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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ImageJ+for+microscopy.">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/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 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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pattern+Recognition+Software+and+Techniques+for+Biological+Image+Analysis.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=PhenoRipper:+software+for+rapidly+profiling+microscopy+images.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+simple+technique+for+reducing+edge+effect+in+cell-based+assays.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Characterizing+heterogeneous+cellular+responses+to+perturbations.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+analysis+of+digital+microscope+images.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Image+calibration+in+fluorescence+microscopy.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Wndchrm+-+an+open+source+utility+for+biological+image+analysis.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Statistical+and+visual+differentiation+of+subcellular+imaging.">Statistical and visual differentiation of subcellular imaging.</a> BMC Bioinform. 10, 94 (2009).</li><li>Huang, K., Murphy, R. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+classification+of+subcellular+patterns+in+multicell+images+without+segmentation+into+single+cells.">Automated classification of subcellular patterns in multicell images without segmentation into single cells.</a> 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 border="0" cellpadding="0" cellspacing="0" width="855">
	<colgroup>
		<col />
		<col />
		<col />
		<col />
	</colgroup>
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">DMEM, High Glucose</td>
			<td style="width:116px;">Invitrogen</td>
			<td align="right" style="width:119px;">11965</td>
			<td style="width:404px;">High Glucose, L-Glutamine</td>
		</tr>
		<tr height="22">
			<td height="22" style="height:22px;width:216px;"></td>
			<td style="width:116px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:216px;">Marker Hoechst 33342</td>
			<td style="width:116px;">Invitrogen&#160;</td>
			<td style="width:119px;">H1399</td>
			<td style="width:404px;">DNA stain, dilution 1:2000 of 5mg/ml stock</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:216px;">Marker phalloidin-Alexa488</td>
			<td style="width:116px;">Invitrogen&#160;</td>
			<td style="width:119px;">A12379</td>
			<td style="width:404px;">Conjugated phallotoxin, dilution 1:200</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:216px;">Primary Antibody &#945;-tubulin</td>
			<td style="width:116px;">Sigma&#160;</td>
			<td style="width:119px;">T9026</td>
			<td style="width:404px;">Primary Ab, mouse monoclonal, dilution 1:200</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:216px;">Secondary Antivody anti-Mouse TRITC</td>
			<td style="width:116px;">Jackson&#160;</td>
			<td style="width:119px;">115-025-166</td>
			<td style="width:404px;">Conjugated secondary Ab, 0.5mg in 1ml PBS + 1ml glycerol</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:216px;">Aldosterone</td>
			<td></td>
			<td></td>
			<td style="width:404px;">concentration used :0.2 &#956;M</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Apicidin</td>
			<td style="width:116px;"></td>
			<td style="width:119px;"></td>
			<td style="width:404px;">concentration used :20 &#956;M</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Colchicine</td>
			<td style="width:116px;"></td>
			<td style="width:119px;"></td>
			<td style="width:404px;">concentration used :0.16 &#956;M</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Cortisol</td>
			<td style="width:116px;"></td>
			<td style="width:119px;"></td>
			<td style="width:404px;">concentration used :0.2 &#956;M</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Dexamethasone</td>
			<td style="width:116px;"></td>
			<td style="width:119px;"></td>
			<td style="width:404px;">concentration used :0.2 &#956;M</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Docetaxel</td>
			<td style="width:116px;"></td>
			<td style="width:119px;"></td>
			<td style="width:404px;">concentration used :0.2 &#956;M</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">M344</td>
			<td style="width:116px;"></td>
			<td style="width:119px;"></td>
			<td style="width:404px;">concentration used :20 &#956;M</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">MS-275</td>
			<td style="width:116px;"></td>
			<td style="width:119px;"></td>
			<td style="width:404px;">concentration used :5 &#956;M</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Nocodazole</td>
			<td style="width:116px;"></td>
			<td style="width:119px;"></td>
			<td style="width:404px;">concentration used :0.3 &#956;M</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Prednisolone</td>
			<td style="width:116px;"></td>
			<td style="width:119px;"></td>
			<td style="width:404px;">concentration used :0.2 &#956;M</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">RU486</td>
			<td style="width:116px;"></td>
			<td style="width:119px;"></td>
			<td style="width:404px;">concentration used :0.2 &#956;M</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Scriptaid</td>
			<td style="width:116px;"></td>
			<td style="width:119px;"></td>
			<td style="width:404px;">concentration used :70 &#956;M</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Taxol</td>
			<td style="width:116px;"></td>
			<td style="width:119px;"></td>
			<td style="width:404px;">concentration used :0.3 &#956;M</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Trichostatin A</td>
			<td style="width:116px;"></td>
			<td style="width:119px;"></td>
			<td style="width:404px;">concentration used :0.2 &#956;M</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Vinorelbine</td>
			<td style="width:116px;"></td>
			<td style="width:119px;"></td>
			<td style="width:404px;">concentration used :0.3 &#956;M</td>
		</tr>
	</tbody>
</table>

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/&#945;-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&#160;and imaged using an...

Watch this Scientific Journal Video about Rapid Analysis and Exploration of Fluorescence Microscopy Images at JoVE.com

Rapid Analysis and Exploration of Fluorescence Microscopy Images

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 ...

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JoVE Journal

Biology

12.1K Views.  UT Southwestern Medical Center. Here we describe a workflow for rapidly analyzing and exploring collections of fluorescence microscopy images using PhenoRipper, a recently developed image-analysis platform.

Video: Rapid Analysis and Exploration of Fluorescence Microscopy Images

Time-lapse Imaging of Mitosis After siRNA Transfection

Changes in cellular organization and chromosome dynamics that occur during mitosis are tightly coordinated to ensure accurate inheritance of genomic and cellular content. Hallmark events of mitosis, such as chromosome movement, can be readily tracked on an individual cell basis using time-lapse fluorescence microscopy of mammalian cell lines expressing specific GFP-tagged proteins. In combination with RNAi-based depletion, this can be a powerful method for pinpointing the stage(s) of mitosis where defects occur after levels of a particular protein have been lowered. In this protocol, we present a basic method for assessing the effect of depleting a potential mitotic regulatory protein on the timing of mitosis. Cells are transfected with siRNA, placed in a stage-top incubation chamber, and imaged using an automated fluorescence microscope. We describe how to use software to set up a time-lapse experiment, how to process the image sequences to make either still-image montages or movies, and how to quantify and analyze the timing of mitotic stages using a cell-line expressing mCherry-tagged histone H2B. Finally, we discuss important considerations for designing a time-lapse experiment. This strategy is complementary to other approaches and offers the advantages of 1) sensitivity to changes in kinetics that might not be observed when looking at cells as a population and 2) analysis of mitosis without the need to synchronize the cell cycle using drug treatments. The visual information from such imaging experiments not only allows the sub-stages of mitosis to be assessed, but can also provide unexpected insight that would not be apparent from cell cycle analysis by FACS.

Here we describe a basic protocol to image and quantify the mitotic timing of live mammalian tissue culture cells after siRNA transfection.

Changes in cellular organization and chromosome dynamics that occur during mitosis are tightly coordinated to ensure accurate inheritance of genomic and ...

Cell Electrofusion Visualized with Fluorescence Microscopy

Cell electrofusion is a safe, non-viral and non-chemical method that can be used for preparing hybrid cells for human therapy. Electrofusion involves application of short high-voltage electric pulses to cells that are in close contact. Application of short, high-voltage electric pulses causes destabilization of cell plasma membranes. Destabilized membranes are more permeable for different molecules and also prone to fusion with any neighboring destabilized membranes. Electrofusion is thus a convenient method to achieve a non-specific fusion of very different cells in vitro. In order to obtain fusion, cell membranes, destabilized by electric field, must be in a close contact to allow merging of their lipid bilayers and consequently their cytoplasm. In this video, we demonstrate efficient electrofusion of cells in vitro by means of modified adherence method. In this method, cells are allowed to attach only slightly to the surface of the well, so that medium can be exchanged and cells still preserve their spherical shape. Fusion visualization is assessed by pre-labeling of the cytoplasm of cells with different fluorescent cell tracker dyes; half of the cells are labeled with orange CMRA and the other half with green CMFDA. Fusion yield is determined as the number of dually fluorescent cells divided with the number of all cells multiplied by two.

In this video we demonstrate efficient electrofusion of cells in vitro by means of modified adherence method using electroporation and the subsequent detection of fused cells visualization with fluorescence microscopy.

Cell electrofusion is a safe, non-viral and non-chemical method that can be used for preparing hybrid cells for human therapy. Electrofusion involves ...

Quantitative Live Cell Fluorescence-microscopy Analysis of Fission Yeast

Several microscopy techniques are available today that can detect a specific protein within the cell. During the last decade live cell imaging using fluorochromes like Green Fluorescent Protein (GFP) directly attached to the protein of interest has become increasingly popular 1. Using GFP and similar fluorochromes the subcellular localisations and movements of proteins can be detected in a fluorescent microscope. Moreover, also the subnuclear localisation of a certain region of a chromosome can be studied using this technique. GFP is fused to the Lac Repressor protein (LacR) and ectopically expressed in the cell where tandem repeats of the lacO sequence has been inserted into the region of interest on the chromosome2. The LacR-GFP will bind to the lacO repeats and that area of the genome will be visible as a green dot in the fluorescence microscope. Yeast is especially suited for this type of manipulation since homologous recombination is very efficient and thereby enables targeted integration of the lacO repeats and engineered fusion proteins with GFP 3. Here we describe a quantitative method for live cell analysis of fission yeast. Additional protocols for live cell analysis of fission yeast can be found, for example on how to make a movie of the meiotic chromosomal behaviour 4. In this particular experiment we focus on subnuclear organisation and how it is affected during gene induction. We have labelled a gene cluster, named Chr1, by the introduction of lacO binding sites in the vicinity of the genes. The gene cluster is enriched for genes that are induced early during nitrogen starvation of fission yeast 5. In the strain the nuclear membrane (NM) is labelled by the attachment of mCherry to the NM protein Cut11 giving rise to a red fluorescent signal. The Spindle Pole body (SPB) compound Sid4 is fused to Red Fluorescent Protein (Sid4-mRFP) 6. In vegetatively growing yeast cells the centromeres are always attached to the SPB that is embedded in the NM 7. The SPB is identified as a large round structure in the NM. By imaging before and 20 minutes after depletion of the nitrogen source we can determine the distance between the gene cluster (GFP) and the NM/SPB. The mean or median distances before and after nitrogen depletion are compared and we can thus quantify whether or not there is a shift in subcellular localisation of the gene cluster after nitrogen depletion.

The fission yeast, Schizosaccharomyces pombe, is a good model system to study basic cellular processes. Here we describe a method to perform quantitative live cell analysis of fission yeast. In this particular experiment we focus on organisation of the genome within the cell nucleus, but the method can also be used to study cytosolic factors.

Several microscopy techniques are available today that can detect a specific protein within the cell. During the last decade live cell imaging using ...

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 ...

Quantitative Analysis of Autophagy using Advanced 3D Fluorescence Microscopy

Prostate cancer is the leading form of malignancies among men in the U.S. While surgery carries a significant risk of impotence and incontinence, traditional chemotherapeutic approaches have been largely unsuccessful. Hormone therapy is effective at early stage, but often fails with the eventual development of hormone-refractory tumors. We have been interested in developing therapeutics targeting specific metabolic deficiency of tumor cells. We recently showed that prostate tumor cells specifically lack an enzyme (argininosuccinate synthase, or ASS) involved in the synthesis of the amino acid arginine1. This condition causes the tumor cells to become dependent on exogenous arginine, and they undergo metabolic stress when free arginine is depleted by arginine deiminase (ADI)1,10. Indeed, we have shown that human prostate cancer cells CWR22Rv1 are effectively killed by ADI with caspase-independent apoptosis and aggressive autophagy (or macroautophagy)1,2,3. Autophagy is an evolutionarily-conserved process that allows cells to metabolize unwanted proteins by lysosomal breakdown during nutritional starvation4,5. Although the essential components of this pathway are well-characterized6,7,8,9, many aspects of the molecular mechanism are still unclear - in particular, what is the role of autophagy in the death-response of prostate cancer cells after ADI treatment? In order to address this question, we required an experimental method to measure the level and extent of autophagic response in cells - and since there are no known molecular markers that can accurately track this process, we chose to develop an imaging-based approach, using quantitative 3D fluorescence microscopy11,12.
Using CWR22Rv1 cells specifically-labeled with fluorescent probes for autophagosomes and lysosomes, we show that 3D image stacks acquired with either widefield deconvolution microscopy (and later, with super-resolution, structured-illumination microscopy) can clearly capture the early stages of autophagy induction. With commercially available digital image analysis applications, we can readily obtain statistical information about autophagosome and lysosome number, size, distribution, and degree of colocalization from any imaged cell. This information allows us to precisely track the progress of autophagy in living cells and enables our continued investigation into the role of autophagy in cancer chemotherapy.

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.

Prostate cancer is the leading form of malignancies among men in the U.S. While surgery carries a significant risk of impotence and incontinence, ...

Analysis of Autophagy in Penicillium chrysogenum by Using Starvation Pads in Combination With Fluorescence Microscopy

The study of cellular quality control systems has emerged as a highly dynamic and relevant field of contemporary research. It has become clear that cells possess several lines of defense against damage to biologically relevant molecules like nucleic acids, lipids and proteins. In addition to organelle dynamics (fusion/fission/motility/inheritance) and tightly controlled protease activity, the degradation of surplus, damaged or compromised organelles by autophagy (cellular &#8216;self-eating&#8217;) has received much attention from the scientific community. The regulation of autophagy is quite complex and depends on genetic and environmental factors, many of which have so far not been elucidated. Here a novel method is presented that allows the convenient study of autophagy in the filamentous fungus Penicillium chrysogenum. It is based on growth of the fungus on so-called &#8216;starvation pads&#8217; for stimulation of autophagy in a reproducible manner. Samples are directly assayed by microscopy and evaluated for autophagy induction / progress. The protocol presented here is not limited for use with P. chrysogenum and can be easily adapted for use in other filamentous fungi.

Analysis of Autophagy in Penicillium chrysogenum by Using Starvation Pads in Combination With Fluorescence Microscopy

A convenient and powerful method for studying autophagy in Penicillium chrysogenum by using starvation pads (mixtures of agarose and tap water in a microscope slide containing a central cavity) is presented here.

The study of cellular quality control systems has emerged as a highly dynamic and relevant field of contemporary research. It has become clear that cells ...

Identification of Kinesin-1 Cargos Using Fluorescence Microscopy

Fluorescence microscopy is employed to identify Kinesin-1 cargos. Recently, the heavy chain of Kinesin-1 (KIF5B) was shown to transport the nuclear transcription factor c-MYC for proteosomal degradation in the cytoplasm. The method described here involves the study of a motorless KIF5B mutant for fluorescence microscopy. The wild-type and motorless KIF5B proteins are tagged with the fluorescent protein tdTomato. The wild-type tdTomato-KIF5B appears homogenously in the cytoplasm, while the motorless tdTomato-KIF5B mutant forms aggregates in the cytoplasm. Aggregation of the motorless KIF5B mutant induces aggregation of its cargo c-MYC in the cytoplasm. Hence, this method provides a visual means to identify the cargos of Kinesin-1. A similar strategy can be utilized to identify cargos of other motor proteins.

Here, a protocol is presented to identify Kinesin-1 cargos. A motorless mutant of the Kinesin-1 heavy chain (KIF5B) aggregates in the cytoplasm and induces aggregation of its cargos. Both aggregates are detected under fluorescence microscopy. A similar strategy can be employed to identify cargos of other motor proteins.

Fluorescence microscopy is employed to identify Kinesin-1 cargos. Recently, the heavy chain of Kinesin-1 (KIF5B) was shown to transport the nuclear ...

Visualizing Stromule Frequency with Fluorescence Microscopy

Stromules, or "stroma-filled tubules", are narrow, tubular extensions from the surface of the chloroplast that are universally observed in plant cells but whose functions remain mysterious. Alongside growing attention on the role of chloroplasts in coordinating plant responses to stress, interest in stromules and their relationship to chloroplast signaling dynamics has increased in recent years, aided by advances in fluorescence microscopy and protein fluorophores that allow for rapid, accurate visualization of stromule dynamics. Here, we provide detailed protocols to assay stromule frequency in the epidermal chloroplasts of Nicotiana benthamiana, an excellent model system for investigating chloroplast stromule biology. We also provide methods for visualizing chloroplast stromules in vitro by extracting chloroplasts from leaves. Finally, we outline sampling strategies and statistical approaches to analyze differences in stromule frequencies in response to stimuli, such as environmental stress, chemical treatments, or gene silencing. Researchers can use these protocols as a starting point to develop new methods for innovative experiments to explore how and why chloroplasts make stromules.

Protocols to investigate the dynamics of chloroplast stromules, the stroma-filled tubules that extend from the surface of chloroplasts, are described.

Stromules, or "stroma-filled tubules", are narrow, tubular extensions from the surface of the chloroplast that are universally observed in plant cells but ...

Open Source High Content Analysis Utilizing Automated Fluorescence Lifetime Imaging Microscopy

We present an open source high content analysis instrument utilizing automated fluorescence lifetime imaging (FLIM) for assaying protein interactions using F&#246;rster resonance energy transfer (FRET) based readouts of fixed or live cells in multiwell plates. This provides a means to screen for cell signaling processes read out using intramolecular FRET biosensors or intermolecular FRET of protein interactions such as oligomerization or heterodimerization, which can be used to identify binding partners. We describe here the functionality of this automated multiwell plate FLIM instrumentation and present exemplar data from our studies of HIV Gag protein oligomerization and a time course of a FRET biosensor in live cells. A detailed description of the practical implementation is then provided with reference to a list of hardware components and a description of the open source data acquisition software written in &#181;Manager. The application of FLIMfit, an open source MATLAB-based client for the OMERO platform, to analyze arrays of multiwell plate FLIM data is also presented. The protocols for imaging fixed and live cells are outlined and a demonstration of an automated multiwell plate FLIM experiment using cells expressing fluorescent protein-based FRET constructs is presented. This is complemented by a walk-through of the data analysis for this specific FLIM FRET data set.

We present an open source high content analysis (HCA) instrument utilizing automated fluorescence lifetime imaging (FLIM) for assaying protein interactions using F&#246;rster resonance energy transfer (FRET) based readouts. Data acquisition for this openFLIM-HCA instrument is controlled by software written in &#181;Manager and data analysis is undertaken in FLIMfit.

We present an open source high content analysis instrument utilizing automated fluorescence lifetime imaging (FLIM) for assaying protein interactions ...

Oligomerization Dynamics of Cell Surface Receptors in Living Cells by Total Internal Reflection Fluorescence Microscopy Combined with Number and Brightness Analysis

Despite the importance and ubiquity of receptor oligomerization, few methods are applicable for detecting clustering events and measuring the degree of clustering. Here, we describe an imaging approach to determine the average oligomeric state of mEGFP-tagged-receptor homocomplexes in the membrane of living cells. The protocol is based on Total Internal Reflection Fluorescence (TIRF) microscopy combined with Number and Brightness (N&#38;B) analysis. N&#38;B is a method similar to fluorescence-correlation spectroscopy (FCS) and photon counting histogram (PCH), which are based on the statistical analysis of the fluctuations of the fluorescence intensity of fluorophores diffusing in and out of an illumination volume during an observation time. In particular, N&#38;B is a simplification of PCH to obtain information on the average number of proteins in oligomeric mixtures. The intensity fluctuation amplitudes are described by the molecular brightness of the fluorophore and the average number of fluorophores within the illumination volume. Thus, N&#38;B considers only the first and second moments of the amplitude distribution, namely, the mean intensity and the variance. This is, at the same time, the strength and the weakness of the method. Because only two moments are considered, N&#38;B cannot determine the molar fraction of unknown oligomers in a mixture, but it only estimates the average oligomerization state of the mixture. Nevertheless, it can be applied to relatively small time series (compared to other moment methods) of images of live cells on a pixel-by-pixel basis, simply by monitoring the time fluctuations of the fluorescence intensity. It reduces the effective time-per-pixel to a few microseconds, allowing acquisition in the time range of seconds to milliseconds, which is necessary for fast oligomerization kinetics. Finally, large cell areas as well as sub-cellular compartments can be explored.

We describe an imaging approach for the determination of the average oligomeric state of mEGFP-tagged-receptor oligomers induced by ligand binding in the plasma membrane of living cells. The protocol is based on Total Internal Reflection Fluorescence (TIRF) microscopy combined with Number and Brightness (N&#38;B) analysis.

Despite the importance and ubiquity of receptor oligomerization, few methods are applicable for detecting clustering events and measuring the degree of ...