Understanding Transcriptomics: A Comprehensive Guide for Researchers

1. Transcriptomics History and practice

2. WT Dwarf Transgenic Other species Label probe + hybridise YFG Nextgene Early RNA analysis used Northerns:…..One gene at a time Tissue sample Quantify RNA levels Extract target RNA

3. Northerns are too slow for Systems Biology where we want to assay ALL transcripts simultaneously Massive Datasets for thousands of genes Genes, protein and metabolites link together into biological SYSTEMS

4. Arabidopsis Merged Network Proteins (red) Metabolites (blue) & Genes (green) 19392 nodes and 72715 edges EXAMPLE: Cytoscape software Allows the visualisation of all transcript levels for an organism This one is based on ARRAY data Arabidopsis transcriptome network (Ma et al. Genome Research 2007)

5. Mass transcript profiling: Transcriptomics Historically (pre-2000): Sequencing ESTs andranking representation Differential display (random 5’ primers + fixed polyA primers) Post 2000:Microarrays & RNAseq….

6. Microarrays Probe preparation Target preparation Acquire or Generate probes ‘All the genes you want’ Extract RNA from your Control AND your Experimental plant Label cDNA from sample 1 RNA …and sample 2 RNA Spot

7. Microarrays • Identify ‘spots’ • remove background • produce ‘red/green’ ratios Hybridise & Scan • Link ratio to relative abundance. • Link spot to gene. • Link genes to each other. Networks / systems

8. Before processing, we have a LOT of spots ‘Landing lights’xyz normalisation After processing, we have a LOT of objective data

9. Learning outcome: What biological questions can be explored with transcriptomics ?

10. Plus hormone vs control (i.e. known / expected challenge) 1. Normal vs challenge (e.g. pathology, induction) 2. Tissue A vs Tissue B (e.g. muscle vs liver) Arrays can separate similar genes Pretend specialist microarray. Only 5 genes ALL responding to a hormone: 1 2 3 4 5 1 2 3 4 5 • The classic types of array experiments: All ‘on’ 1 2 3 4 5 1 2 3 4 5

11. Remember: Genomes are not tidy – duplication is common Plant (arabidopsis) Animal (human) Fungal (yeast) This is a big problem for arrays : Cross - hybridisation

12. Apart from gross syntenic duplication Gene families (recycling of function) is common: e.g. in arabidopsis: Gene family size Proportion of the genome • Conservation at the base-pair level within genes: • 37% of genes highly conserved • (TBLASTX E<10-30) • 10% partially conserved • (TBLASTX E<10-5)

13. Pioneer arrays were cDNAs • Derived from mRNA amplified by reverse transcriptase and cloned. Selected based on partial sequence primed from vector cloning sites (e.g. SP6, T7, T3) • Commonly called ESTs(Expressed Sequence Tags)

14. Gene of interest Example EST sequence 1 Homologous EST sequence 2 Dissimilar EST sequence 3 On the slide 1 2 3 Labelled target cross hybridises ESTs can be misleading

15. Genechips have better specificity Known Gene Sequences 3’ 5’ Algorithmic selection Multiple Short Probes25-mers Hybridisation

16. Biotin-labeled transcripts B B B B B B B B Fragmented cRNA Example single colour target labelling - 3’ IVT Fragment (heat, Mg2+) IVT or WT (Biotin-UTP Biotin-CTP) AAAA RNA Wash & Stain Scan cDNA Hybridise (16 hours)

17. Hybridisation Biotin labelled cRNA Target Antibody detection Detection: Hybridisation and staining Array

18. Each probe call is derived from the 75% quantile of the pixel values (sweet spot). All the probes of a probeset (gene) are combined into ONE measure of expression

19. Data handling: Chips need to be normalised against each other. Each different colour line maps all the intensities of a single chip They are NOT co-incident lines (e.g. yellow and black are outliers) To compare they need to be comparable

20. PA PB PC PD PE PA PB PC PD PE Chip 1 Chip 1 Chip 1 Chip 1 1 2 3 4 5 1.33 2.33 4.66 3.33 7 1.33 2.33 3.33 4.66 7 1 2 4 3 5 Chip 2 Chip 2 Chip 2 Chip 2 7 2 5 3 1 1 2 3 5 7 1.33 2.33 3.33 4.66 7 7 2.33 4.66 3.33 1.33 Chip 3 Chip 3 Chip 3 Chip 3 5 3 4 2 9 4.66 2.33 3.33 1.33 7 2 3 4 5 9 1.33 2.33 3.33 4.66 7 Order by ranks Average the intensities at each rank Reorder by probe Normalisation RMA is a very powerful but simple process that works at the probe level

21. Normalisationmakes data more comparable Sowecanderive / display differentially expressed genes ..as candidates for furtherresearch... RMA trendgraph volcanoplot

22. A complementary solution: RNAseq 3 ‘simple’ steps: Take an RNA sample, 1. sequence it, align it to the genome. 2. count how many times each transcript appears. 3. work out the frequency of each transcript.

23. RNAseq software – Tuxedo suite (2012) • TopHat: Alignment of short RNA-Seq reads • Aligns RNA-Seq reads to genomes using Bowtie. Identifies splice junctions between exons. • Bowtie (2009)*: Ultrafast short read alignment • Aligns short DNA reads at 25 million x 35-bp p/h Cufflinks (includes cuffmerge, cuffcompare, cuffdiff) Uses TopHat to assemble the ‘best’ transcriptome. Estimates relative abundance based on how many reads support each transcript. • CummeRbund: Visualization of RNA-Seq analysis • R package for Cufflinks RNA-Seq output. • Named after the Burrows-Wheeler transform algorithm (BWT)

24. Fragments Per Kilobase per Million reads FDR-adjusted p-values (q-values) - replication

25. Control Mutant

26. Oncewehave candidates - wecandiscovertheirfunction... GO annotations of genes higher in muscle GO annotations of genes higher in liver Graham et al. (2011) Animal

27. ....and use thesetoallocatethosedifferential genes topathwaysandbiological systems