Biology subjects

Angelo, M.

Publications and source records attributed to Angelo, M..

At least 19 recordsLinked to original sources

bioRxiv2026

CytoRipple: Mapping Tissue Polarization through Subcellular Cytoskeleton Dynamics

Spatial multi-omic technologies entail simultaneous measurement of tens-to-thousands of molecular features (e.g. RNA, protein). While cell type and state profiling is the major focus of spatial multi-omic analyses, cytoskeletal proteins are often solely utilized for cell segmentation and quality control. Here, we introduce a new computational framework, CytoRipple, that leverages these concomitant spatially-resolved single-cell measurements of cytoskeleton proteins to identify tissue-level vector fields. Polarization vectors are known to associate with cell movement and phenotype, inferring their physical orientation. We validate these associations across biological contexts and reveal how polarization vector fields can be harnessed to discover multi-cellular structures (e.g. "streams" of cells with similar polarizations). These multi-cellular cytoskeleton dynamics associate with distinct phenomena, such as vector "divergence" with CD8+ T cell activation and "streams" with spatially segmented stages of the anti-tumor immune response. Together, these data support a role for CytoRipple in revealing a dynamic layer across spatial multi-omic platforms.

bioinformatics↗

bioRxiv2026

Transcriptional networks underlying tumour plasticity in small-cell lung cancer

Across human malignancies, the ability of cancer cells to switch states (plasticity) drives progression, metastasis, and therapy resistance 1-3. Here we investigated the transcriptional and epigenetic basis of plasticity in small-cell lung cancer (SCLC), an aggressive, treatment-refractory cancer 4, 5. SCLC tumours display plasticity along a neuroendocrine (NE) to nonNE axis; however, the trajectories of lineage transition and the gene regulatory network underlying it, remain poorly defined 6. Using single-cell multi-omics, we resolved distinct transcriptional SCLC cell states, including a previously unrecognised Intermediate state comprised of low-identity immunogenic SCLC cells that serve as an obligatory route in NE to nonNE lineage transition. Using single-cell gene expression and chromatin accessibility data, we next developed a Boolean Bayes model for transcription factor networks (BoBa-T) to predict regulators of SCLC cell states. We validated RORB as a novel gatekeeper of the NE state that represses the nonNE identity in SCLC. RORB inactivation drives NE cells into the Intermediate state, upregulates immunogenic programs, and inhibits tumour growth in immunocompetent hosts. Our work identifies a plastic, immunogenic waypoint for SCLC lineage switching and reveals strategies to target it therapeutically.

cancer biology↗

bioRxiv2026

Spatially Resolved Epithelial States Delineate Immunosuppressive Niches of Impaired Myeloid Antigen Presentation in PDAC

Bulk transcriptomic classifiers stratify pancreatic ductal adenocarcinoma (PDAC) into classical and basal-like subtypes with prognostic and therapeutic relevance, yet increasing evidence indicates that these epithelial programs frequently coexist within individual tumors. How these intermediate states affect the local tumor microenvironment remains poorly defined. Here, we integrate multiplexed ion beam imaging (MIBI) with bulk RNA sequencing to resolve epithelial subtype identity at the level of spatially contiguous cancer nests and quantify their associated microenvironments. Across 47 primary tumor samples from 34 patients, we identified classical, intermediate, and basal cancer cell states at single-cell resolution and delineated discrete cancer nests with mixed or dominant subtype compositions. Distance-resolved spatial analysis reveals that basal-rich cancer nests are surrounded by locally immunosuppressive microenvironments characterized by reduced expression of MHC class II and co-stimulatory molecules in proximal myeloid cells, independent of myeloid abundance. These regions are enriched in fibroblast-dominated neighborhoods and distinct cell-cell interaction architectures. Using EcoTyper analysis of two independent bulk RNA-seq cohorts, including an OHSU discovery cohort (N = 277 patients) and TCGA as a validation cohort (N = 147 patients), we identified poor-prognosis tumor ecotypes enriched for basal epithelial states that similarly exhibited depleted myeloid antigen presentation signatures, linking spatial niche phenotypes to transcriptional ecotypes and patient outcomes. Together, these findings demonstrate that epithelial subtype programs in PDAC are organized at the level of spatially defined cancer nests and that basal cancer programs reside within localized niches of myeloid antigen presentation dysfunction, linking intratumoral architecture to immune suppression and clinical prognosis.

cancer biology↗

bioRxiv2026

Extracellular Matrix Proteomic Signatures Associate with Disease-Free Survival in Later Events of Ductal Carcinoma In Situ or Invasive Breast Cancer

BackgroundDuctal carcinoma in situ (DCIS) is a noninvasive breast lesion with variable risk of progression to invasive breast cancer (IBC). Current transcription and cell marker investigations suggest ECM decreases in later events but are limited in details of ECM proteomic composition, including post-translational modifications. We investigated whether the extracellular matrix (ECM) proteome alters with later breast events of DCIS or IBC. MethodsECM-targeted mass spectrometry imaging and liquid chromatography-tandem mass spectrometry (LC-MS/MS) were applied to ten tissue microarrays from the Resource of Archival Human Breast Tissue cohort (RAHBT). Primary DCIS specimens (n=136) were analyzed in relation to later events of DCIS (n=40) or IBC(n=30), with a mean follow-up of 192.1 months 95% CI [179.1,205.1]. Statistical modeling, survival analyses, and exploratory machine learning approaches were used to identify ECM peptide signatures associated with later events. ResultsDistinct ECM peptide profiles were associated with later events of DCIS or IBC. Fifteen peptides derived from fibrillar collagens (COL1A1, COL1A2, COL3A1) and elastin, showed significantly reduced abundance in patients who developed IBC. Lower expression of specific collagen peptides associated with overall 19.9% 95% CI [17.92, 21.81] decreased disease-free survival for IBC. Lower expression of these peptides was significantly associated with reduced disease-free survival (age-adjusted hazard ratio [HR] = 2.45, 95% CI: 2.33-2.57; P < 0.05). Patient-matched samples of primary DCIS, later DCIS, and later invasive breast cancer further demonstrated reduction in ECM peptide detection. Exploratory predictive modeling from patient-matched samples achieved high performance (AUROC >0.98, accuracy >93%) in distinguishing primary from later events. Following prior work in the RAHBT cohort, reduction of certain collagen peptides was also observed in primary DCIS samples from higher risk patient groups. ConclusionsECM proteomic remodeling, particularly decreases of specific collagen domains, is strongly associated with later events of DCIS and IBC. These findings highlight ECM proteome as a critical regulator of breast cancer emergence with potential as a prognosticator of risk stratification to guide clinical management of DCIS.

pathology↗

bioRxiv2026

APOE4 Drives Uniquely Dysfunctional Human Microglial States in Alzheimer's Disease

Variation in APOE, notably the {varepsilon}4 allele, profoundly shapes risk and severity of late-onset Alzheimers disease (AD), yet how it remodels human microglial states remains unresolved. We combine spatially resolved proteomic profiling with single-nuclear multiomic analyses to define microglial organization across APOE3/3 and APOE4/4 genotypes in AD. Quantifying condition-associated variation across the cellular manifold reveals a continuous landscape of microglial states. APOE4/4 shifts cells toward terminal states marked by loss of homeostatic identity, metabolic disruption, and incomplete acquisition of disease-associated programs. We identify an APOE4/4-enriched population in AD that exhibits inflammatory signaling without effective metabolic or phagocytic engagement, localizing to niches of gliosis and senescence, and coupled to chronic stress adaptation programs. Together with evidence that APOE4/4 potentiates the activation threshold of nascent microglia, these findings establish a unified framework for human microglial state change, linking genetic risk to spatial and molecular organization of immune responses in the AD brain. Graphical Abstract.APOE4/4 in Alzheimers disease reshapes microglial fate along continuous trajectories characterized by proteomic, transcriptional, and epigenetic programs consistent with chronic stress adaptation, alongside distinct composite spatial niches comprised of astrocytic gliosis and cellular senescence. O_FIG O_LINKSMALLFIG WIDTH=168 HEIGHT=200 SRC="FIGDIR/small/733295v1_ufig1.gif" ALT="Figure 1"> View larger version (75K): org.highwire.dtl.DTLVardef@b2afa4org.highwire.dtl.DTLVardef@12978f8org.highwire.dtl.DTLVardef@1c4e46dorg.highwire.dtl.DTLVardef@170e44d_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2026

Spatial proteomics reveals CD8+ T cell signatures and cellular niches associated with active HIV-1 replication in lymph nodes

Despite its effectiveness in suppressing active HIV-1 replication, antiretroviral therapy (ART) does not eliminate the persistent long-lived pool of HIV-1-infected reservoir cells, preventing the eradication of the infection. Lymphoid tissues are key anatomical sites where these reservoirs persist even in the presence of ART, but the mechanisms that are associated with viral persistence in lymphoid tissues and how tissue networks are reshaped in the setting of viral replication remain incompletely understood. Advances in tissue imaging offer a unique opportunity to characterize immune correlates of viral persistence. Here, we used a spatial proteomic method to map immune microenvironments in HIV-1-infected lymph nodes (LNs) at different stages of infection, including with or without ART. LNs from people with HIV-1 (PWH) were characterized by lower CD4+ T cell counts and higher CD8+ T cell counts in both the whole tissue and within follicles compared to people without HIV (PWOH). CD8+ T cells were more abundant in LN samples with active viral replication, defined by detection of the viral protein p24. Further characterization of p24+ LNs showed that CD8+ T cells located inside of B cell follicles exhibited higher levels of markers associated with immune activation and exhaustion, in addition to the inflammasome protein caspase-1. Using a spatial niche detection method, we found that LNs from PWH with varying levels of viral replication were differentially enriched for CD8+ T cells near antigen-presenting cells, myeloid cells, and fibroblasts. Notably, we found that p24+ cells were less enriched near CD8+ T cells but closer to follicular dendritic cells. Finally, comparing LNs from viremic and aviremic donors, where viremia was defined by detectable plasma viral load, we found low levels of activation markers in CD11c+ cells in aviremic donors, including NLRP3 inflammasome activation. Thus, using spatial proteomics to map the immune landscape in LNs, we identified novel markers characterizing immune cell subsets and tissue microenvironments that were differently enriched in PWH with varying levels of viremia, implying that HIV-1 infection confers long-term changes on the immune landscape in LN tissue. Collectively, these data provide new insights into the complex cell networks associated with viral replication at a key tissue reservoir site, which could be relevant for future HIV-1 cure strategies.

immunology↗

bioRxiv2025

Therapeutic remodeling of the tuberculosis granuloma with 1-methyl-D-tryptophan enhances CD8+ T cell-macrophage interactions

Granulomas, the hallmark of tuberculosis (TB) disease, can both restrict Mycobacterium tuberculosis (Mtb) dissemination and impede its clearance. Recent studies indicate that indoleamine 2,3-dioxygenase (IDO1), an immunosuppressive metabolic enzyme, limits infiltration of activated T cells and can contribute to TB disease progression. Treatment with 1-methyl-D-tryptophan (D-1MT), a small molecule inhibitor that restores mTOR signaling, has been shown to improve immune responses Mtb-infected rhesus macaques. Here, we investigated the impact of D-1MT treatment on TB granuloma architecture using 30-plex high-dimensional issue imaging in rhesus macaques. By spatially mapping 13 distinct cell populations, we found D-1MT treatment corresponded with significantly increased infiltration CD8+ T cells into granulomas compared to untreated controls. Notably, these CD8+ T cells expressed markers of cell proliferation and cytotoxicity. D-1MT enhanced CD8+ T cell infiltration throughout the granuloma, with particularly pronounced effects in the myeloid core, where we observed significantly enhanced spatial interactions between macrophages and CD8+, but not CD4+ T cells. Our results demonstrate that: (i) effective intra-granulomatous Mtb control is associated with the close spatial proximity between CD8+ T cells and macrophages, a feature less abundant in uncontrolled pulmonary TB; (ii) IDO1 induction blocks CD8+ T cell infiltration and reduces T cell activation and proliferation; and (iii) therapeutic strategies, including D-1MT, that improve intra-granulomatous killing hold strong translational potential. Significance statementOur understanding of immune mechanisms within the TB granuloma has advanced greatly with the advent of high-resolution single cell multiplexed imaging. Using such imaging, we show that TB granulomas in rhesus macaques, a highly translational model of human TB pathology, are characterized by IDO1-mediated immunoregulation. Early pharmacologic restoration of mTOR signaling via D-1MT treatment can reduce IDO1 enzymatic activity and facilitate the recruitment and function of CD8+ T cells within the granulomas. These findings reveal specific mechanisms exploited by Mtb to maintain intra-granuloma persistence and underscore immune responses. Future vaccine and therapeutic design should consider these immunoregulatory features to achieve better control of Mtb infection.

immunology↗

bioRxiv2025

RNAGym: Large-scale Benchmarks for RNA Fitness and Structure Prediction

Understanding RNA structure and predicting the functional consequences of mutations are fundamental challenges in computational biology with broad implications for therapeutic development and synthetic biology. Current evaluation of machine learning-based RNA models suffers from disparate experimental datasets and inconsistent performance assessments across different RNA families. To address these challenges, we introduce RNAGym, a large-scale benchmarking framework specifically designed for three core tasks-RNA fitness, secondary structure, and tertiary structure prediction. The framework integrates extensive datasets, including 70 standardized deep mutational scanning assays covering over a million mutations across diverse RNA types; 901k chemical-mapping reactivity profiles for secondary structure; and 215 diverse tertiary structures curated from the PDB. RNAGym is designed to facilitate a systematic comparison of RNA models, offering an essential resource to enhance the understanding and development of these models.

synthetic biology↗

bioRxiv2025

Multi-omic landscape of human gliomas from diagnosis to treatment and recurrence

Gliomas are among the most lethal cancers, with limited treatment options. To uncover hallmarks of therapeutic escape and tumor microenvironment (TME) evolution, we applied spatial proteomics, transcriptomics, and glycomics to 670 lesions from 310 adult and pediatric patients. Single-cell analysis shows high B7H3+ tumor cell prevalence in glioblastoma (GBM) and pleomorphic xanthoastrocytoma (PXA), while most gliomas, including pediatric cases, express targetable tumor antigens in less than 50% of tumor cells, potentially explaining trial failures. Longitudinal samples of isocitrate dehydrogenase (IDH)-mutant gliomas reveal recurrence driven by tumor-immune spatial reorganization, shifting from T-cell and vasculature-associated myeloid cell-enriched niches to microglia and CD206+ macrophage-dominated tumors. Multi-omic integration identified N-glycosylation as the best classifier of grade, while the immune transcriptome best predicted GBM survival. Provided as a community resource, this study opens new avenues for glioma targeting, classification, outcome prediction, and a baseline of TME composition across all stages.

cancer biology↗

bioRxiv2025

The immunometabolic topography of tuberculosis granulomas governs cellular organization and bacterial control

Despite being heavily infiltrated by immune cells, tuberculosis (TB) granulomas often subvert the host response to Mycobacterium tuberculosis (Mtb) infection and support bacterial persistence. We previously discovered that human TB granulomas are enriched for immunosuppressive factors typically associated with tumor-immune evasion, raising the intriguing possibility that they promote tolerance to infection. In this study, our goal was to identify the prime drivers for establishing this tolerogenic niche and to determine if the magnitude of this response correlates with bacterial persistence. To do this, we conducted a multimodal spatial analysis of 52 granulomas from 16 non-human primates (NHP) who were infected with low dose Mtb for 9-12 weeks. Notably, each granulomas bacterial burden was individually quantified allowing us to directly ask how granuloma spatial structure and function relate to infection control. We found that a universal feature of TB granulomas was partitioning of the myeloid core into two distinct metabolic environments, one of which is hypoxic. This hypoxic environment associated with pathologic immune cell states, dysfunctional cellular organization of the granuloma, and a near-complete blockade of lymphocyte infiltration that would be required for a successful host response. The extent of these hypoxia-associated features correlated with worsened bacterial burden. We conclude that hypoxia governs immune cell state and organization within granulomas and is a potent driver of subverted immunity during TB.

immunology↗

bioRxiv2025

Temporal and spatial composition of the tumor microenvironment predicts response to immune checkpoint inhibition

Immune checkpoint inhibition (ICI) has fundamentally changed cancer treatment. However, only a minority of patients with metastatic triple negative breast cancer (TNBC) benefit from ICI, and the determinants of response remain largely unknown. To better understand the factors influencing patient outcome, we assembled a longitudinal cohort with tissue from multiple timepoints, including primary tumor, pre-treatment metastatic tumor, and on-treatment metastatic tumor from 117 patients treated with ICI (nivolumab) in the phase II TONIC trial. We used highly multiplexed imaging to quantify the subcellular localization of 37 proteins in each tumor. To extract meaningful information from the imaging data, we developed SpaceCat, a computational pipeline that quantifies features from imaging data such as cell density, cell diversity, spatial structure, and functional marker expression. We applied SpaceCat to 678 images from 294 tumors, generating more than 800 distinct features per tumor. Spatial features were more predictive of patient outcome, including features like the degree of mixing between cancer and immune cells, the diversity of the neighboring immune cells surrounding cancer cells, and the degree of T cell infiltration at the tumor border. Non-spatial features, including the ratio between T cell subsets and cancer cells and PD-L1 levels on myeloid cells, were also associated with patient outcome. Surprisingly, we did not identify robust predictors of response in the primary tumors. In contrast, the metastatic tumors had numerous features which predicted response. Some of these features, such as the cellular diversity at the tumor border, were shared across timepoints, but many of the features, such as T cell infiltration at the tumor border, were predictive of response at only a single timepoint. We trained multivariate models on all of the features in the dataset, finding that we could accurately predict patient outcome from the pre-treatment metastatic tumors, with improved performance using the on-treatment tumors. We validated our findings in matched bulk RNA-seq data, finding the most informative features from the on-treatment samples. Our study highlights the importance of profiling sequential tumor biopsies to understand the evolution of the tumor microenvironment, elucidating the temporal and spatial dynamics underlying patient responses and underscoring the need for further research on the prognostic role of metastatic tissue and its utility in stratifying patients for ICI.

cancer biology↗

bioRxiv2025

Spatial Profiling Reveals Equivalence-Derived Molecular Signatures of Brain Mimicry and Adaptation in Breast Cancer Brain Metastases

Brain metastases (BrMets), common for advanced-stage breast cancer patients, are associated with poor median survival and accompanied by severe neurologic decline. Halting the progression of breast cancer brain metastases (BCBMs) may require modulation of the tumor microenvironment (TME), yet little is known about the impact of the primary breast TME on brain tropism, or how, once there, metastatic breast cancer cells coexist with brain-resident cells (e.g., neurons and glia). Traditionally, studies in this space have focused on differential expression analysis, overlooking potential insights gained from investigating genes with equivalent expression between groups. This is particularly crucial in distant metastasis, where tumor cells may co-opt the transcriptional programs of the host organ (e.g., brain) to facilitate successful seeding and outgrowth. Prior to our work, no computational framework existed to determine biologically-relevant equivalent gene expression. To resolve molecular mechanisms of BCBM enabled by metastatic cancer cells and/or resident brain cells, we leveraged Nanostring GeoMx to perform spatially-resolved transcriptomic profiling on 235 patient-derived tissue cores from BCBM (including adjacent normal brain), primary invasive breast cancers, and normal (non-cancer) brain; analyzing 18,677 RNAs in 450 areas of interest (AOIs). We introduce the "Equivalent Expression Index" a highly specific and accurate algorithm that identifies statistically significant "Equivalently-Expressed Genes". This method facilitated the identification of molecular remodeling and mimicry genes within tissue-specific TMEs. By integrating differential expression analysis with the Equivalent Expression Index, we discovered multiple novel gene signatures associated with BCBM and primary tumor brain-metastatic potential. We demonstrate that the Equivalent Expression Index is a powerful tool to uncover shared gene expression programs representing the adaptation of metastatic cells and brain-resident cells to the BCBM microenvironment.

cancer biology↗

bioRxiv2025

QUICHE reveals structural definitions of anti-tumor responses in triple negative breast cancer

While recent innovations in spatial biology have driven new insights into how tissue organization is altered in disease, interpreting these datasets in a generalized and scalable fashion remains a challenge. Computational workflows for discovering condition-specific differences in tissue organization typically rely on pairwise comparisons or unsupervised clustering. In many cases, these approaches are computationally expensive, lack statistical rigor, and are insensitive to low-prevalence cellular niches that are nevertheless highly discriminative and predictive of patient outcomes. Here, we present QUICHE - an automated, scalable, and statistically robust method that can be used to discover cellular niches differentially enriched in spatial regions, longitudinal samples, or clinical patient groups. In contrast to existing methods, QUICHE combines local niche detection with interpretable statistical modeling using graph neighborhoods to detect differentially enriched cellular niches, even at low prevalence. Using in silico models and spatial proteomic imaging of human tissues, we demonstrate that QUICHE can accurately detect condition-specific cellular niches occurring at a frequency of 0.5% in fewer than 20% of patient samples, outperforming the next best method which required a patient prevalence of 60% for detection. To validate our approach and understand how tumor structure influences recurrence risk in triple negative breast cancer (TNBC), we used QUICHE to comprehensively profile the tumor microenvironment in a multi-center, spatial proteomics cohort consisting of primary surgical resections, analyzing over 2 million cells from 314 patients across 5 medical centers. We discovered cellular niches that were consistently enriched in key regions of the tumor microenvironment, including the tumor-immune border and extracellular matrix remodeling regions, as well as niches statistically-associated with patient outcomes, including recurrence status and recurrence-free survival. The majority of differential niches (74.2%) were specific to patients that did not relapse and formed a robust interconnected network enriched in monocytes, macrophages, APCs, and CD8T cells with tumor and stroma cells. In contrast, the interaction network for patients that relapsed was notably sparse and enriched in B cells, CD68 macrophages and neutrophils. We validated these findings using two independent cohorts, observing similar cellular interactions and predictive power. Collectively, these results suggest that salient, generalized profiles of productive anti-tumor immune responses are defined by a network of structural engagement between innate and adaptive immunity with tumor and stromal cells, rather than by any single specific cell population. We have made QUICHE freely available as a user-friendly open-source Python package at https://github.com/jranek/quiche.

bioinformatics↗

bioRxiv2024

Accurate in silico predictions of modified RNA interactions to a prototypical RNA-binding protein with {lambda}-dynamics

RNA-binding proteins shape biology through their widespread functions in RNA biochemistry. Their function requires the recognition of specific RNA motifs for targeted binding. These RNA binding elements can be composed of both unmodified and chemically modified RNAs, of which over 170 chemical modifications have been identified in biology. Unmodified RNA sequence preferences for RNA-binding proteins have been widely studied, with numerous methods available to identify their preferred sequence motifs. However, only a few techniques can detect preferred RNA modifications, and no current method can comprehensively screen the vast array of hundreds of natural RNA modifications. Prior work demonstrated that {lambda}-dynamics is an accurate in silico method to predict RNA base binding preferences of an RNA-binding antibody. This work extends that effort by using {lambda}-dynamics to predict unmodified and modified RNA binding preferences of human Pumilio, a prototypical RNA binding protein. A library of RNA modifications was screened at eight nucleotide positions along the RNA to identify modifications predicted to affect Pumilio binding. Computed binding affinities were compared with experimental data to reveal high predictive accuracy. In silico force field accuracies were also evaluated between CHARMM and Amber RNA force fields to determine the best parameter set to use in binding calculations. This work demonstrates that {lambda}-dynamics can predict RNA interactions to a bona fide RNA-binding protein without the requirements of chemical reagents or new methods to experimentally test binding at the bench. Advancing in silico methods like {lambda}-dynamics will unlock new frontiers in understanding how RNA modifications shape RNA biochemistry.

biochemistry↗

bioRxiv2024

IFNγ-Expressing Myeloid Cells Localize within Lipoproteinosis during Drug-Associated Pulmonary Alveolar Proteinosis occurring in Systemic Juvenile Idiopathic Arthritis

In the United States, approximately one in 1000 children are diagnosed with the autoinflammatory disease, Juvenile Idiopathic Arthritis (JIA). A subset of JIA cases manifests as Systemic JIA (sJIA), which is characterized by joint pain, fevers, rashes, and systemic inflammation. Severe pulmonary complications have not historically been associated with sJIA. Since 2010, inhibitors of interleukin-1 and interleukin 6 (IL-1i/IL-6i) are the recommended course of treatment for sJIA, yet recently studies show evidence of a severe drug hypersensitivity reaction implicating these medications in a subset of those treated. With this reaction, sJIA patients can develop severe lung disease, including pulmonary alveolar proteinosis (PAP). As this drug-associated lung disease has only recently been identified, the etiology of sJIA drug-associated PAP (sJIA-daPAP) is poorly understood. We used multiplexed ion beam imaging by time-of-flight (MIBI-TOF) to define the cellular immune infiltrate and describe pathological features of PAP in sJIA-daPAP patients. We found an enrichment of eosinophils, neutrophils, and M2 macrophages within regions of lipoproteinosis. These enriched subsets all upregulate IFN{gamma} within lipoproteinosis, a signature specific to sJIA-daPAP samples compared to non-sJIA-PAP samples. In a cellular neighborhood analysis, we identified that eosinophils, neutrophils and M2 macrophages frequently co-localize within the same cellular microenvironment, especially in lipoproteinosis regions. Therefore, this spatial coordination may be involved in clearance or persistence of lipoproteinosis in sJIA-daPAP. This study provides a comprehensive overview of sJIA-daPAP immune pathology and suggests cellular mechanisms that drive inflammation in sJIA patients experiencing pulmonary complications associated with delayed drug hypersensitivity during IL-1i/IL-6i treatment.

immunology↗

bioRxiv2024

Automated classification of cellular expression in multiplexed imaging data with Nimbus

Multiplexed imaging offers a powerful approach to characterize the spatial topography of tissues in both health and disease. To analyze such data, the specific combination of markers that are present in each cell must be enumerated to enable accurate phenotyping, a process that often relies on unsupervised clustering. We constructed the Pan-Multiplex (Pan-M) dataset containing 197 million distinct annotations of marker expression across 15 different cell types. We used Pan-M to create Nimbus, a deep learning model to predict marker positivity from multiplexed image data. Nimbus is a pre-trained model that uses the underlying images to classify marker expression across distinct cell types, from different tissues, acquired using different microscope platforms, without requiring any retraining. We demonstrate that Nimbus predictions capture the underlying staining patterns of the full diversity of markers present in Pan-M. We then show how Nimbus predictions can be integrated with downstream clustering algorithms to robustly identify cell subtypes in image data. We have open-sourced Nimbus and Pan-M to enable community use at https://github.com/angelolab/Nimbus-Inference.

bioinformatics↗

bioRxiv2024

Epstein-Barr Virus Orchestrates Spatial Reorganization and Immunomodulation within the Classic Hodgkin Lymphoma Tumor Microenvironment

Classic Hodgkin Lymphoma (cHL) is a tumor composed of rare malignant Hodgkin and Reed-Sternberg (HRS) cells nested within a T-cell rich inflammatory immune infiltrate. cHL is associated with Epstein-Barr Virus (EBV) in 25% of cases. The specific contributions of EBV to the pathogenesis of cHL remain largely unknown, in part due to technical barriers in dissecting the tumor microenvironment (TME) in high detail. Herein, we applied multiplexed ion beam imaging (MIBI) spatial pro-teomics on 6 EBV-positive and 14 EBV-negative cHL samples. We identify key TME features that distinguish between EBV-positive and EBV-negative cHL, including the relative predominance of memory CD8 T cells and increased T-cell dysfunction as a function of spatial proximity to HRS cells. Building upon a larger multi-institutional cohort of 22 EBV-positive and 24 EBV-negative cHL samples, we orthogonally validated our findings through a spatial multi-omics approach, coupling whole transcriptome capture with antibody-defined cell types for tu-mor and T-cell populations within the cHL TME. We delineate contrasting transcriptomic immunological signatures between EBV-positive and EBV-negative cases that differently impact HRS cell proliferation, tumor-immune interactions, and mecha-nisms of T-cell dysregulation and dysfunction. Our multi-modal framework enabled a comprehensive dissection of EBV-linked reorganization and immune evasion within the cHL TME, and highlighted the need to elucidate the cellular and molecular fac-tors of virus-associated tumors, with potential for targeted therapeutic strategies.

cancer biology↗

bioRxiv2024

A Spatial Multi-Modal Dissection of Host-Microbiome Interactions within the Colitis Tissue Microenvironment

The intricate and dynamic interactions between the host immune system and its microbiome constituents undergo dynamic shifts in response to perturbations to the intestinal tissue environment. Our ability to study these events on the systems level is significantly limited by in situ approaches capable of generating simultaneous insights from both host and microbial communities. Here, we introduce Microbiome Cartography (MicroCart), a framework for simultaneous in situ probing of host features and its microbiome across multiple spatial modalities. We demonstrate MicroCart by comprehensively investigating the alterations in both gut host and microbiome components in a murine model of colitis by coupling MicroCart with spatial proteomics, transcriptomics, and glycomics platforms. Our findings reveal a global but systematic transformation in tissue immune responses, encompassing tissue-level remodeling in response to host immune and epithelial cell state perturbations, and bacterial population shifts, localized inflammatory responses, and metabolic process alterations during colitis. MicroCart enables a deep investigation of the intricate interplay between the host tissue and its microbiome with spatial multiomics.

immunology↗