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Digital sequencing by Molecular barcodes for accuracy
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PCR duplicates and amplification bias are major issues in current RNAseq workflows, as they
result in biased and inaccurate gene expression profiles
mRNA cDNA
PCR duplicates
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Digital RNAseq for Gene Expression Profiling: Digital RNAseq Webinar Part 2
The document outlines a three-part webinar series on digital RNA sequencing (RNA-seq) aimed at accurate gene expression profiling, featuring speakers from Qiagen. It discusses the advantages and challenges of current gene expression profiling methodologies and introduces Qiagen's targeted RNA panels as a comprehensive solution to improve accuracy while overcoming limitations of traditional methods. The webinars include tutorials on data analysis and applications in various research fields, emphasizing the effectiveness of molecular barcoding technology in achieving unbiased gene expression results.
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Digital RNAseq for Gene Expression Profiling: Digital RNAseq Webinar Part 2
- 1. Sample to Insight Welcometo 3 part webinar series on Digital RNAsequencing Digital RNA sequencing for accurate gene expression profiling Part 1: What is digital RNAseq? Speaker: Eric Lader, Ph.D. Senior Director, Research & Development, QIAGEN Date: Feb 17th, 1 pm EST, 10 am PST, 6 pm GMT Part 2: Digital RNAseq for gene expression profiling Speaker: Raed Samara, Ph.D. Global Product Manager, NGS, QIAGEN Date: Feb 24th, 1 pm EST, 10 am PST, 6 pm GMT Part 3: Molecular Insight into gene expression profiling using digital RNA seq: data analysis tutorial Speaker: Melanie Hussong, Ph.D. Scientist, NGS Jean-Noel Billaud, Ph.D. Principal Scientist. Bioinformatics Date: March 1st, 1 pm EST, 10 am PST, 6 pm GMT
- 2. Sample to Insight QIAseqTargeted RNA Panels for gene expression profiling using Digital RNA sequencing Raed N. Samara, Ph.D. Global Product Manager 2 Molecular barcodes enabling Digital RNAseq
- 3. Sample to Insight Agenda 3 •Introduction • Digital sequencing with QIAseq targeted RNA panels • Application data • Summary
- 4. Sample to Insight Geneexpression profiling 4 Importance Gene expression profiling is central to many biological processes and applications including: Gene expression profiling Cancer research Immune profiling Cell cycle research Changes in signaling pathways Predictive toxicology Biomarker development
- 5. Sample to Insight Geneexpression profiling 5 Current technologies and methodologies Current technology Advantages PCR-based Accuracy Whole transcriptome sequencing (WTS) Throughput power Microarrays Easy data analysis Traditional targeted RNA sequencing Manageable data Low per-sample cost
- 6. Sample to Insight Geneexpression profiling 6 Disadvantages of current technologies and methodologies Current technology Advantages Disadvantages PCR-based Accuracy Limited sample & assay throughput Requires a lot of RNA Whole transcriptome sequencing (WTS) Throughput power Expensive Complex data Microarrays Easy data analysis High background noise Requires a lot of RNA Traditional targeted RNA sequencing Manageable data Low per-sample cost Amplification bias
- 7. Sample to Insight Geneexpression profiling 7 Ideal methodology should combine all advantages and overcome all disadvantages Current technology Advantages Disadvantages PCR-based Accuracy Limited sample & assay throughput Requires a lot of RNA Whole transcriptome sequencing (WTS) Throughput power Expensive Complex data Microarrays Easy data analysis High background noise Requires a lot of RNA Traditional targeted RNA sequencing Manageable data Low per-sample cost Amplification bias
- 8. Sample to Insight QIAseqtargeted RNA panels 8 Digital RNAseq for gene expression profiling A complete set of reagents for cDNA synthesis, enrichment of genes and library construction
- 9. Sample to Insight QIAseqtargeted RNA panels 9 How do they address current limitations? Current technology Disadvantages How QIAseq targeted RNA panels address disadvantages of current technologies PCR-based Limited sample & assay throughput Requires a lot of RNA Profile 1000 genes in 96 samples simultaneously Requires 25 ng RNA Whole transcriptome sequencing (WTS) Expensive Complex data Cost-effective Easy data analysis Microarrays High background noise Requires a lot of RNA Highly specific assays Requires 25 ng RNA Traditional targeted RNA sequencing Amplification bias Digital sequencing removes amplification bias Accuracy NGS power Valuable insight
- 10. Sample to Insight QIAseqtargeted RNA panels 10 What are they? The only sample-to-insight digital RNAseq solution for unbiased gene expression profiling using NGS Integrated library preparation Works with any RNA sample type Compatible with most sequencers Complementary data analysis tool for fold change analysis
- 11. Sample to Insight Digitalsequencing by Molecular barcodes for accuracy 11 PCR duplicates and amplification bias are major issues in current RNAseq workflows, as they result in biased and inaccurate gene expression profiles mRNA cDNA PCR duplicates Ratio of original state of genes 4 1 Amplification bias Ratio of genes based on reads [reads (ratio)] 12 (2) 6 (1) Ratio based on reads Gene A Sample 1 Gene A Sample 2
- 12. Sample to Insight Digitalsequencing by Molecular barcodes for accuracy 12 Molecular barcodes allow the counting of original gene levels instead of PCR duplicates, thereby enabling digital sequencing and resulting in unbiased and accurate gene expression profiles mRNA cDNA Ratio of original state of genes 4 1 Gene A Sample 1 Gene A Sample 2 Ratio of genes based on barcodes 4 1 Tag each gene with unique molecular barcodes Count unique barcodes, not reads
- 13. Sample to Insight QIASeqTargeted RNA Panels overcome traditional targeted RNAseq challenges 13 • Digital sequencing using molecular barcode technology o Tagging each cDNA template with a unique barcode o Counting the number of barcodes to correct any amplification artifacts o Providing unparalleled value: accurate and unbiased gene expression analysis with NGS
- 14. Sample to Insight SimpleProcedure 14 6hours GSP1, GSP2 never see other, thereby minimizing primer dimers
- 15. Sample to Insight Sampleindexes for increased throughput 15 Indexes • Illumina o 12 indexes o 96 indexes – Tube and array formats • Ion Torrent o 12 indexes o 96 indexes − Array format
- 16. Sample to Insight Sampleindexes for increased throughput 16 Indexes • Illumina o 12 indexes o 96 indexes – Tube and array formats • Ion Torrent o 12 indexes o 96 indexes – Array format Sample 63 Sample 1 Sample 17 Sample 24 Sample 30 Sample 96 Sample 48 Sample 71 Sample 56 Sample 35
- 17. Sample to Insight Outstandingperformance metrics 17 Accuracy (vs. qPCR): R2 = 0.90 Specificity (on-target reads): >97% Uniformity (20% of mean): >97% Reproducibility (lab 1 vs. lab 2): R2 = 0.99 Sensitivity: detect ~0.2 copies of RNA per cell Average amplicon 97 bps’; range 95-130 bases (FFPE Compatible!) 150 base single reads more than enough for accurate data 128 copies 1 2 3 4 5 6 7 8 9 10 11 12 13 10 tags
- 18. Sample to Insight 170+pathway and disease focused panels for human 18 Cancer Transcriptome Inflammation & Immunity Transcriptome Signal TransductionPathwayFinder Stem Cell & Differentiation Markers Molecular Toxicology Transcriptome Angiogenesis & Endothelial Cell Biology Apoptosis & Cell Death Extracellular Matrix & Cell Adhesion Molecules
- 19. Sample to Insight Takeguessing out of your analysis 19 Built-in controls • gDNA assay to control for any gDNA contamination in the RNA sample • Avg. tags per target calculated and mRNAs near this number are flagged during analysis as ‘close to noise level’ • HKG assays to normalize data to make sample-to- sample and run-to-run comparisons possible • Flexible – use one, two, all, none or any other genes as normalizers HKG
- 20. Sample to Insight Custompanels 20 Online custom builder • Choose your own gene content from 54,881 human genes and lncRNA • Easy to use online Custom Panel Builder to tailor panel to your research needs o Input list of genes o Select proper controls (genomic DNA contamination control, HKGs) o Output: list of genomic coordinates for primers designed specifically for your genes of interest
- 21. Sample to Insight Extendedpanels 21 Extend an existing panel Extend an existing panel by adding up to 25 additional genes
- 22. Sample to Insight Custombuilder 22 Output Download zip file containing: • Summary file • Bed file All your custom designs are saved for easy retrieval Have questions? Easily contact us Configure and order Custom panel number
- 23. Sample to Insight Custombuilder 23 Summary file Gene ID and symbol Strand of the genome the gene is on Amplicon coordinates Chosen controls are shown here • Single exon (1) means both primers are within one exon • # Gencode basic RNAs: total number of RNA transcripts found for the gene in Gencode • # Gencode basic RNAs matched: # of RNA transcripts targeted by the designed amplicon • # off target genes: rough prediction of # of off target genes that will also get enriched by the primer pair for the target gene • Amplicon not genome unique: reads that will not be able to be uniquely mapped to the genome, so some MT counts will come from another gene
- 24. Sample to Insight Custombuilder 24 Bed file Location of designed amplicon
- 25. Sample to Insight QIAseqtargeted RNA panels 25 • Molecular barcodes • 6 hours to go from RNA to sequencing- ready libraries • Minimal RNA input • Small amplicons • 170+ panels • Platform-independent panels • Up to 96 sample indexes • Custom and extended offerings • Built-in controls • Increased accuracy • Optimization of read budget • Make a call about whether a target is expressed sufficiently in a sample • QA FFPE samples in terms of being able to read rare targets • Prepare libraries in one day for a quick turn around time • Preserve precious samples • Profile RNA from FFPE samples • Content for a wide range of applications • Same panel for different sequencers • Decrease per-sample costs • Flexibility to define your own content • Confidently compare samples Feature Benefit Features and benefits
- 26. Sample to Insight 26 SmallMolecules – Signal Transduction Application • HEK293T Cells were treated with 90 different chemical inhibitors. • The 421 Signal Transduction Gene Panel including the ten Housekeeping reference and six gDNA Control genes were interrogated. • In one day we went from total RNA to sequence ready libraries for all of the 96 samples. The final libraries were quantitated and the sequence-ready libraries were pooled in equimolar and denatured using NaOH. Prior to loading onto a NextSeq sequencing run the denatured libraries were diluted to the appropriate input concentrationto obtain and generate suitable clusters on the NextSeq. • The parameters of the NextSeq sequencing run were dual-indexed, single 151 bp read with a Custom Sequencing Primer. QIASeq Targeted RNA Application Data
- 27. Sample to Insight SmallMolecule Application Data 27 • FASTQ files were generated and uploaded into the Primary Analysis Molecular Tag Counting Portal – QIAseq RNAQuantification • Overview of the Summary Page
- 28. Sample to Insight SmallMolecule Application Data 28 • QIAseq RNA Quantification - Read Details: Unique Captures per Target Gene Count Differential gene expression inter- and intra-samples
- 29. Sample to Insight SmallMolecule Application Data 29 • Changes in gene expressionby these treatments were measured by QIAseq RNA NGS, and fold-changes in gene expressiondue to chemical perturbation were characterized. Upload data to secondary data analysis portal and IPA
- 30. Sample to Insight Foldchanges display 30 Scatter plot and clustergram (HDAC Sample compared to the Control Sample)
- 31. Sample to Insight HDACMechanistic Network in HEK293T Cells Treated with Trichostatin A 31 HDAC is predicted to be inhibited by Trichostatin A and drives a Mechanistic Network along with 18 other regulators.
- 32. Sample to Insight Unparalleledefficiency and flexibility 32 96 samples, 421 genes Parameter QIAseq targeted RNApanels RT-PCR Material required One pool of primers 105 384-well plates Run time 14 hours for NextSeq run 310 hours (2 hours per plate) Hands-on time 3 hours (for 96 samples) 105 hours (one hour per plate) Cost per sample $65 (inclusive of sequencing run) $239
- 33. Sample to Insight 33 Summary •Only requires ~ 20 ng of total RNA. • Requires no rRNA depletion or blocking or dT selection. • Random Molecular barcoding assists in enhanced quantification of transcripts being interrogated. • The design is highly flexible, from 12 to 1000 or more targets, 1 to 96 samples per NGS run. • A complete streamline integrated workflow from sample to insight with using IPA. The Benefits of the QIASeq Targeted RNA-Seq Workflow
- 34. Sample to Insight QIAseqtargeted RNA panels overcome challenges of existing gene expression profiling methods 34 Current technology Disadvantages How QIAseq targeted RNA panels address disadvantages of current technologies PCR-based Limited sample & assay throughput Requires a lot of RNA Profile 1000 genes in 96 samples simultaneously Requires 25 ng RNA Whole transcriptome sequencing (WTS) Expensive Complex data Cost-effective Easy data analysis Microarrays High background noise Requires a lot of RNA Highly specific assays Requires 25 ng RNA Traditional targeted RNA sequencing Amplification bias Digital sequencing removes amplification bias
- 35. Sample to Insight QIASeqTargeted RNA Panel: Sample to Insight workflow 35 • Integrated library preparation • Works with any RNA sample type • Compatible with most sequencers • Complementary data analysis tool for fold changeanalysis Comprehensive suite of tools to gain valuable insight Attend next week’s webinar
- 36. Sample to Insight 36 Thankyou Raed N. Samara, PhD Global Product Manager
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Editor's Notes
- #17 across one to 96 samples simultaneously, thereby decreasing the cost per sample and data point
- #20 HKG assays are used to normalize data to make sample-to-sample and run-to-run comparisons possible.
- #27 Here is an example to demonstrate the capabilities of the QIAseq Targeted RNA system by profiling the expression of 100s of genes in a cell model system’s response to small molecules. The chemical inhibitors are the same as from the SureFIND Transcriptome PCR Array.