Biology subjects

Piya, S.

Publications and source records attributed to Piya, S..

5 recordsLinked to original sources

bioRxiv2026

Deep Learning Enabled 3D Multi-Omic Analysis Reveals Molecular Signatures of Heterogeneous Response to Chemotherapy in Pancreatic Cancer

Resistance to systemic therapy is a major unmet challenge in pancreatic cancer. To identify potential mechanisms of resistance, we developed a novel 3D pipeline in clinical samples that uses deep learning to classify sensitive and persistent tumor cell populations based on morphological features, enabling subsequent molecular characterization of intratumoral heterogeneity. We applied this automated 3D pipeline to a cohort of human pancreatic cancer samples treated with neoadjuvant chemotherapy, identifying heterogeneity in response to therapy both between and within tumors. Application of spatial proteomics to these sensitive and persistent regions identified enhanced epithelial-to-mesenchymal transition and non-classical cell states in persistent cells, confirming our morphological classification. Integration of spatial transcriptomics in multiple pancreatic cancer cohorts associated fibroblast-cancer crosstalk via syndecans with resistance to cytotoxic therapy. Our validated 3D multi-omic pipeline is now poised for application to clinical trials, enabling discovery of resistance mechanisms and design of new therapeutic combinations to circumvent resistance. Statement of significanceWe developed a novel 3D multi-omic pipeline to identify mechanisms of resistance to chemotherapy in clinical samples. This approach associated fibroblast-cancer crosstalk via syndecans with resistance to cytotoxic therapy and is poised for broader application in neoadjuvant clinical trials.

cancer biology↗

bioRxiv2025

SpaceSequest: A unified pipeline for spatial transcriptomics data analysis

BackgroundSpatial transcriptomics has emerged as one of the most powerful tools for gaining biological insights, enabling researchers to uncover intricate relationships between gene expression patterns and tissue architecture. Recent advances in the field have resulted in a variety of new platforms, including Visium, Visium HD, and Xenium from 10x Genomics, as well as GeoMx and CosMx from NanoString Technologies, which has now been acquired by the Bruker Corporation. However, the existence of diverse spatial transcriptomics platforms and various data formats poses challenges in standardizing data analysis. Thus, there remains a critical gap in the availability of a comprehensive pipeline capable of conducting end-to-end analysis that is necessary to extract biological insights from multiple spatial transcriptomics platforms. ResultsHere, we present SpaceSequest, a tailored pipeline that utilizes cutting-edge computational methodologies to conduct a thorough analysis, enabling the extraction of crucial biological insights from five major spatial transcriptomics technologies. SpaceSequest performs (1) standardized quality control and general data processing, (2) key analyses customized for each spatial platform, (3) automated cell type annotation and deconvolution, and (4) high-quality figure and analysis result generation. In addition, SpaceSequest allows for smooth integration with cellxgene VIP and Quickomics for user-friendly data access and interactive visualization. ConclusionsSpaceSequest is a unified and comprehensive pipeline designed for the analysis, visualization, and publication of spatial transcriptomics data from various platforms. The source code is available at https://github.com/interactivereport/SpaceSequest. To facilitate seamless installation and usage, we have also created a detailed Bookdown tutorial that can be accessed through https://interactivereport.github.io/SpaceSequest/tutorial/docs/index.html.

bioinformatics↗

bioRxiv2025

Inhibitors of oncogenic Kras specifically prime CTLA4 blockade to transcriptionally reprogram Tregs and overcome resistance to suppress pancreas cancer

Lack of sustained response to oncogenic Kras (Kras*) inhibition in preclinical models and patients with pancreatic ductal adenocarcinoma (PDAC) emphasizes the need to identify impactful synergistic combination therapies to achieve robust clinical benefit. Kras* targeting results in an influx of T cell infiltrates including Tregs, effector CD8+ T cells and exhausted CD8+ T cells expressing several immune checkpoint molecules in PDAC. Here, we probe whether the T cell influx induced by different Kras* inhibitors enable a therapeutic window to prime adaptive immune response in PDAC. Here we report a specific synergy between KrasG12D allele specific inhibitor, MRTX1133 or multi-selective pan-RAS inhibitor, RMC-6236 and anti-CTLA4 immune checkpoint blockade. In contrast, attempted therapeutic combination with multiple other immune checkpoint inhibitors, including anti-PD1, anti-Tim3, anti-Lag3, anti-Vista and anti-4-1BB agonist antibody failed due to compensatory mechanisms mediated by other checkpoints on exhausted CD8+ T cells. Specifically, anti-CTLA4 therapy in Kras* targeted PDAC transcriptionally reprograms effector T regs to a naive phenotype, reverses CD8+ T cell exhaustion and is associated with recruitment of tertiary lymphoid structures (TLS) containing follicular B cells, interferon (IFN)- stimulated/ activated B cells, plasma cells and germinal center B cells to functionally enable efficacy of immunotherapy with long-term survival. In this regard, inhibition of the TLS with lymphotoxin-{beta} inhibitor (LTBi) or direct B cell depletion reversed the survival benefit conferred by the combination therapy and highlights the function of TLS in generating productive anti-tumor immune responses. Further, single cell ATAC sequencing analysis revealed that transcriptional reprogramming of Tregs is epigenetically regulated by downregulation of AP-1 family of transcription factors including Fos, Fos-b, Jun-b, Jun-d in the IL-35 promoter region. This study reveals an actionable vulnerability in the adaptive immune response in Kras* targeted PDAC with relevant clinical implications.

cancer biology↗

bioRxiv2025

Scaling up spatial transcriptomics for large-sized tissues: uncovering cellular-level tissue architecture beyond conventional platforms with iSCALE

Recent advances in spatial transcriptomics (ST) technologies have transformed our ability to profile gene expression while retaining the crucial spatial context within tissues. However, existing ST platforms suffer from high costs, long turnaround times, low resolution, limited gene coverage, and small tissue capture areas, which hinder their broad applications. Here we present iSCALE, a method that predicts super-resolution gene expression and automatically annotates cellular-level tissue architecture for large-sized tissues that exceed the capture areas of standard ST platforms. The accuracy of iSCALE were validated by comprehensive evaluations, involving benchmarking experiments, immunohistochemistry staining, and manual annotation by pathologists. When applied to multiple sclerosis human brain samples, iSCALE uncovered lesion associated cellular characteristics that were undetectable by conventional ST experiments. Our results demonstrate iSCALEs utility in analyzing large-sized tissues with automatic and unbiased tissue annotation, inferring cell type composition, and pinpointing regions of interest for features not discernible through human visual assessment.

genomics↗

bioRxiv2025

Spatial transcriptomics reveals heterogeneous cell-cell interactions among brain regions in a cuprizone model consistent with multiple sclerosis lesions

The cuprizone (CPZ) model is widely used for modeling demyelination in multiple sclerosis (MS) and for testing potential remyelination therapies. We integrated single-cell and spatial transcriptomics (ST) to fine map the spatial cellular and molecular responses during de and remyelination. ST revealed global demyelination and neuroinflammation in the brain beyond the corpus callosum, with region-specific differences. We identified oligodendroglia and microglia as two major cell types with significant transcriptomic changes in the model. Ligand receptor pairing analyses predicted growth factor and phagocytic pathway enrichment during demyelination, which is consistent with changes in MS lesions. During remyelination, while mature oligodendrocytes nearly reversed their phenotype back to the control state, microglia remained associated with the demyelination phenotype. Finally, astrocytes in the CPZ model had the greatest preservation of disease-associated modules to MS lesions, while the MOL, OPC, and microglia showed moderate to low preservation, which overall suggested that the CPZ model had moderate translatability to chronically active MS lesions.

neuroscience↗