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In silico-labeled ghost cytometry

Ugawa, Masashi ; Kawamura, Yoko ; Toda, Keisuke ; Teranishi, Kazuki ; Morita, Hikari ; Adachi, Hiroaki ; Tamoto, Ryo ; Nomaru, Hiroko ; Nakagawa, Keiji ; Sugimoto, Keiki ; Borisova, Evgeniia ; An, Yuri ; Konishi, Yusuke ; Tabata, Seiichiro ; Morishita, Soji ; Imai, Misa ; Takaku, Tomoiku ; Araki, Marito ; Komatsu, Norio ; Hayashi, Yohei ; Sato, Issei ; Horisaki, Ryoichi ; Noji, Hiroyuki ; Ota, Sadao

eLife, 2021-12, Vol.10 [Periódico revisado por pares]

England: eLife Science Publications, Ltd

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Título:
In silico-labeled ghost cytometry
Autor: Ugawa, Masashi ; Kawamura, Yoko ; Toda, Keisuke ; Teranishi, Kazuki ; Morita, Hikari ; Adachi, Hiroaki ; Tamoto, Ryo ; Nomaru, Hiroko ; Nakagawa, Keiji ; Sugimoto, Keiki ; Borisova, Evgeniia ; An, Yuri ; Konishi, Yusuke ; Tabata, Seiichiro ; Morishita, Soji ; Imai, Misa ; Takaku, Tomoiku ; Araki, Marito ; Komatsu, Norio ; Hayashi, Yohei ; Sato, Issei ; Horisaki, Ryoichi ; Noji, Hiroyuki ; Ota, Sadao
Assuntos: Cell Biology ; Classification ; Coloring Agents - analysis ; Computer Simulation ; Datasets ; Field programmable gate arrays ; Flow cytometry ; Flow Cytometry - instrumentation ; Fluorescence ; Humans ; imaging flow cytometry ; Immunology and Inflammation ; Induced Pluripotent Stem Cells - cytology ; ips cells ; Labeling ; Learning algorithms ; Leukocytes ; Leukocytes - cytology ; Machine Learning ; Medical equipment ; Morphology ; Pluripotency ; Regenerative medicine ; Sensors ; Staining and Labeling - instrumentation ; Stains & staining ; Support vector machines ; Toxicity
É parte de: eLife, 2021-12, Vol.10
Notas: These authors contributed equally to this work.
Descrição: Characterization and isolation of a large population of cells are indispensable procedures in biological sciences. Flow cytometry is one of the standards that offers a method to characterize and isolate cells at high throughput. When performing flow cytometry, cells are molecularly stained with fluorescent labels to adopt biomolecular specificity which is essential for characterizing cells. However, molecular staining is costly and its chemical toxicity can cause side effects to the cells which becomes a critical issue when the cells are used downstream as medical products or for further analysis. Here, we introduce a high-throughput stain-free flow cytometry called in silico-labeled ghost cytometry which characterizes and sorts cells using machine-predicted labels. Instead of detecting molecular stains, we use machine learning to derive the molecular labels from compressive data obtained with diffractive and scattering imaging methods. By directly using the compressive 'imaging' data, our system can accurately assign the designated label to each cell in real time and perform sorting based on this judgment. With this method, we were able to distinguish different cell states, cell types derived from human induced pluripotent stem (iPS) cells, and subtypes of peripheral white blood cells using only stain-free modalities. Our method will find applications in cell manufacturing for regenerative medicine as well as in cell-based medical diagnostic assays in which fluorescence labeling of the cells is undesirable.
Editor: England: eLife Science Publications, Ltd
Idioma: Inglês

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In silico-labeled ghost cytometry

England: eLife Science Publications, Ltd

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