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CellProfiler is provided for free open-source software created to enable biologists without education in computer vision or programming to quantitatively measure phenotypes from countless images automatically. See our papers on analyzing cell images and non-cell images.
CellProfiler Analyst costs nothing open-source software for exploring and analyzing large, high-dimensional image-derived data. It includes machine learning tools for identifying complex and subtle phenotypes. See our papers on data visualization and machine learning.
CellProfiler incorporates a new forum! Visit us at All posts and account information remains to be the same. Also, on November 23, Mark Bray can have a CellProfiler tutorial for the Broad Institute. Email for signup information and be notified about future tutorials
Just announced! Mark Bray on the Broad Institutes Imaging Platform will show a CellProfiler tutorial on the 2015 Annual Society of Biomolecular Imaging and Informatics Meeting in Boston, Ma. The tutorial is going to take place on September 15, 2015. For more information, as well as register, visit /2nd-annual-conference/. We look forward to seeing you there!
The CellProfiler team is content to announce the making of CellProfiler 2.1.1. This can be a scheduled patch release whose primary purpose would be to address any issues by two.1.0. details
Great news! We are content to announce which the NIH continue to support CellProfiler through 2018 R01 GM089652. We are grateful that NIH recognizes the city value of open source tools as well as the variety of research that widely advantages of their continued maintenance. more info
Our CellProfiler software has yielded discoveries in many translational projects, most of which may ultimately possess a direct influence on the treatment of disease. For example, CellProfiler has become used to recognize several small molecules which can be effective in treating particular diseases in mouse models. In certain instances, discoveries made using CellProfiler have even generated planning clinical studies in humans, which often can directly improve patient outcomes. more information
Gain direct access on the CellProfiler team, while supporting the CellProfiler project!
Starting in 2014, we are thrilled to offer the CellProfiler Annual Support and Training Plan, a fortune of services certainly optimize your usage of CellProfiler.
Subscribing organizations will get project-specific image assay development, training, configuration assistance, and custom software engineering to provide features of interest for the open-source CellProfiler project. Support is provided by email, phone, plus-person at our laboratory in Cambridge, Massachusetts. Plus, access to easily installable, code-signed packages of unreleased versions of CellProfiler and 20% discounts on CellProfiler training workshops.
The CellProfiler team is excited to announce the production of CellProfiler 2.1.0!
In this major update, we now have made it better to load complicated collections of images and associated data. The new version is usually many times faster on computers with multiple CPU cores, so very large teams of images very easily processed over a regular computer system. Memory handling is usually improved, and then there are many new image processing options, features, usability enhancements, speed improvements, and bug fixes. More details are in the discharge notes: /CellProfiler/CellProfiler/wiki/CellProfiler-release-notes
Mark-Anthony Bray recently presented a webinar on CellProfiler entitled Using CellProfiler for Biological Image Analysis for CYTO University. Check out the overview and also the webinar.
CellProfiler is provided for free open-source software built to enable biologists without lessons in computer vision or programming to quantitatively measure phenotypes from a huge number of images automatically. See our papers on analyzing cell images and non- cell images.
CellProfiler Analyst is free of charge open-source software for exploring and analyzing large, high-dimensional image-derived data. It includes machine learning tools for identifying complex and subtle phenotypes. See our papers on data visualization and machine learning.
CellProfiler incorporates a new forum! Visit us at All posts and account information continues to be same. Also, on November 23, Mark Bray can have a CellProfiler tutorial for the Broad Institute. Email for signup information also to be notified about future tutorials
Just announced! Mark Bray on the Broad Institutes Imaging Platform will show a CellProfiler tutorial on the 2015 Annual Society of Biomolecular Imaging and Informatics Meeting in Boston, Ma. The tutorial will need place on September 15, 2015. For more information, also to register, visit /2nd-annual-conference/. We look forward to seeing you there!
The CellProfiler team is thrilled to announce the discharge of CellProfiler 2.1.1. This is usually a scheduled patch release whose primary purpose is usually to address any issues by 50 %.1.0. details
Great news! We are thrilled to announce how the NIH continues to support CellProfiler through 2018 R01 GM089652. We are grateful that NIH recognizes the city value of open source tools and also the variety of research that widely advantages from their continued maintenance. more info
Our CellProfiler software has yielded discoveries in many translational projects, most of which may ultimately employ a direct affect the treatment of disease. For example, CellProfiler has become used to recognize several small molecules which can be effective in treating particular diseases in mouse models. In certain cases, discoveries made using CellProfiler have even ended in planning numerous studies in humans, which may directly improve patient outcomes. more info
Tutorial videos for that WormToolbox detailed in a very recent publication in Methods at the moment are accessible at /WormToolbox.
Gain direct access for the CellProfiler team, while supporting the CellProfiler project!
Starting in 2014, we are thrilled to offer the CellProfiler Annual Support and Training Plan, a fortune of services to enable you to optimize your using CellProfiler.
Subscribing organizations will get project-specific image assay development, training, configuration assistance, and custom software engineering to provide features of interest towards the open-source CellProfiler project. Support emerges by email, phone, plus in-person at our laboratory in Cambridge, Massachusetts. Plus, get access to easily installable, code-signed packages of unreleased versions of CellProfiler and 20% discounts on CellProfiler training workshops.
The CellProfiler team is excited to announce the discharge of CellProfiler 2.1.0!
In this major update, we now have made it simpler to load complicated collections of images and associated data. The new version can often be many times faster on computers with multiple CPU cores, so very large teams of images can be processed with a regular laptop or computer. Memory handling is additionally improved, high are many new image processing options, features, usability enhancements, speed improvements, and bug fixes. More details are in the discharge notes: /CellProfilerCellProfiler/wiki/CellProfiler - release-notes
Mark-Anthony Bray recently presented a webinar on CellProfiler entitled Using CellProfiler for Biological Image Analysis for CYTO University. Check out the overview along with the webinar.
Would you want us to share with you about new CellProfiler releases as well as other major announcements? You will get approximately five emails per annum.
If you will find CellProfiler helpful, please cite it.
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The easiest way to learn understanding CellProfiler and CellProfiler Analyst is usually to download our examples and try out!
Download example images together with pipelines getting immediate hands-on experience of using CellProfiler.
Select a good example from the pipeline list below the place that the cell type or object type resembles yours. You can move your mouse over a picture below to view an example in the CellProfiler analysis, or click to discover an expanded view.
Download the example images and pipeline in the list below and run it in CellProfiler to view how it works.
Try the pipeline yourself images.
Adjust the pipeline to distinguish objects properly as part of your images. This frequently includes changing the dimensions range in the objects. See the Tutorials page for step by step instructions.
Still stuck? See if your question has become answered around the forum.
CellProfiler pipelines, from the 2.1.0 version formats.
Example images which might be to be used as input for that pipeline.
These pipelines are manufactured for simple cellular and tissue image assays, and can include some basic measurements. Human cells: Human HT29 cells are fairly smooth and elliptical. This pipeline demonstrates tips on how to accurately identify these cells and the ways to measurements cellular parameters for instance morphology, count, intensity and texture.
Fruit fly cells: In comtrast to your HT29 cells, Drosophila Kc167 cells certainly are a highly textured and clumpy cell type. This pipeline demonstrates how to recognize these clumpy cells and have morphological, intensity and texture measurements.
Tumors: A simple pipeline that identifies and counts tumors in the mouse lung, after which measures their size.
Comet assay This is really a simple illustration showing a DNA damage assay using single cell gel electrophoresis. Here, the measurement interesting is the length and intensity on the comet tail. Also, illumination correction is utilized to reduce background flourescence before measurement. Also shown is usually a silver-stained comet example where the percentage of DNA contained inside tail is calculated.
In addition to cellular object and have identification, these pipelines include some on the more specialized modules in CellProfiler for image pre-processing or measurement. Cell/particle counting, and scoring the amount of stained objects: CellProfiler is often used to count cells or any other objects and also percent-positives, by measuring the per-cell staining intensity. This pipeline shows the way to do those two tasks, and demonstrates how various modules may be employed to accomplish the identical result.
Yeast colony classification: This pipeline demonstrates tips on how to classify and count objects within the basis of their measured features. The example identifies uniformly round objects, in such cases, yeast colonies growing using a dish. The pipeline also shows tips on how to load a template and align it to some cropped image, as well as using illumination correction to subtract for background illumination.
Yeast patch identification: This pipeline identifies patches of yeast growing within a 96 well plate, in the role of an introduction to your grid defintion and identification modules.
Annotation: Example labeling a cell microarray having an annotated grid. Download
Tissue Neighbors: Tissue samples often times have irregularly shaped cells with adjacent edges. This pipeline shows tips on how to input a color tissue image, split it into its component channels, then identify individual cells coming from a particular stain and record the volume of neighbors that cell has.
Wound Healing: In this example, cells are grown being a tissue monolayer. Rather than identifying individual cells, this pipeline quantifies the region occupied through the tissue sample.
Illumination Correction: Illumination correction is usually important for both accurate segmentation as well as for intensity measurements. This example shows that this CorrectIlluminationCalculate and CorrectIlluminationApply modules are accustomed to compensate for that non-uniformities in illumination often seen in microscopy images.
Colocalization: Measuring the colocalization between fluorescently labeled molecules is often a widely used appraoch to study the degree of spatial coincidence and potential interactions among subcellular species, proteins. This example shows what sort of object identifcation and RelateObjects modules are accustomed to measure the level of overlap between two fluorescent channels.
These pipelines are actually developed for high-throughput screens on C. elegans and extract measurements over a per-worm basis. The Worm Toolbox page has further information on this workflow, and also video tutorials, pipelines and image data as well as those described below. from C W
the projects survival is dependent upon it!
Mac OS X 10.5 or later Intel only
New in CellProfiler Analyst 2.0 r11710:
Thumbnail preview in plateviewer was broken.
Densityplot incorrectly always plots ImageNumber on x-axis.
Per-object data points were being interpreted as image keys by scatterplot when plotting per-image x axis vs. per-object y axis.
Last updated on April 9th, 2015
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then open the DMG, copy for your Applications folder
Note: CPAnalyst cannot run without data to assess.
Example data and accompanying images to be used with CPAnalyst 2.0 are offered here.
The following are links to your repositories where one can get the most current version with the source code and accompanying files.
bmcbioinformatics/article/10.1186/1471/2105/9/482
Keep up-to-date with the latest news and content from BMC Bioinformatics and BioMed Central.
Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
Computer Sciences and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
2008 Jones et al; licensee BioMed Central Ltd.
This is undoubtedly an Open Access article distributed underneath the terms in the Creative Commons Attribution License /licenses/by/2.0, which permits unrestricted use, distribution, and reproduction in different medium, provided the main work is properly cited.
Image-based screens can produce countless measured features for each and every of numerous millions of person cells in a very single experiment.
Here, we describe CellProfiler Analyst, open-source software for that interactive exploration and analysis of multidimensional data, particularly data from high-throughput, image-based experiments.
The system enables interactive data exploration for image-based screens and automated scoring of complex phenotypes that want combinations of multiple measured features per cell.
Visual analysis of cell samples has played a dominant role from the history of biology. The scientific community only has begun to scratch the surface of computationally extracting the rich information visible in fluorescence microscopy images of cell samples 1. This capability is increasingly important due to the ease now to systematically perturb cells with libraries of chemicals or gene-perturbing reagents like RNA interference or gene overexpression and collect hundreds of 1000s of images of the cell samples 2, 3. We recently developed open-source image analysis software, CellProfiler, which measures an abundant set of cellular features in images, including size, shape, and staining patterns including intensity, texture, and colocalization 4, 5 webcite. This tool has become useful for extracting image-based measurements to attain sophisticated screens 6 - 8, with numerous more beginning.
The volume and richness of person-cell data from large image-based screens is unprecedented and existing software programs are inadequate with the challenge of knowledge analysis. For analysis of small or quite simple experiments, spreadsheet programs like Microsoft Excel are sufficient, and useful open-source tools exists for analysis and exploration of knowledge from high throughput screens on the whole 9 - 12. Existing software products targeted for image-based screening, however, have several limitations which prevent sophisticated visualization and extraction of info from image-based screens: a they may not be designed for your hierarchical data structure inherent in image-based data each treatment condition is replicated in many samples, each sample is frequently represented by a few images, each image has a population of cells, and every cell has many associated measures, b they neglect the inherent biological variability of cell populations to ensure that assays requiring subpopulation analysis can't be scored, c they are unable to handle the volumes of information typical in image-based experiments, 500 measurements for every single of 100 million individual cells, d they feature limited linking to raw or processed image data or chemical structure data, e they enable only limited statistical analyses with the data, f they may be proprietary and new methods are not easily added, g these are limited to data at a particular image analysis package, h they might require expertise in statistics or programming, and/or i they might need intense hands-on data management.
Given that no existing tools satisfy the specific needs of image-based screens, researchers have needed computational expertise to directly query databases of image-based information using command-line tools. Often, the study best capable to explore and interpret your data lack these computational skills. These researchers are thus less likely to produce serendipitous discoveries or identify quality-control issues into their image-based screens, which inherently contain enormous amounts of real information beyond whatever is pertinent to your original, intended biological question. It is critical to provide exploration tools to screening researchers, tools that employ their understanding from the experiment showcased and their creativity and chance to recognize and interpret patterns and relationships within data. These capabilities flourish when united that has a computers unique power to store, retrieve, display, and quantitatively analyze billions of internet data points.
We therefore sought to build up a software system that could make high-dimensional image-based data exploration easy for researchers who lack computational skills, and flexible for computer scientists who want to build up and add advanced new techniques for image-based screening, for example machine learning-based phenotype scoring. We describe here caused by our work, an open-source computer software called CellProfiler Analyst.
Four varieties of plots include the starting points for investigation of large, multi-dimensional image-based screens in CellProfiler Analyst Figure 1. Importantly, this software are works with the scale of web data typically acquired in image-based screens, which can be many features for every single of numerous millions of cells. Histograms display the distribution of values for just one measured feature by grouping image or object data into evenly spaced bins, on the linear or logarithmic scale Figure 1a. Such plots may help, one example is, to analyze the cell cycle status of samples by plotting per-cell DNA content or to consider outliers for quality control purposes, by plotting per-image cell counts. Two measured features per image or object may be displayed within the same chart using a scatterplot Figure 1b, which is additionally useful for identifying hits as well as for quality control purposes. For example, the researcher can readily exclude out-of-focus images from analysis dependant on measurements produced by CellProfilers Measure Image Quality module. Because data points in scatterplots can occlude 1 another, they can be typically unsuitable for individual cell data where countless millions of internet data points are examined to distinguish interesting subpopulations. For these cases, a density plot is much more appropriate Figure 1c. Every pixel inside the plot represents a histogram bin plus the color with the pixel represents the amount of data points inside the bin. These plots are of help, for instance, for establishing thresholds from which to classify individual cells as positive or negative according to two features, according to two intensity measures such as flow cytometry. To explore over two measured highlights of each image or data point, a parallel coordinate plot is utilized. Parallel coordinate plots 13 allow analysis of multiple dimensions of web data, whereby each measured features scaled 01 values are shown a separate y-axis and individual data points are connected across these multiple axes Figure 1d.
Figure 1. Four varieties of plots can be accomplished. Four varieties of plots produced by CellProfiler Analyst are shown. a A histogram of per-image data the mean division of all cells within the image. b Scatterplot of image data X-axis mean section of all cells from the image; Y-axis mean part of all nuclei from the image. One particular sample as well as its replicates are highlighted as blue data points, as well as the blue lines indicate/- two standard deviations on the mean, although because the info is non-Gaussian, the left standard deviation line is not visible. c Density plot of human cell data X-axis area on the cell; Y-axis area from the nucleus. d Parallel coordinate plot where 6 features are plotted, one on each numbered axis as labeled from the table shown below the plot. The four selected blue data points inside the scatterplot can also be highlighted from the parallel coordinate plot as blue lines. From this plot, it truly is apparent these four data points have high cell and nuclear area two left-most coordinates but low cell count right-most coordinate.
Each data point inside a plot can represent somebody cell or, by comparison, the mean value with the population of cells within a photo. Data can even be grouped by characteristics the samples have in common, chemical name or dose. Multiple experiments that investigate precisely the same set of treatment conditions, substances or RNA interference reagents might be grouped together, which eases analysis of replicates. For all forms of plots, the information to be displayed could be filtered, for instance to plot data only from the single image, from your sample of knowledge points at specified equal intervals, or data that satisfies certain criteria specified by SQL where clauses like CellCount 100.
Data points selected and highlighted a single plot are immediately highlighted in every other open plots a way often called brushing 14 in a way that a sample or group of samples might be examined from the context of other groups of samples Figure 2. This allows, by way of example, the comparison of measurements from samples appealing vs. all samples inside experiment. Brushing helps anyone to more readily examine relationships within the data, specifically when the data incorporates a large volume of attributes or items, when your data spans multiple experiments including, one example is, replicates, or when it can be natural to analyze different parts in the data using different views. The brushing concept is extended in CellProfiler Analyst for situations where multiple experiments are now being simultaneously explored: each time a point corresponding to your particular image is highlighted, all points corresponding to that experimental treatment condition could be highlighted, even if the information comes from multiple experiments which might be being examined together. In the scatterplot in Figure 1b, for instance, four data points are blue because one was originally selected as well as the user requested that replicates with the sample be highlighted.
Figure 2. Relationships among data is usually explored. Data points representing images with good nuclear area averaged within the cells within the image and high DNA intensity averaged on the cells within the image are highlighted in blue by brushing the scatterplot shown in a very. Immediately, the attached points appear blue from the other open scatterplot b, allowing the connection between all in the plotted features to become examined. As well, a DNA content histogram c shows individual cell data in the selected image data points blue, in accordance with all cells inside the experiment red. In this case, the selected blue data points indeed offer an unusual cell cycle distribution fewer 2N cells in accordance with 4N cells in comparison with all cells inside the experiment. Finally, the sample names d, Hairpin column, because of this particular RNA interference experiment corresponding for the selected blue data points may be displayed within a table to view which samples are present within the selected points. The first two columns from the table show additional information about those data points based for the axes on the scatterplot shown in the. The ImageNumber and well columns provide more details about the samples the researcher is investigating.
Interesting data points or groups of data points could be investigated by drilling on to the data in lots of ways Figure 3. For plots showing data points representing image measurements, a data point or list of data points might be selected and also the original images that produced the info point can look Figure 3d. This can reveal artifacts in sample preparation or imaging, including fluorescent test compounds, aggregates or overabundance of staining reagents, fibers, or debris Figure 3g. These artifacts not simply occlude actual cells in images but they can also disrupt the appropriate identification and measurement of remaining cells within the image. For these as well as other reasons, images showing identification outlines presented by image analysis if available may also be shown for selected data points Figure 3e, to spot whether the identification of cells occurred properly. This is surely an important consideration considering the fact that no segmentation algorithms are flawless.
Figure 3. Data points may be investigated. The data points highlighted in blue inside scatterplot a represent replicates of a specific treatment condition rich in mean cell area and nuclear area. To examine the cell cycle distribution of the samples, a DNA content histogram depending on individual cells within those four images was plotted b. For one in the four data points, the researcher displayed a table of the measurements within the database c, the raw image d, or with outlines overlaid e. Each staining/channel red, green, blue might be toggled on or off permitting close examination on the relationships with shod and non-shod. Information at a public website describing the gene tested in one in the samples is displayed f by clicking the info point. Outlier data points for sure measured features, high mean cell actin intensity, high mean cell area, high segmentation threshold, or percent of pixels which might be saturated could mean images with severe artifacts g which should be excluded from analysis. These images is usually identified by their aberrant measurements and excluded from further analysis by gating, selecting simply a subset of information to be plotted and analyzed further.
Additionally, a data point or number of data points might be selected as well as a plot with the measurements of human cells that were within those images could be displayed being a separate subplot. This allows, by way of example, a DNA content histogram indicating cell cycle distribution with the cell population for being displayed for a specific image or group of images appealing Figure 2c and Figure 3b. To investigate the identity of interesting samples, a straightforward list from the treatment problems that produced a pair of data points can seem to get a review Figure 2d. For further investigation, web-based information regarding each images treatment condition is usually launched in a external internet browser Figure 3f, if web addresses connected with each sample are stored inside the database. All available measurements and also other information for a selected sample might be displayed in the simple table and saved like a comma-delimited text declare analysis in another computer software Figure 3c.
Image-based results are tremendously valuable in that multiple single-cell measurements can be obtained. Responses of human cells into a treatment are generally inhomogeneous due to cell cycle variations or differences in protein levels caused by memory or stochastic noise 15, 16. In many cases, one particular measured feature, the total power of red stain inside nucleus can be accustomed to score individual cells plus the only challenge is usually to identify a good threshold for scoring positive cells. This is usually accomplished in CellProfiler Analyst using histograms of person cell data. For complex phenotypes, several popular features of each cell may be needed for effective scoring. In these cases, a density plot showing individual cells Figure 4a can be a good choice for identifying interesting cell subpopulations, by delineating a section with the plot referred to as gating. Whether the gate has the cells interesting can be tested using two features: the Show Object Montage feature to find out what individual cells inside gate appear like Figure 4b, as well as the Show Image feature to discover whether cells within a selected sample are appropriately marked as inside or outside the gate Figure 4c. Once one more, desired subpopulation of cells is gated, the amount of cells that fall within that subpopulation is calculated for every image, for additional statistical analysis Figure 4d. As an illustration, when DNA and phosphorylated Serine 10 of histone H3 are generally stained, an easy two-feature gate in CellProfiler Analyst enables scoring mitotic subphases in human HT29 cells Figure 4e. Many software systems perform image analysis within the fly during image acquisition; in these instances, a threshold value for just a feature of great interest must be chosen in advance to get the screen. By contrast, this equipment in CellProfiler Analyst allow testing the efficacy of scoring dependant on different features and different measurement thresholds.
Figure 4. Cell subpopulations may be identified, examined, and scored. a On a density plot of person cell data log scale: X-axis integrated concentration of nuclear DNA; Y-axis nuclear area, two populations were gated white boxes and also a random number of cells within each subpopulation is shown inside montages about the right b; all gated cells present within a particular sample can be marked c. Samples might be scored d for that number of gated cells and total cells in each sample, the enrichment of this percentage relative towards the overall number of positive cells within the entire experiment Enrichment; by way of example, the initial image listed from the table has 19.311-fold more cells within the subpopulation than typical from the experiment overall, along with the left- and right-tail log
p-values a measure with the statistical significance on the enrichment, based for the number of cells inside the sample. e Gates for anaphase/telophase and late prophase/metaphase info is plotted for all those human HT29 cells inside the experiment 7. X-axis integrated nuclear concentration of DNA, log scale; Y-axis mean nuclear concentration of phospho-histone H3. Random cells falling inside gates are shown from the center of each and every 34 x 34 mm subimage.
If in excess of two features are needed to get a phenotype, sequential gates may be used upon the cell data. This approach is applied as follows: 1 display the full population of cells from an experiment within a density plot, 2 draw a gate around the details points representing potential cells of great interest, 3 adjust the gate to incorporate nearly all positive cells and exclude several negative cells as is possible, 4 plot the resulting gated subpopulation in a very new density plot with two new measurement features as axes, 5 gate the subpopulation again depending on these additional features, and 6 calculate the portion of each images cells that fall from the final gate.
We planned to test CellProfiler Analysts capacity to plot, explore, and filter individual cell data to spot subpopulations defined by a number of morphological features. We chose to spot Drosophila melanogaster Kc167 cells in telophase and metaphase in the cell cycle, using merely a DNA stain. Identification of samples with perturbed cell cycle regulation is of clear importance on track cell biology in addition to cancer studies. Regulators on the cell cycle are actually sought intensively for several years via traditional and high-throughput screens for modifications to overall cell cycle distribution or increased phospho-histone H3 staining, a marker of cells in late G2 and M phase, 17 and references therein. We reasoned that additional genes might exist which, when perturbed, yield increased quantities of metaphase- or telophase-stage nuclei without substantially affecting the general mitotic index phospho-histone H3 staining or cell cycle distribution. While we weren't aware of any positive controls with your a phenotype, we suspected such genes might have already been previously overlooked because we pointed out that not all metaphase nuclei stain brightly for phospho-histone H3 Figure 5a, for unknown reasons. Identifying genes whose RNAi produces cells appearing for being in particular subphases of mitosis, no matter what concomitant phospho-histone H3 staining, is a first step towards understanding these phenomena.
Figure 5. Drosophila cell microarrays. a Phospho-histone H3 staining is frequently dim for metaphase nuclei left and middle vs. right. Scale bar 5
bmcbioinformatics/article/10.1186/1471/2105/9/482
Keep current with the latest news and content from BMC Bioinformatics and BioMed Central.
Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
Computer Sciences and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
2008 Jones et al; licensee BioMed Central Ltd.
This is undoubtedly an Open Access article distributed within the terms on the Creative Commons Attribution License /licenses/by/2.0, which permits unrestricted use, distribution, and reproduction in different medium, provided the first work is properly cited.
Image-based screens can produce countless measured features for every of numerous millions of human cells in a very single experiment.
Here, we describe CellProfiler Analyst, open-source software to the interactive exploration and analysis of multidimensional data, particularly data from high-throughput, image-based experiments.
The system enables interactive data exploration for image-based screens and automated scoring of complex phenotypes which require combinations of multiple measured features per cell.
Visual analysis of cell samples has played a dominant role from the history of biology. The scientific community just has begun to scratch the surface of computationally extracting the rich information visible in fluorescence microscopy images of cell samples 1. This capability is increasingly important in the ease now to systematically perturb cells with libraries of chemicals or gene-perturbing reagents like RNA interference or gene overexpression and collect hundreds of a huge number of images of such cell samples 2, 3. We recently developed open-source image analysis software, CellProfiler, which measures an abundant set of cellular features in images, for instance size, shape, and staining patterns including intensity, texture, and colocalization 4, 5 webcite. This tool may be useful for extracting image-based measurements to gain sophisticated screens 6 - 8, with lots of more beginning.
The volume and richness of person-cell data from large image-based screens is unprecedented and existing software program is inadequate for your challenge of information analysis. For analysis of small or very easy experiments, spreadsheet programs like Microsoft Excel are sufficient, and useful open-source tools are available for analysis and exploration of web data from high throughput screens normally 9 - 12. Existing software systems targeted for image-based screening, however, have a number limitations which prevent sophisticated visualization and extraction of data from image-based screens: a they're not designed with the hierarchical data structure inherent in image-based data each treatment condition is replicated in a number of samples, each sample is normally represented by a few images, each image posesses a population of cells, each cell has countless associated measures, b they disregard the inherent biological variability of cell populations in a way that assays requiring subpopulation analysis are not scored, c they can not handle the volumes of knowledge typical in image-based experiments, 500 measurements per of 100 million individual cells, d they offer limited linking to raw or processed image data or chemical structure data, e they enable only limited statistical analyses from the data, f they can be proprietary and new methods are not easily added, g they're limited to data from the particular image analysis package, h they want expertise in statistics or programming, and/or i they want intense hands-on data management.
Given that no existing tools match the specific needs of image-based screens, researchers have needed computational expertise to directly query databases of image-based information using command-line tools. Often, they best competent to explore and interpret the information lack these computational skills. These researchers therefore are less likely to create serendipitous discoveries or identify quality-control issues within their image-based screens, which inherently contain enormous amounts of real information beyond truley what is pertinent towards the original, intended biological question. It is critical to provide exploration tools to screening researchers, tools that employ their understanding with the experiment you want and their creativity and chance to recognize and interpret patterns and relationships within data. These capabilities flourish when united which has a computers unique power to store, retrieve, display, and quantitatively analyze billions of knowledge points.
We therefore sought to produce a software system that will make high-dimensional image-based data exploration straightforward for researchers who lack computational skills, and flexible for computer scientists who want to build up and add advanced new strategies to image-based screening, for example machine learning-based phenotype scoring. We describe here caused by our work, an open-source software program called CellProfiler Analyst.
Four forms of plots would be the starting points for search for large, multi-dimensional image-based screens in CellProfiler Analyst Figure 1. Importantly, this software are that will work with the scale of information typically acquired in image-based screens, which can be many features for each and every of numerous millions of cells. Histograms display the distribution of values for just one measured feature by grouping image or object data into evenly spaced bins, over a linear or logarithmic scale Figure 1a. Such plots is a good idea, one example is, to analyze the cell cycle status of samples by plotting per-cell DNA content or to check outliers for quality control purposes, by plotting per-image cell counts. Two measured features per image or object could be displayed within the same chart by way of a scatterplot Figure 1b, which can also be useful for identifying hits and then for quality control purposes. For example, the researcher can readily exclude out-of-focus images from analysis dependant on measurements manufactured by CellProfiler s Measure Image Quality module. Because data points in scatterplots can occlude the other person, they're typically unsuitable for individual cell data where numerous millions of knowledge points are examined to distinguish interesting subpopulations. For these cases, a density plot might be more appropriate Figure 1c. Every pixel within the plot represents a histogram bin along with the color with the pixel represents the volume of data points within the bin. These plots are of help, by way of example, for establishing thresholds when to classify individual cells as positive or negative dependant on two features, according to two intensity measures as with flow cytometry. To explore over two measured options that come with each image or data point, a parallel coordinate plot is needed. Parallel coordinate plots 13 allow analysis of multiple dimensions of knowledge, whereby each measured features scaled 01 values are made a separate y-axis and individual data points are connected across these multiple axes Figure 1d.
Figure 1. Four kinds of plots can be achieved. Four forms of plots manufactured by CellProfiler Analyst are shown. a A histogram of per-image data the mean region of all cells inside image. b Scatterplot of image data X-axis mean section of all cells within the image; Y-axis mean section of all nuclei from the image. One particular sample and it is replicates are highlighted as blue data points, plus the blue lines indicate/- two standard deviations on the mean, although because the details is non-Gaussian, the left standard deviation line is not visible. c Density plot of person cell data X-axis area with the cell; Y-axis area in the nucleus. d Parallel coordinate plot where 6 features are plotted, one on each numbered axis as labeled from the table shown below the plot. The four selected blue data points inside the scatterplot are highlighted from the parallel coordinate plot as blue lines. From this plot, it truly is apparent these particular four data points have high cell and nuclear area two left-most coordinates but low cell count right-most coordinate.
Each data point within a plot can represent a person cell or, electrical systems, the mean value from the population of cells within a perception. Data can be grouped by characteristics the samples have in common, chemical name or dose. Multiple experiments that investigate the identical set of treatment conditions, chemical substances or RNA interference reagents is usually grouped together, which eases analysis of replicates. For all forms of plots, the info to be displayed is usually filtered, as an example to plot data only from the single image, at a sample of information points at specified equal intervals, or data that satisfies certain criteria per SQL where clauses like CellCount 100.
Data points selected and highlighted a single plot are immediately highlighted in every other open plots an approach often called brushing 14 in a way that a sample or number of samples is usually examined from the context of other teams of samples Figure 2. This allows, for instance, the comparison of measurements from samples appealing vs. all samples within the experiment. Brushing helps an individual to with less effort examine relationships inside the data, particularly when the data features a large amount of attributes or items, when your data spans multiple experiments including, by way of example, replicates, or when it's natural to analyze different parts with the data using different views. The brushing concept is extended in CellProfiler Analyst for situations where multiple experiments are simultaneously explored: every time a point corresponding into a particular image is highlighted, all points corresponding to that experimental treatment condition could be highlighted, even if the info comes from multiple experiments which can be being examined together. In the scatterplot in Figure 1b, for instance, four data points are blue because one was originally selected plus the user requested that replicates to the sample be highlighted.
Figure 2. Relationships among data might be explored. Data points representing images with higher nuclear area averaged above the cells inside the image and high DNA intensity averaged in the cells inside the image are highlighted in blue by brushing the scatterplot shown in the. Immediately, the related points appear blue inside the other open scatterplot b, allowing their bond between all from the plotted features for being examined. As well, a DNA content histogram c shows individual cell data on the selected image data points blue, in accordance with all cells from the experiment red. In this case, the selected blue data points indeed come with an unusual cell cycle distribution fewer 2N cells in accordance with 4N cells in comparison with all cells inside experiment. Finally, the sample names d, Hairpin column, due to this particular RNA interference experiment corresponding on the selected blue data points may be displayed within a table to discover which samples are present inside the selected points. The first two columns in the table show more information about those data points based for the axes in the scatterplot shown in the. The ImageNumber and well columns provide more details about the samples the researcher is investigating.
Interesting data points or groups of data points could be investigated by drilling into the data in many ways Figure 3. For plots showing data points representing image measurements, a data point or pair of data points is usually selected and also the original images that produced the info point can be Figure 3d. This can reveal artifacts in sample preparation or imaging, for example fluorescent test compounds, aggregates or overabundance of staining reagents, fibers, or debris Figure 3g. These artifacts not simply occlude actual cells in images but tend to also disrupt the right identification and measurement of remaining cells inside the image. For these as well as other reasons, images showing identification outlines due to image analysis if available may also be shown for selected data points Figure 3e, to recognize whether the identification of cells occurred properly. This is definitely an important consideration considering that no segmentation algorithms are flawless.
Figure 3. Data points is usually investigated. The data points highlighted in blue inside scatterplot a represent replicates of a specific treatment condition with higher mean cell area and nuclear area. To examine the cell cycle distribution of the samples, a DNA content histogram according to individual cells within those four images was plotted b. For one on the four data points, the researcher displayed a table of the measurements from the database c, the raw image d, or with outlines overlaid e. Each staining/channel red, green, blue might be toggled on or off permitting close examination with the relationships totally. Information from the public website describing the gene tested in one from the samples has become displayed f by clicking the details point. Outlier data points for several measured features, high mean cell actin intensity, high mean cell area, high segmentation threshold, or percent of pixels which can be saturated could mean images with severe artifacts g which should be excluded from analysis. These images is usually identified by their aberrant measurements and excluded from further analysis by gating, selecting simply a subset of internet data to be plotted and analyzed further.
Additionally, a data point or group of data points could be selected along with a plot with the measurements of human cells that were contained in those images might be displayed as being a separate subplot. This allows, by way of example, a DNA content histogram indicating cell cycle distribution from the cell population to get displayed for a certain image or group of images of great interest Figure 2c and Figure 3b. To investigate the identity of interesting samples, an effective list from the treatment conditions produced a group of data points can look to get a synopsis Figure 2d. For further investigation, web-based details about each images treatment condition could be launched in a external internet browser Figure 3f, if web addresses connected with each sample are stored from the database. All available measurements and also other information for a specific sample might be displayed in a very simple table and saved being a comma-delimited text register for analysis in another computer software Figure 3c.
Image-based details are tremendously valuable in that multiple single-cell measurements are offered. Responses of person cells into a treatment are generally inhomogeneous due to cell cycle variations or differences in protein levels on account of memory or stochastic noise 15, 16. In many cases, 1 measured feature, the total concentration of red stain in the nucleus can be employed to score individual cells along with the only challenge should be to identify the right threshold for scoring positive cells. This might be accomplished in CellProfiler Analyst using histograms of person cell data. For complex phenotypes, several top features of each cell are usually necessary for effective scoring. In these cases, a density plot showing individual cells Figure 4a can be great for identifying interesting cell subpopulations, by delineating a section with the plot known as gating. Whether the gate provides the cells interesting can be tested using two features: the Show Object Montage feature to view what individual cells from the gate seem like Figure 4b, and also the Show Image feature to find out whether cells within a certain sample are appropriately marked as inside or outside the gate Figure 4c. Once one more, desired subpopulation of cells is gated, how many cells that fall within that subpopulation is calculated for every single image, for even more statistical analysis Figure 4d. As an illustration, when DNA and phosphorylated Serine 10 of histone H3 both are stained, a straightforward two-feature gate in CellProfiler Analyst enables scoring mitotic subphases in human HT29 cells Figure 4e. Many software systems perform image analysis for the fly during image acquisition; in such instances, a threshold value for the feature interesting must be chosen in advance gain the screen. By contrast, power tools in CellProfiler Analyst allow testing the efficacy of scoring dependant on different features and different measurement thresholds.
Figure 4. Cell subpopulations could be identified, examined, and scored. a On a density plot of human cell data log scale: X-axis integrated power of nuclear DNA; Y-axis nuclear area, two populations were gated white boxes as well as a random collection of cells within each subpopulation is shown within the montages around the right b; all gated cells present in the particular sample can be marked c. Samples may be scored d to the number of gated cells and total cells in each sample, the enrichment of their percentage relative to your overall portion of positive cells from the entire experiment Enrichment; for instance, the initial image listed within the table has 19.311-fold more cells within the subpopulation than typical inside the experiment overall, as well as the left- and right-tail log
p-values a measure from the statistical significance on the enrichment, based around the number of cells inside the sample. e Gates for anaphase/telophase and late prophase/metaphase info is plotted for many human HT29 cells inside the experiment 7. X-axis integrated nuclear concentration of DNA, log scale; Y-axis mean nuclear power of phospho-histone H3. Random cells falling in the gates are shown inside the center of each and every 34 x 34 mm subimage.
If over two features are needed to get a phenotype, sequential gates is usually used upon the cell data. This approach is applied as follows: 1 display the full population of cells from an experiment in a very density plot, 2 draw a gate around your data points representing potential cells of curiosity, 3 adjust the gate to add nearly all positive cells and exclude numerous negative cells as you can, 4 plot the resulting gated subpopulation in the new density plot with two new measurement features as axes, 5 gate the subpopulation again dependant on these latest features, and 6 calculate the portion of each images cells that fall inside final gate.
We needed to test CellProfiler Analyst s chance to plot, explore, and filter individual cell data to recognize subpopulations defined by a few morphological features. We chose to name Drosophila melanogaster Kc167 cells in telophase and metaphase from the cell cycle, using simply a DNA stain. Identification of samples with perturbed cell cycle regulation is of clear importance to normalcy cell biology together with cancer studies. Regulators on the cell cycle happen to be sought intensively for years via traditional and high-throughput screens for adjustments to overall cell cycle distribution and for increased phospho-histone H3 staining, a marker of cells in late G2 and M phase, 17 and references therein. We reasoned that additional genes might exist which, when perturbed, yield increased amounts of metaphase- or telophase-stage nuclei without substantially affecting the complete mitotic index phospho-histone H3 staining or cell cycle distribution. While we just weren't aware of any positive controls with your a phenotype, we suspected such genes might are already previously overlooked because we pointed out that not all metaphase nuclei stain brightly for phospho-histone H3 Figure 5a, for unknown reasons. Identifying genes whose RNAi produces cells appearing being in particular subphases of mitosis, no matter what concomitant phospho-histone H3 staining, would have been a first step towards understanding these phenomena.
Figure 5. Drosophila cell microarrays. a Phospho-histone H3 staining can often be dim for metaphase nuclei left and middle vs. right. Scale bar 5
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BMC Bioinformatics. 2008; 9: 482.
Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
Computer Sciences and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
Received 2008 Jul 11; Accepted 2008 Nov 15.
This is undoubtedly an Open Access article distributed beneath the terms in the Creative Commons Attribution License /licenses/by/2.0, which permits unrestricted use, distribution, and reproduction in every medium, provided the main work is properly cited.
This article may be cited by other articles in PMC.
Image-based screens can produce numerous measured features for every of many millions of human cells in a very single experiment.
Here, we describe CellProfiler Analyst, open-source software for your interactive exploration and analysis of multidimensional data, particularly data from high-throughput, image-based experiments.
The system enables interactive data exploration for image-based screens and automated scoring of complex phenotypes that need combinations of multiple measured features per cell.
Visual analysis of cell samples has played a dominant role inside the history of biology. The scientific community merely has begun to scratch the surface of computationally extracting the rich information visible in fluorescence microscopy images of cell samples 1. This capability is increasingly important in the ease now to systematically perturb cells with libraries of chemicals or gene-perturbing reagents like RNA interference or gene overexpression and collect hundreds of 1000s of images these cell samples 2, 3. We recently developed open-source image analysis software, CellProfiler, which measures a refreshing set of cellular features in images, for instance size, shape, and staining patterns including intensity, texture, and colocalization 4, 5 This tool has become useful for extracting image-based measurements to gain sophisticated screens 6 - 8, with lots of more happening.
The volume and richness of person-cell data from large image-based screens is unprecedented and existing applications are inadequate for that challenge of information analysis. For analysis of small or really easy experiments, spreadsheet programs like Microsoft Excel are sufficient, and useful open-source tools exists for analysis and exploration of knowledge from high throughput screens generally 9 - 12. Existing software applications targeted for image-based screening, however, have a number of limitations which prevent sophisticated visualization and extraction of knowledge from image-based screens: a they aren't designed with the hierarchical data structure inherent in image-based data each treatment condition is replicated in numerous samples, each sample is normally represented by a number of images, each image includes a population of cells, and every cell has numerous associated measures, b they overlook the inherent biological variability of cell populations to ensure that assays requiring subpopulation analysis is not scored, c they can not handle the volumes of web data typical in image-based experiments, 500 measurements for every of 100 million individual cells, d they give limited linking to raw or processed image data or chemical structure data, e they permit only limited statistical analyses from the data, f they can be proprietary and new methods can't be easily added, g they're limited to data at a particular image analysis package, h they might need expertise in statistics or programming, and/or i they need intense hands-on data management.
Given that no existing tools fulfill the specific needs of image-based screens, researchers have needed computational expertise to directly query databases of image-based information using command-line tools. Often, the study best in a position to explore and interpret the info lack these computational skills. These researchers are thus less likely to generate serendipitous discoveries or identify quality-control issues inside their image-based screens, which inherently contain enormous amounts of data beyond what is pertinent to your original, intended biological question. It is critical to provide exploration tools to screening researchers, tools that employ their understanding from the experiment showcased and their creativity and capacity to recognize and interpret patterns and relationships within data. These capabilities flourish when united which has a computers unique power to store, retrieve, display, and quantitatively analyze billions of information points.
We therefore sought to formulate a software system that could make high-dimensional image-based data exploration practical for researchers who lack computational skills, and flexible for computer scientists who want to formulate and add advanced new means of image-based screening, like machine learning-based phenotype scoring. We describe here the consequence of our work, an open-source software program called CellProfiler Analyst.
Four sorts of plots would be the starting points for quest for large, multi-dimensional image-based screens in CellProfiler Analyst Figure Figure1. 1. Importantly, these power tools are appropriate for the scale of information typically acquired in image-based screens, which can be countless features for each and every of many millions of cells. Histograms display the distribution of values for starters measured feature by grouping image or object data into evenly spaced bins, over a linear or logarithmic scale Figure Figure1a. 1a. Such plots can help, for instance, to analyze the cell cycle status of samples by plotting per-cell DNA content or to consider outliers for quality control purposes, by plotting per-image cell counts. Two measured features per image or object could be displayed around the same chart with a scatterplot Figure Figure1b, 1b, which is additionally useful for identifying hits and then for quality control purposes. For example, the researcher can readily exclude out-of-focus images from analysis dependant on measurements created by CellProfilers Measure Image Quality module. Because data points in scatterplots can occlude one another, they can be typically unsuitable for individual cell data where many millions of internet data points are examined to distinguish interesting subpopulations. For these cases, a density plot is a lot more appropriate Figure Figure1c. 1c. Every pixel inside plot represents a histogram bin along with the color with the pixel represents the volume of data points inside the bin. These plots are helpful, as an example, for establishing thresholds where to classify individual cells as positive or negative according to two features, according to two intensity measures just as flow cytometry. To explore a lot more than two measured highlights of each image or data point, a parallel coordinate plot is employed. Parallel coordinate plots 13 allow analysis of multiple dimensions of web data, whereby each measured features scaled 0 1 values get a separate y-axis and individual data points are connected across these multiple axes Figure Figure1d 1d.
Four forms of plots can be accomplished. Four kinds of plots put together by CellProfiler Analyst are shown. a A histogram of per-image data the mean region of all cells within the image. b Scatterplot of image data X-axis mean part of all cells inside image;
Each data point inside a plot can represent a person cell or, by comparison, the mean value with the population of cells within a perception. Data will also be grouped by characteristics the samples have in common, chemical name or dose. Multiple experiments that investigate the identical set of treatment conditions, chemicals or RNA interference reagents might be grouped together, which eases analysis of replicates. For all varieties of plots, the details to be displayed could be filtered, for instance to plot data only coming from a single image, from your sample of information points at specified equal intervals, or data that satisfies certain criteria specified by SQL where clauses like CellCount 100.
Data points selected and highlighted within a plot are immediately highlighted to all other open plots a method often called brushing 14 in ways that a sample or pair of samples may be examined within the context of other groups of samples Figure Figure2. 2. This allows, one example is, the comparison of measurements from samples appealing vs. all samples within the experiment. Brushing helps the person to with less effort examine relationships inside data, specially when the data features a large amount of attributes or items, when the info spans multiple experiments including, as an example, replicates, or when it truly is natural to look at different parts with the data using different views. The brushing concept is extended in CellProfiler Analyst for situations where multiple experiments are increasingly being simultaneously explored: any time a point corresponding to some particular image is highlighted, all points corresponding to that experimental treatment condition may be highlighted, even if the information comes from multiple experiments that happen to be being examined together. In the scatterplot in Figure Figure1b, 1b, one example is, four data points are blue because one was originally selected as well as the user requested that replicates with the sample be highlighted.
Relationships among data may be explored. Data points representing images with higher nuclear area averaged above the cells within the image and high DNA intensity averaged on the cells inside the image are highlighted in blue by brushing the scatterplot shown
Interesting data points or teams of data points might be investigated by drilling on to the data in a number of ways Figure Figure3. 3. For plots showing data points representing image measurements, a data point or number of data points is usually selected as well as the original images that produced the information point can be shown Figure Figure3d. 3d. This can reveal artifacts in sample preparation or imaging, for example fluorescent test compounds, aggregates or overabundance of staining reagents, fibers, or debris Figure Figure3g. 3g. These artifacts not just occlude actual cells in images but tend to also disrupt the right identification and measurement of remaining cells inside the image. For these along with other reasons, images showing identification outlines caused by image analysis if available can be shown for selected data points Figure Figure3e, 3e, to recognize whether the identification of cells occurred properly. This is surely an important consideration since no segmentation algorithms are flawless.
Data points could be investigated. The data points highlighted in blue inside the scatterplot a represent replicates of a specific treatment condition rich in mean cell area and nuclear area. To examine the cell cycle distribution of the samples, a
Additionally, a data point or list of data points might be selected and also a plot in the measurements of human cells that were found in those images might be displayed like a separate subplot. This allows, one example is, a DNA content histogram indicating cell cycle distribution with the cell population to get displayed for a selected image or number of images of great interest Figure Figure2c 2c and Figure Figure3b. 3b. To investigate the identity of interesting samples, a straightforward list on the treatment issues that produced a number of data points can seem to get a summary Figure Figure2d. 2d. For further investigation, web-based specifics of each images treatment condition could be launched inside an external internet browser Figure Figure3f, 3f, if web addresses connected with each sample are stored inside database. All available measurements and also other information for a selected sample might be displayed in the simple table and saved to be a comma-delimited text register for analysis in another application Figure Figure3c 3c.
Image-based info is tremendously valuable in that multiple single-cell measurements can be obtained. Responses of person cells with a treatment usually are inhomogeneous as a consequence of cell cycle variations or differences in protein levels because of memory or stochastic noise 15, 16. In many cases, one particular measured feature, the total concentration of red stain in the nucleus can be accustomed to score individual cells as well as the only challenge is always to identify the ideal threshold for scoring positive cells. This could be accomplished in CellProfiler Analyst using histograms of person cell data. For complex phenotypes, several popular features of each cell is usually necessary for effective scoring. In these cases, a density plot showing individual cells Figure Figure4a 4a can be ideal for identifying interesting cell subpopulations, by delineating a section in the plot known as gating. Whether the gate is the cells of curiosity can be tested using two features: the Show Object Montage feature to discover what individual cells inside gate seem like Figure Figure4b, 4b, and also the Show Image feature to determine whether cells within a unique sample are appropriately marked as inside or outside the gate Figure Figure4c. 4c. Once the last, desired subpopulation of cells is gated, the volume of cells that fall within that subpopulation is calculated per image, for more statistical analysis Figure Figure4d. 4d. As an illustration, when DNA and phosphorylated Serine 10 of histone H3 are generally stained, an effective two-feature gate in CellProfiler Analyst enables scoring mitotic subphases in human HT29 cells Figure Figure4e. 4e. Many software systems perform image analysis within the fly during image acquisition; in these cases, a threshold value for just a feature of curiosity must be chosen in advance gain the screen. By contrast, power tools in CellProfiler Analyst allow testing the efficacy of scoring according to different features and different measurement thresholds.
Cell subpopulations may be identified, examined, and scored. a On a density plot of human cell data log scale: X-axis integrated concentration of nuclear DNA; Y-axis nuclear area, two populations were gated white boxes along with a random selection
If in excess of two features are needed to get a phenotype, sequential gates may be used upon the cell data. This approach is applied as follows: 1 display the full population of cells from an experiment in a very density plot, 2 draw a gate around the info points representing potential cells of great interest, 3 adjust the gate to incorporate nearly all positive cells and exclude several negative cells as it can be, 4 plot the resulting gated subpopulation in a very new density plot with two new measurement features as axes, 5 gate the subpopulation again according to these latest features, and 6 calculate the number of each images cells that fall inside the final gate.
We planned to test CellProfiler Analysts chance to plot, explore, and filter individual cell data to recognize subpopulations defined by a number of morphological features. We chose to spot Drosophila melanogaster Kc167 cells in telophase and metaphase on the cell cycle, using just a DNA stain. Identification of samples with perturbed cell cycle regulation is of clear importance to normalcy cell biology in addition to cancer studies. Regulators from the cell cycle are actually sought intensively for several years via traditional and high-throughput screens for modifications in overall cell cycle distribution and for increased phospho-histone H3 staining, a marker of cells in late G2 and M phase, 17 and references therein. We reasoned that additional genes might exist which, when perturbed, yield increased quantities of metaphase- or telophase-stage nuclei without substantially affecting the mitotic index phospho-histone H3 staining or cell cycle distribution. While we weren't aware of any positive controls with your a phenotype, we suspected such genes might have already been previously overlooked because we realized that not all metaphase nuclei stain brightly for phospho-histone H3 Figure Figure5a, 5a, for unknown reasons. Identifying genes whose RNAi produces cells appearing to get in particular subphases of mitosis, in spite of concomitant phospho-histone H3 staining, has to be first step towards understanding these phenomena.
Drosophila cell microarrays. a Phospho-histone H3 staining is normally dim for metaphase nuclei left and middle vs. right. Scale bar 5 m. b One cell array, DNA-stained and contrast-enhanced 5 lens. Scale bar 5 mm. c Small
Several groups have tested automated means of scoring mitotic subphases 18 - 20 ; these studies were accomplished by computational tools tailored to your specific assay and frequently relied on multiple cellular stains. Machine learning methods happen to be explored by each of our group among others 21 - 26 and discover Conclusions, but we also wished to explore allowing the consumer to manually pick a small number of highlights of known biological relevance, then sequential gating on those features. This would provide the researcher full control on the features used inside scoring, as well as the scoring can be more readily transferable from experiment towards the next want . small quantity of features are selected. We therefore wanted to get mitotic subphases having a DNA stain only, using supervised number of measurements accompanied by sequential gating on those measurements, within the context of a program usable by the non-computer scientist.
We screened genes using Drosophila RNA interference living cell microarrays 27 - 29 to distinguish gene knockdowns that yield a disproportionate quantity of cells by 50 % sub-phases of mitosis: metaphase and anaphase/telophase generally known as telophase for simplicity. We created and analyzed 5 replicates of your Drosophila array, with 1120 spots of dsRNA over a single microscope slide Figure Figure5b, 5b, including three replicate spots for every of 288 genes mostly kinases and phosphatases, plus 256 negative control spots lacking dsRNA. Some phenotypes created in these Drosophila Kc167 cells cell death are visible at low resolution 5 lens; Figure Figure5c, 5c, but to distinguish telophase and metaphase nuclei we collected individual high definition images within each i'm all over this each slide 40 lens; small component of one image shown in Figure Figure5d 5d.
We began while using telophase phenotype. To determine which measured cellular features could be most effective for scoring, we handpicked representative telophase nuclei and normal G2-phase nuclei from random screening images and created image montages of those two classes Figure Figure6a 6a using Adobe Photoshop. We used CellProfiler to measure nuclear features during these montage images, then exported the outcome to Excel and selected five features to use for sequential gating, based over a combination of biological intuition together with quantitative ability of each one feature to discriminate telophase from normal nuclei, using simple statistical tests in Excel. The selected features included DNA content, intensity, shape, and texture features additional data file 1.
Screen revealed RNA interference samples enriched in telophase- and metaphase-stage nuclei. a Composite images of handpicked Drosophila Kc167 nuclei which are measured to help feature selection and still provide the starting place for developing the gates.
We then interactively developed sequential gates using density plots of the features in CellProfiler Analyst see Gating individual cell data to gain complex phenotypes section. To accomplish this task, CellProfiler was familiar with process the full list of screening images and load the resulting data right into a database 2.8 million cells 396 features/cell 1.1 billion measurements total. This allowed us to show off all individual cells from the experiment inside an initial density plot with a pair of our selected features as axes, , DNA content and size area on the nucleus. We drew a basic gate round the 2N DNA content peak and small nuclear area, and empirically refined the gate for telophase cells by examining images in the gated nuclei and adjusting the gates boundaries accordingly. While automated approaches could certainly identify a boundary based using a researcher-provided training set, this manual approach allows the biologist specifically assess many cells near to the relevant boundaries. Once the appropriate gate was selected for that initial density plot, the subpopulation was transferred to some new density plot with two latest features used as axes as well as the next gate appeared, again choosing the optimal parameters to differentiate telophase nuclei of all other nuclei. This procedure was repeated with the fifth, and final, selected feature. Once a final gate was refined, we applied the sequential gates into a new number of images and confirmed that their scoring was effective Table Table1 1 and Figure Figure6b, 6b, successfully differentiating telophase off their nuclei. In creating the gates, we aimed to minimize the false positive rate while accepting a greater false negative rate Table Table1. 1. We reasoned that true hits might have enough positives to get over this intentionally stringent selection procedure. At this point, we applied the last sequential gates to everyone the cells in order to attain the entire screen for your telophase phenotype. We found that this gates must typically be adjusted slightly between different replicate slides as a result of inter-experiment variability, staining intensity, although experiment-to-experiment normalization methods may very well be explored to cut back this effect.
We separately performed a similar procedure with the metaphase phenotype using four features to tell apart metaphase nuclei coming from all other nuclei; a total list in the 288 genes tested and scores for telophase and metaphase is shown in data file 2.
Rank-ordering samples by the portion of telophase nuclei revealed 4 gene knockdowns using a significant surge in telophase nuclei Figure Figure6c, 6c, first 4 rows. Validating the approach, two on the genes are PP2A complex subunits that are already previously related to mitosis: the PP2A-C catalytic subunit mts CG7109/microtubule star along with a PP2A-A family regulatory subunit CG17291/CG33297/CG13383, Note: dicistronic with CSN8. RNAi against both genes increased the amount of cells that had been phospho-histone H3-positive Figure Figure6c, 6c, fifth column. A third hit, Ck1 Casein kinase 1/CG2028, also has previously been related to mitosis Figure Figure6c, 6c, last column. We seen that its knockdown by RNAi produced nuclei whose chromatin appeared for being slightly less condensed than typical telophase nuclei Figure Figure7, 7, while still more condensed than interphase nuclei. The amount of cells that had been phospho-histone H3-positive was normal Figure Figure6c, 6c, fifth column. Together, these observations report that this defect is situated late-stage telophase/anaphase. The fourth hit would have been a predicted kinase without any functional annotation CG8878. Visual inspection revealed that virtually all nuclei over these samples appeared brighter plus more compact than controls, a subtle but reproducible effect Figure Figure7. 7. This understandably ended in more on the 2N nuclei being counted as having telophase-like morphology. We found the cells wasn't enriched for phospho-histone H3-positivity Figure Figure6c, 6c, fifth column; without further experimentation, it really is unclear whether this can be a true late-stage mitotic phenotype in other words a condensed nuclei phenotype.
Unusual phenotypes were found for telophase hits CG2028 and CG8878. Top: CG2028 knockdown produces many telophase-like nuclei that appear less condensed than typical telophase nuclei but more condensed than interphase nuclei. Bottom: Nearly all nuclei
Interestingly, the sole metaphase hit within this screen Figure Figure6c, 6c, last row would be the B/B56 subfamily regulatory subunit of PP2A CG5643/widerborst, which for the time of our screen had not been connected to cell cycle regulation. The number of cells that have been phospho-histone H3-positive has not been much higher than normal Figure Figure6c, 6c, fifth column. We confirmed by eye the metaphase-inducing phenotype of widerborst knockdown within the original images as well as in separate experiments with two other dsRNAs, including one which was non-overlapping with the first Figure Figure8a. 8a. Widerborst can be an essential gene linked to planar cell polarization 30 and apoptosis 31, 32. Notably, in other contexts circadian clock protein cycling 33 and sensory organ development 34 widerborst is indirectly linked on the B/PR55 subfamily member twins/aar, and that is itself known for being required for metaphase to anaphase transition 35. Our work therefore confirms, with non-overlapping dsRNAs, a recently reported cell cycle regulation role for widerborst 36 and together indicates that it can be unlikely this phenotype is caused by off-target effects 37, 38.
The widerborst RNA interference metaphase arrest phenotype is confirmed in Drosophila and human cells. a A sampling of metaphase nuclei that is generated by widerborst knockdown in Drosophila Kc167 cells top. Scale bar 5 m. Quantitative confirmation
The closest human homolog of widerborst is PPP2R5E, the epsilon isoform of the subfamily of PP2R5 B/PR61/B56 regulatory subunits on the PP2A complex. As yet, no particular function continues to be associated with PPP2R5E. We wondered whether PPP2R5E can be quite a B regulatory subunit that modulates the known role for PP2A in mitosis, given our finding of the homolog widerborsts role in Drosophila. PPP2R5E knockdown wouldn't increase the mitotic index significantly in recent RNA interference screens for increased phospho-histone H3 7, 39, 40. However, once we scored similar PPP2R5E-knockdown images for metaphase morphology, in lieu of phospho-histone H3 levels, we discovered a metaphase-arrest phenotype for PPP2R5E knockdown, confirmed by two different shRNAs Figure Figure8b, 8b, consistent using the phenotype seen for widerborst in Drosophila. Whether widerborst/PPP2R5E are themselves essential for metaphase-to-anaphase transition or whether their depletion causes the phenotype by specifically disrupting the stoichiometry on the relevant PP2A complex remains to get determined. Recent findings that PPP2R5E localizes to centromeres and how the B subfamily of regulatory subunits are essential for proper meiotic sister chromatid separation in fission and budding yeast 41 include the idea that this class of subunits is indeed vital for proper chromatin dynamics during cell division.
We have described here a software system for exploration and analysis of enormous, hierarchical, multi-dimensional data sets. While it is suitable for any type of internet data, players on teams, trees within forests, it really is particularly competent at high-end exploration and analysis of measured features from high-throughput image-based screens for both quality control and identifying hits in the screen. Researchers are this is download the Java source code and add new sorts of plots and analysis tools, for normalizing screen data 9, 10 on the system.
We have demonstrated the utility in this software for interactive data exploration and analysis for intentionally selecting cells with particular measurement values in order gain complex visual phenotypes. Of course, often you will that successfully specify a specific phenotype can be unknown roughly numerous as to create the sequential plotting shown here impractical, deciding on decision boundaries empirically most likely are not optimal to attain the phenotype. For these reasons, we recently added machine-learning methodology to CellProfiler Analyst TRJ, AEC, DMS, PG, unpublished data. Nonetheless, the complete treating features and thresholds made available from sequential gating is very useful in certain instances. Often a researcher would need to ignore certain highlights of positive control cells by way of example, each time a positive control treatment has pleiotropic effects on cells and emphasize other, better-understood cellular features. Interactive observation on the original cellular images while making gating decisions to define a phenotype also leverages the biologists intuition of a phenotype. Within precisely the same open-source software infrastructure, both approaches sequential gating and machine learning very easily applied to large-scale imaging screens.
CellProfiler Analyst could be downloaded for Mac, Windows, and Unix operating systems in the CellProfiler Project website, where it's distributed under an open-source license GNU General Public License, version 2. An archived version is usually available as additional data file 3, submitted using this article. The Examples page on the website provides demonstration movies showing the application in use, a sample database and images, and links with an online forum where questions in regards to the software are answered.
CellProfiler Analyst is made to explore and analyze any MySQL database of image-based screening data that follows a straightforward format: a minimum of one image table with rows corresponding to images and columns of image data instances of columns are: the name with the treatment condition, total intensity with the entire image, mean cell area averaged over-all cells within the image, path towards the original image, and a minumum of one object table with rows corresponding to objects, cells and columns of object data degrees of columns are: area with the cell, concentration of DNA stain from the nucleus, location on the cell from the original image rogues being necessary for viewing individual cells during exploration. This data format is automatically produced if images are analyzed with CellProfiler open-source cell image analysis software 4, featuring a ExportToDatabase module. The data must be normalized for plate-to-plate or spatial-layout variations just before exploration in CellProfiler Analyst. While the application is created to access remote databases because typical data sets are way too large to become stored in physical memory, the Make Local Object Table option allows particularly relevant measurements to get stored locally in memory to speed analysis while still allowing access towards the full dataset inside remote database.
We prepared Drosophila Kc167 cells as previously described 29. In brief, cells were grown on living cell microarrays with spots of double stranded RNA for three days. For confirmation of phenotypes in Drosophila, we grew cells on plain slides for 72 hours, after being pre-treated with dsRNA for a few days. We used images of human HT29 cells as previously described 7.
For the screen with the metaphase and telophase phenotypes, each gene was tested in three replicate spots on five independently prepared cell array slides, along with the results for those genes are shown in Additional file 2. Because the three replicate spots were near one another, cell counts to the groups of three were accumulated but not treated as independent samples. A p-value for every gene on each with the five slides was calculated based around the number of metaphase nuclei found and also the number of cells total, relative on the average area of metaphase nuclei for the entire slide, like a Bernoulli random variable. To add stringency, we report latest results for second- and third-strongest scoring replicates only shown on two separate sheets of Additional file 2. We needed that two or three with the five scores were above a threshold that results within a combined p-value below 0.01. For Bonferroni-adjusted p-values from single experiments, these thresholds are 0.6 for two main experiments away from 5, and 5.2 for three outside of 5. De-enriched samples are listed that has a p-importance of 1 and samples using a p-importance of 1 are ordered by enrichment. Enrichment could be the fold-enrichment on the sample compared to all the samples.
In the bar charts inside fourth column of Figure Figure6c, 6c, we statistically analyzed the cell cycle distribution and cell count for your screens hits. To do this, we first gathered DNA content data, integrated nuclear DNA intensity through the database for those cells within the slides the location where the hits occurred. Then, to normalize for illumination and staining variation between slides and between images, the DNA content measurements were log2-transformed and shifted so how the mode in the DNA content for every image calculated by binning the log2-transformed DNA into 50 bins was corresponding to 1. Based on this normalized log2DNA intensity, cells were then counted as 2N, 4N, and 8N as follows: