Dixit Desh Deepak, Graf Tyler P, McHugh Kevin J, Lillehoj Peter B
Department of Mechanical Engineering, Rice University, Houston, TX, USA.
Department of Bioengineering, Rice University, Houston, TX, USA.
Microsyst Nanoeng. 2025 Feb 28;11(1):36. doi: 10.1038/s41378-025-00881-y.
The quantification of immune cell subpopulations in blood is important for the diagnosis, prognosis and management of various diseases and medical conditions. Flow cytometry is currently the gold standard technique for cell quantification; however, it is laborious, time-consuming and relies on bulky/expensive instrumentation, limiting its use to laboratories in high-resource settings. Microfluidic cytometers offering enhanced portability have been developed that are capable of rapid cell quantification; however, these platforms involve tedious sample preparation and processing protocols and/or require the use of specialized/expensive instrumentation for flow control and cell detection. Here, we report an artificial intelligence-enabled microfluidic cytometer for rapid CD4 T cell quantification in whole blood requiring minimal sample preparation and instrumentation. CD4 T cells in blood are labeled with anti-CD4 antibody-coated microbeads, which are driven through a microfluidic chip via gravity-driven slug flow, enabling pump-free operation. A video of the sample flowing in the chip is recorded using a microscope camera, which is analyzed using a convolutional neural network-based model that is trained to detect bead-labeled cells in the blood flow. The functionality of this platform was evaluated by analyzing fingerprick blood samples obtained from healthy donors, which revealed its ability to quantify CD4 T cells with similar accuracy as flow cytometry (<10% deviation between both methods) while being at least 4× faster, less expensive, and simpler to operate. We envision that this platform can be readily modified to quantify other cell subpopulations in blood by using beads coated with different antibodies, making it a promising tool for performing cell count measurements outside of laboratories and in low-resource settings.
血液中免疫细胞亚群的定量对于各种疾病和医疗状况的诊断、预后及管理至关重要。流式细胞术是目前细胞定量的金标准技术；然而，它费力、耗时且依赖于庞大/昂贵的仪器设备，限制了其仅在资源丰富环境中的实验室使用。已开发出具有更高便携性的微流控细胞仪，能够进行快速细胞定量；然而，这些平台涉及繁琐的样品制备和处理方案，和/或需要使用专门的/昂贵的仪器进行流量控制和细胞检测。在此，我们报告一种基于人工智能的微流控细胞仪，用于全血中CD4 T细胞的快速定量，所需样品制备和仪器设备极少。血液中的CD4 T细胞用抗CD4抗体包被的微珠标记，这些微珠通过重力驱动的段塞流驱动通过微流控芯片，实现无泵操作。使用显微镜相机记录芯片中流动样品的视频，并用基于卷积神经网络的模型进行分析，该模型经过训练以检测血流中微珠标记的细胞。通过分析从健康供体获得的指尖血样评估了该平台的功能，结果显示其定量CD4 T细胞的能力与流式细胞术相似（两种方法之间的偏差<10%），同时速度至少快4倍，成本更低，操作更简单。我们设想，通过使用涂有不同抗体的微珠，该平台可以很容易地进行修改以定量血液中的其他细胞亚群，使其成为在实验室外和资源匮乏环境中进行细胞计数测量的有前途的工具。
