In silico-labeled ghost cytometry
| العنوان: | In silico-labeled ghost cytometry |
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| المؤلفون: | Masashi Ugawa, Yoko Kawamura, Keisuke Toda, Kazuki Teranishi, Hikari Morita, Hiroaki Adachi, Ryo Tamoto, Hiroko Nomaru, Keiji Nakagawa, Keiki Sugimoto, Evgeniia Borisova, Yuri An, Yusuke Konishi, Seiichiro Tabata, Soji Morishita, Misa Imai, Tomoiku Takaku, Marito Araki, Norio Komatsu, Yohei Hayashi, Issei Sato, Ryoichi Horisaki, Hiroyuki Noji, Sadao Ota |
| المصدر: | eLife eLife, Vol 10 (2021) |
| بيانات النشر: | eLife Sciences Publications, Ltd, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | General Immunology and Microbiology, Staining and Labeling, QH301-705.5, leukocytes, Science, General Neuroscience, flow cytometry, ips cells, Induced Pluripotent Stem Cells, General Medicine, Cell Biology, imaging flow cytometry, General Biochemistry, Genetics and Molecular Biology, Machine Learning, Immunology and Inflammation, Medicine, Humans, Computer Simulation, Biology (General), Coloring Agents, Research Article, Human |
| الوصف: | 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. |
| اللغة: | English |
| تدمد: | 2050-084X |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b2b8d87697be43f5b6079e181a950cd2Test http://europepmc.org/articles/PMC8691837Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....b2b8d87697be43f5b6079e181a950cd2 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 2050084X |
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