Helia Biomonitoring, Eindhoven 5612 AR, The Netherlands.
Avebe Innovation Center, Groningen 9747 AA, The Netherlands.
ACS Sens. 2024 Sep 27;9(9):4924-4933. doi: 10.1021/acssensors.4c01586. Epub 2024 Aug 21.
Continuous biosensors measure concentration-time profiles of biomolecular substances in order to allow for comparisons of measurement data over long periods of time. To make meaningful comparisons of time-dependent data, it is essential to understand how measurement imprecision depends on the time interval between two evaluation points, as the applicable imprecision determines the significance of measured concentration differences. Here, we define a set of measurement imprecisions that relate to different sources of variation and different time scales, ranging from minutes to weeks, and study these using statistical analyses of measurement data. The methodology is exemplified for Biosensing by Particle Motion (BPM), a continuous, affinity-based sensing technology with single-particle and single-molecule resolution. The studied BPM sensor measures specific small molecules (glycoalkaloids) in an industrial food matrix (potato fruit juice). Measurements were performed over several months at two different locations, on nearly 50 sensor cartridges with in total more than 1000 fluid injections. Statistical analyses of the measured signals and concentrations show that the relative residuals are normally distributed, allowing extraction and comparisons of the proposed imprecision parameters. The results indicate that sensor noise is the most important source of variation followed by sample pretreatment. Variations caused by fluidic transport, changes of the sensor during use (drift), and variations due to different sensor cartridges and cartridge replacements appear to be small. The imprecision due to sensor noise is recorded over few-minute time scales and is attributed to stochastic fluctuations of the single-molecule measurement principle, false-positive signals in the signal processing, and nonspecific interactions. The developed methodology elucidates both time-dependent and time-independent factors in the measurement imprecision, providing essential knowledge for interpreting concentration-time profiles as well as for further development of continuous biosensing technologies.
连续生物传感器测量生物分子物质的浓度-时间曲线,以便能够对长时间的测量数据进行比较。为了对时间依赖性数据进行有意义的比较,了解测量不精密度如何随两个评估点之间的时间间隔而变化是至关重要的,因为适用的不精密度决定了所测量浓度差异的重要性。在这里,我们定义了一组与不同来源的变化和不同时间尺度(从分钟到周)相关的测量不精密度,并使用测量数据的统计分析来研究这些不精密度。该方法学通过粒子运动生物传感(BPM)进行了举例说明,BPM 是一种连续的、基于亲和力的传感技术,具有单颗粒和单分子分辨率。所研究的 BPM 传感器在工业食品基质(土豆果汁)中测量特定的小分子(糖生物碱)。在两个不同的地点进行了数月的测量,使用近 50 个传感器盒进行了总共超过 1000 次流体注入。对测量信号和浓度进行的统计分析表明,相对残差呈正态分布,允许提取和比较所提出的不精密度参数。结果表明,传感器噪声是最重要的变化来源,其次是样品预处理。由流体传输引起的变化、使用过程中传感器的变化(漂移)以及由于不同的传感器盒和更换传感器盒引起的变化似乎较小。由于传感器噪声引起的不精密度是在几分钟的时间尺度上记录的,归因于单分子测量原理的随机波动、信号处理中的假阳性信号以及非特异性相互作用。所开发的方法阐明了测量不精密度中的时间相关和时间无关因素,为解释浓度-时间曲线以及进一步开发连续生物传感技术提供了必要的知识。