Chun Hyeong Jin, Han Yong Duk, Park Yoo Min, Kim Ka Ram, Lee Seok Jae, Yoon Hyun C
Department of Molecular Science and Technology, Ajou University, Suwon 16499, Korea.
Nano-bio Application Team, National NanoFab Center (NNFC), Daejeon 34141, Korea.
Materials (Basel). 2018 Mar 6;11(3):388. doi: 10.3390/ma11030388.
To overcome the time and space constraints in disease diagnosis via the biosensing approach, we developed a new signal-transducing strategy that can be applied to colorimetric optical biosensors. Our study is focused on implementation of a signal transduction technology that can directly translate the color intensity signals-that require complicated optical equipment for the analysis-into signals that can be easily counted with the naked eye. Based on the selective light absorption and wavelength-filtering principles, our new optical signaling transducer was built from a common computer monitor and a smartphone. In this signal transducer, the liquid crystal display (LCD) panel of the computer monitor served as a light source and a signal guide generator. In addition, the smartphone was used as an optical receiver and signal display. As a biorecognition layer, a transparent and soft material-based biosensing channel was employed generating blue output via a target-specific bienzymatic chromogenic reaction. Using graphics editor software, we displayed the optical signal guide patterns containing multiple polygons (a triangle, circle, pentagon, heptagon, and 3/4 circle, each associated with a specified color ratio) on the LCD monitor panel. During observation of signal guide patterns displayed on the LCD monitor panel using a smartphone camera via the target analyte-loaded biosensing channel as a color-filtering layer, the number of observed polygons changed according to the concentration of the target analyte via the spectral correlation between absorbance changes in a solution of the biosensing channel and color emission properties of each type of polygon. By simple counting of the changes in the number of polygons registered by the smartphone camera, we could efficiently measure the concentration of a target analyte in a sample without complicated and expensive optical instruments. In a demonstration test on glucose as a model analyte, we could easily measure the concentration of glucose in the range from 0 to 10 mM.
为了克服通过生物传感方法进行疾病诊断时的时间和空间限制,我们开发了一种可应用于比色光学生物传感器的新型信号转导策略。我们的研究重点是实现一种信号转导技术,该技术可以将需要复杂光学设备进行分析的颜色强度信号直接转换为可以用肉眼轻松计数的信号。基于选择性光吸收和波长过滤原理,我们的新型光学信号传感器由一台普通电脑显示器和一部智能手机构建而成。在这个信号传感器中,电脑显示器的液晶显示(LCD)面板充当光源和信号引导发生器。此外,智能手机用作光接收器和信号显示器。作为生物识别层,采用了基于透明软材料的生物传感通道,通过目标特异性双酶显色反应产生蓝色输出。我们使用图形编辑软件在LCD显示器面板上显示包含多个多边形(三角形、圆形、五边形、七边形和3/4圆形,每个多边形都与特定的颜色比例相关)的光信号引导图案。在使用智能手机摄像头通过加载目标分析物的生物传感通道作为滤色层观察LCD显示器面板上显示的信号引导图案时,通过生物传感通道溶液中吸光度变化与每种多边形颜色发射特性之间的光谱相关性,观察到的多边形数量会根据目标分析物的浓度而变化。通过简单计数智能手机摄像头记录的多边形数量变化,我们可以在无需复杂且昂贵的光学仪器的情况下,高效测量样品中目标分析物的浓度。在以葡萄糖为模型分析物的演示测试中,我们能够轻松测量0至10 mM范围内的葡萄糖浓度。