J. Saez-Rodriguez

The paper identifies lipid cargoes for half of analyzed human LTPs, revealing selectivity for lipid head groups and acyl chains and new lipid-organelle relationships.

The paper uses single-cell transcriptomics to show how two compartments in medulloblastoma are connected through a migrating precursor stage, revealing spatial and developmental links between proliferating and differentiated tumor cells.

The paper introduces a single-sample workflow, MTBE-SP3, for simultaneous proteomic and metabolomic analysis, showing it reduces technical variation and enables consistent results across biological matrices.

This study builds gene co-expression networks from rat kidney toxicogenomics data and integrates them into a web tool to improve safety assessment and biomarker identification for drug development.

This study uses single-cell RNAseq to identify distinct fibroblast and macrophage activation patterns in murine HFpEF, revealing disease-specific transcriptional signatures and a potential biomarker.

This study uses spatial transcriptomics and single-cell RNA sequencing to analyze colorectal cancer subtypes, revealing intratumoral heterogeneity and cell communication events in CMS2 carcinomas.

This study uses network-based transcriptomic analysis of HepG2 cells exposed to DILI compounds to identify causally linked genes involved in drug-induced liver injury.

This study uses phosphoproteomics to analyze metformin signaling in CRC cell lines, revealing heterogeneous responses and identifying potential drug interactions.

This study evaluates pre-analytical factors affecting plasma and serum proteome and metabolome stability, recommending standardized protocols for clinical use.

The paper introduces TXG-MAPr, a tool for analyzing toxicogenomic data using WGCNA to identify stress response modules across species and platforms.

This study identifies metabolic reprogramming in renal cancer cells, showing how branched-chain amino acid catabolism and urea cycle enzymes support tumor progression and metastasis.

This study shows that unsaturated lipids reduce renal damage in diabetes by increasing lipid storage and protecting against ER stress in proximal tubular cells.

The paper analyzes protein activity changes in cancer using genomics and proteomics data to identify drivers of signaling dysregulation and their impact on patient survival.

This study identifies an immunogenic CD8+ T cell epitope associated with severe SARS-CoV-2 infection and characterizes its pathological features.

The paper hosts a DREAM Challenge to evaluate computational models for predicting drug polypharmacology using perturbational profile data from 32 kinase inhibitors.

This study identifies PYCR1-dependent proline synthesis in cancer-associated fibroblasts as critical for collagen production and tumor progression, suggesting PYCR1 as a therapeutic target to inhibit pro-tumorigenic extracellular matrix deposition.

The paper introduces a statistical framework using uncertainty estimates to identify biomarkers from drug screening data, finding 24 known and 6 novel biomarkers in cancer cell lines.

This paper integrates transcriptomic data from nine CKD entities to delineate their molecular differences and identify potential therapeutic targets.

The paper functionally links gene fusions to cancer cell fitness using drug sensitivity and CRISPR-Cas9 data, identifying rare functional fusions and novel therapeutic targets.

This study uses CRISPR-Cas9 screens in cancer cell lines to identify and prioritize new oncology therapeutic targets based on gene fitness, biomarkers, and tractability.

The paper uses pharmacogenomics data and regression models to identify drug-transporter relationships, uncovering both known and novel associations.

GDSCTools provides a software package for analyzing pharmacogenomic data to identify genomic markers of drug response in cancer.

The paper uses computational methods to infer transcription factor activities from RNA-seq data and shows their utility as markers for drug response in cancer.

The paper uses dynamic logic models of signaling pathways to predict drug sensitivity in colorectal cancer cell lines and identifies drug combinations to overcome resistance.

This paper presents a microfluidics-based method to rapidly screen drug combinations on patient cancer cells, identifying personalized treatment options validated in pancreatic cancer cell lines.