Name: substructure analyzer: a user-friendly workflow for rapid exploration and accurate analysis of cellular bodies in fluorescence microscopy images
Uploaded: 2020-07-15T14:00:00
Description: 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

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).

NOTE: User-friendly tutorials are available on Icy&#8217;s website http://icy.bioimageanalysis.org.
1. Download Icy and the Substructure Analyzer protocol
<ol>
	<li>Download Icy from the Icy website (http://icy.bioimageanalysis.org/download) and download the Substructure Analyzer protocol: http://icy.bioimageanalysis.org/protocols?sort=latest. 
	NOTE: If us...

<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. 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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. 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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 border="0" cellpadding="0" cellspacing="0" style="width:1105px;" width="1104">
	<colgroup>
		<col />
		<col />
		<col />
		<col />
	</colgroup>
	<tbody>
		<tr height="40">
			<td height="40" style="height:40px;width:313px;">16% Formaldehyde solution (w/v) methanol free</td>
			<td style="width:183px;">Thermo Fisher Scientific</td>
			<td style="width:137px;">28908</td>
			<td style="width:472px;">to fix the cells</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:313px;">Alexa Fluor 488 of goat anti-rabbit</td>
			<td>Thermo Fisher Scientific</td>
			<td style="width:137px;">A-11008</td>
			<td>fluorescent secondary antibody</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:313px;">Alexa Fluor 555 of goat anti-mouse</td>
			<td>Thermo Fisher Scientific</td>
			<td>A-21425</td>
			<td>fluorescent secondary antibody</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:313px;">Alexa Fluor 555 Phalloidin</td>
			<td>Thermo Fisher Scientific</td>
			<td style="width:137px;">A34055</td>
			<td>fluorescent secondary antibody</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:313px;">Bovine serum albumin standard (BSA)</td>
			<td style="width:183px;">euromedex</td>
			<td style="width:137px;">04-100-812-E</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">DMEM</td>
			<td>Sigma-Aldrich</td>
			<td style="width:137px;">D5796-500ml</td>
			<td>cell culture medium</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Duolink In Situ Mounting Medium with DAPI</td>
			<td>Sigma-Aldrich</td>
			<td>DUO82040-5ML</td>
			<td>mounting medium</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Human: HeLa S3 cells</td>
			<td>IGBMC, Strasbourg, France</td>
			<td style="width:137px;"></td>
			<td>cell line used to perform the experiments</td>
		</tr>
		<tr height="24">
			<td height="24" style="height:24px;width:313px;">Hydrogen peroxide solution 30% (H2O2)</td>
			<td>Sigma-Aldrich</td>
			<td style="width:137px;">H1009-100ml</td>
			<td>used as a stressing agent</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Lipofectamine 2000 Reagent</td>
			<td>Thermo Fisher Scientific</td>
			<td style="width:137px;">11668-019</td>
			<td>transfection reagent</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:313px;">Mouse monoclonal anti-coilin</td>
			<td style="width:183px;">abcam</td>
			<td style="width:137px;">ab11822</td>
			<td>Coilin-specific antibody</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:313px;">Nikon Optiphot-2 fluorescence microscope</td>
			<td style="width:183px;">Nikon</td>
			<td style="width:137px;"></td>
			<td>epifluoresecence microscope</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Opti-MEM I Reduced Serum Medium</td>
			<td>Thermo Fisher Scientific</td>
			<td>31985062</td>
			<td>transfection medium</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:313px;">PBS pH 7.4 (10x)</td>
			<td style="width:183px;">gibco</td>
			<td style="width:137px;">70011-036</td>
			<td>to wash the cells</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:313px;">Rabbit polyclonal anti-53BP1</td>
			<td>Thermo Fisher Scientific</td>
			<td>PA1-16565</td>
			<td>53BP1-specific antibody</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Rabbit polyclonal anti-EDC4</td>
			<td>Sigma-Aldrich</td>
			<td>SAB4200114</td>
			<td>EDC4-specific antibody</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:313px;">Triton X-100</td>
			<td style="width:183px;">Roth</td>
			<td style="width:137px;">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 ...

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