Intelligent Image-Activated Cell Sorting and Beyond

We present a groundbreaking machine intelligence technology called “intelligent image-activated cell sorting” that achieves high-throughput image-triggered sorting of single cells by integrating high-speed fluorescence microscopy, cell focusing, cell sorting, and deep learning

Intelligent Image-Activated Cell Sorting and Beyond

We present a groundbreaking machine intelligence technology called “intelligent image-activated cell sorting” that achieves high-throughput image-triggered sorting of single cells by integrating high-speed fluorescence microscopy, cell focusing, cell sorting, and deep learning

Intelligent Image-Activated Cell Sorting

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.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

SIRT1 Regulates Thyroid-Stimulating Hormone Release by Enhancing PIP5Kγ[subscript gamma] Activity through Deacetylation of Specific Lysine Residues in Mammals

Background: SIRT1, a NAD-dependent deacetylase, has diverse roles in a variety of organs such as regulation of endocrine function and metabolism. However, it remains to be addressed how it regulates hormone release there. Methodology/Principal Findings: Here, we report that SIRT1 is abundantly expressed in pituitary thyrotropes and regulates thyroid hormone secretion. Manipulation of SIRT1 level revealed that SIRT1 positively regulated the exocytosis of TSH-containing granules. Using LC/MS-based interactomics, phosphatidylinositol-4-phosphate 5-kinase (PIP5K)γ[subscript gamma] was identified as a SIRT1 binding partner and deacetylation substrate. SIRT1 deacetylated two specific lysine residues (K265/K268) in PIP5Kγ[subscript gamma] and enhanced PIP5Kγ[subscript gamma] enzyme activity. SIRT1-mediated TSH secretion was abolished by PIP5Kγ[subscript gamma] knockdown. SIRT1 knockdown decreased the levels of deacetylated PIP5Kγ, PI(4,5)P[subscript 2], and reduced the secretion of TSH from pituitary cells. These results were also observed in SIRT1-knockout mice. Conclusions/Significance: Our findings indicated that the control of TSH release by the SIRT1-PIP5Kγ[subscript gamma] pathway is important for regulating the metabolism of the whole body.Mitsubishi Institute of Life SciencesJapan Society for the Promotion of Science. (WAKATE S grant

Raman image-activated cell sorting

The advent of image-activated cell sorting and imaging-based cell picking has advanced our knowledge and exploitation of biological systems in the last decade. Unfortunately, they generally rely on fluorescent labeling for cellular phenotyping, an indirect measure of the molecular landscape in the cell, which has critical limitations. Here we demonstrate Raman image-activated cell sorting by directly probing chemically specific intracellular molecular vibrations via ultrafast multicolor stimulated Raman scattering (SRS) microscopy for cellular phenotyping. Specifically, the technology enables real-time SRS-image-based sorting of single live cells with a throughput of up to ~100 events per second without the need for fluorescent labeling. To show the broad utility of the technology, we show its applicability to diverse cell types and sizes. The technology is highly versatile and holds promise for numerous applications that are previously difficult or undesirable with fluorescence-based technologies