ReviewACS omega2026
Decoding Cellular Heterogeneity with Microfluidic Single-Cell Secretion Analysis Tools.
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.
What it found
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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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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
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Registered trials
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.