Evidence map›Paper›PMID 40346035›Full record

ArticleNature communications2025

Spatiotemporal deciphering of dynamic the FUS interactome during liquid-liquid phase separation in living cells.

Sunfengda Song, Haiyang Xie, Qingwen Wang, Xinyi Sun, Jiasu Xu, Rui Chen, Yuankang Zhu, Lai Jiang, Xianting Ding

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Sunfengda SongDepartment of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Haiyang XieDepartment of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Qingwen WangDepartment of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Xinyi SunDepartment of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Jiasu XuDepartment of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Rui ChenDepartment of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Yuankang ZhuDepartment of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Lai JiangDepartment of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Xianting DingDepartment of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China. dingxianting@sjtu.edu.cn.ORCID http://orcid.org/0000-0002-1549-3499

Funding

National Natural Science Foundation of China (National Science Foundation of China) T2122002, 22077079, 81871448Science and Technology Commission of Shanghai Municipality (Shanghai Municipal Science and Technology Commission) 22Z510202478Shanghai Municipal Education Commission | E-Institutes of Shanghai Municipal Education Commission 21SG10SJTU | School of Medicine, Shanghai Jiao Tong University (School of Medicine, Shanghai Jiao Tong University) YG2021ZD19, 2020 SJTU-HUJI
6 · The paper itself

Abstract

Liquid-liquid phase separations (LLPS) are membraneless organelles driven by biomolecule assembly and are implicated in cellular physiological activities. However, spatiotemporal deciphering of the dynamic proteome in living cells during LLPS formation remains challenging. Here, we introduce the Composition of LLPS proteome Assembly by Proximity labeling-assisted Mass spectrometry (CLAPM). We demonstrate that CLAPM can instantaneously label and monitor the FUS interactome shifts within intracellular droplets undergoing spatiotemporal LLPS. We report 129, 182 and 822 proteins specifically present in the LLPS droplets of HeLa, HEK 293 T and neuronal cells respectively. CLAPM further categorizes spatiotemporal dynamic proteome in droplets for living neuronal cells and identifies 596 LLPS-aboriginal proteins, 226 LLPS-dependent proteins and 58 LLPS-sensitive proteins. For validation, we uncover 11 previously unknown LLPS proteins in vivo. CLAPM provides a versatile tool to decipher proteins involved in LLPS and enables the accurate characterization of dynamic proteome in living cells.

Indexed as

ProteomeRNA-Binding Protein FUSHEK293 CellsHeLa CellsHumansMass SpectrometryNeuronsOrganellesPhase SeparationProteomicsFUS protein, humanProteomeRNA-Binding Protein FUS

Identifiers

PMID40346035
PMCPMC12064718

What OpenQuestion holds

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Registered trials

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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.