Le Zhang

The paper compares the developmental strategies of the enteric nervous system and intrinsic cardiac nervous system to reveal how organ-specific specialization occurs through distinct molecular and cellular mechanisms.

This paper identifies shared genetic and neuroimmune mechanisms linking type 1 diabetes with neurocognitive traits through genome-wide association and single-cell epigenomic analyses.

GITIII infers cell-cell interactions from spatial transcriptomics data by analyzing cell state and niche correlations using a self-supervised graph transformer model.

BrainBridge integrates single-cell transcriptomic data with phenotypes to model brain biology, prioritizing genes and cell types linked to Alzheimer's, aging, and sex differences.

The paper identifies two specialized intrinsic cardiac neuron subtypes in mice that play distinct roles in maintaining cardiac homeostasis and stress resilience.

The paper identifies increased myeloid-driven inflammation in the CNS of prodromal Parkinson's Disease patients, linking gut and CNS immune responses through shared T cell clones.

This study uses single-nucleus multiome sequencing to identify HIV-associated transcriptional and epigenetic changes in brain regions of individuals with HIV, revealing altered glial cell states and pro-inflammatory signaling.

This study uses single-nucleus RNA sequencing to analyze periventricular white matter in vascular dementia, identifying increased heat shock protein responses as a conserved feature across cell types.

CosGeneGate selects non-redundant, cell-type-specific marker genes for single-cell analysis, outperforming existing methods in downstream applications.

The paper performs single-cell transcriptomic and proteomic analysis of Parkinson's disease brains, identifying cell-type-specific changes and differences from Alzheimer's disease.

This study investigates the distinct roles of PRKCZ and PRKCI in oligodendrocyte differentiation and myelination, showing non-redundant functions in early specification and later maturation stages.

This study uses single-cell RNA sequencing to analyze immune changes after B-cell depletion in MS patients, revealing cell type-specific effects on macrophages, monocytes, and T cells.

A topological machine learning approach applied to single-nucleus RNA sequencing identifies shared glial activation states in AMD, AD, and MS.