Biology subjects

Jesudason, R.

Publications and source records attributed to Jesudason, R..

5 recordsLinked to original sources

bioRxiv2026

A Spatial Proteomic Atlas of Tertiary Lymphoid Structures in Non-Small Cell Lung Cancer Identifies a Novel Predictive Class of Lymphoid Aggregates

Tertiary lymphoid structures (TLS) predict benefit from immune checkpoint inhibitors (CPIs), yet mature, germinal-center-rich TLS are infrequent in solid tumors by histological review. Here, using 38-plex MIBI spatial proteomics across 165 lymphoid structures from 14 NSCLC resections, we establish a continuum of TLS maturity using high dimensional compositional, spatial, and molecular features. We demonstrate that histologically-defined lymphoid aggregates (LA) comprise a heterogeneous class of structures, which span this continuum of maturity. We identify a subset of lymphoid aggregates that harbor follicular dendritic cell networks, T follicular helper cells, and activated B cell states characteristic of mature TLS, yet are not readily distinguished from other LA structures in our histological review. We developed a novel digital pathology classifier to identify mature LAs in CPI trials, and demonstrate in a retrospective analysis of Atezolizumab in advanced NSCLC that the inclusion of mature LAs greatly expands the biomarker-eligible population while maintaining strong predicted benefit. Together, these data redefine the biological spectrum of tumor-associated lymphoid aggregates and provide a framework for implementing maturity-informed TLS biomarker strategies.

cancer biology↗

bioRxiv2026

Spatiotemporal profiling reveals the role of inflammatory niche in driving prostate cancer

Prostate cancer (PCa) is a lethal malignancy that displays profound resistance to immune checkpoint blockade (ICB), via mechanisms that are poorly understood. Here, we investigate the causes of CD8 T cell exhaustion and mechanisms of tumor progression in a PCa animal model, by single cell and spatial profiling, along a time course, following orthotopic transplantation of RB1/TP53/PTEN-deficient mouse organoids, competent to express neoantigens. The resulting tumors were castration resistant, consisting of largely basal and L2 malignant cells with upregulated inflammatory gene programs, and a specific spatial distribution of macrophages, cancer associated fibroblast (CAF) subtypes, and CD8 T-cells that was not previously reported. Using Zman-seq, we demonstrate that the effector function of tumor-infiltrating CD8 T cells was rapidly impaired as early as 24hrs after their infiltration, likely driven by signals from proinflammatory macrophages, Ccl2-Jak2+ inflammatory CAFs, and malignant basal cells, thus driving resistance to ICB. Interestingly, dual blockade of JAK1/2 and PD1 induced potent anti-tumor effects in tumor epithelial cells, decreased malignant epithelial cells and pro-inflammatory macrophages, and increased the proportion of normal (Pi16+) fibroblasts in the TME. Our results underscore the therapeutic potential of targeting JAK1/2 to enhance the efficacy of ICB, providing a rationale for clinical investigation of this combination in PCa.

cancer biology↗

bioRxiv2026

Spatial Decoding of Tertiary Lymphoid Structure Maturation in Non-Small Cell Lung Cancer Using Deep Neural Networks

Understanding the role of tertiary lymphoid structures (TLS) is crucial in non-small cell lung cancer (NSCLC), as they are associated with patient prognosis and treatment outcomes. Specific cellular ecosystems that originate anti-tumor activity or predict immunotherapy response remain poorly characterized. To this end, we developed a high-resolution, multimodal spatial atlas jointly profiling transcriptomics, proteomics, and histology to characterize TLS maturation in NSCLC alongside secondary lymph organs as a baseline. Using this atlas, we proposed a pathologist-in-the-loop framework that combines a variational graph autoencoder (VGAE) with diffusion pseudotime to refine human expert annotations and characterize TLS maturation. These spatial molecular representations were extended to H&E whole-slide images via a vision transformer-based foundation model. Next, we resolved cellular composition, spatial organization, and cell-cell interactions within these data and defined two divergent spatial ecosystems. Clinical evidence suggests that these ecosystems are associated with distinct patient outcomes: a mature germinal center niche with favorable prognoses, and a tumor-macrophage-fibroblast niche with unfavorable prognoses. In summary, our work decodes key components of TLS heterogeneity, identifies hallmark spatial patterns involved in NSCLC adaptive immunity, and provides a framework for translating spatial omics insights into clinical applications.

genomics↗

bioRxiv2023

Highly multiplexed, image-based pooled screens in primary cells and tissues with PerturbView

Optical pooled screening (OPS) is a highly scalable method for linking image-based phenotypes with cellular perturbations. However, it has thus far been restricted to relatively low-plex phenotypic readouts in cancer cell lines in culture, due to limitations associated with in situ sequencing (ISS) of perturbation barcodes. Here, we developed PerturbView, an OPS technology that leverages in vitro transcription (IVT) to amplify barcodes prior to ISS, enabling screens with highly multiplexed phenotypic readouts across diverse systems, including primary cells and tissues. We demonstrate PerturbView in iPSC-derived neurons, primary immune cells, and tumor tissue sections from animal models. In a screen of immune signaling pathways in primary bone marrow-derived macrophages, PerturbView uncovered both known and novel regulators of NF{kappa}B signaling. Furthermore, we combined PerturbView with spatial transcriptomics in tissue sections from a mouse xenograft model, paving the way to in vivo screens with rich optical and transcriptomic phenotypes. PerturbView broadens the scope of OPS to a wide range of models and applications.

systems biology↗

bioRxiv2022

SPEX: A modular end-to-end analytics tool for spatially resolved omics of tissues

Recent advancements in transcriptomics and proteomics have opened the possibility for spatially resolved molecular characterization of tissue architecture with the promise of enabling a deeper understanding of tissue biology in either homeostasis or disease. The wealth of data generated by these technologies has recently driven the development of a wide range of computational methods. These methods have the requirement of advanced coding fluency to be applied and integrated across the full spatial omics analysis process thus presenting a hurdle for widespread adoption by the biology research community. To address this, we introduce SPEX (Spatial Expression Explorer), a web-based analysis platform that employs modular analysis pipeline design, accessible through a user-friendly interface. SPEXs infrastructure allows for streamlined access to open source image data management systems,analysis modules, and fully integrated data visualization solutions. Analysis modules include essential steps covering image processing, single-cell and spatial analysis. We demonstrate SPEXs ability to facilitate the discovery of biological insights in spatially resolved omics datasets from healthy tissue to tumor samples.

bioinformatics↗