Principal Investigators Meetings

Vertical Array Project
Rosana Risques, Martin Judex, Gaelle Rhondeau, John Welsh, Michael McClelland, Charles Berry, Thomas Werner, Miguel Peinado

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Table of Contents:

• Title and Author
• Concept
• Vertical array strategy
• RNA Arbitrarily Primed PCR.RAP-PCR to make LCRs
• RAP-PCR is robust
• Rot curve
• RAP+Array
• Different RAP probes (red) vs. oligo dT probe (green)
• Oligo dT vs RAP
• RAP vs RAP
• Rap vs. QPCR
• Cumulative Coverage of 3800 Genes
• Comparison with Affymetrix GeneChip
• RAP-array cassette
• Vertical Array Design
• Vertical Array Throughput
• Actin vs. Thrombospondin
• Actin vs. Cyclophilin
• Actin vs. Cyclophilin Ratios
• Technical notes
• Uses of vertical arrays
• Transcription factor prediction
• Exemplars
• Drugs
• Experiment size

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Vertical Array Project
Sidney Kimmel Cancer Center, San Diego

Rosana Risques	SKCC
Martin Judex	SKCC
Gaelle Rhondeau	SKCC
John Welsh	SKCC
Michael McClelland	SKCC
Charles Berry	UCSD
Thomas Werner	Genomatix
Miguel Peinado	Barcelona

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Concept

Coverage of thousands of experimental variables using microarrays is expensive and requires thousands of array hybridizations.

QPCR is great for thousands of variables, but can become costly when hundreds of genes must be examined.

"Vertical arrays" are modified dot blots that are economical when thousands of variables and hundreds of genes are to be examined.

The choice between standard “horizontal” arrays, QPCR, and vertical arrays is a matter of cost benefit analysis

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Vertical Array Strategy

1. Make low complexity representations of the mRNA populations.
2. Spot these on a glass slide.
3. Probe one gene at a time, or a few using multichannel fluorescence.

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RNA Arbitrarily Primed PCR RAP-PCR to make LCRs

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RAP-PCR is robust

Triplicates of six time points, serum-starvation and re-feeding.

The fingerprint shows 50-100 bands. Underlying the visible bands are about a thousand bands that are too faint to see. These faint bands come preferentially from the complex class and are reproducible.

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Rot curve

These are at a disadvantage because their low sequence complexity results in only a few poor matches.

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RAP + Array

The RAP-PCR reactions, themselves, are analyzed by hybridizing to a cDNA array.

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Different RAP probes (red) vs. oligo dT probe (green)

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Oligo dT vs RAP

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RAP vs RAP

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RAP vs. QPCR

Genes selected from serum-starvation re-feeding experiment.

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Cumulative Coverage of 3800 Genes

(5 best re-sampling average = 1.7)

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Comparison with Affymetrix GeneChip

RT-PCR across splice junctions confirmed presence of 7 out of 10 messages detected by RAP but undetected by Affy. 3 remaining have not yet been sequenced-confirmed.

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RAP-Array Cassette

(for multiple RAP hybridizations)

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Vertical Array Design

Each experiment is represented by ~10-20 spots made using RAP-PCR.

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Vertical Array Throughput

Each gene is represented in at least one spot per experiment

Three genes at a time can be studied in thousands of experimental contexts.

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Actin vs. Thrombospondin

Fibroblasts from 23 patients and 20 primers at 2 RNA concentrations + controls.

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Actin vs. Cyclophilin

(different actin probe)

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Actin vs. Cyclophilin Ratios

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Technical notes

1. Two-color comparison cannot be implemented in the usual way. Multiple randomly selected genes will be used to calibrate the amount of hybridizable DNA in a spot. Possibly in pools.
2. Redundancy in data occurs because LCRs overlap.
3. Number of RAPs per condition depends on required coverage of exemplar set. Estimate ~20 for 98%.

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Uses of Vertical Arrays

• Transcription Factor Predictor: Classification of exemplars, and co-classification of novel cases using vertical arrays to survey thousands of pleiotropic agents or cell lines
• Drug screening: Survey of the impact of thousands of drugs on hundreds of genes.
• Large sample screening: Survey hundreds of genes in thousands of pathology samples.
• Experiment Archive: Keep representation of mRNA populations from many diverse experiments and laboratories for unplanned discovery as insights evolve.

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Transcription factor prediction
(R33)

1. Use transcription factor target ‘exemplar’ genes and experimental perturbation using drugs and/or different cell lines to build a model of exemplar gene behavior.
2. Select perturbations or cell lines that best distinguish the exemplars from non-exemplars.
3. Use these conditions in standard microarrays to discover targets for the transcription factor.

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Exemplars

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Drugs

Table 3: Agents Reported to Alter AP-1 or NF-kappaB controlled genes
Agent	AP1 Effect	AP1 Ref.*	NF-kB Effect	NF-kB Ref.**	Agent	AP1 Effect	AP1 Ref.*	NF-kB Effect	NF-kB Ref.**
calphostin	-	1	-	1	ionomycin	+	12	±	10
calyculin			+	2	lavendustin	-	1
curcumin	-	2	-	3	lipopolysaccharide	+	13	+	11
cyclic AMP	+	2			lovastatin	-	14
cycloheximide			+	4	mitoxantrone			+	12
dexamethasone	±	3,4	-	5	N-acetylcysteine	-	15	-	13
dithiocarbamate	+	22	-	23	neopterin			+	14
dihydrolioate			-	2	okadaic acid			+	15
forskolin	+	5			pertusis toxin	-	16	-	16
genistine	-	1			PMA	+	18	+	11
GM-CSF	+	6			pyrrolidine	-	15	-	17
H2O2	-	7	+	6	staurosporine	-	19	±	18,19
herbimycin	-	8	-	7	tepoxalin			-	20
hymenialdisine			-	8	thapsigargin	+	20
IGF I	+	9			TNF-alpha	+	21	+	21
IL-1	+	10	+	8	tyloxapol			-	22
*See References for AP-1 agents in Table 3 in Literature Cited
**See References for NF-kappaB agents in Table 3 in Literature Cited

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Experiment size

(for single cell line)

Biomek FX robot to set up reactions, GeneMachines robot for spotting.

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