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Significance Imaging flow cytometry is a powerful tool for analyzing every single cell in a large heterogeneous population but relies on fluorescent labeling, which comes with cytotoxicity, nonspecific binding, and interference with natural cellular functions. This paper presents label-free multicolor chemical imaging flow cytometry based on stimulated Raman scattering (SRS), a highly sensitive method of molecular vibrational spectroscopy. With the help of deep learning, it demonstrates high-precision characterization and classification of microalgal cells and cancer cells without the need for fluorescent labeling., Combining the strength of flow cytometry with fluorescence imaging and digital image analysis, imaging flow cytometry is a powerful tool in diverse fields including cancer biology, immunology, drug discovery, microbiology, and metabolic engineering. It enables measurements and statistical analyses of chemical, structural, and morphological phenotypes of numerous living cells to provide systematic insights into biological processes. However, its utility is constrained by its requirement of fluorescent labeling for phenotyping. Here we present label-free chemical imaging flow cytometry to overcome the issue. It builds on a pulse pair-resolved wavelength-switchable Stokes laser for the fastest-to-date multicolor stimulated Raman scattering (SRS) microscopy of fast-flowing cells on a 3D acoustic focusing microfluidic chip, enabling an unprecedented throughput of up to ∼140 cells/s. To show its broad utility, we use the SRS imaging flow cytometry with the aid of deep learning to study the metabolic heterogeneity of microalgal cells and perform marker-free cancer detection in blood.

A fundamental challenge of biology is to understand the vast heterogeneity of cells, particularly how cellular composition, structure, and morphology are linked to cellular physiology. Unfortunately, conventional technologies are limited in uncovering these relations. We present a real-time machine intelligence technology based on a radically new architecture that realizes real-time image-based intelligent cell search and sorting at an unprecedented rate. The technology integrates high-throughput cell imaging, cell focusing, and cell sorting on a hybrid software-hardware data management infrastructure, enabling real-time automated operation for data acquisition, data processing, decision making, and actuation. Specifically, we use it to demonstrate real-time sorting of microalgal and blood cells based on intracellular protein localization and cell-cell interaction from large heterogeneous populations for studying photosynthesis and atherothrombosis, respectively. Our technology is highly versatile and expected to enable machine-based scientific discovery in biological, pharmaceutical, and medical sciences.

A fundamental challenge of biology is to understand the vast heterogeneity of cells, particularly how cellular composition, structure, and morphology are linked to cellular physiology. Unfortunately, conventional technologies are limited in uncovering these relations. We present a machine-intelligence technology based on a radically different architecture that realizes real-time image-based intelligent cell sorting at an unprecedented rate. This technology, which we refer to as intelligent image-activated cell sorting, integrates high-throughput cell microscopy, focusing, and sorting on a hybrid software-hardware data-management infrastructure, enabling real-time automated operation for data acquisition, data processing, decision-making, and actuation. We use it to demonstrate real-time sorting of microalgal and blood cells based on intracellular protein localization and cell-cell interaction from large heterogeneous populations for studying photosynthesis and atherothrombosis, respectively. The technology is highly versatile and expected to enable machine-based scientific discovery in biological, pharmaceutical, and medical sciences., 世界初のIntelligent Image-Activated Cell Sorterを開発 --細胞画像の深層学習により高速細胞選抜を実現--. 京都大学プレスリリース. 2018-09-05.