Thomas Chartrand

GeoAdvAE integrates unpaired single-cell morphology and transcriptomics to align cell types and reveal disease-associated microglia signatures.

On 495 human Patch-seq neurons, cell type means predicted electrophysiology better than neural networks on scGPT embeddings or gene panels; fusing scGPT embeddings with cell type labels predicted best.

The paper introduces SACCELERATOR, a framework for spatially aware clustering that improves benchmarking by incorporating expert feedback and consensus analysis across diverse datasets.

The paper identifies topographic organization in locus coeruleus norepinephrine neurons, linking their morphology, gene expression, and activity to specific brain regions and learning-related behaviors.

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 introduces THALMANAC, a resource for analyzing single-cell transcriptomic data of the thalamus, revealing gene expression patterns that support and refine thalamic nucleus parcellation.

The paper identifies transcriptomic and morpho-electric diversity in human neocortical layer 1 interneurons, revealing greater subclass distinction compared to mouse.

The paper uses viral labeling and Patch-seq to define morpho-electric properties of GABAergic interneurons in human neocortex, revealing functional insights and reclassifying some SST neurons into PVALB subclass.

The paper investigates target cell-specific synaptic dynamics between excitatory and inhibitory neurons in human neocortex, finding similarities to rodent studies.

The paper generates and evaluates over 130 million single-neuron models with active conductances to link electrophysiology, morphology, and transcriptomics across cortical cell types.

The paper presents a synaptic physiology dataset and analysis platform to study cortical microcircuit structure and synaptic properties in mouse and human neocortex, revealing layer-dependent connectivity and subclass-specific dynamics.

The paper identifies transcriptomic diversity in human supragranular neurons and links it to functional specialization and disease vulnerability.