Biology subjects

Van Batavia, K.

Publications and source records attributed to Van Batavia, K..

4 recordsLinked to original sources

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

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↗

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↗