# Presentation: 265850

## ViaLogy VMAxS Calibration Study:

**ViaLogy VMAxS Calibration Study:**
**        ****A Case Study****	****	**

**	****	****	**
**      ****Sandeep Gulati ********                               Prof. Charles Wang**
** ****Chief Science Officer****	****	****       Director, Microarray Core**
**      ****ViaLogy  Corp.****	****	****    Cedars-Sinai Medical Center **
**	****	****	****	**

**May 2, 2005**

## Calibration Rationale

- As Analyte Volumes Get Smaller It Becomes Increasingly Difficult (Impossible) To Accurately Measure Linear Dose Response Due To Multiple, Random Non-Linear Interactions of Analytes and Background
- There Is Valuable Expression Information In The ‘Noise’
- Platform Calibration Increases Detection Sensitivity, Linear Dynamic Range And Improves Platform Resolution
- The Result – Greater Dynamic Range With Better Reproducibility

## Study Goals

- Independent Analysis of the Same Dataset Yields Same Results
- Experiments Using Identical Samples, Protocols and Systems Produce Statistically Similar Results
- Calibrate The Analysis Method to Deliver Standardized Performance on the Leading Microarray Platforms
- ABI, Affymetrix, Agilent, GE & Illumina
- Initial Platform:  Affymetrix GeneChip®
- Preserve Biological Variance and Dynamic Range
- Preserve Gene Expression Information ‘In The Noise’
- K. I. S.

## Calibration Objectives

- Establish An Analytical Performance Specification for VMAxS
- Phase 1:   		15ug to 100ng starting cRNA
- 			       	Complex biological backgrounds
- 			       	<5% between run CVs
- Phase 2:    	< 100ng starting cRNA
- Select Optimal Feature-level Probes for Quantitation
- Assess response linearity, sensitivity and specificity
- Develop Linearity Model for High, Moderate, Low & Very Low Expressors
- Complex samples with genes exhibiting 6 log variation in expression levels

## Methodology

- Step 1: Calibrate GeneChip® HG U133 Plus 2.0 system based on carefully controlled hybridizations
- Compare results by
-     6 analytical methods
- Step 2: Validate with
-     a set of over 1000
-     experimental arrays

## Dilution Study Design (24 arrays) 

- Stratagene Universal Reference RNA
- Triplicate technical analysis (hybridizations)
- Biotin labeling and fragmentation on bulk sample RNA before aliquots were frozen

## Tissue Mixing Study Design (16 Arrays)  

- Ambion Diploid Lung Total RNA used to emulate a complex background interference
- Duplicate hybridizations

| Tissue Mixing Experiments |  |  |
| --- | --- | --- |
| Stratagene Universal Total RNA | Ambion Diploid Lung Total RNA | Background Interference Ratio |
| (Specific RNA) | (Complex Interference to Specific Signal) |  |
|   | 10g |  1:20   |
| 0.5g + | 5g | 1:10 |
|  | 2.5g | 1:5 |
|  | 1g | 1:2 |
|  | 0.5g | 1:1 |
| 0.1g + | 10g | 1:100 |
|  | 1g | 1:10 |
|  | 0.1g | 1:1  |

## RNA Selection

- Dilution Study
- Commercially Available Reference Material
- Maximal GeneChip (55K probe sets) Coverage
- Abundant Expression Data Available on Stratagene RNA
- Affy chips, RT-PCR, SAGE
- Already Used As A Calibration Assay By Most Labs
- Tissue Mixing
- Commercially Available Material
- Genes Not Expressed In UHRNA (Lung)
- Full Characterization of RNA Is Not Germane To This Study
- But must have some known differences with Reference RNA
- Purpose: Induce Background Interference to Universal RNA
- Simulate  (Signal + 100 X Background)  Interference

## Criteria for Selecting Tracking Genes

- Focus Set**:** 15 Genes Known Present in Stratagene Universal RNA
- 3 genes in 4 expression groups + 3 house keeping genes
- Grouping determined by an alternative method
-   _High Exp_.       :   _Moderate Exp_.  :  _Weak Exp_.   :  _Ultra-Weak Exp_.
-   >1000 fold      :    100 fold           :    10 fold         :          <1
- Typical Of Most Real-life Experiments
- Different Analytical Methods May Show Up To 2-log Range Variation In Between-run CVs Across The 4 Expression Groups
- Significant biology is lost if expression quality is not consistent across different expressors
- Richard Simon (NCI): “.. *very important to have consistent and comparable fold change quality over all types of expressors in same sample so that users can start with small quantities of material and still trust their data**..” *

## Selected Tracking Genes

|  |
| --- |

**Selected Tracking Genes**

- Existing StaRT-PCR Data on Reference RNA
- SAGE Tag Performance

**Notes:**
For calibration it is important to be able to calibrate the system across a large range of concentrations. 
Different platforms have different characteristica in terms of how wide a dynamic range they cover, and how accurate they detect fold changes.
That does not necessarily mean we need to limit the genes employed to that range, as it will certainly change in the future. 

Example, which is also demonstrated by the above table: Going back 10 years, ESTs gained interest as a quantitative measure of global gene expression. Then came the more efficient SAGE libraries, but still the sensitivity was only as good as the depth of the number of ESTs and SAGE tags sequenced per library. Assuming a cell expresses 500,000 mRNAs, sequencing say 100,000 ESTs or SAGE tags from that tissue, would mostly provide you with quantitative numbers of a few highly expressed genes, whereas the medium expressed may be detected, but not in a quantifiable manner, and the low expressed genes (1-5 mRNA per cell) may or may not show up. See table above, under the SAGE tags. None has been sequenced for most of the lower expressed genes as detected by StaRT-PCR. 

Now we have micro-arrays supposedly falling somewhere between SAGE libraries and the RT-PCR methods in terms of sensitivity and the ability to quantitate, detect change folds, and we would like to pusht his towards better fold change accuracy for the low expressed genes or to be able to better quantitate using less material.

Rough guidelines:

500,000 transcripts per cell
mRNA is about 1000 bases
Molecular weight per base: 340 g/mol
Avogadro’s number: 6.02 X 1023 molecules/mol

Thus one cell contains about 0.28 pg mRNA, which is roughly equivalent to 10 pg total RNA. 

Typically recommended is to use 10 ug total RNA for an experiment equivalent to material from about 106 cells. This makes up about 100 ug labeled cRNA after reaction.

So many people talk about detecting one mRNA per cell, if you start out with 106 cells or 10 ug total RNA. 
Looking at the table above, and assuming b-actin is present in about 1200 copies per cell, detecting one copy per cell when looking at 106 cells material, would detect down to about the 1000 mRNAs  per 106 b-actin molecules. This corespond to about where PAK1 is in the table. It is desirably to go several fold below this limit. 

The next picture shows how a gene (STAT1, or transcription factor ISGF-3) at about 10 times this level, can be detected at the highest point in a dilution series, and still be detected through dilution steps down to 100 ng cRNA or about 10 ng total RNA starting material. This gene has been found in the Stratagene Reference total RNA as 3 SAGE tags per 51973 tags. RT-PCR has not been performed on this gene. 

The next graph shows different methods of analysis the Affy chip used in this study. The chip is a human Plus 2 array. Highest conc. to the right is 25 ug cRNA or about 2.5 ug total RNA. The lowest concentration to the left is 100 ng cRNA or about 10 ng total RNA. 

## Data Quality Metrics

- Data Compression**:** Fold Change As Compared to RT-PCR Taqman Assay (CT)
- In general it has been observed that microarray expression levels are compressed  by 3:1;  5:1;  7:1; ... depending on the  different expression groups
- Degradation of compression ratios occurs in the presence of interfering background
- Optimal Probes Selection
- Determine features that exhibit monotonic expression response to dilution; and,
- Can be used for calibration and standardization of expression

## Data Quality Metrics

- Reproducibility
- Across Runs/Triplicates
- Highly Controlled Experiments Performed to Limit Variability
- Operator, batch, instrument, labeling
- All Credible Users/Core Labs Today Employ Tight Controls
- In more complex backgrounds (tissue mixing experiments)
- Assess %CV Expression Degradation Across the 4 Groups

## Dynamic Range

- Analytical Tools Must Address the Range of Biology
- Study Design
- Capture 5 log Variation in Expression for Different Genes
- 2.5 log Variation in Starting Material
- 2 log Variation in Noise
- Overall 6 – 7 log Variation in Expected Expression Performance
- (with 5:1 compression over biology)
- Linearize Expression to Match Taqman CT range: 18±1 to 36±2
- Rationale**:** Do Not Focus Only On A Subset of Genes That Are Concordantly Observed On All Platforms By All Laboratories.
- RE: High Risk of Missing Dynamical Range of Intrinsic Biology
- “Common Good” Likely Is Not Adequate Standard for Clinical Data

## Starting Data

- Processing Raw Data Files (e.g., DAT file for GeneChip®) Produces Better Results Than Starting With Preprocessed CEL Files
- Recommend Having Raw Data Available To Analyze As New Signal Processing Tools Become Available
-  It is suggested that GeneChip data be submitted as DAT files

## Summary

- Clinical Quality Results Are Achievable
- Standardization and Calibration Assays and Protocols Will Produce Data Required for Clinical Quality Standards and Advance Biological and Clinical Decisions
- Broad Platform Calibration Is A Must
- Cost-prohibitive To Validate Every Microarray Result With A Reference Method.
- Strongly Suggest Including Genes With Low Expression In The Control Group For MAQC Study

## Acknowledgements

- FDA	Leming Shi,  Weida Tong
- Cedars-Sinai	Charles Wang, Jun Xu
- Stratagene	Gavin Fisher
- Ambion	Bob Setterquist, David Doris
- ViaLogy	Bud Bromley, Jim Breaux, 		Cecilie Boysen, Sandeep Gulati
- Contact: Bud Bromley   bud.bromley@vialogy.com
- 		+1(858) 832-1099