Qing Hua Nie

Building virtual cell dynamics that evolve with biological measurements

CellCompass, an agentic AI system, models cell dynamics from single-cell measurements and improves predictions by integrating new biological constraints and experimental data.

Epigenetic Coalitions Couple Tissue Growth to Generate Periodic Colour Patterns in Birds

The paper shows how epigenetic coalitions in birds couple tissue growth to generate periodic color patterns through ASIP regulation and feedback loops.

Robust identification of cell-cell communication heterogeneity in single cells

This paper introduces scRICH, a framework for analyzing cell-cell communication in single-cell and spatial transcriptomics data, showing its ability to capture communication heterogeneity and link it to cell fate decisions.

Integrative cross-sample alignment and spatially differential gene analysis for spatial transcriptomics

CODA aligns and compares spatial transcriptomics data across samples, identifying spatially differential genes associated with disease and sleep regulation.

Deciphering interaction syntax via decoupling intrinsic lineages and niche pressure

TRINUS decouples cell lineage identity from niche pressure to uncover interaction syntax in spatial transcriptomics data, showing improved clustering and domain detection across biological systems.

Single-cell characterization of skin response to a bite by West Nile virus-infected mosquito reveals fibroblast-mediated barrier to transmission

This study uses single-cell RNA-sequencing to analyze skin responses to West Nile virus-infected mosquito bites, identifying fibroblasts as key cells restricting viral transmission.

Deep learning linking mechanistic models to single-cell transcriptomics data reveals transcriptional bursting in response to DNA damage

This study introduces DeepTX, a deep learning framework that links mechanistic models to scRNA-seq data to infer transcriptional burst kinetics in response to DNA damage.

Statistically principled feature selection for single cell transcriptomics

The paper introduces a statistically principled feature selection method for scRNA-seq that improves accuracy in identifying rare cell types compared to existing tools.

Morphogen-guided neocortical organoids recapitulate regional areal identity and model neurodevelopmental disorder pathology

This study develops a method to generate neocortical organoids with distinct anterior-posterior molecular signatures and uses them to model Fragile X Syndrome pathology.

Dissecting crosstalk induced by cell-cell communication using single-cell transcriptomic data

The paper introduces SigXTalk, a method to analyze crosstalk in cell-cell communication using scRNA-seq data, identifying shared molecules and regulatory networks in disease conditions and over time.

Sequential emergence and contraction of epithelial subtypes in the prenatal human choroid plexus revealed by a stem cell model

Modeling spatial intercellular communication and multilayer signaling regulations using stMLnet

This paper introduces stMLnet, a method for inferring spatial intercellular communication and multilayer signaling regulations using spatial transcriptomics data, showing its effectiveness across multiple datasets and technologies.

Leveraging gene correlations in single cell transcriptomic data

The paper introduces BigSur, a method for identifying gene-gene correlations in scRNA-seq data without normalization, showing it captures significant correlation structures and aligns with known biological processes.

CellChat for systematic analysis of cell-cell communication from single-cell and spatially resolved transcriptomics

The paper introduces CellChat v2, a tool for analyzing cell-cell communication from single-cell and spatial transcriptomics data, including spatial proximity analysis and expanded ligand-receptor databases.

LMNA-Related Dilated Cardiomyopathy: Single-Cell Transcriptomics during Patient-derived iPSC Differentiation Support Cell type and Lineage-specific Dysregulation of Gene Expression and Development for Cardiomyocytes and Epicardium-Derived Cells with Lamin A/C Haploinsufficiency

This study uses single-cell RNA sequencing of patient-derived iPSCs to identify gene expression and pathway dysregulation in LMNA haploinsufficiency affecting cardiomyocytes and epicardium-derived cells.

Single-cell and spatial transcriptomics of vulvar lichen sclerosus reveal multicompartmental alterations in gene expression and signaling cross-talk

This study uses single-cell and spatial transcriptomics to analyze vulvar lichen sclerosus, identifying molecular changes and signaling pathways involved in the disease.

Spatial-temporal order-disorder transition in angiogenic NOTCH signaling controls cell fate specification

The paper investigates spatial and temporal patterns of Tip/Stalk specification during angiogenesis using a controlled model and mathematical analysis, revealing an order-disorder transition in NOTCH signaling.

Single cell transcriptomics of human skin equivalent organoids

This study uses single-cell RNA sequencing to compare human skin equivalent organoids with in vivo epidermis, identifying both similarities and unique features in their cellular states and differentiation trajectories.

exFINDER: identify external communication signals using single-cell transcriptomics data

exFINDER identifies external signals received by cells from their environment using single-cell transcriptomics data, revealing signaling networks and biological activities.

Inferring neuron-neuron communications from single-cell transcriptomics through NeuronChat

The paper introduces NeuronChat, a tool for inferring neuron-neuron communication networks from single-cell transcriptomics data, validated on human and mouse datasets and applied to autism spectrum disorder.

Screening cell-cell communication in spatial transcriptomics via collective optimal transport

This paper introduces COMMOT, a method for inferring cell-cell communication in spatial transcriptomics, and demonstrates its effectiveness across multiple datasets and technologies.

A multifunctional Wnt regulator underlies the evolution of coat pattern in African striped mice

The paper identifies Sfrp2 as a multifunctional regulator controlling coat pattern and color in African striped mice through transcriptomics, modeling, and gene editing.

UFold: Fast and Accurate RNA Secondary Structure Prediction with Deep Learning

UFold predicts RNA secondary structures using deep learning, outperforming thermodynamic models and other learning-based methods with high accuracy and speed.

Single-cell transcriptomic analysis unveils spinal motor neuron subtype diversity underpinning the water-to-land transition in vertebrates

This study uses single-cell transcriptomics to identify novel motor neuron subtypes in mice, revealing subtype diversity that increases in land animals and continues to evolve in humans.

Dissecting Transition Cells from Single-Cell Transcriptome Data through Multiscale Stochastic Dynamics

MuTrans identifies transition cells and quantifies cell-state transitions using multiscale stochastic dynamics, applied to diverse single-cell datasets.

Multiple morphogens and rapid elongation promote segmental patterning during development

The paper develops a stochastic multiscale cell-based model to study how multiple morphogens and tissue dynamics regulate segment formation in the zebrafish hindbrain.

SAILER: Scalable and Accurate Invariant Representation Learning for Single-Cell ATAC-Seq Processing and Integration

SAILER improves scATAC-seq analysis by learning invariant representations, enhancing clustering and imputation while scaling to large datasets.

Inferring latent temporal progression and regulatory networks from cross-sectional transcriptomic data of cancer samples

This paper introduces PROB, a Bayesian method to infer gene regulatory networks from cross-sectional cancer transcriptomic data by decoding latent temporal progression, outperforming existing methods in pseudotime and GRN inference.

Inference and analysis of cell-cell communication using CellChat

CellChat infers communication probabilities between cell groups using a mass-action model and 2,021 curated ligand-receptor interactions (multimeric complexes, cofactors), then compares signaling networks across scRNA-seq datasets.

Modeling of histone modifications reveals formation mechanism and function of bivalent chromatin

The paper develops a mathematical model to explain the formation and function of bivalent chromatin by analyzing the dynamics of H3K4me3 and H3K27me3 modifications.