This work was supported by grants CRUK 15043 and CRUK 14251.
We thank Daniel Wetterskog and Ka Kei Ho for technical assistance and critical reading of the manuscript.

1. Experimental Perturbation and Cell Labeling for Response Markers (Reverse Transfection siRNA Screen)
<ol>
	<li>In a sterile tissue culture hood pipette 70 &#181;l of 200 nM siRNA in 1x siRNA buffer in wells of a sterile, plain 96 well plate. Dilute transfection lipid into 40 volumes of serum-free DMEM media and dispense 105 &#181;l into each well containing siRNA. 
	NOTE: Diluting 262.5 &#181;l lipid into 10.5 ml of serum-free DMEM yields a master mix suitable for a whole 96 well plate of siRNA, delivering 2.6 &#181;l of lipid per well. Use of 200 nM siRNA starting concentration at this step will deliver a working concentration of 20 nM in step 1.3, but procedures will work for working concentrations down to 5 nM, with the starting concentration adjusted accordingly (i.e. 50 nM). Lower working concentration may reduce off-target false positive scores, although they can reduce the magnitude of on-target response, leading to increase of on-target false negative rates.</li>
	<li>Mix the plate by gentle vibration for ten minutes at room temperature. Sub-divide the resulting 175 &#181;l into three 50 &#181;l replicates per target onto an opaque, tissue culture treated, 96-well plate with a transparent base.</li>
	<li>Reverse transfect by dispensing 8,000 cells per well in 150 &#181;l DMEM containing 10% serum directly onto the 50 &#181;l lipid-siRNA complexes. Use HCT116 human colorectal cells stably expressing a GFP-tagged marker reporting CDK2 activity5,8. No further mixing is necessary. Seal the plate with a sterile, adhesive breathable membrane to control humidity and prevent plate &#8216;edge-effects&#8217; and place the plate into a humidified incubator at 37 &#176;C, 5% CO2 for 48 hr.</li>
	<li>Aspirate the media such that a small residual amount of media remains in the wells. Fix the cells by adding 100 &#181;l of 4% buffered formaldehyde to each well and incubate in a fume hood for 10 min at room temperature.</li>
	<li>Remove the fixing solution by aspirating the plate. At this point either stop the experiment by washing the plate three times with 100 &#181;l phosphate-buffered saline (PBS) and then store sealed, under 100 &#181;l of PBS in the dark at 4 &#176;C for up to a week, or proceed with the permeabilization of the cells. 
	NOTE: We recommend processing plates as soon as possible after fixation, and generally prefer storage of fully processed plates. Biocidal preservatives such as thimerosal, sodium azide, or commercial alternatives may be added to prevent micoroganismal growth. Addition of phosphatase inhibitors helps to preserve phospho-epitopes, and other means to preserve protein modification states may be useful in relevant assay contexts</li>
	<li>Remove PBS from the plate and permeabilize the cells by adding 100 &#181;l of permeabilization solution. Incubate for 10 min at room temperature without shaking. Aspirate the permeabilization solution using a multichannel pipette. Repeat this step three times.</li>
	<li>Block the cells by adding 100 &#181;l block solution per well for 30 min at room temperature. Remove the block solution by aspirating the plate, then probe with 50 &#181;l of anti P-S780 RB1 antibody diluted 500-fold in the block solution for 2 hr in the dark at room temperature.</li>
	<li>Wash the plate three times with 100 &#181;l plate wash solution, leaving the solution on the plate for 5 min each time. Probe the plate overnight in the dark at 4 &#176;C with 50 &#181;l fluorescently-tagged secondary antibody diluted 1,000-fold in block solution supplemented with 2 &#181;M of the chromatin-specific DNA dye Bisbenzimide. Wash the plate three times as before and store sealed, under 100 &#181;l PBS in the dark at 4 &#176;C. Image the plate within two weeks.</li>
</ol>
2. Imaging and Image Segmentation
<ol>
	<li>Use a confocal or spinning-disk fluorescence microscope with a 20X objective to take separate 16-bit, greyscale TIFF images in three channels corresponding to the DNA dye, GFP and immuno-staining fluorophores. Capture many non-overlapping image sets, referred to here as frames, to image approximately 1,000-2,000 cells per well.</li>
	<li>Name the image files systematically so that each file name is a unique combination of&#160; &#8216;experiment name&#8217;, &#8216;well address&#8217;, &#8216;frame number&#8217; and &#8216;channel identifier&#8217;, in this order (Figure 3). The example data set uses &#8220;Blue&#8221; (chromatin DNA staining) or &#8220;Green&#8221; (GFP) or &#8220;Red&#8221; (the immuno-stained fluorophore) as channel identifiers. The well address, frame number and channel identifier are further on referred to as the image metadata. Use the underscore symbol to avoid confusing well and frame metadata.</li>
	<li>Name the files with these metadata elements in the specified order. This is necessary to ensure that the subsequent software steps correctly group sets of images for analysis.</li>
	<li>Download and install the freeware Cell Profiler, Active Perl Community Edition, R statistical programming environment and RStudio. Accept all default options during installation; PC users installing Active Perl should enable all options relating to PATH, file extension association and script mapping where prompted. Active Perl is optional for Mac users, but they will otherwise need to run the Perl script in step 3.2 from the Terminal command line rather than using icon clicking.</li>
	<li>Open the Cell Profiler software, click &#8216;File&#8217;, &#8216;Import Pipeline&#8217; then &#8216;From file&#8217; and select the file 3_channels_pipeline.cppipe (Figures S1A &#38; S1B). The file contains instructions necessary for the software to interpret image file metadata from the file name convention described. Cell Profiler now relates the images, extracts nuclear DNA and antibody intensities from these and uses the GFP channel to calculate the ratio of nuclear versus cytoplasm intensity for each cell detected (Figures 4 &#38; 5).</li>
	<li>Click the button &#8216;View output settings&#8217; in the lower left corner of the Cell Profiler window. At the top of the new screen are text boxes labeled &#8216;Default Input Folder&#8217; and &#8216;Default Output Folder&#8217;. One at a time, click the folder-icons to the right of these boxes and select the location of the image files for analysis and the destination for the extracted data, respectively (Figure S1C).</li>
	<li>Begin the image analysis by pressing the &#8216;Analyze Images&#8217; button in the lower left corner of Cell Profiler. At the bottom of the screen observe the remaining time for the data extraction, the &#8216;Stop Analysis&#8217; and &#8216;Pause&#8217; buttons. If required pause the analysis by selecting the &#8216;Pause&#8217; button at any time, which is useful when watching the images being analyzed (described in step 2.8).</li>
	<li>Optionally, open the windows for any of the image analysis steps by clicking the eye-icons in the panel on the extreme left of the program window (Figure S1D). Observe the &#8216;IdentifyPrimaryObjects&#8217; window and those for &#8216;Secondary&#8217; and &#8216;Tertiary Objects&#8217; to check that the current settings in Cell Profiler to perform image segmentation are suitable (see Figure 1 and Discussion for advice on modifying these settings).</li>
	<li>Click &#8216;Ok&#8217; in the message box that appears when the analysis is complete. Go to the location &#8216;Default Output Folder&#8217; where all the data files with the results are saved as comma-separated-value (.csv) files (Figure S2A).</li>
</ol>
3. Data Extraction
<ol>
	<li>Find the new file &#8216;Nuclei.csv &#8217;, which is included among the output from Cell Profiler. This file contains individual cell data for fluorescent nuclear antibody intensity, nuclear DNA intensity and GFP-CDK2 reporter ratio values (Figures 6A &#38; S2A). 
	NOTE: Different laboratories will want to process this type of data to suit the nature of their own assays. Suggested for the current data is the gating of the cells from each treatment condition according to the antibody data and the GFP-CDK2 reporter values using the provided Perl script &#8216;2_gate_classifier.pl&#8217;.</li>
	<li>Copy the provided Perl script file &#8216;2_gate_classifier.pl&#8217; into the same folder as the &#8216;Nuclei.csv&#8217; data file (Figure S2A). Double-click the icon for the Perl script and, when prompted, type the full name of the data file followed by a &#8216;.csv&#8217; filename name for the file in which the cells are to be gated and finally the gate values for the antibody fluorescence and GFP-CDK2 reporter data. 
	NOTE: How to principally determine gate settings and apply these for analysis of data are discussed below in the Representative Data section and Figure 6 (to analyze the data provided use &#8216;0.004&#8217; and &#8216;1.5&#8217;, respectively). Mac users should run the Perl script from the Terminal command line by typing: &#8216;perl 2_gate_classifier.pl&#8217;.</li>
	<li>Observe the newly created file which combines the raw individual cell assay values from the original Cell Profiler data with sub-population labels that show how each cell from each well performs against both gates (Figure 6C).</li>
	<li>Plot the data for each experimental condition using the individual cell subpopulation labels by opening the RStudio software. Click &#8216;File&#8217; and &#8216;Open File&#8217;, then select the provided &#8216;analysis.r&#8217; file. Observe the commands to plot Figures 6B, 7 and 8 in the upper left window of RStudio (Figure S2B). In the upper left window, between the double quote symbols on lines 5 and 6, type the computer address of the folder containing the gated data. Include the drive letter and the name of the file itself, respectively (e.g., &#8220;C:/analysis folder/analysis output&#8221; and &#8220;nuclei_gated.csv&#8221;). 
	NOTE: If RStudio is used for the first time on a given computer, the R graphics package &#8216;ggplot2&#8217; will need to be installed first. This is a once only step for a new installation of RStudio, after which this step becomes redundant. To install &#8216;ggplot2&#8217;, click the tab called &#8216;Packages&#8217; above the window in the lower right corner of RStudio, click the &#8216;Install Packages&#8217; button that appears beneath this. A new window will appear. Type &#8216;ggplot2&#8217; (omitting quotations) into the &#8216;Packages&#8217; space in this new window and finally click the &#8216;Install&#8217; button to close the window, install the necessary ggplot2 functions and return to the main RStudio window to continue from step 3.6.</li>
	<li>Highlight lines 1 through 17 in the upper left window of RStudio, then click the &#8216;Run&#8217; button. This will enter the experimental data, threshold values and well location details into R (Figure S2C). R will now temporarily hold the relevant data for plotting.</li>
	<li>Highlight individual blocks of the remaining code beneath line 17 and create the corresponding plots by clicking the &#8216;Run&#8217; button as before. Observe the plots in the window in the lower right corner of RStudio and save number of formats by clicking the &#8216;Export&#8217; button (Figure S2D).</li>
	<li>While closing RStudio, click &#8216;Don&#8217;t Save&#8217; when prompted. This prevents confusion on the next use of RStudio, which will otherwise hold data from the previous session.</li>
</ol>

<ol><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 (10), 100(2006).</li>
<li>Khan, A., Eldaly, H., Rajpoot, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+gamma-gaussian+mixture+model+for+detection+of+mitotic+cells+in+breast+cancer+histopathology+images%5BTitle%5D)+AND+%22J+Pathol+Inform%22%5BJournal%5D)">A gamma-gaussian mixture model for detection of mitotic cells in breast cancer histopathology images.</a> J Pathol Inform. 4, 11(2013).</li>
<li>Selzer, P., Beibel, M., Gubler, H., Parker, C. N., Gabriel, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Comparison+of+multivariate+data+analysis+strategies+for+high-content+screening%5BTitle%5D)+AND+%22J+Biomol+Screen%22%5BJournal%5D)">Comparison of multivariate data analysis strategies for high-content screening.</a> J Biomol Screen. 16 (3), 338-347 (2011).</li>
<li>Lyman, S. K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((High+content%2C+high-throughput+analysis+of+cell+cycle+perturbations+induced+by+the+HSP90+inhibitor+XL888%5BTitle%5D)+AND+%22PLoS+One%22%5BJournal%5D)">High content, high-throughput analysis of cell cycle perturbations induced by the HSP90 inhibitor XL888.</a> PLoS One. 6 (3), e17692(2011).</li>
<li>Richardson, E., Stockwell, S. R., Li, H., Aherne, W., Cuomo, M. E., Mittnacht, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Mechanism-based+screen+establishes+signalling+framework+for+DNA+damage-associated+G1+checkpoint+response%5BTitle%5D)+AND+%22PLoS+One%22%5BJournal%5D)">Mechanism-based screen establishes signalling framework for DNA damage-associated G1 checkpoint response.</a> PLoS One. 7 (2), e17692(2012).</li>
<li>Heynen-Genel, S., Pache, L., Chanda, S. K., Rosen, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Functional+genomic+and+high-content+screening+for+target+discovery+and+deconvolution%5BTitle%5D)+AND+%22Expert+Opin+Drug+Discov%22%5BJournal%5D)">Functional genomic and high-content screening for target discovery and deconvolution.</a> Expert Opin Drug Discov. 7 (10), 955-968 (2012).</li>
<li>Krausz, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((High-content+siRNA+screening%5BTitle%5D)+AND+%22Mol+Biosyst%22%5BJournal%5D)">High-content siRNA screening.</a> Mol Biosyst. 3 (4), 232-240 (2007).</li>
<li>Gu, J., Xia, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Cell+Cycle-dependent+Regulation+of+a+Human+DNA+Helicase+That+Localizes+in%5BTitle%5D)+AND+%22DNA+Damage+Foci.+Mol+Biol+Cell%22%5BJournal%5D)">Cell Cycle-dependent Regulation of a Human DNA Helicase That Localizes in.</a> DNA Damage Foci. Mol Biol Cell. 15 (7), 3320-3332 (2004).</li>
<li>Mittnacht, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Control+of+pRB+phosphorylation%5BTitle%5D)+AND+%22Curr+Opin+Genet+Dev%22%5BJournal%5D)">Control of pRB phosphorylation.</a> Curr Opin Genet Dev. 8 (1), 21-27 (1998).</li>
<li>Mittnacht, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+retinoblastoma+protein--from+bench+to+bedside%5BTitle%5D)+AND+%22Eur+J+Cell+Biol%22%5BJournal%5D)">The retinoblastoma protein--from bench to bedside.</a> Eur J Cell Biol. 84 (2-3), 97-107 (2005).</li>
<li>Nybo, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((GFP+imaging+in+fixed+cells%5BTitle%5D)+AND+%22BioTechniques%22%5BJournal%5D)">GFP imaging in fixed cells.</a> BioTechniques. 52 (6), 359-360 (2012).</li>
<li>Schwartz, R. L., Foy, B. D., Phoenix, T. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aSchwartz%2C+RL+%22Learning+Perl+-+Making+Easy+Things+Easy+and+Hard+Things+Possible%22">Learning Perl - Making Easy Things Easy and Hard Things Possible</a>. , 6th ed, O'Reilly Media. 1-363 (2011).</li>
<li>Wickham, H. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aWickham%2C+H+%22ggplot2%3A+Elegant+Graphics+for+Data+Analysis%22">ggplot2: Elegant Graphics for Data Analysis</a>. , Springer. New York. 978-970 (2009).</li>
</ol>

The authors have nothing to disclose

The workflow described constitutes a procedure for multiwell perturbance of cells using siRNA, subsequent marker detection and finally use of a series of software-supported steps to facilitate extraction of quantitative data from the resulting fluorescent microscopy images. The approach is focused on the delivery of nuclear and cytoplasmic intensity values for individual cells, which has broad practical application in many cell-based applications. The example data used here was generated in an siRNA screen setting in which two fluorescent assays for G1 phase cell cycle transit are tested and correlated back to a more direct biophysical measure of nuclear DNA content.
The use of a fluorescent DNA stain to image nuclear DNA is an indispensable step in the image segmentation process as it allows identification of individual cells and the resulting &#8216;Nuclei mask&#8217; serves as the starting point to identify corresponding cytoplasmic regions. The GFP-tagged CDK2 reporter, which is stably expressed in the cells, gives a variable yet consistently higher than background signal in the cytoplasm by which this compartment can be delineated. The same analysis pipeline should be applicable to the analysis of protein translocation events using other suitable fluorescence-linked reporters and their response to perturbance. Also, substituting the GFP-CDK2 reporter with cytoplasm-specific fluorescent dyes would allow the alternative use of this algorithm to measure the dimensions of the cytoplasm and the relative sizes of the cells in the images.
Another design consideration in the image segmentation strategy described here is the use of Cell Profiler to deliver integrated intensity values for the DNA quantification. Integration of the intensity values for the nuclear DNA staining data allow for possible variations in nucleus size, and represents a close match for the quantification profiles seen for propidium iodide stained FACS data. However, integrated intensity may not provide an appropriate means to assess protein function where average concentration, exemplified by mean intensity of antigen fluorescence, is more biologically relevant than the integrated total amount of protein (and associated fluorescence) within a cell compartment. Therefore mean intensity values were used for the P-S780 RB1 and GFP data. The option to alter between the two modes (mean or integrated) of data assessment is found on the &#8216;ExportToSpreadsheet&#8217; panel of the Cell Profiler software.
The analysis settings in the 3_channels_pipeline.cppipe file are optimized for the images in the example data set. Analysis of new images sets with this protocol will require that the file names adopt the naming convention described above (Figure 3). Also, sensitivity values to suit the brightness of nuclear DNA staining and thresholds for background intensities in the new image sets may need to be adjusted within the Cell Profiler settings. Given the key role the DNA staining holds for building the various image segmentation masks, the application of correct sensitivity settings for this channel is key to the successful analysis of new image data with the Cell Profiler software. The provided Cell Profiler settings file (3_channels_pipeline.cppipe) contains notes on the most frequently useful parameters for adapting the analysis to new data. These notes are in the text box at the top of the screen in the Cell Profiler main window and include guidance on changing the sensitivity settings and adjusting the number of channels to be analyzed. As indicted in Protocol section 2.8, to test settings for new image data it may be necessary to observe the image segmentation during image analysis by clicking open the &#8216;eye&#8217; icons for each of the &#8216;Identify&#8230;Objects&#8217; protocol steps (Figure S1D). In particular, visualization of the image data through &#8216;IdentifyPrimaryOjects&#8217; will show if the Nuclei mask is correctly identified from images of the DNA staining. On the Cell Profiler software page for the &#8216;IdentifyPrimaryOjects&#8217; module is the threshold correction factor. Trial and error adjustment of this value will fix most nuclear recognition errors. The values balance the DNA channel against the background intensity for each image. Threshold correction factor values hinge around 1, where greater than this is more stringent (good for clear images) and less than 1 is lenient (suited to images with less contrast between staining and background).
The raw output of individual cell data from Cell Profiler can be analyzed in varying ways to suit the needs of other studies. Shown here is the use of a Perl script to apply gates to two of the parameters measured per cell in order to assist extracting biological trends from the data and permit cross-referencing of the identified subpopulations with additional measurements. Although it is equally possible to include elements of gating within the framework of Cell Profiler, the alternative route used here provides greater flexibility and speed, specifically if large data sets need to be assessed. The slowest stage in the post-image acquisition phases of the current protocol is the running of the Cell Profiler software. Cell profiler here is run without imposing gates to produce an un-gated raw data set which can be reanalyzed with the subsequent Perl script more quickly and, if needed, iteratively with different gate values. Not all studies will know in advance the suitable gate values as this may vary with reagents on any given set of data, and potentially over time. It is, therefore, recommended to generate histograms depicting the raw data distribution obtained from Cell Profiler for positive controls and mock-perturbed cells in order to identify suitable gate values for the parameters of interest.
The Perl scripts are written to accept a rigidly defined column structure of data from Cell Profiler and may stop working if a user modifies the number of parameters output by Cell Profiler using the &#8216;ExportToSpreadsheet&#8217; settings. To help implement modification of the settings notes are included within the Perl script files. To see these view the script in a text editor, preferably a programmer&#8217;s text editor set to color code Perl elements (e.g., http://www.activestate.com/komodo-edit). These notes indicate where to adjust the script to adapt to changes in data format. Similar to the Perl scripts, the R-code file provided (analysis.r), containing the instructions for plotting the figures from the image analysis data, can be read in a text editor or RStudio software to see additional notes on use and adaptation. These notes can be supplemented with details on regular expressions and Perl12 and the ggplot213 package for R, both of which form the basis for how the data is read, annotated and plotted, respectively.
New studies using fluorescence microscopy as well as raw data deposited with open source publications are amenable to methods of analysis such as those described here. The very nature of high-content data lends itself to recursive analysis with different analytic emphases depending on the research interests of any given observer. Although the questions that can be asked of the data are limited by the probes originally used, image data can often be meaningfully reanalyzed beyond the scope of the studies which generated them.

The work presented here describes the use of the freely available software Cell Profiler to perform algorithm-guided breakdown of fluorescent microscopy images of adherent cells to identify individual cells and defined subcellular regions. This approach, referred to as image segmentation, allows the subsequent multi-parametric analysis of the imaged cells by quantifying fluorescently labeled markers localized to each cell or subcellular region (referred to as segmented objects). This workflow constitutes a basis for enabling high-content analysis and is intended to serve as a tool that can be further developed and modified to suit multi-parametric, individual cell analyses in laboratories without access to specialized high-content instruments or proprietary software. The files supplied with this manuscript include a test set of relevant raw image data, algorithm settings and supporting scripts to generate the analysis described. The provided algorithm settings for Cell Profiler are optimized for the example data set and the Discussion section details what adjustments may be necessary to enable use of image data from other studies.
Once quantitative data has been extracted using Cell Profiler, different laboratories may have different requirements for how to use the information presented by the individual cell values in the raw data; shown here is one approach by which gates are applied to the raw data for each assay. Using these gates, the data are transformed into binary terms of response, allowing visualization of trends linking different treatments with the subpopulations of cells undergoing response defined by the gates. The gates are set based on observations of the data distributions obtained for appropriate negative and positive controls for each relevant measurement. The use of gates is just one example of how to manage the raw, cell-based measurements. Also shown here is the use of nuclear DNA intensity measurements in their raw form as a continuous range of values in combination with the gated data. Other approaches to managing image analysis data should be considered, depending on the nature of the study; statistical alternatives to using gates for assigning cells to subpopulations have been reported2 and systematic comparisons of strategies to summarize high-content data across large numbers of parameters have been reported3.
High-content analyses of image data have found use in cellular studies of drug-response, reverse genetics and environmental stress signalling4&#8211;6. The merit of high-content analysis stems from the fact that algorithmic analysis of fluorescence microscopy data allows quantitative and spatial parameters to be considered simultaneously across individual cells7. In this way, cellular outcomes for multiple assays can be cross-referenced, differential behavior of assay-defined cell subpopulations can be tracked within experimental conditions and assays can include consideration of morphological variables. The strategies and analysis workflow discussed here, as for other high-content approaches, are capable of delivering multiplexed data that are cross-referenced to individual cells. High-content methods suit studies that generate fluorescent microscope images and are applicable to analysis of data ranging from tens of images produced in low throughput conventional fluorescence-based microscopy through to the thousands of images produced using automated high-content screening platforms.
The workflow is illustrated here with example data from which separate assays are measured in terms of either nuclear fluorescent marker intensities or nuclear/cytoplasmic translocation of a fluorescent reporter protein, respectively. The workflow is flexible in that these assays can be considered separately or in combination depending on each given research question by different investigators. The example data are produced as part of a RNA interference (RNAi) experiment (Figure 1). Small interfering RNA oligonucleotides (siRNA) are used to knockdown specific proteins in HCT116 human colorectal carcinoma cells which result in changes for two fluorescent reporters of cyclin-dependent kinase (CDK) activity. The CDK6-dependent phosphorylation of the nuclear retinoblastoma protein at serine 780 (P-S780 RB1) is assessed by antibody staining. In the same cells, a green fluorescent protein-tagged reporter of CDK2 activity (GFP-CDK2 reporter) is assessed by its nuclear to cytoplasmic ratio where in the absence of CDK2 activity the reporter resides in the nucleus and upon CDK2 activation shuttles into the cytoplasm8. Additionally, the nuclear DNA of each cell is stained using a DNA-intercalating dye, Bisbenzimide, which serves as a means to identify cells and define nuclei borders in the images as well as a measure of DNA abundance providing information on cell cycle position of the cell (Figure 2).
The activities of CDK6 and CDK2 are detectable as cells transit from G1 to S phase of the cell cycle5 and succeed each other9,10 and, as such, close concordance between the two reporters in individual cells is expected. The demonstration dataset used here analyzes as an example the effect of siRNA targets CDK6, retinoblastoma protein (RB1) and a non-targeting negative control (Table 1). Knockdown of CDK6 should elicit both a decrease of the P-S780 RB1 epitope and an accumulation of cells in G1 phase of the cell cycle. The RB1 knockdown serves as a reagent control for the specificity of the phospho-S780 antibody. Fluorescence microscope images from formalin fixed11, fluorescently stained HCT116 tissue culture cells are used for algorithmic image analysis. The resulting numeric data is then used to cross-reference the reporters and gauge the impact of the different knockdown states.
The potential size of the data produced by this type of analysis can present a challenge to normal analysis tools. For example, the individual cell data can be larger than some spreadsheet software will accommodate. Included are Perl scripts which perform simple, highly-repetitive, supervised processing of the data to aid analysis of large datasets. The Perl scripts are written specifically for the output files produced by Cell Profiler, when processing image files with a specific file naming convention (Figure 3), and allow for variable numbers of fields per well to be used in the analysis. It is frequently important to gate individual cell assay data to track trends in cell subpopulations5 and shown here is the use of a Perl script to flag each cell based on a set gate predetermined for each assay type. Also included are optional Perl scripts which summarize the data outcome for individual wells (or conditions), delivering: percentage of cells within the set gate and the mean values of the raw assay scores. The latter, more homogeneous way of viewing the data, is valid where responses affect all or the majority of cells within a well. As discussed above, such assessment is less useful than that afforded by the individual cell data gating where response is confined to a subset of cells within a population.
The utility of the described workflow is not limited to perturbation by siRNA or the marker assays described. Studies have used this approach to assay responses in tissue culture experiments using combinations of siRNA, chemical inhibitors and radiation treatment and for assessment of markers other than CDK6 and CDK2 activity5.
Conceptually, the experimental strategy allows a variety of biologically useful subcellular regions to be automatically registered in individual cells present in fluorescent microscope images. As such, this approach can yield quantitative, multiplexed data revealing biological information that may be missed through techniques that focus on populations rather than individual cells. With minor modifications, the approach and analysis workflow described can yield quantitative, individual cell data for any fluorescence-based assay outputs and cell-biological responses, where quantitative assessment of DNA content, quantification of nuclear or cytoplasmic fluorescence or the shuttling of markers between these two compartments either individually or in a multiplexed manner is of interest. As publishing requirements increasingly tend towards submission of openly accessible raw data, access to and familiarity with free tools for microscopy image analysis such as those described here will also be of direct interest to labs looking to reanalyze published data.

<table><tbody><tr><td>AllStars negative control siRNA</td><td>Qiagen</td><td>1027280</td><td>Negative control siRNA.</td></tr><tr><td>CDK6 siRNA</td><td>Dharmacon/ Custom synthesis</td><td>NA</td><td>Antisense sequence: 5&#039; CUCUAGGCCAGUCUUCUUCUU</td></tr><tr><td>RB1 siRNA</td><td>Dharmacon/ Custom synthesis</td><td>NA</td><td>Antisense sequence: 5&#039; GGUUCAACUACGCGUGUAATT</td></tr><tr><td>5x siRNA buffer</td><td>Thermo Scientific</td><td>B-002000-UB-100</td><td>To be diluted with nuclease-free, non-DEPC treated water.</td></tr><tr><td>Nulcease-free water (non-DEPC)</td><td>Applied Biosystems</td><td>AM9937</td><td>For dilution of siRNA buffer.</td></tr><tr><td>Hiperfect</td><td>Qiagen</td><td>301705</td><td>Transfection lipid.</td></tr><tr><td>DMEM</td><td>Life Technologies</td><td>41966052</td><td>Pyruvate and high-glucose supplemented tissue culture media.</td></tr><tr><td>96 well tissue culture plate</td><td>Falcon</td><td>3072</td><td>Plain, 96 well tissue culture plate for the parallel, compartmentalized mixing of transfection complexes.</td></tr><tr><td>Packard Viewplate</td><td>Perkin Elmer LAS</td><td>6005182</td><td>96 well TC plate with optical base on which cells are transfected, grown, fixed and eventually imaged. Supplied with opaque, adhesive plate seals, which are used during storage of used plates.</td></tr><tr><td>Breathable membrane</td><td>Alpha Labs</td><td>LW2783</td><td>Sterile, gas-permeable, adhesive membrane.</td></tr><tr><td>Neutral buffered formalin, 10%</td><td>Sigma-Aldrich</td><td>HT5012-1CS</td><td>10% Formalin (4% formaldehyde) fixative used neat in protocol.</td></tr><tr><td>Triton X-100</td><td>Sigma-Aldrich</td><td>X100PC</td><td>Non-ionic detergent</td></tr><tr><td colspan="4">[header]</td></tr><tr><td>Tris</td><td>Sigma-Aldrich</td><td>T1503</td><td>Trizma base (tris(hydroxymethyl)aminomethane)</td></tr><tr><td>Tween 20</td><td>Sigma-Aldrich</td><td>P2287</td><td>For plate wash solution.</td></tr><tr><td>Anti-P-S780 RB1 antibody</td><td>Abcam</td><td>ab32513</td><td>Rabbit monoclonal</td></tr><tr><td>AlexaFluor647 Anti-rabbit</td><td>Invitrogen</td><td>A21245</td><td>Highly cross-adsorbed, fluorescently labelled secondary antibody.</td></tr><tr><td>Hoechst 33342 (Bisbenzimide)</td><td>Sigma-Aldrich</td><td>B2261</td><td>Fluorescent, chromatin-intercalating, DNA dye.</td></tr><tr><td>Permeabilization solution</td><td>NA</td><td>NA</td><td>0.1% Triton X-100 in 50 mM Tris-buffered saline, pH 8.0.</td></tr><tr><td>Plate wash solution</td><td>NA</td><td>NA</td><td>Tris-buffered saline containing 0.1% Tween-20.</td></tr><tr><td>Block solution</td><td>NA</td><td>NA</td><td>5% powdered milk in Tris-buffered saline and 0.1% Tween-20. For blocking plate prior to immuno-staining and dilution of antibodies.</td></tr><tr><td>Cell Profiler 2.1</td><td>Broad Institute</td><td></td><td>http://www.cellprofiler.org/download.shtml</td></tr><tr><td>Active Perl Community Edition</td><td>ActiveState</td><td></td><td>http://www.activestate.com/activeperl/downloads</td></tr><tr><td>R programming environment</td><td>The R Foundation</td><td></td><td>http://www.r-project.org</td></tr><tr><td>Rstudio</td><td>Rstudio</td><td></td><td>http://www.rstudio.com/</td></tr></tbody></table>

workflow for high-content, individual cell quantification of fluorescent markers from universal microscope data, supported by open source software

Advances in understanding the control mechanisms governing the behavior of cells in adherent mammalian tissue culture models are becoming increasingly dependent on modes of single-cell analysis. Methods which deliver composite data reflecting the mean values of biomarkers from cell populations risk losing subpopulation dynamics that reflect the heterogeneity of the studied biological system. In keeping with this, traditional approaches are being replaced by, or supported with, more sophisticated forms of cellular assay developed to allow assessment by high-content microscopy. These assays potentially generate large numbers of images of fluorescent biomarkers, which enabled by accompanying proprietary software packages, allows for multi-parametric measurements per cell. However, the relatively high capital costs and overspecialization of many of these devices have prevented their accessibility to many investigators.
Described here is a universally applicable workflow for the quantification of multiple fluorescent marker intensities from specific subcellular regions of individual cells suitable for use with images from most fluorescent microscopes. Key to this workflow is the implementation of the freely available Cell Profiler software1&#160;to distinguish individual cells in these images, segment them into defined subcellular regions and deliver fluorescence marker intensity values specific to these regions. The extraction of individual cell intensity values from image data is the central purpose of this workflow and will be illustrated with the analysis of control data from a siRNA screen for G1 checkpoint regulators in adherent human cells. However, the workflow presented here can be applied to analysis of data from other means of cell perturbation (e.g., compound screens) and other forms of fluorescence based cellular markers and thus should be useful for a wide range of laboratories.

Watch this Scientific Journal Video about Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software at JoVE.com

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

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.

Advances in understanding the control mechanisms governing the behavior of cells in adherent mammalian tissue culture models are becoming increasingly ...

workflow-for-high-content-individual-cell-quantification-fluorescent

Research

JoVE Journal

Biology

13.0K Views.  UCL Cancer Institute. 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.

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

Visualizing Cell-to-cell Transfer of HIV using Fluorescent Clones of HIV and Live Confocal Microscopy

By fusing the green fluorescent protein to their favorite proteins, biologists now have the ability to study living complex cellular processes using fluorescence video microscopy. To track the movements of the human immunodeficiency virus core protein during cell-to-cell transmission of human immunodeficiency virus, we have GFP-tagged the Gag protein in the context of an infectious molecular clone of HIV, called HIV Gag-iGFP. We study this viral clone using video confocal microscopy. In the following visualized experiment, we transfect a human T cell line with HIV Gag-iGFP, and we use fluorescently labeled uninfected CD4+ T cells to serve as target cells for the virus. Using the different fluorescent labels we can readily follow viral production and transport across intercellular structures called virological synapses. Simple gas permeable imaging chambers allow us to observe synapses with live confocal microscopy from minutes to days. These approaches can be used to track viral proteins as they move in from one cell to the next.

This visualized experiment is a guide for utilizing a fluorescent molecular clone of HIV for live confocal imaging experiments.

By fusing the green fluorescent protein to their favorite proteins, biologists now have the ability to study living complex cellular processes using ...

Immunology and Infection

Quantitative Immunofluorescence to Measure Global Localized Translation

The mechanisms regulating mRNA translation are involved in various biological processes, such as germ line development, cell differentiation, and organogenesis, as well as in multiple diseases. Numerous publications have convincingly shown that specific mechanisms tightly regulate mRNA translation. Increased interest in the translation-induced regulation of protein expression has led to the development of novel methods to study and follow de novo protein synthesis in cellulo. However, most of these methods are complex, making them costly and often limiting the number of mRNA targets that can be studied. This manuscript proposes a method that requires only basic reagents and a confocal fluorescence imaging system to measure and visualize the changes in mRNA translation that occur in any cell line under various conditions. This method was recently used to show localized translation in the subcellular structures of adherent cells over a short period of time, thus offering the possibility of visualizing de novo translation for a short period during a variety of biological processes or of validating changes in translational activity in response to specific stimuli.

This manuscript describes a method to visualize and quantify localized translation events in subcellular compartments. The approach proposed in this manuscript requires a basic confocal imaging system and reagents and is rapid and cost-effective.

The mechanisms regulating mRNA translation are involved in various biological processes, such as germ line development, cell differentiation, and ...

Biochemistry

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

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

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

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

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

Developmental Biology

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

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

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

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

Bioengineering

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, ...

Rapid Analysis and Exploration of Fluorescence Microscopy Images

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

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

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

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 ...

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Cellular processes are complex and result from the interplay between multiple cell types and their environment. Existing cell biology techniques often do not allow for accurate interpretation of this interplay. Using a quantitative imaging-based approach, we present a high-content protocol for characterizing the dynamic phenotypic responses (i.e. morphology changes, proliferation, apoptosis) of heterogeneous cell populations to changes in environmental stimuli. We highlight our ability to distinguish between cell types based upon either fluorescence intensity or inherent morphology features depending on the application. This platform allows for a more comprehensive characterization of subpopulation response to perturbation while utilizing shorter time, smaller amounts of reagents, and lower likelihood of error than traditional cell biology assays. However, in some cases, cell populations may be difficult to identify and quantitate based on complex cellular features and will require additional troubleshooting; we highlight some of these circumstances in the protocol. We demonstrate this application using response to drug in a cancer model; however, it can easily be applied more broadly to other physiological processes. This protocol allows one to identify subpopulations within a co-culture system and characterize the particular response of each to external stimuli.

We have developed a novel protocol for studying heterocellular population dynamics in response to perturbations. This manuscript describes an imaging-based platform that produces quantitative datasets for simultaneous characterization of multiple cellular phenotypes of heterocellular populations in a robust manner.

Cellular processes are complex and result from the interplay between multiple cell types and their environment. Existing cell biology techniques often do ...

Application of Membrane and Cell Wall Selective Fluorescent Dyes for Live-Cell Imaging of Filamentous Fungi

The application of membrane and cell wall selective fluorescent dyes for live-cell imaging analyses of organelle dynamics in fungal cells started two decades ago and since then continues to contribute greatly to our understanding of the filamentous fungal lifestyle. This paper provides a practical guide for the utilization of the two membrane dyes FM 1-43 and FM 4-64 and the four cell wall stains Calcofluor White M2R, Solophenyl Flavine 7GFE 500, Pontamine Fast Scarlet 48 and Congo Red. The focus is on their low-dose application to ascertain artefact-free staining, their co-imaging properties, and their quantitative evaluation. The presented methods are applicable to all filamentous fungal samples that can be prepared in the described ways. The fundamental staining approaches can serve as starting points for adaptations to species that might require different cultivation conditions. First, biophysical and biochemical properties are reviewed as their understanding is essential for using these dyes as truly vital fluorescent stains. Secondly, step-by-step protocols are presented that detail the preparation of various fungal sample types for fluorescent live-cell imaging. Finally, example experiments illustrate different approaches to: (1) identify defects in the spatio-temporal organization of endocytosis in genetic mutants, (2) comparatively characterize shared and distinct co-localization of GFP-labeled target proteins in the endocytic pathway, (3) identify morphogenetic cell wall defects in a genetic mutant, and (4) monitor cell wall biogenesis in real time.

Vital fluorescent dyes are essential tools for live-cell imaging analyses in modern fungal cell biology. This paper details the application of established and lesser-known fluorescent dyes for tracking plasma membrane dynamics, endo-/exocytosis and cell wall morphogenesis in filamentous fungi.

The application of membrane and cell wall selective fluorescent dyes for live-cell imaging analyses of organelle dynamics in fungal cells started two ...

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

Summary

Explore More Videos

Visualizing Cell-to-cell Transfer of HIV using Fluorescent Clones of HIV and Live Confocal Microscopy

Quantitative Immunofluorescence to Measure Global Localized Translation

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

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

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

Quantitative Analysis of Autophagy using Advanced 3D Fluorescence Microscopy

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Open Source High Content Analysis Utilizing Automated Fluorescence Lifetime Imaging Microscopy

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Application of Membrane and Cell Wall Selective Fluorescent Dyes for Live-Cell Imaging of Filamentous Fungi