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Decyder 2d software

Manufactured by GE Healthcare
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The DeCyder 2D software is a data analysis tool developed by GE Healthcare for the processing and analysis of 2D gel electrophoresis images. The software provides automated spot detection, quantification, and matching across multiple gel images, enabling researchers to identify and compare protein expression patterns.

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13 protocols using decyder 2d software

1

Quantitative 2D-DIGE Protein Analysis

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To visualize the labeled and separated proteins after electrophoresis, the 2D-DIGE gels were scanned at a resolution of 100 µm with a Typhoon FLA 9500 (GE Healthcare). Subsequently, to visualize the total protein load, 2D-DIGE gels were fixed (1% citric acid, 30% ethanol) for 60 min and stained with colloidal Coomassie overnight (5% aluminum sulfate hydrate, 10% ethanol, 0.02% Coomassie G250, and 2% o-phosphoric acid). After destaining with deionized water until appropriate background reduction, gels were scanned with a visual scanner (CanoScan 9900F) using a resolution of 300 dpi.
For image analysis scan files of the 2D-DIGE gels were loaded into DeCyder 2D software (GE Healthcare, version 7.2). Spots were detected with an estimate of 5000 spots for the 2D-DIGE gel. Subsequently, a detection area excluding the region of strip application, molecular weight marker, and running front was determined. Spots with a volume below 50,000 were defined to be background. Stained crumbs originating from the dyes were eliminated by excluding spots with an area below 300. False positive spots, for example, produced by dye artifacts within the gel were removed manually. After editing the gels were normalized towards the Cy2 channel (internal standard).
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2

Comparative Proteomics of Canine CSM

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Gels were scanned in a Typhoon 9400 variable mode scanner (GE Healthcare) using the appropriate settings for CyDye fluorophors at 100-micron resolution. Preparative gels (for spot picking and protein identification) were stained with Lava purple general protein stain (Gel Company) according to standard protocols. Gel images were loaded into DeCyder 2D software (GE Healthcare) and analyzed individually. Log-standardized abundance was the variable subjected to statistical analysis. A Student’s t-test was used to detect differences between CSM-affected and control GD (P<0.05). The following three comparisons were made in Decyder: 1) control versus CSM-affected dogs, 2) control versus non-corticosteroid treated CSM-affected dogs, and 3) non-corticosteroid treated CSM-affected versus corticosteroid treated CSM-affected dogs. For each comparison, spots exhibiting a statistically significant change of at least 1.25-fold were selected for subsequent identification.
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3

Differential Proteomic Analysis of Multiple Myeloma

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The 2D-DIGE experiments were performed as per the protocol reported previously by Rapole and co-authors (31 (link)). Briefly, 60 µg of BMIF and serum proteins from controls and MM patients were labeled with 400 pmol of CyDyes Cy5, Cy3 respectively. Moreover, an internal control having 1:1 concentration of both samples was constituted and labeled with Cy2. The labeled samples were rehydrated on a 24 cm linear IPG strip (pH 4–7), and the strips were subjected to isoelectric focusing (IEF) through EttanIPGphor 3 instrument (GE Healthcare, USA). Following the first dimension IEF procedure, the second-dimension SDS-PAGE was performed and the gels were scanned through the Typhoon FLA 9500 biomolecular imager (GE Healthcare, USA). The images were analyzed using DeCyder 2D software; version 7.0 (GE Healthcare, USA) for both the differential in-gel analysis and biological variation analysis (BVA). The statistically significant protein spots passing the t-test (p-value ≤ 0.05) and present in all the gels were selected for MALDI-TOF/TOF identification.
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4

Quantitative Proteomic Profiling of Meningiomas

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Protein extraction and 2D-DIGE were performed as described previously43 (link). Protein extraction from depleted serum samples was performed using TCA/acetone precipitation method43 (link). The two most high abundance serum proteins (albumin and IgG) were depleted using Albumin & IgG Depletion SpinTrap (GE Healthcare) following the manufacturer's instructions. Samples (meningiomas grade I/II and control) were labeled with Cy3 and Cy5, while a mixture of equal amounts of all samples to be analyzed in the experiment, regarded as internal standard, was labeled with the third fluorescent dye; Cy2 according to the manufacturer's instructions (GE Healthcare). DIGE experiments were performed in three technical replicates and dye-swapping was performed while labeling the meningioma and control samples to avoid any type of labeling effects. Image acquisition and data analysis was performed as described previously42 (link). In brief, comparative analysis was performed using two different modules, differential in-gel analysis (DIA) and biological variation analysis (BVA) of DeCyder 2D software; version 7.0 (GE Healthcare). Statistical significance of the average ratio of expressions was analyzed by Student's t-test and ANOVA (p < 0.05).
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5

Differential Proteomics Analysis by 2D-DIGE

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Prepared secreted proteins were separated by 2D-DIGE as described previously [49 (link)]. The Cy2, Cy3, Cy5 labeled samples (50 μg) were mixed and loaded on the strips (linear, 24 cm, pI 4–7, GE Healthcare, USA) for the first dimension separation. Next, the strips were placed on top of 12.5 % SDS-PAGE gels for the second dimension electrophoresis. Protein spots on gels were scanned using an Ettan DIGE Scanner (GE Healthcare, USA) and the images were analyzed using Decyder 2D software (Version 7.0, GE Healthcare, USA). Finally, spots from different gels were matched using Biological Variation Analysis. Only spots present in all gels and which exhibited statistically significant changes in intensity (≥1.5 fold or ≤ −1.5 fold, p <0.05) were considered to be differentially expressed proteins.
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6

2D-DIGE Gel Image Analysis Protocol

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To visualize the labeled and separated proteins after electrophoresis, the 2D-DIGE gels were scanned at a resolution of 100 μm with a Typhoon FLA 9500 (GE Healthcare).
For image analysis, scan files of the 2D-DIGE gels were loaded into DeCyder 2D software (GE Healthcare, version 7.2). Spots were detected with an estimate of 5,000 spots for the 2D-DIGE gel. Subsequently, a detection area excluding the region of strip application, molecular weight marker, and running front was defined. Spots with a volume below 100,000 were defined to be background. Stained crumbs originated from the Dyes were eliminated by excluding spots with an area below 350. False positive spots, for example, produced by dye artefacts within the gel were removed manually. After the editing the gels were normalized towards the Cy2 channel (internal standard).
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7

Flow Cytometry and Invasion Assay Protocol

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All flow cytometry data were analyzed using FlowJo (version 10.6.1). The DeCyder analysis was performed by Applied Biomics using the DeCyder 2D software from GE Healthcare, version 6.5; http://biotech.gsu.edu/core/Documents/Manuals/decyder-v6.5-manual.pdf. For statistical analysis of the invasion assays, 2-tailed Student t tests were performed using GraphPad Prism version 9.
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8

SDS-PAGE Protein Separation and Visualization

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Equilibrated strips were briefly washed in 1x running buffer (25 mM Tris, 192 mM glycine, and 0.2% SDS) and placed on top of 12% acrylamide/bis-acrylamide gels, overlaid with a 0.5% agarose solution. Protein separation was carried out at 10°C, in an Ettan Dalt Six Electrophoresis System (GE Healthcare, USA), 45 mA per gel, until the dye front reached the bottom of the gel. Labeled proteins in each gel were visualized using the Typhoon FLA 9000 scanner (GE Healthcare, USA) at 100 µM image resolution with excitation/emission wavelengths for Cy3 (532/580 nm), Cy5 (633/670 nm), and Cy2 (488/520 nm). Gel images were uploaded and cropped using Image Loader Software (GE Healthcare, USA), then imported to DeCyder 2D software, version 7.0 (GE Healthcare, USA).
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9

DIGE Image Analysis of Pneumococcal Variants

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SDS-PAGE gels were scanned using an Ettan DIGE Imager (GE Healthcare) with a resolution of 100 μm. The exposure times of the individual channels (Cy2, Cy3 and Cy5) were set to yield a maximum of approximately 35,000 intensity units. The resulting images were horizontally flipped before image analysis using ImageQuant TL (Version 7.0, GE Healthcare).
Image analysis was undertaken using DeCyder 2D software (version 7, GE Healthcare). Each gel image was processed separately in the Differential In-gel Analysis (DIA) module of DeCyder prior to export to the Biological Variation Analysis (BVA) module. In all DIGE experiments, protein expression in the T variant of every strain was subjected to statistical comparison with its O counterpart (D39O vs. D39T; WCH16O vs. WCH16T; WCH43O vs. WCH43T) to detect spots that are differentially expressed using unpaired two-tailed Students t-test. Those spots that returned a p-value of <0.05 were accepted. For the second DIGE experiments, spots with a significant p-value were further verified to exhibit a consistent regulation pattern, i.e. up/down regulated in T vs. O in all three strains.
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10

Multivariate Analysis of Fluorescence Imaging

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Fluorescence images were analyzed using DeCyder™ 2D software (v.7.0) and the multivariate statistical module EDA (Extended Data Analyses; GE, Healthcare), as described in Varó et al. (2010 (link)). First, the intra-gel images were individually processed by DeCyder-DIA (Differential In-gel Analyses) software module to co-detect and differentially quantify the protein spots in the images, taking the internal standard as reference to normalize the data, and with the threshold set to 2 standard deviations. Thereafter, the DeCyder-BVA (Biological Variation Analysis) was applied to inter-gel matching, and differences in average ratios of protein expression were analyzed by the Student's t- test and One-Way ANOVA, with p ≤ 0.05 being considered significant. Finally, EDA software was used for multivariate statistical analysis of data. Principal components analyses (PCA) were carried out using an algorithm included in the EDA software, incorporating only data from proteins present in at least 90% of the spot maps and applying a t-test filter (p ≤ 0.05).
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