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1. Multispectral tracing in densely labeled mouse brain with nTracer

   https://doi.org/10.1093/bioinformatics/btz084  

   Roossien, Douglas H. ; Sadis, Benjamin V. ; Yan, Yan ; Webb, John M. ; Min, Lia Y. ; Dizaji, Aslan S. ; Bogart, Luke J. ; Mazuski, Cristina ; Huth, Robert S. ; Stecher, Johanna S. ; et al ( February 2019 , Bioinformatics)

   This note describes nTracer, an ImageJ plug-in for user-guided, semi-automated tracing of multispectral fluorescent tissue samples. This approach allows for rapid and accurate reconstruction of whole cell morphology of large neuronal populations in densely labeled brains.

   nTracer was written as a plug-in for the open source image processing software ImageJ. The software, instructional documentation, tutorial videos, sample image and sample tracing results are available at https://www.cai-lab.org/ntracer-tutorial.

   Supplementary data are available at Bioinformatics online.

    

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2. 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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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. SynQuant: an automatic tool to quantify synapses from microscopy images

   https://doi.org/10.1093/bioinformatics/btz760  

   Wang, Yizhi ; Wang, Congchao ; Ranefall, Petter ; Broussard, Gerard Joey ; Wang, Yinxue ; Shi, Guilai ; Lyu, Boyu ; Wu, Chiung-Ting ; Wang, Yue ; Tian, Lin ; et al ( October 2019 , Bioinformatics)

   Synapses are essential to neural signal transmission. Therefore, quantification of synapses and related neurites from images is vital to gain insights into the underlying pathways of brain functionality and diseases. Despite the wide availability of synaptic punctum imaging data, several issues are impeding satisfactory quantification of these structures by current tools. First, the antibodies used for labeling synapses are not perfectly specific to synapses. These antibodies may exist in neurites or other cell compartments. Second, the brightness of different neurites and synaptic puncta is heterogeneous due to the variation of antibody concentration and synapse-intrinsic differences. Third, images often have low signal to noise ratio due to constraints of experiment facilities and availability of sensitive antibodies. These issues make the detection of synapses challenging and necessitates developing a new tool to easily and accurately quantify synapses.

   We present an automatic probability-principled synapse detection algorithm and integrate it into our synapse quantification tool SynQuant. Derived from the theory of order statistics, our method controls the false discovery rate and improves the power of detecting synapses. SynQuant is unsupervised, works for both 2D and 3D data, and can handle multiple staining channels. Through extensive experiments on one synthetic and three real datasets with ground truth annotation or manually labeling, SynQuant was demonstrated to outperform peer specialized unsupervised synapse detection tools as well as generic spot detection methods.

   Java source code, Fiji plug-in, and test data are available at https://github.com/yu-lab-vt/SynQuant.

   Supplementary data are available at Bioinformatics online.

    

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5. 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)

   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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