Transcript Arraying

Genomic Arrays – an overview
Dr. Colin Campbell
The Central Dogma
Genome
Regulation
Transcription
DNA
Transcription
Translation
mRNA
Protein
Genomics in perspective
Post Genomic Challenges
Sequences available for hundreds of genomes
viruses/plasmids >> mammalian genomes
Genome sequence only the start
Need to understand:
genomic structure, replication, expression
Problem of scale, complexity and diversity
Advent of HTS functional genomic technologies:
microarray, Si RNA, mutagenesis, proteomics, imaging
Post genomic approaches
Functional genomics toolbox
Sequence
Classify
Identify
ascribe function
Monitor expression
all genes used to assemble an organism
Microarrays – a post genomic technology
Mammalian Genome
Database
Gene Expression/Genotyping
Proteomics
Fundamental and applied biomedical research
Supporting Technologies
Statistics/Bioinformatics
HTS Technology Developments: Arraying/ Scanning/ Lab-on-a-chip
Computing/ Databases
Evolution of array technology
Traditional method:
taking gene by gene approach
Insufficient to meet magnitude of problem
Array technology
Developed to provide a systematic way of studying RNA expression,
genotyping, DNA/ RNA interactions and numerous other applications
Array = A regular or uniform arrangement
e.g. of DNA probes or other elements such as proteins or
tissue sections arranged on glass slides or nylon membranes
The Central Dogma
Genome
Regulation
Transcription
DNA
Transcription
Translation
mRNA
Protein
RNA transcription analysis
Expression of RNA assessed by Northern blotting, RNAase protection,
RT-PCR methods
Low to medium throughput approaches.
Do not easily accommodate scale, complexity and diversity challenges
e.g. Northern Blot
Filters exposed to labelled
DNA probe and subject
to radiography
Cell
DNA
mRNA
Denature
Gel electrophoresis,
RNA separated by
Size and blotted
on filter
proteins
RNA transcripts anlysed singly.
Definiton of transcriptome would
take thousands of blots
The microarray solution
cDNA(s) or oligonucleotide(s)
representative of genes
spotted on slide
1
genes
2
3
4
Intensity
value
1
Intensity
value
2
Array
3
=
Relative
Value
+ve = upreg
4
DNA
GENOME
Hybridise to array
Test cDNA
control cDNA
DNA
mRNA
proteins
Reverse
transcribe RNA
Using Cy3 (test RNA)
or Cy5 (control) dCTP
Relative expression of RNA
defined at whole genome level
Microarray options
First attempts at exploiting array approaches
involved filter based screening of clone libraries
Basic genomic and RNA expression studies
Two key innovations have enhanced the utility of genomic microarrays
1. Use of glass substrates to construct miniaturised arrays
DIRECT DEPOSITION: Using automated printers: ~30-40K DNA probe elements
deposited on a glass slide
IN SITU SYNTHESIS: several million individual DNA probe elements
defined by photolithography on silicon wafers
2. The use of fluorescence for detection
Method 1. Array of 5,000 mouse genes
- direct deposition method
The microarray solution
cDNA(s) or oligonucleotide(s)
representative of genes
spotted on slide
1
genes
2
3
4
Intensity
value
1
Intensity
value
2
Array
3
=
Relative
Value
+ve = upreg
4
DNA
GENOME
Hybridise to array
Test cDNA
control cDNA
DNA
mRNA
proteins
Reverse
transcribe RNA
Using Cy3 (test RNA)
or Cy5 (control) dCTP
Relative expression of RNA
defined at whole genome level
Direct deposition DNA microarray scanner image
Method 2. In situ synthesised oligo array - Affymetrix GeneChip® system
Gene Sequence
representative DNA sequences derived from 3’ end of gene
L
25 mer
T G C A T G C A T G C A T G A T G C A T G C A T G
Many million fold bound in specific feature
20 features used to represent one gene
400,000 features per
array representing
~ 12,000 genes
3’
Affymetrix target labelling
Cell/ Tissue of interest
1st strand cDNA synthesis
DNA
AAA
AAA
AAA
AAA
Isolation of
total RNA
2nd strand cDNA synthesis
AAA
TTT
TTT
AAA
TTT
TTT
AAA
TTT
TTT
AAA
TTT
TTT
T7 Promoter incorporated
in first strand synthesis
ds cDNA
Affymetrix labelling and hybridisation
In vitro transcription using
Biotinylated dNTPs
Biotinylated cRNA
Hybridise to Array
L
TTT
b
SA b
b
L
TTT
b
b
SA b
TTT
b
L
b
SA b
TTT
b
L
b
SA b
Affymetrix Gene Chip results
Expression of 10K genes – but what is the result ?
Statistics and Bioinformatics essential
Microarray technology - pros and cons
Scale - true global analyses possible
Semi-quantitative
advantages
High throughput
Sensitivity
Precision
Scale demands stringent QC and analytical routines
disadvantages
Emerging standards for analysis
Relative cost/logistics
Context independent
Microarrays in cancer biology
RNA Expression profiling arrays: Targets > pathways
Genotyping arrays: HTS SNP analysis > gene association studies
Protein arrays: marker sets
Expression based classification to detect dominant patterns of
expression in heterogeneous tumours
Can identify:
•Tumour markers
•Origin of tumour
•Developmental stage
•Metastatic potential
•Therapeutic response profile
•Fundamental insights >> definition of cancer pathways and control
•Contribute to diagnosis, prognosis and therapy.
Clustered gene sets
Interferon related
Breast luminal cell profile
Basal epithelial cell profile
Lung adenocarcinoma
enriched profile
Proliferation gene set
DNA microarrays – a platform technology
DNA microarrays now extensively employed for RNA expression profiling
studies in biomedical research.
Crucial role for statistics, bioinfomatics and computational science to turn HTS data into
useful information (gene targets and pathway definition) for the biologist to interpret
Provides a critical approach to a thorough understanding of fundamental biological
processes. Also contributing to applied areas such as disease diagnosis and definition.
DNA microarrays providing a HTS and global platform technology for numerous
biomedical and genomic research applications
- splicing
- sequencing and SNP analysis (v. high density oligo arrays under development)
- CGH, BAC clones
- epigenetic studies e.g. DNA methylation
- Also, platforms developing for: proteins, cells and tissues
DNA microarray approaches will ultimately replace many of the standard methods genetic
analysis.
Biological context
Full definition of biological processes requires additional contextual
inforrmation (e.g. spatial, temporal, modification)
Methods for precise micro sampling of complex cell populations and tissues
can be combined with microarray readouts.
Initial step involves precise sampling via cell sorting/enrichment or micro-dissection
techniques
Combine with target sample (micro RNA sample) amplification methods to enable
readout on standard DNA microarray platforms
Increases power of analysis and biological interpretation
Future potential in biology and medicine
Array technology will continue to develop for DNA, RNA, protein and various
other physiological measurements.
Developments will require increasing interface of biology with physical sciences
and technology.
Allow new questions to be asked at the whole genome/proteome level.
Integration of HTS genomic, proteomic and cellular readouts will be required to
define biological complexity and approach systems level understanding
Key to this is input from bioinformatics and computational science to analyse,
store and visualise data