The document discusses 2D gel electrophoresis and the limitations of conventional 2D gels. It introduces Difference Gel Electrophoresis (DIGE), which uses spectrally distinct fluorescent dyes to label protein samples before running multiple samples on the same 2D gel. This allows direct comparison of protein abundance levels between samples and eliminates gel-to-gel variation. The document outlines the experimental design, statistical analysis software, and advantages of DIGE over conventional 2D gels such as increased accuracy, reduced variation, and ability to detect small protein differences.

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
1. Only one dye can be used at a time for one gel
6 gels made from the very same sample, run in
parallel (SYPRO Ruby)
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 less
6.
Proteins with PI beyond the pH limits of strips cannot be
focused on strips
Conventional 2-D
control
gel 1
Differences??
treated
gel 2
Are spot differences real?

4. 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

5. What we want ???
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

6. What is DIGE and why is it needed
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.
DIfference Gel Electrophoresis
Experimental
design
Unique dyes
CyDye™ DIGE Fluor minimal dyes
Dyes chemistry
Uniqueness
DeCyder
software
Identification of spots
Codetection of spots
Spot volume ratio
Normalization
Stats t test and ANOVA
The internal standard
Randomization

7. CyDye DIGE Fluor minimal dyes
Chemical 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
Emission
peak (nm)
488
520 (yellow)
Cy3
532
580 (Blue)
Cy5
6. pH insensitive: no change in signal over wide pH
Excitatio
n peak
(nm)
Cy2
5. Photo stable: minimal loss of signal
Fluoropho
re
633
670 (Red)
Sensitivity
Great 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

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
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

10. Randomization
Conditional bias
Are we applying specific dyes to specific sample inadvertently
Biological replicates (sampling bias)
Good experimental practice
A1
A2
A3
A4
B1
B2
B3
B4
C1
C2
C3
C4
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.

11. Using 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
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

12. Internal standard
Advantages of using an
internal standard
Are spot
difference
real
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

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

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
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
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

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 togel result variation. A detection of less than 10% difference between samples can
be made with more than 95% statistical confidence
Six modules in DeCyder
1. Image Loader
2. Batch Processor
3. 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 groups
5. 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 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
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

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