High-throughput imaging flow cytometry by optofluidic time-stretch microscopy

• Published in: Nature protocols Vol. 13; no. 7; pp. 1603 - 1631
• Main Authors: Lei, Cheng, Kobayashi, Hirofumi, Wu, Yi, Li, Ming, Isozaki, Akihiro, Yasumoto, Atsushi, Mikami, Hideharu, Ito, Takuro, Nitta, Nao, Sugimura, Takeaki, Yamada, Makoto, Yatomi, Yutaka, Di Carlo, Dino, Ozeki, Yasuyuki, Goda, Keisuke
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 01.07.2018
• Subjects: Algae; Clean energy; Computer applications; Data management; Data processing; Digital signal processing; Flow cytometry; Flow Cytometry - instrumentation; Flow Cytometry - methods; Green energy; Heterogeneity; High-Throughput Screening Assays - methods; Image acquisition; Image analysis; Image enhancement; Image processing; Image Processing, Computer-Assisted - methods; Learning algorithms; Machine learning; Materials handling; Microfluidics; Microfluidics - instrumentation; Microfluidics - methods; Microscopy; Microscopy - methods; Molecular chains; Optical Imaging - methods; Optics; Sample preparation; Signal processing; Software
• ISSN: 1754-2189; 1750-2799
• DOI: 10.1038/s41596-018-0008-7

The ability to rapidly assay morphological and intracellular molecular variations within large heterogeneous populations of cells is essential for understanding and exploiting cellular heterogeneity. Optofluidic time-stretch microscopy is a powerful method for meeting this goal, as it enables high-throughput imaging flow cytometry for large-scale single-cell analysis of various cell types ranging from human blood to algae, enabling a unique class of biological, medical, pharmaceutical, and green energy applications. Here, we describe how to perform high-throughput imaging flow cytometry by optofluidic time-stretch microscopy. Specifically, this protocol provides step-by-step instructions on how to build an optical time-stretch microscope and a cell-focusing microfluidic device for optofluidic time-stretch microscopy, use it for high-throughput single-cell image acquisition with sub-micrometer resolution at >10,000 cells per s, conduct image construction and enhancement, perform image analysis for large-scale single-cell analysis, and use computational tools such as compressive sensing and machine learning for handling the cellular 'big data'. Assuming all components are readily available, a research team of three to four members with an intermediate level of experience with optics, electronics, microfluidics, digital signal processing, and sample preparation can complete this protocol in a time frame of 1 month.