Intelligent whole-blood imaging flow cytometry for simple, rapid, and cost-effective drug-susceptibility testing of leukemia

Drug susceptibility (also called chemosensitivity) is an important criterion for developing a therapeutic strategy for various cancer types such as breast cancer and leukemia. Recently, functional assays such as high-content screening together with genomic analysis have been shown to be effective fo...

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Veröffentlicht in:Lab on a chip 2019-08, Vol.19 (16), p.2688-2698
Hauptverfasser: Kobayashi, Hirofumi, Lei, Cheng, Wu, Yi, Huang, Chun-Jung, Yasumoto, Atsushi, Jona, Masahiro, Li, Wenxuan, Wu, Yunzhao, Yalikun, Yaxiaer, Jiang, Yiyue, Guo, Baoshan, Sun, Chia-Wei, Tanaka, Yo, Yamada, Makoto, Yatomi, Yutaka, Goda, Keisuke
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Sprache:eng
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Zusammenfassung:Drug susceptibility (also called chemosensitivity) is an important criterion for developing a therapeutic strategy for various cancer types such as breast cancer and leukemia. Recently, functional assays such as high-content screening together with genomic analysis have been shown to be effective for predicting drug susceptibility, but their clinical applicability is poor since they are time-consuming (several days long), labor-intensive, and costly. Here we present a highly simple, rapid, and cost-effective liquid biopsy for ex vivo drug-susceptibility testing of leukemia. The method is based on an extreme-throughput (>1 million cells per second), label-free, whole-blood imaging flow cytometer with a deep convolutional autoencoder, enabling image-based identification of the drug susceptibility of every single white blood cell in whole blood within 24 hours by simply flowing a drug-treated whole blood sample as little as 500 L into the imaging flow cytometer without labeling. Our results show that the method accurately evaluates the drug susceptibility of white blood cells from untreated patients with acute lymphoblastic leukemia. Our method holds promise for affordable precision medicine. The drug susceptibility of leukemia cells in whole blood is evaluated by using extreme-throughput imaging flow cytometry with deep learning.
ISSN:1473-0197
1473-0189
DOI:10.1039/c8lc01370e