Biology subjects

Chen, D. G.

Publications and source records attributed to Chen, D. G..

6 recordsLinked to original sources

bioRxiv2026

EBV Reprograms B Cells in an Autoimmune-Like Fashion in Patients with COVID-19

Epstein-Barr virus (EBV) reprograms B cells in autoimmune disease. Reprogrammed EBV+ B cells activate nearby B and CD4+ T cells, via upregulated antigen presentation and costimulatory machinery, to drive autoimmune pathology. EBV reactivation is a known correlate of long COVID, which is a heterogeneous condition that can bear similarities to autoimmune disease. However, the mechanisms underpinning this association remain unresolved. We report on EBV metabolically reprogrammed B cells in patients with COVID-19. We find EBV+ B cells provide stimulatory signals to bystander B and CD4+ T cells. SARS-CoV-2 infected participants exhibiting elevated fractions of EBV+ B cells present, at convalescence, with dysregulated lipid profiles, increased autoantibody titers, and post-acute symptomology likely reflective of this metabolic reprogramming and cell-cell interactions. Enrichment of our EBV+ B cell signatures seen in patients with COVID-19 is similar in patients with lupus and multiple sclerosis suggesting a potentially shared pathway of EBV-driven dysfunction across diseases.

immunology↗

bioRxiv2026

SpaceBender: Denoising Spatial Transcriptomics Data to Enhance Biological Signals

Spatial transcriptomics (ST) allows for the simultaneous profiling of cell phenotype (e.g. transcriptome) and physical position. Although ST data has brought about numerous new biological insights, it remains limited by noise, largely in the form of RNA diffusion. Here, we introduce SpaceBender which leverages spatial-specific information (e.g. spatial ambient RNA niches) to build upon single-cell denoising strategies. SpaceBender outperforms current ST denoising methods in simulations and in vivo chimeric tissues. Through case studies, we demonstrate how SpaceBender unveils hidden biological insights and increases the significance of said insights as evaluated by statistical testing. Finally, we reveal how SpaceBender may also be applied to subcellular resolution data where it removes off-target expression of neighboring cell type specific marker genes. In all, we present SpaceBender as an ST denoising method, freely available as an open-source package, that may enhance the insights the field may draw from various ST data types.

bioinformatics↗

bioRxiv2026

Single-cell Tree-based Model for Genomic-Disease Association

The rapid maturation of single-cell multi-omics technologies has enabled unprecedented resolution for mapping disease states and identifying disease-associated biomarkers. In practice, biomarkers are often discovered through differential detection that treat genomic features as independent contributors to phenotypes, while the combinatorial interactions that drive clinical outcomes remain a practical challenge. We present scanCT (single-cell analysis of Clinical Tree), a tree-based framework that identifies groups of genomic features associated with distinct disease phenotypes in a highly interpretable manner. scanCT uses an unbiased, model-based variable-selection procedure for data-driven split selection, which is important for handling the diverse distributional properties of single-cell data across modalities. The tree architecture captures feature interaction effects, and the association modeling enables adjustment for confounding factors. We apply scanCT to longitudinal single-cell multi-omics COVID-19 datasets spanning diverse clinical outcomes and multiple time points per patient. scanCT identifies phenotype-specific gene and protein markers while accounting for age and sex, and it reveals interpretable synergistic marker combinations that help explain differences in patient clinical phenotypes.

bioinformatics↗

bioRxiv2025

Learning Human T Cell Behaviors through Generative AI Embeddings of T Cell Receptors

T cells interact with the world through T cell receptors (TCRs). The extent to which TCRs determine T cell behavior has not been comprehensively characterized. Our Tarpon model leverages advances in generative artificial intelligence to synthesize large-scale (>1M sequences) TCR atlases across human development and diseases into actionable insights. Tarpon creates: 1) bespoke sampling functions generating realistic Ag-specific TCRs, 2) embeddings revealing CD4+ and CD8+ single-positive TCR repertoires as distinct with divergent physiochemical properties, and 3) cross-dataset mappings of T cell states that validate fetal CD4+ versus CD8+ TCR differences in adults and find fetal type I innate T cells to map to MAIT and KIR+ adult CD8+ T cells which we verify via whole transcriptome analysis. Tarpon is a resource as a reference of TCRs across human physiological states and as a computational framework to create interpretable TCR embeddings, via physicochemical associations, that have broad implications for the field.

bioinformatics↗

bioRxiv2025

APMAT analysis reveals the association between CD8 T cell receptors, cognate antigen, and T cell phenotype and persistence

Elucidating the relationships between a class I peptide antigen, a CD8 T cell receptor (TCR) specific to that antigen, and the T cell phenotype that emerges following antigen stimulation, remains a mostly unsolved problem, largely due to the lack of large data sets that can be mined to resolve such relationships. Here, we describe Antigen-TCR Pairing and Multiomic Analysis of T-cells (APMAT), an integrated experimental-computational framework designed for the high-throughput capture and analysis of CD8 T cells, with paired antigen, TCR sequence, and single-cell transcriptome. Starting with 951 putative antigens representing a comprehensive survey of the SARS-CoV-2 viral proteome, we utilize APMAT for the capture and single cell analysis of CD8 T cells from 62 HLA A*02:01 COVID-19 participants. We leverage this unique, comprehensive dataset to integrate with peptide antigen properties, TCR CDR3 sequences, and T cell phenotypes to show that distinct physicochemical features of the antigen-TCR pairs strongly associate with both T cell phenotype and T cell persistence. This analysis suggests that CD8+ T cell phenotype following antigen stimulation is at least partially deterministic, rather than the result of stochastic biological properties.

immunology↗

bioRxiv2024

CITE-seq analysis reveals human cytomegalovirus and diabetes-associated adaptive NK cell alterations in cardiovascular disease.

Coronary artery disease (CAD) is a leading cause of mortality worldwide with Diabetes and human cyto-megalovirus (HCMV) infection as risk factors. CADs influence on human NK cells is not well characterized. CITE-seq analysis of a CAD cohort of 61 patients revealed distinctly higher NK cell SPON2 expression and lower IFNG expression in severe CAD patients. Interestingly, HCMV+ patients displayed lower SPON2 ex-pression while diabetes status reversed the HCMV effect. Diabetes led to diminished adaptive Fc{varepsilon}RI{gamma}-/low NK cell frequencies and was associated with a higher PBMC IL15/TGFB transcript ratio, while TGFB in-creased in severe CAD. SPON2 expression corresponded to changes in conventional vs. adaptive NK cell frequencies, and SPON2/IFNG ratio decreased in inflamed plaque tissue with an increased adaptive NK cell gene signature and was increased in severe CAD patients. Our results indicate that the SPON2/IFNG ra-tio and adaptive NK cell gene signature associated with stenosis severity or inflammation in CAD.

immunology↗