Automated counting of mammalian cell colonies

Image Analysis Semi-Automatic System for Colony-Forming-Unit Counting

Background: Accurate quantitative analysis of microorganisms is recognized as an essential tool for gauging safety and quality in microbiology settings in a wide range of fields. The enumeration process of viable microorganisms via traditional culturing techniques are methodically convenient and cost-effective, conferring high applicability worldwide. However, manual counting can be time-consuming, laborious and imprecise. Furthermore, particular cases require an urgent and accurate response for effective processing. Methods: To reduce time limitations and discrepancies, this work introduces an image processing method capable of semi-automatically quantifying the number of colony forming units (CFUs). This rapid enumeration technique enables the technician to provide an expeditious assessment of the microbial load of a given sample. To test and validate the system, three bacterial species were cultured, and a labeled database was created, with subsequent image acquisition. Results: The system demonstrated acceptable classification measures; the mean values of Accuracy, Recall and F-measure were: (1) 95%, 95% and 0.95 for E. coli; (2) 91%, 91% and 0.90 for P. aeruginosa; and (3) 84%, 86% and 0.85 for S. aureus. Conclusions: Evidence related to the time-saving potential of the system was achieved; the time spent on quantification tasks of plates with a high number of colonies might be reduced to a half and occasionally to a third.

A quantum-inspired classifier for clonogenic assay evaluations

Recent advances in Quantum Machine Learning (QML) have provided benefits to several computational processes, drastically reducing the time complexity. Another approach of combining quantum information theory with machine learning—without involving quantum computers—is known as Quantum-inspired Machine Learning (QiML), which exploits the expressive power of the quantum language to increase the accuracy of the process (rather than reducing the time complexity). In this work, we propose a large-scale experiment based on the application of a binary classifier inspired by quantum information theory to the biomedical imaging context in clonogenic assay evaluation to identify the most discriminative feature, allowing us to enhance cell colony segmentation. This innovative approach offers a two-fold result: (1) among the extracted and analyzed image features, homogeneity is shown to be a relevant feature in detecting challenging cell colonies; and (2) the proposed quantum-inspired classifier is a novel and outstanding methodology, compared to conventional machine learning classifiers, for the evaluation of clonogenic assays.

Experimental setup and image processing method for automatic enumeration of bacterial colonies on agar plates

Automated colony counting methods have long been known in Microbiology. Numerous methods for automated image analysis have been described and a wide range of commercial products exists. Known advantages are saving cost by reducing enumeration time, automatic documentation, reproducibility, and operator independence. Still, even today the realization of all advantages of automated image analysis makes it necessary to either invest in an expensive, high performance commercial system, or to acquire expert knowledge in image processing. This is a considerable obstacle for many laboratories, and the reason why manual colony counting is still done frequently. This article describes an easy to apply automatic colony counting system–including suggestions for sample preparation–that can be put into operation with basic knowledge of image processing and low budget.

MF2C3: Multi-Feature Fuzzy Clustering to Enhance Cell Colony Detection in Automated Clonogenic Assay Evaluation

A clonogenic assay is a biological technique for calculating the Surviving Fraction (SF) that quantifies the anti-proliferative effect of treatments on cell cultures: this evaluation is often performed via manual counting of cell colony-forming units. Unfortunately, this procedure is error-prone and strongly affected by operator dependence. Besides, conventional assessment does not deal with the colony size, which is generally correlated with the delivered radiation dose or administered cytotoxic agent. Relying upon the direct proportional relationship between the Area Covered by Colony (ACC) and the colony count and size, along with the growth rate, we propose MF2C3, a novel computational method leveraging spatial Fuzzy C-Means clustering on multiple local features (i.e., entropy and standard deviation extracted from the input color images acquired by a general-purpose flat-bed scanner) for ACC-based SF quantification, by considering only the covering percentage. To evaluate the accuracy of the proposed fully automatic approach, we compared the SFs obtained by MF2C3 against the conventional counting procedure on four different cell lines. The achieved results revealed a high correlation with the ground-truth measurements based on colony counting, by outperforming our previously validated method using local thresholding on L*u*v* color well images. In conclusion, the proposed multi-feature approach, which inherently leverages the concept of symmetry in the pixel local distributions, might be reliably used in biological studies.

Semi-automated counting model for arbuscular mycorrhizal fungi spores using the Circle Hough Transform and an artificial neural network

Arbuscular Mycorrhizae (AM) are mutualistic associations between Arbuscular Mycorrhizal Fungi (AMF) and the roots of many plant species. AMF spores give rise to filaments that develop in the root system of plants and contribute to the absorption of water and some nutrients. This article introduces a semi-automated counting model of AMF spores in slide images based on Artificial Neural Network (ANN). The semi-automated counting of AMF spores facilitates and accelerates the tasks of researchers, who still do the AMF spore counting manually. We built a representative database of spore images, processing images through the Circle Hough Transform (CHT) method and training an ANN to classify patterns automatically. The classification analysis and the performances of the proposed method against the manual method are presented in this paper. The accuracy for the identification of spores by CHT in conjunction to ANN classification in the images was 90%. The results indicate that this method can accurately detect the presence of AMF spores in images as well as count them with a high level of confidence.

Freeware tool for analysing numbers and sizes of cell colonies

The clonogenic cell survival assay is a basic method to study the cytotoxic effect of radiation and chemical toxins. In large experimental setups, counting of colonies by eye is tiresome and prone to bias. Moreover, it is often interesting to quantify the size of individual colonies. Such analyses are largely facilitated by computerised image analysis systems. Although a number of such systems exist, they all focus on enumerating colonies and not on analysing the colony size. We have developed a new software package for both counting colonies and plotting their size distributions. The software called count and Plot HIstograms of Colony Size (countPHICS) consists of two parts: (1) a macro written for ImageJ which analyses computerised images of cell culture dishes or 6-well plates, counts colonies, estimates their size and saves the results in a text file; (2) a program written with QT Creator which reads the text file, plots histograms of colony size distribution and fits the best function. The full program is freely available at: http://www.fuw.edu.pl/~bbrzozow/FizMed/countPHICS.html . In conclusion, our new publically available software will facilitate colony counting and provide additional information on the colony growth rate, which is relevant especially for radiosensitisation studies.

Cell colony counter called CoCoNut

Clonogenic assays are powerful tools for testing cell reproductive death after biological damage caused by, for example, ionizing radiation. Traditionally, the methods require a cumbersome, slow and eye-straining manual counting of viable colonies under a microscope. To speed up the counting process and minimize those issues related to the subjective decisions of the scoring personnel, we developed a semi-automated, image-based cell colony counting setup, named CoCoNut (Colony Counter developed by the Nutech department at the Technical University of Denmark). It consists in an ImageJ macro and a photographic 3D-printed light-box, conceived and demonstrated to work together for Crystal Violet-stained colonies. Careful attention was given to the image acquisition process, which allows background removal (i.e. any unwanted element in the picture) in a minimally invasive manner. This is mainly achieved by optimal lighting conditions in the light-box and dividing the image of a flask that contains viable colonies by the picture of an empty flask. In this way, CoCoNut avoids using aggressive background removal filters that usually lead to suboptimal colony count recovery. The full method was tested with V79 and HeLa cell survival samples. Results were compared to other freely available tools. CoCoNut proved able to successfully distinguish between single and merged colonies and to identify colonies bordering on flask edges. CoCoNut software calibration is fast; it requires the adjustment of a single parameter that is the smallest colony area to be counted. The employment of a single parameter reduces the risk of subjectivity, providing a robust and user-friendly tool, whose results can be easily compared over time and among different bio-laboratories. The method is inexpensive and easy to obtain. Among its advantages, we highlight the possibility of combining the macro with a perfectly reproducible 3D-printed light-box. The CoCoNut software and the 3D-printer files are provided as supporting information (S1 CoCoNut Files).

Area-based cell colony surviving fraction evaluation: A novel fully automatic approach using general-purpose acquisition hardware

BACKGROUND

The current methodology for the Surviving Fraction (SF) measurement in clonogenic assay, which is a technique to study the anti-proliferative effect of treatments on cell cultures, involves manual counting of cell colony forming units. This procedure is operator-dependent and error-prone. Moreover, the identification of the exact colony number is often not feasible due to the high growth rate leading to the adjacent colony merging. As a matter of fact, conventional assessment does not deal with the colony size, which is generally correlated with the delivered radiation dose or the administered cytotoxic agent.

METHOD

Considering that the Area Covered by Colony (ACC) is proportional to the colony number and size as well as to the growth rate, we propose a novel fully automatic approach exploiting Circle Hough Transform, to automatically detect the wells in the plate, and local adaptive thresholding, which calculates the percentage of ACC for the SF quantification. This measurement relies just on this covering percentage and does not consider the colony number, preventing inconsistencies due to intra- and inter-operator variability.

RESULTS

To evaluate the accuracy of the proposed approach, we compared the SFs obtained by our automatic ACC-based method against the conventional counting procedure. The achieved results (r = 0.9791 and r = 0.9682 on MCF7 and MCF10A cells, respectively) showed values highly correlated with the measurements using the traditional approach based on colony number alone.

CONCLUSIONS

The proposed computer-assisted methodology could be integrated in laboratory practice as an expert system for the SF evaluation in clonogenic assays.

Determination of Cell Colony Formation in a High-Content Screening Assay

The use of cell colony formation assays for research and clinical applications to assess the functional integrity of cells after in vitro manipulations is extensive. Key areas include hematopoietic stem cell research, cell transformation studies, and predicting the response of tumors to chemotherapeutic agents. Traditionally, enumeration of colonies has involved laborious and subjective counting by hand using a microscope. Here, laser scanning microplate cytometry has been used to provide an automated high-content readout of the effects of cytostatic agents on colony formation. This approach determines colony number through the application of a volume algorithm. Such an approach permits the differentiation of cytostatic effects where the number of colonies and size remains constant, and cytotoxic effects where the size and number may be reduced. Application of microplate cytometry thus offers significant benefits over alternative analytical methods in the search for novel chemotherapeutic agents.

Real-time bacterial microcolony counting using on-chip microscopy

Observing microbial colonies is the standard method for determining the microbe titer and investigating the behaviors of microbes. Here, we report an automated, real-time bacterial microcolony-counting system implemented on a wide field-of-view (FOV), on-chip microscopy platform, termed ePetri. Using sub-pixel sweeping microscopy (SPSM) with a super-resolution algorithm, this system offers the ability to dynamically track individual bacterial microcolonies over a wide FOV of 5.7 mm × 4.3 mm without requiring a moving stage or lens. As a demonstration, we obtained high-resolution time-series images of S. epidermidis at 20-min intervals. We implemented an image-processing algorithm to analyze the spatiotemporal distribution of microcolonies, the development of which could be observed from a single bacterial cell. Test bacterial colonies with a minimum diameter of 20 μm could be enumerated within 6 h. We showed that our approach not only provides results that are comparable to conventional colony-counting assays but also can be used to monitor the dynamics of colony formation and growth. This microcolony-counting system using on-chip microscopy represents a new platform that substantially reduces the detection time for bacterial colony counting. It uses chip-scale image acquisition and is a simple and compact solution for the automation of colony-counting assays and microbe behavior analysis with applications in antibacterial drug discovery.

Automated counting of bacterial colonies by image analysis

Research on microorganisms often involves culturing as a means to determine the survival and proliferation of bacteria. The number of colonies in a culture is counted to calculate the concentration of bacteria in the original broth; however, manual counting can be time-consuming and imprecise. To save time and prevent inconsistencies, this study proposes a fully automated counting system using image processing methods. To accurately estimate the number of viable bacteria in a known volume of suspension, colonies distributing over the whole surface area of a plate, including the central and rim areas of a Petri dish are taken into account. The performance of the proposed system is compared with verified manual counts, as well as with two freely available counting software programs. Comparisons show that the proposed system is an effective method with excellent accuracy with mean value of absolute percentage error of 3.37%. A user-friendly graphical user interface is also developed and freely available for download, providing researchers in biomedicine with a more convenient instrument for the enumeration of bacterial colonies.

Open Access to High-Content Clonogenic Analysis

Image-processing programs are used to identify and classify eukaryotic cell colonies as spots following seeding at low density on dishes or in multiwell plates. The output from such approaches, however, is generally limited to 1–2 parameters, and there is no ability to extract phenotypic information at the single colony level. Furthermore, there is a lack of user-friendly pipelines for analysis of clonogenicity in the context of high-content analysis. This article describes an experimental and multiparametric image analysis workflow for clonogenic assays in multiwell format, named the Colony Assay Toolbox (CAT). CAT incorporates a cellular-level resolution of individual colonies and facilitates the extraction of phenotypic information, including the number and size of colonies and nuclei, as well as morphological parameters associated with each structure. Furthermore, the pipeline is capable of discriminating between colonies composed of senescent and nonsenescent cells. We demonstrate the accuracy and flexibility of CAT by interrogating the effects of 2 preclinical compounds, Nutlin-3a and ABT-737, on the growth of human osteosarcoma cells. CAT is accessible to virtually all laboratories because it uses common wide-field fluorescent microscopes, the open-source CellProfiler program for colony image analysis, and a single fluorescent dye for all the segmentation steps.

ColonyArea: an ImageJ plugin to automatically quantify colony formation in clonogenic assays

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 IC50 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.

"Broadbeam" irradiation of mammalian cells using a vertical microbeam facility

A "broadbeam" facility is demonstrated for the vertical microbeam at Surrey's Ion Beam Centre, validating the new technique used by Barazzuol et al. (Radiat Res 177:651-662, 2012). Here, droplets with a diameter of about 4 mm of 15,000 mammalian cells in suspension were pipetted onto defined locations on a 42-mm-diameter cell dish with each droplet individually irradiated in "broadbeam" mode with 2 MeV protons and 4 MeV alpha particles and assayed for clonogenicity. This method enables multiple experimental data points to be rapidly collected from the same cell dish. Initially, the Surrey vertical beamline was designed for the targeted irradiation of single cells with single counted ions. Here, the benefits of both targeted single-cell and broadbeam irradiations being available at the same facility are discussed: in particular, high-throughput cell irradiation experiments can be conducted on the same system as time-intensive focused-beam experiments with the added benefits of fluorescent microscopy, cell recognition and time-lapse capabilities. The limitations of the system based on a 2 MV tandem accelerator are also discussed, including the uncertainties associated with particle Poisson counting statistics, spread of linear energy transfer in the nucleus and a timed dose delivery. These uncertainties are calculated with Monte Carlo methods. An analysis of how this uncertainty affects relative biological effect measurements is made and discussed.

A focused scanning vertical beam for charged particle irradiation of living cells with single counted particles

The Surrey vertical beam is a new facility for targeted irradiation of cells in medium with singly counted ions. A duo-plasmatron ion source and a 2 MV Tandem™ accelerator supply a range of ions from protons to calcium for this beamline and microscope endstation, with energy ranges from 0.5 to 12 MeV. A magnetic quadrupole triplet lens is used to focus the beam of ions. We present the design of this beamline, and early results showing the capability to count single ions with 98% certainty on CR-39 track etch. We also show that the beam targeting accuracy is within 5 μm and selectively target human fibroblasts with a <5 μm carbon beam, using γ-H2AX immunofluorescence to demonstrate which cell nuclei were irradiated. We discuss future commissioning steps necessary to achieve submicron targeting accuracy with this beamline.

Portable bacterial identification system based on elastic light scatter patterns

Background

Conventional diagnosis and identification of bacteria requires shipment of samples to a laboratory for genetic and biochemical analysis. This process can take days and imposes significant delay to action in situations where timely intervention can save lives and reduce associated costs. To enable faster response to an outbreak, a low-cost, small-footprint, portable microbial-identification instrument using forward scatterometry has been developed.

Results

This device, weighing 9 lb and measuring 12 × 6 × 10.5 in., utilizes elastic light scatter (ELS) patterns to accurately capture bacterial colony characteristics and delivers the classification results via wireless access. The overall system consists of two CCD cameras, one rotational and one translational stage, and a 635-nm laser diode. Various software algorithms such as Hough transform, 2-D geometric moments, and the traveling salesman problem (TSP) have been implemented to provide colony count and circularity, centering process, and minimized travel time among colonies.

Conclusions

Experiments were conducted with four bacteria genera using pure and mixed plate and as proof of principle a field test was conducted in four different locations where the average classification rate ranged between 95 and 100%.

Optimized digital counting colonies of clonogenic assays using ImageJ software and customized macros: comparison with manual counting

PURPOSE

To develop a digital method for counting colonies that highly replicates manual counting.

MATERIALS AND METHODS

Breast cancer cells were treated with trastuzumab-conjugated gold nanoparticles in combination with X-ray irradiation, (111)In labeled trastuzumab, or γ-radiation, followed by clonogenic assays. Colonies were counted manually or digitally using ImageJ software with customized macros. Key parameters, intensity threshold and minimum colony size, were optimized based on three preliminary manual counts or blindly chosen. The correlation of digital and manual counting and inter- and intra-experimenter variability were examined by linear regression. Survival curves derived from digital and manual counts were compared by F-test (P < 0.05).

RESULTS

Using optimized parameters, digital counts corresponded linearly to manual counts with slope (S) and R(2) value close to 1 and a small y-intercept (y(0)): SK-BR-3 (S = 0.96 ± 0.02, R(2) = 0.969, y(0) = 5.9 ± 2.2), MCF-7/HER2-18 (S = 0.98 ± 0.03, R(2) = 0.952, y(0) = 0.74 ± 0.47), and MDA-MB-231 cells (S = 1.00 ± 0.02, R(2) = 0.995, y(0) = 3.3 ± 4.5). Both reproducibility and repeatability of digital counts were better than the manual method. Survival curves generated from digital and manual counts were not significantly different; P-values were 0.3646 for SK-BR-3 cells and 0.1818 for MCF-7/HER2-18 cells. Using blind parameters, survival curves generated by both methods showed some differences: P-values were 0.0897 for SK-BR-3 cells and 0.0024 for MCF-7/HER2-18 cells.

CONCLUSIONS

The colony counting using ImageJ and customized macros with optimized parameters was a reliable method for quantifying the number of colonies.

Hidden stressors in the clonogenic assay used in radiobiology experiments

While clonogenic assays are extensively used in radiobiology, there is no widely accepted procedure for choosing the composition of the cell culture media. Cell line suppliers recommend a specific culture medium for each cell line, however a researcher will frequently customize this aspect of the protocol by supplementing the recommended support medium with additives. For example, many researchers add antibiotics, in order to avoid contamination of cells and the consequent loss of data, with little discussion of the influence of the antibiotics on the clonogenic survival of the cells. It is assumed that the effect of any variables in the growth medium on cell survival is taken into consideration by comparing the survival fraction relative to that of controls grown under the same conditions. In the search for better cancer treatment, the effect of various stressors on clonogenic cell survival is under investigation. This study seeks to identify and test potential stressors commonly introduced into the cell culture medium, which may confound the response to radiation.

Low-cost, high-throughput, automated counting of bacterial colonies

Research involving bacterial pathogens often requires enumeration of bacteria colonies. Here, we present a low-cost, high-throughput colony counting system consisting of colony counting software and a consumer-grade digital camera or document scanner. We demonstrate that this software, called "NICE" (NIST's Integrated Colony Enumerator), can count bacterial colonies as part of a high-throughput multiplexed opsonophagocytic killing assay used to characterize pneumococcal vaccine efficacy. The results obtained with NICE correlate well with the results obtained from manual counting, with a mean difference of less than 3%. NICE is also rapid; it can count colonies from multiple reaction wells within minutes and export the results to a spreadsheet for data processing. As this program is freely available from NIST, NICE should be helpful in bacteria colony enumeration required in many microbiological studies, and in standardizing colony counting methods.

Modeling light propagation through bacterial colonies and its correlation with forward scattering patterns

Bacterial colonies play an important role in the isolation and identification of bacterial species, and plating on a petri dish is still regarded as the gold standard for confirming the cause of an outbreak situation. A bacterial colony consists of millions of densely packed individual bacteria along with matrices such as extracellular materials. When a laser is directed through a colony, complicated structures encode their characteristic signatures, which results in unique forward scattering patterns. We investigate the connection between the morphological parameters of a bacterial colony and corresponding forward scattering patterns to understand bacterial growth morphology. A colony elevation is modeled with a Gaussian profile, which is defined with two critical parameters: center thickness and diameter. Then, applying the scalar diffraction theory, we compute an amplitude modulation via light attenuation from multiple layers of bacteria while a phase modulation is computed from the colony profile. Computational results indicate that center thickness plays a critical role in the total number of diffraction rings while the magnitude of the slope of a colony determines the maximum diffraction angle. Experimental validation is performed by capturing the scattering patterns, monitoring colony diameters via phase contrast microscope, and acquiring the colony profiles via confocal displacement meter.

Quantification of fluorescence intensity of labeled human mesenchymal stem cells and cell counting of unlabeled cells in phase-contrast imaging: an open-source-based algorithm

Assessment of cell fate is indispensable to evaluate cell-based therapies in regenerative medicine. Therefore, a widely used technique is fluorescence labeling. A major problem still is the standardized, noninvasive, and reliable quantification of fluorescence intensity of adherent cell populations on single-cell level, since total fluorescence intensity must be correlated to the cell number. Consequently, the aim of the present study was to produce and validate an open-source-based algorithm, capable of measuring the total fluorescence intensity of cell populations and assessing the total cell number in phase-contrast images. To verify the algorithms' capacity to assess fluorescence intensity, human mesenchymal stem cells were transduced to stably express enhanced green fluorescent protein and results produced by the algorithm were compared to flow cytometry analysis. No significant differences could be observed at any time (p ≥ 0.443). For validation of the algorithm for cell counting in phase-contrast images, adherent human mesenchymal stem cells were manually counted and compared to results produced by the algorithm (correlation coefficient [CC] r = 0.975), nuclei staining (CC r = 0.997), and hemocytometer (CC r = 0.629). We conclude that applying the developed algorithm in routine practice allows robust, fast, and reproducible assessment of fluorescence intensity and cell numbers in simple large-scale microscopy. The method is easy to perform and open source based.

Counting clonogenic assays from normoxic and anoxic irradiation experiments manually or by using densitometric software

The clonogenic assay is the method of choice to determine cell reproductive death after in vitro irradiation treatment. Traditionally, colony quantification has been performed by manual counting, a very laborious, time-consuming and rather subjective task. In this study, we compared manual counting by two skilled investigators with automated counting using the freely available ClonoCounter program. Five human tumour cell lines were irradiated under normoxia (21% O(2)) or anoxia (<0.1% O(2)), after 24 h or 6 h anoxic preincubation. Colonies were quantified manually or using the ClonoCounter software. A positive correlation between the absolute number of colonies counted manually and automatically was shown. Though there was a general trend of underpredicting the absolute number of cell colonies when counting automatically, survival curves were very similar, and in none of the cell lines were significant differences in radiobiological parameters such as mean inactivation dose, surviving fraction at 2 Gy and oxygen enhancement ratio detected. Our results suggest that the ClonoCounter provides sufficient reliability to be implemented for counting human tumour colonies in in vitro irradiation experiments. In contrast to several previously reported computer-aided colony-counting methods, it is a freely available program, requiring only minimal instrument costs.

RAD51C facilitates checkpoint signaling by promoting CHK2 phosphorylation

The RAD51 paralogues act in the homologous recombination (HR) pathway of DNA repair. Human RAD51C (hRAD51C) participates in branch migration and Holliday junction resolution and thus is important for processing HR intermediates late in the DNA repair process. Evidence for early involvement of RAD51 during DNA repair also exists, but its function in this context is not understood. In this study, we demonstrate that RAD51C accumulates at DNA damage sites concomitantly with the RAD51 recombinase and is retained after RAD51 disassembly, which is consistent with both an early and a late function for RAD51C. RAD51C recruitment depends on ataxia telangiectasia mutated, NBS1, and replication protein A, indicating it functions after DNA end resection but before RAD51 assembly. Furthermore, we find that RAD51C is required for activation of the checkpoint kinase CHK2 and cell cycle arrest in response to DNA damage. This suggests that hRAD51C contributes to the protection of genome integrity by transducing DNA damage signals in addition to engaging the HR machinery.

Automated cell colony counting and analysis using the circular Hough image transform algorithm (CHiTA)

We present an automated cell colony counting method that is flexible, robust and capable of providing more in-depth clonogenic analysis than existing manual and automated approaches. The full form of the Hough transform without approximation has been implemented, for the first time. Improvements in computing speed have facilitated this approach. Colony identification was achieved by pre-processing the raw images of the colonies in situ in the flask, including images of the flask edges, by erosion, dilation and Gaussian smoothing processes. Colony edges were then identified by intensity gradient field discrimination. Our technique eliminates the need for specialized hardware for image capture and enables the use of a standard desktop scanner for distortion-free image acquisition. Additional parameters evaluated included regional colony counts, average colony area, nearest neighbour distances and radial distribution. This spatial and qualitative information extends the utility of the clonogenic assay, allowing analysis of spatially-variant cytotoxic effects. To test the automated system, two flask types and three cell lines with different morphology, cell size and plating density were examined. A novel Monte Carlo method of simulating cell colony images, as well as manual counting, were used to quantify algorithm accuracy. The method was able to identify colonies with unusual morphology, to successfully resolve merged colonies and to correctly count colonies adjacent to flask edges.

Determination of cell colony formation in a high-content screening assay

The use of cell colony formation assays for research and clinical applications to assess the functional integrity of cells after in vitro manipulations is extensive. Key areas include hematopoietic stem cell research, cell transformation studies, and predicting the response of tumors to chemotherapeutic agents. Traditionally, enumeration of colonies has involved laborious and subjective counting by hand using a microscope. Here, laser scanning microplate cytometry has been used to provide an automated high-content readout of the effects of cytostatic agents on colony formation. This approach determines colony number through the application of a volume algorithm. Such an approach permits the differentiation of cytostatic effects where the number of colonies and size remains constant, and cytotoxic effects where the size and number may be reduced. Application of microplate cytometry thus offers significant benefits over alternative analytical methods in the search for novel chemotherapeutic agents.

The evaluation of a semiautomated computer method to determine the effects of DMSO on Giardia lamblia–intestinal cell interaction

In this work, we describe a semiautomated computer method to evaluate the activity of a common drug solvent, dimethyl sulfoxide (DMSO), on in vitro Giardia lamblia-host cell interaction. To compare the number of intestinal cells (IEC-6) and the adhered trophozoites over a specific area in control and treated coculture, a computer routine was created. Using video-light microscopy and digital image-processing tools, the operator was able to count the number of epithelial cells or parasites when they were still lying on the slide surface and without the need to detach them from the substrate for counting with a hemocytometer or other counting devices. Using this strategy, we calculated the total cell number per area and verified the effects of different concentrations of DMSO on G. lamblia-intestinal cell interaction and on the IEC-6 culture. At concentrations of 0.2% and 1%, this solvent produced a fragmentation on the monolayer of epithelial cells. However, DMSO did not affect the attachment of G. lamblia. In the course of these experiments, we compared the semiautomated method to the manual counting method and found that the first one generated smaller standard deviations (SD) than the second.

Photoreceptor counting and montaging of en-face retinal images from an adaptive optics fundus camera

A fast and efficient method for quantifying photoreceptor density in images obtained with an en-face flood-illuminated adaptive optics (AO) imaging system is described. To improve accuracy of cone counting, en-face images are analyzed over extended areas. This is achieved with two separate semiautomated algorithms: (1) a montaging algorithm that joins retinal images with overlapping common features without edge effects and (2) a cone density measurement algorithm that counts the individual cones in the montaged image. The accuracy of the cone density measurement algorithm is high, with >97% agreement for a simulated retinal image (of known density, with low contrast) and for AO images from normal eyes when compared with previously reported histological data. Our algorithms do not require spatial regularity in cone packing and are, therefore, useful for counting cones in diseased retinas, as demonstrated for eyes with Stargardt's macular dystrophy and retinitis pigmentosa.

Role of apoptosis in low-dose hyper-radiosensitivity

Little is known about the mode of cell killing associated with low-dose hyper-radiosensitivity, the radiation response that describes the enhanced sensitivity of cells to small doses of ionizing radiation. Using a technique that measures the activation of caspase 3, we have established a relationship between apoptosis detected 24 h after low-dose radiation exposure and low-dose hyper-radiosensitivity in four mammalian cell lines (T98G, U373, MR4 and 3.7 cells) and two normal human lymphoblastoid cell lines. The existence of low-dose hyper-radiosensitivity in clonogenic survival experiments was found to be associated with an elevated level of apoptosis after low-dose exposures, corroborating earlier observations (Enns et al., Mol. Cancer Res. 2, 557-566, 2004). We also show that enriching populations of MR4 and V79 cells with G(1)-phase cells, to minimize the numbers of G(2)-phase cells, abolished the enhanced low-dose apoptosis. These cell-cycle enrichment experiments strengthen the reported association between low-dose hyper-sensitivity and the radioresponse of G(2)-phase cells. These data are consistent with our current hypothesis to explain low-dose hyper-radiosensitivity, namely that the enhanced sensitivity of cells to low doses of ionizing radiation reflects the failure of ATM-dependent repair processes to fully arrest the progression of damaged G(2)-phase cells harboring unrepaired DNA breaks entering mitosis.

Colony size measurement of the yeast gene deletion strains for functional genomics

BACKGROUND

Numerous functional genomics approaches have been developed to study the model organism yeast, Saccharomyces cerevisiae, with the aim of systematically understanding the biology of the cell. Some of these techniques are based on yeast growth differences under different conditions, such as those generated by gene mutations, chemicals or both. Manual inspection of the yeast colonies that are grown under different conditions is often used as a method to detect such growth differences.

RESULTS

Here, we developed a computerized image analysis system called Growth Detector (GD), to automatically acquire quantitative and comparative information for yeast colony growth. GD offers great convenience and accuracy over the currently used manual growth measurement method. It distinguishes true yeast colonies in a digital image and provides an accurate coordinate oriented map of the colony areas. Some post-processing calculations are also conducted. Using GD, we successfully detected a genetic linkage between the molecular activity of the plant-derived antifungal compound berberine and gene expression components, among other cellular processes. A novel association for the yeast mek1 gene with DNA damage repair was also identified by GD and confirmed by a plasmid repair assay. The results demonstrate the usefulness of GD for yeast functional genomics research.

CONCLUSION

GD offers significant improvement over the manual inspection method to detect relative yeast colony size differences. The speed and accuracy associated with GD makes it an ideal choice for large-scale functional genomics investigations.

Counting colonies of clonogenic assays by using densitometric software

Clonogenic assays are a useful tool to test whether a given cancer therapy can reduce the clonogenic survival of tumour cells. A colony is defined as a cluster of at least 50 cells which can often only be determined microscopically. The process of counting colonies is very extensive work and so we developed software that is able to count the colonies automatically from scanned flasks. This software is made freely available by us with a detailed description how to use and install the necessary features.

Scoring CFU-GM colonies in vitro by data fusion: A first account

OBJECTIVE

In vitro models of hematopoiesis used in investigative hematopathology and in safety studies on candidate drugs, involve clonogenic assays on colony-forming unit granulocyte macrophage (CFU-GM). These assays require live and unstained colonies to be counted. Most laboratories still rely on visual scoring, which is time-consuming and error-prone. As a consequence, automated scoring is highly desired. An algorithm that recognizes and scores CFU-GM colonies by data fusion has been developed. Some preliminary results are presented in this article.

METHODS

CFU-GM assays were carried out on hematopoietic progenitors (human umbilical cord blood cells) grown in methylcellulose. Colony images were acquired by a digital camera and stored.

RESULTS

The classifier was designed to process images of layers sampled from a three-dimensional (3D) domain and forming a stack. Structure and texture information was extracted from each image. Classifier training was based on a 3D colony model applied to the image stack. The number of scored colonies (assigned class) was required to match the count supplied by the human expert (class of belonging). The trained classifier was validated on one more stack and then applied to a stack with overlapping colonies. Scoring in distortion- and caustic-affected border areas was also successfully demonstrated. Because of hardware limitations, compact colonies in some cases were missed.

CONCLUSIONS

The industry's scoring methods all rely on structure alone and process 2D data. Instead, the classifier here fuses data from a whole stack and is capable, in principle, of high-throughput screening.

Automated counting of mammalian cell colonies by means of a flat bed scanner and image processing

BACKGROUND

Clonogenic assays are used frequently to measure the cell killing and mutagenic effects of radiation and other agents. Clonogenic assays carried out manually are tedious and time-consuming and involve a significant element of subjectivity. However, several commercial automatic colony counters are available. Based on CCD video imaging and image analysis they are relatively expensive and can analyze only one petri dish at a time.

METHOD

We have developed a cheaper and more efficient device, which employs a flat bed scanner to image 12 60-mm petri dishes at a time. Two major problems in automated colony counting are the clustering of colonies and edge effects. By using standard image analysis and implementing an inflection point algorithm, these problems were greatly diminished. The resulting system was compared with two manual colony counts, as well as with automated counts with the Oxford Optronix ColCount colony counter for cell lines V79 and HaCaT.

RESULTS

Comparisons assuming the manual counts to be correct showed that our automatic counter was slightly more accurate than the commercial unit.

CONCLUSIONS

As a whole, our automated colony counter performed significantly better than the commercial unit with regard to processing time, cost and accuracy.

PARP-1, PARP-2, and the cellular response to low doses of ionizing radiation

PURPOSE

Poly(ADP-ribose) polymerase-1 (PARP-1) is rapidly and directly activated by single-strand breaks and is required for efficient base excision repair. These properties indicate that inhibition of PARP-1 might enhance the cellular response to low doses of radiation. We tested the effect of chemical inhibition of PARP-1 on low-dose clonogenic survival in a number of cell lines and the low-dose radiation response of a PARP-1 knockout murine cell line.

METHODS AND MATERIALS

Clonogenic cell survival of V79-379A and CHO-K1 hamster fibroblasts, T98G and U373-MG human glioma cells, and 3T3 mouse embryo fibroblast PARP-1 knockout cells was measured using a precise flow cytometry-based plating assay. Chemical inhibitors of PARP enzymes were tested for their effect on clonogenic survival after a range of ionizing radiation doses.

RESULTS

Chemical inhibition of PARP activity induced marked radiosensitization of V79, CHO, and exponentially growing T98G cells in the 0.05-0.3-Gy range. This effect was not seen in U373 cells or in confluent T98G populations. Low-dose radiosensitization was not apparent in PARP-1 knockout cells.

CONCLUSION

Low-dose radiosensitization of actively dividing tumor cells by PARP-1 inhibitors suggests that they may have a role in enhancing the efficacy of ultrafractionated or low-dose-rate radiotherapy regimens. We hypothesize that PARP-2 compensates for the absence of PARP-1 in the knockout cell line.

An association between the radiation-induced arrest of G2-phase cells and low-dose hyper-radiosensitivity: a plausible underlying mechanism?

The survival of asynchronous and highly enriched G1-, S- and G2-phase populations of Chinese hamster V79 cells was measured after irradiation with 60Co gamma rays (0.1-10 Gy) using a precise flow cytometry-based clonogenic survival assay. The high-dose survival responses demonstrated a conventional relationship, with G2-phase cells being the most radiosensitive and S-phase cells the most radioresistant. Below 1 Gy, distinct low-dose hyper-radiosensitivity (HRS) responses were observed for the asynchronous and G2-phase enriched cell populations, with no evidence of HRS in the G1- and S-phase populations. Modeling supports the conclusion that HRS in asynchronous V79 populations is explained entirely by the HRS response of G2-phase cells. An association was discovered between the occurrence of HRS and the induction of a novel G2-phase arrest checkpoint that is specific for cells that are in the G2 phase of the cell cycle at the time of irradiation. Human T98G cells and hamster V79 cells, which both exhibit HRS in asynchronous cultures, failed to arrest the entry into mitosis of damaged G2-phase cells at doses less than 30 cGy, as determined by the flow cytometric assessment of the phosphorylation of histone H3, an established indicator of mitosis. In contrast, human U373 cells that do not show HRS induced this G2-phase checkpoint in a dose-independent manner. These data suggest that HRS may be a consequence of radiation-damaged G2-phase cells prematurely entering mitosis.

An objective method to measure cell survival by computer-assisted image processing of numeric images of Petri dishes

This work establishes an objective method to measure cell clonogenic survival by computer-assisted image processing using images of cell cultures fixed and stained in Petri dishes. The procedure, developed by Samba Technologies, consists of acquiring Petri dish pictures with a desktop scanner and analysing them by computer, using algorithms based on the 'top hat' filter. The results from the automated count for the cell line SQ20B are compared with those found by two observers, before and after normalization of the counting. After normalization, the shape of the survival curves of the 'manual' counting of the Petri dishes shows a good correlation between both observers. The software enables the small visible differences in count between observers to be eliminated. The comparison between the absolute number of colonies shows an increased difference between the two manual scorings that can be as great as 67 colonies, whereas the difference between the two automated counts is never greater than 8 colonies. These results demonstrate that the 'manual' count is inter- and intraobserver variable, whereas the automatic count performs reproducible cell colony counts, thereby minimizing user-generated bias. The large amount of data produced also gives information about cell and colony characteristics. Thus, this computer-assisted method has considerably improved the reliability of our statistical results.

Model-based automated detection of mammalian cell colonies

Manually counting cell colonies, especially those that originate from fibroblast cell lines, is a time-consuming, eye-straining and tedious task in which consistency of counting is difficult to maintain. In this paper we present a novel model-based image segmentation method, which employs prior knowledge about the shape of a colony with the aim to automatically detect isolated, touching and overlapping cell colonies of various sizes and intensities. First, a set of hypothetical model instances is generated by using a robust statistical approach to estimate the model parameters and a novel confidence measure to quantify the difference between a model instance and the underlying image. Second, the model instances matching the individual colonies in the image are selected from the set by a minimum description length principle. The procedure was applied to images of Chinese hamster lung fibroblast cell line DC3F, which forms poorly defined or 'fuzzy' colonies. The correlation with manual counting was determined and the cell survival curves obtained by automated and manual counting were compared. The results obtained show that the proposed automatic procedure was capable to correctly identify 91% of cell colonies typical of mammalian cell lines.