Continuous Biosensor Based on Particle Motion: How Does the Concentration Measurement Precision Depend on Time Scale?

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.

Real-time continuous monitoring of dynamic concentration profiles studied with biosensing by particle motion.

Real-time monitoring-and-control of biological systems requires lab-on-a-chip sensors that are able to accurately measure concentration-time profiles with a well-defined time delay and accuracy using only small amounts of sampled fluid. Here, we study real-time continuous monitoring of dynamic concentration profiles in a microfluidic measurement chamber. Step functions and sinusoidal oscillations of concentrations were generated using two pumps and a herringbone mixer. Concentrations in the bulk of the measurement chamber were quantified using a solution with a dye and light absorbance measurements. Concentrations near the surface were measured using a reversible cortisol sensor based on particle motion. The experiments show how the total time delay of the real-time sensor has contributions from advection, diffusion, reaction kinetics at the surface and signal processing. The total time delay of the studied real-time cortisol sensor was ∼90 seconds for measuring 63% of the concentration change. Monitoring of sinusoidal cortisol concentration-time profiles showed that the sensor has a low-pass frequency response with a cutoff frequency of ∼4 mHz and a lag time of ∼60 seconds. The described experimental methodology paves the way for the development of monitoring-and-control in lab-on-a-chip systems and for further engineering of the analytical characteristics of real-time continuous biosensors.

High-Throughput Single-Molecule Sensors: How Can the Signals Be Analyzed in Real Time for Achieving Real-Time Continuous Biosensing?

Single-molecule sensors collect statistics of single-molecule interactions, and the resulting data can be used to determine concentrations of analyte molecules. The assays are generally end-point assays and are not designed for continuous biosensing. For continuous biosensing, a single-molecule sensor needs to be reversible, and the signals should be analyzed in real time in order to continuously report output signals, with a well-controlled time delay and measurement precision. Here, we describe a signal processing architecture for real-time continuous biosensing based on high-throughput single-molecule sensors. The key aspect of the architecture is the parallel computation of multiple measurement blocks that enables continuous measurements over an endless time span. Continuous biosensing is demonstrated for a single-molecule sensor with 10,000 individual particles that are tracked as a function of time. The continuous analysis includes particle identification, particle tracking, drift correction, and detection of the discrete timepoints where individual particles switch between bound and unbound states, yielding state transition statistics that relate to the analyte concentration in solution. The continuous real-time sensing and computation were studied for a reversible cortisol competitive immunosensor, showing how the precision and time delay of cortisol monitoring are controlled by the number of analyzed particles and the size of the measurement blocks. Finally, we discuss how the presented signal processing architecture can be applied to various single-molecule measurement methods, allowing these to be developed into continuous biosensors.

Sensing Methodology for the Rapid Monitoring of Biomolecules at Low Concentrations over Long Time Spans.

Studies on the dynamics of biological systems and biotechnological processes require measurement techniques that can reveal time dependencies of concentrations of specific biomolecules, preferably with small time delays, short time intervals between subsequent measurements, and the possibility to record over long time spans. For low-concentration biomolecules, these requirements are very challenging since low-concentration assays are typically slow and require new reagents in every assay. Here, we present a sensing methodology that enables rapid monitoring of picomolar and sub-picomolar concentrations in a reversible affinity-based assay, studied using simulations. We demonstrate that low-concentration biomolecules can be monitored with small time delays, short time intervals, and in principle over an endless time span.

Real-Time Detection of State Transitions in Stochastic Signals from Biological Systems.

Robust analysis of signals from stochastic biomolecular processes is critical for understanding the dynamics of biological systems. Measured signals typically show multiple states with heterogeneities and a wide range of state lifetimes. Here, we present an algorithm for robust detection of state transitions in experimental time traces where the properties of the underlying states are a priori unknown. The method implements a maximum-likelihood approach to fit models in neighboring windows of data points. Multiple windows are combined to achieve a high sensitivity for state transitions with a wide range of lifetimes. The proposed maximum-likelihood multiple-windows change point detection (MM-CPD) algorithm is computationally extremely efficient and enables real-time signal analysis. By analyzing both simulated and experimental data, we demonstrate that the algorithm provides accurate change point detection in time traces with multiple heterogeneous states that are a priori unknown. A high sensitivity for a wide range of state lifetimes is achieved.

Modular, Lightweight, Wireless Potentiostat-on-a-Disc for Electrochemical Detection in Centrifugal Microfluidics.

Interfacing electrochemical sensors in a lab-on-a-disc (LoD) system with a potentiostat is often tedious and challenging. We here present the first multichannel, modular, lightweight, and wirelessly powered, custom-built potentiostat-on-a-disc (PoD) for centrifugal microfluidic applications. The developed potentiostat is in the form factor of a typical digital video disc (DVD) and weighs only 127 g. The design of the potentiostat facilitates easy and robust interfacing with the electrodes in the LoD system, while enabling real-time electrochemical detection during rotation. The device can perform different electroanalytical techniques such as cyclic voltammetry, square wave voltammetry, and amperometry while being controlled by custom-made software. Measurements were conducted with and without rotation using both in-house fabricated and commercial electrodes. The performance of the PoD was in good agreement with the results obtained using a commercial potentiostat with a measured current resolution of 200 pA. As a proof of concept, we performed a real-time release study of an electrochemically active compound from microdevices used for drug delivery.