Nima, J. P. | Search bioRxiv

bioRxiv2024

Patches: A Representation Learning framework for Decoding Shared and Condition-Specific Transcriptional Programs in Wound Healing

Single-cell genomics enables the study of cell states and cell state transitions across biological conditions like aging, drug treatment, or injury. However, existing computational methods often struggle to simultaneously disentangle shared and condition-specific transcriptional patterns, particularly in experimental designs with missing data, unmatched cell populations, or complex attribute combinations. To address these challenges, Patches identifies universal transcriptomic features alongside condition-dependent variations in scRNA-seq data. Using conditional subspace learning, Patches enables robust integration, cross-condition prediction, and biologically interpretable representations of gene expression. Unlike prior methods, Patches excels in experimental designs with multiple attributes, such as age, treatment, and temporal dynamics, distinguishing general cellular mechanisms from condition-dependent changes. We applied Patches to both simulated data and real transcriptomic datasets from skin injury models, focusing on the effects of aging and drug treatment. Patches revealed shared wound healing patterns and condition-specific changes in cell behavior and extracellular matrix remodeling. These insights deepen our understanding of tissue repair and can identify potential biomarkers for therapeutic interventions, particularly in contexts where the experimental design is complicated by missing or difficult-to-collect data.

genomics↗

bioRxiv2024

A heterogenous pharmaco-transcriptomic landscape induced by targeting a single oncogenic kinase

Over-activation of the epidermal growth factor receptor (EGFR) is a hallmark of glioblastoma. However, EGFR-targeted therapies have led to minimal clinical response. While delivery of EGFR inhibitors (EGFRis) to the brain constitutes a major challenge, how additional drug-specific features alter efficacy remains poorly understood. We introduce SCHEMATIC, which integrates multiplex single-cell chemical transcriptomics with deep-generative classification to resolve chemotype-specific and shared programs and apply it to to define the molecular response of glioblastoma to EGFRis. We identify programs that differ by the chemical properties of EGFRis, including induction of adaptive transcription and modulation of immunogenic gene expression. We find that induction of an adaptive transcriptional program is associated with persistence of surviving cells after EGFR inhibition, and that concurrent EGFR/PI3K inhibition attenuates this program. We also find that pro-immunogenic expression changes associated with a subset of tyrphostin-family EGFR inhibitors are accompanied by enhanced antigen-specific cytotoxic T-cell killing in vitro. Our study provides a framework that considers each agents unique and often unknown poly-pharmacology to prioritize compounds pre-clinically that induce favorable molecular responses.

genomics↗