SpotDSQ: a 2D-gel image analysis tool for protein spot detection, segmentation and quantification

The field of proteomics offers powerful methods for studying and analyzing protein expression levels in cells. The Two-Dimensional Gel Electrophoresis technique is a well-established proteomics technique focusing on protein separation and identification that provides digital images containing thousands of protein spots. 2D-gel images are then segmented into spots and background in order to quantify the expression levels of proteins located on a single gel or of differentially expressed proteins between samples from a series of 2D-gels. Spot detection and segmentation are complex and arduous tasks due to the inherent characteristics of 2D-gel images. Several software packages and experimental methods are available for 2D-gel image analysis, achieving different levels of success, each one having its respective advantages and drawbacks. A common characteristic is their dependency on user intervention in order to achieve optimal results, a process that leads to subjective and usually non-reproducible results. In this work, the authors present SpotDSQ, a software tool for 2D-gel electrophoresis image analysis that incorporates novel algorithms for accurate spot detection, segmentation and quantification. SpotDSQ provides high quality automatic protein spot detection, segmentation and quantification by employing novel algorithms that outperform state-of-the-art alternatives. Local automated multi-thresholding along with a modified version of the grow-cut segmentation algorithm is utilized in order to detect areas containing spots as well as the spot centers. The segmentation process is then guided by the information gathered during the detection step, utilizing the detected spot centers as seeds for a region growing approach that separates spot areas, while morphological operators are then utilized in order to accurately detect the protein spots boundaries. SpotDSQ offers an easy-to-use graphical user interface that requires no special training to operate. Results are exported as images and text data in order to facilitate further analysis. The performance of SpotDSQ was evaluated on real as well as synthetic 2D-gel images using well established statistical measures. Spot detection performance was evaluated by means of precision, sensitivity, and the F-measure. Volumetric overlap, volumetric error and volumetric overlap error were utilized for evaluating the segmentation performance. The high F-measure (94.8%) value, the low volumetric overlap error (8.3%), and the accurate spot boundaries achieved by SpotDSQ indicate its effectiveness compared to alternative methods. Experimental results show that SpotDSQ outperforms state-of-the-art software packages as well as methods proposed in the literature, achieving high accuracy and reduced errors. The advantages of SpotDSQ indicate that it has the potential to be a powerful and reliable tool for 2D-gel image analysis in biomedical laboratories.