Mr. Jun Lu Profile

Jun Lu

at Fudan Univ

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Proceedings Article | 19 December 2022 Presentation

Unsupervised spectral denoising and classification for spectroscopic single-molecule localization

Dandan Xu, Jun Lu, Lei Xu, Wei Wang, Biqin Dong

Proceedings Volume 12320, 1232013 (2022) https://doi.org/10.1117/12.2644092

KEYWORDS: Super resolution, Multiplexing, Denoising, Signal to noise ratio, Molecules, Microscopy, Algorithm development, Spectroscopy, Spectral resolution, Single molecule spectroscopy

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The spectroscopic Single-Molecule Localization Microscopy (sSMLM) is an emerging tool offering exciting new capabilities for single-molecule localization and tracking. By simultaneously capturing the spatial location and full spectra of fluorescent emission from single molecules, sSMLM can significantly extend the number of distinct target species for multiplexed super-resolution imaging and offer desirable resolving power for functional super-resolution imaging. However, extracting accurate spectral information in sSMLM remains challenging due to the poor signal-to-noise ratio (SNR) of spectral images set by limited photon budget from single-molecule fluorescent emission and inherent electronic noise during the image acquisition using digital cameras. Here, we developed an unsupervised learning-based method consisting of a convolutional autoencoder and an unsupervised clustering algorithm for high-fidelity spectral denoising and high-accurate molecular discrimination. Compared to existing learning-based spectral classification methods used in sSMLM, highly accurate molecular discrimination can be achieved even at low photon budget while high-SNR spectra can be simultaneously recovered without undermining the fidelity of the spectral details. Since the developed data processing flow can be directly integrated into any current sSMLM hardware, we hope the unsupervised spectral denoising and classification method can facilitate highly accurate molecular discrimination for multiplexed super-resolution imaging and single-molecule tracking.

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