Evidence map›Paper›PMID 40118840›Full record

ArticleNature communications2025

Fast segmentation and multiplexing imaging of organelles in live cells.

Karl Zhanghao, Meiqi Li, Xingye Chen, Wenhui Liu, Tianling Li, Yiming Wang, Fei Su, Zihan Wu, Chunyan Shan, Jiamin Wu and 4 more

Abstract read
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 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
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

14 authors.

Karl Zhanghao *Department of Biomedical Engineering, College of Future Technology, Peking University, Beijing, China.ORCID http://orcid.org/0000-0002-8331-6240
Meiqi Li *Department of Biomedical Engineering, College of Future Technology, Peking University, Beijing, China. limeiqi@pku.edu.cn.ORCID http://orcid.org/0000-0003-3586-176X
Xingye ChenDepartment of Automation, Tsinghua University, Beijing, China.
Wenhui LiuDepartment of Automation, Tsinghua University, Beijing, China.
Tianling LiState Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China.
Yiming WangSchool of Life Sciences, Peking University, Beijing, China.
Fei SuInstitute for Biomedical Materials & Devices (IBMD), University of Technology Sydney, Sydney, NSW, Australia.
Zihan WuUTS-SUStech Joint Research Centre for Biomedical Materials & Devices, College of Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China.
Chunyan ShanSchool of Life Sciences, Peking University, Beijing, China.ORCID http://orcid.org/0000-0002-5532-2811
Jiamin WuDepartment of Automation, Tsinghua University, Beijing, China.ORCID http://orcid.org/0000-0003-3479-1026
Yan ZhangSchool of Life Sciences, Peking University, Beijing, China.ORCID http://orcid.org/0000-0003-4168-8852
Jingyan FuState Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China.ORCID http://orcid.org/0000-0002-5896-8132
Peng XiDepartment of Biomedical Engineering, College of Future Technology, Peking University, Beijing, China. xipeng@pku.edu.cn.ORCID http://orcid.org/0000-0001-6626-4840
Dayong JinZhejiang Provincial Engineering Research Center for Organelles Diagnostics and Therapy, Eastern Institute of Technology, Ningbo, Zhejiang, China. dayong.jin@uts.edu.au.ORCID http://orcid.org/0000-0003-1046-2666

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Studying organelles' interactome at system level requires simultaneous observation of subcellular compartments and tracking their dynamics. Conventional multicolor approaches rely on specific fluorescence labeling, where the number of resolvable colors is far less than the types of organelles. Here, we use a lipid-specific dye to stain all the membrane-associated organelles and spinning-disk microscopes with an extended resolution of ~143 nm for high spatiotemporal acquisition. Due to the chromatic polarity sensitivity, high-resolution ratiometric images well reflect the heterogeneity of organelles. With deep convolutional neuronal networks, we successfully segmented up to 15 subcellular structures using one laser excitation. We further show that transfer learning can predict both 3D and 2D datasets from different microscopes, different cell types, and even complex systems of living tissues. We succeeded in resolving the 3D anatomic structure of live cells at different mitotic phases and tracking the fast dynamic interactions among six intracellular compartments with high robustness.

Indexed as

OrganellesAnimalsFluorescent DyesHeLa CellsHumansImaging, Three-DimensionalMicroscopy, FluorescenceFluorescent Dyes

Identifiers

PMID40118840
PMCPMC11928634

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.