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RNA sequencing module Wednesday 15.10.14 – Day1 09.00 Welcome 09.10 RNA sequencing introduc>on 10.00 RNA-‐seq data analysis –Introduc>on 10.45 RNA-‐seq – prac>cal part 1 12.00 Lunch 12.45 RNA-‐seq – prac>cal part 2 Thursday 16.10.14 – Day2 09.00 Introduc>on to genome-‐guided transcriptome assembly 09.30 Transcriptome assembly – prac>cal part 1 12.00 Lunch 12.45 Transcriptome assembly – prac>cal part 2 14.30 Func>onal annota>on 15.00 Alterna>ve RNA-‐seq applica>ons 15.30 Ques>ons and discussion

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Dr. Susanne Lorenz

Genomics Core Facility Helse Sør-‐Øst

Dept. of Tumor Biology

The Norwegian Radium Hospital, OUS

RNA sequencing -‐ Introduc5on

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exon 1 exon 2 exon 3 exon 4

exon 1 exon 2 exon 3 exon 4 exon 1 exon 2 exon 3 exon 4

Genome

pre-‐ mRNA

Blood Brain Transcrip2on

AAAAAAAAAA mRNA

Splicing, Poly(A) tailing

AAAAAAAAAA

From a Gene to RNA

Transcript A1 Transcript A2

Gene A

à  messanger RNA (mRNA) will be translated into protein (coding RNAs) à  in human 20.000-‐25.000 protein coding genes

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exon 1 exon 2

exon 1 exon 2

Genome

Blood Brain Transcrip2on

Non-‐coding RNA

Spligcing Transcript

Gene B

à  Non-‐coding RNA (ncRNA) will not be translated into protein à  Some types of ncRNAs have a polyA-‐tail, others not à  Three main categories: houskeeping RNAs, short (< 200 bp) and long ncRNAs (>200bp)

pre Non-‐ coding RNA

From a Gene to RNA

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Non-‐coding RNAs

Category Name Length (bp) Func2on

Housekeeping RNAs

Ribosomal RNA (rRNA) 120-‐5000 ribosome structure

Transfer RNA (tRNA) 73-‐94 protein transla>on

small nuclear RNA (snRNA) ~ 150 splicing

small nucleolar RNA (snoRNA) 70-‐200 post-‐transcrip>onal modifica>on

Short non coding RNAs

(smallRNAs)

micro RNAs 16-‐30 (21-‐24) transla>onal repression

PIWI-‐interac>ng RNAs 26-‐31 regulate transposon ac>vity and chroma>n state

promotor-‐associated short RNAs ~18 may regulate gene expression at

chroma>n level

Long non coding RNAs

long intergenic ncRNA > 200 epigene>c, transcrip>onal and post-‐transcrip>onal regula>on

pseudogenes > 200 compe>>ve endogenous RNA

Enhancer RNA 50-‐2000 not known

An>sense RNA > 200 gene expression

long intronic ncRNA > 200 not known

Repeat associated long RNA > 200 not known

à  Ribosomal RNA represents a challenge for RNA sequencing as it cons>tutes up to 80 % of total RNA

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RNA sequencing

What is RNA sequencing?

Massive parallel sequencing to characterize and quanDfy transcriptomes (all acDvely transcribed genes) What does RNA sequencing offer?

•  Iden>fica>on of all ac>vely transcript genes in a cell type/>ssue •  Differen>ally gene expression

•  Iden>fica>on of new transcripts •  Detec>ng of alterna>ve splicing events •  Detec>on of fusion transcripts •  Strand-‐specific measurements •  Muta>on analysis – expression level of genomic muta>ons, RNA edi>ng

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RNA sequencing in comparison

“RNA-‐Seq: a revolu>onary tool for transcriptomics” Wang Z. et al., 2009 Nature Reviews

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RNA sequencing protocols

1. mRNA (protein coding) stranded sequencing à only Poly-‐A tail RNA à no rRNA contamina>on but genes encoding proteins of the

ribosome

2. total RNA stranded transcriptome (ribosomal RNA deple>on) à total RNA isola>on followed by rRNA deple>on à generates informa>on about all RNA molecules except rRNAs and RNA molecules longer than 120 bp

3. Capturing systems for stranded RNA-‐sequencing

à hybridiza>on based à dependent on annota>on à increased sequencing depth at coding regions à capable for very low star>ng material (10 ng)

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Illumina TruSeq strand-‐specific RNA protocols

1. Poly-A selection

mRNA Sequencing Total RNA Sequencing

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Illumina TruSeq strand-‐specific RNA protocols

Flow cell

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Strand-‐specific total RNA sequencing-‐ advantages

§  more even coverage along the transcript à significant less 3´ -‐bias compared to Poly-‐A tailing à more accurate quan>fica>on of gene expression

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Fresh frozen high quality sample (RNA RIN value 9.0)

Formalin-‐fixed paraffin-‐embedded sample (RNA RIN value 6.0)

§  robust and efficient method even for low quality samples

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§  Improved discrimina>on of overlapping transcripts à more accurate quan>fica>on of gene expression

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1. Hybridiza5on and amplifica5on on the flow cell

RNA sequencing -‐ Illumina 2. Sequencing

4. Millions of short sequences in fastq format

> HWUSI-EAS100R:6:73:941:1973#0/1 AGCGTAACCGGTAACGATAGCAGAT @ HWUSI-EAS100R:6:73:941:1973#0/1 bbbbbbbb%%%++)(%%%%)1**((((***+

3. Image analysis and base calling

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RNA sequencing -‐ Illumina

Read1 Read2

cDNA fragment

Single-‐end sequencing (Read1 only)

Paired-‐end sequencing (Read1 and Read2)

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Scien5fic RNA sequencing case 1

“Au>sm spectrum disorder (ASD) is a common, highly heritable neuro-‐developmental condi>on characterized by marked gene>c heterogeneity.” RNAseq is used to inves>gate gene expression in au>s>c brain compared to normal brain.

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Transcriptomic analysis of au5s5c brain

Heatmap of the top 200 differen>ally expressed genes between au>sm and control cortex samples

à  dis>nct clustering of the majority of au>sm cortex samples, in contrast to genomic heterogeneity (shown in GWAS study)

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A) Significant expression differences between frontal and temporal cortex in control samples (top) and au>sm samples (bomom).

B) Top 20 genes differen>ally expressed between frontal and temporal cortex in controls. None of the genes show significant expression differences between frontal and temporal cortex in au>sm.


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Results: §  Dis>nct transcriptomic differences between au>sm and control

cortex samples even if heterogeneous at genomic level (WGAS)

§  Gene ontology analysis showed down-‐regulated genes related to synap>c func>on, whereas up-‐regulated genes were related to immune and inflammatory response

§  Consistent expression in frontal and temporal cortex compared to differen>al expression in normal samples

à  Gained knowledge about biology behind the disease that can improve the development of diagnosis and treatment strategies

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”To idenDfy the precise geneDc elements and study the exclusive nature of three immunohistochemically different breast cancer types, we employed massively parallel mRNA sequencing.”

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PCA plots showing the clustering of the TNBC (magenta), Non-‐TNBC (Red) and HER2-‐posi>ve (green) breast cancer samples based on the transcriptomic expression profiles. Table showing the number of sta>s>cally significant differen>ally expressed transcripts.

Transcriptomic landscape of breast cancer through mRNA sequencing

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The table presents the six most common highly abundant primary transcripts and all of the associated informa>on. The bomom four lines of the table show the primary transcript expression profiles specific for the TNBC and Non-‐TNBC (APOE) and HER2-‐posi>ve (FN1, PP1B and OAZ1) groups.

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§  Compara>ve transcriptomic analyses elucidated differen>ally expressed transcripts between the three breast cancer groups, iden>fying several new modulators of breast cancer. §  Iden>fica>on of common transcrip>onal regulatory elements, such as highly abundant primary transcripts, including osteonec>n, RACK1, calnexin, calre>culin, FTL, and B2M, and ‘‘genomic hotspots’’ enriched in primary transcripts between the three groups. §  The study opens previously unexplored niches that could enable a bemer understanding of the disease and the development of poten>al interven>on strategies.

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Integra5ve annota5on of human large intergenic noncoding RNAs reveals global proper5es and specific subclasses Moran N. Cabili, Cole Trapnell, […], and John L. Rinn (2011)

In this study a reference catalog of > 8000 human lincRNAs is defined and characterize by sequence, structural and transcrip>onal features across 24 >ssues and cell types.

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Integra5ve annota5on of human large intergenic noncoding RNAs

Computa>onal approach for comprehensive annota>on of lincRNAs

B A

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Expression level of lincRNAs and protein coding genes across the >ssues (color intensity represents frac>onal density across the row)

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(B) Expression abundance of 1508 highest expressed lincRNAs compared to 8906 highest expressed protein coding genes à lincRNAs are lower expressed (C) Distribu>on of maximal >ssue specificity scores calculated from data in A à lincRNAs show higher >ssue specificity

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Non-‐coding RNAs in human diseases

HOTAIR binds to polycomp proteins that remodel chroma>n marks what leads to epigene>c silencing of i.e. HOXD and increases invasiveness of cancer cells.

lincRNA HOTAIR

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BACE1-‐AS, an an>sense lncRNA regulates the expression of the sense BACE1 gene (labelled BACE1-‐S in the figure) through the stabiliza>on of its mRNA. BACE1-‐AS is elevated in Alzheimer’s disease, increasing the amount of BACE1 protein and, subsequently, the produc>on of β-‐amyloid pep>de.

lncRNA in Alzheimer`s disease


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The loss of the snoRNA in PWS changes the alterna>ve splicing of the serotonin receptor HTR2C precursor mRNA (pre-‐mRNA), resul>ng in a protein with reduced func>on.

snoRNA in Prader-‐Willi syndrome

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RNA seq data set for the prac5al part

Aim: Iden5fica5on of dysregulated genes in osteosarcoma

•  Most common primary malignant tumours of bone

•  occurs mainly in long bone (arm and leg) Children/adolescents

•  High grade tumours that are very aggressive

•  Complex genomic aberra5ons

à  The high number of genomic aberra>ons is likely to have an effect on genes expression

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