Evidence map›Paper›PMID 42540323›Full record

ReviewACS omega2026

Decoding Cellular Heterogeneity with Microfluidic Single-Cell Secretion Analysis Tools.

Faqin Zhao, Xiaobei Chen, Yueyue Ma, Xianwei Liang, Anqi Liu, Chengjun Wu, Shuai Yuan, Jiu Deng

Abstract readReview
In one paragraph

Review in ACS omega, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Faqin ZhaoSchool of Health and Life Sciences, University of Health and Rehabilitation Sciences, Qingdao 266113, China.
Xiaobei ChenSchool of Health and Life Sciences, University of Health and Rehabilitation Sciences, Qingdao 266113, China.
Yueyue MaSchool of Health and Life Sciences, University of Health and Rehabilitation Sciences, Qingdao 266113, China.
Xianwei LiangSchool of Health and Life Sciences, University of Health and Rehabilitation Sciences, Qingdao 266113, China.
Anqi LiuComprehensive Ward, Guizhou Provincial People's Hospital, Guiyang 550002, China.
Chengjun WuSchool of Health and Life Sciences, University of Health and Rehabilitation Sciences, Qingdao 266113, China.
Shuai YuanSchool of Health and Life Sciences, University of Health and Rehabilitation Sciences, Qingdao 266113, China.ORCID https://orcid.org/0009-0006-4002-3858
Jiu DengSchool of Health and Life Sciences, University of Health and Rehabilitation Sciences, Qingdao 266113, China.ORCID https://orcid.org/0000-0003-1802-3123

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cellular heterogeneity is a fundamental determinant of diverse physiological and pathological processes, yet its functional manifestation, the dynamic secretion of proteins, remains challenging to interrogate on the single-cell level. Traditional methods, while invaluable, are constrained by limited multiplexing capacity, inadequate temporal resolution, and reliance on specialized infrastructure, impeding the comprehensive dissection of cellular diversity. Microfluidic technologies have emerged as transformative platforms that address these limitations, enabling high-throughput, multiparameter secretion profiling with spatiotemporal precision. This review systematically synthesizes recent advances in microfluidic single-cell secretion analysis. We first examined the conceptual framework underlying cellular heterogeneity and the inherent constraints of conventional approaches. Subsequently, we analyze core microfluidic principles for single-cell manipulation, focusing on microstructures for isolation and strategies for capturing and quantifying the secreted proteins. We then explored the expanding applications of these technologies across biological and biomedical research. Finally, we discuss emerging opportunities at the convergence of microfluidics with multiomics integration and artificial intelligence, outlining future trajectories for this rapidly evolving field.

Identifiers

PMID42540323
PMCPMC13425492

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.