Department of Biomedical Engineering, The University of Arizona, Tucson, AZ, USA.
Department of Bio-Industrial Machinery Engineering, Kyungpook National University, Daegu, Republic of Korea.
Methods Mol Biol. 2021;2182:83-101. doi: 10.1007/978-1-0716-0791-6_9.
Previous studies from our lab have created a simple procedure for single-cell count of bacteria on a paper chip platform using optical detection from a smartphone. The procedure and steps employed are outlined along with the lessons learned and details of certain steps and how the design was optimized. Smartphone optical detection is easy to use, low cost, and potentially field deployable, which can be useful for early and rapid detection of pathogens. Smartphone imaging of a paper microfluidic chip preloaded with antibody-conjugated particles provides an adaptable platform for detection of different bacterial targets. The paper microfluidic chip was fabricated with a multichannel design. Each channel was preloaded with either a negative control of bovine serum albumin (BSA) conjugated particles, anti-Salmonella Typhimurium-conjugated particles with varying amounts (to cover different ranges of assay), or anti-Escherichia coli-conjugated particles. Samples were introduced to the paper microfluidic chip using pipetting. Antigens of Salmonella Typhimurium traveled through the channel by capillary action confined within the paper fibers surrounded by the hydrophobic barrier. The paper channel was observed to act as a filter for unwanted particles and contaminants found in field samples. Serial dilutions of known concentrations of bacterial targets were also tested using this procedure to construct a standard curve prior to the assays. The antibody-conjugated particles were able to immunoagglutinate which was quantified through evaluation of Mie scatter intensity. This Mie scattering was quantified in images taken with a smartphone at an optimized angle and distance. Mie scatter simulation provided a method of optimizing the experimental setup and could translate easily to other types of target sample matrices. A smartphone application was developed to help the user position the smartphone optimally in relation to the paper microfluidic chip. The application integrated both image capturing capability and a simple image processing algorithm that calculated bacteria concentrations. The detection limit was at a single-cell level with a total assay time ranging from 90 to less than 60 s depending on the target.
先前，我们实验室开发了一种在纸基芯片平台上利用智能手机光学检测进行细菌单细胞计数的简单方法。文中概述了该方法的步骤和流程，包括所获得的经验教训，以及某些步骤的详细信息和设计优化。智能手机光学检测易于使用、成本低，并且具有潜在的现场部署能力，这对于早期快速检测病原体非常有用。预先加载有抗体偶联颗粒的智能手机对纸微流控芯片进行成像，为不同细菌靶标的检测提供了一个适应性强的平台。该纸微流控芯片采用多通道设计。每个通道预先加载有阴性对照牛血清白蛋白（BSA）偶联颗粒、不同量（涵盖不同检测范围）的抗鼠伤寒沙门氏菌偶联颗粒或抗大肠埃希氏菌偶联颗粒。使用移液管将样品引入纸微流控芯片。抗原鼠伤寒沙门氏菌通过毛细作用在纸纤维内部移动，纸纤维被疏水屏障包围。纸通道被观察到可以作为一个过滤器，过滤野外样本中存在的不需要的颗粒和污染物。使用该程序还测试了已知浓度的细菌靶标的系列稀释液，以便在检测前构建标准曲线。抗体偶联颗粒能够发生免疫凝集，通过 Mie 散射强度的评估进行定量。智能手机在优化的角度和距离下拍摄的图像中对 Mie 散射进行了定量。Mie 散射模拟提供了一种优化实验设置的方法，并且可以很容易地转化为其他类型的目标样本矩阵。开发了一个智能手机应用程序来帮助用户将智能手机相对于纸微流控芯片进行最佳定位。该应用程序集成了图像捕获功能和一个简单的图像处理算法，用于计算细菌浓度。检测限达到单细胞水平，总检测时间取决于目标，范围从 90 秒以下到 60 秒以下。
