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Transcript
Introduction Into The Gene Expression Platform
of the IVM
1. Principles and important terminology
2. RNA Preparation and quality controls
3. Data handling
4. Costs
5. Protocols
6. Information for collaboration partners
7. Downloads
1. Principles and Terminology
1. Principles and Terminology
The human, murine, and other genome projects plus the
availability of robust hardware- and software platforms to
produce and evaluate microarrays have enabled genome-wide
gene expression analyses, i.e. to quantify all mRNAs (> 30 000)
of a total RNA extract relative to another RNA extract, within 48
hours. The platform used by the IVM (Affymetrix) is equipped
with a hybridization oven, a washing station, a scanner and
advanced software. The latter allows for mathematical,
statistical, and information technology-based evaluation of the
arrays.
1. Principles and Terminology
Available: Whole Genome Arrays of Several Species
Affymetrix produces expressionsarrays of several species
(human, mouse, C. elegans and others; test the link !).
These are available in different formats.
Dependent on format and protocol 0,5 - 5 µg total RNA is
required per array.
1. Principles and Terminology
Production of Arrays
Through Photolithography 25mer socalled „Perfect Match“ (PM)
oligonucleotides (ON) whose sequences are derived from the genome
projects are synthesized on a glass slide. To subtract unspecific
hybridizations a „Miss Match“ (MM) ON is also synthesized, that
differs from the PM ON by a single nucleotide exchange at position
13. This results in PM – MM ON pairs, i.e. „probe pairs“. Signals of
MM ONs are subtracted from the corresponding PM ON thereby
enhancing sensitivity and specificity of each PM ON. Each mRNA
sequence represented by a „Probe Set“ consists of 11 probe pairs.
This allows for statistical analyses and thus quality assessment of
each measurement.
1. Principles and Terminology
The Principle: „Probe Set“
Miss Match (MM)
Perfect Match (PM)
Probe Pair
Nucleotidaustausch
an Pos. 13
Feature
Probe Set
1. Principles and Terminology
Synthesis of the „Probes“
1. Principles and Terminology
Washing and Scanning
Fluidics
Station
1. Principles and Terminology
Internal „Built-In“ Controls on The Array
•Percent present
Probe quality and reproducibility
•Background and „Noise“
Scanner electric and hybridization
•3‘ - 5‘ Degradation Pattern of Housekeeping Genes
Quality of cRNA probe (checks all procedures)
•Spiked oligo controls
Hybridization, Staining (Efficiency and Linearity)
•poly(A)-RNA spikes
Quality of cRNA Synthesis
2. RNA Preparation and Quality Control
A high quality RNA preparation is critical to generate an array of
high quality. Degradation and contamination need to be avoided.
We recommend the Qiagen RNeasy Lipid Tissue Mini Kit.
In addition, sample preps, storage conditions, and
homogenization prior to RNA extraction are important.
Protocols need to be worked out for each sample (cultured cell,
tissue, type of organ).
2. RNA Preparation and Quality Controls
RNA Preparation
2. RNA Preparation and Quality Controls
RNA Integrity Using the “Agilent” System
22 . 5
No short RNA
fragments
should be
visible here
20 . 0
28s - 18s
ratio >1.8
is required
F lu o re s c e n c e
17 . 5
15 . 0
12 . 5
10 . 0
7. 5
5. 0
0. 0
19
24
29
34
2 8 S
1 8 S
2. 5
39
T im e (s ec ond s )
44
49
54
59
Example and Stages of RNA Degradation
4 .0
30
1 7 .5
1 2 .5
3 .5
1 0 .0
7 .5
10
F lu o re s c e n c e
15
F lu o re s c e n c e
F lu o re s c e n c e
F lu o re s c e n c e
20
Agilent Lab on a Chip
1 2 .5
1 5 .0
25
1 0 .0
7 .5
5 .0
5 .0
2 .5
2 .0
1 .5
1 .0
2 .5
5
19
24
29
34
39
T i m e (s e c o n d s )
9180
44
49
54
59
19
24
29
34
39
T i m e (s e c o n d s )
2474
44
0 .0
49
54
59
19
24
29
34
39
T i m e (s e c o n d s )
971
2 8 S
1 8 S
0 .0
2 8 S
0 .5
1 8 S
2 8 S
1 8 S
2 .5
0
GAPDH
Transcripts / ng RNA
3 .0
44
0 .0
49
54
59
19
24
29
34
39
T i m e (s e c o n d s )
44
49
54
145
Short = degraded RNA fragments
59
3. Data Evaluation
There are numerous approaches. Which one to choose
depends on the questions asked in the experiment.
Data evaluation is principally done in three steps:
Raw data screening including „report“ on quality parameters.
Statistical evaluation and application of „filters“.
Annotation of genes and functional evaluation.
3. Data Evaluation
Tools we Use to Evaluate Data
Qualitäty
Raw Data Analysis
Data Bank
Control
GCOS
GCOS Manager
GCOS (Report)
Function Evaluation
GeneSpring, NetAffx, Gene
Ontology, GenMapp, RefSeq,
Unigene
Statistics
Excel, GeneSpring
Clustering
GeneSpring,
Connect Raw Data
with Software
Access, GeneSpring, Excel
Display Results
GeneSpring, Excel, Fatigo
3. Data Evaluation
Raw Data Evalution Using GCOS
DAT-File
7 x 7 Pixel per Feature
CEL-File
A Number per
Feature
CHP-File
Signal Intensity giving
Detection p-value per
Probeset
For Each Arrray there is a „Report“ Giving Quality Check on Entire Experiment
3. Data Evaluation
The „Call“
The statistics of the probe pairs, i.e. of a gene/mRNA, are converted
by GCOS into a „call“.
„Absent“ call (not detectable): Detection p-value > 0,065
„Marginal“ call (maybe detectable): Detection p-value 0,065 - 0,05
„Present call (expressed): Detection p-value < 0,05
Present means that the gene is significantly expressed, absent means
gene is not expressed or expression is < sensitivity of probeset.
3. Data Evaluation
The „Normalization“
To compare data from different arrays, data need to be adjusted or
„normalized“.
There are several possibilities to do that.
We use:
Standard: Scaling to a target value of 500 at mean.
If saturated: Scaling to a target of 500 at median.
Tests in general: Logarithmization and Scaling per gene
at the 50th percentile.
3. Data Evaluation
The „Scatter Plot“
Easiest evaluation of a 2 array experiment (control versus experimental)
is the Scatter Plot. Results are plotted against each other logarithmically.
Red: Present - Present; Yellow: Absent - Absent: Blue: Absent - Present
> 30 fold differentially
expressed gene
FoldChange lines, 2x,
3x, 10x, 30x
3. Data Evaluation
Statistics and Filters
To perform statistics 3 repeated measurements are needed. This
yields a p value. Filters then reduce the amount of data.
.
Filter:
1. Signal intensity value
2. Detection p-Wert
3. Fold Change
4. p-Wert of experiment
.
This results in a list of candidate genes that are - most likely differentially expressed.
The stringency of 1. to 4. determines the quality of the candidate list.
3. Data Evaluation
Reduced „Straying“ Through Generation of Means
3. Data Evaluation
Filter
3. Data Evaluation
Combination of Filters: List of Genes
3. Data Evaluation
The „Annotation“
Problem: The investigator gets a list of genes that he doesn´t know:
 Needed: Rapid procedure to identify the genes.
 Generate data banks and structure your gene lists.
 Test the links below !
List of Affymetrix Numbers
via Access, GeneSpring, NetAffx
Relate to Data Bank Terminology
- Pubmed
- UniGene
- LocusLink / Entrez Gene
- OMIM
- Ensembl
- ...
4. Cost
The cost per array: 800.- € bis 1250.- €.
Depending on:
Array type and reagent/work load/experiment.
6. Information for collaborating partners
Contact per mail:
andreas.habenicht@mti.uni-jena.de
Discussion and advice
Sample transfer with „filled-in“ form (available at IVM)
Generation of Microarrays
Transfer of raw data files and Excel files
(Software tools available at IVM)
7. Downloads
•Contract
•Excel scheme for evaluating data
•Manual for Excel scheme
•Sheet „Project form“