to high-sensitivity single-cell and spatial resolution. We detail how next-generation platforms, such as TIMS-PASEF and Orbitrap-Astral, have enabled deeper and faster phenotypic profiling of limited brain samples. However, the proteome coverage remains constrained by dynamic range, sample loss, ionisation bias and incomplete detection of low-abundance regulatory proteins. We further examine how such studies have revealed the proteomic remodelling that drives lineage specification and synaptic plasticity by linking temporal protein expression waves to biological function. Crucially, we delineate the clinical translational trajectory, illustrating how aberrant signatures are verified in cerebrospinal fluid (CSF) and validated in plasma to support precision medicine. Finally, we argue for the necessity of "fused" multi-omics integration and Artificial Intelligence (AI) to decode the non-linear molecular logic of brain pathology.
Arup Acharjee, Nitika Rai, Akash A. Patel et al.· Biochimica et Biophysica Act...· 0 citations
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.
I. Koh, E. Chang, Youngseok Choi et al.· bioRxiv· 0 citations
Precise delineation of cortical layers is fundamental for understanding human brain organization, cell-type architecture, and disease-related tissue alterations. However, traditional anatomy-based methods often lack molecular resolution and suffer from inter-observer subjectivity. Here, we present gene expression-defined cortical layers (GD-Ls) using the BayesSpace algorithm, a high-resolution framework for cortical parcellation based on spatial transcriptomics. Compared with traditional anatomy-based approaches, GD-Ls more accurately resolve laminar boundaries and capture fine-scale laminar heterogeneity, including sublayer-like domains within L1, L3, and L6, as well as a molecularly distinct transition zone at the gray-white matter interface. Validation across diverse cortical lobes, multiple spatial platforms, and independent healthy postmortem datasets demonstrates that GD-Ls capture the intrinsic molecular architecture of the cortex irrespective of tissue source. Furthermore, cross-species analyses show that this framework is extensible to macaque and mouse cortices. Crucially, GD-Ls successfully identify subtle laminar disorganization and aberrant cellular and molecular signatures in pathologically altered tissues, which are often missed by conventional histology. Together, GD-Ls provide an objective and reproducible tool for standardized cortical mapping and for identifying early pathological signatures in the human brain. The source code is available on GitHub (https://github.com/YanrongWei/GD-Ls).
Yanrong Wei, Youzhe He, Yuyang Liu et al.· Genome Medicine· 0 citations
Dysconnectivity in schizophrenia is a pervasive concept across various levels of systems biology. To better understand disrupted patterns of molecular connectivity in schizophrenia, we apply innovative approaches to gene co-expression networks starting from multiple regions of postmortem brain (bulk RNA-Seq from dorso-lateral prefrontal cortex- (DLPFC) (Ndonors=297; sex: M/F = 212/85), hippocampus (Ndonors=250; sex: M/F = 181/69) and caudate (Ndonors=349; sex: M/F = 242/107). Here we identify differentially connected genes (DCG) in schizophrenia networks that deviate from architectural relationships characteristic of neurotypical gene networks based on three network metrics- total connectivity (kTot), clustering coefficient (C), and intra-module degree (kIn). We find multiple DCG consistent across all brain regions, most of which we then independently confirm in brain single nuclei (snRNAseq) data and in four independent human iPSC-derived brain organoids. DCG specific for each network parameter shows enrichment in schizophrenia genetic signal, in pathways prominently related to neurons and oligodendroglia functionality, and in cell-type specific co-expression patterns that differ between neurotypical and schizophrenia, implicating neuronal-oligodendroglia incoordination. WGCNA provides insight into which genes belong to a network. Here, the authors propose a refined approach to define dynamic gene relationships, able to identify aberrant neuron-oligodendroglia interactions when applied to the study of schizophrenia.
E. Radulescu, P. Vértes, Shizhong Han et al.· Nature Communications· 0 citations