Takeaki Sugimura

TL;DR: This work presents a machine-intelligence technology based on a radically different architecture that realizes real-time image-based intelligent cell sorting at an unprecedented rate and is expected to enable machine-based scientific discovery in biological, pharmaceutical, and medical sciences.

TL;DR: 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, which demonstrates high-precision characterization and classification of microalgal cells and cancer cells without the need for fluorescent labeling.

TL;DR: Raman image-activated cell sorting is demonstrated by directly probing chemically specific intracellular molecular vibrations via ultrafast multicolor stimulated Raman scattering (SRS) microscopy for cellular phenotyping and holds promise for numerous applications that were previously difficult or undesirable with fluorescence-based technologies.

TL;DR: This protocol describes how to perform high-throughput imaging flow cytometry by optofluidic time-stretch microscopy and uses computational tools such as compressive sensing and machine learning for handling the cellular ‘big data’.

TL;DR: An optomechanical imaging method is presented that overcomes the trade-off by virtually freezing the motion of flowing cells on the image sensor to effectively achieve 1000 times longer exposure time for microscopy-grade fluorescence image acquisition.