Peng Qiu

This study develops and tests nano-prodrugs of indole-3-propionic acid (IPA) to treat experimental autoimmune arthritis, showing improved lymphoid exposure and immunomodulatory effects.

The paper presents the Human Gut Cell Atlas v1.0, a unified reference of gut cell diversity, revealing how tissue structure influences cellular composition and enabling disease and organoid analysis.

This study uses a multispecies comparative framework to analyze forebrain cell types across vertebrates, revealing conserved transcriptional programs and lineage-specific modifications.

This paper introduces OncoSCOUT, a method using engineered T cells with protease-activatable receptors to detect early-stage cancer with improved sensitivity compared to existing biomarkers.

This study shows that delivering a stable isomer of Resolvin D1 via a hydrogel improves muscle regeneration and reduces inflammation in volumetric muscle loss injuries.

scDisInFact disentangles batch and condition effects in scRNA-seq data, improving batch correction, gene detection, and perturbation prediction across multiple batches and conditions.

The paper introduces SuPERR-seq, a workflow for multi-omics single-cell data integration that improves cell-type classification and identifies novel immune cell subsets.

The paper presents a computational pipeline using UMAP and SHAP to quantify cell-type-specific differences across single-cell datasets and identify responsible genes.

The paper organizes a DREAM challenge to benchmark predictive models of single-cell signaling using mass cytometry data from breast cancer cell lines.

The paper predicts cellular positions in the Drosophila embryo using scRNA-seq data by benchmarking methods against spatially annotated genes.

The paper introduces a shape-to-graph mapping method for quantifying and classifying complex geometries in biological images using graph-based metrics and machine learning.

GLaMST reconstructs lineage trees from BCR sequencing data by interpolating unobserved sequences using a minimum spanning tree, outperforming existing methods in simulations with high mutation rates.

This paper presents a method that uses dropout patterns in single-cell RNA-seq data as a signal for cell type identification, showing that binarized data can effectively cluster cells and identify cell-type specific pathways.

This study rapidly generates proteomic data for NCI-60 cancer cell lines and identifies drug response determinants by integrating proteomic, genetic, and transcriptomic data.