Biology subjects

Ye, C. J.

Publications and source records attributed to Ye, C. J..

At least 19 recordsLinked to original sources

bioRxiv2026

Longitudinal linear-epitope profiling of SLE identifies novel antigens associated with disease and disease activity

Systemic lupus erythematosus (SLE) is the prototypical systemic autoimmune disease defined by the production of autoantibodies directed against diverse self-antigens. Antinuclear antibodies (ANA) are central to SLE diagnosis, and anti-double-stranded DNA (anti-dsDNA) antibodies are clinically used to track disease activity. However, these biomarkers provide only partial insight into the full spectrum of autoimmune changes in patients. To expand the known landscape of SLE autoreactivity shared among patients and associated with disease activity, we performed an unbiased characterization of 1,268 SLE plasma samples from 695 SLE patients and 301 healthy controls using phage immunoprecipitation and sequencing (PhIP-seq). This technique preferentially displays linear epitopes, and thus provides a complementary set of autoreactivities to previously discovered autoreactive targets. Despite the marked clinical heterogeneity of SLE, machine learning applied to this data surfaced a shared, reproducible signature that robustly discriminates between SLE and healthy controls in two validation cohorts. Autoreactivities in SLE were significantly enriched for RNA-binding protein targets. Autoreactive proteins containing Basic Acidic Dipeptides (BAD) repeat domains, features associated with biomolecular condensate formation, distinguished patients with high disease activity (by SLEDAI score) from patients with no disease activity. Among all shared autoreactivities, a peptide derived from YTHDF2 emerged as the most prevalent target across SLE cohorts occurring in ~25% of SLE patients and displayed striking sequence similarity to a peptide derived from the Epstein Barr virus (EBV) lytic switch protein BZLF1. Competition assays demonstrated higher-affinity binding to the viral peptide, supporting a model in which antiviral humoral responses to EBV may drive secondary epitope spreading toward molecularly mimicked self-antigens. Remarkably, orthogonal validation using the Luminex bead platform containing a panel of only 10 peptides, including BZLF1, demonstrated strong discrimination between SLE and healthy controls (AUC = 0.95). Together, these findings identify a rich, greatly expanded landscape of autoreactivities in SLE, candidate biomarkers associated with high disease activity, and provide mechanistic insight linking EBV-driven immunity to epitope-specific autoimmunity in lupus.

immunology↗

bioRxiv2026

A genome-wide CRISPR activation map of surface protein expression in human CD4 T cells

Surface proteins define T cell identity and function, but the abundance of each protein is not determined by transcription alone. Existing genome-wide CRISPR screens in primary human T cells either profile the transcriptome or isolate cells based on a single functional or protein phenotype. Here we present SCITO-Perturb-seq, a novel platform that couples combinatorial-indexed single-cell cytometry sequencing with pooled CRISPR activation (CRISPRa) to map the causal regulation of 201 surface proteins across 3.6 million human CD4 T cells. We find that 16% of activated genes significantly alter the expression of at least one surface protein. By applying semi-nonnegative matrix factorization to the perturbation effect matrix, we identified five modules corresponding to known CD4 T cell states. Notably, these modules group surface proteins by their shared response to perturbation, revealing coordinated regulation of proteins that are not co-expressed in unperturbed cells. SCITO-Perturb-seq represents the first genome-wide CRISPRa screen paired with direct, high-dimensional surface protein profiling, providing a comprehensive regulatory map of the CD4 T cell surface proteome.

genomics↗

bioRxiv2026

DPP9-mediated inflammasome repression protects against checkpoint inhibitor lung toxicity

Over one million patients receive cancer immunotherapy annually, yet the mechanisms underlying life-threatening immune-mediated toxicities remain poorly understood. Checkpoint inhibitor pneumonitis (CIP) is the leading cause of immunotherapy-related mortality, with a case fatality rate approaching 10%, and no genetic risk factors have been described to date. We identified Dipeptidyl-peptidase 9 (DPP9) as the first genetic susceptibility gene for CIP in a clinico-genomics cohort of 4,397 patients treated with immune checkpoint inhibitors. Mechanistically, DPP9 suppresses CARD8 inflammasome activation and IL-18 secretion in human monocytes, a pathway which is engaged prior to CIP onset, with IL-18 selectively elevated in the plasma of patients who subsequently develop CIP. Myeloid-restricted ablation of Dpp8 and Dpp9 in mice recapitulated the pulmonary histopathological and immunological hallmarks of CIP, including granuloma formation, accumulation of IFN{gamma}-producing T cells and monocyte-derived macrophages. Each of these phenotypes were driven by excessive IL-18 secretion. Together, these findings establish DPP9 as a genetic determinant of CIP and nominate IL-18 blockade as a mechanistically rational therapeutic strategy.

immunology↗

bioRxiv2026

Spatially resolved, multimodal in vivo Perturb-seq using antibody-based cell hashing

Large-scale perturbation screens have begun to map cell intrinsic gene-function relationships, yet how genes shape tissue architecture remains largely unexplored. To address this gap, we developed PerturbSpace, a novel approach that integrates CRISPR perturbations with spatially hashed single-cell multiomics. This approach enables the first high-throughput, spatially resolved Perturb-seq analysis across complex tissue architecture in vivo. Notably, PerturbSpace enables spatial transcriptome-wide perturbation readouts at organ scale and can be seamlessly integrated with orthogonal modalities. We combine PerturbSpace with surface proteomics and expressed lineage tracing barcodes to demonstrate multimodal compatibility. We use PerturbSpace to study the genetic determinants of tissue architecture. First, we map how 40 transcriptional regulators determine the size and lineage composition of colonies in the spleen during regenerative hematopoiesis. Second, we characterize immune-niche interactions in the liver by dissecting the extrinsic effects mediated by cytokine-secreting immune cells on their neighboring cells. Collectively, our work establishes PerturbSpace as a scalable and cost-effective approach for transcriptome-wide spatial profiling of whole cells while remaining compatible with the single-cell multiomics workflows that the field has already adopted at scale.

genomics↗

bioRxiv2026

Extending structural surfaceomics to identify aberrant conformations of tumor surface proteins as potential immunotherapy targets

The complement of tumor cell surface proteins, or "surfaceome", is a rich source of potential immunotherapy targets. To move beyond expression-based target discovery, we previously described "structural surfaceomics," combining crosslinking mass spectrometry (XL-MS) with surface protein biotinylation to identify conformation-selective targets. In our prior work, we applied this method to a single model of acute myeloid leukemia (AML), identifying active integrin beta-2 as a promising target. Here, we expand structural surfaceomics to identify additional immunotherapy targets and surface protein biology across additional models of AML, multiple myeloma, and prostate cancer, as well as donor peripheral blood mononuclear cells. Utilizing these models and different chemical crosslinkers, we compile an extensive database of 5,209 crosslinks. We characterize both shared and unique crosslink-based features, identifying 1,612 disease model-specific crosslinks, including 212 potentially defining tumor-specific conformations based on distance constraint violations relative to AlphaFold predictions. We further implement a suite of emerging modeling tools to predict tumor-specific protein structures. We probe crosslinking patterns suggesting multiple myeloma-specific CD48 and AML-specific integrin 1/{beta}4 heterodimer conformations. This work establishes a resource for cancer structural biology by implementation of structural surfaceomics. Our findings also point toward more realistic protein design models, potentially enabling systematic detection of targetable cancer-specific epitopes for next-generation immunotherapies.

cancer biology↗

bioRxiv2025

Determining gene specificity from multivariate single-cell RNA sequencing data

An important application of single-cell genomics experiments is to identify genes specific to biological categories or experimental conditions. Although numerous approaches have been proposed to identify such genes, we consider an axiomatic approach based on defining properties that a specificity measure should have. This leads us to develop ember (Entropy Metrics for Biological ExploRation), which we show is the only method satisfying four key desired properties for a specificity measure. Applying ember to eight tissues from eight founder mouse strains, we find that gene specificity is often unintuitive: canonical markers can be supplanted, housekeeping genes are context-dependent, and mouse strain can drive unexpected cell type switching. Unsupervised learning on entropy metrics uncovers shared genes specialized to male gonads and kidney, as well as genes specific to non-consecutive developmental stages in the kidney. To facilitate further exploration of gene specificity in mice, we have also developed a comprehensive specificity database, along with a web interface, API and MCP server. Extending ember to a human PBMC dataset collected from 255 diverse individuals, we find that variation in PBMCs is largely localized to classical monocytes. We also find genes with unique specificity by sex, age and ancestral background. Together, these applications establish ember as a powerful tool and provide a roadmap for elucidating the impact of human genetic variation using the murine model.

bioinformatics↗

bioRxiv2025

ANTIPODE Provides a Global View of Cell Type Homology and Transcriptomic Divergence in the Developing Mammalian Brain

Diverse neurons and glia are generated in conserved spatial and temporal sequences during mammalian brain development. Divergence in gene regulatory networks can alter brain composition, scaling, timing, and function. However, resolving the identity, extent, and principles of gene regulatory divergence requires cellular-resolution surveys spanning brain regions and species and improved methods for defining cell type homologies. Here, we present ANTIPODE, a deep-learning variational inference framework that simultaneously integrates single-cell datasets, identifies homologous cell types, and parcellates differential expression across cell types, modules, and covariance. Applying ANTIPODE to a census of the whole developing macaque brain and a meta-atlas of human, macaque, and mouse brain development, we find broad conservation of initial neuron classes but widespread regulatory divergence within homologous types, shaped by genomic context, cell lineage, and developmental timing. Together, ANTIPODE provides a formalized and interpretable framework for cross-species single-cell analysis and reveals principles of gene regulatory divergence in mammalian brain evolution.

developmental biology↗

bioRxiv2025

Base-editing a single missense mutation in A20 enhances CAR-T cell efficacy

T cell exhaustion limits the efficacy of cancer immunotherapies. Here, we performed genome-wide loss-of-function screening in repetitively stimulated human T cells and identified the mulitfunctional ubiquitin-modifying protein A20/TNFAIP3 as a major negative regulator of exhausted T cell persistence. Protein large language modeling, deep base-editing mutagenesis, and studies in immunocompetent mice with domain-specific inactivating mutations revealed A20s non-enzymatic M1 ubiquitin-binding zinc finger 7 (A20ZF7) motif as critical to suppression of anti-tumor immunity. A20ZF7-deficient CD8+ tumor-infiltrating lymphocytes (TILs) resisted terminal exhaustion and circumvented an unappreciated mechanism restraining perforin degranulation in terminally exhausted cells. Human chimeric antigen receptor (CAR)-T cells engineered via base-editing to inactivate A20ZF7 via a single missense mutation also resisted exhaustion, secreted more perforin and robustly suppressed cancer in vivo. These studies pinpoint A20ZF7 as a novel T cell checkpoint and reveal precision base-editing of missense mutations as an effective approach to enhance CAR-T cell therapy.

immunology↗

bioRxiv2025

Programmable microparticles rewire CAR signaling to enable super-physiological expansion of human T cells in vitro

T cell proliferative capacity and persistence critically determine the therapeutic success of chimeric antigen receptor (CAR) T cells. However, it remains unknown if and how human CAR-T cells can be externally programmed to reach maximal proliferative capacity. Here, we use programmable PLGA microparticles functionalized with CAR-antigens and CD28-costimulatory antibodies (CAREp) to repeatedly stimulate human CD8+ CAR-T cells in vitro. CAREp-stimulated CAR-T cells expanded continuously for over 100 days--versus [~]30 days with tumor cell stimulation--and achieved up to 1018-fold cumulative expansion, greatly surpassing CD3/28-Dynabeads. Early-phase transcriptomic responses-- upregulation of DNA repair, cell cycle, telomere maintenance, and mitochondrial pathways--aligned with long-term outcomes: massive proliferation, telomere stability, robust respiration, and preserved progenitor phenotype by single-cell sequencing. Differentiation and exhaustion signals were broadly suppressed. Transient telomerase activity further supported physiologic expansion. These findings demonstrate that nanoscale-controlled extracellular cues can rewire intracellular signaling to drive durable, super-physiological expansion of functional CAR-T cells.

immunology↗

bioRxiv2025

Transcriptomic analysis reveals immune signatures associated with specific cutaneous manifestations of lupus in systemic lupus erythematosus

Systemic lupus erythematosus (SLE) presents with diverse heterogenous cutaneous manifestations. However, the molecular and immunologic pathways driving specific cutaneous manifestations of SLE are poorly understood. Here, we leverage transcriptomics from a large well-phenotyped longitudinal cohort of SLE patients to map molecular pathways linked to ten distinct SLE-related rashes. Through whole blood and immune cell-sorted bulk RNA sequencing, we identified immune signatures specific to cutaneous subtypes of SLE. Subacute cutaneous lupus (SCLE) exhibited broad upregulation of interferon, TNF-, and IL6-JAK-STAT3 pathways suggesting potential unique therapeutic responses to JAK and type I interferon inhibition. While interferon signaling is prominent in SCLE, discoid lupus, and acute lupus, it is unexpectedly attenuated in patients with skin and mucosal ulcers. Pathway and cell-type enrichment analysis revealed unexpected roles for CD14+ monocytes in photosensitivity of SLE and NK cells in alopecia, mucosal ulceration, and livedo reticularis. These findings illuminate the immune heterogeneity of rashes in SLE, highlighting subtype-specific mechanistic targets, and presenting opportunities for precision therapies for SLE-associated skin phenotypes.

bioinformatics↗

bioRxiv2025

Defining the host dependencies and the transcriptional landscape of RSV infection and bystander activation

Respiratory syncytial virus (RSV) is a globally prevalent pathogen, causes severe disease in older adults, and is the leading cause of bronchiolitis and pneumonia in the United States for children during their first year of life [1]. Despite its prevalence worldwide, RSV-specific treatments remain unavailable for most infected patients. Here, we leveraged a combination of genome-wide CRISPR knockout screening and single-cell RNA sequencing to improve our understanding of the host determinants of RSV infection and the host response in both infected cells, and uninfected bystanders. These data reveal temporal transcriptional patterns that are markedly different between RSV infected and bystander activated cells. Our data show that expression of interferon-stimulated genes is primarily observed in bystander activated cells, while genes implicated in the unfolded protein response and cellular stress are upregulated specifically in RSV infected cells. Furthermore, genome-wide CRISPR screens identified multiple host factors important for viral infection, findings which we contextualize relative to 29 previously published screens across 17 additional viruses. These unique data complement and extend prior studies that investigate the proinflammatory response to RSV infection, and juxtaposed to other viral infections, provide a rich resource for further hypothesis testing. ImportanceRespiratory syncytial virus (RSV) is a leading cause of lower respiratory tract infection in infants and the elderly. Despite its substantial global health burden, RSV-targeted treatments remain unavailable for the majority of individuals. While vaccine development is underway, a detailed understanding of the host response to RSV infection and identification of required human host factors for RSV may provide insight into combatting this pathogen. Here, we utilized single-cell RNA sequencing and functional genomics to understand the host response in both RSV infected and bystander cells, identify what host factors mediate infection, and contextualize these findings relative to dozens of previously reported screens across 17 additional viruses.

microbiology↗

bioRxiv2025

Phenotypic pleiotropy of missense variants in human B cell-confinement receptor P2RY8

Missense variants can have pleiotropic effects on protein function and predicting these effects can be difficult. We performed near-saturation deep mutational scanning of P2RY8, a G-protein-coupled receptor that promotes germinal center B cell confinement. We assayed the effect of each variant on surface expression, migration, and proliferation. We delineated variants that affected both expression and function, affected function independently of expression, and discrepantly affected migration and proliferation. We also used cryo-electron microscopy to determine the structure of activated, ligand-bound P2RY8, providing structural insights into the effects of variants on ligand binding and signal transmission. We applied the deep mutational scanning results to both improve computational variant effect predictions and to characterize the phenotype of germline variants and lymphoma-associated variants. Together, our results demonstrate the power of integrating deep mutational scanning, structure determination, and in silico prediction to advance the understanding of a receptor important in human health.

genomics↗

bioRxiv2025

Single-cell RNA sequencing of peripheral blood defines two immunological subtypes of Sjögren's disease

Sjogrens disease (SjD) is a heterogeneous autoimmune disorder with significant clinical and molecular diversity. While anti-SSA antibodies serve as a hallmark serological biomarker, nearly half of patients lack them, raising fundamental questions about distinct pathogenic mechanisms. To address this, we performed single-cell RNA sequencing with surface protein profiling on 1.5 million PBMCs from 333 participants in the Sjogrens International Collaborative Clinical Alliance cohort, comparing those with and without SjD and stratifying them by anti-SSA status. Our analysis identified two immunologically distinct subtypes of SjD, with SSA-positive patients exhibiting a dominant and persistent IFN-I signature, profoundly impacting immune cell maturation. Notably, transitional B cells were disproportionately affected, displaying signs of premature maturation, reduced BCR diversity, and shorter CDR3 regions, reinforced interactions with proinflammatory cells, thereby fostering an environment conducive to autoreactivity. By contrast, SSA-negative SjD participants lacked an upregulated IFN-I signature, challenging prevailing pathogenic models and suggesting alternative immune dysregulation pathways. These findings support a two-disease model of SjD and highlight transitional B cells as both a key biomarker and a potential therapeutic target. Targeting IFN-I signaling and transitional B cell maturation may represent a novel therapeutic avenue to modulate immune dysregulation and prevent autoreactivity in SjD.

genomics↗

bioRxiv2025

A20's Linear Ubiquitin Binding Motif Restrains Pathogenic Activation of TH17/22 cells and IL-22 Driven Enteritis

A20, encoded by the TNFAIP3 gene, is a protein linked to Crohns disease and celiac disease in humans. We now find that mice expressing point mutations in A20s M1 ubiquitin binding motif (ZF7) spontaneously develop proximate enteritis that requires both luminal microbes and T cells. Cellular and transcriptomic profiling reveal expansion of TH17/22 cells and aberrant expression of IL-17A and IL-22 in intestinal lamina propria of A20ZF7 mice. While deletion of IL-17A from A20ZF7/ZF7 mice exacerbates enteritis, deletion of IL-22 abrogates intestinal epithelial cell hyperproliferation, barrier dysfunction, and alarmin expression. A20ZF7/ZF7 TH17/22 cells autonomously express more ROR{psi}t and IL-22 after differentiation in vitro. ATAC sequencing identified an enhancer region upstream of the Il22 gene in A20ZF7/ZF7 T cells, and this enhancer demonstrated increased activating histone acetylation coupled with exaggerated Il22 transcription. Finally, CRISPR/Cas9-mediated ablation of A20ZF7 in human T cells increases ROR{psi}t expression and IL22 transcription. These studies link A20s M1 ubiquitin binding function with ROR{psi}t expression, epigenetic activation of TH17/22 cells, and IL-22 driven enteritis.

immunology↗

bioRxiv2024

Learning antibody sequence constraints from allelic inclusion

Antibodies and B-cell receptors (BCRs) are produced by B cells, and are built of a heavy chain and a light chain. Although each B cell could express two different heavy chains and four different light chains, usually only a unique pair of heavy chain and light chain is expressed--a phenomenon known as allelic exclusion. However, a small fraction of naive-B cells violate allelic exclusion by expressing two productive light chains, one of which has impaired function; this has been called allelic inclusion. We demonstrate that these B cells can be used to learn constraints on antibody sequence. Using large-scale single-cell sequencing data from humans, we find examples of light chain allelic inclusion in thousands of naive-B cells, which is an order of magnitude larger than existing datasets. We train machine learning models to identify the abnormal sequences in these cells. The resulting models correlate with antibody properties that they were not trained on, including polyreactivity, surface expression, and mutation usage in affinity maturation. These correlations are larger than what is achieved by existing antibody modeling approaches, indicating that allelic inclusion data contains useful new information. We also investigate the impact of similar selection forces on the heavy chain in mouse, and observe that pairing with the surrogate light chain significantly restricts heavy chain diversity.

immunology↗

bioRxiv2024

Current genomic deep learning models display decreased performance in cell type specific accessible regions

AbstractO_ST_ABSBackgroundC_ST_ABSA number of deep learning models have been developed to predict epigenetic features such as chromatin accessibility from DNA sequence. Model evaluations commonly report performance genome-wide; however, cis regulatory elements (CREs), which play critical roles in gene regulation, make up only a small fraction of the genome. Furthermore, cell type specific CREs contain a large proportion of complex disease heritability. ResultsWe evaluate genomic deep learning models in chromatin accessibility regions with varying degrees of cell type specificity. We assess two modeling directions in the field: general purpose models trained across thousands of outputs (cell types and epigenetic marks), and models tailored to specific tissues and tasks. We find that the accuracy of genomic deep learning models, including two state-of-the-art general purpose models - Enformer and Sei - varies across the genome and is reduced in cell type specific accessible regions. Using accessibility models trained on cell types from specific tissues, we find that increasing model capacity to learn cell type specific regulatory syntax - through single-task learning or high capacity multi-task models - can improve performance in cell type specific accessible regions. We also observe that improving reference sequence predictions does not consistently improve variant effect predictions, indicating that novel strategies are needed to improve performance on variants. ConclusionsOur results provide a new perspective on the performance of genomic deep learning models, showing that performance varies across the genome and is particularly reduced in cell type specific accessible regions. We also identify strategies to maximize performance in cell type specific accessible regions.

genomics↗

bioRxiv2024

Variants in tubule epithelial regulatory elements mediate most heritable differences in human kidney function

Kidney disease is highly heritable; however, the causal genetic variants, the cell types in which these variants function, and the molecular mechanisms underlying kidney disease remain largely unknown. To identify genetic loci affecting kidney function, we performed a GWAS using multiple kidney function biomarkers and identified 462 loci. To begin to investigate how these loci affect kidney function, we generated single-cell chromatin accessibility (scATAC-seq) maps of the human kidney and identified candidate cis-regulatory elements (cCREs) for kidney podocytes, tubule epithelial cells, and kidney endothelial, stromal, and immune cells. Kidney tubule epithelial cCREs explained 58% of kidney function SNP-heritability and kidney podocyte cCREs explained an additional 6.5% of SNP-heritability. In contrast, little kidney function heritability was explained by kidney endothelial, stromal, or immune cell-specific cCREs. Through functionally informed fine-mapping, we identified putative causal kidney function variants and their corresponding cCREs. Using kidney scATAC-seq data, we created a deep learning model (which we named ChromKid) to predict kidney cell type-specific chromatin accessibility from sequence. ChromKid and allele specific kidney scATAC-seq revealed that many fine-mapped kidney function variants locally change chromatin accessibility in tubule epithelial cells. Enhancer assays confirmed that fine-mapped kidney function variants alter tubule epithelial regulatory element function. To map the genes which these regulatory elements control, we used CRISPR interference (CRISPRi) to target these regulatory elements in tubule epithelial cells and assessed changes in gene expression. CRISPRi of enhancers harboring kidney function variants regulated NDRG1 and RBPMS expression. Thus, inherited differences in tubule epithelial NDRG1 and RBPMS expression may predispose to kidney disease in humans. We conclude that genetic variants affecting tubule epithelial regulatory element function account for most SNP-heritability of human kidney function. This work provides an experimental approach to identify the variants, regulatory elements, and genes involved in polygenic disease.

genetics↗

bioRxiv2023

Single cell sequencing as a general variant interpretation assay

The human genome contains [~]70 million possible protein-altering variants, the vast majority of which are of uncertain clinical significance. Closing this gap is essential for accurate diagnosis of disease-causing variants and understanding their mechanisms of action. Towards this goal, we developed a pooled perturbation approach combining saturation mutagenesis with single cell RNA sequencing to map the effects of every single nucleotide variant in a gene. We sequenced [~]440,000 cells expressing variants in CDKN2A (p16INK4a), TP53, and SOD1, observing almost all possible protein-coding variants, with a mean of 61 cells per variant. Using single cell gene expression signatures, we show that each gene may contain multiple types of pathogenic variants that affect distinct downstream pathways. We demonstrate that single cell expression signatures outperform existing bulk experimental assays and computational models for predicting pathogenicity, and summarize both the utility and potential limitations of single cell sequencing as a general variant interpretation assay.

genomics↗