Subject trainingon

Computational Tools for Animal Genome Resource Data Analysis

Dec 02-13, 2013

DiGE

Dr Karan Veer SinghScientist

National Bureau of Animal Genetic ResourcesKarnal

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

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??

1. Only one dye can be used at a time for one gel

2. We run as many gel as many samples are there

3. Cannot control the variation in loss of proteins for each gel

4. Differential analysis is difficult

5. Statistical confidence is less6. Proteins with PI beyond the pH limits of strips cannot be

focused on strips

Variation in 2D gel

System related variations

1. 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.

2. 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

1. Differences due to disease state, drug treatment, life cycle stage

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 ???

Covalent derivatization of proteins with fluorophores in complex protein mixturesprior 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 dyesDyes chemistryUniqueness

The internal standardRandomization

Identification of spotsCodetection of spotsSpot volume ratioNormalizationStats t test and ANOVA

DIfference Gel Electrophoresis

CyDye DIGE Fluor minimal dyesChemical description

1. Spectrally distinct: resolvable dyes (Cy™2, Cy3 and Cy5), discrete signals

2. Size and charge matched: labeled samples co-migrate within gel

3. 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.

4. 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

5. Photo stable: minimal loss of signal

6. pH insensitive: no change in signal over wide pH

SensitivityGreat sensitivity: down to 25 pg of a single protein, and a linear response to protein concentration up to five orders of magnitude (105).

*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/cm2 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)

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.

Experimental design

1. Inclusion of an internal standard sample on each gel

2. The requirement for biological replicates such as multiple cultures, tissue etc.

3. Randomization of samples to produce unbiased results, thus conforming with best experimental practice

4. No gel replicates of the same sample is needed

RandomizationConditional bias

Are we applying specific dyes to specific sample inadvertentlyBiological replicates (sampling bias)

Good experimental practice

Randomize - within each groupLabel 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

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

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

Experimental design 2D DIGEExperimental design 1 color 2 D

1-color 2-DNo automation (complex)SlowPoor accuracy 24 gels, labor intensive

DeCyder™ Differential AnalysisAnalysis automatedRapid data analysisHigh accuracy for Quantification /trend mapping12 gelsAnalysis fast and highly automated

A comparison between classical 2-D and 2-D DIGE

How to use internal standard

DIGE offers

AccuracyBetter interpretation of results

Reduces the impact of uncontrolled gel to gel variationReduces the number of gels

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 theexperiment

Co-detects image pairsRemoves background

Removes dust particlesNormalises images

Matches up to 500 image pairst-test and ANOVA calculated for each spot

Data displayed as Trend analysis graphlow user interaction,

high throughput and low experimental variation

Intra gel co-detection

Inter-gel matchingIt is important to ensure that the same protein spots are compared between gels.

Master image Spot map species

Protein abundance/Differential expressionDirect 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.

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 AnalysisDiscriminant Analysis

Image analysisDeCyder 2D with or without EDAImageMaster 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 DeCyder1. Image Loader2. Batch Processor3. DIA (Differential In-gel Analysis): background subtraction, in-gel normalization and gel artifact

removal.4. BVA (Biological Variation Analysis): Matching of multiple images from different gels to provide

statistical data on differential protein abundance levels between multiple groups5. 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

6. 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 AnalysisPattern analysisDiscriminant analysisInterpretation

Image loadingNaming gel imagesGel 01 Standard Cy2.gel,Gel 01 (Time1_Dose2) Cy3.gelGel 01 (Time2_Dose2) Cy5.gel

Differential 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

Analytical experiments design

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