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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. 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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3. 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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4. FunImageJ: a Lisp framework for scientific image processing

   https://doi.org/10.1093/bioinformatics/btx710  

   Harrington, Kyle I. S. ; Rueden, Curtis T. ; Eliceiri, Kevin W. ; Murphy, ed., Robert ( November 2017 , Bioinformatics)

   FunImageJ is a Lisp framework for scientific image processing built upon the ImageJ software ecosystem. The framework provides a natural functional-style for programming, while accounting for the performance requirements necessary in big data processing commonly encountered in biological image analysis.

   Freely available plugin to Fiji (http://fiji.sc/#download). Installation and use instructions available at http://imagej.net/FunImageJ.

   Supplementary data are available at Bioinformatics online.

    

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5. 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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