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Biology subjects

Ruiz, L. C.

Publications and source records attributed to Ruiz, L. C..

2 recordsLinked to original sources

A unified network systems approach uncovers a core novel program underlying T follicular helper cell differentiation

T follicular helper (Tfh) cells are central to the adaptive immune response and exhibit remarkable functional diversity and plasticity. The complex nature of Tfh cell populations, inconsistent findings across experimental systems and potential differences across species have fueled ongoing debate regarding core regulatory pathways that govern Tfh differentiation. Many studies have experimentally investigated individual proteins and circuits involved in Tfh differentiation in limited contexts, each providing only a partial understanding of the process. To address this, we adopted a novel multi-scale network systems approach that incorporates both regulatory and protein-protein interactions. Our approach integrates diverse data types, captures regulation across multiple levels of immune system organization, and recapitulates known drivers. Further, we discover a core Tfh gene set that is conserved across tissue types and disease contexts, and is consistent across data modalities - bulk, single-cell and spatial. While components of this set have been individually reported, a novel aspect of our work lies in the discovery, characterization, and connectivity of this core signature using a single unbiased approach. Using this method, we also uncover a novel function of IL-12, a molecule with reported conflicting functions, in the regulation of Tfh differentiation. Notably, we find that, in both humans and mice, IL-12 is permissive for the differentiation of Tfh precursors, but blocks subsequent differentiation into GC Tfh cells. Overall, this work elucidates novel networks with unexplored roles in governing Tfh cell differentiation across species and tissues, paving the way for novel -therapeutic interventions.

immunology↗

Drug and single-cell gene expression integration identifies sensitive and resistant glioblastoma cell populations

Glioblastoma (GBM) remains the most common and lethal adult malignant primary brain cancer with few treatment options. A significant issue hindering GBM therapeutic development is intratumor heterogeneity. GBM tumors contain neoplastic cells within a spectrum of different transcriptional states. Identifying effective therapeutics requires a platform that predicts the differential sensitivity and resistance of these states to various treatments. Here, we developed a novel framework, ISOSCELES (Inferred cell Sensitivity Operating on the integration of Single-Cell Expression and L1000 Expression Signatures), to quantify the cellular drug sensitivity and resistance landscape. Using single-cell RNA sequencing of newly diagnosed and recurrent GBM tumors, we identified compounds from the LINCS L1000 database with transcriptional response signatures selectively discordant with distinct GBM cell states. We validated the significance of these findings in vitro, ex vivo, and in vivo, and identified a novel combination of an OLIG2 inhibitor and Depatux-M for GBM. Our studies suggest that ISOSCELES identifies cell states sensitive and resistant to targeted therapies in GBM and that it can be applied to identify new synergistic combinations. HighlightsO_LIIntegration of GBM single-cell RNA sequencing data with L1000-derived drug response signatures facilitates clustering of tumor cells and small molecules on cell-drug connectivity. C_LIO_LICell-drug connectivity predicts the identities of drug-sensitive and resistant cell states. C_LIO_LIIn silico perturbation analysis using cell-drug connectivity predicts drug-induced changes in the cell-drug connectivity landscape in vivo. C_LIO_LIIn silico perturbation analysis to predict drug-induced changes in the tumor cell-drug connectivity landscape predicts drug combinations that synergize in vivo to extend survival. C_LI

cancer biology↗