Shizhong Han

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.

The paper identifies brain-peripheral DNA methylation sites (BrESS) that consistently predict brain methylation across tissues, showing they are not linked to disease heritability.

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.

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 identifies distinct DNA methylation mechanisms for common and rare noncoding genetic risk in brain disorders using deep learning and single-nucleus DNAm data.

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 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.

The paper shows a copper-containing nanozyme worsens alpha-synucleinopathy by increasing nitrosative stress, while a copper-free version reduces pathology in neurodegenerative models.

The paper identifies genes with disrupted connectivity in schizophrenia gene co-expression networks, revealing atypical neuronal-glial interactions and synaptic transmission disturbances.

The paper identifies distinct α-synuclein strains associated with cognitive decline in Parkinson's disease and shows AI-based analysis of these strains improves clinical stratification.

This paper introduces a transformer-based model to predict cell type-specific DNA methylation regulatory variants and improves fine mapping for brain disorders.

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 identifies widespread genetic influences on DNA methylation in the human brain and links schizophrenia GWAS variants to epigenetic changes.

This study uses machine learning to predict autism risk genes by integrating gene expression, constraint metrics, and variation features, identifying known and novel candidates.

The paper integrates brain DNA methylation data with GWAS to identify psychiatric risk genes, finding heritable CpG sites and methylation-trait associations linked to schizophrenia and bipolar disorder.