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2010 06 sq-03_lakdawalla_transcriptome_sequencing

This document discusses RNA sequencing (RNA-Seq) and related techniques. It introduces total RNA sequencing which allows sequencing of all RNA molecules from a sample, including non-coding RNA, using less than 100ng of input RNA. Double-strand specific nuclease (DSN) normalization is described as a method to normalize total RNA sequencing libraries. The document also discusses applying RNA sequencing to analyze formalin-fixed paraffin-embedded (FFPE) samples.

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© 2009 Illumina, Inc. All rights reserved. Illumina, illuminaDx, Solexa, Making Sense Out of Life, Oligator, Sentrix, GoldenGate, GoldenGate Indexing, DASL, BeadArray, Array of Arrays, Infinium, BeadXpress, VeraCode, IntelliHyb, iSelect, CSPro, and GenomeStudio are registered trademarks or trademarks of Illumina, Inc. All other brands and names contained herein are the property of their respective owners. Welcome! Abizar Lakdawalla, PhD alakdawalla@illumina.com •Pervasive transcription •Antisense transcription •Non-coding RNA •Promoter associated RNA •Micro-RNA/mRNA targets •Enhancer RNAs •Gene fusions Transcriptome sequencing Goodbye! Arrays
2 2 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Human Genome circa2010
3 3 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE
4 4 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE RNA families RNA Coding PolyA mRNA Non-PolyA mRNA Non-coding StructuralDNA associated Replisome DNA Repair Telomeric DNA methylation (piRNA) RNA associated Ribosome associated rRNA Regulatory Micro RNA TSS associated Anti-sense Enhancer RNA
5 5 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Genome Analyzer Transcriptional Analysis RNA from mitochondria RNA from nucleus RNA from cytoplasm Sub-fractionate the RNA •Poly-A RNA •RNA without rRNA •RNA without … Sequence the whole RNA Tag Sequence (a snippet)
6 6 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Tag Sequencing AAAAA mRNA / Total RNA Sequencing Adapter Ligation GATC AAAAA TTTTT- CTAGMmeI AAAAA TTTTT- 1stand 2ndStrand cDNA Synthesis Restriction Enzyme Digestion GATC AAAAA TTTTT-
7 7 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE 2ndPrimer Addition via PCR Seq. Primer Primer Tag Primer Digital Tag mRNA Sample Preparation GATC AAAAA TTTTT- CTAG MmeI NN MmeI Digestion Ligation of 1stPrimer NN NN
8 8 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Gene Expression 250 samples per run (HiSeq 2000)
9 9 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE RNA-Seq: Method AAAA AAAAAAA AAAAAA Illumina Sequencing cDNAsample Genome position Relative # of reads
10 10 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE We can calculate the expression ratio of any two samples at every region across the entire genome
11 11 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE
12 12 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE mRNA-SeqData is Information RichmRNA Expression ProfilingAlternative Splicing Analysis Analysis of expressed SNPs and mutationsAnalysis of Allelic-specific ExpressionChimericTranscript DiscoveryGene Discovery and Annotation
1133 Revolution – RNA-Seq – PCR-free – Ribo-Seq – CLIP-Seq – Normalization - FFPE Base Position 51,165,353 51,166,593 51,167,833 51,169,073 51,170,313 51,171,553 51,172,793 51,174,033 51,175,273 51,176,513 Cytogenetic Band q13.13 Sequence (+) Sequence is too dense for display. BCnormal_619K… KRT6A Base Position 151,798,793 151,800,683 151,802,573 151,804,463 151,806,353 151,808,243 151,810,133 151,812,023 151,813,913 Cytogenetic Band q21.3 Sequence (+) Sequence is too dense for display. BCnormal_619K… S100A2 Base Position 56,150,388 56,152,598 56,154,808 56,157,018 56,159,228 56,161,438 56,163,648 56,165,858 56,168,068 Cytogenetic Band q13.33 Sequence (+) Sequence is too dense for display. BCnormal_619K… KLK6 KLK6 normal tumor KRT6A S100A2 KLK6
1144 Revolution – RNA-Seq – PCR-free – Ribo-Seq – CLIP-Seq – Normalization - FFPE Tissue-specific genes Base Position 125,121,475 125,121,905 125,122,335 125,122,765 125,123,195 125,123,625 125,124,055 125,124,485 125,124,915 Cytogenetic Band q24.2 Sequence (+) Sequence is too dense for display. adipose PATE1 PATE1 Gene Prostate And Testis Expressed 1
15 15 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Read Counts for All RefSeq Genes
16 16 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE RNA-seq and Microarray ComparedExpression levels are shown, as measured by RNA-Seqand tiling arrays for Saccharomycescerevisiaecells. Agree for genes with medium levels of expression, but correlation is very low for genes with either low or high expression levels.
17 17 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Dynamic Range Accuracy and Sensitivity Performance of RNA sequencing is superior to other gene expression profiling methods
18 18 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE mRNA-Seq
19 19 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Transcribed region of PMPCB gene is clearly longer than RefSeqannotation Overlapping of PMPCB and DNAJC2 genes on two strands
2200 Revolution – RNA-Seq – PCR-free – Ribo-Seq – CLIP-Seq – Normalization - FFPE 30%
21 21 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE cSNP Analysis Homozygous Heterozygous
2222 Revolution – RNA-Seq – PCR-free – Ribo-Seq – CLIP-Seq – Normalization - FFPE Differential Allelic Expression
23 23 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Read Counts for All RefSeq Exons
24 24 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Transcriptome isoforms –
25 25 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Making Sense“… studying transcription for a long time and never seen this kind of transcription before … but we also see a polymerase that appears to be pointing in the wrong direction.” - Seila/Sharp, MIT “The gene, in other words, is in an identity crisis.”
26 26 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Gene Fusions
27 27 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE
28 28 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Foxa2 Binds in Unknown Regions … two remote peaks in regions containing no known genes. The first peak (unknown #4) lies close to a mouse EST, while the second peak (unknown #5) overlaps and is close to highly conserved sequence regions.
29 29 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE
30 30 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE More to small RNA than known microRNAsmiRNA Fragment Potential Novel Content rRNA, tRNA, adaptor contamination Standard Protocol:<3% sRNA v1.5 30 nt band: 6% sRNA v1.5 22 nt band:8% Non-coding RNA Fragment
31 31 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE More to small RNAs ...
32 32 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Identification of micro-RNA targetsHenderson, 2009
33 33 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE ArgonauteHITS-CLIP decodes microRNA- mRNA interaction maps. Chi SW, ZangJB, MeleA, Darnell RB. Nature (2009) 460: 479-86. Covalently crosslink native argonauteprotein–RNA gives you Ago–miRNAand Ago–mRNA binding sites. Ago-mRNA tags to genome
3344 Revolution – RNA-Seq – PCR-free – Ribo-Seq – CLIP-Seq – Normalization - FFPE Ribo-Seq. RNA engaged in translation Ribo-Seq RNA-Seq
35 35 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE RNA Normalization
36 36 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Genome Analyzer Transcriptional Analysis RNA from mitochondria RNA from nucleus RNA from cytoplasm Sub-fractionate the RNA •Poly-A RNA •RNA without rRNA •RNA without … Sequence the whole RNA Tag Sequence (a snippet)
37 37 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Cot Curves and cDNA Normalization rRNA mtRNA mRNA
38 38 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Double strand nuclease normalization mRNA-Seq Purified Total RNA Poly-A Selection RNA Fragmentation cDNA Synthesis* Adapter Ligation & PCR Total RNA-Seq Purified Total RNA RNA Fragmentation cDNA Synthesis* Adapter Ligation & PCR DSN Normalization
39 39 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE DSN Library Normalization Process Overview From Evrogen.com Library is melted at 98ºC Library is hybridized at 68ºC DSN Cleavage PCR to enrich library Grow Clusters and Sequence
40 40 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Total RNA-SeqMethod OverviewRNA from any organism –bacteria, plants, animalsLess than 100 nanogramsof total RNAFull-length cDNA coverage ofall RNA moleculesWorks with low quality RNA –even FFPE preparationsNormalization protocol can be used with most RNA-Seqprotocols
41 41 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Comparison of Output from Different Protocols
42 42 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Many ncRNAsare often transcribed from the same strand as mRNA Poly-A + DSN Total RNA + DSN
43 43 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Total RNA + DSN Poly-A + DSN Many non-coding RNAs are seen with TotalRNA-Seq
44 44 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE DSN Normalization has little effect on most mRNAs Poly-A mRNA (no DSN) Poly-A mRNA (+ DSN)
45 45 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Plot shows gene level count data for all RefSeqGenes Total RNA vs. Poly-A mRNA Many non-coding RNAs are observed with Total RNA-SeqMethod Total RNA Poly-A mRNA
4466 Revolution – RNA-Seq – PCR-free – Ribo-Seq – CLIP-Seq – Normalization - FFPE -10 -5 0 5 10 -10 -5 0 5 10 Log2 sequencing count ratio (brain vs UHR) Log2 qPCR ratio (brain vs UHR) R = 0.947 Slope = 1.01 Fold Change for qRT-PCR: Brain/UHR Fold Change for Total RNA-SEQ: Brain/UHR Key Variables: RNA is degraded No Poly-A Selection + DSN Normalization 0.1 μg input total RNA Fold Change of Total RNA-Seq vs. qRT-PCR
47 47 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE RNA-Seqin FFPE
48 48 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE RNA-SeqProtocol Variations mRNA-Seq Purified Total RNA Poly-A Selection RNA Fragmentation cDNA Synthesis* Adapter Ligation & PCR Total RNA-Seq Purified Total RNA RNA Fragmentation cDNA Synthesis* Adapter Ligation & PCR NormalizationTotalRNA-SeqTotal RNA from FFPEcDNA Synthesis* Adapter Ligation & PCRNormalization
49 49 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Zoom in Collaboration with Chuck Perou -UNC Total RNA-Seqprotocol on FFPE SamplesTwo Cancer Samples –Her2 and Basal breast cancer sub-typesTwo types of RNA preparations –Total RNA from fresh/frozen tissue and highly degraded FFPE RNARIN of the fresh samples are 9.3 and 8.3 Fresh Samples FFPE Samples
5500 Revolution – RNA-Seq – PCR-free – Ribo-Seq – CLIP-Seq – Normalization - FFPE 10 -2 10 -1 10 0 10 1 10 2 10 3 10 4 s5_csv1.7_c Counts s1_csv1.7_c Counts vs s5_csv1.7_c Counts 10-2 10-1 100 101 102 103 104 s1_csv1.7_c Counts R2 = 0.89 FFPE Total RNA-Seq vs. Fresh Total RNA-Seq Pair-wise Comparison of FFPE
51 51 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE MUCL1(mucin-like 1 gene) FFPE -Basal FFPE -Her2 Fresh mRNA –Basal Fresh mRNA -Her2 Fresh Total RNA -Basal Fresh Total RNA –Her2 Fresh Total RNA -Basal Fresh Total RNA –Her2 Replicate 1 Replicate 2
52 52 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Whole Transcriptome Studies on HiSeq 2000 System Format PF reads Average Error Rates (PhiXspike-ins) Yield Individual human tissues 2 X 50 2.55 B 0.22%, 0.44% >120 Gbin <5 days Individual human tissues 1 X 75 1.27 B 0.37%, 0.52% >95 Gbin <4 days Total human transcriptome 1 X 100 1.19 B 0.67%, 0.81% >115 Gbin <5 days Body Map 2.0 ProjectAdrenal, Adipose, Brain, Breast, Colon, Heart, Kidney, Liver, Lung, Lymph Node, Ovary, Prostate, Skeletal Muscle, Testis, Thyroid, White Blood Cells INDIVIDUAL HUMAN TISSUES 16 tissues, 1 lane per tissue Standard Poly-A mRNA-SeqLibrary Preps TOTAL HUMAN TRANSCRIPTOME Equal Mixture of 16 Human Tissues Strand-specific mRNA-Seq SUMMARY OF RUN METRICS
53 53 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE PCR-free
54 54 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE No PCR No PCR PCR PCR GAPDH
55 55 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE No PCR No PCR PCR PCR APOB
5566 Revolution – RNA-Seq – PCR-free – Ribo-Seq – CLIP-Seq – Normalization - FFPE No PCR vs PCR (15 cycles) 10 0 10 1 10 2 10 3 10 4 10 5 HCT10738 Counts (Raw) HCT10736 Counts (Raw) vs HCT10738 Counts (Raw) 100 101 102 103 104 105 HCT10736 Counts (Raw) R2=0.9637
57 57 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Thank you Summary RNA-Seq= more publications! DATA ANALYSES
58 58 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Samples/flow cell and cost/sample (all consum.) GAIIe GAIIx HiScanSQ HiSeq 2000 DGE (25bp, 2 million) 60 €230 125 €205 125 €204 250 €192 RNA-Seq(75x2, 20 million) 6 €1585 13 €898 13 €835 25 €522 ChIP-Seq(TF) 125 €252 250 €231 250 €229 500 €219 Virus/BAC clones (100x2, 30x) 2,770 €209 5,555 €207 5,555 €207 11,111 €206 Bact./GWAS (100x2, 30x) 277 €242 556 €224 556 €222 1,111 €214 Huexome(100x2, 30x) 28 €568 56 €387 56 €368 111 €287 Hugenome (100x2, 30x) 0.28 €36,444 0.56 €18,325 0.56 €16,436 1.11 €8,321 €5,616 @300Gb/run
59 59 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE1270 genes respond to beta-estradiol, includes several direct targets of Eralpha, organized in a gene regulation cascade, stemming from ligand-activated receptor and reaching a large number of downstream targets via AP-2gamma, B-cell activating transcription factor, E2F1 and 2, E74-like factor 3, GTF2IRD1, hairy and enhancer of split homologue-1, MYB, SMAD3, RARalpha, and RXRalphatranscription factors. MicroRNAsintegral components of this gene regulation network; miR-107, miR-424, miR-570, miR-618, and miR-760 are regulated by 17beta-estradiol along with other microRNAsthat can target a significant number of transcripts belonging to one or more estrogen- responsive gene clusters. Estrogen receptor alpha controls a gene network in luminal-like breast cancer cells comprising multiple transcription factors and microRNAs. CicatielloL … Am J Pathol(2010) 176: 2113-30.
6600 Revolution – RNA-Seq – PCR-free – Ribo-Seq – CLIP-Seq – Normalization - FFPE Genomic DNA X X Restriction Enzyme Digestion Size Selection (200+25bp) Bisulfite Treatment Sequence Ends of Selected Fragments Data Analysis 200 +/- 25 bp Reduced Representation Bisulfite Seq
61 61 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE BC Cell Line RRBS Data -MMEL1 Gene MCF7 ZR-75-1 T47DBT474 MDA-MB-231 MDA-MB-468 BT20 MCF10A
62 62 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE BC Cell Line RRBS Data -CD247 Gene MCF7 ZR-75-1T47D BT474 MDA-MB-231 MDA-MB-468 BT20 MCF10A
63 63 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE BC Cell Line RRBS Data -CDH13 Gene MCF7ZR-75-1 T47D BT474 MDA-MB-231 MDA-MB-468 BT20 MCF10A
64 64 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Correlation of RRBS and mRNA-SeqData for PRKD1 Gene chr14:29,027,620-29,554,515 PRKD1 gene methylation for 5’ CpG island region been dramatically changed and anti-correlates well with mRNA levels MCF7 ZR-75-1T47D BT474 MDA-MB-231 MDA-MB-468 BT20 MCF10A 109 37 0 283 0 0 0 418 mRNA levels BT474mRNA levels
65 65 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Lung Breast CDH13(H-cadherin): increased methylation in lung tumor 3% ave 47% ave 4% ave 10% ave Study both individual CpG sites and CpG Islands
6666 Revolution – RNA-Seq – PCR-free – Ribo-Seq – CLIP-Seq – Normalization - FFPE CpG island sumMeth value in twins 0 5000 10000 15000 20000 25000 30000 35000 0 5000 10000 15000 20000 25000 30000 35000 twin1 twin2 Twin 101 Twin 001 0 5000 10000 15000 20000 25000 30000 35000 0 5000 10000 15000 20000 25000 30000 35000 normal breast Breabseta sCt taumnocr Ber Normal Breast 0 5000 10000 15000 20000 25000 30000 0 5000 10000 15000 20000 25000 30000 lung normal lung tumor B Normal Lung Lung Cancer Comparison of Methylation Summarized for ~ 25K CpG Islands
67 67 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Whole Genome Bisulfite Sequencing Reduced Representation Bisulfite Sequencing
68 68 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE CpG Island Methylation Changes & Pathways What are in these pathways that change? Top 3 Pathways by Enrichment cAMP-mediated Signaling (p = 2.09e-8) G-Protein Coupled Receptor Signaling (p = 4.17e-7) Axonal Guidance Signaling (p = 1.18e-6) Cell Death RelatedPathways Implication: increased methylation of cell death related pathways could promote cell growth and cancer progression
69 69 Revolution –RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Experimental Data Available RNA-Seq 50 bppaired-end standard mRNA-Seq 100 bpsingle-read directional mRNA-Seq DNA Methylation -RRBS CNV-low pass genomic DNA sequencing Small RNA Analysis

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2010 06 sq-03_lakdawalla_transcriptome_sequencing

  • 1.
    © 2009 Illumina,Inc. All rights reserved. Illumina, illuminaDx, Solexa, Making Sense Out of Life, Oligator, Sentrix, GoldenGate, GoldenGate Indexing, DASL, BeadArray, Array of Arrays, Infinium, BeadXpress, VeraCode, IntelliHyb, iSelect, CSPro, and GenomeStudio are registered trademarks or trademarks of Illumina, Inc. All other brands and names contained herein are the property of their respective owners. Welcome! Abizar Lakdawalla, PhD alakdawalla@illumina.com •Pervasive transcription •Antisense transcription •Non-coding RNA •Promoter associated RNA •Micro-RNA/mRNA targets •Enhancer RNAs •Gene fusions Transcriptome sequencing Goodbye! Arrays
  • 2.
    2 2 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Human Genome circa2010
  • 3.
    3 3 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE
  • 4.
    4 4 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE RNA families RNA Coding PolyA mRNA Non-PolyA mRNA Non-coding StructuralDNA associated Replisome DNA Repair Telomeric DNA methylation (piRNA) RNA associated Ribosome associated rRNA Regulatory Micro RNA TSS associated Anti-sense Enhancer RNA
  • 5.
    5 5 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Genome Analyzer Transcriptional Analysis RNA from mitochondria RNA from nucleus RNA from cytoplasm Sub-fractionate the RNA •Poly-A RNA •RNA without rRNA •RNA without … Sequence the whole RNA Tag Sequence (a snippet)
  • 6.
    6 6 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Tag Sequencing AAAAA mRNA / Total RNA Sequencing Adapter Ligation GATC AAAAA TTTTT- CTAGMmeI AAAAA TTTTT- 1stand 2ndStrand cDNA Synthesis Restriction Enzyme Digestion GATC AAAAA TTTTT-
  • 7.
    7 7 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE 2ndPrimer Addition via PCR Seq. Primer Primer Tag Primer Digital Tag mRNA Sample Preparation GATC AAAAA TTTTT- CTAG MmeI NN MmeI Digestion Ligation of 1stPrimer NN NN
  • 8.
    8 8 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Gene Expression 250 samples per run (HiSeq 2000)
  • 9.
    9 9 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE RNA-Seq: Method AAAA AAAAAAA AAAAAA Illumina Sequencing cDNAsample Genome position Relative # of reads
  • 10.
    10 10 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE We can calculate the expression ratio of any two samples at every region across the entire genome
  • 11.
    11 11 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE
  • 12.
    12 12 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE mRNA-SeqData is Information RichmRNA Expression ProfilingAlternative Splicing Analysis Analysis of expressed SNPs and mutationsAnalysis of Allelic-specific ExpressionChimericTranscript DiscoveryGene Discovery and Annotation
  • 13.
    1133 Revolution –RNA-Seq – PCR-free – Ribo-Seq – CLIP-Seq – Normalization - FFPE Base Position 51,165,353 51,166,593 51,167,833 51,169,073 51,170,313 51,171,553 51,172,793 51,174,033 51,175,273 51,176,513 Cytogenetic Band q13.13 Sequence (+) Sequence is too dense for display. BCnormal_619K… KRT6A Base Position 151,798,793 151,800,683 151,802,573 151,804,463 151,806,353 151,808,243 151,810,133 151,812,023 151,813,913 Cytogenetic Band q21.3 Sequence (+) Sequence is too dense for display. BCnormal_619K… S100A2 Base Position 56,150,388 56,152,598 56,154,808 56,157,018 56,159,228 56,161,438 56,163,648 56,165,858 56,168,068 Cytogenetic Band q13.33 Sequence (+) Sequence is too dense for display. BCnormal_619K… KLK6 KLK6 normal tumor KRT6A S100A2 KLK6
  • 14.
    1144 Revolution –RNA-Seq – PCR-free – Ribo-Seq – CLIP-Seq – Normalization - FFPE Tissue-specific genes Base Position 125,121,475 125,121,905 125,122,335 125,122,765 125,123,195 125,123,625 125,124,055 125,124,485 125,124,915 Cytogenetic Band q24.2 Sequence (+) Sequence is too dense for display. adipose PATE1 PATE1 Gene Prostate And Testis Expressed 1
  • 15.
    15 15 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Read Counts for All RefSeq Genes
  • 16.
    16 16 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE RNA-seq and Microarray ComparedExpression levels are shown, as measured by RNA-Seqand tiling arrays for Saccharomycescerevisiaecells. Agree for genes with medium levels of expression, but correlation is very low for genes with either low or high expression levels.
  • 17.
    17 17 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Dynamic Range Accuracy and Sensitivity Performance of RNA sequencing is superior to other gene expression profiling methods
  • 18.
    18 18 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE mRNA-Seq
  • 19.
    19 19 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Transcribed region of PMPCB gene is clearly longer than RefSeqannotation Overlapping of PMPCB and DNAJC2 genes on two strands
  • 20.
    2200 Revolution –RNA-Seq – PCR-free – Ribo-Seq – CLIP-Seq – Normalization - FFPE 30%
  • 21.
    21 21 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE cSNP Analysis Homozygous Heterozygous
  • 22.
    2222 Revolution –RNA-Seq – PCR-free – Ribo-Seq – CLIP-Seq – Normalization - FFPE Differential Allelic Expression
  • 23.
    23 23 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Read Counts for All RefSeq Exons
  • 24.
    24 24 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Transcriptome isoforms –
  • 25.
    25 25 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Making Sense“… studying transcription for a long time and never seen this kind of transcription before … but we also see a polymerase that appears to be pointing in the wrong direction.” - Seila/Sharp, MIT “The gene, in other words, is in an identity crisis.”
  • 26.
    26 26 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Gene Fusions
  • 27.
    27 27 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE
  • 28.
    28 28 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Foxa2 Binds in Unknown Regions … two remote peaks in regions containing no known genes. The first peak (unknown #4) lies close to a mouse EST, while the second peak (unknown #5) overlaps and is close to highly conserved sequence regions.
  • 29.
    29 29 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE
  • 30.
    30 30 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE More to small RNA than known microRNAsmiRNA Fragment Potential Novel Content rRNA, tRNA, adaptor contamination Standard Protocol:<3% sRNA v1.5 30 nt band: 6% sRNA v1.5 22 nt band:8% Non-coding RNA Fragment
  • 31.
    31 31 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE More to small RNAs ...
  • 32.
    32 32 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Identification of micro-RNA targetsHenderson, 2009
  • 33.
    33 33 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE ArgonauteHITS-CLIP decodes microRNA- mRNA interaction maps. Chi SW, ZangJB, MeleA, Darnell RB. Nature (2009) 460: 479-86. Covalently crosslink native argonauteprotein–RNA gives you Ago–miRNAand Ago–mRNA binding sites. Ago-mRNA tags to genome
  • 34.
    3344 Revolution –RNA-Seq – PCR-free – Ribo-Seq – CLIP-Seq – Normalization - FFPE Ribo-Seq. RNA engaged in translation Ribo-Seq RNA-Seq
  • 35.
    35 35 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE RNA Normalization
  • 36.
    36 36 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Genome Analyzer Transcriptional Analysis RNA from mitochondria RNA from nucleus RNA from cytoplasm Sub-fractionate the RNA •Poly-A RNA •RNA without rRNA •RNA without … Sequence the whole RNA Tag Sequence (a snippet)
  • 37.
    37 37 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Cot Curves and cDNA Normalization rRNA mtRNA mRNA
  • 38.
    38 38 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Double strand nuclease normalization mRNA-Seq Purified Total RNA Poly-A Selection RNA Fragmentation cDNA Synthesis* Adapter Ligation & PCR Total RNA-Seq Purified Total RNA RNA Fragmentation cDNA Synthesis* Adapter Ligation & PCR DSN Normalization
  • 39.
    39 39 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE DSN Library Normalization Process Overview From Evrogen.com Library is melted at 98ºC Library is hybridized at 68ºC DSN Cleavage PCR to enrich library Grow Clusters and Sequence
  • 40.
    40 40 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Total RNA-SeqMethod OverviewRNA from any organism –bacteria, plants, animalsLess than 100 nanogramsof total RNAFull-length cDNA coverage ofall RNA moleculesWorks with low quality RNA –even FFPE preparationsNormalization protocol can be used with most RNA-Seqprotocols
  • 41.
    41 41 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Comparison of Output from Different Protocols
  • 42.
    42 42 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Many ncRNAsare often transcribed from the same strand as mRNA Poly-A + DSN Total RNA + DSN
  • 43.
    43 43 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Total RNA + DSN Poly-A + DSN Many non-coding RNAs are seen with TotalRNA-Seq
  • 44.
    44 44 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE DSN Normalization has little effect on most mRNAs Poly-A mRNA (no DSN) Poly-A mRNA (+ DSN)
  • 45.
    45 45 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Plot shows gene level count data for all RefSeqGenes Total RNA vs. Poly-A mRNA Many non-coding RNAs are observed with Total RNA-SeqMethod Total RNA Poly-A mRNA
  • 46.
    4466 Revolution –RNA-Seq – PCR-free – Ribo-Seq – CLIP-Seq – Normalization - FFPE -10 -5 0 5 10 -10 -5 0 5 10 Log2 sequencing count ratio (brain vs UHR) Log2 qPCR ratio (brain vs UHR) R = 0.947 Slope = 1.01 Fold Change for qRT-PCR: Brain/UHR Fold Change for Total RNA-SEQ: Brain/UHR Key Variables: RNA is degraded No Poly-A Selection + DSN Normalization 0.1 μg input total RNA Fold Change of Total RNA-Seq vs. qRT-PCR
  • 47.
    47 47 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE RNA-Seqin FFPE
  • 48.
    48 48 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE RNA-SeqProtocol Variations mRNA-Seq Purified Total RNA Poly-A Selection RNA Fragmentation cDNA Synthesis* Adapter Ligation & PCR Total RNA-Seq Purified Total RNA RNA Fragmentation cDNA Synthesis* Adapter Ligation & PCR NormalizationTotalRNA-SeqTotal RNA from FFPEcDNA Synthesis* Adapter Ligation & PCRNormalization
  • 49.
    49 49 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Zoom in Collaboration with Chuck Perou -UNC Total RNA-Seqprotocol on FFPE SamplesTwo Cancer Samples –Her2 and Basal breast cancer sub-typesTwo types of RNA preparations –Total RNA from fresh/frozen tissue and highly degraded FFPE RNARIN of the fresh samples are 9.3 and 8.3 Fresh Samples FFPE Samples
  • 50.
    5500 Revolution –RNA-Seq – PCR-free – Ribo-Seq – CLIP-Seq – Normalization - FFPE 10 -2 10 -1 10 0 10 1 10 2 10 3 10 4 s5_csv1.7_c Counts s1_csv1.7_c Counts vs s5_csv1.7_c Counts 10-2 10-1 100 101 102 103 104 s1_csv1.7_c Counts R2 = 0.89 FFPE Total RNA-Seq vs. Fresh Total RNA-Seq Pair-wise Comparison of FFPE
  • 51.
    51 51 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE MUCL1(mucin-like 1 gene) FFPE -Basal FFPE -Her2 Fresh mRNA –Basal Fresh mRNA -Her2 Fresh Total RNA -Basal Fresh Total RNA –Her2 Fresh Total RNA -Basal Fresh Total RNA –Her2 Replicate 1 Replicate 2
  • 52.
    52 52 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Whole Transcriptome Studies on HiSeq 2000 System Format PF reads Average Error Rates (PhiXspike-ins) Yield Individual human tissues 2 X 50 2.55 B 0.22%, 0.44% >120 Gbin <5 days Individual human tissues 1 X 75 1.27 B 0.37%, 0.52% >95 Gbin <4 days Total human transcriptome 1 X 100 1.19 B 0.67%, 0.81% >115 Gbin <5 days Body Map 2.0 ProjectAdrenal, Adipose, Brain, Breast, Colon, Heart, Kidney, Liver, Lung, Lymph Node, Ovary, Prostate, Skeletal Muscle, Testis, Thyroid, White Blood Cells INDIVIDUAL HUMAN TISSUES 16 tissues, 1 lane per tissue Standard Poly-A mRNA-SeqLibrary Preps TOTAL HUMAN TRANSCRIPTOME Equal Mixture of 16 Human Tissues Strand-specific mRNA-Seq SUMMARY OF RUN METRICS
  • 53.
    53 53 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE PCR-free
  • 54.
    54 54 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE No PCR No PCR PCR PCR GAPDH
  • 55.
    55 55 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE No PCR No PCR PCR PCR APOB
  • 56.
    5566 Revolution –RNA-Seq – PCR-free – Ribo-Seq – CLIP-Seq – Normalization - FFPE No PCR vs PCR (15 cycles) 10 0 10 1 10 2 10 3 10 4 10 5 HCT10738 Counts (Raw) HCT10736 Counts (Raw) vs HCT10738 Counts (Raw) 100 101 102 103 104 105 HCT10736 Counts (Raw) R2=0.9637
  • 57.
    57 57 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Thank you Summary RNA-Seq= more publications! DATA ANALYSES
  • 58.
    58 58 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Samples/flow cell and cost/sample (all consum.) GAIIe GAIIx HiScanSQ HiSeq 2000 DGE (25bp, 2 million) 60 €230 125 €205 125 €204 250 €192 RNA-Seq(75x2, 20 million) 6 €1585 13 €898 13 €835 25 €522 ChIP-Seq(TF) 125 €252 250 €231 250 €229 500 €219 Virus/BAC clones (100x2, 30x) 2,770 €209 5,555 €207 5,555 €207 11,111 €206 Bact./GWAS (100x2, 30x) 277 €242 556 €224 556 €222 1,111 €214 Huexome(100x2, 30x) 28 €568 56 €387 56 €368 111 €287 Hugenome (100x2, 30x) 0.28 €36,444 0.56 €18,325 0.56 €16,436 1.11 €8,321 €5,616 @300Gb/run
  • 59.
    59 59 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE1270 genes respond to beta-estradiol, includes several direct targets of Eralpha, organized in a gene regulation cascade, stemming from ligand-activated receptor and reaching a large number of downstream targets via AP-2gamma, B-cell activating transcription factor, E2F1 and 2, E74-like factor 3, GTF2IRD1, hairy and enhancer of split homologue-1, MYB, SMAD3, RARalpha, and RXRalphatranscription factors. MicroRNAsintegral components of this gene regulation network; miR-107, miR-424, miR-570, miR-618, and miR-760 are regulated by 17beta-estradiol along with other microRNAsthat can target a significant number of transcripts belonging to one or more estrogen- responsive gene clusters. Estrogen receptor alpha controls a gene network in luminal-like breast cancer cells comprising multiple transcription factors and microRNAs. CicatielloL … Am J Pathol(2010) 176: 2113-30.
  • 60.
    6600 Revolution –RNA-Seq – PCR-free – Ribo-Seq – CLIP-Seq – Normalization - FFPE Genomic DNA X X Restriction Enzyme Digestion Size Selection (200+25bp) Bisulfite Treatment Sequence Ends of Selected Fragments Data Analysis 200 +/- 25 bp Reduced Representation Bisulfite Seq
  • 61.
    61 61 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE BC Cell Line RRBS Data -MMEL1 Gene MCF7 ZR-75-1 T47DBT474 MDA-MB-231 MDA-MB-468 BT20 MCF10A
  • 62.
    62 62 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE BC Cell Line RRBS Data -CD247 Gene MCF7 ZR-75-1T47D BT474 MDA-MB-231 MDA-MB-468 BT20 MCF10A
  • 63.
    63 63 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE BC Cell Line RRBS Data -CDH13 Gene MCF7ZR-75-1 T47D BT474 MDA-MB-231 MDA-MB-468 BT20 MCF10A
  • 64.
    64 64 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Correlation of RRBS and mRNA-SeqData for PRKD1 Gene chr14:29,027,620-29,554,515 PRKD1 gene methylation for 5’ CpG island region been dramatically changed and anti-correlates well with mRNA levels MCF7 ZR-75-1T47D BT474 MDA-MB-231 MDA-MB-468 BT20 MCF10A 109 37 0 283 0 0 0 418 mRNA levels BT474mRNA levels
  • 65.
    65 65 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Lung Breast CDH13(H-cadherin): increased methylation in lung tumor 3% ave 47% ave 4% ave 10% ave Study both individual CpG sites and CpG Islands
  • 66.
    6666 Revolution –RNA-Seq – PCR-free – Ribo-Seq – CLIP-Seq – Normalization - FFPE CpG island sumMeth value in twins 0 5000 10000 15000 20000 25000 30000 35000 0 5000 10000 15000 20000 25000 30000 35000 twin1 twin2 Twin 101 Twin 001 0 5000 10000 15000 20000 25000 30000 35000 0 5000 10000 15000 20000 25000 30000 35000 normal breast Breabseta sCt taumnocr Ber Normal Breast 0 5000 10000 15000 20000 25000 30000 0 5000 10000 15000 20000 25000 30000 lung normal lung tumor B Normal Lung Lung Cancer Comparison of Methylation Summarized for ~ 25K CpG Islands
  • 67.
    67 67 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Whole Genome Bisulfite Sequencing Reduced Representation Bisulfite Sequencing
  • 68.
    68 68 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE CpG Island Methylation Changes & Pathways What are in these pathways that change? Top 3 Pathways by Enrichment cAMP-mediated Signaling (p = 2.09e-8) G-Protein Coupled Receptor Signaling (p = 4.17e-7) Axonal Guidance Signaling (p = 1.18e-6) Cell Death RelatedPathways Implication: increased methylation of cell death related pathways could promote cell growth and cancer progression
  • 69.
    69 69 Revolution–RNA-Seq–PCR-free –Ribo-Seq–CLIP-Seq–Normalization -FFPE Experimental Data Available RNA-Seq 50 bppaired-end standard mRNA-Seq 100 bpsingle-read directional mRNA-Seq DNA Methylation -RRBS CNV-low pass genomic DNA sequencing Small RNA Analysis