The

state

of

the

art

in

the

analysis

of

two-dimensional

gel

electrophoresis

images

Software-based

image

analysis

is

a

crucial

step

in

the

biological

interpretation

of

two-dimensional

gel

electrophoresis

experiments

Recent

significant

advances

in

image

processing

methods

combined

with

powerful

computing

hardware

have

enabled

the

routine

analysis

of

large

experiments

We

cover

the

process

starting

with

the

imaging

of

gels

quantitation

of

spots

creation

of

expression

profiles

to

statistical

expression

analysis

followed

by

the

presentation

of

results

Challenges

for

analysis

software

as

well

as

good

practices

are

highlighted

We

emphasize

image

warping

and

related

methods

that

are

able

to

overcome

the

difficulties

that

are

due

to

varying

migration

positions

of

spots

between

gels

Spot

detection

quantitation

normalization

and

the

creation

of

expression

profiles

are

described

in

detail

The

recent

development

of

consensus

spot

patterns

and

complete

expression

profiles

enables

one

to

take

full

advantage

of

statistical

methods

for

expression

analysis

that

are

well

established

for

the

analysis

of

DNA

microarray

experiments

We

close

with

an

overview

of

visualization

and

presentation

methods

-LRB-

proteome

maps

-RRB-

and

current

challenges

in

the

field

Introduction

The

last

decade

in

life

sciences

was

deeply

influenced

by

the

development

of

the

Omics

technologies

-LRB-

genomics

transcriptomics

proteomics

and

metabolomics

-RRB-

which

aim

for

a

global

view

on

biological

systems

With

these

tools

at

hand

the

scientific

community

is

striving

to

build

functional

models

to

develop

a

global

understanding

of

the

living

cell

The

analysis

of

the

proteome

as

the

final

level

of

gene

expression

started

out

with

techniques

based

on

gel

electrophoresis

-LRB-

O'Farrell

Klose

-RRB-

and

extended

its

reach

with

semi-gel-free

and

shot

gun

gel-free

liquid

chromatography

mass

spectrometry

-LRB-

LC

MS

-RRB-

based

techniques

in

recent

years

A

comprehensive

evaluation

of

the

relative

merits

and

weaknesses

of

gel-based

and

gel-free

methods

is

beyond

the

scope

of

our

review

Studies

that

compare

the

performance

on

similar

samples

-LRB-

Wolff

et

al.

-RRB-

indicate

that

the

methods

are

complementary

i.e.

their

analytical

windows

overlap

but

each

has

an

exclusive

set

of

proteins

or

modifications

that

were

not

identified

with

the

other

Quantitative

analysis

based

on

LC

MS

techniques

is

still

in

an

early

stage

when

considering

available

software

and

algorithms

Here

we

focus

on

the

computerized

analysis

of

gels

which

are

widely

used

in

the

scientific

community

gels

may

separate

up

to

protein

spots

on

one

gel

-LRB-

Klose

and

Kobalz

-RRB-

In

a

suitably

equipped

and

experienced

lab

environment

gels

are

easy

to

handle

and

they

can

be

produced

in

a

highly

parallelized

way

The

software

has

meanwhile

reached

a

level

that

allows

for

routine

analysis

of

a

large

amount

of

samples

with

an

investment

of

time

that

is

much

smaller

than

the

efforts

needed

for

the

wet

lab

work

The

routine

application

of

potent

and

easy-to-use

software

systems

has

been

enabled

by

several

improvements

in

gel

image

acquisition

and

analysis

technology

over

the

last

decades

-LRB-

Aittokallio

et

al.

Dowsey

et

al.

-RRB-

Among

these

milestones

one

should

mention

the

introduction

of

the

first

computer-based

analysis

systems

still

without

a

graphical

user

interface

in

the

late

among

them

ELSIE

-LRB-

Bossinger

et

al.

Vo

et

al.

-RRB-

GELLAB

-LRB-

Lemkin

and

Lipkin

b

c

Lemkin

-RRB-

TYCHO

-LRB-

Anderson

et

al.

-RRB-

HERMeS

-LRB-

Tarroux

et

al.

Vincens

and

Tarroux

-RRB-

GESA

-LRB-

Rowlands

et

al.

-RRB-

and

LIPS

-LRB-

Skolnick

et

al.

-RRB-

This

first

generation

of

image

analysis

programs

was

followed

by

-LRB-

Olson

and

Miller

-RRB-

Melanie

-LRB-

Appel

et

al.

based

on

-RRB-

and

QUEST

-LRB-

Garrels

-RRB-

since

the

These

programs

used

X-Windows-based

graphical

user

interfaces

on

computer

workstations

In

the

following

years

the

QUEST

TYCHO

and

Melanie

programs

evolved

to

the

commercially

available

PDQuest

Kepler

and

Melanie

III

While

in

the

beginning

such

software

needed

exceptionally

equipped

workstations

in

Nonlinear

-LRB-

Newcastle

UK

-RRB-

introduced

Phoretix

the

first

gel

analysis

software

running

on

desktop

PCs

With

the

dropping

hardware

prices

the

Melanie

and

PDQuest

systems

were

also

ported

to

PCs

While

until

then

of

the

available

systems

gave

visual

feedback

on

the

quality

of

spot

matching

Melanie

II

introduced

image

adjustment

based

on

a

global

polynomial

transformation

of

the

image

's

geometry

This

simplified

the

comparison

of

the

raw

images

but

only

incompletely

addressed

the

problem

of

positional

variations

caused

by

the

electrophoretical

separation

process

Horgan

et

al.

-LRB-

-RRB-

used

for

the

first

time

a

superimposing

of

false-colored

images

to

simplify

the

finding

of

differences

in

spot

patterns

Unlü

et

al.

-LRB-

-RRB-

and

Bernhardt

et

al.

-LRB-

-RRB-

suggested

different

approaches

that

use

superimposed

and

congruent

false

color

images

for

the

comparison

of

gels

This

technology

was

improved

by

establishing

positional

correction

by

image

warping

of

the

raw

gel

images

and

commercialized

with

the

first

version

of

the

software

from

DECODON

-LRB-

Greifswald

Germany

-RRB-

coinciding

with

Compugen

's

software

in

-LRB-

Smilansky

-RRB-

Both

systems

were

able

to

completely

remove

distortions

from

the

gel

images

and

bring

the

spot

patterns

into

congruency

See

Table

for

a

list

of

current

software

Table

commercial

software

products

for

gel

image

analysisCompanyProductsBio-Rad

Hercules

CA

USA

ProteomWeaverCompugen

Tel

Aviv

Israel

-LRB-

discontinued

-RRB-

DECODON

Greifswald

Germany

Healthcare

ImageMaster

Platinum

Genebio

Geneva

Switzerland

Melanie

-LRB-

ImageMaster

Platinum

-RRB-

Nonlinear

Dynamics

Newcastle

UK

SameSpotsSyngene

Cambridge

UK

With

the

ever-increasing

capacity

of

available

hardware

more

advanced

image

processing

methods

became

feasible

They

use

all

of

the

available

image

information

instead

of

condensing

it

to

spot

boundaries

before

processing

This

simplifies

image

comparison

and

speeds

up

analysis

dramatically

but

still

produces

expression

profiles

with

gaps

which

significantly

impede

reliable

gene

expression

analysis

The

most

recent

milestone

introduced

by

us

in

addressed

this

problem

An

algorithm

to

combine

the

information

of

several

gels

into

a

so-called

fusion

gel

makes

it

possible

to

generate

a

proteome

map

that

is

representative

for

the

whole

experiment

-LRB-

Luhn

et

al.

-RRB-

On

a

proteome

map

one

can

detect

all

spots

of

a

whole

experiment

in

a

single

gel

image

whereas

the

average

images

proposed

earlier

suffer

from

dilution

effects

for

weak

and

rare

spots

The

spots

detected

there

can

serve

as

a

spot

consensus

pattern

that

is

valid

for

the

whole

gel

set

of

the

experiment

The

consensus

spot

pattern

is

then

transferred

according

to

the

warping

transform

and

used

on

all

gels

This

allows

for

matching

spots

and

in

turn

complete

expression

profiles

for

reliable

statistical

analysis

-LRB-

Voigt

et

al.

Höper

et

al.

-RRB-

Depending

on

the

workflow

one

can

put

gel

image

analysis

software

into

two

broad

categories

Spot

detection

first

these

are

the

classical

packages

where

the

image

information

is

first

condensed

into

a

set

of

spot

centers

boundaries

and

possibly

spot

volumes

for

each

image

Spot

matching

and

subsequent

creation

of

expression

profiles

are

done

based

on

the

data

about

spot

geometry

and

volumes.Image

warping

first

these

are

packages

where

image

warping

is

applied

to

remove

running

differences

between

gels

based

on

the

whole

image

information

Spot

detection

is

a

separate

and

independent

step

The

creation

of

expression

profiles

is

critically

informed

-LRB-

and

improved

-RRB-

by

the

data

about

positional

differences

between

gels

that

were

gained

in

the

first

step

Historically

the

spot

detection

first

workflow

was

the

only

feasible

way

to

proceed

due

to

the

limitations

of

available

hardware

Software

based

on

the

second

image

warping

first

workflow

including

Compugen

's

DECODON

's

and

most

recently

Nonlinear

's

SameSpots

is

able

to

overcome

many

of

the

difficulties

in

spot

matching

that

are

hard

to

deal

with

when

different

migration

positions

add

to

the

uncertainty

of

multiple

separate

spot

detections

With

the

subsequent

introduction

of

consensus

spot

patterns

-LRB-

in

and

SameSpots

-RRB-

one

is

able

to

virtually

eliminate

matching

problems

by

using

consistent

spot

patterns

throughout

the

experiment

Recent

comparisons

between

separate

spot

detection

and

using

a

consensus

spot

pattern

-LRB-

Eravci

et

al.

-RRB-

show

that

the

latter

approach

is

able

to

find

substantially

more

differentially

expressed

proteins

in

a

much

shorter

time

In

other

words

valuable

information

is

lost

due

to

spot

matching

problems

that

are

inevitable

when

using

the

classical

approach

We

expect

other

vendors

to

evolve

their

products

to

use

a

workflow

that

is

based

on

image

warping

and

consensus

spot

patterns

over

the

following

years

The

typical

workflow

of

a

proteomics

analysis

using

the

image

warping

first

approach

and

consensus

spot

patterns

can

be

described

as

follows

-LRB-

Fig.

-RRB-

Performing

a

biological

experiment

or

selecting

a

biological

object

of

interest

The

first

sample

preparation

step

is

freezing

the

sample

in

the

current

state

This

includes

inactivation

of

all

cellular

processes

that

may

change

the

proteome

composition

preventing

protease

action

disintegration

of

the

cell

material

keeping

or

bringing

the

proteins

into

solution

removing

or

destroying

macromolecules

that

may

disturb

the

subsequent

steps

of

the

protocol

-LRB-

RNase

and/or

DNase

treatment

and

centrifuging

for

cell

debris

removal

-RRB-

Alternatively

or

in

combination

with

radiolabeling

covalent

fluorescent

labeling

of

proteins

can

be

applied

here.Bringing

the

proteins

into

the

gel

and

performing

the

separation

by

combining

isoelectrofocussing

in

the

first

and

sodium

dodecyl

sulfate

-LRB-

SDS

-RRB-

electrophoresis

in

the

second

dimension

An

alternate

approach

uses

the

combination

of

two

detergent

treatments

that

resolve

the

protein

molecules

differently

resulting

in

a

scattered

diagonal

spot

pattern

-LRB-

or

gel

electrophoresis

-RRB-

A

variety

of

staining

techniques

can

be

applied

before

or

after

separation

to

enable

spot

detection.Capturing

the

gel

images

by

using

scanners

charge-coupled

device

-LRB-

CCD

-RRB-

camera-based

or

laser

imaging

devices

Depending

on

the

protein

labeling

or

staining

techniques

a

compatible

imaging

device

has

to

be

chosen

The

capturing

process

results

in

one

or

more

digitized

computer

images

per

gel

that

can

be

displayed

with

common

image

analysis

software

The

image

capture

step

transforms

the

quantitative

information

of

the

gel

into

computer-readable

data.Correction

of

positional

spot

variations

by

image

warping

electrophoresis

results

in

spot

patterns

with

variations

in

the

spot

positions

between

gels

Therefore

gel

images

are

positionally

corrected

by

a

combination

of

global

and

local

image

transforms

-LRB-

image

warping

-RRB-

The

information

about

differences

in

spot

positions

that

was

gained

in

this

step

is

reused

later

for

image

fusion

and

for

the

transfer

of

the

consensus

spot

pattern.Image

fusion

and

proteome

maps

condense

the

image

information

of

the

whole

experiment

into

one

fusion

image

also

called

a

proteome

map

The

proteome

map

contains

the

information

of

all

protein

spots

ever

detected

in

the

experiment.Spot

detection

is

performed

on

the

proteome

map

As

a

result

a

consensus

spot

pattern

is

generated

which

is

valid

for

all

gels

in

the

experiment

It

describes

the

position

and

the

general

shape

of

all

protein

spots

from

the

experiment.For

spot

quantitation

and

building

expression

profiles

the

consensus

spot

pattern

is

applied

to

all

gel

images

of

the

experiment

-LRB-

Fig.

-RRB-

The

image

transformation

-LRB-

step

-RRB-

assures

that

all

spots

of

the

consensus

pattern

arrive

at

their

correct

position

A

remodeling

step

makes

sure

that

the

predetermined

spot

boundaries

from

the

consensus

are

adapted

to

the

real

gray

levels

observed

on

the

target

image

All

boundaries

of

the

consensus

pattern

can

be

found

on

every

gel.Expression

profile

analysis

identifies

interesting

spots

which

will

be

marked

for

further

analysis

protein

identification

and

interpretation.Fig

work

flow

of

a

proteomics

experiment

in

Sample

preparation

gel

electrophoresis

gels

are

stained/detected

and

digitized

spot

positions

are

aligned

across

gel

images

by

warping

a

proteome

map/fusion

gel

image

is

generated

by

combining

the

images

using

a

union

fusion

the

union

fusion

image

serves

as

basis

for

constructing

the

consensus

spot

pattern

for

the

whole

experiment

the

consensus

spot

pattern

is

transferred

to

all

images

and

subsequently

remodeled

expression

profiles

are

extracted

and

analyzed

to

find

relevant

proteinsFig

spot

pattern

applied

to

four

gel

images

-LRB-

a

d

-RRB-

before

remodeling

of

spot

shapes

The

consensus

spot

pattern

is

generated

by

spot

detection

on

the

synthetic

fusion

image

-LRB-

e

-RRB-

which

was

computed

from

the

original

images

Protein

staining

Various

techniques

are

available

to

make

the

separated

spots

detectable

-LRB-

Fig.

-RRB-

A

direct

method

that

suggests

itself

is

the

detection

of

the

UV-induced

autofluorescence

of

proteins

-LRB-

Roegener

et

al.

-RRB-

Unfortunately

it

is

still

not

available

for

routine

analysis

although

it

generates

promising

results

Fig.

labeling

staining

and

tagging

techniques

for

the

selective

detection

of

proteins

By

multiplexing

detection

approaches

image

analysis

may

relate

different

subsets

of

the

proteome

such

as

phosphorylated

or

glycosylated

proteins

Ideally

a

dye

should

bind

noncovalently

to

the

protein

after

a

linear

response

curve

It

should

allow

for

a

detection

of

very

low

protein

amounts

because

protein

concentrations

in

biological

systems

may

vary

by

six

or

more

orders

of

magnitude

-LRB-

Corthals

et

al.

-RRB-

At

the

same

time

saturation

effects

have

to

be

avoided

because

they

impede

normalized

quantitation

In

practice

depending

on

the

stain

that

was

used

a

gel

image

analysis

may

give

quantitative

or

only

qualitative

results

for

most

or

only

a

subfraction

of

the

most

intense

spots

Common

approaches

for

the

detection

of

protein

amounts

use

dyes

that

ideally

bind

to

proteins

much

stronger

than

to

the

gel

matrix

or

other

compounds

accompanying

the

electrophoresis

process

Most

fluorescent

dyes

are

more

sensitive

than

quantitative

visible

stains

-LRB-

Table

-RRB-

Like

most

absorbing

dyes

fluorescent

stains

give

a

measure

of

the

accumulated

amount

of

proteins

within

the

sample

There

are

ruthenium-based

dyes

-LRB-

-RRB-

available

that

covalently

bind

to

the

already

separated

proteins

-LRB-

Tokarski

et

al.

-RRB-

They

also

give

a

measure

of

the

amount

of

protein

and

they

can

be

used

like

noncovalently

binding

fluorescent

dyes

but

after

separation

Table

most

commonly

used

dyes

in

gelsDyePrincipleSensitivityQuantitationAmount/signalCoomassie

Brilliant

BlueAbsorptionVery

lowAfter

calibrationNonlinearColloidal

Coomassie

BlueAbsorption

-LRB-

very

-RRB-

highAfter

calibrationNonlinearSilver

StainingAbsorptionVery

highImpossibleLogisticSypro

RubyFluorescenceHighYesLinearRuthenium

II

tris

-LRB-

bathophenanthroline

disulfonate

-RRB-

FluorescenceHighYesLinearFlamingoFluorescenceHighYesLinearLava

PurpleFluorescenceHighYesLinearKryptonFluorescenceVery

highYesLinear

The

most

striking

advantage

of

applying

covalently

binding

dyes

before

separation

is

the

possibility

to

run

multiple

samples

in

one

gel

Provided

that

the

dyes

induce

the

same

positional

shift

in

a

protein

's

position

samples

separated

in

parallel

will

give

exactly

congruent

patterns

As

long

as

the

number

of

samples

equals

the

number

of

available

dyes

patterns

with

perfect

positional

identity

can

be

generated

Difference

gel

electrophoresis

-LRB-

DIGE

-RRB-

was

the

first

approach

that

used

such

a

sample

multiplexing

in

Unlü

et

al.

-LRB-

-RRB-

by

simultaneously

separating

samples

labeled

with

and

within

the

same

gel

If

the

amount

of

samples

exceeds

the

number

of

available

dyes

-LRB-

dyes

max

samples

per

gel

-RRB-

more

than

one

gel

have

to

be

used

giving

rise

to

positional

variation

between

gels

just

like

in

the

traditional

setups

Dye

multiplexing

allows

for

a

quantitative

normalization

over

several

gels

by

using

an

internal

standard

i.e.

a

mixture

of

equal

aliquots

of

every

sample

under

analysis

-LRB-

Alban

et

al.

-RRB-

The

internal

standard

is

separated

together

with

sample

pairs

on

every

gel

and

serves

as

quantitative

reference

Protein

stainings

give

a

measurement

of

the

current

protein

amount

They

are

well

suited

for

knock

out

experiments

or

analyses

of

long-term

experiments

in

which

treatment

or

stimulus

effects

have

time

to

manifest

themselves

as

observable

changes

in

protein

levels

Staining

techniques

that

measure

the

accumulated

quantity

of

proteins

can

overlook

minor

changes

in

protein

quantities

because

they

disappear

within

noise

or

systematic

errors

CVs

between

and

for

the

same

spot/same

sample

separated

on

different

gels

were

reported

by

different

authors

-LRB-

e.g.

Nishihara

and

Champion

Eravci

et

al.

-RRB-

This

means

that

a

change

of

a

protein

species

can

not

be

reliably

detected

That

is

why

for

short-term

stimulus

response

experiments

in

vivo

labeling

techniques

can

give

a

more

detailed

picture

of

a

changing

proteome

Characterizing

specific

protein

properties

There

is

a

broad

range

of

techniques

available

that

are

able

to

detect

specific

protein

features

-LRB-

Fig.

-RRB-

such

as

phosphorylation

The

general

approach

is

to

combine

a

conventional

staining

method

that

displays

all

separated

proteins

with

a

specialized

stain

that

highlights

protein

features

giving

separate

images

that

can

be

recombined

using

software

The

Diamond

ProQ

-LRB-

Steinberg

et

al.

-RRB-

and

Emerald

ProQ

-LRB-

Patton

and

Beechem

-RRB-

stains

bind

specifically

but

noncovalently

to

phosphorylated

and

glycosylated

proteins

respectively

Multiplexing

the

phosphoprotein

or

glycoprotein

pattern

with

the

total

protein

clearly

indicates

the

modified

protein

species

For

a

quantitative

analysis

of

phosphoproteins

in

a

bacterial

system

see

Eymann

et

al.

-LRB-

-RRB-

For

the

redox

state

detection

of

proteins

two

assays

for

oxidized

thiols

based

on

a

radiolabeled

tag

-LRB-

Leichert

and

Jakob

-RRB-

or

a

fluorescent

tag

-LRB-

Hochgräfe

et

al.

-RRB-

have

been

suggested

In

addition

to

visualizing

the

protein

's

thiol

state

image

multiplexing

allows

the

calculation

of

the

degree

of

oxidation

An

assay

for

the

detection

of

carbonyl

groups

which

are

an

indicator

for

irreversible

protein

oxidation

has

been

successfully

applied

by

Dukan

and

Nyström

-LRB-

-RRB-

or

Mosterz

and

Hecker

-LRB-

-RRB-

This

approach

uses

immunodetection

of

derivated

carbonyl

groups

Protein

synthesis

at

a

given

point

in

time

can

be

detected

by

using

in

vivo

labeling

with

radioisotopes

Radiolabeling

is

perfectly

suitable

for

the

detection

of

changes

in

protein

synthesis

during

stimulus

response

studies

Especially

radioactively

labeled

amino

acids

like

methionine

or

cysteine

or

compounds

are

applicable

After

separation

the

incorporated

material

is

analyzed

by

phosphorimaging

-LRB-

Amemiya

and

Miyahara

Johnston

et

al.

-RRB-

This

delivers

data

with

a

linear

correlation

between

radioactivity

and

measured

signal

over

nearly

five

orders

of

magnitude

The

striking

advantage

of

radiolabeling

in

stimulus/response

experiments

is

the

ability

to

detect

fast

but

relatively

small

changes

in

protein

quantities

-LRB-

Bernhardt

et

al.

Fig.

-RRB-

Fig.

amount

-LRB-

green

-RRB-

and

protein

synthesis

-LRB-

red

-RRB-

in

a

heat

shock

experiment

of

Bacillus

subtilis

The

synthesis

patterns

can

differ

dramatically

between

different

stimuli

but

can

be

easily

related

using

the

protein

amount

patterns

The

spot

patterns

may

be

very

different

when

only

special

protein

subsets

are

imaged

so

they

are

hard

to

compare

Fortunately

the

combination

of

autoradiography

with

total

protein

staining

can

help

to

solve

this

problem

Autoradiograph

and

densito

fluorograph

from

the

same

gel

can

be

aligned

very

easily

-LRB-

Fig.

-RRB-

because

normally

only

minor

gel

distortions

can

occur

due

to

staining

and

washing

steps

Images

with

the

total

protein

amount

may

then

help

in

finding

correspondences

between

gels

because

total

protein

patterns

change

only

to

a

small

extent

so

they

can

be

easily

aligned

Protein

degradation

has

been

measured

by

using

in

vivo

pulse

chase

radiolabeling

as

well

-LRB-

Kock

et

al.

-RRB-

The

degradation

of

a

radiolabeled

subfraction

of

cellular

proteins

is

observed

in

a

time

course

experiment

by

using

a

series

of

gels

and

looking

for

proteins

whose

signals

disappear

with

time

Radiophosphate

labeling

can

be

used

for

in

vivo

detection

of

short-time

effects

in

protein

phosphorylation

in

the

cell

Eymann

et

al.

suggested

to

support

the

analysis

with

a

Diamond

ProQ

-LRB-

Invitrogen

-RRB-

stained

pattern

The

Diamond

ProQ

stain

binds

highly

specific

to

phosphorylated

proteins

but

also

to

a

lower

extent

to

nonphosphorylated

ones

This

allows

for

the

determination

of

landmarks

between

stained

and

radiophosphate-labeled

protein

patterns

that

can

be

used

to

superimpose

and

stained

protein

patterns

-LRB-

Fig.

-RRB-

Fig.

protein

amount

-LRB-

green

-RRB-

Diamond

ProQ

Phosphoprotein

staining

-LRB-

red

-RRB-

and

in

vivo

phosphoprotein

labeling

-LRB-

blue

-RRB-

in

an

exponentially

growing

B.

subtilis

sample

While

the

green

and

blue

subimages

seem

to

be

almost

complementary

the

red

subimage

highlights

spots

from

the

protein

level

pattern

as

well

as

from

the

phosphate

autoradiograph

so

it

can

be

used

to

find

correspondences

Western

blots

are

used

for

the

detection

of

immunogenic

proteins

as

suggested

by

Haas

et

al.

Western

blots

are

also

suitable

for

finding

modified

forms

of

known

proteins

-LRB-

using

protein-specific

antibodies

-RRB-

or

for

the

detection

of

protein

features

-LRB-

phosphoaminoacid

antibodies

-RRB-

Again

superimposing

the

stained

gel

and

the

Western

blot

can

highlight

the

relevant

spots

For

a

more

detailed

overview

on

protein

detection

techniques

see

Patton

-LRB-

-RRB-

Recording

and

preparation

of

raw

image

data

Digital

images

of

gels

are

acquired

using

scanners

or

CCD-camera-based

systems

Scanners

produce

an

image

by

moving

a

light

source

and

sensor

element

with

CCDs

over

the

gel

The

resolution

of

the

final

image

is

defined

by

the

density

of

sensors

within

the

CCD

element

the

speed

and

the

frequency

of

the

measurements

In

CCD-camera-based

systems

the

gel

is

projected

onto

a

CCD

sensor

array

through

a

photographic

lens

so

the

gel

is

measured

as

a

whole

Because

the

photographic

lens

is

an

optical

system

based

on

refraction

removal

of

distortions

is

part

of

the

system

's

image

preparation

Techniques

compensating

for

lightfield

variations

are

often

applied

because

the

central

CCD

sensors

collect

more

light

than

those

at

the

borders

The

number

of

single

CCD

elements

on

the

camera

chip

determines

the

resolution

of

the

final

gel

image

Hybrid

systems

aim

to

combine

the

speed

of

a

camera

with

the

resolution

of

a

scanner

by

moving

a

camera

over

the

gel

to

produce

image

tiles

that

are

later

assembled

using

image

processing

The

challenge

is

to

accurately

remove

illumination

differences

and

distortions

caused

by

the

photographic

lens

to

avoid

discontinuities

in

adjacent

tiles

Some

scanners

have

the

ability

to

measure

white

light

fluorescence

and

radiation

within

one

device

as

well

as

simultaneous

scanning

for

different

wavelengths

Scanners

usually

provide

higher

resolution

than

CCD

cameras

while

consuming

more

processing

time

per

image

For

technical

information

consult

Miura

-LRB-

-RRB-

and

Tan

et

al.

-LRB-

-RRB-

The

general

rule

in

gel

image

analysis

is

that

the

quality

of

the

raw

data

has

a

significant

impact

on

the

final

result

Therefore

it

is

essential

to

avoid

experimentally

caused

artifacts

and

to

configure

the

scanning

devices

in

the

best

possible

way

Background

artifacts

and

noise

influence

the

spot

detection

and

quantitation

process

Gel

disruptions

may

truncate

spots

speckles

may

mislead

the

spot

detection

or

distort

quantitation

noise

can

cover

low

intensity

spots

background

increases

quantity

and

reduces

dynamic

range

etc.

Background

may

be

caused

by

insufficiently

erased

imaging

plates

-LRB-

phosphorimaging

-RRB-

insufficient

destaining

fluorescing

glass

plates

gel

coverings

and

backings

Furthermore

misusing

optical

filters

for

fluorescence

imaging

may

cause

background

Noise

can

be

produced

by

high

photo-multiplier

tube

voltages

which

leads

to

the

amplification

of

random

signals

Phosphor

screens

that

have

not

been

used

for

a

longer

time

accumulate

noise

Many

software

packages

allow

for

postscan

image

manipulations

One

has

to

distinguish

between

image

manipulations

that

do

not

change

the

quantitative

information

and

those

that

do

incurring

some

loss

of

data

in

the

process

All

operations

that

leave

pixels

intact

do

not

change

the

measured

data

e.g.

rotations

in

steps

mirrorings

and

cropping

-LRB-

removing

areas

from

the

images

that

do

not

contain

information

of

interest

-RRB-

Linear

enhancements

of

resolution

and

gray

levels

can

be

undone

without

data

loss

and

do

not

influence

quantitative

data

because

normalization

is

used

in

spot

quantitation

On

the

other

hand

many

operations

that

are

used

for

image

enhancement

cause

minor

changes

in

spot

detection

and

spot

quantitation

and

should

be

avoided

if

possible

e.g.

free

rotations

or

free

scaling

change

gray

level

distribution

of

the

manipulated

image

Image

warping

leads

to

changed

quantitative

data

so

quantitation

should

be

done

on

the

original

images

or

the

warping

should

incorporate

a

factor

for

volume

compensation

-LRB-

Dowsey

et

al.

-RRB-

to

minimize

quantitative

side

effects

Several

types

of

image

filtering

algorithms

are

used

by

gel

image

analysis

software

to

remove

background

and

noise

-LRB-

Fig.

-RRB-

These

filters

are

applied

within

the

software

for

correct

quantitation

and

for

optimizing

the

appearance

of

the

image

on

the

computer

screen

Fig.

of

the

raw

image

into

background

noise

and

cleaned

images

Image

filters

can

be

used

to

determine

background

and

noise

leaving

the

quantitative

protein

spot

information

in

the

cleaned

image

a

Raw

image

b

speckles

and

noise

c

background

d

cleaned

image

Finally

there

are

operations

that

should

definitely

be

avoided

because

they

result

in

data

loss

for

example

gamma

correction

changes

the

gray

levels

nonlinearly

blurring

and

converting

to

JPEG

format

loose

data

etc.

Another

hazard

lies

in

the

application

of

general

purpose

image

manipulation

software

to

special

purpose

file

formats

In

the

process

of

for

example

cropping

a

gel

file

in

Photoshop

essential

calibration

information

can

be

lost

in

the

resulting

file

Therefore

it

is

advisable

to

use

specialized

software

-LRB-

e.g.

the

software

that

came

with

the

scanner

or

a

gel

image

analysis

program

-RRB-

that

understands

the

characteristics

of

the

file

format

Removing

variations

in

spot

positions

warping

Unfortunately

the

position

of

a

protein

on

a

gel

fluctuates

from

separation

to

separation

Even

a

very

experienced

experimenter

will

not

be

able

to

produce

perfect

gels

whose

spot

patterns

show

exact

congruency

-LRB-

Fig.

-RRB-

Reasons

for

changing

spot

positions

may

be

variations

in

the

pH

value

of

the

running

buffer

problems

of

incomplete

polymerization

of

the

gel

matrix

current

leakage

-LRB-

Gustafsson

et

al.

-RRB-

air

bubbles

in

the

gel

or

highly

abundant

proteins

that

may

influence

the

pH

gradient

in

the

IPG

gel

by

their

own

locally

concentrated

buffer

capacity

Some

of

these

problems

can

be

mitigated

by

using

the

DIGE

setup

or

similar

techniques

-LRB-

see

below

-RRB-

that

let

multiple

samples

comigrate

on

the

same

gel

However

differing

spot

positions

will

still

occur

in

any

nontrivial

experiment

that

includes

more

than

one

gel

Differences

in

spot

positions

are

a

major

challenge

in

image

processing

because

they

impede

accurate

spot

matching

and

thus

the

construction

of

expression

profiles

Fig.

gel

image

registration

by

warping

Two

images

are

combined

pixel

by

pixel

using

a

false

color

display

-LRB-

a

-RRB-

Vectors

connecting

corresponding

points

-LRB-

spots

-RRB-

on

both

images

are

determined

automatically

-LRB-

b

-RRB-

Transforming

the

image

geometry

-LRB-

warping

-RRB-

according

to

the

vectors

produces

an

exact

overlay

-LRB-

c

-RRB-

Corresponding

spots

-LRB-

black

color

-RRB-

as

well

as

differences

in

spot

patterns

can

be

easily

identified

Data

about

differences

in

spot

position

are

used

in

later

image

analysis

steps

-LRB-

image

fusion

transfer

of

consensus

spot

pattern

-RRB-

It

turns

out

that

variations

in

spot

positions

are

localized

i.e.

spots

that

are

close

together

on

the

gels

will

have

similar

gel-to-gel

variations

in

their

position

-LRB-

Fig.

-RRB-

This

suggests

the

possibility

of

eliminating

the

positional

variations

by

applying

an

image

processing

technique

called

warping

In

a

more

general

context

the

image

processing

task

is

known

as

image

registration

it

is

for

example

used

to

combine

satellite

images

from

the

same

region

that

were

taken

at

different

times

and

angles

In

a

sense

image

registration

compensates

for

variations

in

the

lab

process

that

could

not

be

controlled

otherwise

During

image

registration

similar

regions

or

corresponding

spots

are

searched

on

both

gels

in

a

more

or

less

automated

way

This

results

in

pairs

of

landmark

coordinates

that

link

corresponding

regions

of

an

image

pair

to

each

other

The

actual

transformation

of

one

image

to

fit

another

is

not

linear

-LRB-

i.e.

no

simple

rotation

or

scaling

suffices

-RRB-

and

it

can

vary

considerably

between

regions

It

is

important

to

note

that

automatic

image

registration

uses

the

entire

available

image

information

and

can

be

done

independently

of

spot

detection

Artifacts

like

gel

disruptions

finger

prints

and

speckles

may

disturb

the

finding

of

the

correct

warp

transform

and

should

be

experimentally

avoided

or

removed

by

the

analysis

software

Image

registration

techniques

have

been

introduced

by

Compugen

's

and

DECODON

's

in

and

are

meanwhile

adopted

by

other

software

packages

For

surveys

of

image

registration

as

applied

to

gels

see

Aittokallio

et

al.

-LRB-

-RRB-

and

Dowsey

et

al.

-LRB-

-RRB-

Once

suitable

warpings

between

gel

pairs

have

been

produced

and

checked

using

e.g.

dual

channel

images

-LRB-

Fig.

c

-RRB-

they

can

be

combined

to

allow

for

the

registration

of

every

gel

in

the

experiment

to

every

other

gel

By

knowing

the

necessary

transforms

between

the

images

the

software

can

essentially

remove

all

differences

in

spot

positions

as

needed

for

e.g.

precise

spot

matching

or

fusion

images

-LRB-

see

below

-RRB-

In

rare

cases

no

suitable

warp

transform

can

be

found

e.g.

if

spots

switched

their

relative

positions

or

if

the

patterns

are

so

different

that

no

obvious

landmark

pairs

can

be

found

-LRB-

e.g.

stimulus/in

vivo

labeling

experiments

dye

multiplex

experiments

-RRB-

Under

these

circumstances

alternative

experimental

setups

should

be

used

One

example

is

the

linking

of

gel

images

via

comigration

of

differently

labeled

samples

in

the

same

gel

-LRB-

DIGE

-RRB-

or

by

using

helper

gels

containing

mixtures

of

two

samples

A

and

B

to

find

an

implicit

warp

transform

between

two

gel

images

via

a

helper

image

-LRB-

Eymann

et

al.

-RRB-

Spot

detection

and

quantitation

The

goals

of

this

step

are

to

find

the

spot

positions

find

their

surrounding

boundary

and

determine

their

quantities

There

are

two

basic

approaches

that

are

used

in

current

software

image

segmentation

and

model-based

quantitation

The

segmentation

approach

partitions

the

image

into

nonoverlapping

segments

essentially

classifying

each

pixel

as

belonging

to

a

certain

spot

or

as

being

part

of

the

background

between

spots

Spot

boundaries

and

quantities

are

then

derived

from

the

spot

's

pixels

The

segmentation

of

the

image

can

take

various

characteristics

of

the

image

into

account

raw

intensity

slope

and

classification

of

pixels

in

the

surrounding

region

The

advantage

of

this

approach

is

that

the

image

is

clearly

separated

into

spots

and

nonspot

areas

which

are

easy

to

assess

by

a

user

If

the

software

allows

editing

of

spot

boundaries

then

any

desired

spot

shape

can

in

principle

be

obtained

Model-based

approaches

try

to

model

a

spot

's

intensity

as

a

Gaussian

normal

distribution

or

some

variant

thereof

A

spot

's

quantity

and

boundaries

are

then

derived

from

the

model

-LRB-

Fig.

-RRB-

The

use

of

a

Gaussian

is

motivated

by

the

shape

of

spots

-LRB-

Fig.

-RRB-

and

by

general

considerations

on

diffusion

processes

in

the

gel

Model-based

approaches

limit

the

range

of

possible

spot

shapes

thus

leading

to

more

natural

outlines

On

the

other

hand

irregular

spot

shapes

are

poorly

represented

by

simple

models

Spot

models

can

be

used

in

the

subsequent

quantitation

with

overlapping

spots

being

represented

as

the

sum

of

multiple

single-spot

models

offers

a

hybrid

between

segmentation

and

modeling

starting

with

segmentation

spots

are

modeled

as

Gaussians

and

their

nonoverlapping

boundaries

are

derived

from

the

models

-LRB-

Fig.

-RRB-

Fig.

boundaries

for

high

-LRB-

a

-RRB-

and

low

abundance

-LRB-

b

-RRB-

spotsFig

boundaries

produced

by

segmentation

-LRB-

a

-RRB-

and

subsequent

modeling

-LRB-

b

-RRB-

The

particulars

of

the

methods

used

for

spot

detection

are

often

proprietary

information

of

the

software

vendors

and

thus

not

publicly

available

For

a

survey

of

published

work

see

Dowsey

et

al.

-LRB-

-RRB-

The

spot

detection

process

can

be

controlled

by

setting

software-specific

parameters

such

as

expected

size

of

a

spot

in

pixels

or

even

expected

number

of

spots

Due

to

ambiguities

in

the

gel

images

-LRB-

merged

spots

weak

spots

noise

-RRB-

automated

spot

detection

can

only

be

a

heuristic

process

in

some

areas

The

user

will

therefore

sometimes

want

to

change

the

spot

pattern

by

removing

spots

splitting

spot

clusters

or

joining

spots

Manual

intervention

has

a

downside

as

well

Individual

users

have

different

perceptions

about

the

correct

spot

shapes

so

reproducibility

between

different

operators

of

the

software

suffers

It

is

therefore

advisable

to

reduce

the

necessary

manual

interventions

to

a

minimum

e.g.

by

defining

points

as

markers

for

the

creation

of

new/splitting

of

existing

spots

and

letting

the

software

determine

the

adapted

boundaries

In

the

simplest

case

one

assumes

that

gray

values

found

in

the

image

file

are

directly

proportional

to

image

intensities

and

by

extension

protein

quantity

in

the

small

gel

area

corresponding

to

the

pixel

However

more

advanced

imaging

equipment

utilizes

calibration

information

that

should

be

used

to

arrive

at

correct

quantities

Calibration

inherent

in

the

file

format

-LRB-

Fig.

-RRB-

Some

imaging

devices

can

measure

more

intensity

values

than

what

fit

into

the

available

image

file

formats

For

example

if

the

imaging

device

can

measure

a

range

of

intensity

values

this

range

can

not

be

stored

into

a

TIFF

image

because

the

file

format

only

provides

possible

gray

levels

per

pixel

One

way

to

deal

with

this

is

to

transform

the

measured

intensity

values

linearly

into

the

range

of

gray

levels

in

the

image

file

Alternatively

because

for

lower

signal

intensities

higher

accuracy

is

desirable

a

nonlinear

function

-LRB-

e.g.

square

root

transform

as

illustrated

in

Fig.

-RRB-

is

applied

by

some

imaging

devices

This

provides

a

more

precise

representation

of

lower-intensity

pixels

at

the

price

of

lower

accuracy

for

high

intensities

The

corresponding

vendor-specific

file

formats

e.g.

Fuji

's

IMG/INF

format

-LRB-

Fujifilm

Düsseldorf

Germany

-RRB-

and

the

GEL

format

used

in

devices

by

Molecular

Dynamics

-LRB-

Sunnyvale

CA

USA

-RRB-

and

GE

Healthcare

-LRB-

Munich

Germany

-RRB-

have

to

be

interpreted

accordingly

by

the

analysis

software

Fig.

of

a

gray

level

calibration

curve

that

is

used

in

special

image

file

formats

Gray

levels

found

in

the

image

file

have

to

be

interpreted

according

to

the

curve

before

being

summed

up

for

quantitation

The

curve

has

lower

slope

in

the

low

intensity

range

resulting

in

better

quantitative

resolution

for

weak

signals

Calibration

of

the

device

Another

type

of

calibration

may

have

to

be

applied

to

eliminate

variance

between

imaging

devices

of

the

same

model

While

most

of

the

laser

scanners

in

the

market

have

a

built-in

autocalibration

many

flatbed

scanners

need

to

be

calibrated

manually

This

can

be

done

by

using

calibration

wedges

which

are

offered

by

several

resellers

e.g.

Stouffer

Industries

-LRB-

Mishawaka

IN

USA

-RRB-

Danes-Picta

-LRB-

Praha

Czech

Republic

-RRB-

UVP

-LRB-

Upland

CA

USA

-RRB-

Secondary

calibration

can

be

applied

if

the

user

knows

about

the

relationship

of

protein

amount

and

measured

signal

Finally

one

can

take

into

account

the

dye-specific

response

curve

for

protein

staining

Protein

concentration

wedges

may

help

to

find

a

fluorescent

or

absorptive

stain-specific

transfer

function

that

may

help

to

derive

the

protein

amount

from

the

emitted

light

or

the

measured

absorption

respectively

It

is

to

be

expected

that

even

different

protein

species

have

different

response

curves

resulting

from

their

biochemical

properties

Image

background

is

generated

by

material

that

is

stained

but

not

part

of

a

protein

spot

The

method

of

background

subtraction

can

have

a

significant

influence

on

spot

quantities

therefore

it

is

important

that

background

quantities

are

made

explicit

by

the

software

instead

of

being

silently

subtracted

from

a

spot

's

quantity

Background

levels

can

vary

considerably

between

regions

on

a

gel

and

between

gels

Some

background

subtraction

methods

are

based

on

the

gray

levels

at

the

spot

boundaries

other

approaches

are

based

on

the

entire

available

image

data

One

example

for

a

background

estimation

that

is

based

on

the

spot

boundary

is

DeCyder

's

-LRB-

GE

Healthcare

-RRB-

rule

Background

is

determined

by

the

tenth

percentile

value

of

all

intensity

values

on

the

boundary

A

background

model

based

on

local

minima

was

used

by

Tyson

et

al.

-LRB-

-RRB-

The

Melanie

II

software

-LRB-

Appel

et

al.

-RRB-

calculated

background

based

on

a

polynomial

that

is

fitted

to

image

intensities

A

related

approach

is

the

rolling

ball

method

-LRB-

Skolnick

-RRB-

that

determines

background

levels

by

fitting

a

sphere

into

the

landscape

of

the

image

-LRB-

see

Fig.

-RRB-

The

sphere

needs

to

be

large

enough

such

that

the

ball

will

not

go

too

deep

into

the

spots

Background

levels

are

then

determined

relative

to

the

center

of

the

ball

when

it

touches

the

image

surface

Fig.

subtraction

using

the

rolling

ball

approach

Normalization

of

spot

quantities

Normalization

procedures

aim

to

mitigate

systematic

differences

between

images

Such

variation

can

occur

in

protein

loading

imaging

exposure

times

and

dye/staining

efficiency

Normalization

starts

with

raw

spot

volumes

and

relates

them

to

spot

volumes

on

the

same

or

on

other

gels

The

simplest

common

procedure

is

to

normalize

for

total

volumes

The

corresponding

normalization

rule

is

to

set

the

total

spot

quantity

to

on

each

gel

image

For

every

spot

its

proportion

of

total

quantity

is

then

computed

This

rule

results

from

the

assumption

that

there

is

the

same

total

protein

amount

on

all

gels

With

this

procedure

errors

such

as

different

protein

loads

differences

in

staining

times

scanner

exposure

time

or

detector

sensitivity

can

be

compensated

For

some

instances

the

total

spot

intensity

is

distorted

by

one

or

a

few

very

strong

spots

leading

to

a

skewing

in

the

scatter

plot

-LRB-

Fig.

-RRB-

A

countermeasure

is

to

exclude

some

of

the

strongest

spots

-LRB-

e.g.

the

strongest

spots

-RRB-

from

the

calculation

of

total

spot

intensity

i.e.

removal

of

outliers

Fig.

plot

-LRB-

a

-RRB-

of

logarithmic

spot

quantities

on

two

gels

from

different

samples

Spots

were

normalized

based

on

total

spot

quantity

b

The

quantile

quantile

plot

-LRB-

QQ

plot

-RRB-

of

the

same

data

Spots

are

sorted

by

quantity

separately

on

each

gel

spots

of

corresponding

ranks

are

plotted

The

QQ

plot

makes

it

easier

to

compare

the

spot

volume

distributions

in

an

ideal

experiment

all

points

would

lie

on

the

diagonal

line

The

diagram

shows

that

the

quantity

distributions

on

both

gels

are

nearly

equal

indicating

a

successful

normalization

A

more

general

approach

is

to

postulate

that

the

spot

intensity

distributions

-LRB-

or

some

of

the

distribution

's

parameters

-RRB-

are

equal

between

gels/gel

images

Therefore

e.g.

DeCyder

-LRB-

GE

Healthcare

-RRB-

tries

to

match

the

distributions

by

adapting

parameters

of

a

normal

distribution

Another

approach

is

to

use

only

a

subset

for

normalization

e.g.

by

declaring

that

certain

spots

belong

to

housekeeping

genes

It

is

hard

to

justify

a

subset

like

this

given

that

their

being

unaffected

by

the

phenomenon

under

study

is

just

a

hypothesis

In

principle

one

could

also

spike

the

gel

with

a

defined

mixture

of

proteins

with

known

quantities

We

are

however

not

aware

of

any

published

work

that

used

this

approach

More

advanced

normalization

methods

that

were

originally

developed

for

the

analysis

of

microarray

data

have

been

successfully

applied

in

the

context

of

gel

analysis

-LRB-

Fodor

et

al.

-RRB-

Spatial

bias

i.e.

a

systematic

increase

or

decrease

in

spot

intensities

in

a

gel

region

can

be

observed

in

experiments

and

could

be

corrected

by

incorporating

it

in

an

error

model

analogous

to

those

used

in

microarray

analysis

-LRB-

Kreil

et

al.

-RRB-

The

implicit

assumption

in

all

normalization

procedures

is

that

the

majority

of

proteins

are

not

affected

by

the

phenomenon

under

study

or

the

technical

variations

in

the

process

Therefore

the

global

intensity

distributions

should

be

equal

The

similarity

of

two

distributions

can

be

assessed

using

a

quantile

quantile

-LRB-

QQ

-RRB-

plot

-LRB-

Fig.

-RRB-

It

is

a

two-dimensional

plot

where

spots

are

compared

based

on

their

rank

i.e.

the

strongest

spot

on

one

image

is

plotted

against

the

strongest

spot

on

another

image

etc.

For

identical

distributions

the

QQ

plot

is

a

diagonal

line

Systematic

deviations

like

an

overall

higher

intensity

are

easily

visible

Normalization

procedures

that

take

into

account

only

intensities

on

one

image

might

be

called

vertical

-LRB-

using

columns

in

an

imagined

spot

intensity

table

-RRB-

they

modify

values

based

on

values

in

the

same

column

-LRB-

i.e.

image

-RRB-

A

different

class

of

normalizations

uses

intensities

from

other

gels/images

these

might

be

called

horizontal

normalizations

The

most

prominent

example

is

the

DIGE

setup

using

a

common

internal

standard

on

all

gels

usually

in

the

channel

-LRB-

Alban

et

al.

-RRB-

The

internal

standard

is

composed

by

mixing

all

samples

from

the

experiment

in

equal

amounts

Spot

intensities

are

then

normalized

by

dividing

them

by

the

intensity

of

the

corresponding

spot

on

the

standard

image

-LRB-

channel

on

the

same

gel

-RRB-

In

addition

to

the

gel-to-gel

variations

these

normalization

methods

have

to

cope

with

differing

backgrounds

and

differing

distributions

of

spot

quantities

between

dyes

The

resulting

statistics

are

very

similar

to

the

relative

expression

levels

that

are

produced

in

competitive

hybridization

microarray

experiments

For

a

critique

of

the

then-current

DIGE

normalization

used

in

DeCyder

and

an

alternative

normalization

method

see

Karp

et

al.

-LRB-

-RRB-

As

a

result

of

the

spot

detection

and

quantitation

step

the

user

gets

a

variety

of

data

on

each

gel

spot

including

normalized

spot

quantity

background

spot

outline

position

of

the

spot

center

and

spot

quality

measures

Of

course

it

would

be

desirable

to

relate

spot

intensities

that

are

obtained

from

gel

image

analysis

directly

to

protein

molecule

counts

in

the

original

sample

However

the

relation

between

original

protein

quantity

in

the

sample

and

measured

spot

intensity

is

influenced

by

various

intervening

factors

loss

of

sample

during

entry

into

the

IEF

gel

efficiency

of

transfer

from

first

to

second

dimension

protein

loss

during

staining

staining

efficiency

a

protein

's

staining

curve

over

time

staining

curve

over

concentration

anddye

bleaching

Given

the

biochemical

diversity

of

protein

molecules

it

is

to

be

expected

that

there

are

some

proteins

with

a

nonlinear

relation

between

concentration

and

intensity

As

a

result

one

expects

to

obtain

only

quantitative

results

of

a

relative

nature

referring

to

the

same

protein

species

similar

to

this

protein

is

two

times

stronger

in

sample

A

than

in

sample

B.

It

is

essential

to

track

the

protein

across

gels

for

this

relative

quantitation

to

work

Although

the

limiting

factors

mentioned

above

may

seem

a

bit

discouraging

recent

controlled

experiments

-LRB-

Eravci

et

al.

-RRB-

show

that

even

a

change

in

quantity

can

be

reliably

detected

for

a

substantial

fraction

of

the

proteins

provided

that

one

controls

experimental

variation

and

software-related

problems

that

have

a

diminishing

effect

on

reproducibility

-LRB-

mainly

spot

matching

see

below

-RRB-

Building

expression

profiles

For

comparing

the

spot

intensities

over

a

whole

experiment

each

spot

on

a

certain

gel

has

to

be

mapped

to

the

corresponding

spots

on

the

other

gels

in

a

process

called

spot

matching

The

quality

of

the

matching

depends

on

the

quality

and

the

reproducibility

of

electrophoretic

separation

and

spot

detection

as

well

as

on

the

methods

employed

by

the

software

In

the

ideal

analysis

spot

matching

would

produce

exactly

one

expression

profile

for

every

protein

species

that

is

visible

on

any

gel

The

challenges

in

spot

matching

lie

in

the

differences

in

migration

positions

between

gels

changes

in

the

spot

pattern

itself

-LRB-

i.e.

proteins

that

are

missing

or

very

weak

in

one

of

the

samples

-RRB-

ambiguities

in

the

gel

data

-LRB-

e.g.

more

or

less

well-resolved

spot

clusters

-RRB-

Due

to

these

difficulties

spot

matching

has

been

alongside

with

spot

editing

the

most

time-consuming

step

in

the

analysis

when

using

traditional

software

The

traditional

approach

consists

of

doing

separate

spot

detections

for

each

individual

gel

image

of

the

experiment

This

results

in

differing

spot

patterns

even

for

replicate

samples

In

a

subsequent

step

the

user

has

to

revise

these

results

and

if

necessary

to

correct

missing

or

false

positive

spots

Furthermore

regions

that

were

detected

as

separate

spots

on

one

gel

but

as

a

single

spot

on

another

have

to

be

split

or

joined

by

hand

Unfortunately

these

problems

grow

with

the

number

of

gels

-LRB-

Voss

and

Haberl

-RRB-

severely

limiting

the

statistical

benefits

that

come

with

larger

sample

numbers

For

example

in

a

study

that

used

plasma

from

five

different

individuals

taken

at

six

time

points

-LRB-

Fodor

et

al.

-RRB-

there

were

fewer

than

spots

matched

across

at

least

of

the

total

DIGE

gels

out

of

spots

total

Although

it

seems

possible

with

enough

manual

work

to

improve

expression

profiles

both

the

inherent

ambiguity

in

the

images

and

the

sheer

amount

of

work

necessary

result

in

the

acceptance

of

mismatches

and

gaps

in

expression

profiles

with

classical

packages

Spot-matching-related

problems

affect

the

statistical

analysis

Gaps

in

expression

profiles

have

to

be

treated

as

missing

values

Mismatches

are

the

equivalent

of

substituting

a

random

value

into

the

expression

profile

Both

effects

decrease

the

statistical

confidence

substantially

To

overcome

these

problems

a

different

approach

to

construct

expression

profiles

has

been

suggested

and

implemented

in

recent

years

The

approach

is

based

on

a

spot

consensus

pattern

that

is

derived

from

all

gel

images

of

an

experiment

and

then

applied

to

each

gel

image

of

the

analysis

set

-LRB-

Figs.

and

-RRB-

The

consensus

pattern

is

produced

by

spot

detection

on

a

fused

image

that

essentially

contains

all

spots

in

the

experiments

-LRB-

union

fusion

Luhn

et

al.

-RRB-

Image

fusion

is

a

method

to

combine

multiple

images

into

a

new

synthetic

image

where

each

pixel

is

a

function

of

corresponding

pixels

in

the

input

images

The

resulting

image

looks

like

a

real

gel

image

and

more

importantly

all

spots

from

the

experiment

are

represented

on

it

Thus

spots

that

are

present

only

on

a

few

of

the

gels

can

be

located

in

the

fused

image

and

properly

separated

from

those

surrounding

them

When

the

detection

and

editing

of

the

consensus

spot

pattern

is

finished

spot

boundaries

are

transferred

back

to

the

original

images

This

transfer

uses

the

transformations

that

were

produced

from

the

warping

step

and

used

earlier

to

create

the

fused

image

The

spot

sizes

and

intensities

differ

from

gel

to

gel

so

a

spot

remodeling

step

is

applied

to

make

the

spot

boundaries

fit

to

the

gray

level

distributions

of

the

original

gel

images

The

spot

quantities

are

then

calculated

as

usual

by

summing

the

pixel

intensities

within

the

spot

boundaries

Because

each

gel

has

the

same

spot

pattern

-LRB-

modified

by

the

warping

transform

-RRB-

a

matching

of

the

spots

is

quite

easy

and

results

in

spot

matching

and

complete

expression

profiles

For

spots

that

are

absent

on

some

gels

the

method

will

nevertheless

produce

a

spot

quantity

which

will

be

near

zero

after

background

is

subtracted

In

essence

one

could

describe

the

spot

transfer

approach

as

quantitation

of

corresponding

gel

image

regions

The

resulting

expression

profiles

were

shown

to

be

superior

to

those

produced

by

the

classical

method

-LRB-

separate

spot

detections

on

each

gel

-RRB-

both

in

terms

of

quality

as

well

as

time

needed

for

the

analysis

-LRB-

Eravci

et

al.

-RRB-

Analyzing

gene

expression

Having

expression

profiles

at

hand

we

can

now

proceed

to

address

the

important

biological

questions

of

the

phenomenon

under

study

Overall

the

task

is

to

distinguish

those

proteins

that

are

relevant

or

interesting

from

those

that

are

relatively

unchanged

It

is

often

the

case

that

gel

studies

are

exploratory

in

nature

i.e.

that

one

wants

to

generate

hypotheses

and

find

proteins

whose

role

can

later

be

validated

using

other

methods

An

example

of

this

is

the

discovery

of

protein

biomarkers

where

one

searches

proteins

that

are

consistently

associated

with

a

phenotype

Another

group

of

applications

is

concerned

with

elucidating

and

interpreting

subsets

of

proteins

that

are

involved

in

a

biological

process

e.g.

coregulated

proteins

induced

by

an

external

stimulus

-LRB-

Bernhardt

et

al.

-RRB-

Methods

ranging

from

the

classical

statistical

tests

to

machine

learning

are

applied

to

address

these

questions

As

in

all

experimental

work

the

design

of

the

experiment

is

of

crucial

importance

to

its

success

It

is

expedient

to

ask

for

statistical

advice

early

to

be

able

to

make

optimal

use

of

resources

and

limited

amounts

of

sample

material

For

all

but

the

most

exploratory

experiments

one

will

want

to

use

replicates

biological

replicates

to

address

genetic

and

environmental

variability

and

maybe

technical

replicates

-LRB-

i.e.

multiple

separations

of

the

same

sample

-RRB-

to

address

the

variability

in

the

gel

electrophoresis

process

itself

General

guidelines

concerning

the

use

of

replicates

and

statistical

analyses

accompanying

published

data

have

been

put

forward

in

Wilkins

et

al.

-LRB-

-RRB-

Of

course

a

higher

number

of

replicates

increase

the

number

of

proteins

that

can

be

identified

as

being

relevant

with

statistical

confidence

A

recommendation

for

the

simplest

possible

case

-LRB-

two

sample

groups

-RRB-

is

worked

out

in

Hunt

et

al.

-LRB-

-RRB-

Karp

and

Lilley

-LRB-

-RRB-

present

a

power

and

cost-benefit

analysis

for

DIGE

experiments

and

DeCyder

software

that

can

be

used

as

a

model

for

other

experimental

setups

The

type

of

replicate

affects

the

statistical

model

that

should

be

used

When

applying

statistical

tests

technical

replicates

can

not

necessarily

be

viewed

as

independent

observations

so

a

more

refined

statistical

analysis

e.g.

nested

analysis

of

variance

-LRB-

ANOVA

-RRB-

can

give

better

results

-LRB-

Karp

et

al.

-RRB-

Pooling

of

samples

should

be

kept

to

a

minimum

because

it

diminishes

-LRB-

or

prevents

-RRB-

the

ability

to

assess

biological

variation

within

a

sample

type

In

Molloy

et

al.

-LRB-

-RRB-

the

effects

of

technical

and

biological

variance

are

investigated

for

samples

of

different

types

-LRB-

bacteria

cell

lines

primary

cultures

human

samples

-RRB-

For

guidance

on

experiment

design

see

also

Hunt

et

al.

-LRB-

-RRB-

Of

course

experimental

variability

is

different

between

laboratories

and

the

software

and

analysis

method

used

can

have

a

crucial

influence

on

the

outcome

's

quality

The

studies

cited

here

use

separate

spot

detections

on

each

gel

and

often

bias

results

by

selecting

only

spots

that

were

matched

across

a

sufficient

number

of

gel

images

At

the

level

of

expression

profiles

gel

analysis

is

very

similar

to

the

analysis

of

DNA

microarrays

so

a

lot

of

the

methods

applied

there

can

be

used

with

minor

adaptations

In

fact

some

of

the

work

on

machine

learning

on

gel

data

predates

the

microarray

technology

-LRB-

e.g.

Appel

et

al.

-RRB-

Let

us

highlight

the

main

differences

between

gel

data

and

microarray

data

before

we

proceed

to

describe

the

methods

in

detail

Running

differences

between

gels

add

a

source

of

errors

for

spot

matching

whereas

in

microarray

data

matching

is

trivial

because

every

gene

is

spotted

at

a

known

row

and

column.Spot

detection

in

gels

is

much

harder

because

spots

may

not

be

distinct.Gene

information

is

not

readily

available

for

spots

so

it

is

harder

to

correlate

or

cross-validate

expression

profiles

with

gene

annotations

The

first

two

characteristics

have

the

greatest

impact

on

the

quality

of

statistical

analysis

Running

differences

and

spot

shape

ambiguities

-LRB-

e.g.

distinct

spots

on

one

gel

vs

overlapping

spots

on

another

-RRB-

create

spot

matching

problems

that

can

lead

to

faulty

assembly

of

expression

profiles

and

gaps

-LRB-

missing

values

-RRB-

By

using

consensus

spot

patterns

-LRB-

see

above

-RRB-

that

are

transferred

onto

all

images

one

obtains

consistent

expression

profiles

Of

course

the

ambiguities

inherent

in

electrophoresis

e.g.

masking

of

weak

spots

by

strong

ones

in

a

nearby

position

-LRB-

Pietrogrande

et

al.

Campostrini

et

al.

-RRB-

as

well

as

operator-related

variance

are

still

potential

sources

of

errors

and

variation

Currently

all

gel

analysis

software

packages

come

with

some

basic

internal

statistical

analysis

facilities

The

advantage

of

using

these

facilities

as

opposed

to

external

programs

is

that

the

analysis

of

expression

profiles

is

tightly

integrated

with

image

analysis

For

example

it

is

easy

to

see

a

section

of

all

gels

around

a

given

spot

that

was

flagged

as

being

differentially

expressed

All

packages

support

the

export

of

expression

data

in

tabular

form

so

more

advanced

methods

can

be

used

Beyond

the

image

analysis

packages

there

are

a

few

commercial

and

noncommercial

options

for

the

statistical

analysis

of

gel

data

Genedata

's

Expressionist

-LRB-

GeneData

Basel

-RRB-

and

DeCyder

EDA

-LRB-

GE

Healthcare

-RRB-

are

products

that

offer

multivariate

statistics

tailored

for

gel

image

data

General-purpose

statistics

packages

like

the

free

and

open-source

R

-LRB-

-RRB-

have

extensive

facilities

for

higher-level

methods

such

as

principal

component

analysis

-LRB-

PCA

-RRB-

and

clustering

The

R-based

BioConductor

package

-LRB-

-RRB-

provides

access

to

a

wide

variety

of

data

analysis

methods

and

graphics

facilities

that

were

developed

for

microarray

data

While

these

command-line-oriented

packages

offer

great

flexibility

and

control

as

well

as

some

of

the

latest

methods

in

the

field

their

learning

curve

can

be

steep

A

more

interactive

and

visual

approach

to

data

analysis

is

offered

by

the

open-source

TIGR

Multiple

Experiment

Viewer

MeV

-LRB-

Saeed

et

al.

-RRB-

MeV

combines

interactive

visualization

of

microarray

data

with

a

wide

choice

of

analysis

methods

such

as

hierarchical

clustering

self-organizing

maps

and

PCA

-LRB-

Fig.

-RRB-

Fig.

using

a

consensus

spot

pattern

in

-LRB-

a

-RRB-

complete

expression

profiles

-LRB-

b

-RRB-

are

generated

Profiles

can

be

imported

into

DNA

array

analysis

software

-LRB-

here

TIGR

MultiExperiment

Viewer

TMEV

-RRB-

With

appropriate

data

transformations

and

normalization

many

approaches

for

data

analysis

known

from

DNA

arrays

can

be

used

for

proteome

data

Hierarchical

clustering

-LRB-

c

-RRB-

and

self-organizing

maps

-LRB-

d

-RRB-

group

proteins

by

similarity

of

their

expression

profiles

Template

matching

-LRB-

e

-RRB-

can

be

used

to

find

proteins

that

conform

to

an

expression

pattern

given

by

the

user

Terrain

maps

-LRB-

f

-RRB-

can

give

a

high

level

overview

of

a

data

set

where

correlations

of

protein

expression

profiles

are

shown

as

distances

in

two

dimensions

and

protein

density

is

shown

in

the

third

dimension

-LRB-

height

-RRB-

Hypothesis-driven

methods

In

the

simplest

case

the

experiment

is

a

comparison

of

two

samples

A

and

B

-LRB-

e.g.

treated

vs

control

mutant

vs

wild

type

etc.

-RRB-

The

task

then

is

finding

those

proteins

that

show

significant

differences

in

expression

levels

Normally

one

will

have

several

replicates

per

sample

and

a

statistical

test

is

employed

to

find

differentially

expressed

proteins

Certainly

the

most

popular

test

in

this

area

is

Student

's

t

test

where

the

hypothesis

is

that

the

means

of

expression

levels

in

samples

A

and

B

are

the

same

Rejecting

the

hypothesis

then

means

that

the

protein

under

test

is

differentially

expressed

One

has

to

keep

in

mind

that

the

t

test

makes

the

assumption

that

spot

quantities

within

replicates

follow

a

normal

distribution

which

should

in

principle

be

tested

separately

Additional

tests

are

in

use

-LRB-

ProteomWeaver

Bio-Rad

Hercules

CA

USA

-RRB-

that

do

not

depend

on

the

normality

assumption

-LRB-

nonparametric

tests

-RRB-

like

the

two-sample

Wilcoxon

test

also

known

as

the

Mann

Whitney

U

test

-LRB-

Conover

-RRB-

In

addition

to

statistical

tests

often

a

fold-change

criterion

is

imposed

on

the

spots

The

reasoning

behind

this

is

that

even

true

changes

in

intensity

are

hard

to

verify

independently

if

they

are

small

and

that

at

least

intuitively

a

high

fold-change

in

a

protein

's

intensity

is

unlikely

to

be

the

result

of

mere

chance

For

small

numbers

of

replicates

the

fold-change

criterion

is

sometimes

the

only

one

used

When

applying

statistical

tests

to

gel

data

one

is

faced

with

the

so-called

multiple

hypothesis

testing

problem

For

each

expression

profile

a

separate

test

is

done

Each

test

has

a

certain

probability

of

giving

a

false

positive

result

i.e.

a

protein

spot

is

declared

to

be

differentially

expressed

while

the

difference

was

due

to

pure

chance

The

large

number

of

tests

can

produce

a

high

number

of

false

positives

For

example

in

an

experiment

with

spots

per

gel

an

accepted

false-positive

rate

alpha

of

will

result

in

proteins

that

are

found

to

be

differentially

expressed

although

the

difference

is

the

result

of

mere

chance

Various

procedures

try

to

overcome

this

problem

by

adjusting

alpha

in

an

adequate

way

Bonferroni

correction

-LRB-

Weisstein

-RRB-

controls

the

probability

that

there

is

one

single

error

made

in

the

whole

analysis

-LRB-

family-wise

error

rate

-RRB-

i.e.

that

there

is

at

least

one

false-positive

protein

However

this

method

is

usually

too

conservative

because

in

practice

it

is

acceptable

to

have

some

false

positives

depending

on

the

cost

of

repeating

or

validating

the

corresponding

results

The

proportion

of

false

positives

in

the

result

set

is

controlled

by

the

False

Discovery

Rate

-LRB-

FDR

-RRB-

approach

-LRB-

Benjamini

and

Hochberg

Pawitan

et

al.

-RRB-

The

false

discovery

rate

is

the

rate

of

false

positive

results

among

all

profiles

that

were

tested

positive

While

it

is

difficult

to

estimate

the

false

discovery

rate

the

approach

in

Benjamini

and

Hochberg

-LRB-

-RRB-

gives

a

simple

procedure

to

control

it

i.e.

make

sure

the

false

discovery

rate

is

below

a

given

bound

Overall

the

FDR

approach

allows

one

to

strike

a

balance

between

the

need

to

find

statistically

valid

proteins

of

interest

and

the

additional

cost

that

is

associated

with

following

up

on

false

positives

For

background

and

applications

of

FDR

and

related

methods

the

reader

is

advised

to

consult

the

reviews

in

Manly

et

al.

-LRB-

-RRB-

and

Pounds

-LRB-

-RRB-

For

more

complicated

experiment

designs

involving

multiple

factors

ANOVA

can

be

used

-LRB-

Weisstein

Karp

et

al.

-RRB-

The

basic

idea

of

the

method

is

to

find

out

to

what

extent

the

observed

variances

between

different

samples

can

be

explained

by

the

experimental

parameters

as

opposed

to

biological

or

technical

variation

Hypothesis-independent

methods

Hypothesis-independent

methods

were

developed

for

the

discovery

of

patterns

in

large

quantities

of

possibly

high-dimensional

data

in

the

fields

of

data

mining

and

machine

learning

As

we

expect

a

small

number

of

fundamental

biological

processes

to

be

reflected

in

the

expression

patterns

of

a

large

number

of

proteins

it

makes

sense

to

apply

these

methods

to

the

analysis

of

experiments

Again

much

of

the

work

on

microarray

analysis

can

be

transferred

easily

because

the

fundamental

unit

of

data

is

an

expression

profile

When

using

separate

spot

detections

on

every

gel

the

missing

values

will

have

to

be

dealt

with

by

the

statistical

method

for

example

by

missing

value

imputation

A

large

percentage

of

missing

values

decreases

the

utility

of

all

statistical

methods

that

is

why

we

recommend

using

the

consensus

spot

pattern

approach

described

above

Hierarchical

clustering

refers

to

a

group

of

methods

that

aim

to

group

expression

profiles

or

gels

by

similarity

forming

separate

clusters

that

can

be

further

analyzed

Hierarchical

clustering

of

gels

can

be

used

to

detect

outliers

and

to

identify

structures

in

the

experiment

Ideally

the

cluster

composition

will

reflect

the

structure

of

the

experiment

e.g.

replicates

and

images

from

the

same

sample

should

end

up

in

the

same

cluster

-LRB-

Fig.

-RRB-

Clustering

of

images

is

a

good

first

step

in

assessing

the

quality

of

the

quantitative

data

Clustering

of

expression

profiles

is

done

to

identify

proteins

with

similar

behavior

implying

that

they

are

coregulated

or

at

least

correlated

Again

it

is

hoped

that

the

cluster

structure

maps

to

functional

groups

or

coregulated

proteins

The

global

nature

of

the

cluster

display

allows

for

a

broad

overview

and

the

forming

of

hypotheses

that

can

then

be

tested

However

in

contrast

to

the

situation

in

microarray

data

with

gels

biological

annotations

of

proteins

are

not

available

until

after

protein

identification

making

it

harder

to

correlate

expression

behavior

to

function

The

methods

and

software

tools

applied

in

the

microarray

analysis

are

applicable

here

and

the

choices

a

user

has

to

make

are

essentially

the

same

-LRB-

Meunier

et

al.

-RRB-

The

first

choice

is

the

normalization

method

e.g.

to

standardize

expression

profiles

to

be

of

mean

zero

and

variance

one

Then

a

similarity

measure

between

expression

profiles

has

to

be

defined

e.g.

correlation

or

the

Euclidean

distance

Taken

together

with

further

choices

such

as

using

single

average

or

complete

linkage

to

connect

clusters

these

combine

to

create

a

variety

of

possible

clusterings

Fig.

of

a

heat

map

of

a

hierarchical

clustering

of

an

experiment

consisting

of

individuals

with

replicate

gels

each

and

average

fusion

image

per

individual

Clustering

was

done

for

gels

-LRB-

columns

-RRB-

and

expression

profiles

-LRB-

rows

-RRB-

simultaneously

Gels

are

color

coded

by

sample

replicates

have

the

same

color

sample

A

is

colored

in

shades

of

blue

sample

B

is

colored

in

shades

of

red

The

clusterdendrogram

for

gels

shows

that

replicates

were

clustered

together

and

samples

are

roughly

grouped

in

the

higher

level

clusters

The

clustering

did

not

use

any

sample

or

replicate

information

The

left-most

replicate

group

is

probably

an

outlier

as

it

branches

off

early

in

the

dendrogram

Notice

also

the

cluster

structure

in

the

rows

grouping

proteins

with

similar

expression

profiles

-LRB-

row

dendrogram

not

shown

-RRB-

Expression

profiles

were

generated

by

spot

transfer

hence

the

absence

of

missing

values

Only

about

of

all

expression

profiles

are

shown

Another

method

that

has

been

applied

with

great

success

in

a

variety

of

areas

like

semantic

text

analysis

-LRB-

Deerwester

et

al.

-RRB-

and

face

recognition

-LRB-

Turk

and

Pentland

-RRB-

is

PCA

or

the

related

singular

value

decomposition

The

basic

idea

is

to

find

a

projection

from

a

high-dimensional

space

into

a

low-dimensional

space

-LRB-

e.g.

the

plane

-RRB-

such

that

the

structure

especially

the

variation

in

the

data

is

preserved

The

principal

components

are

the

directions

along

which

the

variation

is

maximal

They

can

be

interpreted

as

hidden

parameters

of

a

process

or

experiment

The

tutorial

Shlens

-LRB-

-RRB-

provides

an

accessible

introduction

the

book

chapter

Wall

et

al.

-LRB-

-RRB-

gives

background

and

motivation

for

microarray

analysis

As

with

clustering

PCA

can

be

done

for

gels

or

expression

profiles

In

the

first

variant

each

gel

image

is

considered

as

a

vector

with

coordinates

given

by

the

spot

intensities

on

that

gel

For

example

an

experiment

with

gels

from

sample

A

and

gels

from

sample

B

and

spots

on

each

gel

would

be

modeled

as

a

set

-LRB-

or

point

cloud

-RRB-

of

vectors

in

space

The

goal

of

PCA

is

then

to

find

a

projection

of

the

point

cloud

in

two

or

three-dimensional

space

such

that

as

much

as

possible

the

variation

of

the

point

cloud

is

preserved

One

hopes

that

the

gels

from

different

samples

will

be

in

separate

regions

of

the

resulting

diagram

-LRB-

Fig.

-RRB-

The

principal

components

can

then

be

interpreted

as

typical

spot

patterns

or

eigengels

Their

coordinates

can

be

analyzed

to

determine

which

spots

are

contributing

most

to

the

variance

making

them

candidates

for

protein

identification

and

biological

interpretation

Fig.

of

gels

from

patients

Gels

are

color

coded

according

to

sample

-LRB-

sample

a

shades

of

blue

sample

b

shades

of

red

-RRB-

Notice

how

replicate

gels

are

grouped

closely

together

We

have

chosen

the

projection

onto

the

second

and

third

principal

components

because

it

shows

a

good

separation

between

samples

When

PCA

is

applied

to

the

expression

profiles

in

our

example

we

would

consider

a

point

cloud

of

vectors

-LRB-

one

vector

for

each

expression

profile

-RRB-

with

dimensions

-LRB-

the

expression

levels

on

the

gels

-RRB-

The

result

is

a

display

of

the

proteins

where

-LRB-

hopefully

-RRB-

proteins

with

close

positions

are

biologically

related

Consider

a

time

series

experiment

where

proteins

are

switched

on

and

off

in

stages

If

there

is

a

hidden

parameter

such

as

a

stage

in

the

cell

cycle

it

will

have

a

systematic

influence

on

the

expression

levels

and

thus

increase

the

variance

for

the

genes

taking

part

in

it

This

increased

variance

will

then

become

part

of

the

directions

that

are

used

for

the

projection

-LRB-

the

principal

components

-RRB-

The

principal

components

were

also

called

eigengenes

they

can

be

seen

as

typical

expression

profiles

see

for

example

Alter

et

al.

-LRB-

-RRB-

and

Holter

et

al.

-LRB-

-RRB-

Presentation

and

visualization

from

spots

to

proteome

maps

For

the

presentation

of

an

analysis

as

well

as

throughout

the

whole

process

going

back

to

original

images

is

often

useful

Showing

whole

images

or

sections

thereof

in

combination

with

the

processed

spot

data

-LRB-

Fig.

-RRB-

is

therefore

a

feature

offered

by

all

analysis

packages

Let

us

start

with

the

display

of

a

single

gel

image

The

gray

level

resolution

of

current

imaging

devices

-LRB-

usually

bit

for

different

values

-RRB-

is

much

higher

than

what

a

computer

screen

can

display

Therefore

the

gel

images

have

to

be

adapted

or

enhanced

by

histogram

manipulations

that

cause

a

certain

loss

of

information

Fig.

image

tiles

before

-LRB-

a

-RRB-

and

after

-LRB-

b

-RRB-

multiway

histogram

equalization

After

the

equalization

the

difference

in

the

highlighted

spot

-LRB-

middle

row

left

and

right

images

-RRB-

is

clearly

visible

as

shown

in

the

expression

profile

-LRB-

c

-RRB-

The

next

more

complex

display

is

for

comparison

of

a

pair

of

gel

images

e.g.

treated

vs

untreated

or

wild

type

vs

knockout

There

are

two

possibilities

The

gels

can

be

shown

side

by

side

or

the

gels

are

shown

superimposed

in

a

dual

channel

image

-LRB-

see

Fig.

-RRB-

For

both

types

a

histogram

equalization

is

necessary

to

give

the

user

a

visual

estimate

of

relative

spot

quantities

Background

can

be

removed

before

display

depending

on

the

method

that

is

used

for

background

detection

Even

after

equalization

the

visual

impression

of

spot

volumes

can

be

misleading

for

example

when

comparing

a

small

dark

spot

with

a

spot

that

is

less

intense

but

larger

Before

gel

images

are

superimposed

in

false

colors

histograms

should

be

equalized

and

the

chosen

colors

should

be

of

similar

apparent

intensity

This

makes

sure

that

one

image

does

not

dominate

the

others

and

negatively

influence

the

estimation

of

quantities

Ideally

the

chosen

colors

are

of

similar

luminance

and

located

on

opposite

sides

of

the

color

wheel

Triple

channel

images

are

sometimes

used

-LRB-

red-green-blue

channels

see

Fig.

-RRB-

but

difficult

to

interpret

for

the

untrained

eye

For

the

display

of

multiple

gels

one

often

shows

tiles

containing

spots

of

interest

in

a

side-by-side

display

Here

a

multiway

equalization

of

histograms

is

necessary

to

give

suitable

visual

impressions

of

spot

intensities

Figure

shows

the

effect

of

multiway

equalization

on

the

display

of

a

series

of

image

tiles

With

proper

equalization

quantity

the

visual

impression

closely

corresponds

to

the

normalized

quantities

shown

in

the

expression

profile

chart

-LRB-

Fig.

-RRB-

Presentations

of

large

gel

series

for

example

for

time

course

experiments

or

large

projects

containing

a

lot

of

replicates

will

require

too

much

screen

space

for

a

side-by-side

display

Image

warping

can

help

in

making

an

animation

of

gel

series

from

time

courses

by

assuring

that

spot

positions

do

not

jump

between

frames

Bernhardt

et

al.

-LRB-

-RRB-

used

a

combination

of

dual

channel

images

showing

protein

amount

-LRB-

stained

proteins

false

colored

green

-RRB-

and

current

protein

synthesis

-LRB-

methionine

labeling

false

colored

red

-RRB-

to

monitor

the

fate

of

each

protein

during

the

development

of

a

bacterial

culture

along

a

time

line

-LRB-

-RRB-

Especially

for

the

collection

of

protein

identifications

and

the

presentation

of

proteome

maps

it

is

very

useful

to

condense

spot

pattern

information

from

a

multitude

of

gels

into

a

single

reference

image

This

can

be

done

by

collecting

spot

identifications

on

a

single

representative

gel

-LRB-

Eymann

et

al.

-RRB-

When

using

image

fusion

with

the

union

or

max

intensity

combination

function

a

proteome

map

can

be

generated

that

shows

all

proteins

that

were

observed

in

the

experiment

The

map

shows

realistically

looking

spot

shapes

and

does

not

ignore

differentially

expressed

spots

or

dilute

rarely

occurring

spots

like

it

would

be

the

case

with

average

images

-LRB-

Tam

le

et

al.

-RRB-

A

related

feature

was

implemented

in

Proteomweaver

-LRB-

Bio-Rad

-RRB-

that

allows

for

the

combination

of

different

narrow

pI-gradients

into

a

global

proteome

map

This

composite

map

utilizes

the

much

better

resolution

of

narrow

pH-gradient

strips

and

supports

image

analysis

as

if

the

data

came

from

a

single

very

wide

gel

A

proteome

map

normally

serves

as

basis

for

further

especially

physiologically

oriented

research

and

is

comparable

with

a

DNA

array

layout

The

proteome

map

defines

at

which

positions

a

protein

spot

was

identified

and

can

be

recovered

during

gel

analysis

A

variety

of

proteome

maps

of

many

kinds

of

samples

is

available

Most

of

them

show

about

a

thousand

different

identified

protein

spots

Additional

data

can

be

attached

to

spots

using

labels

e.g.

protein

identification

or

functional

information

Especially

in

gel

regions

with

a

very

dense

spot

pattern

it

is

a

big

challenge

to

display

the

protein

information

without

obscuring

image

information

with

spot

labels

In

proteome

maps

color

can

be

used

for

encoding

the

association

of

a

protein

to

a

regulatory

group

-LRB-

Voigt

et

al.

-RRB-

In

Fig.

proteins

above

a

defined

induction

factor

were

grouped

by

showing

them

in

the

same

color

If

several

induction

groups

are

displayed

in

parallel

Venn

diagrams

for

defining

the

protein

set

's

color

are

used

The

presented

stress

proteome

map

shows

combinations

of

expression

behavior

in

response

to

four

analyzed

stimuli

In

general

color

coding

of

protein

subsets

can

be

applied

for

any

way

of

allocating

spots

to

-LRB-

possibly

overlapping

-RRB-

categories

Fig.

maps

with

spot

color

coding

a

Stress

proteome

map

of

B.

subtilis

-LRB-

compare

Tam

le

et

al.

-RRB-

Spots

were

color

coded

according

to

their

induced

expression

in

response

to

different

stress

factors

b

Proteome

map

of

B.

subtilis

in

a

glucose

starvation

time

course

experiment

Spots

were

color

coded

according

to

the

growth

phase

Color

coding

also

works

for

highlighting

proteins

that

show

elevated

expression

levels

at

certain

points

in

a

time

course

experiment

Also

here

the

information

of

a

time-course-associated

gel

series

is

condensed

in

a

proteome

map

For

each

time

point

a

color

is

defined

in

the

proteome

map

which

is

then

assigned

to

spots

that

have

their

maximal

expression

level

at

that

time

point

-LRB-

Fig.

-RRB-

In

contrast

to

a

tabular

display

of

spot

quantities

a

proteome

map

retains

that

spatial

relations

between

spots

as

well

as

typical

spot

forms

It

is

thus

easier

to

relate

visually

to

newly

produced

gel

images

The

color

coding

of

spots

allows

for

easy

visual

identification

of

interesting

subsets

of

the

proteome

Conclusions

Advances

in

software

algorithms

and

experimental

methods

have

kept

gels

competitive

with

and

complementary

to

other

methods

for

proteome

analysis

While

still

not

fully

automatic

the

software-based

analysis

is

not

the

time-consuming

bottleneck

in

a

proteomics

experiment

anymore

With

complete

expression

profiles

one

is

able

to

take

full

advantage

of

statistical

methods

-LRB-

e.g.

hierarchical

clustering

PCA

-RRB-

that

were

established

in

the

analysis

of

DNA

microarray

data

We

see

three

main

directions

for

future

improvements

of

the

available

software

Firstly

the

basic

image

processing

algorithms

i.e.

spot

detection

and

image

registration

-LRB-

warping

-RRB-

should

be

improved

to

a

point

where

the

need

for

a

human

operator

is

limited

to

checking

the

results

Ongoing

research

in

automated

registration

and

the

potential

for

even

greater

processing

power

with

grid

computing

-LRB-

Dowsey

et

al.

-RRB-

are

promising

steps

towards

this

goal

The

proteomics

community

can

advance

the

state

of

the

art

by

freely

sharing

raw

data

from

a

wide

variety

of

experiments

and

by

establishing

benchmarks

Standardization

efforts

of

data

formats

and

reporting

requirements

are

being

established

for

the

gel

electrophoresis

process

-LRB-

MIAPE

GEL

currently

open

for

public

feedback

before

publication

at

-RRB-

a

similar

standard

for

image

processing

and

documentation

of

results

is

under

development

Both

are

coordinated

by

the

HUPO

Proteomics

Standards

Initiative

-LRB-

-RRB-

The

second

direction

for

improvement

is

defined

by

the

further

adoption

of

advanced

statistical

methods

for

the

analysis

of

large

gel

experiments

It

seems

plausible

that

large

numbers

of

images

from

multiple

studies

could

be

aggregated

to

give

richer

functional

profiles

of

proteins

that

show

expression

levels

across

a

wider

range

of

samples

and

conditions

The

third

direction

is

putting

results

from

gel

analysis

into

a

larger

context

by

combining

spot

data

with

functional

annotations

and

data

from

other

experimental

techniques

Visualizing

the

result

in

biological

terms

-LRB-

metabolic

pathways

functional

categories

etc.

-RRB-

will

make

it

possible

to

gain

new

insights

from

the

growing

amounts

of

data

accumulated

by

the

Omics

technologies

image analysis
proteome maps
warping
statistics