Nathan Salomonis

LungChat is a conversational multi-agent AI system that integrates multi-omics analysis and biomedical discovery, identifying potential therapeutics for pulmonary diseases.

This study uses single nucleus RNA sequencing to identify altered angiogenic signaling in JDM muscle, highlighting vascular and IFN pathways.

This study uses single-cell RNA sequencing in rhesus macaques to identify transitional states and signaling shifts in nephron progenitor cells during late gestation, suggesting a mechanism for lateral branch nephrogenesis.

This study shows Blimp1 restricts clonal dominance in germinal center B cells by controlling chromatin accessibility and BCR signaling during the light-to-dark zone transition.

This study uses single-cell multiomic analysis to reveal genotype-immunophenotype relationships and clonal evolution in NPM1-mutant AML across disease stages.

This study identifies BM adipocyte-derived Resistin, regulated by RXR signaling, as critical for HSC self-renewal and hematopoietic regeneration after myeloablation.

This study defines the transcriptional landscape of CD8+ T cells in pediatric liver transplant rejection using single-cell sequencing and identifies persistent CD8+ clones associated with graft failure.

This study investigates the role of DEK in mammary epithelium proliferation and shows it promotes cell cycle progression through H3K27me3 epigenetic modifications.

The paper identifies two categories of transcription factor isoforms with distinct molecular functions and shows they contribute to gene regulatory complexity.

This paper introduces BayesTS, a Bayesian probabilistic score for tumor specificity to prioritize drug targets, demonstrating its effectiveness across multiple tissues and data types.

This study uses iPSC-derived neurons to show that FMRP deficiency in fragile X syndrome leads to m6A-mediated transcriptome dysregulation, causing synaptic dysfunction and suggesting therapeutic targets.

This study shows cholestasis shifts hepatic Tregs to a Th17 phenotype and impairs their suppressor function, with bile acid levels influencing Treg activity and liver injury.

The paper identifies a splicing mechanism in AML independent of mutations, showing it drives poor prognosis and can be targeted with PRMT5 inhibition.

The paper constructs a single-cell reference atlas for human and mouse lung tissues to improve cell type annotation and data integration.

scTriangulate identifies high-confidence cell populations and modality-specific subtypes by combining clustering results from uni- and multimodal single-cell data using a game-theory framework.

The paper describes the LungMAP Portal Ecosystem, a centralized resource integrating multi-omics and imaging data across species to enable standardized analysis of lung biology and disease.

The paper uses transcriptomic data from amniotic fluid and fetal tissues to identify biomarkers of fetal maturity in rhesus macaques, showing that cell-free RNA can predict lung compliance and gestational age.

CellDrift identifies temporally varied perturbation patterns in single-cell data by analyzing covarying gene expression across time, outperforming existing methods in imputation and pattern detection.

This study shows that blocking inflammation in preterm primates prevents lung injury, preserving alveolar structure and function similar to chronic lung disease in newborns.

The paper shows age-dependent changes in nephron progenitor translation and signaling that influence nephrogenesis timing, with Tsc1 affecting Wnt signaling balance.

This study investigates circadian rhythms in human intestinal organoids, showing circadian phase-dependent necrotic cell death responses to TcdB and identifying anti-phasic gene expression between mouse and human enteroids.

DeepImmuno predicts and generates immunogenic peptides using deep learning models, outperforming existing methods and providing insights into T cell antigen recognition.

DoubletDecon identifies and removes doublets from scRNA-seq data using deconvolution and gene expression analysis.

The paper introduces an improved clustering algorithm for scRNA-seq data that outperforms existing methods on benchmark datasets and scales to large-scale data.

DoubletDecon identifies and removes doublets in scRNA-Seq data using deconvolution and cell-state gene expression analysis, improving cell state delineation.

cellHarmony matches and compares single-cell transcriptomes to identify disease-specific gene regulatory programs and cell-state changes.