1. Bruce Blumberg (blumberg@uci.edu)
BioSci D145 Lecture #5
Bruce Blumberg
4103 Nat Sci 2 - office hours Tu, Th 3:30-5:00 (or by appointment)
phone
TA – Bassem Shoucri
4351 Nat Sci 2, , 3116 Office hours M 2-4
lectures will be posted on web pages after lecture
Last year’s midterm is now posted. It is useful to work through the problems to see what sort of questions I ask and how best to study.
BioSci D145 lecture 1 page 1 ©copyright Bruce Blumberg All rights reserved

2. Human genome, mouse, rat, Drosophila, C. elegans “finished”
Functional Genomics - Analysis of gene function on a whole genome basis
Genome projects
DNA sequencing
Human genome, mouse, rat, Drosophila, C. elegans “finished”
model organisms progressing rapidly
Lots of new genes, but many lack known function
Functional genomics
Identification of gene functions
associate functions with new genes coming from genome projects
function of genes identified from characterizing diseases or mutants
Identification of genes by their function
discovery of new genes
BioSci D145 lecture 6 page 2 ©copyright Bruce Blumberg All rights reserved

3. *Methods of profiling gene expression – large scale to whole genome
What are the possibilities
Array – micro or macro
Sequence sampling
SAGE – serial analysis of gene expression
Massively parallel signature sequencing (RNA-seq, Illumina, 454)
DNA microarray analysis was, until now totally dominant method
Two basic flavors
Spotted (spot DNA onto support)
cDNA microarrays
Oligonucleotide arrays
Moderately expensive
Synthesized (use photolithography to synthesize oligos onto silicon or other suitable support
Affymetrix Gene Chips dominate
VERY expensive
Both are in wide use and suitable for whole genome analysis
BioSci D145 lecture 6 page 3 ©copyright Bruce Blumberg All rights reserved

4. Source material is prepared cDNAs are PCR amplified OR
Spotted arrays
Source material is prepared
cDNAs are PCR amplified OR
Oligonucleotides synthesized
Spotted onto treated glass slides
RNA prepared from 2 sources
Test and control
Labeled probes prepared from RNAs
Incorporate label directly
Or incorporate modified NTP and label later
Or chemically label mRNA directly
Hybridize, wash, scan slide
Express as ratio of one channel to other after processing
BioSci D145 lecture 6 page 4 ©copyright Bruce Blumberg All rights reserved

5. Stanford type microarrayer
DNA microarray types
Stanford type microarrayer
Printing method
Reminiscent of fountain pen
BioSci D145 lecture 6 page 5 ©copyright Bruce Blumberg All rights reserved

6. Amino-allyl labeled 1st strand cDNA
Strategy to identify RAR target genes
Agonist - TTNPB
Antgonist - AGN193109
Harvest st 18
Poly A+ RNA
Poly A+ RNA
Amino-allyl labeled 1st strand cDNA
Amino-allyl labeled 1st strand cDNA
Alexa Fluor 555 (cy3)
Alexa Fluor 647 (cy5)
Alexa Fluor 555 (cy3)
Alexa Fluor 647 (cy5)
Probe microarrays
upregulated
downregulated
BioSci D145 lecture 6 page 6 ©copyright Bruce Blumberg All rights reserved

7. Statistical analysis of output – VERY IMPORTANT!
DNA microarray
Statistical analysis of output – VERY IMPORTANT!
Replicates are very important
Preprocessing of data is needed
To remove spurious signals
BioSci D145 lecture 6 page 7 ©copyright Bruce Blumberg All rights reserved

8. Custom arrays possible and affordable
DNA microarray
Advantages
Custom arrays possible and affordable
Ratio of fluorescence is robust and reproducible
Disadvantages
Availability of chips
Expense of production on your own
Technical details in preparation
BioSci D145 lecture 6 page 8 ©copyright Bruce Blumberg All rights reserved

9. High density arrays are synthesized directly on support
Affymetrix GeneChips
High density arrays are synthesized directly on support
4 masks required per cycle -> 100 masks per chip (25-mers)
Pentium IV requires about 30 masks
G.P. Li in Engineering directs a UCI facility that can make just about anything using photolithography
BioSci D145 lecture 5 page 9 ©copyright Bruce Blumberg All rights reserved

10. Affymetrix GeneChips Streptavidin/phycoerythrin
BioSci D145 lecture 5 page 10 ©copyright Bruce Blumberg All rights reserved

11. Each gene is represented by a series of oligonucleotide pairs
Affymetrix GeneChips
Each gene is represented by a series of oligonucleotide pairs
One perfect match
One with a single mismatch
Only hybridization to perfect match but not mismatch is considered to be real
Gene is considered “detected” if > ½ of oligo pairs are positive
Number of pairs depends on organism and how well characterized array behavior is
Human uses 8 pairs
Xenopus uses 16 pairs
BioSci D145 lecture 5 page 11 ©copyright Bruce Blumberg All rights reserved

12. Result is in single color
Affymetrix GeneChips
Result is in single color
Always need two chips – control and experimental for each condition
Also need replicates for each condition
For diverse biological samples (e.g., humans) 10 replicates required!
For less diverse samples (cell lines) probably 5 replicates needed
Advantages
Commercially available
Standardized
Disadvantages
About $1000 to buy, probe and process each chip (at UCI)!
About $700 elsewhere
May not be available for your organism of interest
No ability to compare probes directly on the same chip
Must rely on technology
BioSci D145 lecture 5 page 12 ©copyright Bruce Blumberg All rights reserved

13. Identifying genes expressed in one condition vs. another
DNA microarrays
What are they good for?
Identifying genes expressed in one condition vs. another
One tissue vs. another (heart vs liver)
Tissue vs. tumor (liver vs. hepatocarcinoma)
In response to a treatment (e.g., RA)
In response to disease (e.g., after viral infection)
Building expression profiles
Tissues
Cancers
Developmental stages
Expressed genes
Identifying organisms in food
Array can identify which animals are present in a mix
BioSci D145 lecture 5 page 13 ©copyright Bruce Blumberg All rights reserved

14. What are they good for? (contd)
DNA microarrays
What are they good for? (contd)
Response of animal to drugs or chemicals
Toxicogenomics
Pharmacogenomics
Diagnostics
SNP analysis to identify disease loci
Specific testing for known diseases
BioSci D145 lecture 5 page 14 ©copyright Bruce Blumberg All rights reserved

15. Signal intensity (or signal/noise) Improved dyes, label uniformly
DNA microarrays
What are the limitations of microarray technology? What sorts of factors might confound the experiment?
Signal intensity (or signal/noise)
Improved dyes, label uniformly
Biological variation (samples are inherently different)
Sufficient # of replicates is key
keep individuals separate
Not all mRNAs will be present at sufficient levels to detect
Amplification, but beware of bias
Good statistical analysis is required
Bayesian statistics are best (Pierre Baldi is local expert)
calculating the probability of a new event on the basis of earlier probability estimates which have been derived from empiric data
i.e., don’t assume random distribution in datasets, calculate probability based on real data
Bayesian approach great for small number of replicates, converges on t-test at high number of replicates
BioSci D145 lecture 5 page 15 ©copyright Bruce Blumberg All rights reserved

16. Where/when are they expressed? Known genes (e.g. from genome projects)
Functional Genomics - The challenge: Many new genes of unknown function
Where/when are they expressed?
Known genes (e.g. from genome projects)
Gene chips (Affymetrix)
Microarrays (Oligo, cDNA, protein)
Novel genes
Differential display
Expression profiling
SAGE and related approaches
What do they interact with?
Biochemical methods
Yeast two, three hybrid screening
Phage display
Expression cloning
Proteomics
2 dimensional gel electrophoresis
Mass spectrometry
Protein microarrays
BioSci D145 lecture 5 page 16 ©copyright Bruce Blumberg All rights reserved

17. Methods of profiling gene expression (small number of genes)
How to evaluate gene expression?
Old, low-throughput - prepare RNA sample and perform
Northern blot – immobilize RNA on filter, probe
Quantitative WHY?
Nuclease protection
quantitative
In situ hybridization
Not quantitative – enzymatic reaction
Newer, high throughput methods
RT-PCR
Can be quantitative
Quantitative real time RT-PCR
Or prepare protein samples and evaluate proteins
Western blot - detect protein of interest with specific antibody.
ELISA – enzyme linked immunosorbent assay quantitative
RIA – radioimmunoassay - quantitative
Probe is in excess
BioSci D145 lecture 5 page 17 ©copyright Bruce Blumberg All rights reserved

18. Analysis of mRNA - size and splicing
Quantitation of mRNA levels
possible methods
Northern analysis
nuclease protection
RT-PCR
measure steady state mRNA levels (production/degradation)
mRNA size determination –
Northern blot only way
good RNA size markers = accurate sizing
which to use, poly A+ or total RNA?
A+ much more sensitive (50-100x)
what about mRNAs with no or short tails?
total RNA much simpler
gel limitations – 20 μg/lane is practical limit
what is a key factor in sizing mRNAs?
Appropriate size standards larger and smaller than target
BioSci D145 lecture 5 page 18 ©copyright Bruce Blumberg All rights reserved

19. Analysis of mRNA - quantitation (contd)
Nuclease protection assays
approach
hybridize a single-stranded (SS) probe (DNA or RNA) to RNA sample
probe must be larger than protected region
digest remaining single stranded regions
electrophorese on denaturing polyacrylamide gel
advantages
less sensitive to slightly degraded mRNA
absolutely quantitative
can tolerate large amounts of RNA (100+ μg)
allows detection of rare transcripts
but gives high background
multiple simultaneous detection
disadvantages
more tedious than Northern
no blot to reuse
multiple simultaneous detection hard to optimize
BioSci D145 lecture 5 page 19 ©copyright Bruce Blumberg All rights reserved

20. Analysis of mRNA - quantitation (contd)
RT-PCR - reverse transcriptase mediated PCR
approach
reverse transcribe mRNA -> cDNA
amplify with specific primers
quantitate
flavors
relative quantitation – compare to invariant gene
absolute quantitation
by comparison to synthetic reference
competitive PCR
various fluorescent dye mediated methods
advantages
very fast and simple
works with tiny amounts of material
limitations
RT efficiency differs by mRNAs
Must be in linear amplification range
Errors increase exponentially with amplification
BioSci D145 lecture 5 page 20 ©copyright Bruce Blumberg All rights reserved

21. Analysis of mRNA - quantitation (contd)
RT-PCR reverse transcriptase mediated PCR
relative concentration determination
perform multiplex reaction using two primer sets
1 for reference, 1 experimental
advantages
no fancy equipment required
disadvantages
careful attention to linear region for both primer sets
often must add one set during reaction
companies claim to have products that eliminate this need
more than 2 primer sets are not reliable
BioSci D145 lecture 5 page 21 ©copyright Bruce Blumberg All rights reserved

22. Analysis of mRNA - quantitation (contd)
RT-PCR (contd)
absolute concentration determination real time PCR
Taqman, molecular beacons
Fluorescent methods that allow direct quantitation of PCR product
approach
special oligonucleotide that has a fluor and a quenching group on it.
When whole, no fluorescence
perform PCR reaction, if primer anneals, Taq polymerase removes the reporter group which can now fluoresce
BioSci D145 lecture 5 page 22 ©copyright Bruce Blumberg All rights reserved

23. Analysis of mRNA - quantitation (contd)
RT-PCR (contd)
absolute concentration determination - Taqman, etc
Fluorescence detected continuously in real time
advantages
can be detected in real time with proper instrument
no difficulties with linearity
multiplexing of probes possible (limited by available dyes)
very good for clinical diagnostics
disadvantages
requires instrument
varies from expensive to extremely expensive
Not of equal quality
need to make custom oligos - can be expensive
must know something about relative abundance of mRNAs before setting up reactions
careful optimization required for best results
primer concentrations
target concentrations
BioSci D145 lecture 5 page 23 ©copyright Bruce Blumberg All rights reserved

24. Analysis of mRNA - quantitation (contd)
RT-PCR (contd)
absolute concentration determination – Sybr Green
Alternative real time RT-PCR utilizes a single dye
approach
Extend a single template
Detect ds DNA with a specific dye
BioSci D145 lecture 5 page 24 ©copyright Bruce Blumberg All rights reserved

25. Analysis of mRNA - quantitation (contd)
RT-PCR (contd)
absolute concentration determination – Sybr green
Plot lift off time
Generate standard curve
BioSci D145 lecture 5 page 25 ©copyright Bruce Blumberg All rights reserved

26. Analysis of mRNA - quantitation (contd)
RT-PCR Sybr Green (contd)
Advantages
No special primers needed
Single dye, simple
Fast, robust and quantitative
Good for routine use
Disadvantages
Need instrument
Single dye, can’t multiplex
Problems with multiple fragments
Melting curves required
Absolute quantitation requires std curve
BioSci D145 lecture 5 page 26 ©copyright Bruce Blumberg All rights reserved

27. Other methods of transcriptome analysis - parallel
Microarray was once the dominant method
Direct RNA sequencing methods are rapidly displacing microarrays
SAGE (serial analysis of gene expression)
Nanostring is modern implementation
Short sequences
RNAseq
Directly sequence large numbers of RNAs
Longer sequences
SAGE
Relies on generating many very short sequences and matching these to the genome
10 bp = short SAGE
17 bp = “long” SAGE
BioSci D145 lecture 5 page 27 ©copyright Bruce Blumberg All rights reserved

28. Other methods of transcriptome analysis - parallel
SAGE (continued)
What is the obvious shortcoming of this method?
Sequences may not be unique and could have difficulty mapping to the genome
BioSci D145 lecture 5 page 28 ©copyright Bruce Blumberg All rights reserved

29. Other methods of transcriptome analysis - parallel
RNA seq – Ali Mortazavi is local expert
Use of massively parallel sequencing allows precise quantitation of transcript
Also allows discovery of rare splice forms
Discovery of unexpected transcripts
Main problem is in mapping sequence calls to genome
Sequencing has 1-2% errors which can make mapping to genome fail
or induce “in silico cross-hybridization”
Mapping to incorrect genomic location
BioSci D145 lecture 5 page 29 ©copyright Bruce Blumberg All rights reserved

30. Assumes you know all the transcripts
Microarray vs. RNAseq
Microarray
Assumes you know all the transcripts
Any sequence you did not know was expressed will not be there.
except whole genome tiling arrays – Kapranov paper
Detection limit issues
Signal-noise ratio
Well validated , expression analysis can be quantitative
RNAseq
No assumption re transcripts but need genome sequence
Can discover novel sequences or new splice forms not yet characterized
Detection limits are not a problem – can detect small #
Getting better, expression analysis can be quantitative
Still have issues mapping sequences to genome
Should improve over time
BioSci D145 lecture 5 page 30 ©copyright Bruce Blumberg All rights reserved