Cited by 39Single-cell and spatial transcriptomicsGene Regulatory Network AnalysisGene expression and cancer classification

Squidiff: predicting cellular development and responses to perturbations using a diffusion model

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He, Siyu, Zhu, Yuefei, Tavakol, Daniel Naveed, Ye, Haotian, Lao, Yeh-Hsing, Zhu, Zixian, Xu, Cong, Chauhan, Shradha +6 more·2026·Nature Methods

Published version of a preprint

Abstract

Single-cell sequencing has revolutionized our understanding of cellular heterogeneity and responses to environmental stimuli. However, mapping transcriptomic changes across diverse cell types in response to various stimuli and elucidating underlying disease mechanisms remains challenging. Here we present Squidiff, a diffusion model-based generative framework that predicts transcriptomic changes across diverse cell types in response to environmental changes. We demonstrate the robustness of Squidiff across cell differentiation, gene perturbation and drug response prediction. Through continuous denoising and semantic feature integration, Squidiff learns transient cell states and predicts high-resolution transcriptomic landscapes over time and conditions. Furthermore, we applied Squidiff to model blood vessel organoid development and cellular responses to neutron irradiation and growth factors. Our results demonstrate that Squidiff enables in silico screening of molecular landscapes and cellular state transitions, facilitating rapid hypothesis generation and providing valuable insights into the regulatory principles of cell fate decisions.

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  1. [16]Pandey K, Mukherjee A, Rai P. & Kumar A. VAEs meet Diffusion Models: Efficient and High-Fidelity Generation. in NeurIPS 2021 Workshop on Deep Generative Models and Downstream Applications (2021).

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Authors
He, S., Zhu, Y., Tavakol, D. N., Ye, H., Lao, Y.-H., Zhu, Z., Xu, C., Chauhan, S., Garty, G., Tomer, R., Vunjak-Novakovic, G., Zou, J., Azizi, E., & Leong, K. W.
Affiliations
  • Columbia University (US)
  • Stanford University (US)
  • University at Buffalo, State University of New York (US)
  • Columbia University Irving Medical Center (US)
Corresponding author
  • Zou, James
  • Azizi, Elham
  • Leong, Kam W.
Venue
Nature Methods
Published
2025-11-03
Volume
23
Issue
1
Pages
65-77
ISSN
  • 1548-7091 (ISSN-L)
  • 1548-7105
OpenAlex
W4415790290
Cited by
38
References
68
Citations by year
2026: 34 · 2025: 3 · 2024: 1
FWCI
11.12
Type
journal article
Language
en
Open access
Locations
  • Nature Methods (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • Research Square (acceptedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • Europe PMC (PubMed Central) (submittedVersion, cc-by-nc-nd)
Funders
  • U.S. Department of Health & Human Services | NIH | National Cancer Institute (P30CA013696)
  • National Aeronautics and Space Administration (NNX16AO69A-RAD0104)
  • U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (U19 AI067773)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Keywords
diffusion models, single-cell transcriptomics, cell differentiation, gene perturbation, drug response prediction, blood vessel organoids, neutron irradiation, growth factor responsiveness, cell fate determination, generative models, virtual screening

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