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.