Department of Electrical and Computer Engineering, Boston University, 8 St. Mary's Street, Boston, MA 02215, USA.
Department of Bioengineering, University of California, Berkeley, CA 94720, USA; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158, USA.
Biosens Bioelectron. 2020 Aug 15;162:112258. doi: 10.1016/j.bios.2020.112258. Epub 2020 May 4.
Bacterial infectious diseases are a major threat to human health. Timely and sensitive pathogenic bacteria detection is crucial in bacterial contaminations identification and preventing the spread of infectious diseases. Due to limitations of conventional bacteria detection techniques there have been concerted research efforts towards developing new biosensors. Biosensors offering label-free, whole bacteria detection are highly desirable over those relying on label-based or pathogenic molecular components detection. The major advantage is eliminating the additional time and cost required for labeling or extracting the desired bacterial components. Here, we demonstrate rapid, sensitive and label-free Escherichia coli (E. coli) detection utilizing interferometric reflectance imaging enhancement allowing visualizing individual pathogens captured on the surface. Enabled by our ability to count individual bacteria on a large sensor surface, we demonstrate an extrapolated limit of detection of 2.2 CFU/ml from experimental data in buffer solution with no sample preparation. To the best of our knowledge, this level of sensitivity for whole E. coli detection is unprecedented in label-free biosensing. The specificity of our biosensor is validated by comparing the response to target bacteria E. coli and non-target bacteria S. aureus, K. pneumonia and P. aeruginosa. The biosensor's performance in tap water proves that its detection capability is unaffected by the sample complexity. Furthermore, our sensor platform provides high optical magnification imaging and thus validation of recorded detection events as the target bacteria based on morphological characterization. Therefore, our sensitive and label-free detection method offers new perspectives for direct bacterial detection in real matrices and clinical samples.
细菌性传染病是对人类健康的重大威胁。及时、敏感的致病菌检测对于细菌污染鉴定和传染病的防控至关重要。由于传统细菌检测技术的局限性，人们一直在努力开发新的生物传感器。与依赖基于标记或致病分子成分检测的传感器相比，提供无标记、全细菌检测的生物传感器更受青睐。其主要优势在于消除了标记或提取所需细菌成分所需的额外时间和成本。在这里，我们利用干涉反射成像增强技术，展示了快速、敏感和无标记的大肠杆菌（E. coli）检测，该技术能够可视化表面捕获的单个病原体。借助我们在大传感器表面上计数单个细菌的能力，我们在没有样品制备的缓冲溶液中从实验数据中推断出检测限为 2.2 CFU/ml。据我们所知，这种无标记生物传感技术对全大肠杆菌的检测灵敏度是前所未有的。通过比较目标细菌大肠杆菌和非目标细菌金黄色葡萄球菌、肺炎克雷伯菌和铜绿假单胞菌的响应，验证了我们生物传感器的特异性。生物传感器在自来水中的性能证明其检测能力不受样品复杂性的影响。此外，我们的传感器平台提供高光学倍率成像，因此可以根据形态特征验证基于目标细菌的记录检测事件。因此，我们的敏感无标记检测方法为直接在实际基质和临床样本中进行细菌检测提供了新的视角。
