Biology subjects

Hickey, J. W.

Publications and source records attributed to Hickey, J. W..

At least 19 recordsLinked to original sources

bioRxiv2026

Spatial profiling of pooled mRNA-LNP delivery in vivo with NanoSTAMP

Existing pooled lipid nanoparticle (LNP) screens lack spatial information on formulation localization, cellular uptake, and associated multicellular tissue responses. Here we introduce NanoSTAMP, a spatially resolved, pooled in vivo screening platform for barcoded LNP libraries that uses fluorescence in situ hybridization (FISH)-based barcode readout and is compatible with spatial omics. NanoSTAMP links LNP formulation to cell-type specific uptake, cargo expression, and nearby multicellular neighborhoods within intact tissue, which enables spatially-informed design of RNA delivery, establishing tissue architecture as a dimension of LNP performance.

bioengineering↗

bioRxiv2026

Multiscale harmonization and semantic integration of biomedical data enable biological insights through immersive exploration

Single-cell atlassing efforts like the Human BioMolecular Atlas Program (HuBMAP) and the Cellular Senescence Network (SenNet) are producing multiscale datasets across the healthy, adult, human body, but this data is typically explored only on 2D screens with limited 3D affordances, even though understanding a cells location within a tissue, organ, and body requires reasoning across many orders of spatial magnitude. The Human Reference Atlas (HRA) provides standard terminologies and a Common Coordinate Framework (CCF) for harmonizing such data spatially and semantically. Building on the HRA Organ Gallery in virtual reality (VR) application, we present "HRA: Powers of Ten," which integrates, harmonizes, and visualizes biological data from these efforts immersively in VR using a Multiscale Elevator System that lets users descend, like riding an elevator through an inverted skyscraper, from a whole body view of 81 reference organs to datasets across 5 organs (lymph node, brain, large intestine, small intestine, liver), 5 assay types (CODEX, Visium, v-CyCIF, Xenium, SBF-SEM), and 4 spatial scales. Contributed by Data Providers, these VR scenes enable biological insights into senescence patterns, cellular neighborhoods, 3D tissue reconstruction, and subcellular liver architecture. A standard operating procedure supports adding further datasets. Freely available on the Meta Store to over 20 million headset owners, the application, data, and code are open-source.

bioinformatics↗

bioRxiv2026

Foundation cell segmentation models performance on live microscopy and spatial-omics data

Accurate cell segmentation is an essential step for quantitative analysis of biological imaging data. Recent advances in deep learning have led to the development of generalist segmentation models that perform robustly across multiple imaging modalities, including label-free phase contrast, fluorescence cell culture, and multiplexed fluorescence tissue imaging. However, systematic comparisons of these models at the level of downstream biological analysis remain limited. To address this gap, we evaluated several recent segmentation models, including Cellpose cyto3, Cellpose-SAM, {micro}SAM, and CellSAM, on phase contrast and fluorescence cell culture images. In addition, Mesmer and InstanSeg were included for benchmarking on multiplexed fluorescence tissue images generated using CO-Detection by IndEXing (CODEX). We found that Cellpose-SAM achieved strong performance on phase contrast images, while SAM-based models consistently performed well on fluorescence cell culture data. In contrast, no single model consistently outperformed others on CODEX datasets. Instead, each model exhibited distinct strengths and limitations, which led to differences in downstream analyses, including clustering and cell type identification. Together, our study emphasizes the importance of selecting segmentation models based on dataset characteristics and analytical goals, rather than relying on a single universal approach.

bioinformatics↗

bioRxiv2026

Engineering age-adaptive mRNA lipid nanoparticle cancer vaccines via reprogramming systemic gene expression

Lipid nanoparticle (LNP)-based mRNA vaccines have transformed cancer immunotherapy, yet their efficacy in older individuals, who represent the majority of cancer patients, remains poorly understood. Here, we uncover a critical and previously underappreciated barrier to mRNA LNP cancer vaccine performance in aged hosts: impaired systemic transgene expression. Using the SM-102 mRNA LNPs as a benchmark formulation, we show that while local immune activation and antigen presentation at the injection site and draining lymph nodes remain largely intact with age, transgene expression in peripheral organs, including the liver, lungs, and spleen, is markedly reduced. This deficit limits the magnitude and durability of CD8 and CD4 T cell responses and substantially compromises tumour control. Transcriptomic profiling further reveals that attenuated transgene expression parallels broad attenuation of antigen processing, presentation, and activation pathways in immune cells from aged animals, implicating impaired systemic mRNA translation as a central driver of the downstream immune defects and antitumour efficacy loss. Building on these insights, we identify a rationally selected LNP formulation that reestablishes distal antigen expression across age groups as an engineering strategy to revive T cell immunity, achieving full rescue of therapeutic efficacy in aged mice without additional intervention. Together, these findings establish systemic mRNA translation as a tuneable lever of vaccine performance and highlight that optimizing LNP formulations to sustain systemic transgene expression across age groups may enable next-generation, age-adaptive mRNA vaccines for cancer and other diseases of aging.

bioengineering↗

bioRxiv2026

MINGL Quantifies Borders, Gradients, and Heterogeneity in Multicellular Tissue Organization

Tissues are organized with interacting multicellular organizational units whose interfaces and transitions shape function in health and disease. Current spatial-omics analyses typically assign cells to a single cellular neighborhood--ignoring natural gradients, heterogeneity, and borders. Here we present MINGL (Mixture-based Identification of Neighborhood Gradients with Likelihood estimates), a probabilistic framework that converts existing neighborhood annotations into continuous measures of tissue architecture. MINGL models each cell by multi-membership probabilities across hierarchical organizational units and uses these probabilities to identify enriched cells at interfaces between units, constructs interaction networks across hierarchical scales, quantifies compositional gradient transitions, measures context-specific composition heterogeneity, and provides a starting point for neighborhood resolution. Across multiple spatial-omic datasets spanning melanoma, healthy intestine, and Barretts Esophagus progression, MINGL detected innate immune-enriched interfaces at tumor and anatomical interfaces, plasma cell niches linking cellular neighborhoods, distinct regimes of sharp and gradual transitions between organizational states, and disease-associated neighborhood remodeling. By treating neighborhood assignment uncertainty as a biological signal rather than noise, MINGL unifies discrete and continuous representations of tissue organization and makes tissue architecture measurable, comparable, and scalable across biological scales and spatial-omics platforms.

systems biology↗

bioRxiv2026

MORPHE: Bridging Image Generation and Spatial Omics for Tissue Synthesis

Spatially resolved omics technologies reveal tissue organization at single-cell resolution but remain limited by the cost of the assays, incomplete spatial coverage, 2D-only imaging, and experimental artifacts. These factors motivate the need for in silico methods that can reconstruct or extend tissue context beyond what current spatial measurements provide. We present MORPHE (MOdeling of stRuctured sPatial High-dimensional Embeddings), an AI framework that learns to synthesize biologically faithful tissue architecture directly from spatial-omics data. MORPHE introduces a graph-informed probabilistic embedding that maps discrete cell identities and their spatial relationships into a continuous RGB-like latent space compatible with diffusion modeling. This representational bridge enables spatial cellular maps to leverage large pre-trained image-generative models while preserving biological interpretability upon decoding. By modeling cells as the fundamental units of generation and learning how their identities and spatial relationships collectively give rise to large-scale tissue structure, MORPHE enables generation and reconstruction of tissue architecture at single-cell resolution. We applied the method across large-scale single-cell proteomic datasets from the intestine and single-cell transcriptomic datasets from the brain, showing computational scalability acrosss millions of cells. We used MORPHE on these datasets to outpaint beyond experimentally restricted fields of view, inpaint missing or experimentally damaged tissue regions, and perform cross-tissue imputation, connecting separated tissue regions into a single contiguous sample in both 2D and 3D. MORPHE represents a new class of tissue generation algorithms that will help solve current limitations and challenges with single-cell spatial-omics datasets.

systems biology↗

bioRxiv2025

Fluid-Squid: DIY Multiplexed Imaging of Cells and Tissues

Recent advances in multiplexed single-cell characterization have revolutionized our insight into cell biology, but many available technologies remain limited by high costs or a lack of customizability. To address these challenges, we developed Fluid-Squid, a cost-effective, quantitative imaging platform that integrates automated fluidics to support customizable, do-it-yourself multiplexed imaging workflows. Using Fluid-Squid, we successfully imaged fresh frozen human intestinal tissues with a 36-plex oligonucleotide-barcoded antibody panel and further demonstrated the feasibility of lyophilizing such multiplexed panels. We also adapted existing multiplexed imaging workflows to characterize individual cells to identify immune cell populations, phenotype, and antigen-specific cells from mouse splenocytes and human peripheral blood mononuclear cells (PBMCs) with a 39-antibody panel. To further expand its utility, we developed a barcoding strategy that allows for the pooling and simultaneous staining of multiple samples, reducing time, costs, and batch effects in single-cell experiments. This approach facilitated rapid titration to optimize antibody concentrations and assess the impact of various blood preparation methods on cell type retention. Overall, our work provides a new open-source framework for automated fluidics and microscopy in a flexible, cost-effective platform, empowering adaptable multiplexed characterization of both single cells and tissues.

bioengineering↗

bioRxiv2025

Exploring endothelial cell environments across organs in spatially resolved omics data

Endothelial cells are ubiquitously present in the human body and line the luminal surface of blood and lymphatic vessels. The oxygen-dependence of cells impacts their proximity to blood vessels, and consequently, to endothelial cells depending on their functional properties and priorities. This paper presents cell-to-nearest-endothelial-cell distance distributions for various cell types using 399 spatially resolved omics datasets from 14 studies comprising 12 tissue types with a total of 47,349,496 cells. Additionally, we developed an open-source web-based interactive tool, Cell Distance Explorer, that allows researchers to interactively visualize cell graphs and linkages in 2D and 3D datasets. Finally, we present a hierarchical neighborhood analysis focused on the endothelial cell neighborhoods in small and large intestine datasets. This paper provides an open-access resource (datasets, tools, and analyses) to characterize and compare cell distances and cell neighborhoods in spatially resolved omics data.

bioinformatics↗

bioRxiv2025

A Transient Immunostimulatory Niche Synergizes Adoptive and Endogenous Immunity for Enhanced Tumor Control

Adoptive Cell Therapy (ACT) has achieved curative responses in hematological malignancies, yet its translation to solid tumors remains limited by manufacturing bottlenecks, systemic toxicities, and poor T-cell infiltration and persistence within the immunosuppressive tumor microenvironment (TME). Here, we report the development and mechanism of ACTIVATE (Adoptive Cell Therapy and Immunostimulatory Vehicle for Anti-Tumor Efficacy), which leverages an injectable hydrogel depot technology that forms a transient inflammatory niche for localized co-delivery of adoptive T cells and native cytokines. By tuning cytokine identity, ACTIVATE enables precise modulation of T-cell expansion, effector function, and interaction with endogenous immune networks. We found that enhancing T-cell proliferation alone is insufficient to drive robust tumor control; instead, coordinated engagement of both adoptive and endogenous immune responses is critical for durable anti-tumor efficacy. In vivo, this orchestration via ACTIVATE led to enhanced infiltration and cytotoxicity of both adoptive and host-derived immune effectors, while driving robust recruitment and activation of T cells, B cells, dendritic cells, and macrophages in the tumor-draining lymph nodes. This local immune activation can further reshape the TME, promoting antigen presentation and suppressing immunoregulatory populations, thus enhancing anti-tumor efficacy in murine melanoma and lymphoma models. These findings establish ACTIVATE as a modular platform for orchestrating coordinated immune responses to improve ACT outcomes in solid tumors.

immunology↗

bioRxiv2025

Alterations of the composition and spatial organization of the microenvironment following non-dysplastic Barrett's esophagus through progression to cancer

Barretts esophagus (BE), a metaplastic condition that is the only known precursor for esophageal adenocarcinoma (EAC), is relatively common, but progression to cancer is infrequent. BE is inflamed but the contribution of the immune system to the carcinogenic process is unknown. To this end, we contrasted non-dysplastic metaplasia of BE patients, captured when they did not progress (non-progressors), did subsequently, but had not yet progressed (pre-progressors) or had already progressed to EAC (progressors). Using spatial multiplexed 56-protein analysis, serial laser capture microdissection (LCM) RNAseq and shallow whole genome sequencing, we identified prooncogenic immune neighbourhoods and dysregulated immune cell populations predictive of subsequent progression to EAC. Indeed, spatial analysis revealed that M1 macrophages, regulatory natural killer (NK) cells, neutrophils and altered ratios of intraepithelial CD4+ and CD8+ lymphocytes typify tumor microenvironmental (TME) changes associated with cancer initiation. Spatially derived cell-to-cell interactions revealed progression-specific immune cell interaction signatures predominantly involving M1 macrophages NK cells and plasma cells. Furthermore, LCM RNAseq analysis identified gene expression hot signatures enriched in pre-progression and progression samples. Notably, we also observed a correlation between immune cells and copy number alterations in progressor metaplasia. By exposing coordinated changes in the immune cell landscape in patients at high risk of developing EAC, this multi-omic dataset provides novel diagnostic and therapeutic opportunities

cancer biology↗

bioRxiv2025

Spatially organized inflammatory myeloid-CD8+ T cell aggregates linked to Merkel-cell Polyomavirus driven Reorganization of the Tumor Microenvironment

Merkel cell carcinoma (MCC) is an aggressive skin cancer with high propensity for metastasis, caused by Merkel-cell-polyomavirus (MCPyV), or chronic UV-light-exposure. How MCPyV spatially modulates immune responses within the tumor microenvironment and how such are linked to patient outcomes remains unknown. We interrogated the cellular and transcriptional landscapes of 60 MCC-patients using a combination of multiplex proteomics, in-situ RNA-hybridization, and spatially oriented transcriptomics. We identified a spatial co-enrichment of activated CD8+ T-cells and CXCL9+PD-L1+ macrophages at the invasive front of virus-positive MCC. This spatial immune response pattern was conserved in another virus-positive tumor, HPV+ head-and-neck cancer. Importantly, we show that virus-negativity correlated with high risk of metastasis through low CD8+ T-cell infiltration and the enrichment of cancer-associated-fibroblasts at the tumor boundary. By contrast, responses to immune-checkpoint blockade (ICB) were independent of viral-status but correlated with the presence of a B-cell-enriched spatial contexts. Our work is the first to reveal distinct immune-response patterns between virus-positive and virus-negative MCC and their impact on metastasis and ICB-response.

immunology↗

bioRxiv2025

MONTAGE: A Computation Framework to Identify Spatially Resolved Functional Enrichment Gradients in the Tissue Microenvironment via Spatial Communities

Single-cell spatial-omics has advanced our understanding of how tumor microenvironment contributes to cancer progression. However, most single-cell spatial-omics studies focus on cell types and neighborhoods, offering limited information about functional and clinical relevance of cellular organization. We introduce MONTAGE, a computational framework to reconstruct, functionally analyze, and identify clinically relevant cellular spatial communities (SCs). MONTAGE generates a gene signature matrix of SCs from integrated atlases that offer single-cell resolution with transcriptomics, and reconstructs tissue maps ("montages"), reflecting sequential SC compositional changes along spatial gradients of biological function enrichments. The MONTAGE signature matrix can also be used to deconvolve bulk and spot-based spatial transcriptomic data, and combining with clinical metadata, allows assessing SC clinical relevance. In a head and neck cancer study, MONTAGE reveals SCs enriched with malignant cells and granulocytes shifting to SCs enriched with macrophages and fibroblasts along gradient of epithelial-mesenchymal transition, and clinical prognostic significance of the SCs.

systems biology↗

bioRxiv2025

A Spatial Multi-Omic Framework Identifies Gliomas Permissive to TIL Expansion

Tumor-infiltrating lymphocyte (TIL) therapy is effective in several tumor types; however, its feasibility in immune subversive tumors like glioblastoma is unclear. We expanded TILs from glioblastoma specimens and observed marked variability in yield, composition, function, and TCR clonality. By interrogating TILs expanded ex vivo alongside their sourced glioma tissue, we sought to identify determinants of successful (TIL+) vs unsuccessful TIL expansion (TIL-). Expanded TILs were predominantly effector memory CD4 cells and exhibited oligoclonal TCR enrichment. Despite similar T cell abundance in TIL vs TIL- tumors, TIL tumors exhibited distinct spatial organization and cellular interactions, including increased endothelial-immune interactions/ proximity and enrichment of vascular-associated niches. CD4 T cells localized near CD68 macrophages in TIL tumors, while they were positioned near CD163 CD206 macrophages in TIL- tumors. Thus, TIL expansion and functionality are linked to spatial organization and myeloid context; these features may enable biomarker-driven stratification for future TIL therapy in gliomas.

cancer biology↗

bioRxiv2025

mRNA lipid nanoparticle-incorporated nanofiber-hydrogel composite generates a local immunostimulatory niche for cancer immunotherapy

Hydrogel materials have emerged as versatile platforms for various biomedical applications. Notably, the engineered nanofiber-hydrogel composite (NHC) has proven effective in mimicking the soft tissue extracellular matrix, facilitating substantial recruitment of host immune cells and the formation of a local immunostimulatory microenvironment. Leveraging this feature, here we report an mRNA lipid nanoparticle (LNP)-incorporated NHC microgel matrix, termed LiNx, by incorporating LNPs loaded with mRNA encoding tumour antigens. Harnessing the potent transfection efficiency of LNPs in antigen-presenting cells (APCs), LiNx demonstrates remarkable immune cell recruitment, antigen expression and presentation, and cellular interaction. These attributes collectively create an immunostimulating milieu and yield a potent immune response achievable with a single dose, comparable to the conventional three-dose LNP immunization regimen. Further investigations reveal that the LiNx not only generates heightened Th1 and Th2 responses but also elicits a distinctive Type 17 T helper cell-mediated response pivotal for bolstering antitumour efficacy. Our findings elucidate the mechanism underlying LiNxs role in potentiating antigen-specific immune responses, presenting a new strategy for cancer immunotherapy.

bioengineering↗

bioRxiv2024

SPACEc: A Streamlined, Interactive Python Workflow for Multiplexed Image Processing and Analysis

Multiplexed imaging technologies provide insights into complex tissue architectures. However, challenges arise due to software fragmentation with cumbersome data handoffs, inefficiencies in processing large images (8 to 40 gigabytes per image), and limited spatial analysis capabilities. To efficiently analyze multiplexed imaging data, we developed SPACEc, a scalable end-to-end Python solution, that handles image extraction, cell segmentation, and data preprocessing and incorporates machine-learning-enabled, multi-scaled, spatial analysis, operated through a user-friendly and interactive interface.

bioinformatics↗

bioRxiv2024

Human BioMolecular Atlas Program (HuBMAP): 3D Human Reference Atlas Construction and Usage

The Human BioMolecular Atlas Program (HuBMAP) aims to construct a reference 3D structural, cellular, and molecular atlas of the healthy adult human body. The HuBMAP Data Portal (https://portal.hubmapconsortium.org) serves experimental datasets and supports data processing, search, filtering, and visualization. The Human Reference Atlas (HRA) Portal (https://humanatlas.io) provides open access to atlas data, code, procedures, and instructional materials. Experts from more than 20 consortia are collaborating to construct the HRAs Common Coordinate Framework (CCF), knowledge graphs, and tools that describe the multiscale structure of the human body (from organs and tissues down to cells, genes, and biomarkers) and to use the HRA to understand changes that occur at each of these levels with aging, disease, and other perturbations. The 6th release of the HRA v2.0 covers 36 organs with 4,499 unique anatomical structures, 1,195 cell types, and 2,089 biomarkers (e.g., genes, proteins, lipids) linked to ontologies and 2D/3D reference objects. New experimental data can be mapped into the HRA using (1) three cell type annotation tools (e.g., Azimuth) or (2) validated antibody panels (OMAPs), or (3) by registering tissue data spatially. This paper describes the HRA user stories, terminology, data formats, ontology validation, unified analysis workflows, user interfaces, instructional materials, application programming interface (APIs), flexible hybrid cloud infrastructure, and previews atlas usage applications.

bioinformatics↗

bioRxiv2023

Integrating Multiplexed Imaging and Multiscale Modeling Identifies Tumor Phenotype Transformation as a Critical Component of Therapeutic T Cell Efficacy

Cancer progression is a complex process involving interactions that unfold across molecular, cellular, and tissue scales. These multiscale interactions have been difficult to measure and to simulate. Here we integrated CODEX multiplexed tissue imaging with multiscale modeling software, to model key action points that influence the outcome of T cell therapies with cancer. The initial phenotype of therapeutic T cells influences the ability of T cells to convert tumor cells to an inflammatory, anti-proliferative phenotype. This T cell phenotype could be preserved by structural reprogramming to facilitate continual tumor phenotype conversion and killing. One takeaway is that controlling the rate of cancer phenotype conversion is critical for control of tumor growth. The results suggest new design criteria and patient selection metrics for T cell therapies, call for a rethinking of T cell therapeutic implementation, and provide a foundation for synergistically integrating multiplexed imaging data with multiscale modeling of the cancer-immune interface.

systems biology↗

bioRxiv2023

Concerted epithelial and stromal changes during progression of Barrett's Esophagus to invasive adenocarcinoma exposed by multi-scale, multi-omics analysis

Esophageal adenocarcinoma arises from Barretts esophagus, a precancerous metaplastic replacement of squamous by columnar epithelium in response to chronic inflammation. Multi-omics profiling, integrating single-cell transcriptomics, extracellular matrix proteomics, tissue-mechanics and spatial proteomics of 64 samples from 12 patients paths of progression from squamous epithelium through metaplasia, dysplasia to adenocarcinoma, revealed shared and patient-specific progression characteristics. The classic metaplastic replacement of epithelial cells was paralleled by metaplastic changes in stromal cells, ECM and tissue stiffness. Strikingly, this change in tissue state at metaplasia was already accompanied by appearance of fibroblasts with characteristics of carcinoma-associated fibroblasts and of an NK cell-associated immunosuppressive microenvironment. Thus, Barretts esophagus progresses as a coordinated multi-component system, supporting treatment paradigms that go beyond targeting cancerous cells to incorporating stromal reprogramming. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=187 SRC="FIGDIR/small/544265v1_ufig1.gif" ALT="Figure 1"> View larger version (62K): org.highwire.dtl.DTLVardef@64c375org.highwire.dtl.DTLVardef@15c6ef2org.highwire.dtl.DTLVardef@177dee0org.highwire.dtl.DTLVardef@c945e7_HPS_FORMAT_FIGEXP M_FIG Graphical AbstractTo obtain a comprehensive picture of the coordinated changes in epithelial, stromal and immune compartments during development of Barretts-associated esophageal adenocarcinoma, patient-matched samples corresponding to various phases of disease progression were collected from 12 patients, each of which had at a given time point lesions at multiple stages progression (matched-normal, metaplasia, dysplasia, and carcinoma). Matched "normal" gastric tissues were also collected. These sample were analyzed by single cell RNA-sequencing (scRNAseq) for single-cell resolution transcriptomics and Copy Number Variant (CNV), by proteomics for extracellular matrix (ECM) proteins, by Atomic Force Microscopy (AFM for tissue stiffness and by CODEX spatial proteomics imaging The integrative multi-omics analysis exposed drastic alterations in cell type composition and shifts in cell states in all three compartments. A large subpopulation of fibroblasts absent in the normal esophagus and characteristic of dysplasia and adenocarcinoma sample, that based on markers would indeed be considered cancer associated fibroblasts (CAF), appeared already in the metaplastic phase. This fibroblast subpopulation had transcriptomes virtually indistinguishable with fibroblasts of the cancer free gastric epithelium in these patients C_FIG

cancer biology↗