David Zemmour

This study identifies STING as a therapeutic target for Kawasaki disease by analyzing spatial immune architecture in cardiac inflammation.

The paper introduces immgenT-GP, a gene-program framework that identifies 200 reproducible gene programs in mouse T cells, capturing lineage identity, activation states, and tissue location.

The paper profiles mouse CD4+ T cells across >700 samples to identify 20 molecular states, revealing diverse effector functions and unexpected similarities between Tfh and chronically stimulated states.

The paper defines a unified framework for Treg heterogeneity using a joint RNA-protein model, identifying eight conserved clusters across tissues and conditions.

immgenT creates a comprehensive T-cell molecular atlas across mouse organs and disease states, identifying 8 lineages and 107 clusters using deep generative models and integrating external datasets.

The paper establishes immgenT-CD8 as a comprehensive reference for mouse CD8αβ T cell differentiation states across diverse immunological contexts.

The paper analyzes the αβTCR repertoire across 734 mouse samples to uncover patterns in TCR diversity, non-random joining, and clonal expansion.

The paper compares T cell landscapes in mice with normalized microbial exposure (NME) and specific pathogen-free (SPF) conditions, finding increased effector memory CD8+ T cells in NME mice.

This study shows that antigen encounter in the epidermis enhances TRM persistence and proliferation through TGF{beta} signaling and TGF{beta}RIII expression.

The paper identifies TCR-V gene usage patterns in T cells sorted by anti-TCR mAbs using single-cell RNAseq and finds enriched ERV1-family LTR expression in specific TRBV regions.

The paper examines T cell changes in mammary glands during pregnancy and lactation, finding an expansion of T effector cells and similarities to intestinal T cells.

This study identifies histopathological and molecular features linked to J-pouch failure in IBD patients using single-cell spatial transcriptomics.

This study uses a literature-derived knowledge graph to improve scRNA-seq annotation by leveraging gene-cell type associations from biomedical texts.

The paper uses a data science platform to integrate biomedical text and single-cell RNA-seq data to identify new SARS-CoV-2 receptor expression patterns and potential infection targets.