A Deep Generative Model for Deciphering Cellular Dynamics and In Silico Drug Discovery in Complex Diseases

Authors

• Yumin Zheng
• Jonas C Schupp
• Taylor Adams
• Geremy Clair
• Aurelien Justet
• Farida Ahangari
• Xiting Yan
• Paul Hansen
• Marianne Carlon
• Emanuela Cortesi
• Marie Vermant
• Robin Vos
• Laurens J De Sadeleer
• Ivan O Rosas
• Ricardo Pineda
• John Sembrat
• Melanie Königshoff
• John E McDonough
• Bart M Vanaudenaerde
• Wim A Wuyts
• Naftali Kaminski
• Jun Ding

Language

English

Publication Date

12-1-2025

Journal

Nature Biomedical Engineering

DOI

10.1038/s41551-025-01423-7

PMID

40542107

PMCID

PMC12705450

PubMedCentral® Posted Date

6-20-2025

PubMedCentral® Full Text Version

Post-print

Abstract

Human diseases are characterized by intricate cellular dynamics. Single-cell transcriptomics provides critical insights, yet a persistent gap remains in computational tools for detailed disease progression analysis and targeted in silico drug interventions. Here we introduce UNAGI, a deep generative neural network tailored to analyse time-series single-cell transcriptomic data. This tool captures the complex cellular dynamics underlying disease progression, enhancing drug perturbation modelling and screening. When applied to a dataset from patients with idiopathic pulmonary fibrosis, UNAGI learns disease-informed cell embeddings that sharpen our understanding of disease progression, leading to the identification of potential therapeutic drug candidates. Validation using proteomics reveals the accuracy of UNAGI's cellular dynamics analysis, and the use of the fibrotic cocktail-treated human precision-cut lung slices confirms UNAGI's predictions that nifedipine, an antihypertensive drug, may have anti-fibrotic effects on human tissues. UNAGI's versatility extends to other diseases, including COVID, demonstrating adaptability and confirming its broader applicability in decoding complex cellular dynamics beyond idiopathic pulmonary fibrosis, amplifying its use in the quest for therapeutic solutions across diverse pathological landscapes.

Keywords

Humans, Drug Discovery, Idiopathic Pulmonary Fibrosis, Computer Simulation, Single-Cell Analysis, Neural Networks, Computer, COVID-19, Lung, SARS-CoV-2, Nifedipine, Proteomics, Transcriptome, Deep Learning, Disease Progression, Models, Biological, Virtual drug screening, Biomedical engineering, Machine learning, Drug discovery, Computational models

Published Open-Access

yes

Recommended Citation

Zheng, Yumin; Schupp, Jonas C; Adams, Taylor; et al., "A Deep Generative Model for Deciphering Cellular Dynamics and In Silico Drug Discovery in Complex Diseases" (2025). Faculty, Staff and Students Publications. 6788.
https://digitalcommons.library.tmc.edu/baylor_docs/6788