Luning Yang

This study uses single-cell RNA sequencing to analyze human retinal samples and identifies microglial states and neural-immune interactions in diabetic retinopathy.

The paper identifies cell-type-specific aging signatures in human retinal cells using single-cell RNA sequencing and machine learning, revealing molecular changes and vulnerabilities associated with aging.

The paper constructs a single-cell transcriptomic atlas of the human retina from Chinese donors, identifying 10 cell types and subtypes, and mapping their molecular programs and biological functions.

The paper generates a scRNA-seq reference atlas of living and early post-mortem human retina, revealing distinct transcriptional dynamics and a novel mlCone cluster.

This study identifies a novel genetic variant associated with widespread pain using UK Biobank data and finds sex-specific genetic differences.

The paper identifies cell-type-specific biomarkers for diabetic retinopathy using single-cell transcriptomics and machine learning, achieving high classification accuracy across disease states.

This study identifies 13 genetic loci associated with gout using GWAS in the UK Biobank, with four novel loci, and finds sex-specific genetic influences and associations with metabolic and immune traits.

This paper conducts a GWAS on 441,757 UK Biobank participants to identify genetic variants associated with knee pain, finding 10 significant loci, including eight novel ones, and sex-specific associations.