ChiSCAT: unsupervised learning of recurrent cellular micro-motion patterns from a chaotic speckle pattern
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913364480884736 |
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| author | Trelin, Andrii Kussauer, Sophie Weinbrenner, Paul Clasen, Anja David, Robert Rimmbach, Christian Reinhard, Friedemann |
| author_facet | Trelin, Andrii Kussauer, Sophie Weinbrenner, Paul Clasen, Anja David, Robert Rimmbach, Christian Reinhard, Friedemann |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_16931 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ChiSCAT: unsupervised learning of recurrent cellular micro-motion patterns from a chaotic speckle pattern Trelin, Andrii Kussauer, Sophie Weinbrenner, Paul Clasen, Anja David, Robert Rimmbach, Christian Reinhard, Friedemann Optics Quantitative Methods 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. |
| title | ChiSCAT: unsupervised learning of recurrent cellular micro-motion patterns from a chaotic speckle pattern |
| topic | Optics Quantitative Methods |
| url | https://arxiv.org/abs/2405.16931 |