Evidence map›Paper›PMID 39819845›Full record

ArticleMicrosystems & nanoengineering2025

Ultra-sensitive fluorescence-activated droplet single-cell sorting based on Tetramer-HCR-EvaGreen amplification.

Long Chen, Yi Xu, Lele Zhou, Ding Ma, Rong Zhang, Yifan Liu, Xianqiang Mi

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Microsystems & nanoengineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. 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

7 authors.

Long Chen *Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China.
Yi Xu *Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China.
Lele ZhouSchool of Physical Science and Technology, ShanghaiTech University, Shanghai, 201210, China.
Ding MaShanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China.
Rong ZhangSchool of Physical Science and Technology, ShanghaiTech University, Shanghai, 201210, China.
Yifan LiuSchool of Physical Science and Technology, ShanghaiTech University, Shanghai, 201210, China. liuyf6@shanghaitech.edu.cn.ORCID http://orcid.org/0000-0002-2989-6280
Xianqiang MiShanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China. mixq@mail.sim.ac.cn.

Funding

Science and Technology Commission of Shanghai Municipality (Shanghai Municipal Science and Technology Commission) 20511107600
6 · The paper itself

Abstract

The current single-cell analysis technologies such as fluorescence-activated cell sorting (FACS) and fluorescence-activated droplet sorting (FADS) could decipher the cellular heterogeneity but were constrained by low sorting performance and cell viability. Here, an ultra-sensitive single-cell sorting platform has been developed by integrating the FADS technology with Tetramer-HCR-EvaGreen (THE) fluorescence signal amplification. The THE system produced much higher fluorescence signal than that of the single Tetramer or Tetramer-HCR signal amplification. Upon application to target MCF-7 cells, the platform exhibited high efficacy and selectivity while maintaining more than 95% cell viability. The THE-FADS achieved sorting efficiencies of 55.5% and 50.3% with purities of 91% and 85% for MCF-7 cells in PBS solutions and simulated serum samples, respectively. The sorted MCF-7 cells showed similar proliferation together with CK19 and EGFR mRNA expression compared with the control cells. The established THE-FADS showed the promising prospects to cellular heterogeneity understanding and personalized medicine.

Identifiers

PMID39819845
PMCPMC11739583

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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.