Microfluidic Single‐Cell Omics Analysis

• Published in: Small (Weinheim an der Bergstrasse, Germany) Vol. 16; no. 9; pp. e1903905 - n/a
• Main Authors: Xu, Xing, Wang, Junxia, Wu, Lingling, Guo, Jingjing, Song, Yanling, Tian, Tian, Wang, Wei, Zhu, Zhi, Yang, Chaoyong
• Format: Journal Article
• Language: English
• Published: Germany Wiley Subscription Services, Inc 01-03-2020
• Subjects: Biological activity; Computational Biology - methods; Computational Biology - trends; Differentiation (biology); Microfluidic Analytical Techniques; Microfluidics; Microfluidics - methods; Microfluidics - trends; Nanotechnology; omics; single cells; Single-Cell Analysis - methods; Single-Cell Analysis - trends; omics; microfluidics; single cells
• ISSN: 1613-6810; 1613-6829
• DOI: 10.1002/smll.201903905

The commonly existing cellular heterogeneity plays a critical role in biological processes such as embryonic development, cell differentiation, and disease progress. Single‐cell omics‐based heterogeneous studies have great significance for identifying different cell populations, discovering new cell types, revealing informative cell features, and uncovering significant interrelationships between cells. Recently, microfluidics has evolved to be a powerful technology for single‐cell omics analysis due to its merits of throughput, sensitivity, and accuracy. Herein, the recent advances of microfluidic single‐cell omics analysis, including different microfluidic platform designs, lysis strategies, and omics analysis techniques, are reviewed. Representative applications of microfluidic single‐cell omics analysis in complex biological studies are then summarized. Finally, a few perspectives on the future challenges and development trends of microfluidic‐assisted single‐cell omics analysis are discussed.
Microfluidics is considered as a powerful methodology for single‐cell omics analysis with merits in throughput, sensitivity, and accuracy, which can be applied in the study of complex biological processes to identify different cell populations, discover new cell types, reveal informative cell features, and uncover significant interrelationships between cells.