DNA Microarrays Introduction Part 2. Todd Lowe BME/BIO 210 April 11, 2007

Transcription

1 DNA Microarrays Introduction Part 2 Todd Lowe BME/BIO 210 April 11, 2007

2 Reading Assigned For Friday, please read two papers and be prepared to discuss in detail: Comprehensive Identification of Cell Cycle-related Genes of the Yeast Saccharomyces cerevisiae by Microarray Hybridization, Spellman, Sherlock, Zhang, Iyer, Anders, Eisen, Brown, Botstein, & Futcher Molecular Biology of the Cell, December 1998 "Genomic expression programs in the response of yeast cells to environmental changes", Gasch AP, Spellman PT, Kao CM, Carmel- Harel O, Eisen MB, Storz G, Botstein D, Brown PO, Mol Biol Cell 2000 Dec; 11:

3 Array: Different Growth Conditions Standard condition Altered growth condition or genetic background

4 Pros/Cons of Different Technologies Spotted Arrays relative cheap to make (~$10 slide) flexible - spot anything you want Cheap so can repeat experiments many times highly variable spot deposition usually have to make your own Accuracy at extremes in range may be less Affy Gene Chips expensive ($500 or more) limited types avail, no chance of specialized chips fewer repeated experiments usually more uniform DNA feaures Can buy off the shelf Dynamic range may be slightly better

5 Microarray Platforms Summary Type of Array Technology Probe Length Max Features Feature Size Cost/array Applications Traditional Spotted Arrays Robot spotting Pre-made DNA 2-color hyb (Cy3/Cy5) Any (35nt- PCR product size) Affymetrix Photolithography 25 nt ~6.5 in Million situ; 1-color biotin NibleGen Micro-mirrors 50-75nt 385, um ~$ CGH, CHiP-chip, (DLP light gene expression projector) in situ synth; 2-color hyb CombiMatrix CMOS in situ 18-50nt 12,000 ~$ color or 1-color ~50,000 ~100 um 5-11 um Cheap Major cost is upfront DNA synthesis ~$100 or less $500+ Single use Can be reused, 4 times total Agilent Ink jet in situ; nt 244,000 73x124 um $ color hyb Single use (1-color?) Gene expression, CGH, CHiP chip Gene expression, CGH, tiling arrays, CHiP chip, SNP detection CGH, gene expression, SNP detection Gene expression, CGH, ChIP chip

6 Spotted Arrays Movie of a commerical robotic spotter, similar to the two Brown lab-style robots here on campus: Movie applying labeled sample to spotted microarray for hybridization

7 Affymetrix Array Production Photo-lithography demo movie, presented by Eric Lander at YouTube: Source:

8 NibleGen Maskless Micromirror in situ synthesis

9 CombiMatrix Arrays CombiMatrix s core technology is a specially modified semiconductor adapted for biological applications. These integrated circuits contain arrays of microelectrodes that are individually addressable using embedded logic circuitry on the chip. Placed in a specially designed fluidic chamber, the chip digitally directs the molecular assembly of biopolymers in response to a digital command.

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11 Affymetrix Hybridization / Labeling

12 Image Analysis & Data Visualization Cy3 Cy5 Cy5 Cy3 log 2 Cy5 Cy Experiments Genes Underexpressed Overexpressed fold 2 4 8

13 Typical Goal: Identify genes involved in a specific biological process (i.e. heat shock) Guilt by association - assumption that genes with same pattern of changes in expression are involved the same pathway If we know what some genes in a group are doing, it hints that others with similar expression may be involved in same cellular process

14 Classification In some cases, don t need to know what any genes in a group are doing to be useful Total expression profile can be used as a fingerprint identifying cell type example: Tumor classification - predict outcome / prescribe appropriate treatment based on clustering with known outcome tumors

15 Types of Analysis to Form Gene Associations Hierarchical clustering Partitioning of Data in Groups (supervised & unsupervised) Self-organizing maps (SOM) K-means clustering Gene shaving Support vector machines (SVM) Modeling Singular value decomposition (SVD) / Principle component analysis (PCA) Ranking Tests

16 New Data ScanAlyze Data flow Cluster Database Data Selection SOM K-means Complete Data Table (cdt) SVD

17 Developing New Methods How do you know when your method performs better than a previous method? A gold standard test set for benchmarking array data doesn t exist There is too much biology we don t know: if a new method classifies a gene in the wrong gene group, is it recognizing new biology, or just getting it wrong??

18 Limitations of Arrays Do not necessarily reflect true levels of proteins - protein levels are regulated by translation initiation & degradation as well Generally, do not prove new biology - simply suggest genes involved in a process, a hypothesis that will require traditional experimental verification Expensive! $20-$100K to make your own / buy enough arrays to get publishable data

19 Practical Problems Distinguishing background noise from low level, biologically important signals Difficult to independently verify so many data points, so success of array based on anecodotal evidence Multiple repetitions of array experiments is crucial, not always possible with limited samples Not easy to dissect specific from non-specific expression changes for a spec. experiment Example: common stress response in yeast