Biomedical subjects

Rahul Satija

Publications and source records attributed to Rahul Satija.

3 recordsLinked to original sources

PubMed2026

Splenic extramedullary hematopoiesis in myelofibrosis is shaped by transcriptomic and epigenetic dysregulation.

Myelofibrosis (MF) is a chronic, progressive myeloproliferative neoplasm characterized by bone marrow fibrosis, ineffective blood cell production, and neoplastic extramedullary hematopoiesis (EMH) occurring primarily within the spleen. To explore the molecular mechanisms underlying splenic EMH, we performed single-cell transcriptional and chromatin profiling of cells from MF spleens that had been surgically removed. We demonstrate significant expansion of hematopoietic stem and progenitor cells, coupled with aberrant differentiation toward the erythroid and megakaryocytic lineages, associated with a significant enrichment of inflammatory pathways with enhanced NF-κB signaling and IFN responses, as well as dysregulation of the inferred function of differentiation-defining transcription factors. Finally, we report a significant remodeling of the immune microenvironment in MF spleens, characterized by emergence of dysfunctional T cell subsets and inflammatory memory B cells, suggesting the concomitant establishment of a pro-inflammatory and immune-tolerant tumor microenvironment within the spleen that influences hematopoietic cell differentiation and impairs tumor immune surveillance.

Primary Myelofibrosis↗

PubMed2025

Mapping transcriptional responses to cellular perturbation dictionaries with RNA fingerprinting.

Single-cell perturbation dictionaries provide systematic measurements of how cells respond to genetic and chemical perturbations, and create the opportunity to assign causal interpretations to observational data. Here, we introduce RNA fingerprinting, a statistical framework that maps transcriptional responses from new experiments onto reference perturbation dictionaries. RNA fingerprinting learns denoised perturbation "fingerprints" from single-cell data, then probabilistically assigns query cells to one or more candidate perturbations while accounting for uncertainty. We benchmark our method across ground-truth datasets, demonstrating accurate assignments at single-cell resolution, scalability to genome-wide screens, and the ability to resolve combinatorial perturbations. We demonstrate its broad utility across diverse biological settings: identifying context-specific regulators of p53 under ribosomal stress, characterizing drug mechanisms of action and dose-dependent off-target effects, and uncovering cytokine-driven B cell heterogeneity during secondary influenza infection in vivo. Together, these results establish RNA fingerprinting as a versatile framework for interpreting single-cell datasets by linking cellular states to the underlying perturbations which generated them.

Journal Article↗

PubMed2025

Iterative, multimodal, and scalable single-cell profiling for discovery and characterization of signaling regulators.

Cell signaling plays a critical role in regulating cellular state, yet uncovering regulators of signaling pathways and understanding their molecular consequences remains challenging. Here, we present an iterative experimental and computational framework to identify and characterize regulators of signaling proteins, using the mTOR marker phosphorylated RPS6 (pRPS6) as a case study. We present a customized workflow that uses the 10x Flex assay to jointly profile intracellular protein levels, transcriptomes, and CRISPR perturbations in single cells. We use this to generate a "glossary" dataset of paired protein-RNA measurements across targeted perturbations, which we leverage to train a predictive model of pRPS6 levels based solely on transcriptomic data. Applying this model to a genome-wide Perturb-seq dataset enables in silico screening for pRPS6 and nominates novel regulators of mTOR signaling. Experimental validation confirms these predictions and reveals mechanistic diversity among hits, including changes in signaling output driven by anabolic activity, cellular proliferation and multiple stress pathways. Our work demonstrates how integrated experimental and computational approaches provide a scalable framework for multimodal phenotyping and discovery.

Journal Article↗