ChiSCAT: unsupervised learning of recurrent cellular micro-motion patterns from a chaotic speckle pattern

TL;DR

ChiSCAT is a novel interferometric imaging technique that detects cellular micro-motions associated with action potentials using chaotic speckle illumination, combined with unsupervised learning to identify motion patterns.

Contribution

The paper introduces ChiSCAT, a simple, sensitive, and large-field imaging method that detects nanometer-scale cellular motions and employs unsupervised learning to analyze the data.

Findings

Successfully detected micro-motions in cardiomyocytes

Recovered underlying motion patterns using unsupervised learning

Potential applicability in scattering tissues like brain

Abstract

There is considerable evidence that action potentials are accompanied by "intrinsic optical signals", such as a nanometer-scale motion of the cell membrane. Here we present ChiSCAT, a technically simple imaging scheme that detects such signals with interferometric sensitivity. ChiSCAT combines illumination by a {\bf ch}aotic speckle pattern and interferometric scattering microscopy ({\bf iSCAT}) to sensitively detect motion in any point and any direction. The technique features reflective high-NA illumination, common-path suppression of vibrations and a large field of view. This approach maximizes sensitivity to motion, but does not produce a visually interpretable image. We show that unsupervised learning based on matched filtering and motif discovery can recover underlying motion patterns and detect action potentials. We demonstrate these claims in an experiment on blebbistatin-paralyzed cardiomyocytes. ChiSCAT promises to even work in scattering tissue, including a living brain.