ChiSCAT: unsupervised learning of recurrent cellular micro-motion patterns from a chaotic speckle pattern
arxiv(2024)
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 chaotic speckle pattern and interferometric scattering
microscopy (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.
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