DsSMLM
Deep-learning algorithm for spectroscopic single-molecule localization microscopy
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The 2014 Nobel Prize-winning technology, Super-resolution fluorescence microscopy, enables imaging of cellular structures beyond the optical diffraction limit resolution (~250 nm), closer to the molecular scale. Among various super-resolution microscopy techniques, single-molecule localization microscopy (SMLM), such as Stochastic Optical Reconstruction Microscopy (STORM), Photo-activated Localization Microscopy (PALM), and DNA Points Accumulation for imaging in nanoscale topography (DNA-PAINT), provides a spatial resolution of approximately 20 nm. Using spatial along with spectral information, a novel spectroscopic single-molecule localization microscopy (sSMLM) technique achieves even higher spatial resolution (sub-10 nm). This technique also offers the unique advantage of simultaneous multicolor super-resolution imaging, visualizing multiple structures within the cell in a single round of acquisition.
Dr. Gaire is a trained researcher who uses STORM and sSMLM microscopy techniques for multidimensional, multicolor SMLM imaging. He has expertise in all aspects of SMLM imaging: instrumentation, sample preparation, image acquisition, algorithm development (classical and machine learning), and image analysis. In this project, he developed a new deep-learning-based algorithm, DsSMLM, for post-processing of sSMLM imaging data.
This research is highlighted on the cover of Journal of Biomedical Optics Vol 26/No. 06. Link for the journal!
DsSMLM, a post processing algorithm for sSMLM imaging. (Image from publication [1]
DsSMLM performance on simulated sSMLM Data (a) Spatial image with ground truth coordinated (green plus) and DsSMLM predicted coordinates (orange circle); (b) Representative DsSMLM reconstructed spectral PSF compared with simulated and ground truth spectral PSF of AF647 dye. Scale bars=0.5 μm. (c) Spectral plots of images in (b). Noisy is the spectrum of simulated PSF. The emission spectrum from DsSMLM is smooth and very close to the ground-truth image with the matching peak. (Image from publication [1])
Related Publication:
Sunil Kumar Gaire, Ali Daneshkhah, Ethan Flowerday, Ruyi Gong, Jane Frederick, and Vadim Backman "Deep learning-based spectroscopic single-molecule localization microscopy," Journal of Biomedical Optics 29(6), 066501 (May 2024).
Sunil Kumar Gaire, et al. "Simultaneous Multicolor Spectroscopic Single-molecule Localization Microscopy Image Reconstruction using Machine Learning," Optica Imaging Congress, August 2023.
Sunil Kumar Gaire, Ethan Flowerday, Jane Frederick, Ruyi Gong, Sravya Prabhala, Leslie Ying, Hao F. Zhang, and Vadim Backman, "Deep Learning-based Spectroscopic Single-molecule Localization Microscopy for Simultaneous Multicolor Imaging, Imaging and Applied Optics Congress, 2022.
Collaborator
Vadim Backman, PhD
Sachs Family Professor of Biomedical Engineering and Medicine
Backman Laboratory, Northwestern University, Evanston, IL
This work is supported by:
NC A&T Startup fund
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