Panos Roussos

The paper maps gene expression, chromatin accessibility, and DNA methylation in developing human brain cells to understand regulatory interactions and their role in disease.

The paper introduces a probabilistic whitening transformation for high-dimensional genetic data, showing it is faster and more accurate than existing methods for imputing z-statistics and identifying genetic regulatory signals.

This study uses single-cell RNA sequencing to track the maturation of human choroid plexus organoids and identifies a stage-dependent window of developmental plasticity.

This study compares transcriptome and proteome profiles of three microglia sources, identifying shared surface markers and HERV expression patterns, suggesting MDMi as a practical model for functional studies.

GROMTools, a sparse-weight C++/R engine streaming PLINK2 genotypes, reproduces PrediXcan and PLINK2 GReX imputations with about 100-fold less CPU time and 33-fold less peak memory.

Crumblr is a statistical method for analyzing changes in cell type composition across studies, showing increased power over existing methods in simulations and applied to multiple single-cell RNA-seq datasets.

The paper introduces dreamlet, an R package for efficient differential expression analysis of large-scale single-cell transcriptomics data, showing improved speed and memory usage compared to existing methods.

This study performs a single-nucleus transcriptome-wide association study to identify gene-trait associations in brain disorders, revealing cell-type-specific genetic regulation and ancestry-specific patterns.

The paper constructs a single-nucleus atlas of genetic regulation in the human brain, identifying cell-type-specific regulatory effects and disease-associated genes.

The paper profiles human myeloid cells across aging and Alzheimer's disease, identifies AD-associated changes, and links GPNMB to neuroprotection via TREM2.

The paper introduces spatialproteomics, a Python toolbox for analyzing highly multiplexed fluorescence images, demonstrating its use on lymph node and lymphoma data.

The paper identifies three gene regulatory states associated with cognitive resilience and pathogenesis in Alzheimer's disease using single-cell data from the dorsolateral prefrontal cortex.

The paper examines how gene dosage, polygenic background, and environment influence complex traits like height and BMI using large-scale data from multiple cohorts.

The paper introduces ME-seq, a technology that profiles DNA methylation, gene expression, and chromatin accessibility in single nuclei, revealing early microglial priming in Alzheimer's disease.

The paper constructs a single-cell transcriptomic atlas of the human dorsolateral prefrontal cortex across neurodegenerative and neuropsychiatric diseases, revealing shared and distinct molecular mechanisms.

The paper develops an integrative sequencing strategy to catalog NRXN1 isoforms across human brain tissues and hiPSC organoids, revealing cell-type-specific splicing patterns and therapeutic potential of ASO targeting.

This study creates a single-nucleus transcriptomic atlas of Parkinson's disease progression, identifying cell-type-specific gene expression changes and linking them to genetic risk factors and disease stages.

The paper identifies ZBTB7A as a key regulator of MDD-specific chromatin signatures and astrocyte-mediated stress vulnerability in the orbitofrontal cortex.

This study identifies D1 and D2 dopamine receptor-expressing neurons in the mouse ventral hippocampus and shows they mediate opposing approach/avoidance behaviors during anxiety-related tasks.

The paper presents a long-read RNA-seq atlas of microglia isoforms, identifying novel isoforms and their associations with genetic risk loci in Alzheimer's and Parkinson's disease.

This study develops a human brain tissue model (miBrain) to show that APOE4 astrocytes with cholesterol dysregulation promote α-synuclein pathology through endolysosomal dysfunction.

This paper models diagnostic code dropout in EHR data to improve phenotyping for schizophrenia, enhancing polygenic score transferability across ancestries.

This paper presents a computational drug repurposing framework that identifies FDA-approved drugs for COVID-19 by integrating genetic expression data and EMR data, finding four compounds with protective effects in validation.

This paper integrates GWAS and transcriptomic data using PrediXcan and a deep learning model to identify genetic factors and drug candidates for Alzheimer's disease.

This study profiles small RNAs in schizophrenia and bipolar disorder, finding altered sncRNA abundance and links to accelerated aging and key biological processes.

The paper maps transcriptomic and chromatin accessibility profiles across 25 human brain regions and identifies genes linked to neuropsychiatric disorder risk.

This study creates a single-nucleus transcriptomic atlas of the human DLPFC across the lifespan, revealing three transcriptomic phases and ten age-related trajectories linked to neurodevelopmental and neurodegenerative disease risk.

The paper analyzes transcriptomic differences in human brain cells with nine high-risk neuropsychiatric CNVs compared to non-carriers, finding widespread gene expression disruptions and shared functional pathways.

The paper identifies a conserved excitatory neuron gradient in human mediodorsal thalamus using single-nucleus and spatial transcriptomics, linking it to psychiatric disorder risk.

Population-stratified GWAS of 2,068 traits in 635,969 Million Veteran Program participants, 29% genetically non-European, yields 98,485 associations and 57,601 fine-mapped signals with largely shared cross-population architecture.