Biology subjects

Zemmour, D.

Publications and source records attributed to Zemmour, D..

14 recordsLinked to original sources

bioRxiv2026

Spatial profiling of Kawasaki disease cardiac inflammation identifies STING as a therapeutic target

The cellular subsets and in situ mechanisms underlying the development of coronary arteritis in Kawasaki disease (KD), the leading cause of acquired heart disease in children, remain poorly defined. We characterized the cellular composition and spatial organization of cardiac inflammation in autopsy coronary artery (CA) specimens from KD patients using 64-plex immunofluorescence. We identified spatially organized immune infiltrates of neutrophils, macrophages, and T cells associated with arterial wall destruction and remodeling, as well as disrupted stromal/muscle cell colocalization and diffuse myocardial inflammation. Spatial analysis also revealed expression of the innate immune sensor STING in myeloid cells in KD CA tissues. Pharmacological and genetic inhibition of STING reduced vasculitis development in a mouse model of KD. These findings define the spatial immune architecture of KD cardiac inflammation, reveal inflammatory injury beyond the coronary arteries, and identify STING as a potential therapeutic target for treatment-resistant or severe KD.

immunology↗

bioRxiv2026

A Gene-Program Architecture of Mouse T cells

We present immgenT-GP, a gene-program framework for resolving mouse T cell heterogeneity across the immgenT atlas. On ~ 800,000 T cells spanning lineages, organs, and immune challenges, we defined 200 reproducible gene programs, discovered by empirical Bayes matrix factorization approach and validated through a new deep learning approach, that capture major axes of T cell variation, including lineage identity, activation states and tissue location. Gene-program analysis complemented cluster-based annotation by decomposing T cell states into molecular modules, revealing quantitative, shared, modules not represented with discrete labels alone. Across tissues, gene programs reflected both tissue-imposed programs and changes in cluster composition. Integrating GP activity with cell-surface marker expression from the CITE-seq data, revealed that markers can report different programs depending on lineage and context. Together, immgenT-GP extends the atlas from a map of T cell states to a molecular reference of the programs that underlie them.

genomics↗

bioRxiv2026

immgenT CD4: a reference landscape of mouse CD4+ T cell

CD4+ T cells are orchestrators of the immune system with diverse effector functions. Their full molecular diversity remains unclear due to the lack of a unified framework. Within the immgenT project, we profiled RNA, surface markers, and TCR clonotypes in conventional CD4+ T cells across >700 samples. Integration with a joint RNA-protein deep generative model revealed an ensemble of 20 CD4 states that account for all cells across tissues and challenges. Small, highly polarized clusters ("tips") that evoke Th1, Th2, Th17, and Tfh states co-exist with a majority of activated cells with mixed programs occupying "midland" states. Unlike CD8+ T cells, memory CD4+ cells largely mapped to the same states as effectors. Resting states proved quite diverse, and we uncovered unexpected similarities between Tfh and chronically stimulated states. Together, immgenT provides a unified molecular reference for CD4+ T cell diversity.

immunology↗

bioRxiv2026

Diverse Microbial Exposure Enhances CD8+ T Cell Effector Memory Output and Function

Mice with normalized microbial exposure (NME) harbor an immune system that more accurately reflects that of humans compared to mice maintained as specific pathogen-free (SPF). An explanation for the observed alterations in the composition of the T cell compartment in NME mice has not been reported. We compared the T cell landscape in NME versus SPF mice at baseline and after acute LCMV infection. Using the immgenT dataset, we found no unique T cell populations in NME, but the landscape shifted towards activated T cells with increased propensity for effector functions and improved pathogen clearance. CD8+ KLRG1+ cells (immgenT CD8_cl12) are significantly expanded in NME mice. Their predominance was a result of both increased formation and the conversion of other memory populations to a KLRG1+ phenotype. Thus, NME mice provide insight into a diverse T cell compartment rich with cells previously found to be limited in SPF mice.

immunology↗

bioRxiv2026

The CD8 immgenT framework as a universal reference of mouse CD8 Tαβ cell differentiation states

Mouse CD8+ T cell differentiation has been studied extensively in models of infections and tumors, yet no unified framework spans the full spectrum of immunological contexts. Within the immgenT project, we profiled RNA, surface markers, and TCR clonotypes in conventional CD8+ T cells across >600 samples, spanning multiple perturbations, tissues, and timepoints. Twenty-one clusters across naive, effector, circulating memory, tissue-resident memory, progenitor-exhausted, and terminally-exhausted CD8+ T cell compartments emerged, with striking molecular convergence across acute and chronic infections, tumors, autoimmunity, aging, and homeostasis, illustrating that shared transcriptional states support protective or dysfunctional outcomes depending on developmental history and microenvironment. We validate immgenT as a comprehensive reference by integrating external datasets from conditions not represented in immgenT and by defining a flow cytometry panel spanning the CD8+ T cell landscape. Thus, immgenT-CD8 provides a molecular framework for harmonizing CD8+ T cell literature and clarifies relationships across diverse immune challenges.

immunology↗

bioRxiv2026

immgenT Treg: A Reference Landscape of Regulatory T Cell States in Mice

CD4+FoxP3+ regulatory T cells (Tregs) are central to immunity, tolerance, and tissue homeostasis, yet their extensive heterogeneity lacks a unifying framework. Within the immgenT project, we profiled gene expression, surface markers and TCR clonotypes of mouse Tregs. Using a joint RNA-protein deep generative model, we define the Treg landscape, organized around eight conserved clusters shared across tissues and conditions, with immune context reshaping their relative abundance rather than generating new states, including a prominent circulating effector Treg population enriched in select non-lymphoid tissues. We validate this framework by integrating external datasets from conditions not represented in immgenT and by defining a flow cytometry panel spanning the Treg landscape. Together, immgenT provides a scalable, reusable reference that unifies Treg heterogeneity across tissues and immune challenges.

immunology↗

bioRxiv2026

The αβTCR repertoire at scale in the immgenT dataset

The immense T cell receptor (TCR) repertoire is shaped by VDJ combinatorial diversity, imprecise rearrangements, and clonal selection. The immgenT Project generated scRNA and TCRseq to map paired {beta}TCR repertoires across 734 mouse samples from diverse tissues and challenge conditions. Compositional analysis uncovered some extreme junctional architectures. Beyond probabilistic V and J pairing, over-represented joins suggested non-randomness in fine joining, broadening the precedent of quasi-invariant iNKT and MAIT TCRs. We charted public clonotypes linked to self or environmental antigens in the main lineages. Tissue analyses revealed compartmentalized tissue-specific expansions. Unproductive and productive rearrangements of a V gene appeared to interfere specifically with each other, at chromatin or RNA levels. Unexpectedly, allelic exclusion at TCR{beta} proved less stringent than thought, and we identified rearrangements of TCR in immature pre-T stages. This organism-wide look into the TCR repertoire offers novel insights on the evolutionary and immunological pressures on TCR repertoire selection.

immunology↗

bioRxiv2025

Epidermal Resident Memory T Cell Fitness Requires Antigen Encounter in the Skin

CD8+ tissue resident memory T cells (TRM) develop from effectors that seed peripheral tissues where they persist providing defense against subsequent challenges. TRM persistence requires autocrine TGF{beta} transactivated by integrins expressed on keratinocytes. TRM precursors that encounter antigen in the epidermis during development outcompete bystander TRM for TGF{beta} resulting in enhanced persistence. ScRNA-seq analysis of epidermal TRM revealed that local antigen experience in the skin resulted in an enhanced differentiation signature in comparison with bystanders. Upon recall, TRM displayed greater proliferation dictated by affinity of antigen experienced during epidermal development. Finally, local antigen experienced TRM differentially expressed TGF{beta}RIII, which increases avidity of the TGF{beta}RI/II receptor complex for TGF{beta}. Selective ablation of Tgfbr3 reduced local antigen experienced TRM capacity to persist, rendering them phenotypically like bystander TRM. Thus, antigen driven TCR signaling in the epidermis during TRM differentiation results in a lower TGF{beta} requirement for persistence and increased proliferative capacity that together enhance epidermal TRM fitness.

immunology↗

bioRxiv2025

Histopathologic Evaluation and Single-Cell Spatial Transcriptomics of the Colon Reveal Cellular and Molecular Abnormalities Linked to J-Pouch Failure in Patients with Inflammatory Bowel Disease.

Background and AimsTotal abdominal colectomy (TAC) with a staged ileal pouch-anal anastomosis (IPAA) is a common surgical treatment for ulcerative colitis (UC). However, a significant percentage of patients experience pouch failure, leading to morbidity. This retrospective case-control study identified histopathological features of the TAC specimen associated with pouch failure and investigated the molecular mechanisms of this susceptibility using single-cell spatial transcriptomics. MethodsWe analyzed a cohort of 417 patients who underwent IPAA between 2000-2010 at the University of Chicago Medical Center for up to 18 years. Histological examination of TAC specimens focused on disease activity, depth of inflammation, and specific features, including granulomas and deep ulcers. A subset of patients was profiled using single-cell spatial transcriptomics to map gene expression and immune cell interactions in relation to the risk of pouch failure. ResultsThe 18-year pouch failure risk was 23%, with post-procedure diagnosis of CD as a major risk factor (HR = 4.3, 95% CI: 2.3-8.1) as well as high-risk histologic features, including deep chronic inflammation (HR = 21, 95% CI: 11-41) and severe disease activity (HR = 14, 95% CI: 5.7-32) in TAC specimens. Spatial transcriptomics showed immune infiltration of T and myeloid cells, reduced myocyte-glial interactions, and cytokine signaling pathways such as IL-10, IL-1{beta}, and type I/II interferons, associated with an increased risk of pouch failure. ConclusionHistological features and spatial molecular profiling are predictive of IPAA failure. These findings support the use of histologic evaluation and targeted molecular analysis of the TAC specimen to identify high-risk patients and improve IPAA outcomes.

pathology↗

bioRxiv2024

Genomic Specificity of Anti-TCR mAbs determined by single-cell RNAseq

T cells play a pivotal role in the immune system, relying on their somatically rearranged T cell receptor (TCR) to recognize peptide-MHC complexes. A comprehensive and extensively used set of monoclonal antibodies (mAbs) against TCR Variable regions was generated in the previous century. The separate identification of mAb-specific TCR-V proteins and TRV genes has resulted in multiple nomenclatures, making their relationships unclear. To formally re-establish this link and determine patterns of reactivity within TRV subfamilies, we sorted T cells positive for any one of a panel of 22 anti-V mAbs and determined their TRV genes by single-cell TCRseq. RNAseq data revealed consistently higher expression of repeated elements from the ERV1-family LTR RLTR6Mm (mapping to Gm20400) in cells utilizing TRBV segments encoded within a 66kb genomic region between TRBV23 and TRBV30. Our findings provide a comprehensive resource for anti-TCR mAb specificity and insight into V-gene usage biases and T cell function.

immunology↗

bioRxiv2023

PD-1 Mediated Regulation of Unique Activated CD8+ T Cells by NK Cells in the Submandibular Gland

The increasing utilization of anti-PD-1 immune checkpoint blockade (ICB) has led to the emergence of immune-related adverse events (irAEs), including sicca syndrome. Interestingly, we found that the submandibular gland (SMG) of PD-1 deficient mice harbors a large population of CD8+ T cells, reminiscing ICB induced sicca. This phenotype was also observed in the SMG of both NK cell-depleted C57BL/6 animals and NK cell-deficient animals. Mechanistically, using mice conditionally deficient for PD-L1 in the NK cell lineage, we discovered that NK cells regulate CD8+ T cell homeostasis via the PD-1/PD-L1 axis in this organ. Importantly, single-cell RNA sequencing of PD-1 deficient SMG CD8+ T cells reveals a unique transcriptional profile consistent with TCR activation. These cells have limited TCR diversity and phenotypically overlap with GzmK+ CD8+ T autoimmune cells identified in primary Sjogrens syndrome patients. These insights into NK cell immunoregulation in the SMG, and the consequences of disrupted CD8+ T cell homeostasis, provide opportunities for preventing the development of irAEs. HighlightsO_LIElevated CD8+ T cells in the submandibular gland (SMG) of PD-1 deficient mice parallel sicca-like irAEs seen in ICB patients. C_LIO_LIIn addition to their previously described hyporesponsive phenotype, NK cells in the SMG regulate CD8+ T cell homeostasis through the PD-L1/PD-1 axis. C_LIO_LIPD-1 deficient SMG CD8+ T cells display unique transcriptional profiles associated with proinflammatory functions, TCR activation, interferon stimulation, and exhaustion. C_LIO_LIOligoclonal expansion and similarities in TCR sequences indicate T cell activation and a preference for recognizing specific antigens. C_LI

immunology↗

bioRxiv2021

A literature-derived knowledge graph augments the interpretation of single cell RNA-seq datasets

Technology to generate single cell RNA-sequencing (scRNA-seq) datasets and tools to annotate them have rapidly advanced in the past several years. Such tools generally rely on existing transcriptomic datasets or curated databases of cell type defining genes, while the application of scalable natural language processing (NLP) methods to enhance analysis workflows has not been adequately explored. Here we deployed an NLP framework to objectively quantify associations between a comprehensive set of over 20,000 human protein-coding genes and over 500 cell type terms across over 26 million biomedical documents. The resultant gene-cell type associations (GCAs) are significantly stronger between a curated set of matched cell type-marker pairs than the complementary set of mismatched pairs (Mann Whitney p < 6.15x10-76, r = 0.24; cohens D = 2.6). Building on this, we developed an augmented annotation algorithm that leverages GCAs to categorize cell clusters identified in scRNA-seq datasets, and we tested its ability to predict the cellular identity of 185 clusters in 13 datasets from human blood, pancreas, lung, liver, kidney, retina, and placenta. With the optimized settings, the true cellular identity matched the top prediction in 66% of tested clusters and was present among the top five predictions for 94% of clusters. Further, contextualization of differential expression analyses with these GCAs highlights poorly characterized markers of established cell types, such as CLIC6 and DNASE1L3 in retinal pigment epithelial cells and endothelial cells, respectively. Taken together, this study illustrates for the first time how the systematic application of a literature derived knowledge graph can expedite and enhance the annotation and interpretation of scRNA-seq data.

genomics↗

bioRxiv2020

Single cell analysis of FOXP3 deficiencies in humans and mice unmasks intrinsic and extrinsic CD4+ T cell perturbations

ABSTRACTFOXP3 deficiency in humans with IPEX syndrome and mice results in fatal systemic autoimmunity by altering regulatory T cell (Treg) physiology, but actual cellular and molecular mechanisms of disease are unclear, part because Treg surface markers may be unreliable in disease states. We used deep profiling by flow cytometry, population and single-cell RNAseq to analyze Tregs and conventional (Tconv) CD4+ T lymphocytes in cohorts of IPEX patients with a range of genetic lesions, and in Foxp3-deficient mice. In all patients and mice, heterogeneous Treg-like cells with an active FOXP3 locus were observed, some differing very little from normal Tregs, others more distant. Tconv showed no widespread activation or Th bias. The dominant mark was a monomorphic signature equally affecting all CD4+ T cells, unexpectedly dampening tumor-Treg and cytokine-signaling modules. In mixed bone marrow chimeras, WT Tregs exerted dominant suppression, normalizing the states of mutant Treg and Tconv, extinguishing the disease signature, and revealing a small gene cluster truly regulated, cell-intrinsically, by FOXP3. These results suggest a two-step pathogenesis model, with therapeutic implications: limited downregulation of a few core Treg genes de-represses a systemic mediator(s), which imprints the disease signature on all T cells, and further dampens Treg function.Competing Interest StatementThe authors have declared no competing interest.View Full Text

immunology↗

bioRxiv2020

Knowledge synthesis from 100 million biomedical documents augments the deep expression profiling of coronavirus receptors

The COVID-19 pandemic demands assimilation of all available biomedical knowledge to decode its mechanisms of pathogenicity and transmission. Despite the recent renaissance in unsupervised neural networks for decoding unstructured natural languages, a platform for the real-time synthesis of the exponentially growing biomedical literature and its comprehensive triangulation with deep omic insights is not available. Here, we present the nferX platform for dynamic inference from over 45 quadrillion possible conceptual associations extracted from unstructured biomedical text, and their triangulation with Single Cell RNA-sequencing based insights from over 25 tissues. Using this platform, we identify intersections between the pathologic manifestations of COVID-19 and the comprehensive expression profile of the SARS-CoV-2 receptor ACE2. We find that tongue keratinocytes, airway club cells, and ciliated cells are likely underappreciated targets of SARS-CoV-2 infection, in addition to type II pneumocytes and olfactory epithelial cells. We further identify mature small intestinal enterocytes as a possible hotspot of COVID-19 fecal-oral transmission, where an intriguing maturation-correlated transcriptional signature is shared between ACE2 and the other coronavirus receptors DPP4 (MERS-CoV) and ANPEP (-coronavirus). This study demonstrates how a holistic data science platform can leverage unprecedented quantities of structured and unstructured publicly available data to accelerate the generation of impactful biological insights and hypotheses. The nferX Platform Single-cell resource - https://academia.nferx.com/

genomics↗