Name: Rapid Analysis and Exploration of Fluorescence Microscopy Images (Video) | JoVE Journal
Uploaded: 2014-03-19T13:00:00.000+00:00
Duration: 11 min 41 s
Description: Learn rapid fluorescence microscopy image analysis using the PhenoRipper platform for phenotypic profiling. This method automates identification of cellular phenotypes and reduces analysis time significantly.

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5 minutes

We thank Adam Coster and all other members of the Altschuler and Wu labs for helpful feedback and discussions. This research was supported by the National Institute of Health grants R01 GM085442 and CA133253 (S.J.A.), R01 GM081549 (L.F.W.), CPRIT RP10900 (L.F.W), and the Welch Foundation I-1619 (S.J.A.) and I-1644 (L.F.W.).

1. Preparing Samples and Imaging
<ol>
	<li>Cell culture
	<ol>
		<li>Split cultures routinely and maintain at 20-70% confluence. One day prior to the experiment, grow HeLa cells in DMEM (Dulbecco&#39;s Modified Eagle Medium, see Table of Materials) and supplemented with 10% FBS (Fetal Bovine Serum) and 1x penicillin/streptomycin.</li>
		<li>On day of experiment, split cells growing in culture (about 50% confluent).</li>
		<li>Wash cells with PBS (Phosphate Buffered Saline), expose to approximately 2 ml of trypsin/EDTA (Ethylenediaminetetraacetic acid) for a few seconds. Aspirate excess trypsin/EDTA and keep cells bathed in a thin film of trypsin/EDTA in the hood at RT&#160;for ~3 min.</li>
		<li>Mechanically shake flasks twice and detach cells with serum supplemented media. Count cells and dilute to 5,000&#160;cells/50 &#181;l of media and immediately plate 50 &#956;l/well onto a 384-well glass bottom plate using an automated liquid handling robot (only 30 wells of the plate are used here).</li>
		<li>Incubate plate at RT for 30 min&#160;to minimize edge effects in wells5. After RT incubation, place plates in a 37 &#176;C/5% CO2 incubator overnight.</li>
		<li>Each drug will be assayed in duplicate. Add 25 &#181;l of drug to the corresponding wells on the plate already containing media.</li>
		<li>Fix plate with 4% paraformaldehyde solution in PBS at 24 hr&#160;post drug treatment.</li>
	</ol>
	</li>
	<li>Staining 
	The specific primary antibodies, fluorescently-labeled secondary antibodies and other fluorescent stains that were used here are listed in Table of Materials.
	<ol>
		<li>Permeabilize fixed cells with 0.2% Triton X-100 solution in TBS (Tris-buffered saline) for 3 min, and wash with TBST (Tris-buffered saline + Tween 20) 3x. Add 5% BSA (Bovine Serum Albumin) solution in TBST for blocking.</li>
		<li>Incubate plate at RT for 2&#160;hr&#160;and then wash with TBST 3x.</li>
		<li>Make primary antibody dilutions in 5% BSA in TBST (see Table of Materials) and add 10 &#181;l/well. Incubate plate at RT for 2 hr, and then wash 3x with TBST.</li>
		<li>Make secondary antibody dilutionin 5% BSA in TBST (see Table of Materials) and add 10 &#181;l/well.</li>
		<li>Incubate plate in the dark at RT for 2 hr&#160;and then wash twice with TBST.</li>
		<li>Prepare dilutions for Hoechst and phalloidin (see Table of Materials) in PBS and add 10 &#956;l/well.</li>
		<li>Incubate the plate in the dark for 30 min&#160;at RT.</li>
		<li>After 2&#160;TBST washes, cover plates in foil and store at 4 &#176;C O/N.</li>
	</ol>
	</li>
	<li>Imaging 
	Acquire images using an epifluorescence microscope using a 20X&#160;objective with 1x1 camera binning. Acquire sixteen images for each well, for a total of 480 images (x3 channels).</li>
</ol>
2. Analyzing Images
Download and install PhenoRipper from phenoripper.org, and launch PhenoRipper to begin analysis of images. PhenoRipper currently supports TIFF, PNG, and JPEG and all other formats supported by MATLAB (microscope software typically support export into TIFF).
2.1 Loading Data
<ol>
	<li>No standard naming/organization convention exists for microscopy images, which can make loading images and associating metadata (i.e. experimental annotation such as treatment type, well number and so on) the most challenging part of the workflow. Choose one of the presented methods (described below).
	<ol>
		<li>PhenoLoader: Choose this option to semi-automatically load data when the file name and/or directory structure contain enough information to group images as desired (e.g. B16\HeLa_Drug5-DAPI.png might indicate that the image is from well B16, cells are treated with the drug whose ID is 5, and the image captures the nuclear DAPI stain - with this naming convention, all images from well A1 or treated with Drug 5 could be easily grouped). The wizard will guide the user step by step to associate metadata with image groups and define the markers as follows:
		<ol>
			<li>Click on &#34;File Structure&#34; to access to the PhenoLoader window.</li>
			<li>Click on &#34;Define Root Directory&#34; to select the directory containing the image dataset. Then filter the files that are not used (Unused Files) for the analysis (optional, can be left empty).</li>
			<li>Click on &#34;Define Markers&#34; to specify the biomarkers used in the experiment and associate them with the images. Specifically, click on an image file then select a portion of the filename that uniquely identifies the biomarkers. Finally, enter a name for each biomarker.</li>
			<li>Click on &#34;Define Metadata&#34; to associate information with each image. Specifically, click on an image file, and then select a portion of the filename identifying image information (e.g.&#160;&#34;B16&#34; for the well name). A window asking for a group name (e.g.&#160;&#34;Well&#34;) will pop up, enter a name. Members of the group extracted from the filename will be shown on the group table (e.g.&#160;the full list of well names). To add additional information not present in the filename, click on the &#34;(optional) Next&#34; button. Click on &#34;Add Metadata Group&#34;, enter the additional group name (e.g.&#160;&#34;Drug&#34;) and for each value of the predefined group (e.g. &#34;Well&#34;), enter its value (e.g. &#34;Taxol&#34;). There is no limit to the number of additional groups that can be added.</li>
			<li>Finally, click on &#34;Create Metadatafile&#34; to generate the metadata file and continue the analysis. If a metadata file has been previously generated, the steps above may be avoided by directly loading the file using the option &#34;Metadata File&#8221; (described in 3 below).</li>
		</ol>
		</li>
		<li>Regular Expression Based Templates: Regular expressions are a standard approach for matching patterns in text. Choose this option when the file name and/or directory structure contain enough information to group images and the naming convention matches one of the pre-existing templates. Custom templates based on regular expressions may also be manually defined and reused. This advanced option is only recommended for users familiar with regular expressions.</li>
		<li>Metadata File: Choose this option for ultimate customizability. Define and load a comma separated values (CSV) file containing filenames and metadata associated with each image. An example of the file format is available on the download part of the web site (phenoripper.org). Simply load the file through the interface to display the information it contains.</li>
	</ol>
	</li>
	<li>Select the metadata field that defines replicates in the experiment to avoid redundant sampling. For data sets that contain replicates (e.g. all images in a well should be similar), grouping them together (e.g. by well) can improve performance. In most cases it is reasonable to choose the default option &#34;Random (don&#39;t know)&#34; which selects a random subset.</li>
</ol>
2.2 Setting Analysis Parameters
Click on &#34;Set Parameters&#34; to define the parameters required to process the dataset. The parameters to define are on the left panel. The right panel displays a merged sample of the dataset. Click on the left/right arrows to switch among different images.
<ol>
	<li>Threshold Intensity: Choose an appropriate threshold value to roughly identify the cellular portions (rather than the background, noncellular regions) of images. A threshold is precalculated by PhenoRipper, but may be adjusted to improve highlighting of cellular regions using the scrollbar available on top of the image. If a chosen threshold does not work across all images, lower the threshold so as to include noncellular regions rather than dropping cellular regions.</li>
	<li>Block Size: Choose an appropriate block size (in pixels) to grid the image. Blocks of specified size will be superimposed on the image. Adjust block size to obtain an average of 20-30 blocks/cell. To superimpose the grid over the image click on the check box in front of the &#34;Block Size&#34; field.</li>
	<li>Optional Parameters: If the auto-scaled images do not display markers with desired relative intensities (e.g. one color is saturated) or if markers need to be dropped from the analysis, use the &#34;Adjust Channel Intensity&#34; option. To identify cellular regions on a subset of markers use the &#34;Channels used for Threshold&#34; option. Default choices of other parameters are usually adequate, but if required, adjust them using the &#34;Advanced Analysis Options&#34;.</li>
	<li>Run the analysis by pressing &#34;PhenoRip&#34; on the main application panel. PhenoRipper will automatically identify recurrent block types in the images. Phenotypic profiles will be generated for each image based on the fractions of the different block types in it.</li>
</ol>
3. Exploring Results
<ol>
	<li>Explore the relationships between the phenotypic profiles of the images using the PhenoBrowser (Figure 1). The PhenoBrowser interface consists of four panels: the top left panel is a graphical representation (scatter plot) of the relative similarity between different images/conditions, the two panels on the right display sample images of selected conditions, and the final panel (bar chart) compares their phenotypic profiles.</li>
	<li>&#34;Discard Empty Frames&#34; using the &#34;Data Processing&#34; menu.</li>
	<li>Remove poor quality images: First, to study individual images turn off grouping from the &#34;Data Processing&#34; menu. Next, click through the outlier points in the scatter plot and discard low quality images (e.g. out of focus, staining artifact).</li>
	<li>Explore the data: Decide which metadata field defines a basic unit of comparison (e.g. to compare drugs, images with the same value of the metadata field &#34;drug&#34; need to be grouped together). Collapse all images with the same value of this metadata field into a single data point with &#34;Group By&#34;. For example, grouping by drug will average the images within each drug to yield one point per drug. Nearby (or distant) points in the scatter plot reflect conditions that elicit similar (or dissimilar) cellular phenotypes. Color or label points (data display menu) based on available metadata to aid interpretability. The 3D&#160;plot is rotatable by clicking on the &#34;rotatable&#34; check box and by clicking on the left button and dragging the mouse over it.</li>
	<li>Select two different points to compare them directly. The image panels will display sample images for each point, and the bar chart panel will show the block types whose occurrence frequencies are most different between the two points.</li>
	<li>Launch the clustergram from the &#34;Clustergram menu&#34; to provide a more global view of the relationship between block types and experimental conditions. By grouping both block types and conditions, this representation highlights the cellular phenotypes that best distinguish groups of conditions (Figure 5).</li>
</ol>

<ol><li>Collins, T. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((ImageJ+for+microscopy%5BTitle%5D)+AND+%22Biotechniques%22%5BJournal%5D)">ImageJ for microscopy.</a> Biotechniques. 43, 25-30 (2007).</li>
<li>Carpenter, A. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((CellProfiler%3A+image+analysis+software+for+identifying+and+quantifying+cell+phenotypes%5BTitle%5D)+AND+%22Genome+Biol%22%5BJournal%5D)">CellProfiler: image analysis software for identifying and quantifying cell phenotypes.</a> Genome Biol. 7, (2006).</li>
<li>Shamir, L., Delaney, J. D., Orlov, N., Eckley, D. M., Goldberg, I. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Pattern+Recognition+Software+and+Techniques+for+Biological+Image+Analysis%5BTitle%5D)+AND+%22PLoS+Comput.+Biol%22%5BJournal%5D)">Pattern Recognition Software and Techniques for Biological Image Analysis.</a> PLoS Comput. Biol. 6, (2010).</li>
<li>Rajaram, S., Pavie, B., Wu, L. F., Altschuler, S. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((PhenoRipper%3A+software+for+rapidly+profiling+microscopy+images%5BTitle%5D)+AND+%22Nat.+Methods%22%5BJournal%5D)">PhenoRipper: software for rapidly profiling microscopy images.</a> Nat. Methods. 9, 635-637 (2012).</li>
<li>Lundholt, B. K., Scudder, K. M., Pagliaro, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+simple+technique+for+reducing+edge+effect+in+cell-based+assays%5BTitle%5D)+AND+%22J.+Biomol.+Screen%22%5BJournal%5D)">A simple technique for reducing edge effect in cell-based assays.</a> J. Biomol. Screen. 8, 566-570 (2003).</li>
<li>Slack, M. D., Martinez, E. D., Wu, L. F., Altschuler, S. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Characterizing+heterogeneous+cellular+responses+to+perturbations%5BTitle%5D)+AND+%22Proc.+Natl.+Acad.+Sci.+U.S.A%22%5BJournal%5D)">Characterizing heterogeneous cellular responses to perturbations.</a> Proc. Natl. Acad. Sci. U.S.A. 105, 19306-19311 (2008).</li>
<li>Wolf, D. E., Samarasekera, C., Swedlow, J. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Quantitative+analysis+of+digital+microscope+images%5BTitle%5D)+AND+%22Methods+Cell.+Biol%22%5BJournal%5D)">Quantitative analysis of digital microscope images.</a> Methods Cell. Biol. 81, 365-396 (2007).</li>
<li>Zwier, J. M., Van Rooij, G. J., Hofstraat, J. W., Brakenhoff, G. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Image+calibration+in+fluorescence+microscopy%5BTitle%5D)+AND+%22J+Microsc%22%5BJournal%5D)">Image calibration in fluorescence microscopy.</a> J Microsc. 216, 15-24 (2004).</li>
<li>Shamir, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Wndchrm+-+an+open+source+utility+for+biological+image+analysis%5BTitle%5D)+AND+%22Source+Code+Biol.+Med%22%5BJournal%5D)">Wndchrm - an open source utility for biological image analysis.</a> Source Code Biol. Med. 3, 13(2008).</li>
<li>Hamilton, N. A., Wang, J. T., Kerr, M. C., Teasdale, R. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Statistical+and+visual+differentiation+of+subcellular+imaging%5BTitle%5D)+AND+%22BMC+Bioinform%22%5BJournal%5D)">Statistical and visual differentiation of subcellular imaging.</a> BMC Bioinform. 10, 94(2009).</li>
<li>Huang, K., Murphy, R. F. Automated classification of subcellular patterns in multicell images without segmentation into single cells. IEEE Int. Symp. Biomed. Imag. Macro Nano, 2004 Apr 15-18, 2, 1139-1142 (2004).</li>
</ol>

The authors have nothing to disclose.

The workflow described here allows fast and easy characterization and comparison of microscopy images. We first demonstrated how this workflow can help experiment optimization, for example by quickly performing quality control on microscopy images. Next, we demonstrated its potential for analyzing high-throughput screening data: we were able to group drugs based on their mechanism of action. The grouping of drugs was comparable to that found using more complex methods6, even though PhenoRipper was orders of ma...

Over the past decade, rapid advances in imaging technology have given many labs the ability to perform high-throughput microscopy. The challenge has now shifted from one of imaging to one of analysis. How can we characterize, compare, and explore the torrent of complex yet subtle phenotypes generated by a typical high-throughput imaging screen?
A number of image-analysis and informatics platforms have given users sophisticated toolboxes1-3 for extracting biological information from large collections of images. Yet many significant obstacles remain in analyzing data from high-throughput image-based screens. Difficulties involved with choosing appropriate characterizations of the images and fine tuning of algorithmic parameters (to the system of interest) often result in long setup times, particularly for users without image-analysis expertise. In particular, cell segmentation, the process of identifying individual cells in an image, is a major bottleneck in this regard. Segmentation can be highly sensitive to experimental parameters such as cell type, cell density and bio-markers, and frequently requires repeated adjustment for a large data set. For these reasons, image analysis is often an independent, terminal step performed by specialists rather than an integrated part of the experimental workflow. This precludes the rapid feedback cycle between analysis and experiment, a crucial part of assay development.
Here we describe an alternative workflow that is optimized for high-throughput fluorescence microscopy and avoids many of the aforementioned difficulties. To this end, we make use of PhenoRipper4, an open-source software platform for rapid exploration and analysis of microscopy images. Significantly, PhenoRipper avoids many of the challenges involved with cell segmentation by not attempting to identify single cells. Instead, PhenoRipper divides images into pixel blocks of subcellular size and characterizes images in terms of the blocks they contain. Specifically, PhenoRipper profiles blocks in terms of marker-colocalization-based features and automatically identifies4 the most representative block types in the training images. PhenoRipper then assigns to each block the most similar representative block type, and finally characterizes images based on the types of the blocks they contain.
Compared to more traditional single-cell-based approaches, this approach requires minimal fine tuning and expertise. As such, users only need to set two parameters: the block size and a threshold intensity (to discard noncellular portions of an image), both of which are set with graphical feedback. Importantly, the workflow described here has been shown4 to scale easily to much larger data sets, where the speed and minimal fine tuning provides large time and reliability gains. In practice, we find that this approach reduces the time required both for setup and running by multiple orders of magnitude4.
The primary output of PhenoRipper is a visual representation of image similarity, which enables the user to identify groups of experimental conditions that cause cells to display similar phenotypes. The groupings PhenoRipper finds typically have comparable &#34;biological interpretability&#34; to those generated by more time-consuming, cell-based methods4. In practice, this means that often, within half an hour of performing a typical 96-well fluorescence microscopy experiment, an experimenter without prior image-analysis experience can get a reliable readout about the performance of his or her experiment. The experimenter can then start exploring the relationships between different experimental conditions and relating these relationships to image phenotypes.
We demonstrate this workflow here by analyzing the effects of drugs in distinct mechanistic classes on HeLa cells stained for DNA and cytoskeletal markers. Images of cells treated with the same class of drugs were grouped together, and poor quality images (staining artifacts, out of focus cells and so on) appeared as outliers, making them easy to identify and potentially discard.
While this workflow is useful for a large number of image-based assays, there are clearly many situations where a different approach could potentially be more informative. For example, the output from PhenoRipper is primarily a relative comparison of fluorescence patterns across experimental conditions. If an absolute characterization of a specific single cell trait was required instead (for example the number of speckles in a cell), a single-cell-based analysis would be required. Nonetheless, even in this type of situation, PhenoRipper is likely to detect changes in these single-cell phenotypes and therefore be a useful tool for assay optimization.

<table><tbody><tr><td>DMEM, High Glucose</td><td>Invitrogen</td><td>11965</td><td>High Glucose, L-Glutamine</td></tr><tr><td>Marker Hoechst 33342</td><td>Invitrogen</td><td>H1399</td><td>DNA stain, dilution 1:2,000 of 5 mg/ml stock</td></tr><tr><td>Marker phalloidin-Alexa Fluor 488</td><td>Invitrogen</td><td>A12379</td><td>Conjugated phallotoxin, dilution 1:200</td></tr><tr><td>Primary Antibody &amp;alpha;-tubulin</td><td>Sigma</td><td>T9026</td><td>Primary Ab, mouse monoclonal, dilution 1:200</td></tr><tr><td>Secondary Antivody anti-Mouse TRITC</td><td>Jackson</td><td>115-025-166</td><td>Conjugated secondary Ab, 0.5 mg in 1 ml PBS + 1 ml glycerol</td></tr><tr><td>Aldosterone</td><td></td><td></td><td>concentration used: 0.2 &amp;mu;M</td></tr><tr><td>Apicidin</td><td></td><td></td><td>concentration used: 20 &amp;mu;M</td></tr><tr><td>Colchicine</td><td></td><td></td><td>concentration used: 0.16 &amp;mu;M</td></tr><tr><td>Cortisol</td><td></td><td></td><td>concentration used: 0.2 &amp;mu;M</td></tr><tr><td>Dexamethasone</td><td></td><td></td><td>concentration used: 0.2 &amp;mu;M</td></tr><tr><td>Docetaxel</td><td></td><td></td><td>concentration used: 0.2 &amp;mu;M</td></tr><tr><td>M344</td><td></td><td></td><td>concentration used: 20 &amp;mu;M</td></tr><tr><td>MS-275</td><td></td><td></td><td>concentration used: 5 &amp;mu;M</td></tr><tr><td>Nocodazole</td><td></td><td></td><td>concentration used: 0.3 &amp;mu;M</td></tr><tr><td>Prednisolone</td><td></td><td></td><td>concentration used: 0.2 &amp;mu;M</td></tr><tr><td>RU486</td><td></td><td></td><td>concentration used: 0.2 &amp;mu;M</td></tr><tr><td>Scriptaid</td><td></td><td></td><td>concentration used: 70 &amp;mu;M</td></tr><tr><td>Taxol</td><td></td><td></td><td>concentration used: 0.3 &amp;mu;M</td></tr><tr><td>Trichostatin A</td><td></td><td></td><td>concentration used: 0.2 &amp;mu;M</td></tr><tr><td>Vinorelbine</td><td></td><td></td><td>concentration used: 0.3 &amp;mu;M</td></tr></tbody></table>

rapid analysis and exploration of fluorescence microscopy images

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 tested the ability of this workflow to group drugs based on their mechanism of action. HeLa cells were seeded in 30 wells of a 384-well plate and stained for DNA/actin/α-tubulin. The specific primary antibodies, fluorescently-labeled secondary antibodies, and other fluorescent stains that were used are listed in Table of Materials. Wells were treated with 15 drugs belonging to three mechanistic classes (histone deacetylase inhibiting, microtubule targeting and DNA damaging) for 24 hr and imaged using an epifluore...

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 ...

The overall goal of this procedure is to use an image analysis platform to rapidly analyze and explore fluorescent microscopy images. This is accomplished by first seeding cells into a plate, adding perturbations to each well and incubating the plate. Images are then acquired using an automated fluorescence microscope, and the images are semi automatically loaded onto the pheno ripper image analysis platform using the pheno loader tool.After setting the analysis parameters, pheno ripper automatically identifies recurrent block types in the images and generates phenotypic profiles for each image that can be explored using the pheno browser. Ultimately, the final cluster gram shows relationships between different conditions and image features, allowing for the comparison of complex yet subtle phenotypes generated by the high throughput imaging screen. The main advantage of this technique over existing methods that require automated identification of individual cells is that it is much faster and easier to use.This method can help answer key questions in the biomedical field, such as how different drugs affect cellular processes. This procedure begins with cell culture and cell fixing as described in the text protocol stain the fixed cells using the specific primary antibodies, fluorescently labeled secondary antibodies and other fluorescent stains that can be found in the text protocol. After cell staining, acquire images using an epi fluorescence microscope.In this example, we used 20 x magnification with one by one camera.Benning. We acquired 16 images for each well for a total of 480 images. To analyze images, first, download and install pheno ripper from pheno ripper.org and launch pheno ripper. To begin analysis of images to semi automatically load images and associated metadata, click file structure to access pheno loader in pheno loader. Select the directory containing the image data set and specify the file extension of the images if required.Filter files that are not used for analysis. Click on define markers to specify the biomarkers used in the experiment and associate them with the images. Specifically click on an image file and then select a portion of the file path that uniquely identifies the biomarkers.Finally, enter a name for each biomarker. The next step is to click on define metadata. To associate information with each image, click on an image file and then select a portion of the file path identifying image information.A window asking for a group name will pop up. Enter a name. Members of the group extracted from the file name will be shown on the group table.This information can be used during visualization of the analysis results. To add additional information not present in the file name, click on the add additional information button, then select Add metadata group, and enter the additional group name. Followed by each value for the predefined group, there is no limit to the number of additional groups that can be added.Finally, click on create metadata file to generate a pheno ripper readable annotation of the data, select the metadata field that defines replicates in the experiment to avoid redundant sampling. For a data set that contains replicates, grouping them together can improve performance. Next, click on set parameters to define the parameters required to process the data set.The parameters to define are on the left panel. The right panel displays emerge. Sample of the data set.Click on the left or right. Arrows to switch among different images is choose an appropriate threshold value to roughly identify the cellular portions of the images. A threshold is pre-calculated by pheno ripper, but may be adjusted to improve highlighting of cellular regions using the scroll bar available on top of the image.If a chosen threshold does not work across all images, lower the threshold so as to include non cellular regions rather than dropping cellular regions. Next, choose an appropriate block size measured in pixels to subdivide the image into a grid of blocks. Adjust the block size to obtain an average of 20 to 30 blocks per cell to superpose the grid over the image.Click on the checkbox in front of the block size field. If the auto scaled images do not display markers with the desired relative intensities or if markers need to be dropped from the analysis, use the adjust channel intensity option to identify cellular regions on a subset of markers. Select the channels used for threshold option default.Choices of other parameters are usually adequate, but if required, adjust them. Using the advanced analysis options, run the analysis by pressing pheno rip on the main application panel. Pheno ripper will automatically identify recurrent block types in the images.Phenotypic profiles will be generated for each image based on the fractions of the different block types in it. Explore the relationships between the phenotypic profiles of the images using the pheno browser. The pheno browser interface consists of four panels.The top left panel is a graphical representation of the relative similarity between different images and experimental conditions. The two panels on the right display sample images of the selected conditions. The final panel compares the phenotypic profiles of the selected conditions.First turn off grouping from the data processing menu to examine individual images. Next, click through the outlier points in the scatter plot and discard low quality images. Decide which metadata field defines a basic unit of comparison.For example, to compare drugs group together images with the same value of the metadata field drug. Each point now represents a single drug using the data display menu, color or label points based on available metadata to aid interpretability. The distance between points reflects the relative phenotypic similarity of the corresponding experimental conditions.Closer points suggest more similarity. The three dimensional plot is rotatable by selecting the rotatable checkbox. Drag the mouse over the graph while holding down the left mouse button.Select two different points to compare them directly. The image panels will display sample images for each and the bar chart panel will show the block types that best distinguish the two points. Launch the cluster gram from the cluster gram menu to provide a more global view of the relationship between block types and experimental conditions by grouping both block types and conditions.This representation highlights the cellular phenotypes that best distinguish groups of conditions. Pheno ripper was used to analyze hela cells that were fluorescently stained for DNA Acton and alpha tubulin and imaged after treatment with histone deacetylase, inhibiting microtubule targeting and DNA damaging drugs. The foreground threshold of these images was set to 5%and the block size to 20 pixels analysis of this dataset took approximately 10 minutes on a standard desktop computer.The pheno browser interface was used to explore the results. The relative phenotypic similarities between images are visualized using multidimensional scaling. Each point represents a single image colored by its drug's mechanism of action.This plot shows that phenotypic profiles can be used to group drugs by their mechanism of action. Imaging artifacts such as pore staining and empty frames were easy to identify since they stood out as clear outliers. A cluster gram of the data relates this grouping to the phenotypic profiles here.Each row represents a single drug and each column a block type. The gray scale values reflect the phenotypic profiles. A lighter value indicates a higher fraction of that block type hierarchical clustering largely separates the different drugs by drug class.Once mastered, this technique can be done in 10 minutes if it is performed properly. Following this procedure, other methods such as IRA profiling can be performed. This will help answer additional questions such as how different genes affect similar pathways.

rapid-analysis-and-exploration-of-fluorescence-microscopy-images

Research

JoVE Journal

Biology

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.

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

Autophagy is a ubiquitous process that enables cells to degrade and recycle proteins and organelles. We apply advanced fluorescence microscopy to visualize and quantify the small, but essential, physical changes associated with the induction of autophagy, including the formation and distribution of autophagosomes and lysosomes, and their fusion into autolysosomes.

Quantitative Analysis of Autophagy using Advanced 3D Fluorescence Microscopy

We present a simple protocol to obtain fluorescence microscopy movies of growing yeast cells, and a GUI-based software package to extract single-cell time series data. The analysis includes automated lineage and division time assignment integrated with visual inspection and manual curation of tracked data.

Acquiring Fluorescence Time-lapse Movies of Budding Yeast and Analyzing Single-cell Dynamics using GRAFTS

Presented is a flexible informatics workflow enabling multiplexed image-based analysis of fluorescently labeled cells. The workflow quantifies nuclear and cytoplasmic markers and computes marker translocation between these compartments. Procedures are provided for perturbation of cells using siRNA and reliable methodology for marker detection by indirect immunofluorescence in 96-well formats.

Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software

Accurate quantification of vesicular trafficking events often provides key insights into roles for specific proteins and the effects of mutations. This paper presents methods for using superecliptic pHluorin, a pH-sensitive GFP variant, as a tool for quantification of endocytic events in living cells using quantitative fluorescence microscopy and flow cytometry.

Applications of pHluorin for Quantitative, Kinetic and High-throughput Analysis of Endocytosis in Budding Yeast

We present an open source high content analysis (HCA) instrument utilizing automated fluorescence lifetime imaging (FLIM) for assaying protein interactions using Förster resonance energy transfer (FRET) based readouts. Data acquisition for this openFLIM-HCA instrument is controlled by software written in µManager and data analysis is undertaken in FLIMfit.

Open Source High Content Analysis Utilizing Automated Fluorescence Lifetime Imaging Microscopy

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 in Fluorescently-labeled Tissues Using a Widefield High-content Analysis System

Spectral imaging has become a reliable solution for identification and separation of multiple fluorescence signals in a single sample and can readily distinguish signals of interest from background or autofluorescence. Excitation-scanning hyperspectral imaging improves on this technique by decreasing the necessary image acquisition time while simultaneously increasing the signal-to-noise ratio.

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

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.

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

Here, a protocol is presented for optically extracting and cataloging innate cellular fluorescence signatures (i.e., cellular autofluorescence) from every individual live cell distributed in a three-dimensional space. This method is suitable for studying the innate fluorescence signature of diverse biological systems at a single-cell resolution, including cells from bacteria, fungi, yeasts, plants, and animals.

Reconstruction of Single-Cell Innate Fluorescence Signatures by Confocal Microscopy

An accessible, open-source microfluidic workflow is presented for the parallelized analysis of plasmid retention in bacteria. By employing fluorescence microscopy to quantify plasmid presence in single-cell microcolonies within gel microdroplets, this method provides a precise, accessible, and scalable alternative to traditional plate counting.

Plasmid Stability Analysis with Open-Source Droplet Microfluidics

Learn rapid fluorescence microscopy image analysis using the PhenoRipper platform for phenotypic profiling. This method automates identification of cellular phenotypes and reduces analysis time significantly.

Rapid Analysis and Exploration of Fluorescence Microscopy Images

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