Biqing Zhu

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

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

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 uses single nucleus transcriptomics to identify transcriptional changes in CSP KO mice, revealing repression of synaptic pathways and upregulation of autophagy in neurons, along with altered neuron-glia interactions.

This paper introduces a statistical framework, cWAS, to identify cell types with genetically regulated proportions associated with complex diseases using integrated genetic and transcriptomics data.

The paper presents a cost-effective, non-commercial protocol for normalizing TCRβ CDR3 sequencing data to correct amplification bias in bulk TCR repertoire profiling.

The paper introduces MRFscRNAseq, a Markov Random Field model for identifying cell-type specific differentially expressed genes in scRNA-seq data, showing improved power over conventional methods.

ProtAnno automates cell type annotation for single cell proteomics data by integrating multiple reference sources, achieving accurate and robust results on manually labeled datasets.