FGT Part 9 - Genomic Profiling

Many diseases are associated with genomic changes in the genome. For example is cancer and genetic disorder. Changes in the genome can be in the form of gaining or loss of genetic material, or rearrangement of whole chromosomes or smaller section of the chromosome. But, difference in genomes between individual is also an important source of diversity. Therefore, it is interesting to try to profile genomic differences in different individual.

One example is profiling breast cancer. This profiling technique involves classification analysis. What is done is that we take many breast cancer samples from different patients, profile them, and group cells together in terms of their expression by microarrays. This way we will get a classification of different groups of patients whose disease shares common properties. Then, we use combination of data (genotyping info) to determine how gene expression changes and figure out what causes the change.

Early application of genome profiling take a lot of samples from each cell line representing differences in genetic diversity, let these cells grow in culture → treat them with drugs (die or survive)→ connect response of drug to an expression profile for each cell line.

Spectra-paratyping

- attach fluorescent label to chromosomes

- selection for the fluorescent label?→ chromosome will lose it?

Leukaemia example

- chromosomal translocation that generates a fusion protein at the juntcion point responsible for the disease state

- here one want to know where the junction point is

Array Comparative Genome Hybridization (aCGH)

ACGH make use of microarrays, mostly done in dual-channel array (almost obsolete, except special applications like aCGH). The idea is to put two probes onto one array, hybridise them, and look at the differences between them. So, we are comparing the sample with unmodified genome (control) as reference. The goal is to find regions of change which common in samples. We expect 1:1 ratio when control & tumour chromosome length is the same. By using order normalised measurement along the chromosome, we can detect loss/gain by looking for shift in the ratio. We might expect for example 1.5 fold increase when gained 1 chromosome for diploid cell, or 2 fold decrease when lost 1 chromosome. 

This technology is very cheap so it is good for population study, and is widely accessible with relatively good resolution. In population studies, we might have lots of samples and lots of genotypes which from that we can see emerging patterns. But, when understanding cancer, the technology become problematic. Because in cancer, genome gets destabilised. and we get some patterns that are random and others patterns that started the change initially. So, how to identify the latter changes This will bring lots of data together). We need to find 'change points' (patterns that change initially and drive disease).

Single Nucleotide Polymorphism (SNP) Array 

SNP Arrays uses the same array technology, but instead of printing oligonucleotides that represent parts of the transcript, oligos represents genomic changes (SNPs). Therefore, we can identify copy number of variations in a population. 

Affymetrix SNP data enables (1) identification of copy number variants using Comparative Genomic Hybridization. (2) Ploidy status, and (3) Loss of heterozygosity, where one parental chromosome is missing causes to duplication of other parental chromosome

Exome Sequencing

Exome sequencing look at SNPs in exonic regions. In assumption that only coded transcript (protein encoding) which have SNPs may lead to changes (might be wrong). Therefore, disease associated SNPs mostly happens there. - identify exons and sequence them→ compare to a reference genome. - in case one has a library of SNPs: can look up difference between reference and sequenced exons in a database → gives confidence if SNPs are credible or error from sequencing

- need the reference!

- kind of a hybride between microarray and sequencing

- cutting down necessary sequencing by 20-fold→ concentrate on exonic regions

Conclusion

- aCGH: cheap, measure lots of samples but relatively low resolution

- SNP Arrays: good resolution but expensive

- Exome sequencing:  more info but more expensive

Proteomics -2D Differential Gel Electrophoresis

The technique separates proteins according to two independent properties in two discrete steps: (1) Isoelectric focusing (IEF), which separates proteins according to their isoelectric points (pI), and (2) SDS-polyacrylamide gel electrophoresis (SDS-PAGE), which separates them according to their molecular weights (MW).

The power of 2-D electrophoresis as a biochemical separation technique has been recognized since its introduction. Its application, however, has become increasingly significant as a result of a number of developments in separation techniques, image analysis, and protein characterization.

2-D Fluorescence Difference Gel Electrophoresis (2-D DIGE) is a variant of two-dimensional gel electrophoresis that offers the possibility to include an internal standard so that all samples—even those run on different gels—can easily be compared and accurately quantitated.