Microarray Gene Expression Analysis at CNIO

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1 Microarray Gene Expression Analysis at CNIO Orlando Domínguez Genomics Unit Biotechnology Program, CNIO 8 May 2013

2 Workflow, from samples to Gene Expression data Experimental design user/gu/ubio Samples user quality is critical (be better than for q-rt-pcr) issues: integrity, purity (contaminants, DNA, GuTh) Lab-chip Bioanalyzer (Genomics Unit) Sample processing > Data GU Data analysis UBio Nature of samples transcribed mrna, transcribed [micro] mirna

3 Multifunctional genomic spaces 5 transcripts from PISD phosphatidylserine decarboxylase Annotated RefSeq form of PISD 4 transcripts from the antisense strand MYC binding regions

4 Interrogating the genomic space, platforms std. microarray (few probes per transcript isoform)

5 Interrogation platforms, microarray or deep-sequencing std. microarray (few probes per transcript isoform) exon microarray (exon-specific probes)

6 Interrogation platforms, microarray or deep-sequencing RNAseq (may be strand-specific) - only polyadenylated transcripts (from which +50% do not correspond to annotated protein-coding genes) - coding AND/OR non-coding - array-like (quantification) performance (or better) - quantification, coding polymorphisms, gene fusions (richer than microarray) drawbacks: complex analysis, vs. simpler microarrays, with faster answers

7 (you re not looking to a few genes, but to thousands) Raw materials: invest in the best RNA from your sample First step: Sample qualification Lab-chip Analysis (2100 Bioanalyzer) RNA Integrity Number (RIN) > 8.0

8 Sample qualification Lab-chip Analysis (2100 Bioanalyzer)

9 Hybridization-based approaches. DNA microarray NHGRI P21 AKT HER2-neu besides Gene Expression applications (mrna, microrna) also genomic DNA targets (promoters, CpG islands, acgh (array- Comparative Genomic Hybridization)) from 700 mirna genes to 1M tiled genomic targets from 20ng total RNA protocols for 100s of cells possible

10 One [microarray] platform. Many applications

11 Experimental design, what samples, what comparisons Not only care on the analysis of your quantitative data.... but also plan carefully your experiment don t want to waste hard work and money don t want a limited capacity to answer all your questions Goal definition to be able to answer the right questions what comparisons do I want to do? Experimental design conditions, replicates (biological, technical) identical procedure, different biological sample identical procedure, same biological sample time series, age of mice, statistics Laboratory work user randomization, highest sample quality Data analysis UBio

12 Experimental layout: 1-colour vs. 2-colour systems

13 Correlation between systems 1 colour 2 colours 30% 70% 30%

14 [2-colour] Reference design Normal Treated each sample of interest is paired with the same reference sample Treated 1 Pooled normal Ref. Ref. Treated 2 Treated 3

15 Should I pool my samples? Often motivated by insufficient quantity of RNA Sometimes, proposed to control for biological variability Purpose: to comprehend, not eliminate biological variability population s mean can not be estimated from pooled samples mean differences between groups can be estimated from pooled samples DON T pool if you look for subtype classification Pooling is OK for use as a reference in a 2-colour system

16 Experimental layout: 1-colour vs. 2-colour systems Extendability: - open-ended > can add additional samples at a later time - reference designs need exactly the same reference

17 The statistical analysis of samples infers features from the population do those samples represent the population? Populations don t use to be homogeneous, do they? A correct population sampling is extremely important, in order to avoid misleading data randomize, avoid nonrandom samples employ a good sample size (=biological replicates) optimal replication depends on the variability in the population x x x x x x x x

18 Different levels of variability in the experiment Two sorts of replication available variability due to strain, disease states, treatment variation, and environmental factors in addition to the variability introduced by subsequent technical levels (RNA isolation, labeling, etc.) >technical replicates (hybridizing same sample on multiple arrays) are important to ensure the procedure is running properly, >biological replicates (multiple samples of the same condition) are needed to enable transcriptional measurements to be generalized across biological samples

19 replication at the level of microarray hyb (when in need to know how precise the measures are) replication at the level of biology

20 Limited resources and Replication Efficacy of an experimental design Degrees of Freedom 5 DF 0 = Number of samples Number of groups appropriate Number of samples Number of groups + DF 0 Expmntal. vs Untreated, groups [2] + DF 0 [5] 7 samples 0 closer multiple of 2 = 8 samples G.A. Churchill. Nature Genetics 32: (2002) Lower independent replication can require a posteriori hit validation on independent series of samples

21 q-pcr, RPA, or Northern blot analyses are not necessarily needed to confirm a microarray result good quality microarrays the confirmation approach doesn t need to be too redundant the confirmation assays can be based on the hypotheses that link the microarray changes to the phenotype

22 Animal studies. How homogeneous should the sampling be? Should it be Standardized or Heterogenized (??) Homogenization in age, sex, body weight, test procedures, etc STANDARDIZATION reduces within-experiment variation... but it is also limited to the specific conditions employed HETEROGENIZATION or Systematic Variation IT IMPROVES REPRODUCIBILITY Systematic variation improves reproducibility of animal experiments. Richter SH et al., Nature Methods 7:167, 2010.

23 enzymatic sample labeling hyb image quantitation

24 Signal acquisition (raw data)

25 Deliverables

26 Deliverables

27 Deliverables

28 Getting practical

29

30 Microarray Service Requests.xls

31 Microarray Service Requests.xls

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