The clonogenic or colony formation assay is a widely used method to study the number and size of cancer cell colonies that remain after irradiation or cytotoxic agent administration and serves as a measure for the anti-proliferative effect of these treatments. Alternatively, this assay is used to quantitate the transforming potential of cancer associated genes and chemical agents. Therefore, there is a need for a simplified and standardized analysis of colony formation assays for both routine laboratory use and for parallelized automated analysis. Here we describe the freely available ImageJ-plugin “ColonyArea”, which is optimized for rapid and quantitative analysis of focus formation assays conducted in 6- to 24-well dishes. ColonyArea processes image data of multi-well dishes, by separating, concentrically cropping and background correcting well images individually, before colony formation is quantitated. Instead of counting the number of colonies, ColonyArea determines the percentage of area covered by crystal violet stained cell colonies, also taking the intensity of the staining and therefore cell density into account. We demonstrate that these parameters alone or in combination allow for robust quantification of IC 50 values of the cytotoxic effect of two staurosporines, UCN-01 and staurosporine (STS) on human glioblastoma cells (T98G). The relation between the potencies of the two compounds compared very well with that obtained from an absorbance based method to quantify colony growth and to published data. The ColonyArea ImageJ plugin provides a simple and efficient analysis routine to quantitate assay data of one of the most commonly used cellular assays. The bundle is freely available for download as supporting information. We expect that ColonyArea will be of broad utility for cancer biologists, as well as clinical radiation scientists.

Funding: This work was supported by the Academy of Finland fellowship grant, the Sigrid Juselius Foundation, the Cancer Society of Finland and the Marie-Curie Reintegration Grant to DA. JW was supported by funding from the Foundation of the Finnish Cancer Institute and the Sigrid Juselius Foundation. The funders had no role in study design, data collection and analysis decision to publish, or preparation of the manuscript.

Copyright: © 2014 Guzmán et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

As an alternative to counting and quantifying individual colonies, it is much simpler to determine the percentage of the well area that is covered by colonies (colony area percentage) to quantify clonogenic cell growth [7] , [14] – [17] . We here describe the ImageJ plugin, “ColonyArea”, which determines the colony area percentage and an intensity weighted colony area percentage (colony intensity percentage) from flatbed scanner acquired images of colony formation assays conducted in multi-well plates. The plugin is user-friendly, as it basically only requires 1) the selection of a rectangular ROI (region of interest) that encompasses wells to be analyzed, and 2) choice of the well-plate type by the user. We test our plugin by quantifying the susceptibility of T98G human glioblastoma cell growth to two different staurosporines, UCN-01 and staurosporine (STS), both well known inhibitors of protein kinases and prominent anti-proliferative drugs [18] – [21] . We validate the accuracy of our results by recovering the different potencies of UCN-01 and STS, as well as by direct comparison of obtained data with data generated by the absorption based method from Kueng et al. [9] .

Colony counting can be done either with the slow and subjective, manual (human) counting or using a large variety of devices and programs that accelerate and automate counting [2] , [4] , [10] – [13] . The major image analysis challenges are the identification and separation of colonies, as well as integration of the sizes of colonies. Colony number and size would reflect cell survival and proliferation, respectively. Image analysis may require expensive equipment (e.g. GelCount hardware described in [10] ), or the use of commercialized software (ScanCount, [2] and MetaMorph, [11] ). Alternatively, free distribution software can be employed, either as standalone solutions (CellProfiler, [12] and OpenCFU, [13] ) or as macros for ImageJ (National Institutes of Health, Bethesda MD – USA) [4] .

Traditionally clonogenic assays have been performed by counting colonies or foci of cells, which typically comprised >50 densely-packed cells [1] , [3] . Cells are usually identified by staining with a crystal violet dye [3] , which primarily binds to polyanionic sugar molecules such as DNA in the nucleus of mammalian cells [8] . If solubilized from stained cells, measuring the absorption of the crystal violet dye can be used to quantify cellular growth [9] , however with the disadvantage that the cellular sample is destroyed.

Since the introduction of clonogenic assays in 1956 by Puck and Marcus [1] , they have become the method of choice to determine the survival and growth of cells, in particular cancer cell lines, after treatment with ionizing radiation or to determine the effectiveness of cytotoxic agents [2] – [4] . A clonogenic assay evaluates the potential of a single cell to resist treatments and grow into a colony, which lent the assay the alternative name of colony formation assay [3] . In addition, the colony formation assay has also gained significance to evaluate the transforming or colony growth potential of oncogenes, such as H-ras or CIP2A [5] – [7] .

Results and Discussion

We have developed “ColonyArea”, a java-based free distribution plugin for the open-source image analysis software ImageJ. ColonyArea precisely and rapidly quantifies scanned images of colony formation assays (Figure 1). It is already set up to operate on standard 6-, 12- and 24-well cell culture plates, and can be further customized to handle other multi-well formats. In the following, we describe how the plugin automatically separates all user-selected wells in an image, eliminates the background and quantifies colony formation.

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larger image TIFF original image Download: Figure 1. Flow chart of the processing steps in the ColonyArea plugin. Steps performed by the user are represented by ovals and the grey shapes are those requiring user input. All other shapes represent steps performed by the three macros Colony_area (rounded rectangles), Colony_thresholder (hexagons) and Colony_measurer (stars) that are packaged as one plugin file. https://doi.org/10.1371/journal.pone.0092444.g001

An additional file placed as Information S1 and termed ‘ColonyArea.zip’, contains the plugin and user manual. This file is also freely available on our website http://www.btk.fi/research/research-groups/abankwa/downloads/ and on the webpage of the European Data Infrastructure (EUDAT) https://b2share.eudat.eu/record/45 through their service B2SHARE.

Automatic well separation and cropping The ColonyArea plugin consists of a bundle of the actual java-file and macros. For simplicity we refer to this bundle as the ‘plugin’. The plugin procedure uses a scanned image of a multi-well assay plate (Figure 2A) and after separating individual wells performs all processing and analysis steps well-specifically, for example background thresholding. In order to separate the wells from a plate image, the java file “Colony_area” starts by using information about the plate type and the number of selected wells, which are both provided by the user to create a mask that will set the intensity of those pixels belonging to the space in between wells to zero. The size and shape of the mask depend on the above user information and typical plate dimensions of 6- to 24-well plates. Dimensions as published by a major manufacturer (CELLSTAR, Greiner Bio-One) are stored in the plugin. Manual comparison with plates from Millipore and BD Biosciences gave similar dimensions. However, it is possible that the user provides these dimensions, as detailed in the manual. Next this image is converted into a grey scale 8-bit image (Figure 2B). To circumvent issues associated with cell growth abnormalities on the well edges, these are eliminated from analysis using a concentrical cropping mask, which reduces each well diameter by 5% from the edges. Subsequently, the macro separates and further crops each well image such that the edge of the well is aligned with the edges of the individual image. In the last step, an image stack is created, containing all the wells that were selected from the original image (Figure 2C). PPT PowerPoint slide

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larger image TIFF original image Download: Figure 2. Identification of wells and generation of a well image stack. (A) Scanned image of a 12-well plate showing different levels of colony formation of drug treated T98G human glioblastoma cells. (B) Same image as in (A) after automatic identification of the wells. The image was converted into an 8-bit greyscale and spaces between wells were removed using a mask. (C) Each well image was then concentrically cropped and added to an image stack to allow for the analysis of each well individually. https://doi.org/10.1371/journal.pone.0092444.g002

Identification of the background with “Colony_thresholder” Since colony formation assays typically involve staining of the cells with a crystal-violet dye [3], pixels corresponding to regions with cells will appear darker and have smaller grey-values than those without cells. The macro “Colony_thresholder” of our plugin recognizes these differences in intensity and determines per well image the intensity value that separates the background (high intensity values) from stained cell colonies (low intensity values), in other words the background threshold (Figure 3). PPT PowerPoint slide

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larger image TIFF original image Download: Figure 3. Determination of the background threshold. (A) Two 8-bit greyscale images of wells showing high (left) and lower (right) intensities of cell staining with similar colony density. (B) For each case, the colony area percentage is plotted as a function of the applied intensity threshold. At this point, the colony area percentage corresponds to the percentage of the well area that is selected based on the criterion that each pixel in the area has an intensity value below a given intensity threshold. (C) First and (D) second derivatives of the colony area percentage function shown in (B), which allow identifying the correct intensity threshold. After the correct threshold has been identified, the colony area parameter gives the percentage of the well area that is occupied by cells. In all plots (B–D), the highlighted region represents the intensity range where only cells are selected. Above that intensity threshold the background starts to be included, which identifies this intensity value as the background threshold. https://doi.org/10.1371/journal.pone.0092444.g003 If we plot the colony area percentage function in dependence of an applied intensity threshold, i.e. for now the area selected with an intensity value below a given intensity threshold, we observe that above a certain intensity threshold value the selected area function suddenly increases to 100% (Figure 3B). This is the intensity value, above which also high grey-value/ intensity pixels of the background are selected wrongly as ‘cells’ and it therefore corresponds to the actual background threshold. Identification of this transition point is facilitated, by determining the first (Figure 3C) and second derivative (Figure 3D) of the area percentage function, which identifies this point as a local minimum or zero-intercept on the x-axis, respectively. The algorithm inside Colony_thresholder calculates these three functions and explores them to identify the transition point that represents the background threshold. Since there is a possibility that there are multiple local minima and zero-intercepts on the second and third derivative, which would hamper automatic threshold identification, the obtained background thresholds from wells of the same plate are then undergoing a consistency check. First, the maximum intensity in the wells is computed and all the well images are linearly scaled such that their maximum intensity is 200. This is done to counter the effect of non-uniform illumination, or any backlight correction of the scanner. Then the scaled threshold values are compared and any value that deviates more than 50 intensity units from the average is flagged for reevaluation. In this case, the background threshold for that particular well is recalculated restricting the above background analysis procedure to intensity values within the range of the average background threshold ±50 intensity units, instead of using the full, initial range of 0 to 255. Then the threshold obtained from the restricted range is scaled back to the initial intensity values and applied to a copy of the initial well image. In a last step, ColonyArea uses these thresholded copy images and the initial 8-bit images of the wells (Figure 4A), to create a new set of images, where the intensity is zero for pixels that do not contain cells and a number between 1 and 255 for pixels that do contain cells. Images in this new set are then inverted so higher intensity values reflect the increasing density of cells at any given pixel (Figure 4B). These thresholded images are used in subsequent quantitation steps. PPT PowerPoint slide

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larger image TIFF original image Download: Figure 4. Removal of the background. (A) 8-bit greyscale images of individual wells showing different levels of colony formation of T98G cells treated with indicated concentrations of staurosporine. (B) Same individual wells after thresholding and background removal by the macro “Colony_thresholder”. Color bar represents the intensity scale displayed in the thresholded wells. Zero intensity (white) corresponds to areas where no cells were identified (background). https://doi.org/10.1371/journal.pone.0092444.g004 In order to allow the user to verify correct background identification, ColonyArea displays the stacks of well images before and after thresholding side-by-side. In case the user is not satisfied with the result, ColonyArea is equipped with an option for user defined manual thresholding, which is described in the plugin manual provided with the download bundle.