Intelligent Image-Activated Cell Sorting.
By Nao Nitta, Takeaki Sugimura, A. Isozaki, H. Mikami, K. Hiraki, S. Sakuma, T. Iino, F. Arai, Taichiro Endo, Yasuhiro Fujiwaki and 41 more. Cell, 2018
467 citations recorded by the source corpus.
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.
Subfields: Biomedical Engineering, Biophysics, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/13bf7c069d7103392e672a3812043fce3af6e0c8
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In Biomedical Engineering
In Molecular Biology
Citations in the corpus
- Deep learning for cellular image analysis (2019, methodology): “Using this technology, Nitta et al. 85 isolated cells on the basis of protein localization and cell-to-cell interactions.”