Lorenzo Bonaguro

ConvexGating automatically learns unbiased, interpretable gating strategies for flow cytometry data, improving consistency and reducing contamination in cell population identification.

The paper develops an in vitro platform to screen immune senomodulators using human-derived immune cells, enabling personalized anti-aging treatments through multi-scale molecular analysis.

This study shows that Satb1 downregulation prevents autoimmunity by maintaining Tfh cell homeostasis and preventing excessive GC formation and auto-antibody secretion.

The paper identifies OSMR and SST as molecular markers for human dermal sleeping nociceptors, providing new therapeutic targets for neuropathic pain.

This study shows that yolk sac-derived Kupffer cells regulate neonatal hepatocyte metabolism via Igf1 signaling, essential for glycogen storage and TCA cycle function.

This study identifies molecular factors shaping brain-resident CD8+ T cell formation and function in health and disease.

This study shows that primary cilia dysfunction in Bbs8-/- mice reduces adipose stem cells and promotes fibrogenic progenitors via disrupted Hedgehog signaling, affecting adipogenesis and lipid uptake.

The paper introduces cyCONDOR, a computational framework for high-dimensional cytometry data analysis, offering scalable and user-friendly tools for preprocessing, clustering, and downstream biological interpretation.

The paper characterizes human CMV-specific CD8+ T cells using a five-layer multi-omics approach and identifies novel T cell receptor sequences for CMV epitopes.

This paper integrates transcriptome and chromatin state data to develop a scalable pipeline for drug discovery, predicting cancer cell line sensitivity and guiding compound development.

This study shows that Creld2 is essential for maintaining liver metabolism homeostasis by linking the unfolded protein response (UPR) to metabolic regulation, with sex-specific effects in fatty liver disease.

This paper introduces Swarm Learning, a decentralized machine learning approach for disease classification using transcriptomic data, demonstrating its effectiveness in developing classifiers for COVID-19, tuberculosis, and leukemias while preserving privacy.

The paper generates synthetic embryo-like structures from ESCs through transcription factor-mediated reprogramming, showing implantation and decidual tissue formation.

This study uses single-cell RNA-seq and computational methods to analyze macrophage states in COPD patients, revealing transcriptional changes and metabolic shifts associated with the disease.