Xiaojie Qiu

The paper uses CRISPR-based lineage recording and single-cell RNA sequencing to show that mesodermal cell diversity in mouse embryos arises from spatially distinct, fate-biased progenitor pools rather than simple branching lineages.

This study introduces match-seq, a method to trace cell-cell interactions in vivo using CRISPR screens, revealing genetic regulators of tumor-immune interactions.

Bio-Babel reconstructs computational biology software in new ecosystems, enabling agent-callable usage and revealing pancreatic differentiation defects under SWI/SNF loss.

Navigo models mouse embryogenesis to predict cell fates and disease by learning developmental trajectories from scRNA-seq data.

U-Probe is a universal probe design platform that enables agentic, AI-assisted creation of FISH probes for spatial-omics applications, validated across multiple scenarios.

This paper introduces scAPEX-seq, a method to map subcellular transcriptomes, and identifies CTSW as a regulator of CAR T cell function in tumor co-cultures.

PantheonOS introduces a multi-agent framework for automated genomics analysis, achieving super-human performance in batch correction and gene panel selection across diverse biological tasks.

This study uses a knockout village framework to analyze how mutations in diabetes genes alter pancreatic beta cell development, revealing non-canonical cell fate shifts and identifying ISL1 as a key regulator.

MERFISH+ enables high-resolution spatial multi-omics mapping of gene expression and chromatin organization in developing human hearts with subcellular resolution.

OSK factors rejuvenate aged RPE cells by activating a Tet2-independent oxidative resilience pathway through GSTA4, restoring vision and reversing age-related degeneration.

DANCE 2.0 automates and interprets single-cell preprocessing by recommending optimal pipelines for various tasks, improving performance across six major analyses.

This paper introduces Tabula, a privacy-preserving foundation model for single-cell transcriptomics using federated learning and tabular modeling, achieving better performance with less data and revealing regulatory logic across biological systems.

Focus improves cell type annotation by modeling subcellular RNA distribution in spatial transcriptomics data and identifies cell type-specific molecular pathways.

This study creates a gene regulation map of the human fetal heart to interpret genetic variants associated with congenital heart disease and cardiac traits, identifying valvular interstitial cells as key regulators.

Storm infers RNA velocity using metabolic labeling data by solving stochastic differential equations, revealing time-dependent transcriptional regulations in transient processes.

This study uses Perturb-seq to functionally interrogate SARS-CoV-2 host factors in human lung epithelial cells, identifying key factors that influence infection and host response.

This paper introduces Graph-Dynamo, a framework that learns stochastic cellular state transition dynamics from single cell data using graph-based methods and Fokker-Planck equations.

This paper introduces TEMPOmap, a method to profile subcellular RNA kinetics in single cells, revealing kinetic gene clusters with distinct functional roles.

Spateo provides a framework for spatiotemporal modeling of single-cell spatial transcriptomics data to study cell-cell interactions and organ development in 3D space.

The paper introduces dynamo, a framework for estimating RNA velocities and predicting cell fates using single-cell RNA-seq data, applied to various biological processes.

The paper uses Stereo-seq to map single-cell transcriptomes of axolotl telencephalon during development and regeneration, identifying a key radial glial cell subtype involved in early wound healing.

This paper introduces scNT-Seq, a method to measure newly-transcribed and pre-existing RNAs in single cells, revealing gene regulatory networks and RNA dynamics in neurons and embryonic stem cells.

This paper introduces Scribe, a toolkit for causal gene regulatory network inference from single-cell data, showing that pseudotime data performs worse than true time series data.

The paper investigates how thyroid hormone regulates different maturation paths in zebrafish pigment cell lineages through transcriptomic profiling and trajectory analysis.

The paper analyzes MYOD-mediated reprogramming of human fibroblasts using single-cell RNA-Seq to identify molecular determinants of reprogramming success and failure.

The paper profiles chromatin accessibility in Drosophila embryos to uncover cis-regulatory programs and cell fate determinants during development.

Monocle 2 learns branched single-cell trajectories without supervision via reversed graph embedding, revealing a non-fusing myoblast outcome and knockout myeloid cells diverted onto opposite fate branches.

This paper introduces sci-RNA-seq, a combinatorial indexing method for single-cell RNA sequencing, and applies it to profile 42,035 C. elegans cells, identifying 27 cell types and their specific genes.

The paper introduces Cicero, a statistical method to link regulatory elements to target genes using single-cell chromatin accessibility data, and applies it to study myogenesis.