AI-Powered, Temperature-Resolved Centrifugal Microfluidics for Rapid 3D Phase-Diagram Generation of Biomolecular Condensates.

Jiashuo Li, Pengjie Li, Kaiting Zhang, Yongqi Guo, Peng Chen, Bi-Feng Liu, Yiwei Li

Abstract read

In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

What it found

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Jiashuo Li *Key Laboratory of Molecular Biophysics of MOE and Hubei Bioinformatics and Molecular Imaging Key Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China.

Pengjie Li *Key Laboratory of Molecular Biophysics of MOE and Hubei Bioinformatics and Molecular Imaging Key Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China.ORCID https://orcid.org/0000-0002-8057-368X

Kaiting Zhang *Key Laboratory of Molecular Biophysics of MOE and Hubei Bioinformatics and Molecular Imaging Key Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China.

Yongqi GuoKey Laboratory of Molecular Biophysics of MOE and Hubei Bioinformatics and Molecular Imaging Key Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China.

Peng ChenKey Laboratory of Molecular Biophysics of MOE and Hubei Bioinformatics and Molecular Imaging Key Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China.

Bi-Feng LiuKey Laboratory of Molecular Biophysics of MOE and Hubei Bioinformatics and Molecular Imaging Key Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China.ORCID https://orcid.org/0000-0002-2135-0873

Yiwei LiKey Laboratory of Molecular Biophysics of MOE and Hubei Bioinformatics and Molecular Imaging Key Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China.ORCID https://orcid.org/0000-0002-5203-0290

Funding

China Postdoctoral Science Foundation 2024M760996China Postdoctoral Science Foundation 2024T170307Fundamental Research Funds for Central Universities 2024JYCXJJ028Guangdong Basic and Applied Basic Research Foundation 2025A1515011573National Key Research and Development Program of China 2024YFF1207300National Natural Science Foundation of China 12102142National Natural Science Foundation of China 21775049National Natural Science Foundation of China 22074047National Natural Science Foundation of China 22404063National Natural Science Foundation of China 31700746National Natural Science Foundation of China 82170883

6 · The paper itself

Abstract

Biomolecular phase separation drives the formation of membraneless organelles and is implicated in numerous diseases. Mapping multidimensional phase diagrams is essential for understanding condensate assembly mechanisms and developing therapeutic interventions, yet current methods are slow, resource-intensive, and often limited to a single, frequently non-physiological, temperature. Here, we present T-PhaseMap, an AI-powered centrifugal microfluidic platform that integrates programmable concentration-gradient generation, standalone temperature control, and deep-learning analysis to construct 3D phase diagrams across composition-temperature space within 30 min. We demonstrate a dual-scale strategy that enables broad-range exploration of phase space and localized refinement of critical transition boundaries. Meanwhile, tunable tri-nodal temperature control at 25°C, 37°C, and 45°C provides a structured thermal dimension for systematically evaluating temperature-dependent phase behavior. A VGG-16-based four-class classifier further distinguishes two-phase condensates, single-phase states, boundary states, and aggregate-like phenotypes, achieving approximately 99.3% accuracy on an independent test set of 720 images. Applied to heparin sodium-mediated condensate modulation, T-PhaseMap revealed a 17.6% temperature-dependent variation in apparent suppression potency across 25°C-45°C. This platform provides a rapid and objective tool for physiologically relevant phase-diagram generation and high-throughput screening of condensate-targeting therapeutics.

Indexed as

3D phase diagrambiomolecular phase separationcentrifugal microfluidicsdeep learning

Identifiers

PMID42675591

DOI10.1002/advs.77523

PMCPMC13530018

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.