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1. A Fiji process for quantifying fluorescent puncta in linear cellular structures

   https://doi.org/10.17912/micropub.biology.001003  

   Hulsey-Vincent, Heino; Alvinez, Neriah; Witus, Samuel; Kowalski, Jennifer R; Dahlberg, Caroline (January 2023, microPublication Biology)

   Understanding the cell biology of protein trafficking and homeostasis requires reproducible methods for identifying and quantifying proteins within cells or cellular structures. Imaging protocols for measuring punctate protein accumulation in linear structures, for example the neurites of C. elegans, have relied on proprietary software for a full range of analysis capabilities. Here we describe a set of macros written for the NIH-supported imaging software ImageJ or Fiji (Fiji is Just ImageJ) that reliably identify protein puncta so that they can be analyzed with respect to intensity, density, and width at half-maximum intensity (Full-Width, Half-Maximum, FWHM). We provide an explanation of the workflow, data outputs, and limitations of the Fiji macro. As part of this integration, we also provide two independent data sets with side-by-side analyses using the proprietary IgorPro software and the Fiji macro (Hulsey-Vincent, et al. A, B., 2023 submitted). The Fiji macro is an important new tool because it provides robust, reproducible data analysis in a free, open-source format. 

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2. Symmetrically dispersed spectroscopic single-molecule localization microscopy

   https://doi.org/10.1038/s41377-020-0333-9  

   Song, Ki-Hee; Zhang, Yang; Brenner, Benjamin; Sun, Cheng; Zhang, Hao F. (May 2020, Light: Science & Applications)

   Abstract Spectroscopic single-molecule localization microscopy (sSMLM) was used to achieve simultaneous imaging and spectral analysis of single molecules for the first time. Current sSMLM fundamentally suffers from a reduced photon budget because the photons from individual stochastic emissions are divided into spatial and spectral channels. Therefore, both spatial localization and spectral analysis only use a portion of the total photons, leading to reduced precisions in both channels. To improve the spatial and spectral precisions, we present symmetrically dispersed sSMLM, or SDsSMLM, to fully utilize all photons from individual stochastic emissions in both spatial and spectral channels. SDsSMLM achieved 10-nm spatial and 0.8-nm spectral precisions at a total photon budget of 1000. Compared with the existing sSMLM using a 1:3 splitting ratio between spatial and spectral channels, SDsSMLM improved the spatial and spectral precisions by 42% and 10%, respectively, under the same photon budget. We also demonstrated multicolour imaging of fixed cells and three-dimensional single-particle tracking using SDsSMLM. SDsSMLM enables more precise spectroscopic single-molecule analysis in broader cell biology and material science applications. 

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3. Improving spatial precision and field-of-view in wavelength-tagged single-particle tracking using spectroscopic single-molecule localization microscopy

   https://doi.org/10.1364/AO.415275  

   Brenner, Benjamin; Song, Ki-Hee; Sun, Cheng; Zhang, Hao_F (April 2021, Applied Optics)

   Spectroscopic single-molecule localization microscopy (sSMLM) generates super-resolution images of single molecules while simultaneously capturing the spectra of their fluorescence emissions. However, sSMLM splits photons from single-molecule emissions into a spatial channel and a spectral channel, reducing both channels’ precisions. It is also challenging in transmission grating-based sSMLM to achieve a large field-of-view (FOV) and avoid overlap between the spatial and spectral channels. The challenge in FOV has further significance in single-molecule tracking applications. In this work, we analyzed the correlation between the spatial and spectral channels in sSMLM to improve its spatial precision, and we developed a split-mirror assembly to enlarge its FOV. We demonstrate the benefits of these improvements by tracking quantum dots. We also show that we can reduce particle-identification ambiguity by tagging each particle with its unique spectral characteristics. 

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4. Sub‐10 nm Distance Measurements between Fluorophores using Photon‐Accumulation Enhanced Reconstruction

   https://doi.org/10.1002/adpr.202000038  

   Dong, Biqin; Song, Ki-Hee; Davis, Janel_L; Zhang, Hao_F; Sun, Cheng (November 2020, Advanced Photonics Research)

   Single‐molecule localization microscopy (SMLM) precisely localizes individual fluorescent molecules within the wide field of view (FOV). However, the localization precision is fundamentally limited to around 20 nm due to the physical photon limit of individual stochastic single‐molecule emissions. Using spectroscopic SMLM (sSMLM) to resolve their distinct fluorescence emission spectra, individual fluorophore is specifically distinguished and identified, even the ones of the same type. Consequently, the reported photon‐accumulation enhanced reconstruction (PACER) method accumulates photons over repeated stochastic emissions from the same fluorophore to significantly improve the localization precision. This work shows the feasibility of PACER by resolving quantum dots that are 6.1 nm apart with 1.7 nm localization precision. Next, a Monte Carlo simulation is used to investigate the success probability of the PACER's classification process for distance measurements under different conditions. Finally, PACER is used to resolve and measure the lengths of DNA origami nanorulers with an inter‐molecular spacing as small as 6 nm. Notably, the demonstrated sub‐2 nm localization precision bridges the detection range between Förster resonance energy transfer (FRET) and conventional SMLM. Fully exploiting the underlying imaging capability can potentially enable high‐throughput inter‐molecular distance measurements over a large FOV. 

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5. Accelerating multicolor spectroscopic single-molecule localization microscopy using deep learning

   https://doi.org/10.1364/BOE.391806  

   Kumar_Gaire, Sunil; Zhang, Yang; Li, Hongyu; Yu, Ray; Zhang, Hao_F; Ying, Leslie (April 2020, Biomedical Optics Express)

   Spectroscopic single-molecule localization microscopy (sSMLM) simultaneously provides spatial localization and spectral information of individual single-molecules emission, offering multicolor super-resolution imaging of multiple molecules in a single sample with the nanoscopic resolution. However, this technique is limited by the requirements of acquiring a large number of frames to reconstruct a super-resolution image. In addition, multicolor sSMLM imaging suffers from spectral cross-talk while using multiple dyes with relatively broad spectral bands that produce cross-color contamination. Here, we present a computational strategy to accelerate multicolor sSMLM imaging. Our method uses deep convolution neural networks to reconstruct high-density multicolor super-resolution images from low-density, contaminated multicolor images rendered using sSMLM datasets with much fewer frames, without compromising spatial resolution. High-quality, super-resolution images are reconstructed using up to 8-fold fewer frames than usually needed. Thus, our technique generates multicolor super-resolution images within a much shorter time, without any changes in the existing sSMLM hardware system. Two-color and three-color sSMLM experimental results demonstrate superior reconstructions of tubulin/mitochondria, peroxisome/mitochondria, and tubulin/mitochondria/peroxisome in fixed COS-7 and U2-OS cells with a significant reduction in acquisition time. 

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