CometQ: An automated tool for the detection and quantification of DNA damage using comet assay image analysis
Introduction
DNA damage analysis has great significance in the field of medical research. Human cells are constantly attacked by several harmful agents generated by both exogenous and endogenous processes which lead to DNA damage. The majority of damages are induced by reactive oxygen species (ROS) and reactive nitrogen species (RNS). These oxidative DNA damages are critical risk factors for cancer, ageing, neuro degenerative diseases, heart diseases, Parkinson's disease, Schizophrenia and Alzheimer's disease [1], [2], [3], [4], [5]. DNA damage analysis provides important information regarding early biological effects of hazardous chemicals and relevant information about the different stages of diseases. This information can be used by clinicians and pathologists for planning treatment and determining the best course of intervention. Hence, an accurate, fast and sensitive method for the analysis of DNA damage is highly demanded [6].
Comet assay or single-cell gel electrophoresis (SCGE) is a widely accepted method for DNA damage analysis [7], [8]. The comet assay is well known for its simplicity, low cost, robust statistical analysis, requirement of small number of cells per sample and high sensitivity for detecting low levels of DNA damage. Comet assay finds extensive use in the area of testing new chemicals for genotoxicity, monitoring environmental contamination with genotoxins, human biomonitoring and molecular epidemiology, diagnosis of genetic disorders and fundamental research in DNA damage and repair.
Comet assay procedure starts with separating the lymphocyte from blood samples. The cells are embedded in agarose gel and subjected to lysis. The slides are then incubated in alkaline electrophoresis buffer for alkaline unwinding, followed by electrophoresis. During electrophoresis, negatively charged DNA particles move towards the anode and form a comet like structure based on different levels of DNA damage. After neutralisation, slides are stained with suitable DNA-binding dye for the proper visualization of DNA through fluorescent/optical microscope. Different variations of this procedure are available to detect different types of DNA damage [9]. DNA damage is quantified by evaluating various parameters [10] of the individual comets from the comet assay images.
Comets can be scored using either by manual methods or by using software tools. In manual method, comets are scored by measuring the length of a comet tail using a photomicrograph or using a graticule, which is laborious and gives only limited information [11]. Visual scoring is another approach under manual method. Both these methods are time consuming and require skilled operators. But still, visual scoring is used as a benchmark to validate the results obtained through automated algorithms. Semi-automated [12], [13], [14], [15] and fully automated [16], [17], [18], [19], [20], [21], [22], [23] tools are available for comet scoring. CASP [14], [15] and Comet Score are two semi-automated tools freely available in the Internet. Here also, the requirement of experts at different stages makes the technique user dependent, tedious and time consuming. Most of the automated tools are integrated with in-house microscope set up, and hence these tools are not freely accessible. They are very expensive and the source code cannot be modified. Moreover, most of the algorithms except Cell profiler and OpenComet are developed for fluorescent stained images.
This paper presents CometQ, an automated tool for the detection and quantification of DNA damage by analysing comet assay images. The system mainly consists of four stages: (1) classifier, (2) comet segmentation, (3) comet partitioning and (4) comet quantification. CometQ is a robust algorithm which automatically identifies the type of input image (fluorescent or silver stained images) as well as the level of DNA damage (heavily damaged or lightly/moderately damaged) present in these images and directs it to the appropriate segmentation methods for the identification of actual comets. A classifier stage, based on SVM is designed and implemented at the front end, to categorise the input image into one of the above four groups to ensure proper routing. Comet segmentation is followed by comet partitioning which is implemented using a novel technique coined as modified fuzzy clustering. Comet parameters are calculated in the comet quantification stage and are saved in an excel file. This is an open source standalone application developed using Matlab software. CometQ is validated using silver stained images from 40 Schizophrenia patients with different severity levels and 56 fluorescent stained images collected from Internet sources. Patients with schizophrenia have greater DNA damage than the normal population due to various mechanisms involving the redox status in these patients [24], [25], and hence are chosen as the main cases in this study. Even though this package is specifically developed for clinical applications, it can be used in other research applications where DNA damage analysis is performed using comet assay images.
The rest of the paper is organised as follows. In Section 2, different methods used for the implementation of CometQ are discussed. This section describes the CometQ user interface, comet assay procedure and the algorithm developed for comet assay image analysis. Section 3 elaborates the performance of CometQ. Section 4 highlights the salient features of the proposed method and comparison with the most recent related work. The paper concludes with a brief summary in Section 5.
Section snippets
CometQ user interface
Fig. 1 shows the user interface for CometQ. The user has to first select the images (any number of images from a folder) for analysis. Many of the common image file formats like BMP, TIFF, JPG etc., are supported. Next, the user selects an output directory and a file name for storing the comet parameters in MS-Excel format. In order to have a robust output, provision is given to select the objective magnification. An error message will pop up if the user forgets to select any of the input
Classifier
From our dataset, hundred images consisting of both fluorescent and silver stained images are selected for training. Classifier is tested with the remaining data and an accuracy of 100% is obtained for Classifier 1. Classifier 2 is trained with silver stained images alone and Classifier 3 with fluorescent stained images alone. For selecting the best model, Classifiers 2 and 3 are trained using both linear kernel and radial basis function (RBF) kernel with different combinations of features.
Discussion
CometQ, a fully automated and an efficient method for DNA damage analysis using comet assay images, is capable of analysing both silver stained and fluorescent stained images. The proposed software automatically selects the most suitable comet segmentation method depending on the type of input images and levels of DNA damage. After comet segmentation, the individual comets are partitioned into head, halo, tail and background using FCM. Then the output of FCM is modified with clustering and
Conclusion
A fully automated tool for DNA damage analysis, using both fluorescent stained and silver stained comet assay images, for clinical applications, is developed and implemented in this work. The proposed software consists of three classifiers based on SVM to categorize the input images into four classes: silver stained images with lightly or moderately damaged cells, silver stained images with heavily damaged cells, fluorescent stained images with lightly or moderately damaged cells and
References (30)
- et al.
DNA damage and repair efficiency in lymphocytes from schizophrenic patients
Cancer Lett
(2004) - et al.
DNA damage in diabetes: correlation with a clinical marker
Free Radic. Biol. Med
(1998) - et al.
DNA damage in bipolar disorder
Psychiatry Res
(2007) - et al.
Microelectrophoretic study of radiation-induced DNA damages in individual mammalian cells
Biochem. Biophys. Res. Commun
(1984) - et al.
A simple technique for quantitation of low levels of DNA damage in individual cells
Exp. Cell Res
(1988) - et al.
The comet assay: a sensitive method for detecting DNA damage in individual cells
Methods
(2009) - et al.
A public domain image-analysis program for the single-cell gel-electrophoresis (comet) assay
Mutat. Res
(2000) - et al.
A cross-platform public domain pc image-analysis program for the comet assay
Mutat. Res
(2003) - et al.
Curve fitting of combined comet intensity profiles: a new global concept to quantify DNA damage by the comet assay
Chemometr. Intell. Lab. Syst
(2004) - et al.
Validation of an automatic comet assay analysis system integrating the curve fitting of combined comet intensity profiles
Mutat. Res
(2008)
Automatic analysis of silver-stained comets by cellprofiler software
Mutat. Res
Opencomet: an automated tool for comet assay image analysis
Redox Biol
Quantification of DNA damage by the analysis of silver stained comet assay images
IRBM
Increased systemic oxidatively generated DNA and RNA damage in schizophrenia
Psychiatry Res
An improved automatic detection of true comets for DNA damage analysis
Procedia Comput. Sci
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