Interference
from nonspecific binding imposes a fundamental limit
in the sensitivity of biosensors that is dependent on the affinity
and specificity of the available sensing probes. The dynamic single-molecule
sensing (DSMS) strategy allows ultrasensitive detection of biomarkers
at the femtomolar level by identifying specific binding according
to molecular binding traces. However, the accuracy in classifying
binding traces is not sufficient from separate features, such as the
bound lifetime. Here, we establish a DSMS workflow to improve the
sensitivity and linearity by classifying molecular binding traces
in surface plasmon resonance microscopy with multiple kinetic features.
The improvement is achieved by correlation analysis to select key
features of binding traces, followed by unsupervised k-clustering.
The results show that this unsupervised classification approach improves
the sensitivity and linearity in microRNA (hsa-miR155-5p, hsa-miR21-5p, and hsa-miR362-5p) detection to achieve a limit of detection at the subfemtomolar
level.