Model-free machine learning-based 3D single molecule localisation microscopy

Boland, M. d.; Flaxman, S.; Lightley, J. P. E.; Garcia, E.; Kumar, S.; Dunsby, C.; Neil, M. A. A.; French, P. P. W.; Cohen, E. A. K.

doi:10.1101/2024.10.14.618179

bioRxiv · 10.1101/2024.10.14.618179

Model-free machine learning-based 3D single molecule localisation microscopy

Boland, M. d.·Flaxman, S.·Lightley, J. P. E.·Garcia, E.·Kumar, S.·Dunsby, C.·Neil, M. A. A.·French, P. P. W.·Cohen, E. A. K.

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Abstract

Single molecule localisation microscopy (SMLM) can provide two-dimensional super-resolved image data from conventional fluorescence microscopes, while three dimensional (3D) SMLM usually involves a modification of the microscope, e.g. to engineer a predictable axial variation in the point spread function. Here we demonstrate a 3D SMLM approach (we call "easyZloc") utilising a lightweight Convolutional Neural Network that is generally applicable, including with "standard" (unmodified) fluorescence microscopes, and which we consider may be practically useful in a high throughput SMLM workflow. We demonstrate the reconstruction of nuclear pore complexes with comparable performance to previously reported methods but with a significant reduction in computational power and execution time. 3D reconstructions of the nuclear envelope and a tubulin sample over a larger axial range are also shown.

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Boland, M. d., Flaxman, S., Lightley, J. P. E., Garcia, E., Kumar, S., Dunsby, C., Neil, M. A. A., French, P. P. W., Cohen, E. A. K.. 2024-10-15. Model-free machine learning-based 3D single molecule localisation microscopy. https://doi.org/10.1101/2024.10.14.618179

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