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
The Institute of Technological Sciences, Wuhan University, Wuhan, China.
The Key Laboratory of Transients in Hydraulic Machinery of Ministry of Education, School of Power and Mechanical Engineering, Wuhan University, Wuhan, China.
Lab Chip. 2023 Mar 14;23(6):1703-1712. doi: 10.1039/d2lc01048h.
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 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.
急性白血病（AL）是最具生命威胁的疾病之一。准确的AL分型可显著改善其预后。然而，传统的AL分型方法通常需要细胞染色，既耗时又费力。此外，它们的性能受到荧光标记的特异性和可用性的高度限制，难以满足临床环境中AL分型的要求。在此，我们展示了通过微流控芯片上的智能光学时间拉伸（OTS）成像流式细胞术进行AL分型。具体而言，我们采用OTS显微镜以无标记方式在1 m/s的高流速下捕获临床骨髓样本中细胞的图像，空间分辨率为780 nm。然后，为了展示我们方法在临床样本特征多样情况下的临床实用性，我们设计并构建了一个深度卷积神经网络（CNN）来分析细胞图像并确定每个样本的AL类型。我们测量了30个临床样本，其中包括7个急性淋巴细胞白血病（ALL）样本、17个急性髓细胞白血病（AML）样本和6个健康供体的样本，总共采集了227620张图像。结果表明，我们的方法能够以95.03%的准确率区分ALL和AML，据我们所知，这在无标记AL分型中创下了记录。除了AL分型，我们相信我们方法的高通量、高精度和无标记操作使其成为科研和临床环境中细胞分析的潜在解决方案。
