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4 October 2022 Seeing the invisible: deep learning optical microscopy for label-free biomolecule screening in the sub-10 kDa regime (Conference Presentation)
Henrik Klein Moberg, Christoph Langhammer, Daniel Midtvedt, Barbora Spackova, Bohdan Yeroshenko, David Albinsson, Joachim Fritzsche, Giovanni Volpe
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Proceedings Volume PC12204, Emerging Topics in Artificial Intelligence (ETAI) 2022; PC122040C (2022) https://doi.org/10.1117/12.2632311
Event: SPIE Nanoscience + Engineering, 2022, San Diego, California, United States
Abstract
We show that a custom ResNet-inspired CNN architecture trained on simulated biomolecule trajectories surpasses the performance of standard algorithms in terms of tracking and determining the molecular weight and hydrodynamic radius of biomolecules in the low-kDa regime in NSM optical microscopy. We show that high accuracy and precision is retained even below the 10-kDa regime, constituting approximately an order of magnitude improvement in limit of detection compared to current state-of-the-art, enabling analysis of hitherto elusive species of biomolecules such as cytokines (~5-25 kDa) important for cancer research and the protein hormone insulin (~5.6 kDa), potentially opening up entirely new avenues of biological research.
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Henrik Klein Moberg, Christoph Langhammer, Daniel Midtvedt, Barbora Spackova, Bohdan Yeroshenko, David Albinsson, Joachim Fritzsche, and Giovanni Volpe "Seeing the invisible: deep learning optical microscopy for label-free biomolecule screening in the sub-10 kDa regime (Conference Presentation)", Proc. SPIE PC12204, Emerging Topics in Artificial Intelligence (ETAI) 2022, PC122040C (4 October 2022); https://doi.org/10.1117/12.2632311
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KEYWORDS
Optical microscopy
Light scattering
Microscopy
Computer simulations
Scattering
Detection and tracking algorithms
Interferometry
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