Leonardo Collado‐Torres

The paper uses spatial transcriptomics to analyze molecular differences in mood disorders and biological sex in the human prefrontal cortex, identifying sex- and disorder-specific gene expression patterns.

This study maps the spatiomolecular organization of the human habenula using multi-omic approaches, identifying molecular signatures and spatial domains linked to psychiatric disorders.

This study compares the molecular effects of prenatal nicotine exposure and maternal smoking during pregnancy on mouse brain development and adulthood, finding distinct gene expression changes in pups but not adults.

This study creates a spatiomolecular atlas of the human nucleus accumbens using spatial transcriptomics and single-nucleus RNA sequencing, revealing spatial gradients and conserved features across species.

This study identifies transcriptomic changes in rat habenula and amygdala after chronic fentanyl self-administration, linking them to human cell types and risk genes for addiction.

The paper integrates spatial transcriptomics and single-nucleus RNA-seq data from the human dACC to define molecular profiles, spatial domains, and functional insights.

This study maps molecular and genetic signatures of schizophrenia in human prefrontal cortex across spatial scales, revealing altered BDNF-TrkB signaling and inhibitory circuit dysfunction.

The paper provides an open-source online book for analyzing spatial transcriptomics data using Bioconductor in R, with reproducible code and datasets.

The paper investigates how APOE genotype affects gene expression in the human entorhinal cortex, identifying an oligodendrocyte subtype as a potential mediator of Alzheimer's disease risk.

The paper performs transcriptomic analysis of the human habenula and identifies molecular changes associated with schizophrenia risk and illness state.

The paper analyzes the transcriptomic landscape of the human locus coeruleus in relation to Alzheimer's disease risk factors and neuromelanin content using spatially-resolved transcriptomics.

The paper integrates single-nucleus and spatial transcriptomics data to map the molecular landscape of the human hippocampus, identifying cell types and spatial domains.

lute improves cell composition estimation in bulk RNA-seq by accounting for varying cell sizes, using simulated and real datasets to demonstrate its accuracy.

The paper benchmarks cellular deconvolution methods using a multi-assay dataset from human prefrontal cortex, finding Bisque and hspe to be the most accurate.

This study analyzes age-related changes in gene expression in the human dentate gyrus, identifying shifts in neurogenesis, maturation, and neuroinflammation across the lifespan.

The paper examines mis-splicing across human introns, tissues, and age using RNA-sequencing data, finding correlations with spliceosome components and age-related neurodegenerative disease genes.

The paper introduces visiumStitched, a tool for stitching Visium spatial transcriptomics data across multiple capture areas, enabling seamless analysis without data loss.

The paper investigates ligand-receptor interactions (LRIs) in neurological diseases and finds that 96.8% of disease-associated LRIs are interconnected, highlighting their role in disease comorbidity and potential drug targets.

The paper identifies 46,802 enhancers and 164 psychiatric disorder variants with regulatory effects in the developing cortex and organoids using massively parallel reporter assays and deep learning.

The paper processes single-nuclei multi-omics data from 388 human brains to identify regulatory elements and build cell-type networks, revealing changes in aging and neuropsychiatric disorders.

The paper generates a molecular neuroanatomical map of the human prefrontal cortex using spatial transcriptomics and single nucleus RNA-sequencing, revealing novel spatial domains and cell-cell interactions linked to psychiatric disorders.

This study investigates TrkB signaling in the lateral septum using RNA-seq, identifying gene expression changes linked to psychiatric and neurodegenerative disorders.

The paper re-analyzes RNA sequencing data comparing gene expression in living and postmortem human brain tissue, finding that living samples had lower quality, and questions the validity of previous conclusions about postmortem studies.

The paper introduces a data-driven method to identify Total RNA Expression Genes (TREGs) for estimating RNA abundance in heterogeneous cell types and identifies AKT3 as a top TREG in brain regions.

The paper uses spatial proteogenomics to analyze gene expression changes in Alzheimer's disease brains linked to amyloid-beta and tau pathology.

BiocMAP is a GPU-accelerated pipeline for WGBS data that reduces computational time and memory usage through efficient alignment and data representation.

The paper identifies cell type-specific activity-regulated gene expression patterns in mouse hippocampal neurons using snRNA-seq and ECS, revealing divergent transcriptomic responses and enriched biological pathways.

The paper examines how genetic ancestry influences gene expression and DNA methylation in admixed African/Black Americans, showing environmental and genetic factors affect brain disease risk.

The paper characterizes the molecular landscape of the human locus coeruleus using spatially-resolved transcriptomics and single-nucleus RNA-sequencing, providing a publicly accessible resource.

Samui is a web-based tool for visualizing and annotating spatially-resolved transcriptomics data, enabling fast exploration of large images and their features.