RNA sequencing: advances and opportunities

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Adapted version of my technical Journal club presentation on RNA sequencing.

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RNA sequencing: advances and opportunities

  1. 1. advances and opportunities Paolo Dametto May 2012
  2. 2. Is there a correlation between the size of the genome and the morphological complexity? 0 Only to a certain extent! 0 There is not a clear correlation between the size of a genome and the overall complexity of an organism
  3. 3. Is there a correlation between the number of genes and morphological complexity ? 0 Once again, only to a certain extent 0 The complexity of an organism increases much more than the number of genes
  4. 4. 0 It is not needed to increase the variety of the pieces available in order to increase the complexity of a construction, but you have to increase the complexity of the project Transcription factors Operators Enhancers Promoters ncRNA (e.g. involved in alternative splicing and miRNA) 0 Antisense transcripts 0 0 0 0 0 Intergenic DNA 30% Introns 24% Transposons 45% Exons 1%
  5. 5. Genetic expression workflow 1951-1965 Nowadays RNA
  6. 6. The Transcriptome Messenger RNA Ribosomal RNA Signal recognition particle RNA Transfer RNA Transfer-messenger RNA RNAs involved in protein synthesis mRNA Codes for protein rRNA Translation 7SL RNA or Membrane integration SRP RNA tRNA Translation tmRNA Rescuing stalled ribosomes All organisms All organisms All organisms All organisms Bacteria RNAs involved in post-transcriptional modification or DNA replication Type Abbr. Function Distribution Eukaryotes and Small nuclear RNA snRNA Splicing and other functions archaea Eukaryotes and Small nucleolar RNA snoRNA Nucleotide modification of RNAs archaea SmY RNA SmY mRNA trans-splicing Nematodes Type of snoRNA; Nucleotide modification of Small Cajal body-specific RNA scaRNA RNAs Kinetoplastid Guide RNA gRNA mRNA nucleotide modification mitochondria Ribonuclease P RNase P tRNA maturation All organisms Ribonuclease MRP RNase MRP rRNA maturation, DNA replication Eukaryotes Y RNA RNA processing, DNA replication Animals Telomerase RNA Telomere synthesis Most eukaryotes Type Antisense RNA Abbr. aRNA Cis-natural antisense transcript CRISPR RNA crRNA Long noncoding RNA MicroRNA Piwi-interacting RNA Small interfering RNA Trans-acting siRNA Repeat associated siRNA Long ncRNA miRNA piRNA siRNA tasiRNA rasiRNA Regulatory RNAs Function Transcriptional attenuation / mRNA degradation / mRNA stabilisation / Translation block Gene regulation Resistance to parasites, probably by targeting their DNA Various Gene regulation Transposon defense, maybe other functions Gene regulation Gene regulation Type of piRNA; transposon defense Distribution All organisms Bacteria and archaea Eukaryotes Most eukaryotes Most animals Most eukaryotes Land plants Drosophila
  7. 7. Transcriptomic 0 To catalogue all species of transcripts; 0 To determine the transcriptional structure of genes, in terms of their starting site, 5’ and 3’ ends, splicing patterns and other post-transcriptional modification; 0 To quantify the changing expression levels of each transcript during development and under different conditions.  Various technologies have been developed to deduce and quantify the transcriptome, including hybridization-based approaches (microarray) or sequence-based approaches (RNA-seq)
  8. 8. Hybridization-based approach: Microarray technology… 0 High-throughput 0 Fast 0 Relatively inexpensive 0 Multiple applications: 0 gene expression profiling 0 gene fusions detection 0 alternative splicing detection 0 SNP detection 0 Tiling array 0 ChIP
  9. 9. …and its limitations 0 Reliance upon existing knowledge about genome sequence 0 High background levels owing to cross-hybridization 0 A limited dynamic range of detection due to both background and saturation of signals 0 Comparing expression levels across different experiments is often difficult and can require complicated normalization methods
  10. 10. Sequence-based approaches 0 Directly determine the cDNA sequence, hence defining the 1. 2. corresponding mRNA Sanger sequencing of cDNA or EST libraries 0 Low-throughput, expensive, generally not quantitative Tag-based methods were developed: SAGE, CAGE, MPSS 0 Still expensive because based on Sanger sequencing, short tags cannot be uniquely mapped to the reference genome, isoforms are generally not distiguishable 3. RNA-seq, based on NGS technologies 0 By analyzing the transcriptome at spectacular and unprecedented depth and accuracy, thousands of new transcripts variants and isoforms have been shown to be expressed in mammalian tissues or organs 0 it greatly accelerated our understanding of the complexity of gene expression, regulation and networks for mammalian cells
  11. 11. Roche/454 Illumina/Solexa Life/SOLiD NGS Helicos/tSMS Pacific Biosciences Life/Ion Torrent
  12. 12. A typical RNA-seq experiment
  13. 13. RNA-seq for detection of alternative splicing events
  14. 14. Challenges for RNA-seq Library construction 0 Larger RNA molecules must be fragmented into smaller pieces (200-500bp) to be compatible with most deep-sequencing technologies  RNA fragmentation has little bias over the transcript body, but is depleted for transcript ends compared with other methods  cDNA fragmentation is usually strongly biased towards the identification of sequences from the 3’ ends of transcripts
  15. 15. Challenges for RNA-seq Bioinformatic challenges 0 Development of efficient methods to store, retrieve and process large amounts of data: ELAND, SOAP, MAQ and RMAP  High-quality reads are selected and matched against a reference genome, or they are first assembled into contigs before alignining them to the genomic sequence to reveal transcription structure 1. 2. Junctions reads are difficult to map:  a junction library containing all known and predicted junction sequences has been created and junction reads are mapped there Many reads match multiple locations in the genome (e.g. repetitive regions)  Multi-matched reads are assigned proportionally to the number of reads mapped to their neighbouring unique sequences  Roche 454 to obtain longer reads (250 bp)  Paired-end sequencing strategy (Solexa)
  16. 16. Challenges for RNA-seq Defining transcription level 0 RNA-seq can be used to determine levels more accurately than microarrays. In principle, it is possible to determine the absolute quantity of every molecule in a cell population, and directly compared results between experiments. 0 Gene expression level is deduced from the total number of reads that fall into the exons of a 1. RNA fragmentation + cDNA synthesis (exons’ body-biased): gene, normalized by the length of exons that can be uniquely mapped read counts from a window near the 3’end are used 2. cDNA fragmentation (3’end-biased): 0 0 RNA-seq can capture transcriptome dynamics across different tissues or conditions without sophisticated normalization of data sets.
  17. 17. Life/SOLiD 0 mRNA-seq on a single mouse blastomere and oocyte 0 They detected the expression of 75% (5270) more genes than microarray techniques 0 They identified 1753 previously unknown splice junctions called by at least 5 read 0 8-19% of the genes with multiple known transcript isoforms expressed at least two isoforms in the same blastomere or oocyte 0 Dicer1-/- and Ago2-/- oocytes show 1696 and 1553 genes, respectively, to be upregulated compared to wild-type controls, with 619 genes in common
  18. 18. Mitinouri S. et al, Nat Protocol, 2007 5 min > 30 min 64% genes 3 min > 6 min (80-130 bp)
  19. 19. High accuracy of the sequencing technique and mapping algorithms
  20. 20. Comparison of mRNA-Seq and microarray assays 0 Microarray analysis of 320 blastomeres found 6650 genes in common with RNA-seq. Overall RNA-seq detected 60% more genes compared with microarray. 0 mRNA-Seq missed 5.7% of the transcripts (400 genes) 0 327/400 genes had fluorescence intensity on the chip lower than 100 0 9/11 genes tested by RT-PCR were found to be false positive 0 Cross-hybridization 0 Stochastically, some low-expressed genes on a single cell can be either on or off. 0 Very similar expression pattern compared to a NIH mouse array 0 380 genes detected by RNA-seq were chosen and tested by RT-PCR. 71% were clearly confirmed
  21. 21. New splice isoforms identified by mRNA-seq 1. Generation of a library containing all possible combinations of exon-exon junctions as 84-bp sequences, with 42-bp from each exon 3. Matching between RNA-seq reads and the new library 2. Removing of all known exon junctions Results 0 One blastomere: 6701 and 1753 new junctions with at least 2 or 5 reads, respectively 0 8/8 confirmed by RT-PCR 0 One mature oocyte: 9012 and 2070 new junctions 0 335 genes (19% of all known genes with at least two known isoforms) expressed more than two transcripts insoforms in a single blastomere, at the same time
  22. 22. RNA-seq to dissect functional differences: Dicer1-/- vs WT 0 Two separately processed single wild-type mature oocytes showed very similar transcriptome profiles. Same results for Dicer1-/- oocytes 0 Differences between Ago2-/- and WT were clearly less than that between Dicer1-/- and WT >> this observation correlates with the fact that Ago2-/- oocytes phenotype is similar but milder than that of Dicer1-/-
  23. 23. RNA-seq to dissect functional differences: Dicer1-/- vs WT 0 Single-exon resolution of RNA-seq with low or even no background: in Dicer1-/- oocytes, exon 23 is deleted by loxP-directed Cre recombination. Result confirmed by TaqMan assay. 0 Abnormal upregulation was detected for three genes Ccne1, Dppa5 and Klf2 and confirmed by RT-PCR. They may contribute to the compromised developental potential of Dicer1-/- and Ago2-/- oocytes
  24. 24. RNA-seq to dissect functional differences: Dicer1-/- vs WT Overall results Dicer1-/Upregulated 1696 Downregulated 1571 Ago2-/Upregulated 619 Downregulated 589 Upregulated 1553 Downregulated 1121 Core candidates to dissect the function of microRNAs and endogenous small interfering RNAs involved in oogenesis
  25. 25. Conclusions 0 mRNA-seq on a single mouse blastomere > small amount of starting material 0 7% > 64% of full-length cDNAs captured 0 They detected the expression of 75% (5270) more genes than microarray techniques and identified 1753 previously unknown splice junctions 0 8-19% of the genes with multiple known transcript isoforms expressed at least two isoforms in the same blastomere or oocyte 0 Dicer1-/- and Ago2-/- oocytes show 1696 and 1553 genes, respectively, to be upregulated compared to wild-type controls, with 619 genes in common Limitations  Only poly(A) mRNA are captured (e.g. histone mRNA is not detected)  For mRNAs longer than 3 Kb, the 5’end will not be characterized  The assay uses double-stranded cDNAs but cannot discriminate between sense and antisense
  26. 26. Helicos/tSMS (DRS) 0 cDNA synthesis introduces multiple biases: 0 Erases RNA strand information 0 Spurious second-strand cDNA artefacts can be introduced, owing to the DNAdependent DNA polymerase (DDDP) activites 0 Artefactual cDNAs due to template switching 0 Error prone and inefficiency of the enzyme  Direct single molecule RNA sequencing without prior conversion of RNA to cDNA >> it captures all RNAs  The sequencing was performed on Poly(A)+ S.cerevisiae RNA strain
  27. 27. PAPI enzyme add ~150 bp to the 3’end
  28. 28. DRS sequencing read-length statistics Pilot experiment with oligoribonucleotides 0 48.5% of aligned reads have a sequence length of at least 20 nucleotides (nt) 0 38 nt is the longest read with no errors 0 Errors: 4% 0 2-3% missing base errors 0 1-2% insertion rate 0 0.1%-0.3% substitution errors Poly(A)+ S.cerevisiae (Clontech) 0 Femtomoles quantities of RNA needed 0 120 cycles in 3 days 0 41261 reads of > 20 nt, average of 28 nt 0 50 nt is the longest read 0 19501 reads (48.4%) aligned to the yeast genome using the BLAT algorithm
  29. 29. DRS sequencing read-length statistics 0 Of the aligned reads, 91% were within 400 nt downstream of annotated yeast gene 3’ ORF ends 0 Most of the reads were in close proximity to EST 3’ ends 0 ~2% of the total reads were from ribosomal RNAs and small nucleolar RNAs, indicating that at least a fraction of those can be polyadenylated posttranscriptionally.
  30. 30. 0 The emerging discoveries on the link between polyadenylation and disease states (oculopharyngeal muscular dystrophy, thalassemias, thrombophilia, and IPEX syndrome ) underline the need to fully characterize genome-wide polyadenylation states. Here, we report comprehensive maps of global polyadenylation events in human and yeast generated using refinements to the Direct RNA Sequencing technology. This direct approach provides a quantitative view of genome-wide polyadenylation states in a strand-specific manner and requires only attomole RNA quantities. The polyadenylation profiles revealed an abundance of unannotated polyadenylation sites, alternative polyadenylation patterns, and regulatory element-associated poly(A)+ RNAs. We observed differences in sequence composition surrounding canonical and noncanonical human polyadenylation sites, suggesting novel noncoding RNA-specific polyadenylation mechanisms in humans. Furthermore, we observed the correlation level between sense and antisense transcripts to depend on gene expression levels, supporting the view that overlapping transcription from opposite strands may play a regulatory role. Our data provide a comprehensive view of the polyadenylation state and overlapping transcription.
  31. 31. Conclusions 0 Requirement of minor RNA quantities 0 No biases due to cDNA synthesis, end repair, ligation and amplification procedures 0 Potentially useful to study short RNA species Future perspective 1. Generation of a complete catalogue of transcripts that are derived from genomes ranging from those of simple unicellular organisms to complex mammalian cells, normal or disease tissues, single-cells and formalin-paraffin embedded tissues 2. Generation of complex biological networks in a wide range of biological specimens 3. Use of these networks to fully understand the biological pathways that are active in various physiological conditions  Immediate application in clinical diagnostic: analyses of extracellular nucleic acid (e.g. fetal RNA) and cells (e.g. circulating tumor cells)

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