Lecture 189Functional Genomics Based on chapter 8 Functional and Comparative Genomics Copyright © 2010 Pearson Education Inc.

Lecture 189Functional GenomicsBased on chapter 8 Functional and Comparative Genomics

Copyright © 2010 Pearson Education Inc.

1 - RNA Expression Analysis – Determining Genomewide RNA Expression Levels

• Genomewide RNA expression analysis• Types of microarrays• Making microarrays • Hybridization to microarrays

7 - Genomic Expression Analysis Methods

1. Microarraysa. Hybridization based

2. SAGE – Serial analysis of gene expression

3. MPSS – Massively parallel signature sequencing

8 - Nucleic Acid Hybridization

1. Measurements of RNA abundance by microarrays based on hybridization

a. Between complementary strands of RNA and DNA

b. Or two complementary DNA strands

2. Similar in principle to RNA blot (Northern blot)

9 - Hybridization Issues

1. RNA integrity must be verified

a. If RNA degraded, hybridization not quantitative

2. DNA Probe must be in excess of bound RNA

3. Hybridization must be for a sufficient time to allow probe to find target RNA

4. Comparison between samples requires loading control

10 - Northern Blots vs. Microarrays

1. Global expression analysis: microarrays

a. RNA levels of every gene in the genome analyzed in parallel

2. Northern blot

a. Only 1 gene at a time

target –

loading – control

11 - Basics of Microarrays

1. DNA probe attached to solid support

a. Glass, plastic, or nylon

2. RNA or cDNA is labeled

a. Usually indirectly

3. Bound DNA is the equivalent of the “probe”

a. Labeled RNA (cDNA) is the “target”

4. Each “probe” is specific for a different gene.

12 - Microarray Hybridization

1. Usually comparative

a. Ratio between two samples

2. Examples

a. Tumor vs. normal tissue

b. Drug treatment vs. no treatment

c. Embryo vs. adult

mRNA

cDNA

DNAmicroarray

samples

13 - How Microarrays are Made: Spotted Microarrays

1. DNA mechanically placed on glass slide

2. Need to deliver nanoliter to picoliter volumes

a. Too small for normal pipetting devices

3. Robot “prints,” or “spots,” DNA in specific places

14 - DNA spotting I

1. DNA spotting usually uses multiple pins

2. DNA in microtiter plate

3. DNA usually PCR amplified

4. Oligonucleotides can also be spotted

17 - How Microarrays are Made: Affymetrix GeneChips

1. Oligonucleotides synthesized on silicon chip

a. One base at a time

2. Uses process of photolithography

a. Developed for printing computer circuits

18 - Affymetrix GeneChips

1. Oligonucleotidesa. Usually 20–25 bases in lengthb. 10–20 different oligonucleotides for each gene

2. Oligonucleotides for each gene selected by computer program to be the following:

a. Unique in genomeb. Nonoverlapping

3. Composition based on design rulesa. Empirically derived

19 - Photolithography

1. Light-activated chemical reaction

a. For addition of bases to growing oligonucleotide

2. Custom masks

a. Prevent light from reaching spots where bases not wanted

lamp mask chip

20 - Example: Building Oligonucleotides by Photolithography

1. Want to add nucleotide G

2. Mask all other spots on chip

3. Light shines only where addition of G is desired

4. G added and reacts

5. Now G is on subset of oligonucleotides

light

21 - Example: Adding a Second Base

1. Want to add T 2. New mask covers

spots where T not wanted

3. Light shines on mask4. T added 5. Continue for all four

bases6. Need 80 masks for

total7. 20-mer

oligonucleotide

light

23 - Target labeling: Fluorescent cDNA

1. cDNA made using reverse transcriptase

2. Fluorescently labeled nucleotides added

3. Labeled nucleotides incorporated into cDNA

25 - Labels

1. Cy3 and Cy5a. Fluoresce at different wavelengths

b. Used for competitive hybridization

2. Biotina. Binds to fluorescently labeled avidin

b. Used with Affymetrix GeneChips

27 - Scanning of Microarrays

1. Confocal laser scanning microscopy

2. Laser beam excites each spot of DNA

3. Amount of fluorescence detected

4. Different lasers used for different wavelengthsa. Cy3b. Cy5

laserdetection

28 - Analysis of Hybridization

1. Results given as ratios

2. Images use colors:Cy3 = Green

Cy5 = red

Yellow

3. Yellow is equal intensity or no change in expression

29 - Example of Spotted Microarray

1. RNA from irradiated cells (red)

2. Compare with untreated cells (green)

3. Most genes have little change (yellow)

4. Gene CDKN1A: red = increase in expression

5. Gene Myc: green = decrease in expression

CDKNIA

MYC

2 – Yeast Cell Cycle Experimental

3 - Analysis of cell-cycle regulation

1. Yeast cells stopped at different stages of cell cycle G1, S, G2, and M

2. RNA extracted from each stage

3. Control RNA from unsynchronized culture

4 - Results of cell-cycle analysis

1. 800 genes identified whose expression changes during cell cycle

2. Grouped by peak expression

a. M/G1, G1, S, G2, and M

3. Four different treatments used to synchronize cells

a. All gave similar results

4. Results from Spellman et al., 1998; Cho et al., 1998

5 - Cell-cycle regulated genes

Each gene is a line on the longitudinal axis

Treatments in different panels

Cell-cycle stages are color coded at top

Vertical axis groups genes by stage in which expression peaks

Brown and Botstein, 1999

Alpha cdc15 cdc28 Elu

M/G1

G1

S

G2

M

6 - Affymetrix GeneChip experiment

1. RNA from different types of brain tumors extracted

2. Extracted RNA hybridized to GeneChips containing approximately 6,800 human genes

3. Identified gene expression profiles specific to each type of tumor

7 - Profiling tumors1. Image portrays gene

expression profiles showing differences between different tumors

2. Tumors:

a. MD (medulloblastoma)

b. Mglio (malignant glioma)

c. Rhab (rhabdoid)

d. PNET (primitive neuroectodermal tumor)

3. Ncer: normal cerebella

8 - Cancer Diagnosis by Microarray

1. Gene expression differences for medulloblastoma correlated with response to chemotherapy

2. Those who failed to respond had a different profile from survivors

3. Can use this approach to determine treatment

60 d

iffer

ent

sam

ple

s

9 - Analysis of microarray results

1. Inherent variability: need for repetitiona. Biological and technical replicates

2. Analysis algorithmsa. Based on statistical models

3. Means of generating hypotheses that need to be tested

10 – Serial Analysis of Gene Expression (SAGE)

1. Serial analysis of gene expression

2. Concept: sequence a small piece of each cDNA in a library

a. Gives measure of abundance of each RNA species

3. Methoda. Cut off “tag” from each cDNA

b. Ligate tags together into a concatemer

c. Sequence the concatemer

13 - SAGE IV

1. Sequence the concatemers

2. Identify tag borders a. Size of tag and restriction-enzyme sites

3. Compare tag sequences to database

4. Abundance of tag is measure of abundance of that RNA species

14 - MPSS I

1. Massively parallel signature sequencing

2. Means of determining abundance of RNA species

3. Unique tags added to cDNAs

4. Tags hybridized to oligonucleotides on microbeads

Slide 15 – MPSS I

Sequencing performed in glass chamber

Initiated by restriction enzyme revealing four-base overhang

Hybridization of four-base adapters used to read sequence

Number of times a particular sequence is found is measure of RNA abundance