Evidence map›Paper›PMID 39856037›Full record

ArticleNature communications2025

In-situ profiling of glycosylation on single cells with surface plasmon resonance imaging.

Xiaoyin Liu, Jinbiao Ma, Yunrui Zhang, Yi Xu, Yunxiao Wang, Dehong Yang, Di Wang, Qingjun Liu, Fenni Zhang

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. Review
  2. Review
  3. Spatial Glyco-Codes Define Human Liver Pathology and Progression.bioRxiv : the preprint server for biology · 2026
    Article
  4. Article
  5. Article
  6. Article
  7. Nanoscale Mapping of the Subcellular Glycosylation Landscape.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
  8. Review
  9. Review
  10. Article
  11. 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.

Xiaoyin LiuBiosensor National Special Laboratory, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China.
Jinbiao MaBiosensor National Special Laboratory, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China.
Yunrui ZhangBiosensor National Special Laboratory, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China.
Yi XuStomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Hangzhou, China.
Yunxiao WangBiosensor National Special Laboratory, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China.
Dehong YangBiosensor National Special Laboratory, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China.
Di WangBiosensor National Special Laboratory, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China.ORCID http://orcid.org/0000-0003-1581-4982
Qingjun LiuBiosensor National Special Laboratory, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China.ORCID http://orcid.org/0000-0002-9442-9355
Fenni ZhangBiosensor National Special Laboratory, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China. fennizhang@zju.edu.cn.ORCID http://orcid.org/0000-0002-4369-8563

Funding

National Natural Science Foundation of China (National Science Foundation of China) 82102180Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation) LZ24H180001
6 · The paper itself

Abstract

Cellular glycosylation is crucial for cell recognition, signal transduction, and the development of various diseases, especially in tumor initiation, progression, and metastasis. Current glycosylation profiling methods normally involve laborious sample processing and labeling and lack in-situ quantitative analysis. Here, we present a direct optical method to investigate and quantify the glycan expression on single cells based on lectin-glycan kinetic quantification with plasmonic imaging. Three unlabeled lectins (WGA, SBA, ConA) are employed as probes to bind with specific glycans, and binding kinetics are assessed to determine glycosylation profiles. The result reveals cell-to-cell heterogeneity in glycosylation patterns. To demonstrate the capability of our method, the glycosylation profiling of four distinct cell lines is explored, showing obvious alterations in glycan expression related to tumor initiation, progression, and metastasis. This approach enables direct quantification of glycosylation and binding kinetics, providing insights into tumor cell glycosylation mechanisms and potential applications in disease diagnosis and treatment.

Indexed as

PolysaccharidesSingle-Cell AnalysisSurface Plasmon ResonanceCell Line, TumorConcanavalin AGlycosylationHumansKineticsLectinsConcanavalin ALectinsPolysaccharides

Identifiers

PMID39856037
PMCPMC11759948

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.