Department of Electrical Engineering, University of California Los Angeles (UCLA), CA 90095, USA.
Lab Chip. 2015 Mar 7;15(5):1284-93. doi: 10.1039/c4lc01358a.
Rapid and sensitive detection of waterborne pathogens in drinkable and recreational water sources is crucial for treating and preventing the spread of water related diseases, especially in resource-limited settings. Here we present a field-portable and cost-effective platform for detection and quantification of Giardia lamblia cysts, one of the most common waterborne parasites, which has a thick cell wall that makes it resistant to most water disinfection techniques including chlorination. The platform consists of a smartphone coupled with an opto-mechanical attachment weighing ~205 g, which utilizes a hand-held fluorescence microscope design aligned with the camera unit of the smartphone to image custom-designed disposable water sample cassettes. Each sample cassette is composed of absorbent pads and mechanical filter membranes; a membrane with 8 μm pore size is used as a porous spacing layer to prevent the backflow of particles to the upper membrane, while the top membrane with 5 μm pore size is used to capture the individual Giardia cysts that are fluorescently labeled. A fluorescence image of the filter surface (field-of-view: ~0.8 cm(2)) is captured and wirelessly transmitted via the mobile-phone to our servers for rapid processing using a machine learning algorithm that is trained on statistical features of Giardia cysts to automatically detect and count the cysts captured on the membrane. The results are then transmitted back to the mobile-phone in less than 2 minutes and are displayed through a smart application running on the phone. This mobile platform, along with our custom-developed sample preparation protocol, enables analysis of large volumes of water (e.g., 10-20 mL) for automated detection and enumeration of Giardia cysts in ~1 hour, including all the steps of sample preparation and analysis. We evaluated the performance of this approach using flow-cytometer-enumerated Giardia-contaminated water samples, demonstrating an average cyst capture efficiency of ~79% on our filter membrane along with a machine learning based cyst counting sensitivity of ~84%, yielding a limit-of-detection of ~12 cysts per 10 mL. Providing rapid detection and quantification of microorganisms, this field-portable imaging and sensing platform running on a mobile-phone could be useful for water quality monitoring in field and resource-limited settings.
快速灵敏地检测饮用水和娱乐水中的病原体对于治疗和预防与水有关的疾病至关重要，尤其是在资源有限的环境中。在这里，我们展示了一种用于检测和定量贾第虫包囊的现场便携且具有成本效益的平台，贾第虫包囊是最常见的水传播寄生虫之一，它具有厚的细胞壁，使其能够抵抗大多数水消毒技术，包括氯化消毒。该平台由智能手机和重量约 205 克的光电机械附件组成，它利用手持式荧光显微镜设计与智能手机的相机单元对齐，以对定制的一次性水样盒进行成像。每个样品盒由吸收垫和机械滤膜组成；使用 8μm 孔径的膜作为多孔间隔层，以防止颗粒回流到上膜，而 5μm 孔径的上膜用于捕获用荧光标记的单个贾第虫包囊。采集滤膜表面的荧光图像（视场：~0.8cm²），通过手机无线传输，并通过机器学习算法在服务器上进行快速处理，该算法是基于贾第虫包囊的统计特征进行训练的，以自动检测和计数膜上捕获的包囊。结果在不到 2 分钟内传回手机，并通过在手机上运行的智能应用程序显示。该移动平台以及我们定制的样品制备方案，使我们能够在大约 1 小时内分析大量水（例如 10-20mL），以实现对贾第虫包囊的自动检测和计数，包括样品制备和分析的所有步骤。我们使用流式细胞仪计数的贾第虫污染水样评估了这种方法的性能，结果表明，我们的滤膜上的包囊捕获效率平均约为 79%，基于机器学习的包囊计数灵敏度约为 84%，检测限约为 10 毫升 12 个包囊。该便携式成像和传感平台能够快速检测和定量微生物，可用于现场和资源有限的环境中的水质监测。
