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Abstract

Interferometric scattering (iSCAT) microscopy is a label-free optical method capable of detecting single proteins, localizing
their binding positions with nanometer precision, and measuring their mass. In the ideal case, iSCAT is limited by shot noise
so that collection of more photons should allow its detection sensitivity to biomolecules of arbitrarily low mass. However, a
number of technical noise sources combined with speckle-like background fluctuations have restricted the detection limit in
iSCAT. Here, we show that an unsupervised machine learning isolation forest algorithm for anomaly detection pushes the
mass sensitivity limit by a factor of four to below 10 kDa. We implement this scheme both with a user-defined feature matrix
and a self-supervised FastDVDNet and validate our results with correlative fluorescence images recorded in total internal
reflection mode. Our work opens the door to the optical detection of small traces of disease markers such as alpha-synuclein,
chemokines, and cytokines.