Extracting quantitative information from proteomic 2-D gels

Lecture in the bioinformatics course ”Gene expression and cell models”

April 20, 2005

John Gustafsson

Mathematical Statistics

Chalmers

Proteomics lectures:starting points

• Anders’ starting point this Monday:– Let’s say that we want to study life at the protein

level – what technologies do we have at hand?

• Today’s lecture:– How can we get (large-scale) quantitative

measurements of protein amounts? So that we can do statistics and bioinformatics

• Proteomics• The 2-D gel technology• Extracting quantitative information

– Image analysis of 2-D gels• Comparison with microarrays• Statistic analysis of quantitative 2-D gel data

Content and structure

ProteomicsDNA

mRNA

ProductionModification Degradation

Localisation

Interaction

ACTIVITY

P

TDP

Co-factors

2-D gels

2-D gel electrophoresis: Protein separation and quantification

”protein soup”

spot volume protein quantity

mo

lecu

lar

size

molecular charge

acidic alkaline

sma

llla

rge

A typical 2-D gel experiment

statistical analysis

conclusions

protein extracts

biological experimentcontrol treatment

2-D gel images

2-D gel electrophoresis

quantified data

image analysis

25211511

225221215211

125121115111

mmmm zzzz

zzzz

zzzz

matrix with

spot volume data

rows: proteins (many)

columns: gels (few)

experimental design

Example:

The image analysis task

• The task1. In each gel image: Find and quantify the protein

spots

2. In the group of gel images: Match protein spots in different images that correspond to the same protein

• Issues– automation– time

Pseudo-color superposition 1(3)0M NaCl 1M NaCl

Pseudo-color superposition 2(3)OM NaCl 1M NaCl

Pseudo-color superposition 3(3)(red: 0M NaCl, blue: 1M NaCl)

The standard solution – workflow

In each gel image1. Background subtraction

2. Spot detection

3. Spot quantification

In the group of gel images4. Spot pattern matching

1. Background subtraction

Before After

- =

2. Spot detection / image segmentation

3. Spot quantification

spot volume protein quantity

4. Spot pattern matching

The typical 2-D gel experiment

statistical analysis

conclusions

protein extracts

biological experimentcontrol treatment

2-D gel images

2-D gel electrophoresis

quantified data

image analysis

25211511

225221215211

125121115111

mmmm zzzz

zzzz

zzzz

matrix with

spot volume data

rows: proteins (many)

columns: gels (few)

experimental design

Example:

Limitations

• Technological– hydrofobic proteins don’t

dissolve– limited pI/size coverage– limited labeling/staining

• Image analytical– Limited global matching

efficiency of automatic algorithms

– Need for time consuming manual guidance

– ”The image analysis bottle-neck”

Limited global matching efficiency

Voss and Haberl (2000)

Incomplete spot detection: Faint spots

Detected

Not detected

Incomplete spot detection:Close spots

• Proteomics• The 2-D gel technology• Extracting quantitative information

– Image analysis of 2-D gels• Comparison with microarrays• Statistic analysis of quantitative 2-D gel data

Content and structure – revisited

Comparison with microarrays

2-D gels Microarrays

Labeling one channel* one or two-color

Background subtr. yes yes

Spot detection HARD easy

Spot quantitation can be difficult quite easy

Spot matching HARD known

Identification MS or reference atlas known

*) recently also two-color

Variability

normal 1M NaCl

normal 1M NaCl

biol

ogic

al r

eplic

atio

ns

growth condition

Variance versus mean dependence

• A dot in the plot:– the measurement of one

protein

• The quadratic dependence indicates a multiplicative error structure

(2x5 gel set; normal growth condition)

slope=2 variance mean2

Why transform the data?

• A mathematical data transformation can be used to – Make errors more normally distributed– Stabilize variance versus mean dependence

• Then the model on transformed scale is more simple than on original scale

• Simplifies the subsequent analysis

Logarithmic data transformation

• Stabilized variance versus mean dependence after a logarithmic data transformation

(2x5 gel set; normal growth condition)

Statistical analysis of quantitative 2-D gel data

Examples:• Test of differential expression• Cluster analysis

– cluster proteins – cluster cell/tissue samples

• Classification– classify tissue samples (i.e. tumor classes)

• Proteomics• The 2-D gel technology• Extracting quantitative information

– Image analysis of 2-D gels• Comparison with microarrays• Statistic analysis of quantitative 2-D gel data

Summary

An alternative approach to the matching problem

• The standard solution– First spot detection– Then matching of point patterns

• An alternative, recent approach– Matching at the pixel level– Computationally heavy

Gel matching at the pixel level

Reference image

Image warping

Original image Aligned image

Future alternatives to quantitative 2-D gels?

• Quantitative masspectrometry• Protein arrays