Stuard Barta

This study uses single-nucleus RNA-seq, ATAC-seq, and Multiome to analyze Alzheimer's disease across multiple brain regions, revealing shared and region-specific cellular vulnerabilities.

This study uses multimodal Patch-seq data to show that gene expression gradients in mouse basal ganglia neurons correlate with spatial location, physiology, and morphology.

The paper develops a cross-species enhancer-AAV toolkit for targeting specific cell types in the basal ganglia across species, validated in mouse and macaque.

The paper analyzes multi-modal Patch-seq data from macaque striatum to reveal morphoelectric diversity and functional specialization of neuronal cell types, highlighting primate-specific features and differences from rodents.

The paper creates a cross-species spinal cord cell type atlas integrating data from mouse, macaque, and human, identifying conserved and species-specific features.

The paper integrates multimodal data to characterize human neocortical glutamatergic neurons, revealing morphoelectric properties that distinguish transcriptomic subclasses and highlight species-specific differences.

The paper maps AD-related pathological changes in the caudate nucleus at cellular resolution, identifying distinct features and cell type-specific responses.

This paper creates a cross-species spatial transcriptomic atlas of the basal ganglia in human, rhesus macaque, and common marmoset, revealing conserved molecular organization and cell type-specific compartments.

The paper presents a spatially resolved transcriptomic and gene regulatory atlas of primate subcortical regions, revealing spatially tuned transcriptional and regulatory programs across cell types.

The paper presents an enhancer AAV toolbox for targeting and perturbing striatal cell types and circuits in mice, with validated specificity and cross-species applicability.

The paper develops a toolkit of transgenic mouse lines and enhancer AAVs for selective targeting of cortical cell populations using transcriptomic and epigenomic data.

The paper introduces MerQuaCo, an open-source tool to automatically detect and quantify imperfections in spatial transcriptomics data, improving quality assessment.