Jun Ding

Dissecting and steering cell dynamics using spatially-informed RNA velocity with veloAgent

Brent Yoon

RNA velocity enables inference of cell state transitions from single-cell transcriptomics by modeling transcriptional dynamics from spliced … (see more)and unspliced mRNA. However, existing methods overlook spatial context and struggle to scale to large datasets, limiting insights into tissue organization and dynamic processes. We introduce veloAgent, a deep generative and agent-based framework that estimates gene- and cell-specific transcriptional kinetics while integrating spatial information through agent-based simulations of local microenvironments. By leveraging both molecular and spatial cues, veloAgent improves velocity accuracy and achieves sublinear memory scaling, enabling efficient analysis of large and multi-batch spatial datasets. A distinctive feature of veloAgent is its in silico perturbation module, which allows targeted manipulation of spatial velocity vectors to simulate regulatory interventions and predict their impact on cell fate dynamics. These capabilities position veloAgent as a scalable and versatile framework for dissecting spatially resolved cellular dynamics and guiding cell fate manipulation across diverse biological processes.

2026-01-08

bioRxiv (preprint)

doi.org

SIDISH integrates single-cell and bulk transcriptomics to identify high-risk cells and guide precision therapeutics through in silico perturbation

Yasmin Jolasun

Kailu Song

Yumin Zheng

Jingtao Wang

Gregory Fonseca

David H. Eidelman

Jun Ding

Single-cell RNA sequencing (scRNA-seq) provides high-resolution insights into cellular heterogeneity but remains costly, restricting its use… (see more) to small cohorts that often lack comprehensive clinical data, reducing translational relevance. In contrast, bulk RNA sequencing is scalable and cost-effective but obscures critical single-cell insights. We introduce SIDISH, a neural network framework that integrates the granularity of scRNA-seq with the scalability of bulk RNA-seq. Using a variational autoencoder, deep Cox regression, and transfer learning, SIDISH identifies high-risk cell populations while enabling robust clinical predictions from large-cohort data. Its in silico perturbation module identifies therapeutic targets by simulating interventions that reduce high-risk cells associated with adverse outcomes. SIDISH also generalizes to spatial transcriptomics, identifying high-risk cells and mapping them within their native tissue microenvironment. Applied across diverse diseases, SIDISH establishes the link between cellular dynamics and clinical phenotypes, facilitating biomarker discovery and precision medicine. By unifying single-cell insights with large-scale clinical data, SIDISH advances computational tools for disease risk assessment and therapeutic prioritization, offering an integrative and scalable approach to precision medicine. SIDISH integrates single-cell and bulk RNA sequencing data using deep learning to identify high-risk cell populations and prognostic biomarkers, enabling in silico perturbations that could guide precision therapeutics and advance personalized medicine.

2025-12-09

Nature Communications (published)

doi.org

scGALA advances graph link prediction-based cell alignment for comprehensive data integration and harmonization

Guo Jiang

Kailu Song

Gregory J. Fonseca

Darcy E. Wagner

Iain C. Clark

Hui Wang

Jun Ding

Single-cell technologies have transformed our understanding of cellular heterogeneity through multimodal data acquisition. However, robust c… (see more)ell alignment remains a major challenge for data integration and harmonization, including batch correction, label transfer, and multi-omics integration. Many existing methods constrain alignment based on rigid feature-wise distance metrics, limiting their ability to capture accurate cell correspondence across diverse cell populations and conditions. We introduce scGALA, a graph-based learning framework that redefines cell alignment by combining graph attention networks with a score-driven, task-independent optimization strategy. scGALA constructs enriched graphs of cell-cell relationships by integrating gene expression profiles with auxiliary information, such as spatial coordinates, and iteratively refines alignment via self-supervised graph link prediction, where a deep neural network is trained to identify and reinforce high-confidence correspondences across datasets. In extensive benchmarks, scGALA identifies over 25 percent more high-confidence alignments without compromising accuracy. By improving the core step of cell alignment, scGALA serves as a versatile enhancer for a wide range of single-cell data integration tasks.

2025-11-25

Nature Communications (published)

doi.org

DENetwork unveils non-differentially expressed genes with functional relevance across conditions through information flow perturbation

Bowen Zhao

Ting-Yi Su

Jingtao Wang

Quazi S. Islam

Kailu Song

Steven K. Huang

Matthieu Allez

Gregory J. Fonseca

Carolyn J. Baglole

Jun Ding

Differential gene expression (DE) analysis of RNA-sequencing (RNA-seq) data is a standard approach for identifying phenotypic differences be… (see more)tween conditions. However, traditional DE methods such as DESeq2 focus on expression changes alone, often overlooking non-differentially expressed (non-DE) genes that may play key regulatory roles. This limits their ability to identify upstream drivers of transcriptomic variation. To address this gap, we introduce DENetwork, a network-based approach that prioritizes genes based on their influence on global information flow. Each gene is scored using an in silico knockout strategy that quantifies its impact across the inferred gene network, capturing both DE and non-DE genes with potential functional relevance. DENetwork deciphers intricate regulatory and signaling networks driving transcriptomic variations between conditions with distinct phenotypes. Across simulated and disease-relevant RNA-seq datasets, DENetwork identifies non-DE regulators enriched in known pathways and phenotypic associations, providing mechanistic insights missed by standard DE analysis, with implications for target discovery and intervention.

2025-11-14

Nucleic Acids Research (published)

doi.org

SUICA: Learning Super-high Dimensional Sparse Implicit Neural Representations for Spatial Transcriptomics

Qingtian Zhu

Yumin Zheng

Yuling Sang

Yifan Zhan

Ziyan Zhu

Jun Ding

Yinqiang Zheng

Spatial Transcriptomics (ST) is a method that captures gene expression profiles aligned with spatial coordinates. The discrete spatial distr… (see more)ibution and the super-high dimensional sequencing results make ST data challenging to be modeled effectively. In this paper, we manage to model ST in a continuous and compact manner by the proposed tool, SUICA, empowered by the great approximation capability of Implicit Neural Representations (INRs) that can enhance both the spatial density and the gene expression. Concretely within the proposed SUICA, we incorporate a graph-augmented Autoencoder to effectively model the context information of the unstructured spots and provide informative embeddings that are structure-aware for spatial mapping. We also tackle the extremely skewed distribution in a regression-by-classification fashion and enforce classification-based loss functions for the optimization of SUICA. By extensive experiments of a wide range of common ST platforms under varying degradations, SUICA outperforms both conventional INR variants and SOTA methods regarding numerical fidelity, statistical correlation, and bio-conservation. The prediction by SUICA also showcases amplified gene signatures that enriches the bio-conservation of the raw data and benefits subsequent analysis.

2025-10-06

Proceedings of the 42nd International Conference on Machine Learning (published)

proceedings.mlr.press

SUICA: Learning Super-high Dimensional Sparse Implicit Neural Representations for Spatial Transcriptomics

Qingtian Zhu

Yumin Zheng

Yuling Sang

Yifan Zhan

Ziyan Zhu

Jun Ding

Yinqiang Zheng

Spatial Transcriptomics (ST) is a method that captures gene expression profiles aligned with spatial coordinates. The discrete spatial distr… (see more)ibution and the super-high dimensional sequencing results make ST data challenging to be modeled effectively. In this paper, we manage to model ST in a continuous and compact manner by the proposed tool, SUICA, empowered by the great approximation capability of Implicit Neural Representations (INRs) that can enhance both the spatial density and the gene expression. Concretely within the proposed SUICA, we incorporate a graph-augmented Autoencoder to effectively model the context information of the unstructured spots and provide informative embeddings that are structure-aware for spatial mapping. We also tackle the extremely skewed distribution in a regression-by-classification fashion and enforce classification-based loss functions for the optimization of SUICA. By extensive experiments of a wide range of common ST platforms under varying degradations, SUICA outperforms both conventional INR variants and SOTA methods regarding numerical fidelity, statistical correlation, and bio-conservation. The prediction by SUICA also showcases amplified gene signatures that enriches the bio-conservation of the raw data and benefits subsequent analysis. The code is available at https://github.com/Szym29/SUICA.

2025-10-06

Proceedings of the 42nd International Conference on Machine Learning (published)

doi.org

arxiv.org

Inhibition of epithelial cell YAP-TEAD/LOX signaling attenuates pulmonary fibrosis in preclinical models

Darcy Elizabeth Wagner

Hani N. Alsafadi

Nilay Mitash

Aurelien Justet

Qianjiang Hu

Ricardo Pineda

Claudia Staab-Weijnitz

Martina Korfei

Nika Gvazava

Kristin Wannemo

Ugochi Onwuka

Molly Mozurak

Adriana Estrada-Bernal

Juan Cala Garcia

Katrin Mutze

Rita Costa

Deniz Bölükbas

John Stegmayr

Wioletta Skronska-Wasek

Stephan Klee … (see 14 more)

Chiharu Ota

Hoeke A. Baarsma

Jingtao Wang

John Sembrat

Anne Hilgendorff

Jun Ding

Andreas Günther

Rachel Chambers

Ivan O Rosas

Stijn de Langhe

Naftali Kaminski

Mareike Lehmann

Oliver Eickelberg

Melanie Königshoff

Idiopathic pulmonary fibrosis (IPF) is a progressive and lethal disease characterized by excessive extracellular matrix deposition. Current … (see more)IPF therapies slow disease progression but do not stop or reverse it. The (myo)fibroblasts are thought to be the main cellular contributors to excessive extracellular matrix production in IPF. Here we show that fibrotic alveolar type II cells regulate production and crosslinking of extracellular matrix via the co-transcriptional activator YAP. YAP leads to increased expression of Lysl oxidase (LOX) and subsequent LOX-mediated crosslinking by fibrotic alveolar type II cells. Pharmacological YAP inhibition via verteporfin reverses fibrotic alveolar type II cell reprogramming and LOX expression in experimental lung fibrosis in vivo and in human fibrotic tissue ex vivo. We thus identify YAP-TEAD/LOX inhibition in alveolar type II cells as a promising potential therapy for IPF patients.

2025-08-02

Nature Communications (published)

doi.org

Inhibition of epithelial cell YAP-TEAD/LOX signaling attenuates pulmonary fibrosis in preclinical models

Darcy Elizabeth Wagner

Hani N. Alsafadi

Nilay Mitash

Aurelien Justet

Qianjiang Hu

Ricardo Pineda

Claudia Staab-Weijnitz

Martina Korfei

Nika Gvazava

Kristin Wannemo

Ugochi Onwuka

Molly Mozurak

Adriana Estrada-Bernal

Juan Cala Garcia

Katrin Mutze

Rita Costa

Deniz Bölükbas

John Stegmayr

Wioletta Skronska-Wasek

Stephan Klee … (see 14 more)

Chiharu Ota

Hoeke A. Baarsma

Jingtao Wang

John Sembrat

Anne Hilgendorff

Jun Ding

Andreas Günther

Rachel Chambers

Ivan O Rosas

Stijn de Langhe

Naftali Kaminski

Mareike Lehmann

Oliver Eickelberg

Melanie Königshoff

Idiopathic pulmonary fibrosis (IPF) is a progressive and lethal disease characterized by excessive extracellular matrix deposition. Current … (see more)IPF therapies slow disease progression but do not stop or reverse it. The (myo)fibroblasts are thought to be the main cellular contributors to excessive extracellular matrix production in IPF. Here we show that fibrotic alveolar type II cells regulate production and crosslinking of extracellular matrix via the co-transcriptional activator YAP. YAP leads to increased expression of Lysl oxidase (LOX) and subsequent LOX-mediated crosslinking by fibrotic alveolar type II cells. Pharmacological YAP inhibition via verteporfin reverses fibrotic alveolar type II cell reprogramming and LOX expression in experimental lung fibrosis in vivo and in human fibrotic tissue ex vivo. We thus identify YAP-TEAD/LOX inhibition in alveolar type II cells as a promising potential therapy for IPF patients.

2025-08-02

Nature Communications (published)

doi.org

Inhibition of epithelial cell YAP-TEAD/LOX signaling attenuates pulmonary fibrosis in preclinical models

Darcy Elizabeth Wagner

Hani N. Alsafadi

Nilay Mitash

Aurelien Justet

Qianjiang Hu

Ricardo Pineda

Claudia Staab-Weijnitz

Martina Korfei

Nika Gvazava

Kristin Wannemo

Ugochi Onwuka

Molly Mozurak

Adriana Estrada-Bernal

Juan Cala Garcia

Katrin Mutze

Rita Costa

Deniz Bölükbas

John Stegmayr

Wioletta Skronska-Wasek

Stephan Klee … (see 14 more)

Chiharu Ota

Hoeke A. Baarsma

Jingtao Wang

John Sembrat

Anne Hilgendorff

Jun Ding

Andreas Günther

Rachel Chambers

Ivan O Rosas

Stijn de Langhe

Naftali Kaminski

Mareike Lehmann

Oliver Eickelberg

Melanie Königshoff

Idiopathic pulmonary fibrosis (IPF) is a progressive and lethal disease characterized by excessive extracellular matrix deposition. Current … (see more)IPF therapies slow disease progression but do not stop or reverse it. The (myo)fibroblasts are thought to be the main cellular contributors to excessive extracellular matrix production in IPF. Here we show that fibrotic alveolar type II cells regulate production and crosslinking of extracellular matrix via the co-transcriptional activator YAP. YAP leads to increased expression of Lysl oxidase (LOX) and subsequent LOX-mediated crosslinking by fibrotic alveolar type II cells. Pharmacological YAP inhibition via verteporfin reverses fibrotic alveolar type II cell reprogramming and LOX expression in experimental lung fibrosis in vivo and in human fibrotic tissue ex vivo. We thus identify YAP-TEAD/LOX inhibition in alveolar type II cells as a promising potential therapy for IPF patients.

2025-08-02

Nature Communications (published)

doi.org

Computational Tracking of Cell Origins Using CellSexID from Single-Cell Transcriptomes

Huilin Tai

Qian Li

Jingtao Wang

Jiahui Tan

Bowen Zhao

Ryann Lang

Basil J. Petrof

Jun Ding

Cell tracking in chimeric models is essential yet challenging, particularly in developmental biology, regenerative medicine, and transplanta… (see more)tion research. Existing methods such as fluorescent labeling and genetic barcoding are technically demanding, costly, and often impractical for dynamic or heterogeneous tissues. Here, we introduce CellSexID, a computational framework that leverages sex as a surrogate marker for cell origin inference. Using a machine learning model trained on single-cell transcriptomic data, CellSexID accurately predicts the sex of individual cells, enabling in silico distinction between donor and recipient cells in sex-mismatched settings. The model identifies minimal sex-linked gene sets through ensemble feature selection and has been validated using both public datasets and experimental flow sorting, confirming the biological relevance of predicted populations. We further demonstrate CellSexID’s applicability beyond chimeric models, including organ transplantation and multiplexed sample demultiplexing. As a scalable and cost-effective alternative to physical labeling, CellSexID facilitates precise cell tracking and supports diverse biomedical applications involving mixed cellular origins.

2025-07-30

bioRxiv (preprint)

doi.org

DOLPHIN advances single-cell transcriptomics beyond gene level by leveraging exon and junction reads

Kailu Song

Yumin Zheng

Bowen Zhao

David H. Eidelman

Jian Tang

Jun Ding

2025-07-04

Nature Communications (published)