G.H. was supported by a graduate fellowship from the Minist&#232;re D&#233;l&#233;gu&#233; &#224; la Recherche et aux Technologies. L.H. was supported by a graduate fellowship from the Institut de Canc&#233;rologie de Lorraine (ICL), whereas Q.T. was supported by a public grant overseen by the French National Research Agency (ANR) as part of the second &#8220;Investissements d&#8217;Avenir&#8221; program FIGHT-HF (reference: ANR-15-RHU4570004). This work was funded by CNRS and Universit&#233; de Lorraine (UMR 7365).

<ol>
	<li>M&#246;ckl, L., Lamb, D. C., Br&#228;uchle, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Super-resolved+fluorescence+microscopy:+Nobel+Prize+in+Chemistry+2014+for+Eric+Betzig,+Stefan+Hell,+and+William+E.+Moerner.">Super-resolved fluorescence microscopy: Nobel Prize in Chemistry 2014 for Eric Betzig, Stefan Hell, and William E. Moerner.</a> Angewandte Chemie. 53 (51), 13972-13977 (2014).</li><li>Meijering, E., Carpenter, A. E., Peng, H., Hamprecht, F. A., Olivo-Marin, J. -. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Imagining+the+future+of+bioimage+analysis.">Imagining the future of bioimage analysis.</a> Nature Biotechnology. 34 (12), 1250-1255 (2016).</li><li>Wiesmann, V., Franz, D., Held, C., M&#252;nzenmayer, C., Palmisano, R., Wittenberg, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Review+of+free+software+tools+for+image+analysis+of+fluorescence+cell+micrographs.">Review of free software tools for image analysis of fluorescence cell micrographs.</a> Journal of Microscopy. 257 (1), 39-53 (2015).</li><li>de Chaumont, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Icy:+an+open+bioimage+informatics+platform+for+extended+reproducible+research.">Icy: an open bioimage informatics platform for extended reproducible research.</a> Nature Methods. 9 (7), 690-696 (2012).</li><li>Girish, V., Vijayalakshmi, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Affordable+image+analysis+using+NIH+Image/ImageJ.">Affordable image analysis using NIH Image/ImageJ.</a> Indian J Cancer. 41 (1), 47 (2004).</li><li>Zaitoun, N. M., Aqel, M. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Survey+on+image+segmentation+techniques.">Survey on image segmentation techniques.</a> Procedia Computer Science. 65, 797-806 (2015).</li><li>. MetaMorph Microscopy Automation and Image Analysis Software Available from: <a target="_blank" href="https://www.moleculardevices.com/products/cellular-imaging-systems/acquisition-and-analysis-software/metamorph-microscopy">https://www.moleculardevices.com/products/cellular-imaging-systems/acquisition-and-analysis-software/metamorph-microscopy</a> (2018)</li><li>. NIS-Elements Imaging Software Available from: <a target="_blank" href="https://www.nikon.com/products/microscope-solutions/lineup/img_soft/nis-element">https://www.nikon.com/products/microscope-solutions/lineup/img_soft/nis-element</a> (2014)</li><li>Meyer, F., Beucher, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Morphological+segmentation.">Morphological segmentation.</a> Journal of Visual Communication and Image Representation. 1 (1), 21-46 (1990).</li><li>Schieber, M., Chandel, N. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ROS+Function+in+Redox+Signaling+and+Oxidative+Stress.">ROS Function in Redox Signaling and Oxidative Stress.</a> Current Biology. 24 (10), 453-462 (2014).</li><li>D'Autr&#233;aux, B., Toledano, M. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ROS+as+signalling+molecules:+mechanisms+that+generate+specificity+in+ROS+homeostasis.+Nature+Reviews.">ROS as signalling molecules: mechanisms that generate specificity in ROS homeostasis. Nature Reviews.</a> Molecular Cell Biology. 8 (10), 813-824 (2007).</li><li>Davalli, P., Mitic, T., Caporali, A., Lauriola, A., D'Arca, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ROS,+Cell+Senescence,+and+Novel+Molecular+Mechanisms+in+Aging+and+Age-Related+Diseases.">ROS, Cell Senescence, and Novel Molecular Mechanisms in Aging and Age-Related Diseases.</a> Oxidative Medicine and Cellular Longevity. 2016, 3565127 (2016).</li><li>Disher, K., Skandalis, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evidence+of+the+modulation+of+mRNA+splicing+fidelity+in+humans+by+oxidative+stress+and+p53.">Evidence of the modulation of mRNA splicing fidelity in humans by oxidative stress and p53.</a> Genome. 50 (10), 946-953 (2007).</li><li>Takeo, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Oxidative+stress-induced+alternative+splicing+of+transformer+2&#946;+(SFRS10)+and+CD44+pre-mRNAs+in+gastric+epithelial+cells.">Oxidative stress-induced alternative splicing of transformer 2&#946; (SFRS10) and CD44 pre-mRNAs in gastric epithelial cells.</a> American Journal of Physiology - Cell Physiology. 297 (2), 330-338 (2009).</li><li>Seo, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Oxidative+Stress+Triggers+Body-Wide+Skipping+of+Multiple+Exons+of+the+Spinal+Muscular+Atrophy+Gene.">Oxidative Stress Triggers Body-Wide Skipping of Multiple Exons of the Spinal Muscular Atrophy Gene.</a> PLOS ONE. 11 (4), 0154390 (2016).</li><li>Will, C. L., Luhrmann, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Spliceosome+Structure+and+Function.">Spliceosome Structure and Function.</a> Cold Spring Harbor Perspectives in Biology. 3 (7), 003707 (2011).</li><li>Ljosa, V., Sokolnicki, K. L., Carpenter, A. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Annotated+high-throughput+microscopy+image+sets+for+validation.">Annotated high-throughput microscopy image sets for validation.</a> Nature Methods. 9 (7), 637-637 (2012).</li><li>Wang, Q., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cajal+bodies+are+linked+to+genome+conformation.">Cajal bodies are linked to genome conformation.</a> Nature Communications. 7, (2016).</li><li>Carpenter, A. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CellProfiler:+image+analysis+software+for+identifying+and+quantifying+cell+phenotypes.">CellProfiler: image analysis software for identifying and quantifying cell phenotypes.</a> Genome Biology. 7, 100 (2006).</li><li>McQuin, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CellProfiler+3.0:+Next-generation+image+processing+for+biology.">CellProfiler 3.0: Next-generation image processing for biology.</a> PLoS Biology. 16 (7), 2005970 (2018).</li></ol>

The authors have nothing to disclose.

An increasing number of free software tools are available for the analysis of fluorescence cell images. Users must correctly choose the adequate software according to the complexity of their problematic, to their knowledge in image processing, and to the time they want to spend in their analysis. Icy, CellProfiler, or ImageJ/Fiji are powerful tools combining both usability and functionality3. Icy is a stand-alone tool that presents a clear graphical user interface (GUI), and notably its &#8220;Pro...

Light microscopy and, more particularly, fluorescence microscopy are robust and versatile techniques commonly used in biological sciences. They give access to the precise localization of various biomolecules like proteins or RNA through their specific fluorescent labeling. The last decade has been characterized by rapid advances in microscopy and imaging technologies as evidenced by the 2014 Nobel Prize in Chemistry awarding Eric Betzig, Stefan W. Hell and William E. Moerner for the development of super-resolved fluorescence microscopy (SRFM)1. SFRM bypasses the diffraction limit of traditional optical microscopy to bring it into the nanodimension. Improvement in techniques like live-imaging or high throughput screening approaches also increases the amount and the complexity of the data to treat for each experiment. Most of the time, researchers are faced with high heterogeneous populations of cells and want to analyze phenotypes at the single-cell level.
Initially, analyses such as foci counting were performed by eye, which is preferred by some researchers since it provides full visual control over the counting process. However, manual analysis of such data is too time consuming, leads to variability between observers, and does not give access to more complex features so that computer-assisted approaches are becoming widely used and almost unavoidable2. Bioimage informatics methods substantially increase the efficiency of data analysis and are free of the unavoidable operator subjectivity and potential bias of the manual counting analysis. The increased demand in this field and the improvement of computer power led to the development of a large number of image analysis platforms. Some of them are freely available and give access to various tools to perform analysis with personal computers. A classification of open access tools has been recently established3 and presents Icy4 as a powerful software combining usability and functionality. Moreover, Icy has the advantage of communicating with ImageJ.
For users without image analysis expertise, the main obstacles are&#160;to choose the appropriate tool according to the problematic and correctly tune&#160;parameters that are often not well understood. Moreover, setup times are often long. Icy proposes a user-friendly point-and-click interface named &#8220;Protocols&#8221; to develop workflow by combining some plugins found within an exhaustive collection4. The flexible modular design and the point-and-click interface make setting up an analysis feasible for non-programmers. Here we present a workflow called Substructure Analyzer, developed in Icy&#8217;s interface, whose function is to analyze fluorescent signals in specific cellular compartments and measure different features as brightness, foci number, foci size, and spatial distribution. This workflow addresses several issues such as quantification of signal translocation, analysis of transfected cells expressing a fluorescent reporter, or analysis of foci from different cellular substructures in individual cells. It allows the simultaneous processing of multiple images, and output results are exported to a tab-delimited worksheet that can be opened in commonly used spreadsheet programs.
The Substructure Analyzer pipeline is presented in Figure 1. First, all the images contained in a specified folder are pre-processed to improve their signal to noise ratio. This step increases the efficiency of the following steps and decreases the running time. Then, the Regions of Interest (ROIs), corresponding to the image areas where the fluorescent signal should be detected, are identified and segmented. Finally, the fluorescent signal is analyzed, and results are exported into a tab-delimited worksheet.
Object segmentation (detection of boundaries) is the most challenging step in image analysis, and its efficiency determines the accuracy of the resulting cell measurements. The first objects identified in an image (called primary objects) are often nuclei from DNA-stained images (DAPI or Hoechst staining), although primary objects can also be whole cells, beads, speckles, tumors, or whatever stained objects. In most biological images, cells or nuclei touch each other or overlap causing the simple and fast algorithms to fail. To date, no universal algorithm can perform perfect segmentation of all objects, mostly because their characteristics (size, shape, or texture) modulate the efficiency of segmentation5. The segmentation tools commonly distributed with microscopy software (such as the MetaMorph Imaging Software by Molecular Devices6, or the NIS-Elements Advances Research software by Nikon7) are generally based on standard techniques such as correlation matching, thresholding, or morphological operations. Although efficient in basic systems, these overgeneralized methods rapidly present limitations when used in more challenging and specific contexts. Indeed, segmentation is highly sensitive to experimental parameters such as cell type, cell density, or biomarkers, and frequently requires repeated adjustment for a large data set. The Substructure Analyzer workflow integrates both simple and more sophisticated algorithms to propose different alternatives adapted to image complexity and user needs. It notably proposes the marker-based watershed algorithm8 for highly clustered objects. The efficiency of this segmentation method relies on the selection of individual markers on each object. These markers are manually chosen most of the time to get correct parameters for full segmentation, which is highly time consuming when users face a high number of objects. Substructure Analyzer proposes an automatic detection of these markers, providing a highly efficient segmentation process. Segmentation is, most of the time, the limiting step of image analysis and can considerably modify the processing time depending on the resolution of the image, the number of objects per image, and the level of clustering of objects. Typical pipelines require a few seconds to 5 minutes per image on a standard desktop computer. Analysis of more complex images can require a more powerful computer and some basic knowledge in image analysis.
The flexibility and functionality of this workflow are illustrated with various examples in the representative results. The advantages of this workflow are notably displayed through the study of nuclear substructures under oxidative stress (OS) conditions. OS corresponds to an imbalance of the redox homeostasis in favor of oxidants and is associated with high levels of reactive oxygen species (ROS). Since ROS act as signaling molecules, changes in their concentration and subcellular localization affect positively or negatively a myriad of pathways and networks that regulate physiological functions, including signal transduction, repair mechanisms, gene expression, cell death, and proliferation9,10. OS is thus directly involved in various pathologies (neurodegenerative and cardiovascular diseases, cancers, diabetes, etc.), but also cellular aging. Therefore, deciphering the consequences of OS on the human cell&#8217;s organization and function constitutes a crucial step in the understanding of the roles of OS in the onset and development of human pathologies. It has been established that OS regulates gene expression by modulating transcription through several transcription factors (p53, Nrf2, FOXO3A)11, but also by affecting the regulation of several co- and post-transcriptional processes such as alternative splicing (AS) of pre-RNAs12,13,14. Alternative splicing of primary coding and non-coding transcripts is an essential mechanism that increases the encoding capacity of the genomes by producing transcript isoforms. AS is performed by a huge ribonucleoprotein complex called spliceosome, containing almost 300 proteins and 5 U-rich small nuclear RNAs (UsnRNAs)15. Spliceosome assembly and AS are tightly controlled in cells and some steps of the spliceosome maturation occur within membrane-less nuclear compartments named Cajal Bodies. These nuclear substructures are characterized by the dynamic nature of their structure and their composition, which are mainly conducted by multivalent interactions of their RNA and protein components with the coilin protein. Analysis of thousands of cells with the Substructure Analyzer workflow allowed characterization of never described effects of OS on Cajal Bodies. Indeed, obtained data suggest that OS modifies the nucleation of Cajal Bodies, inducing a nucleoplasmic redistribution of the coilin protein into numerous smaller nuclear foci. Such a change of the structure of Cajal Bodies might affect the maturation of the spliceosome and participate in AS modulation by OS.

<table><tbody><tr><td>16% Formaldehyde solution (w/v) methanol free</td><td>Thermo Fisher Scientific</td><td>28908</td><td>to fix the cells</td></tr><tr><td>Alexa Fluor 488 of goat anti-rabbit</td><td>Thermo Fisher Scientific</td><td>A-11008</td><td>fluorescent secondary antibody</td></tr><tr><td>Alexa Fluor 555 of goat anti-mouse</td><td>Thermo Fisher Scientific</td><td>A-21425</td><td>fluorescent secondary antibody</td></tr><tr><td>Alexa Fluor 555 Phalloidin</td><td>Thermo Fisher Scientific</td><td>A34055</td><td>fluorescent secondary antibody</td></tr><tr><td>Bovine serum albumin standard (BSA)</td><td>euromedex</td><td>04-100-812-E</td><td></td></tr><tr><td>DMEM</td><td>Sigma-Aldrich</td><td>D5796-500ml</td><td>cell culture medium</td></tr><tr><td>Duolink In Situ Mounting Medium with DAPI</td><td>Sigma-Aldrich</td><td>DUO82040-5ML</td><td>mounting medium</td></tr><tr><td>Human: HeLa S3 cells</td><td>IGBMC, Strasbourg, France</td><td></td><td>cell line used to perform the experiments</td></tr><tr><td>Hydrogen peroxide solution 30% (H&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;2&lt;/sub&gt;)</td><td>Sigma-Aldrich</td><td>H1009-100ml</td><td>used as a stressing agent</td></tr><tr><td>Lipofectamine 2000 Reagent</td><td>Thermo Fisher Scientific</td><td>11668-019</td><td>transfection reagent</td></tr><tr><td>Mouse monoclonal anti-coilin</td><td>abcam</td><td>ab11822</td><td>Coilin-specific antibody</td></tr><tr><td>Nikon Optiphot-2 fluorescence microscope</td><td>Nikon</td><td></td><td>epifluoresecence microscope</td></tr><tr><td>Opti-MEM I Reduced Serum Medium</td><td>Thermo Fisher Scientific</td><td>31985062</td><td>transfection medium</td></tr><tr><td>PBS pH 7.4 (10x)</td><td>gibco</td><td>70011-036</td><td>to wash the cells</td></tr><tr><td>Rabbit polyclonal anti-53BP1</td><td>Thermo Fisher Scientific</td><td>PA1-16565</td><td>53BP1-specific antibody</td></tr><tr><td>Rabbit polyclonal anti-EDC4</td><td>Sigma-Aldrich</td><td>SAB4200114</td><td>EDC4-specific antibody</td></tr><tr><td>Triton X-100</td><td>Roth</td><td>6683</td><td>to permeabilize the cells</td></tr></tbody></table>

substructure analyzer: a user-friendly workflow for rapid exploration and accurate analysis of cellular bodies in fluorescence microscopy images

The last decade has been characterized by breakthroughs in fluorescence microscopy techniques illustrated by spatial resolution improvement but also in live-cell imaging and high-throughput microscopy techniques. This led to a constant increase in the amount and complexity of the microscopy data for a single experiment. Because manual analysis of microscopy data is very time consuming, subjective, and prohibits quantitative analyses, automation of bioimage analysis is becoming almost unavoidable. We built an informatics workflow called Substructure Analyzer to fully automate signal analysis in bioimages from fluorescent microscopy. This workflow is developed on the user-friendly open-source platform Icy and is completed by functionalities from ImageJ. It includes the pre-processing of images to improve the signal to noise ratio, the individual segmentation of cells (detection of cell boundaries) and the detection/quantification of cell bodies enriched in specific cell compartments. The main advantage of this workflow is to propose complex bio-imaging functionalities to users without image analysis expertise through a user-friendly interface. Moreover, it is highly modular and adapted to several issues from the characterization of nuclear/cytoplasmic translocation to the comparative analysis of different cell bodies in different cellular sub-structures. The functionality of this workflow is illustrated through the study of the Cajal (coiled) Bodies under oxidative stress (OS) conditions. Data from fluorescence microscopy show that their integrity in human cells is impacted a few hours after the induction of OS. This effect is characterized by a decrease of coilin nucleation into characteristic Cajal Bodies, associated with a nucleoplasmic redistribution of coilin into an increased number of smaller foci. The central role of coilin in the exchange between CB components and the surrounding nucleoplasm suggests that OS induced redistribution of coilin could affect the composition and the functionality of Cajal Bodies.

All the described analyses have been performed on a standard laptop (64-bit, quad-core processor at 2.80 GHz with 16 GB random-access memory (RAM)) working with the 64-bit version of Java. Random-access memory is an important parameter to consider, depending on the amount and the resolution of images to analyze. Using the 32-bit version of Java limits the memory to about 1300 MB, which could be unsuitable for big data analysis, whereas the 64-bit version allows increasing the memory allocated to Icy. <strong class="xfig"...

Watch this Scientific Journal Video about Substructure Analyzer: A User-Friendly Workflow for Rapid Exploration and Accurate Analysis of Cellular Bodies in Fluorescence Microscopy Images at JoVE.com

Substructure Analyzer: A User-Friendly Workflow for Rapid Exploration and Accurate Analysis of Cellular Bodies in Fluorescence Microscopy Images

We present a freely available workflow built for rapid exploration and accurate analysis of cellular bodies in specific cell compartments in fluorescence microscopy images. This user-friendly workflow is designed on the open-source software Icy and also uses ImageJ functionalities. The pipeline is affordable without knowledge in image analysis.

The last decade has been characterized by breakthroughs in fluorescence microscopy techniques illustrated by spatial resolution improvement but also in ...

substructure-analyzer-user-friendly-workflow-for-rapid-exploration

Research

JoVE Journal

Bioengineering

7.9K Views.  Université de Lorraine, CNRS, IMoPA F54000 Nancy France. Substructure analyzer is een gebruiksvriendelijke workflow die automatische analyse uitvoert van meerdere procesmicroscopiestatistieken. Het betekent niet nul ton van open source software Icy en ook met behulp van machine functionaliteiten. Belangrijk is dat deze workflow is betaalbaar resort produceren kennis en beelden analyse.Multi-channel beelden worden geladen binnen de workflow en voorbewerkt om de signaal-ruisverhouding te verbeteren en om beeldvorming of defecten te verwijderen...

Video: Substructure Analyzer: A User-Friendly Workflow for Rapid Exploration and Accurate Analysis of Cellular Bodies in Fluorescence Microscopy Images

Light Sheet-based Fluorescence Microscopy of Living or Fixed and Stained Tribolium castaneum Embryos

The red flour beetle Tribolium castaneum has become an important insect model organism in developmental genetics and evolutionary developmental biology. The observation of Tribolium embryos with light sheet-based fluorescence microscopy has multiple advantages over conventional widefield and confocal fluorescence microscopy. Due to the unique properties of a light sheet-based microscope, three dimensional images of living specimens can be recorded with high signal-to-noise ratios and significantly reduced photo-bleaching as well as photo-toxicity along multiple directions over periods that last several days. With more than four years of methodological development and a continuous increase of data, the time seems appropriate to establish standard operating procedures for the usage of light sheet technology in the Tribolium community as well as in the insect community at large. This protocol describes three mounting techniques suitable for different purposes, presents two novel custom-made transgenic Tribolium lines appropriate for long-term live imaging, suggests five fluorescent dyes to label intracellular structures of fixed embryos and provides information on data post-processing for the timely evaluation of the recorded data. Representative results concentrate on long-term live imaging, optical sectioning and the observation of the same embryo along multiple directions. The respective datasets are provided as a downloadable resource. Finally, the protocol discusses quality controls for live imaging assays, current limitations and the applicability of the outlined procedures to other insect species.
This protocol is primarily intended for developmental biologists who seek imaging solutions that outperform standard laboratory equipment. It promotes the continuous attempt to close the gap between the technically orientated laboratories/communities, which develop and refine microscopy methodologically, and the life science laboratories/communities, which require 'plug-and-play' solutions to technical challenges. Furthermore, it supports an axiomatic approach that moves the biological questions into the center of attention.

Light Sheet-based Fluorescence Microscopy of Living or Fixed and Stained Tribolium castaneum Embryos

Imaging the morphogenesis of insect embryos with light sheet-based fluorescence microscopy has become state of the art. This protocol outlines and compares three mounting techniques appropriate for Tribolium castaneum embryos, introduces two novel custom-made transgenic lines well suited for live imaging, discusses essential quality controls and indicates current experimental limitations.

The red flour beetle Tribolium castaneum has become an important insect model organism in developmental genetics and evolutionary developmental biology. ...

Developmental Biology

Automated Quantification of Synaptic Fluorescence in C. elegans

Synapse strength refers to the amplitude of postsynaptic responses to presynaptic neurotransmitter release events, and has a major impact on overall neural circuit function. Synapse strength critically depends on the abundance of neurotransmitter receptors clustered at synaptic sites on the postsynaptic membrane. Receptor levels are established developmentally, and can be altered by receptor trafficking between surface-localized, subsynaptic, and intracellular pools, representing important mechanisms of synaptic plasticity and neuromodulation. Rigorous methods to quantify synaptically-localized neurotransmitter receptor abundance are essential to study synaptic development and plasticity. Fluorescence microscopy is an optimal approach because it preserves spatial information, distinguishing synaptic from non-synaptic pools, and discriminating among receptor populations localized to different types of synapses. The genetic model organism Caenorhabditis elegans is particularly well suited for these studies due to the small size and relative simplicity of its nervous system, its transparency, and the availability of powerful genetic techniques, allowing examination of native synapses in intact animals.
Here we present a method for quantifying fluorescently-labeled synaptic neurotransmitter receptors in C. elegans. Its key feature is the automated identification and analysis of individual synapses in three dimensions in multi-plane confocal microscope output files, tabulating position, volume, fluorescence intensity, and total fluorescence for each synapse. This approach has two principal advantages over manual analysis of z-plane projections of confocal data. First, because every plane of the confocal data set is included, no data are lost through z-plane projection, typically based on pixel intensity averages or maxima. Second, identification of synapses is automated, but can be inspected by the experimenter as the data analysis proceeds, allowing fast and accurate extraction of data from large numbers of synapses. Hundreds to thousands of synapses per sample can easily be obtained, producing large data sets to maximize statistical power. Considerations for preparing C. elegans for analysis, and performing confocal imaging to minimize variability between animals within treatment groups are also discussed. Although developed to analyze C. elegans postsynaptic receptors, this method is generally useful for any type of synaptically-localized protein, or indeed, any fluorescence signal that is localized to discrete clusters, puncta, or organelles.
The procedure is performed in three steps: 1) preparation of samples, 2) confocal imaging, and 3) image analysis. Steps 1 and 2 are specific to C. elegans, while step 3 is generally applicable to any punctate fluorescence signal in confocal micrographs.

Automated Quantification of Synaptic Fluorescence in C. elegans

The abundance of neurotransmitter receptors clustered at synapses strongly influences synaptic strength. This method quantifies fluorescently-labeled neurotransmitter receptors in three dimensions with single-synapse resolution in C. elegans, allowing hundreds of synapses to be rapidly characterized within a single sample without distortions introduced by z-plane projection.

Synapse strength refers to the amplitude of postsynaptic responses to presynaptic neurotransmitter release events, and has a major impact on overall ...

Neuroscience

Automated Slide Scanning and Segmentation in Fluorescently-labeled Tissues Using a Widefield High-content Analysis System

Automated slide scanning and segmentation of fluorescently-labeled tissues is the most efficient way to analyze whole slides or large tissue sections. Unfortunately, many researchers spend large amounts of time and resources developing and optimizing workflows that are only relevant to their own experiments. In this article, we describe a protocol that can be used by those with access to a widefield high-content analysis system (WHCAS) to image any slide-mounted tissue, with options for customization within pre-built modules found in the associated software. Not originally intended for slide scanning, the steps detailed in this article make it possible to acquire slide scanning images in the WHCAS which can be imported into the associated software. In this example, the automated segmentation of brain tumor slides is demonstrated, but the automated segmentation of any fluorescently-labeled nuclear or cytoplasmic marker is possible. Furthermore, there are a variety of other quantitative software modules including assays for protein localization/translocation, cellular proliferation/viability/apoptosis, and angiogenesis that can be run. This technique will save researchers time and effort and create an automated protocol for slide analysis.

Here we describe a protocol for the automated segmentation of fluorescently labeled tissues on slides using a widefield high-content analysis system (WHCAS). This protocol has wide-ranging applications in any field which involves the quantitation of fluorescent markers in biological tissues including the biological sciences, medical engineering, and health sciences.

Automated slide scanning and segmentation of fluorescently-labeled tissues is the most efficient way to analyze whole slides or large tissue sections. ...

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

Cell polarity is a macroscopic phenomenon established by a collection of spatially concentrated molecules and structures that culminate in the emergence of specialized domains at the subcellular level. It is associated with developing asymmetric morphological structures that underlie key biological functions such as cell division, growth, and migration. In addition, the disruption of cell polarity has been linked to tissue-related disorders such as cancer and gastric dysplasia.
Current methods to evaluate the spatiotemporal dynamics of fluorescent reporters in individual polarized cells often involve manual steps to trace a midline along the cells&#39; major axis, which is time consuming and prone to strong biases. Furthermore, although ratiometric analysis can correct the uneven distribution of reporter molecules using two fluorescence channels, background subtraction techniques are frequently arbitrary and lack statistical support.
This manuscript introduces a novel computational pipeline to automate and quantify the spatiotemporal behavior of single cells using a model of cell polarity: pollen tube/root hair growth and cytosolic ion dynamics. A three-step algorithm was developed to process ratiometric images and extract a quantitative representation of intracellular dynamics and growth. The first step segments the cell from the background, producing a binary mask through a thresholding technique in the pixel intensity space. The second step traces a path through the midline of the cell through a skeletonization operation. Finally, the third step provides the processed data as a ratiometric timelapse and yields a ratiometric kymograph (i.e., a 1D spatial profile through time). Data from ratiometric images acquired with genetically encoded fluorescent reporters from growing pollen tubes were used to benchmark the method. This pipeline allows for faster, less biased, and more accurate representation of the spatiotemporal dynamics along the midline of polarized cells, thus advancing the quantitative toolkit available to investigate cell polarity. The AMEBaS Python source code is available at:&#160;https://github.com/badain/amebas.git

Current methods for analyzing the intracellular dynamics of polarized single cells are often manual and lack standardization. This manuscript introduces a novel image analysis pipeline for automating midline extraction of single polarized cells and quantifying spatiotemporal behavior from time lapses in a user-friendly online interface.

Cell polarity is a macroscopic phenomenon established by a collection of spatially concentrated molecules and structures that culminate in the emergence ...

An Analytical Tool that Quantifies Cellular Morphology Changes from Three-dimensional Fluorescence Images

The most common software analysis tools available for measuring fluorescence images are for two-dimensional (2D) data that rely on manual settings for inclusion and exclusion of data points, and computer-aided pattern recognition to support the interpretation and findings of the analysis. It has become increasingly important to be able to measure fluorescence images constructed from three-dimensional (3D) datasets in order to be able to capture the complexity of cellular dynamics and understand the basis of cellular plasticity within biological systems. Sophisticated microscopy instruments have permitted the visualization of 3D fluorescence images through the acquisition of multispectral fluorescence images and powerful analytical software that reconstructs the images from confocal stacks that then provide a 3D representation of the collected 2D images. Advanced design-based stereology methods have progressed from the approximation and assumptions of the original model-based stereology1 even in complex tissue sections2. Despite these scientific advances in microscopy, a need remains for an automated analytic method that fully exploits the intrinsic 3D data to allow for the analysis and quantification of the complex changes in cell morphology, protein localization and receptor trafficking. 
Current techniques available to quantify fluorescence images include Meta-Morph (Molecular Devices, Sunnyvale, CA) and Image J (NIH) which provide manual analysis. Imaris (Andor Technology, Belfast, Northern Ireland) software provides the feature MeasurementPro, which allows the manual creation of measurement points that can be placed in a volume image or drawn on a series of 2D slices to create a 3D object. This method is useful for single-click point measurements to measure a line distance between two objects or to create a polygon that encloses a region of interest, but it is difficult to apply to complex cellular network structures. Filament Tracer (Andor) allows automatic detection of the 3D neuronal filament-like however, this module has been developed to measure defined structures such as neurons, which are comprised of dendrites, axons and spines (tree-like structure). This module has been ingeniously utilized to make morphological measurements to non-neuronal cells3, however, the output data provide information of an extended cellular network by using a software that depends on a defined cell shape rather than being an amorphous-shaped cellular model. To overcome the issue of analyzing amorphous-shaped cells and making the software more suitable to a biological application, Imaris developed Imaris Cell. This was a scientific project with the Eidgen&ouml;ssische Technische Hochschule, which has been developed to calculate the relationship between cells and organelles. While the software enables the detection of biological constraints, by forcing one nucleus per cell and using cell membranes to segment cells, it cannot be utilized to analyze fluorescence data that are not continuous because ideally it builds cell surface without void spaces. To our knowledge, at present no user-modifiable automated approach that provides morphometric information from 3D fluorescence images has been developed that achieves cellular spatial information of an undefined shape (Figure 1). 
We have developed an analytical platform using the Imaris core software module and Imaris XT interfaced to MATLAB (Mat Works, Inc.). These tools allow the 3D measurement of cells without a pre-defined shape and with inconsistent fluorescence network components. Furthermore, this method will allow researchers who have extended expertise in biological systems, but not familiarity to computer applications, to perform quantification of morphological changes in cell dynamics.

We developed a software platform that utilizes Imaris Neuroscience, ImarisXT and MATLAB to measure the changes in morphology of an undefined shape taken from three-dimensional confocal fluorescence of single cells. This novel approach can be used to quantify changes in cell shape following receptor activation and therefore represents a possible additional tool for drug discovery.

The most common software analysis tools available for measuring fluorescence images are for two-dimensional (2D) data that rely on manual settings for ...

Biology

Visualization and Analysis of mRNA Molecules Using Fluorescence In Situ Hybridization in Saccharomyces cerevisiae

The Fluorescence in situ Hybridization (FISH) method allows one to detect nucleic acids in the native cellular environment. Here we provide a protocol for using FISH to quantify the number of mRNAs in single yeast cells. Cells can be grown in any condition of interest and then fixed and made permeable. Subsequently, multiple single-stranded deoxyoligonucleotides conjugated to fluorescent dyes are used to label and visualize mRNAs. Diffraction-limited fluorescence from single mRNA molecules is quantified using a spot-detection algorithm to identify and count the number of mRNAs per cell. While the more standard quantification methods of northern blots, RT-PCR and gene expression microarrays provide information on average mRNAs in the bulk population, FISH facilitates both the counting and localization of these mRNAs in single cells at single-molecule resolution.

Visualization and Analysis of mRNA Molecules Using Fluorescence In Situ Hybridization in Saccharomyces cerevisiae

This protocol describes an experimental procedure for performing Fluorescence in situ Hybridization (FISH) for counting mRNAs in single cells at single-molecule resolution.

The Fluorescence in situ Hybridization (FISH) method allows one to detect nucleic acids in the native cellular environment. Here we provide a protocol for ...

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Despite rapid advances in high-throughput microscopy, quantitative image-based assays still pose significant challenges. While a variety of specialized image analysis tools are available, most traditional image-analysis-based workflows have steep learning curves (for fine tuning of analysis parameters) and result in long turnaround times between imaging and analysis. In particular, cell segmentation, the process of identifying individual cells in an image, is a major bottleneck in this regard.
Here we present an alternate, cell-segmentation-free workflow based on PhenoRipper, an open-source software platform designed for the rapid analysis and exploration of microscopy images. The pipeline presented here is optimized for immunofluorescence microscopy images of cell cultures and requires minimal user intervention. Within half an hour, PhenoRipper can analyze data from a typical 96-well experiment and generate image profiles. Users can then visually explore their data, perform quality control on their experiment, ensure response to perturbations and check reproducibility of replicates. This facilitates a rapid feedback cycle between analysis and experiment, which is crucial during assay optimization. This protocol is useful not just as a first pass analysis for quality control, but also may be used as an end-to-end solution, especially for screening. The workflow described here scales to large data sets such as those generated by high-throughput screens, and has been shown to group experimental conditions by phenotype accurately over a wide range of biological systems. The PhenoBrowser interface provides an intuitive framework to explore the phenotypic space and relate image properties to biological annotations. Taken together, the protocol described here will lower the barriers to adopting quantitative analysis of image based screens.

Here we describe a workflow for rapidly analyzing and exploring collections of fluorescence microscopy images using PhenoRipper, a recently developed image-analysis platform.

Despite rapid advances in high-throughput microscopy, quantitative image-based assays still pose significant challenges. While a variety of specialized ...

3D Reconstruction and Analysis of Thin Subcellular Neuronal Structures using Focused-Ion Beam Scanning Electron Microscopy Data

Recent advances in scanning electron microscope technologies now permit the rapid three-dimensional (3D) analysis of ultrathin subcellular processes. Here, a methodological pipeline is presented to identify, visualize, and analyze thin neuronal processes, such as those that project into the presynaptic boutons of other neurons (termed 'spinules'). Using freely available software packages, this protocol demonstrates how to use a decision tree to identify common neuronal subcellular structures using morphological criteria within focused ion beam scanning electron microscopy (FIB-SEM) image volumes, with particular attention on identifying a diversity of spinules projecting into presynaptic boutons. In particular, this protocol describes how to trace spinules within neuronal synapses to produce 3D reconstructions of these thin subcellular projections, their parent neurites, and postsynaptic partners. Additionally, the protocol includes a list of freely available open-source software programs for analyzing FIB-SEM data and offers tips (e.g., smoothing, lighting) toward improving 3D reconstructions for visualization and publication. This adaptable protocol offers an entry point into the rapid nanoscale analysis of subcellular structures within FIB-SEM image volumes.

A flexible methodological pipeline to identify, visualize, and quantify thin subcellular neuronal processes within focused ion beam scanning electron microscopy image volumes using user-friendly open-source software packages.

Recent advances in scanning electron microscope technologies now permit the rapid three-dimensional (3D) analysis of ultrathin subcellular processes. ...

Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper

Super-resolution fluorescence microscopy is currently revolutionizing cell biology research. Its capacity to break the resolution limit of around 300 nm allows for the routine imaging of nanoscale biological complexes and processes. This increase in resolution also means that methods popular in electron microscopy, such as single-particle analysis, can readily be applied to super-resolution fluorescence microscopy. By combining this analytical approach with super-resolution optical imaging, it becomes possible to take advantage of the molecule-specific labeling capacity of fluorescence microscopy to generate structural maps of molecular elements within a metastable structure. To this end, we have developed a novel algorithm &#8212; VirusMapper &#8212; packaged as an easy-to-use, high-performance, and high-throughput ImageJ plugin. This article presents an in-depth guide to this software, showcasing its ability to uncover novel structural features in biological molecular complexes. Here, we present how to assemble compatible data and provide a step-by-step protocol on how to use this algorithm to apply single-particle analysis to super-resolution images.

This manuscript uses the Fiji-based open-source software package VirusMapper to apply single-particle analysis to super-resolution microscopy images in order to generate precise models of nanoscale structure.

Super-resolution fluorescence microscopy is currently revolutionizing cell biology research. Its capacity to break the resolution limit of around 300 nm ...

Quantitative Approaches for Studying Cellular Structures and Organelle Morphology in Caenorhabditis elegans

Defining the cellular mechanisms underlying disease is essential for the development of novel therapeutics. A strategy frequently used to unravel these mechanisms is to introduce mutations in candidate genes and qualitatively describe changes in the morphology of tissues and cellular organelles. However, qualitative descriptions may not capture subtle phenotypic differences, might misrepresent phenotypic variations across individuals in a population, and are frequently assessed subjectively. Here, quantitative approaches are described to study the morphology of tissues and organelles in the nematode Caenorhabditis elegans using laser scanning confocal microscopy combined with commercially available bio-image processing software. A quantitative analysis of phenotypes affecting synapse integrity (size and integrated fluorescence levels), muscle development (muscle cell size and myosin filament length), and mitochondrial morphology (circularity and size) was performed to understand the effects of genetic mutations on these cellular structures. These quantitative approaches are not limited to the applications described here, as they could readily be used to quantitatively assess the morphology of other tissues and organelles in the nematode, as well as in other model organisms.

Quantitative Approaches for Studying Cellular Structures and Organelle Morphology in Caenorhabditis elegans

This study outlines quantitative measurements of synaptic size and localization, muscle morphology, and mitochondrial shape in C. elegans using freely available image processing tools. This approach allows future studies in&#160;C. elegans&#160;to quantitatively compare the extent of tissue and organelle structural changes as a result of genetic mutations.

Defining the cellular mechanisms underlying disease is essential for the development of novel therapeutics. A strategy frequently used to unravel these ...

Substructure Analyzer: A User-Friendly Workflow for Rapid Exploration and Accurate Analysis of Cellular Bodies in Fluorescence Microscopy Images

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