Typing of acute leukemia by intelligent optical time-stretch imaging flow cytometry on a chip. Issue 6 (17th February 2023)

• Record Type: Journal Article
• Authors: Weng, Yueyun; Shen, Hui; Mei, Liye; Liu, Li; Yao, Yifan; Li, Rubing; Wei, Shubin; Yan, Ruopeng; Ruan, Xiaolan; Wang, Du; Wei, Yongchang; Deng, Yunjie; Zhou, Yuqi; Xiao, Tinghui; Goda, Keisuke; Liu, Sheng; Zhou, Fuling; Lei, Cheng
• Is Part Of: Lab on a chip. Volume 23:Issue 6(2023)
• Page Start: 1703
• Page End: 1712
• Publication Date: 2023-02-17
• Subjects: Miniature electronic equipment -- Periodicals; Combinatorial chemistry -- Periodicals; Biotechnology -- Periodicals; 543.0813
• DOI: 10.1039/d2lc01048h
• Languages: English
• ISSNs: 1473-0197

Abstract

Abstract : Intelligent optical time-stretch imaging flow cytometry on a chip is developed for high-throughput, high-accuracy and label-free acute leukemia typing. Abstract : Acute leukemia (AL) is one of the top life-threatening diseases. Accurate typing of AL can significantly improve its prognosis. However, conventional methods for AL typing often require cell staining, which is time-consuming and labor-intensive. Furthermore, their performance is highly limited by the specificity and availability of fluorescent labels, which can hardly meet the requirements of AL typing in clinical settings. Here, we demonstrate AL typing by intelligent optical time-stretch (OTS) imaging flow cytometry on a microfluidic chip. Specifically, we employ OTS microscopy to capture the images of cells in clinical bone marrow samples with a spatial resolution of 780 nm at a high flowing speed of 1 m s −1 in a label-free manner. Then, to show the clinical utility of our method for which the features of clinical samples are diverse, we design and construct a deep convolutional neural network (CNN) to analyze the cellular images and determine the AL type of each sample. We measure 30 clinical samples composed of 7 acute lymphoblastic leukemia (ALL) samples, 17 acute myelogenous leukemia (AML) samples, and 6 samples from healthy donors, resulting in a total of 227 620 images acquired. Results show that our method can distinguish ALL and AML with an accuracy of 95.03%, which, to the best of our knowledge, is a record in label-free AL typing. In addition to AL typing, we believe that the high throughput, high accuracy, and label-free operation of our method make it a potential solution for cell analysis in scientific research and clinical settings.