MRTX1133 sensitivty across the PRISM cohort of cell lines.

Abstract Introduction Glycans are key regulators of immune recognition, yet how endogenous human lectins interpret dynamic glycan changes at single-cell resolution remains poorly understood. Existing single-cell omics platforms do not measure functional glycan-lectin interactions, limiting the discovery of glycan-mediated immunoregulatory programs. To address this gap, we developed scGOAT-seq, a multimodal single-cell method that integrates DNA-barcoded recombinant human lectins with transcriptomic profiling to generate physiologically grounded readouts of functional glycan states across diverse immune contexts. Methods We curated and validated a panel of recombinant human lectins, including Siglec-7, Siglec-9, Siglec-15, Galectin-8/9, DC-SIGN, and MBL, that collectively span major mammalian glycan classes and applied them using scGOAT-seq on PBMCs under different immune perturbations. Results Applied to perturbed peripheral immunity, scGOAT-seq revealed stimulus-specific remodeling of Siglec-ligand landscapes that demarcate discrete immune activation states. In CD4 T cells, Siglec-9L and Siglec-15L distinguished immuno-metabolic, transitional, and effector-like activation programs, whereas Siglec-7L uniquely tracked IL-2-independent activation pathways associated with innate-like stimulation. To probe functional consequences of disrupting these pathways, we blocked Siglec-7/9 receptors during LPS stimulation. Blockade reduced Siglec-ligand display, rewired glycan landscapes across monocytes, NK cells, and T cells, and amplified inflammatory, metabolic, and cytotoxic transcriptional programs. These blockade-induced signatures showed prognostic relevance in TCGA kidney cancer datasets, highlighting their potential translational significance. Conclusion Together, scGOAT-seq defines a powerful framework for functional glycan profiling in single cells, revealing previously unrecognized glycan-defined T cell states and offering new avenues for diagnostic stratification and therapeutic glyco-engineering. Funding Source MIT Koch Institute Traditional Bridge Award, MIT School of Science Research Innovation Award, MIT Koch Institute Graduate Fellowship Topic Categories Technological Innovations in Immunology (TECH)

Genetic and copy number variants at resistance to adagrasib or sotorasib across the novel cohort of PDAC and GI cancer patients.

Supplemental Table 5A: Drug sensitivity metrics from MRTX1133-treated KRASG12D patient-derived organoids. Supplemental Table 5B: MRTX1133 dose response across KRASG12D mutated patient-derived organoids.

Crohn’s disease is an inflammatory bowel disease (IBD) commonly treated through anti-TNF blockade. However, most patients still relapse and inevitably progress. Comprehensive single-cell RNA-sequencing (scRNA-seq) atlases have largely sampled patients with established treatment-refractory IBD, limiting our understanding of which cell types, subsets, and states at diagnosis anticipate disease severity and response to treatment. Here, through combining clinical, flow cytometry, histology, and scRNA-seq methods, we profile diagnostic human biopsies from the terminal ileum of treatment-naïve pediatric patients with Crohn’s disease (pediCD; n=14), matched repeat biopsies (pediCD-treated; n=8) and from non-inflamed pediatric controls with functional gastrointestinal disorders (FGID; n=13). To resolve and annotate epithelial, stromal, and immune cell states among the 201,883 baseline single-cell transcriptomes, we develop a principled and unbiased tiered clustering approach, ARBOL. Through flow cytometry and scRNA-seq, we observe that treatment-naïve pediCD and FGID have similar broad cell type composition. However, through high-resolution scRNA-seq analysis and microscopy, we identify significant differences in cell subsets and states that arise during pediCD relative to FGID. By closely linking our scRNA-seq analysis with clinical meta-data, we resolve a vector of T cell, innate lymphocyte, myeloid, and epithelial cell states in treatment-naïve pediCD (pediCD-TIME) samples which can distinguish patients along the trajectory of disease severity and anti-TNF response. By using ARBOL with integration, we position repeat on-treatment biopsies from our patients between treatment-naïve pediCD and on-treatment adult CD. We identify that anti-TNF treatment pushes the pediatric cellular ecosystem towards an adult, more treatment-refractory state. Our study jointly leverages a treatment-naïve cohort, high-resolution principled scRNA-seq data analysis, and clinical outcomes to understand which baseline cell states may predict Crohn’s disease trajectory.

Supplemental Table 4A: Differential gene expression analysis between MRTX1133 sensitive and resistant KRASG12D cell lines. Supplemental Table 4B: Analysis of Copy number and RPPA datasets between MRTX1133 sensitive and resistant KRASG12D cell lines.

Abstract Introduction Solid tumors are populated with functional, non-circulating virus-specific CD8+ T cells, primed for rapid response to re-infection. Immunosurveillance by these memory T cells is evident across multiple tissues and solid tumors. Activation of these bystander cells triggers clearance of poorly immunogenic tumors in mice and is phenocopied in human tumor explants through unknown mechanisms — putatively involving the adjuvanting of tumor-specific T cell responses — providing the basis for a Phase I immunotherapy trial termed peptide alarm therapy (PAT). Methods To define mechanisms of PAT mediated tumor clearance in murine models, we used single-cell multi-omics, targeted immune cell depletions, and genetic manipulations. We mapped how viral peptides influence antiviral T cell states and by what axes they signal to activate the innate and adaptive immune system to mount an effective response against malignant cells. We validated our multi-omic approach via in vivo studies to measure treatment efficacy in the context of key immune cell depletion and pathway inhibitions. Results We initially hypothesized that tumors were cleared in an antigen-specific manner; however, PAT cured without conventional recognition-dependent mechanisms and in the absence of any tumor-specific TCRab T cell. Mechanistically, robust T cell activation recruited immune cells, utilized innate leukocytes, and triggered a tumoricidal combination of effector molecules and panoptotic pathways, resulting in tumor-specific clearance independent of conventional T cell mechanisms. IFN-γ, TNF, and NO induced caspase-dependent death, recapitulating melanoma cures in mice or human melanoma cell death in vitro. Gene expression signatures of immune and tumor cell types involved in this panoptotic pathway were predictive of survival among human melanoma patients. Conclusion Thus, triggering productive T cell activation within tumors can be sufficient for immunotherapy, without needing to induce or rescue cancer-specific responses. Funding Source F30CA253992, R01CA238439, DP1238659 Topic Categories Computational and Systems Immunology (COMP)

Supplemental Figure 1: Acquired resistance to KRASG12C inhibition in PDAC and other GI cancers. Supplemental Figure 2: MRTX1133 sensitivity across KRASG12D mutant in vitro models of PDAC. Supplemental Figure 3: Isogenic models of acquired resistance to MRTX1133. Supplemental Figure 4: In vivo treatment and tumor monitoring for the KPC PDAC mouse model. Supplemental Figure 5: Genomic characterization of KPC tumors. Supplemental Figure 6: snRNA-seq quality metrics and description of the tumor microenvironment. Supplemental Figure 7: Identification and characterization of malignant cell populations. Supplemental Figure 8: Characterization of malignant metaprograms in KPC tumors. Supplemental Figure 9: Treatment with MRTX1133 induces modest changes in the immune microenvironment following tumor regression. Supplemental Figure 10: Treatment of 6694C2-LM tumors with MRTX1133 reduces granulocytes but has little effect on T cells. Supplemental Figure 11: Neoadjuvant and adjuvant therapy in a metastatic model of PDAC.

List of genes in each NMF metaprograms

Characterization of acquired resistance to KRASG12C inhibition across studies and cancer types.

Clustering is commonly used in single-cell RNA sequencing (scRNA-seq) to assess cellular heterogeneity, but standard methods often require user-specified heuristics and rely on post-selective differential expression analyses, which often lead to inflated false discovery rates. Here, we present NCLUSION: a nonparametric infinite mixture model that leverages Bayesian sparse priors to identify marker genes and cluster single-cell expression data simultaneously. NCLUSION uses a variational inference algorithm, which enables it to scale up to millions of cells. Through simulations and analyses of publicly available scRNA-seq studies, we demonstrate that NCLUSION (1) matches the performance of other state-of-the-art clustering techniques with significantly reduced runtime and (2) provides statistically robust and biologically relevant transcriptomic signatures for each of the clusters it identifies. Overall, NCLUSION represents a reliable hypothesis-generating tool for understanding patterns of expression variation present in single-cell populations.

Mutation and sequence alterations from whole exome sequencing of in vitro models of acquired resistance to MRTX1133.

Throughout the female reproductive lifespan, the ovary undergoes hundreds of cycles of follicle development, ovulation and tissue regeneration. How aging disrupts the coordination of such precise, multicellular interactions across time and space is not well understood. Using Slide-seq, a near-cellular spatial transcriptomics method, here we profile 22 mouse ovaries across the reproductive cycle and chronological ages, capturing 610,620 spots across 69 spatial profiles. We develop a novel segmentation pipeline to examine the multicellular dynamics of 358 oocytes, 668 follicles and 236 corpora lutea to find that aging impairs the spatial and temporal coordination required for folliculogenesis even before reproductive cycles cease. These disruptions are characterized by altered immune cell dynamics, inflammatory signaling and global tissue disorganization, which impair the cyclic remodeling required for ovarian function. Our findings reveal how multicellular niches orchestrate ovarian function and demonstrate that age-related breakdown in tissue organization precedes the end of fertility.

List of reagents and antibodies used in the study

Supplemental Table 7A: Clinical evaluation and characterisitcs of KPC mice treated until clinical endpoint. Supplemental Table 7B: Clinical evaluation and characterisitcs of KPC mice treated for 3 days.

A hallmark of HIV infection is disruption of intestinal barrier integrity that persists in people with HIV (PWH) despite treatment with antiretroviral therapy (ART). This disruption is central to HIV disease progression, yet the causes remain incompletely understood. We report a mechanism by which immunometabolic defects in colon-resident CD8+ T cells in PWH lead to intestinal epithelial apoptosis and disruption of intestinal barrier integrity. We show that in PWH, these cells downregulate the lipid sensor peroxisome proliferator-activated receptor-γ (PPARγ), which results in reduced intracellular lipid droplets, impaired fatty acid oxidation, and acquisition of lipids by CD8+ T cells from intestinal epithelial cells, which then contributes to epithelial cell death. Our findings indicate that HIV-associated immunometabolic dysregulation of colon CD8+ T cells leads to loss of intestinal epithelial homeostasis. These results identify potential strategies to reduce comorbidities in PWH and other disorders with disrupted intestinal barrier integrity.

Ovulation is a spatiotemporally coordinated process that involves several tightly controlled events, including oocyte meiotic maturation, cumulus expansion, follicle wall rupture and repair, and ovarian stroma remodeling. To date, no studies have detailed the precise window of ovulation at single-cell resolution. Here, we performed parallel single-cell RNA-seq and spatial transcriptomics on paired mouse ovaries across an ovulation time course to map the spatiotemporal profile of ovarian cell types. We show that major ovarian cell types exhibit time-dependent transcriptional states enriched for distinct functions and have specific localization profiles within the ovary. We also identified gene markers for ovulation-dependent cell states and validated these using orthogonal methods. Finally, we performed cell-cell interaction analyses to identify ligand-receptor pairs that may drive ovulation, revealing previously unappreciated interactions. Taken together, our data provides a rich and comprehensive resource of murine ovulation that can be mined for discovery by the scientific community.

Phenotypic drug screening remains constrained by the vastness of chemical space and the technical challenges of scaling experimental workflows. To overcome these barriers, computational methods have been developed to prioritize compounds, but they rely on either single-task models lacking generalizability or heuristic-based genomic proxies that resist optimization. We designed an active deep learning framework that leverages omics to enable scalable, optimizable identification of compounds that induce complex phenotypes. Our generalizable algorithm outperformed state-of-the-art models on classical recall, translating to a 13- to 17-fold increase in phenotypic hit rate across two hematological discovery campaigns. Combining this algorithm with a lab-in-the-loop signature refinement step, we achieved an additional twofold increase in hit rate along with molecular insights. In sum, our framework enables efficient phenotypic hit identification campaigns, with broad potential to accelerate drug discovery.

Delineating cell populations is crucial for understanding immune function in health and disease. Spatial omics technologies offer insights by capturing three complementary domains: single-cell molecular biomarker expression, cellular spatial relationships and tissue architecture. However, current computational methods often fail to fully integrate these multidimensional data, particularly for immune cell populations and intrinsic functional states. We introduce Cell Local Environment and Neighborhood Scan (CellLENS), a self-supervised computational method that learns cellular representations by fusing information across three spatial omics domains (expression, neighborhood and image). CellLENS markedly enhances de novo discovery of biologically relevant immune cell populations at fine granularity by integrating individual cells’ molecular profiles with their neighborhood context and tissue localization. By applying CellLENS to diverse spatial proteomic and transcriptomic datasets across multiple tissue types and disease settings, we uncover unique immune cell populations functionally stratified according to their spatial contexts. Our work demonstrates the power of multi-domain data integration in spatial omics to reveal insights into immune cell heterogeneity and tissue-specific functions. In this Technical Report, the authors present CellLENS, a computational method that enhances immune cell identification and analyses by integrating cross-domain information in spatial multiomics data.

Developing a working knowledge of immune dynamics during prolonged infection and treatment has become critical for both advancing HIV cure strategies and understanding non-AIDS comorbidities, given rises in the age and average time spent on antiretroviral therapy (ART) among people living with HIV. However, at present, we do not fully appreciate the ways in which prolonged suppressive therapy influences immune function. Toward addressing this key knowledge gap comprehensively, we applied single-cell RNA-sequencing (scRNA-seq) to longitudinally profile peripheral blood mononuclear cells from SIV-infected non-human primates longitudinally. Our data reveal significant immune shifts during acute and chronic infection, as well as over five years of subsequent ART. We observe a decline in CD4+ T cells and an increase in aberrant B cells and CD16+ monocytes during untreated chronic infection, as well as widespread dampened transcriptional activity. Further, we uncover transcriptional signatures suggestive of unresolved immune dysregulation during long-term suppressive therapy - most prominently among myeloid cell populations. By examining concurrent measurements of intact proviral DNA, we link peripheral responses to reservoir size via IPDA. We furthermore identify ribosomal-associated pathways as key differentiators of infection stage, treatment status, and time on ART. Finally, we tested whether previously published transcriptional correlates of differential outcomes (e.g. viral rebound, vaccine efficacy) changed over time on ART. Overall, our findings capture dynamic immune remodeling from acute infection through long-term ART, highlighting complexities in achieving complete immune recovery that may influence future therapeutic strategies.