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

Towards continuous monitoring of TNF-α at picomolar concentrations using biosensing by particle motion

The ability to continuously monitor cytokines is desirable for fundamental research studies and healthcare applications. Cytokine release is characterized by picomolar circulating concentrations, short half-lives, and rapid peak times. Here, we describe the characteristics and feasibility of a particle-based biosensing technique for continuous monitoring of TNF-α at picomolar concentrations. The technique is based on the optical tracking of particle motion and uses an antibody sandwich configuration. Experimental results show how the analyte concentration influences the particle diffusivity and characteristic response time of the sensor, and how the sensitivity range depends on the antibody functionalization density. Furthermore, the data clarifies how antibodies supplemented in solution can shorten the characteristic response time. Finally, we demonstrate association rate-based sensing as a first step towards continuous monitoring of picomolar TNF-α concentrations, over a period of 2 h with delay times under 15 min. The insights from this research will enable the development of continuous monitoring sensors using high-affinity binders, providing the sensitivity and speed needed in applications like cytokine monitoring.

Sandwich Immunosensor Based on Particle Motion: How Do Reactant Concentrations and Reaction Pathways Determine the Time-Dependent Response of the Sensor?

To control and optimize the speed of a molecular biosensor, it is crucial to quantify and understand the mechanisms that underlie the time-dependent response of the sensor. Here, we study how the kinetic properties of a particle-based sandwich immunosensor depend on underlying parameters, such as reactant concentrations and the size of the reaction chamber. The data of the measured sensor responses could be fitted with single-exponential curves, with characteristic response times that depend on the analyte concentration and the binder concentrations on the particle and substrate. By comparing characteristic response times at different incubation configurations, the data clarifies how two distinct reaction pathways play a role in the sandwich immunosensor, namely, analyte binding first to particles and thereafter to the substrate, and analyte binding first to the substrate and thereafter to a particle. For a concrete biosensor design, we found that the biosensor is dominated by the reaction pathway where analyte molecules bind first to the substrate and thereafter to a particle. Within this pathway, the binding of a particle to the substrate-bound analyte dominates the sensor response time. Thus, the probability of a particle interacting with the substrate was identified as the main direction to improve the speed of the biosensor while maintaining good sensitivity. We expect that the developed immunosensor and research methodology can be generally applied to understand the reaction mechanisms and optimize the kinetic properties of sandwich immunosensors with particle labels.

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.