2D-gel spot detection and segmentation based on modified image-aware grow-cut and regional intensity information

Kostopoulou, Eirini; Katsigiannis, Stamos; Maroulis, Dimitris

doi:10.1016/j.cmpb.2015.06.007

Cited by 8 publications

(5 citation statements)

References 37 publications

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“…In part, this is also due to the development and commercial availability of (1) high-resolution imaging equipment supporting multiwavelength excitation and emission (e.g., Odyssey imager (Licor, Lincoln NB); Typhoon TM FLA-9000 (GE Healthcare); Typhoon 5 Biomolecular Imager (GE Healthcare)); and (2) image analysis software enabling high resolution spot identification (i.e., signal above local background) and detailed quantitative analyses (e.g., Delta2D (DECODON)). There is also ongoing development of quantitative image analysis approaches that may lead to the critical extraction of still more/better data from 2D gels [ 148 , 149 , 150 , 151 , 152 , 153 ]. Furthermore, the immunoblotting of 2D gels, even after staining, provides the most direct approach to quickly identifying proteoforms [ 3 , 52 , 154 , 155 , 156 , 157 , 158 , 159 , 160 , 161 , 162 , 163 , 164 , 165 ], provided the antibodies used have been critically vetted (including to PTM [ 166 ]), and even then there are caveats to consider (e.g., blockade of antibody binding by a given PTM) [ 10 ].…”

Section: Recognising and Addressing Critical Issuesmentioning

confidence: 99%

Proteomics—The State of the Field: The Definition and Analysis of Proteomes Should Be Based in Reality, Not Convenience

Coorssen,

Padula

2024

Proteomes

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With growing recognition and acknowledgement of the genuine complexity of proteomes, we are finally entering the post-proteogenomic era. Routine assessment of proteomes as inferred correlates of gene sequences (i.e., canonical ‘proteins’) cannot provide the necessary critical analysis of systems-level biology that is needed to understand underlying molecular mechanisms and pathways or identify the most selective biomarkers and therapeutic targets. These critical requirements demand the analysis of proteomes at the level of proteoforms/protein species, the actual active molecular players. Currently, only highly refined integrated or integrative top-down proteomics (iTDP) enables the analytical depth necessary to provide routine, comprehensive, and quantitative proteome assessments across the widest range of proteoforms inherent to native systems. Here we provide a broad perspective of the field, taking in historical and current realities, to establish a more balanced understanding of where the field has come from (in particular during the ten years since Proteomes was launched), current issues, and how things likely need to proceed if necessary deep proteome analyses are to succeed. We base this in our firm belief that the best proteomic analyses reflect, as closely as possible, the native sample at the moment of sampling. We also seek to emphasise that this and future analytical approaches are likely best based on the broad recognition and exploitation of the complementarity of currently successful approaches. This also emphasises the need to continuously evaluate and further optimize established approaches, to avoid complacency in thinking and expectations but also to promote the critical and careful development and introduction of new approaches, most notably those that address proteoforms. Above all, we wish to emphasise that a rigorous focus on analytical quality must override current thinking that largely values analytical speed; the latter would certainly be nice, if only proteoforms could thus be effectively, routinely, and quantitatively assessed. Alas, proteomes are composed of proteoforms, not molecular species that can be amplified or that directly mirror genes (i.e., ‘canonical’). The problem is hard, and we must accept and address it as such, but the payoff in playing this longer game of rigorous deep proteome analyses is the promise of far more selective biomarkers, drug targets, and truly personalised or even individualised medicine.

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Section: Recognising and Addressing Critical Issuesmentioning

confidence: 99%

Proteomics—The State of the Field: The Definition and Analysis of Proteomes Should Be Based in Reality, Not Convenience

Coorssen,

Padula

2024

Proteomes

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“…Esta técnica permite separar objetos del fondo a partir de un umbral sobre la intensidad de los pixeles [20]. En procesamiento de imágenes 2DGE es común encontrar enfoques multi-umbral para la detección de las proteínas [38], [45]. Por ejemplo, Kostopoulou y colaboradores dividen la imagen en ventanas de tamaño fijo, en cada una de las cuales se aplica la umbralización [39], [45].…”

Section: ) Umbralizaciónunclassified

“…El principal problema que presentan los algoritmos de umbralización es su alta sensibilidad al ruido, anomalías y fondo no homogéneo de las imágenes 2DGE, lo cual resulta en la detección de falsas proteínas [45]. Por esta razón, se encuentra en la literatura la combinación de las técnicas de umbralización con otros métodos de procesamiento de imágenes.…”

Section: ) Umbralizaciónunclassified

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Procesamiento de imágenes de electroforesis bidimensional: una revisión

et al. 2019

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Este artículo presenta una revisión de las técnicas y algoritmos aplicados al procesamiento y análisisde imágenes de electroforesis bidimensional (2DGE). La electroforesis bidimensional permite separar cientos de proteínas en un único gel, mostrando un patrón característico. En el análisis de imágenes es importante una correcta detección de las proteínas presentes, ya que cualquier error en esta etapa puede llevar a la detección de falsas proteínas, o a obviar proteínas importantes, pero de baja abundancia, lo cual afectaría los resultados del análisis. Técnicas de segmentación son empleadas para separar las proteínas del fondo y encontrar anomalías. Los métodos empleados para la segmentación de imágenes 2DGE se pueden clasificar como: basados en detección de bordes, morfológicos, umbralización y basados en regiones. En muchos estudios proteómicos se hace necesario la fusión o registro de imágenes para la identificación y comparación de patrones de varias muestras diferentes. Para este proceso de fusión se pueden usar las imágenes originales o los resultados de la segmentación. A pesar de los avances significativos en el campo de procesamiento de imágenes 2DGE, no se encuentran en la literatura métodos completamente automatizados. Las herramientas comerciales disponibles para el análisis y procesamiento de imágenes 2DGE requieren que el usuario seleccione adecuadamente ciertos parámetros, de los cuales dependen los resultados arrojados por el software. Este artículo revisa diferentes técnicas para el procesamiento de las imágenes 2DGE. Especial atención es dada a las técnicas de segmentación y registro de imágenes.

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“…For 2-DGE image analysis, techniques are required to detect protein spots, to segment and to quantify the protein expression level based on the number of pixels [15] , [16] . An additional step of image alignment is performed in order to match the corresponding protein spots from different images [17] . However, due to technical difficulties inherent to 2-DGE, anomalies are often found in gel images, such as noise around protein spots, vertical and horizontal streaking, saturation of certain protein spots, presence of very faint protein spots, as well as non-linear intensity of protein spots [14] , [18] .…”

Section: Introductionmentioning

confidence: 99%

Preprocessing of 2-Dimensional Gel Electrophoresis Images Applied to Proteomic Analysis: A Review

Goez

Torres-Madronero

Röthlisberger

et al. 2018

Genomics, Proteomics &Amp; Bioinformatics

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Various methods and specialized software programs are available for processing two-dimensional gel electrophoresis (2-DGE) images. However, due to the anomalies present in these images, a reliable, automated, and highly reproducible system for 2-DGE image analysis has still not been achieved. The most common anomalies found in 2-DGE images include vertical and horizontal streaking, fuzzy spots, and background noise, which greatly complicate computational analysis. In this paper, we review the preprocessing techniques applied to 2-DGE images for noise reduction, intensity normalization, and background correction. We also present a quantitative comparison of non-linear filtering techniques applied to synthetic gel images, through analyzing the performance of the filters under specific conditions. Synthetic proteins were modeled into a two-dimensional Gaussian distribution with adjustable parameters for changing the size, intensity, and degradation. Three types of noise were added to the images: Gaussian, Rayleigh, and exponential, with signal-to-noise ratios (SNRs) ranging 8–20 decibels (dB). We compared the performance of wavelet, contourlet, total variation (TV), and wavelet-total variation (WTTV) techniques using parameters SNR and spot efficiency. In terms of spot efficiency, contourlet and TV were more sensitive to noise than wavelet and WTTV. Wavelet worked the best for images with SNR ranging 10–20 dB, whereas WTTV performed better with high noise levels. Wavelet also presented the best performance with any level of Gaussian noise and low levels (20–14 dB) of Rayleigh and exponential noise in terms of SNR. Finally, the performance of the non-linear filtering techniques was evaluated using a real 2-DGE image with previously identified proteins marked. Wavelet achieved the best detection rate for the real image.

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2D-gel spot detection and segmentation based on modified image-aware grow-cut and regional intensity information

Cited by 8 publications

References 37 publications

Proteomics—The State of the Field: The Definition and Analysis of Proteomes Should Be Based in Reality, Not Convenience

Proteomics—The State of the Field: The Definition and Analysis of Proteomes Should Be Based in Reality, Not Convenience

Procesamiento de imágenes de electroforesis bidimensional: una revisión

Preprocessing of 2-Dimensional Gel Electrophoresis Images Applied to Proteomic Analysis: A Review

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