Decoding Cellular Heterogeneity with Microfluidic Single-Cell Secretion Analysis Tools
Faqin Zhao1, Xiaobei Chen1, Yueyue Ma1
1School of Health and Life Sciences, University of Health and Rehabilitation Sciences, Qingdao 266113, China.
ACS Omega
|August 1, 2026
Summary
Microfluidic technologies enable high-throughput single-cell protein secretion analysis, overcoming limitations of traditional methods. This approach enhances understanding of cellular heterogeneity in health and disease.
Area of Science:
- Biotechnology
- Cell Biology
- Biomedical Engineering
Background:
- Cellular heterogeneity significantly impacts physiological and pathological processes.
- Analyzing single-cell protein secretion is crucial but limited by traditional methods' low throughput, temporal resolution, and multiplexing capacity.
- Existing techniques hinder comprehensive dissection of cellular diversity.
Purpose of the Study:
- To systematically review recent advances in microfluidic technologies for single-cell secretion analysis.
- To highlight how microfluidics addresses limitations of conventional methods in studying cellular heterogeneity.
- To explore future directions integrating microfluidics with multiomics and AI.
Main Methods:
- Review of microfluidic principles for single-cell manipulation, isolation, and protein capture.
- Analysis of strategies for quantifying secreted proteins at the single-cell level.
- Synthesis of current applications in biological and biomedical research.
Main Results:
- Microfluidic platforms offer high-throughput, multiparameter secretion profiling with spatiotemporal precision.
- These technologies overcome the constraints of traditional methods for studying cellular heterogeneity.
- Diverse applications across biological and biomedical fields have been demonstrated.
Conclusions:
- Microfluidic single-cell secretion analysis is a transformative technology for dissecting cellular diversity.
- Future integration with multiomics and artificial intelligence promises further advancements.
- This field offers significant potential for understanding complex biological systems and diseases.
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