Microfluidic platform for omics analysis on single cells with diverse morphology and size: A review

文献类型： 外文期刊

第一作者： Lin, Shujin

作者： Lin, Shujin;Han, Xiao;Li, Ling;Gao, Haibing;Lin, Shujin;Lin, Yao;Feng, Dan;Lin, Yao;Feng, Dan;Han, Xiao;Li, Ling

作者机构：

期刊名称：ANALYTICA CHIMICA ACTA （ 影响因子：6.2； 五年影响因子：5.9 ）

ISSN： 0003-2670

年卷期： 2024 年 1294 卷

页码：

收录情况： SCI

摘要： Background: Microfluidic techniques have emerged as powerful tools in single -cell research, facilitating the exploration of omics information from individual cells. Cell morphology is crucial for gene expression and physiological processes. However, there is currently a lack of integrated analysis of morphology and single -cellomics information. A critical challenge remains: what platform technologies are the best option to decode omics data of cells that are complex in morphology and size? Results: This review highlights achievements in microfluidic-based single -cell omics and isolation of cells based on morphology, along with other cell sorting methods based on physical characteristics. Various microfluidic platforms for single -cell isolation are systematically presented, showcasing their diversity and adaptability. The discussion focuses on microfluidic devices tailored to the distinct single -cell isolation requirements in plants and animals, emphasizing the significance of considering cell morphology and cell size in optimizing single -cell omics strategies. Simultaneously, it explores the application of microfluidic single -cell sorting technologies to single -cell sequencing, aiming to effectively integrate information about cell shape and size. Significance and novelty: The novelty lies in presenting a comprehensive overview of recent accomplishments in microfluidic-based single -cell omics, emphasizing the integration of different microfluidic platforms and their implications for cell morphology -based isolation. By underscoring the pivotal role of the specialized morphology of different cells in single -cell research, this review provides robust support for delving deeper into the exploration of single -cell omics data.

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