Browsing by Author "Yasumoto, Atsushi"

Now showing 1 - 3 of 3

Intelligent classification of platelet aggregates by agonist type
(2020-05-12)
Zhou, Yuqi
;
Yasumoto, Atsushi
;
Lei, Cheng
;
Huang, Chun-Jung
;
Kobayashi, Hirofumi
Platelets are anucleate cells in blood whose principal function is to stop bleeding by forming aggregates for hemostatic reactions. In addition to their participation in physiological hemostasis, platelet aggregates are also involved in pathological thrombosis and play an important role in inflammation, atherosclerosis, and cancer metastasis. The aggregation of platelets is elicited by various agonists, but these platelet aggregates have long been considered indistinguishable and impossible to classify. Here we present an intelligent method for classifying them by agonist type. It is based on a convolutional neural network trained by high-throughput imaging flow cytometry of blood cells to identify and differentiate subtle yet appreciable morphological features of platelet aggregates activated by different types of agonists. The method is a powerful tool for studying the underlying mechanism of platelet aggregation and is expected to open a window on an entirely new class of clinical diagnostics, pharmacometrics, and therapeutics.
Intelligent frequency-shifted optofluidic time-stretch quantitative phase imaging
(2020-01-06)
Wu, Yunzhao
;
Zhou, Yuqi
;
Huang, Chun-Jung
;
Kobayashi, Hirofumi
;
Yan, Sheng
Optofluidic time-stretch quantitative phase imaging (OTS-QPI) is a powerful tool as it enables high-throughput (>10,0(X)cell/s) QPI of single live cells. OTS-QPI is based on decoding temporally stretched spectral interferograms that carry the spatial profiles of cells flowing on a microfluidic chip. However, the utility of OTS-QPI is troubled by difficulties in phase retrieval from the high-frequency region of the temporal interferograms, such as phase-unwrapping errors, high instrumentation cost, and large data volume. To overcome these difficulties, we propose and experimentally demonstrate frequency-shifted OTS-QPI by bringing the phase information to the baseband region. Furthermore, to show its boosted utility, we use it to demonstrate image-based classification of leukemia cells with high accuracy over 96% and evaluation of drug-treated leukemia cells via deep learning. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
Intelligent whole-blood imaging flow cytometry for simple, rapid, and cost-effective drug-susceptibility testing of leukemia
(2019-08-21)
Kobayashi, Hirofumi
;
Lei, Cheng
;
Wu, Yi
;
Huang, Chun-Jung
;
Yasumoto, Atsushi
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 mu 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.