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.

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.

Interfacing electrochemical sensors in 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 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, amperometry while being controlled by custom-made software. Measurements were conducted with and without rotation using both inhouse 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.

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.