DiGE....2-D gel electrophoresis

2-D gel electrophoresis techniques..application and Utility

1. Subject training on Computational Tools for Animal Genome Resource Data Analysis Dec 02-13, 2013 DiGE Dr Karan Veer Singh Scientist National Bureau of Animal Genetic Resources Karnal

2. 2-D gel electrophoresis 2D analysis experiments commonly address questions like Protein level Differences caused by disease state drug treatment life-cycle stage Some protein level differences studied are small and the results are affected by experimental variation originating both from the system and from inherent biological variation Misdiagnosis is dangerous

3. Limitations of conventional 2D gel 6 gels made from the very same sample , run in parallel (SYPRO Ruby) Conventional 2-D control gel 1 treated gel 2 Are spot differences real? Differences?? Only one dye can be used at a time for one gel We run as many gel as many samples are there Cannot control the variation in loss of proteins for each gel Differential analysis is difficult Statistical confidence is less Proteins with PI beyond the pH limits of strips cannot be focused on strips

4. Variation in 2D gel System related variations Gel-to-gel variation , which can result from differences in electrophoretic conditions between first dimension strips or second dimension gels, gel distortions , sample application variation and user-to-user variation . Variation due to user-specific editing and interpretation when using the data analysis software. Inherent biological variation Inherent biological variation arises from intrinsic differences that occur within a population. For example, differences from animal-to-animal , plant-to-plant or culture-to-culture which have been subjected to identical conditions Induced biological change Differences due to disease state, drug treatment, life cycle stage

5. Least gel to gel variation Normalization of biological variation Least number of gels run Differential expression analysis Statistical confidence in presenting our result How to avoid uncontrolled protein loss Is there any way out What we want ???

6. Covalent derivatization of proteins with fluorophores in complex protein mixtures prior to IEF and SDS-PAGE allows detection and quantification of differences in protein abundance between different biological samples within one single gel DIGE system allows the inherent biological variation to be effectively differentiated from induced biological changes DIGE system is capable of detecting and quantifying differences as small as 10% between samples (above system variation) with greater than 95% statistical confidence . What is DIGE and why is it needed Unique dyes Experimental design DeCyder software CyDye™ DIGE Fluor minimal dyes Dyes chemistry Uniqueness The internal standard Randomization Identification of spots Codetection of spots Spot volume ratio Normalization Stats t test and ANOVA DI fference G el E lectrophoresis

7. CyDye DIGE Fluor minimal dyes Chemical description Spectrally distinct : resolvable dyes (Cy™2, Cy3 and Cy5), discrete signals Size and charge matched: labeled samples co-migrate within gel Each adds 450 Da to the mass of the protein. This mass shift does not effect the pattern visible on a second dimension SDS PAGE gel. multiplexing possible: A protein labeled with any of the CyDye DIGE Fluor minimal dyes will migrate to the same position on the second dimension SDS PAGE gel Photo stable: minimal loss of signal pH insensitive : no change in signal over wide pH Sensitivity Great sensitivity : down to 25 pg of a single protein, and a linear response to protein concentration up to five orders of magnitude (10 5 ). *silver stain detects 1–60 ng of protein with a dynamic range of less than two orders of magnitude *Comassie Brilliant Blue sensitivity = 0.5µg/cm 2 of protein present in a gel matrix Fluorophore Excitation peak (nm) Emission peak (nm) Cy2 488 520 (yellow) Cy3 532 580 (Blue) Cy5 633 670 (Red)

8. Protein labeling Minimal labeling: With CyDye DIGE Fluor minimal dyes 50 μg protein is labeled in each reaction with 400 pmole dyes. The ratio ensures that the dyes label approximately 1–2% of lysine residues so each labeled protein carries only one dye label and is vizualised as a single protein spot. The lysine amino acid in proteins carries an intrinsic +1 charge at neutral or acidic pH. CyDye DIGE Fluor minimal dyes also carry a +1 charge which, when coupled to the lysine, replaces the lysine’s +1 charge with its own, ensuring that the pI of the protein does not significantly alter .

9. Experimental design Inclusion of an internal standard sample on each gel The requirement for biological replicates such as multiple cultures, tissue etc. Randomization of samples to produce unbiased results, thus conforming with best experimental practice No gel replicates of the same sample is needed

10. Randomization Conditional bias Are we applying specific dyes to specific sample inadvertently Biological replicates (sampling bias) Good experimental practice Randomize - within each group Label half of each group with Cy™3 and half with Cy5 Randomization of samples Randomization of samples across gels removes any bias from the experiments such as experimental conditions , sample handling and labeling Even if the system related result variation is low using DIGE System it is good laboratory practice to distribute individual experimental samples evenly between different CyDye DIGE Fluor dyes and different gels to avoid systematic errors. A1 A2 A3 A4 B1 B2 B3 B4 C1 C2 C3 C4

11. Using internal standard Benefits of the internal standard Gel-to-gel (system) variation is eliminated The internal standard appears on all gels and contains all spots (average) Easier gel-to-gel matching (between identical spot maps) 2-D DIGE is the only protein 2-D approach which allows multiplexing 2-D DIGE is the only 2-D approach enabling use of an internal standard DeCyder™ 2-D Differential Analysis Software is designed to work with an internal standard The internal standard is used to match and normalize the protein patterns across different gels thereby negating the problem of inter-gel variation , a common problem in standard “one sample per gel” 2D electrophoresis experiments The internal standard allows accurate quantization of differences between samples

12. Internal standard Advantages of using an internal standard Accurate quantification and accurate spot statistics between gels Increased confidence in matching between gels Flexibility of statistical analysis depending on the relationship between samples Separation of induced biological change from system variation and inherent biological variation Are spot difference real

13. Experimental design 2D DIGE Experimental design 1 color 2 D 1-color 2-D No automation (complex) Slow Poor accuracy 24 gels, labor intensive DeCyder™ Differential Analysis Analysis automated Rapid data analysis High accuracy for Quantification /trend mapping 12 gels Analysis fast and highly automated A comparison between classical 2-D and 2-D DIGE

14. How to use internal standard

15. DIGE offers Accuracy Better interpretation of results Reduces the impact of uncontrolled gel to gel variation Reduces the number of gels

16. DeCyder 2D To compare protein spot volumes across a range of experimental samples and gels, two distinct steps are required • Intra-gel co-detection of sample and internal standard protein spots • Inter-gel matching of internal standard samples across all gels within the experiment Co-detects image pairs Removes background Removes dust particles Normalises images Matches up to 500 image pairs t-test and ANOVA calculated for each spot Data displayed as Trend analysis graph low user interaction , high throughput and low experimental variation

17. Intra gel co-detection

18. Inter-gel matching It is important to ensure that the same protein spots are compared between gels. Master image Spot map species

19. Protein abundance/Differential expression Direct comparison of spot volume or compare the ratio of spot volume of sample to the internal standard??? Differences in spot intensity that may arise due to experimental factors during the process of 2D electrophoresis, such as protein loss during sample transfer, will be the same for each sample within a single gel, including the internal standard .

20. Statistical tests of protein abundance in DeCyder 2D Student’s T-test and ANOVA. The statistical tests compare the average ratio and variation within each group to the average ratio and variation in the other groups to see if any change between the groups is significant. Extended Data Analysis (EDA) module of DeCyder 2D Multivariate statistical analyses such as Principal Component Analysis (PCA), Pattern Analysis Discriminant Analysis

21. Image analysis DeCyder 2D with or without EDA ImageMaster 2D Platinum These dedicated 2D software products use the internal standard to minimize gel to-gel result variation. A detection of less than 10% difference between samples can be made with more than 95% statistical confidence Six modules in DeCyder Image Loader Batch Processor DIA (Differential In-gel Analysis): background subtraction, in-gel normalization and gel artifact removal. BVA (Biological Variation Analysis): Matching of multiple images from different gels to provide statistical data on differential protein abundance levels between multiple groups XML Toolbox: Extraction of user specific data from XML files generated in either the Batch, DIA or BVA modules. This data can be saved in either text or html format enabling users to access data from DeCyder 2D workspaces in other applications EDA (Extended Data Analysis): Multivariate analysis of data from several BVA workspaces. EDA is an add-on module for the DeCyder 2D software and can handle up to 1000 spot maps Principal Component Analysis Pattern analysis Discriminant analysis Interpretation

22. Image loading Naming gel images Gel 01 Standard Cy2.gel, Gel 01 (Time1_Dose2) Cy3.gel Gel 01 (Time2_Dose2) Cy5.gel D ifferential In-Gel Analysis (DIA) - performs spot co-detection (up to 10,000) spot quantification by normalization and ratio calculation Contrast adjustment (~65000 vs 256 grey scale) Biological Variation Analysis (BVA) - processes multiple gel images - performs gel to gel matching of spots - allowing quantitative comparisons of protein expression across multiple gels

23. Analytical experiments design

24. DIGE summary 3 different CyDye DIGE fluors are available Complete system approach from sample preparation to MS ID Sample multiplexing - up to 3 samples on each gel Fluorescence detection with wide dynamic range Automated high throughput image analysis platform Statistics associated with results