Biology subjects

Bhattacharjee, N.

Publications and source records attributed to Bhattacharjee, N..

3 recordsLinked to original sources

bioRxiv2026

A multitask single-cell analysis framework with knowledge graph as a prior

Single-cell RNA sequencing has reshaped cancer biology by revealing the cellular and molecular diversity of the tumor microenvironment, yet most analysis pipelines still treat clustering, annotation, pathway scoring, metabolic inference, and cell-cell communication as separate tasks. Here, we present scKNIFE (Knowledge graph-based NMF for Inference of Functional Entities), a unified graph-regularized non-negative matrix factorization framework that jointly reconstructs single-cell expression profiles and infers activities of diverse biological entities, including pathways, cell-type markers, metabolic reactions and tasks, ligand-receptor interactions, and cell-state programs. By embedding a heterogeneous knowledge graph directly into the factorization objective, scKNIFE propagates information between biologically related entities through Laplacian regularization while sparsity constraints preserve interpretability. The integrated prior graph spans 47,274 nodes and 506,620 edges, assembled from complementary resources including Gene Ontology, Reactome, Hallmark gene sets, Human-GEM, CellChat, PanglaoDB, Cytopus, and Tabula Muris-derived annotations. Across multiple cancer-focused single-cell datasets, scKNIFE yields competitive to leading performance for clustering and cell-type annotation, while also recovering biologically coherent metabolic programs that agree with dedicated metabolism-focused methods and capture therapy-response-associated states. In addition, the framework supports downstream inference of cell-type-specific biological activities from a single latent representation. Together, these results establish scKNIFE as a modular and extensible framework for end-to-end biological interpretation of single-cell cancer transcriptomics

cancer biology↗

bioRxiv2025

Mechanism-aware inference of response to targeted cancer therapies

Targeted therapies like small-molecule inhibitors often work by blocking proteins that cancer cells rely on for survival. Omics based modeling of drug sensitivity alone lack mechanistic grounding. We propose FORGE (Factorization Of Response and Gene Essentiality) a simple yet powerful joint matrix factorization framework that co-models drug response and target gene essentiality, enabling the stratification of promising treatment groups for targeted therapy consideration. FORGE also provides Benefit Score -- a predictive score that estimates treatment efficacy from basal gene expression profiles. We validated the predictive performance of FORGE across multiple targeted therapies, including Erlotinib (EGFR inhibitor) and Daporinad (NAMPT inhibitor). Our meta-analysis of large scale in-vitro studies underscores FORGEs ability to identify common determinants of drug vulnerabilities and target gene essentiality. Such convergences were not observed when treatment vulnerabilities and gene essentialities were modeled independently. We also demonstrated the universality of Erlotinib Benefit Scores by transferring transformations learned from high-throughput drug response studies across other published datasets, including the TAHOE-100M single-cell perturbation atlas and patient-derived xenograft studies. FORGE successfully identified key regulators within the molecular pathways targeted by these therapies, reinforcing its potential for mechanistically grounded treatment stratification.

cancer biology↗

bioRxiv2020

A Microfluidic Platform for Functional Testing of Cancer Drugs on Intact Tumor Slices

Present approaches to assess cancer treatments are often inaccurate, costly, and/or cumbersome. Functional testing platforms that use live tumor cells are a promising tool both for drug development and for identifying the optimal therapy for a given patient, i.e. precision oncology. However, current methods that utilize patient-derived cells from dissociated tissue typically lack the microenvironment of the tumor tissue and/or cannot inform on a timescale rapid enough to guide decisions for patient-specific therapy. We have developed a microfluidic platform that allows for multiplexed drug testing of intact tumor slices cultured on a porous membrane. The device is digitally-manufactured in a biocompatible thermoplastic by laser-cutting and solvent bonding. Here we describe the fabrication process in detail, we characterize the fluidic performance of the device, and demonstrate on-device drug-response testing with tumor slices from xenografts and from a patient colorectal tumor.

bioengineering↗