Evaluation of colony-forming ability experiments using normal and DNA repair-deficient human fibroblast strains and an automatic colony counter

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.

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.

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.

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.

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.

Automated counting of mammalian cell colonies

Investigating the effect of low-dose radiation exposure on cells using assays of colony-forming ability requires large cell samples to maintain statistical accuracy. Manually counting the resulting colonies is a laborious task in which consistent objectivity is hard to achieve. This is true especially with some mammalian cell lines which form poorly defined or 'fuzzy' colonies, typified by glioma or fibroblast cell lines. A computer-vision-based automated colony counter is presented in this paper. It utilizes novel imaging and image-processing methods involving a modified form of the Hough transform. The automated counter is able to identify less-discrete cell colonies typical of these cell lines. The results of automated colony counting are compared with those from four manual (human) colony counts for the cell lines HT29, A172, U118 and IN1265. The results from the automated counts fall well within the distribution of the manual counts for all four cell lines with respect to surviving fraction (SF) versus dose curves, SF values at 2 Gy (SF2) and total area under the SF curve (Dbar). From the variation in the counts, it is shown that the automated counts are generally more consistent than the manual counts.

DNA repair synthesis following irradiation with 254-nm and 312-nm ultraviolet light is not diminished in fibroblasts from patients with dysplastic nevus syndrome

The DNA excision repair capacity of 23 primary fibroblast lines from patients with dysplastic nevus syndrome was investigated and DNA repair synthesis ("unscheduled DNA synthesis") was determined after UV exposure. Seventeen fibroblast lines from normal donors served as controls. The dose/response experiments included up to ten dose levels and two wavelength ranges: UV-C (using a low-pressure mercury lamp emitting predominantly 254-nm light) and UV-B (artificial "sunlamp" radiation centering around 312-nm light). For each dose level, silver grains over fibroblast nuclei were counted by visual inspection. Twelve cell lines were also evaluated for both UV wavelength ranges using a new semi-automatic image analyzing system. This system included components for rapid sequential identification of both fibroblast nuclei and silver grains sited above them. Silver grains over 100 nuclei were determined for each UV dose level. Dose/response curves were established and analyzed by linear regression. As a quantitative term for assessing DNA excision repair capacity of a cell line we calculated the linear increase (G0) in the number of grains per nucleus, when the UV dose was multiplied by the factor e (i.e. 2.72). The sensitivity of grain detection and resolution of overlapping grains was approximately threefold better in visual than in automatic counting, especially when there were more than 70 grains over nuclei. The time required for visual counting, however, was tenfold that of automatic counting. The variance-weighted mean G0v.w of all fibroblast lines from patients with dysplastic nevus syndrome was found to be 79.1 (+/- 1.8- grains/nucleus, that of fibroblast lines from normal donors was 74.2 (+/- 1.7) grains/nucleus. This difference revealed a slightly better repair capability for cell lines from patients but was at the borderline of detection and, therefore, should not be overinterpreted. From the experimental accuracy achieved by determination of the variance-weighted means of the two groups, we would have been able to detect a difference of 7 and more grains [> 2 x (sigma normal+sigma patients)]. The variance-weighted mean G0v.w of all fibroblast lines from patients with dysplastic nevus syndrome was found to be 76.4 (+/- 1.4) grains/nucleus, whereas that of fibroblast lines from normal donors was only 66.6 (+/- 1.8) grains/nucleus. This difference was statistically significant and, contrary to expectation, revealed better, not worse post-UV DNA repair capability in cell lines from patients that in those from normal donors.(ABSTRACT TRUNCATED AT 400 WORDS)

6-Methylguanine and 6-methylguanosine inhibit colony-forming ability in a malignant xeroderma pigmentosum cell line but not in other xeroderma pigmentosum and normal human fibroblast strains after treatment with 1-(2-chloroethyl)-1-nitroso-3-(2-hydroxyethyl)-urea

The XP cell strain XP29MA, its malignant counterpart XP29MAmal and a normal human fibroblast strain were tested for colony-forming ability after treatment with HECNU in the presence of m6G, m6Gua, and he7G. In XP29MAmal, inhibition of post-HECNU colony-forming ability was 35% when 0.25 mM of either m6G or m6Gua were present, whereas in XP29MA and the normal fibroblast strain no inhibition was detected. The he7G caused a similar but smaller inhibitory effect in XP29MAmal, but failed to do so in XP29MA. HECNU predominantly exerts its killing effect by alkylating O-6 of DNA-bound guanine and causing DNA interstrand crosslinks. Alkylation of O-6 of guanine can be repaired by 6-methylguanine-DNA methyltransferase. From our experiments we conclude that in XP29MAmal this methyltransferase was inhibited in the presence of the 6-alkylguanines, thus leaving more 2-chloroethylated sites in DNA unrepaired. This results in sensitization in terms of decreased colony-forming ability observed only in the malignant cell line.