QIAseq Targeted DNA, RNA and Fusion Gene Panels
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Tumor heterogeneity has been known for a while but quantifying heterogeneity is still a challenge. NGS is the method of choice in the analysis of tumor heterogeneity, however, there are some inherent challenges associated with it. These include false positives, gaps in the gene due to overrepresentation and incomplete representation of low-frequency transcripts – all contributing to an inaccurate picture. Conventional library prep strategies for NGS are based on PCR, which introduces sequence-based bias and amplification noise, leading to these inaccuracies. In this webinar, we will cover 1. Principles of UMI and the new QIAseq product porfolio 2. How UMI along with SPE (single primer extension) allows for increased uniformity across difficult-to-sequence regions, removal of library construction bias, improved data analysis and sequencing optimization 3. How data generated from using UMI and SPE is directly comparable to analysis derived from whole transcriptome and exome sequencing 4. Application of UMI and SPE in the discovery of novel gene fusions and in the analysis of gene expression and genetic variation
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QIAseq Targeted DNA, RNA and Fusion Gene Panels
- 1. Sample to Insight QIAseq Targeted Sequencing 1PowerPoint Style Guide, 07.10.2015 Samuel Rulli, Ph.D. Global Product Manager QIAGEN
- 2. Sample to Insight Legal disclaimer 2 • QIAGEN products shown here are intended for molecular biology applications. These products are not intended for the diagnosis, prevention or treatment of a disease. • For up-to-date licensing information and product-specific disclaimers, see the respective QIAGEN kit handbook or user manual. QIAGEN kit handbooks and user manuals are available at www.QIAGEN.com or can be requested from QIAGEN Technical Services or your local distributor.
- 3. Sample to Insight Tumor heterogeneity 3 • Tumors are notorious for being a mixed population of cancer cells and infiltrating cells • There is often a limited amount of sample available • FFPE samples are a necessary part of most cancer research programs
- 4. Sample to Insight Tumor heterogeneity 4 • Tumor heterogeneity leads to ◦ Highly variable cancers ◦ Differential response to treatment – Targeted – Non-targeted ◦ A need to be monitored over time, especially during treatment • Low-frequency gene mutations are as important as high-frequency ones • The challenge is to identify these low frequency events
- 5. Sample to Insight Using targeted sequencing to understand tumor heterogeneity 5 • Targeted sequencing is uniquely positioned to address these problems • Needs a small amount of sample input • Robust even when using FFPE or damaged samples • Accessible to researchers with bench-top NGS instruments • Simplified bioinformatics Variant? Fusion? GEX? miRNA? Variant? Fusion? GEX? miRNA? Variant? Fusion? GEX? miRNA?
- 6. Sample to Insight Contents 6 Principles of unique molecular indexes (UMIs)1 Single primer extension (SPE) vs. PCR for library construction2 UMIs and SPE in action – gene expression analysis3 DNA variant analysis and novel gene fusion discoveries with UMIs and SPE4 Summary/questions5
- 7. Sample to Insight Contents 7 Principles of unique molecular indexes (UMIs)1 Single primer extension (SPE) vs. PCR for library construction2 UMIs and SPE in action – gene expression analysis3 DNA variant analysis and novel gene fusion discoveries with UMIs and SPE4 Summary/questions5
- 8. Sample to Insight Principles of unique molecular indexes (UMIs) 8 PCR duplication and amplification bias are major issues in current RNAseq workflows, as they result in biased and inaccurate gene expression profiles mRNA copies cDNA Original gene ratio status mRNA ratio based on reads (reads ratio) Gene A Sample 1 Gene A Sample 2 Raw reads 4 1 Number of reads 1 2 6 12
- 9. Sample to Insight 9 Targeted RNAseq is a “read-based” approach to understanding gene expression How do we go from “reads” to counting transcripts? Principles of unique molecular indexes (UMIs)
- 10. Sample to Insight 10 Targeted RNAseq is a “read”-based approach to understanding gene expression How do we go from “reads” to counting transcripts? Principles of unique molecular indexes (UMIs)
- 11. Sample to Insight Principles of unique molecular indexes (UMIs) 11 mRNA copies cDNA Original gene ratio status mRNA ratio based on reads Gene A Sample 1 Gene A Sample 2 UMI reads 4 1 1 2 Molecular indexes allow the counting of original transcript levels instead of PCR duplicates, thereby enabling digital sequencing and resulting in unbiased and accurate gene expression profiles Tag each transcript with UMIs mRNA ratio based on UMIs 1 4 Count UMIs, not reads
- 12. Sample to Insight 12 During mRNAseq, each capture event is archived with an UMI 12 random bases 16.7 million indexes The strategy for measuring gene expression uses UMI-gene-specific primer The strategy for measuring DNA variant and fusion gene is slightly different, but the principle is the same. Principles of unique molecular indexes (UMIs)
- 13. Sample to Insight Contents 13 Principles of unique molecular indexes (UMIs)1 Single primer extension (SPE) vs. PCR for library construction2 UMIs and SPE in action – gene expression analysis3 DNA variant analysis and novel gene fusion discoveries with UMIs and SPE4 Summary/questions5
- 14. Sample to Insight Single primer extension 14 5’ 3’ 3’ 5’ cDNA 5’ 3’ 5’ Captures information Single primer
- 15. Sample to Insight 15 Advantages of SPE: • Needs only a single region for primer design ◦ Unlocks entire transcriptome, genome and fusion genes ◦ Having to use half the number of primers lowers cost and allows for greater content during multiplexing • Able to adapt to G/C-rich and difficult-to-PCR regions ◦ Allows you to sequence almost everything • Uniform reaction ◦ Uniform library construction – uniform sequencing • Works very well on FFPE, fragmented and low quality samples Disadvantages of SPE: • Extra step in library construction ◦ May add 1 hour to total workflow Single primer extension
- 16. Sample to Insight Contents 16 Principles of unique molecular indexes (UMIs)1 Single primer extension (SPE) vs. PCR for library construction2 UMIs and SPE in action – gene expression analysis3 DNA variant analysis and novel gene fusion discoveries with UMIs and SPE4 Summary/questions5
- 17. Sample to Insight 17 Small molecules/signal transduction application Experiment: identify novel compounds that modulate known signal transduction pathways Cells Treated cells RNA UMIs and SPE in action: a gene expression example
- 18. Sample to Insight 18 Small molecules/signal transduction application • Cells are treated with different chemical inhibitors • RNA is isolated • Libraries are built using QIAseq Targeted RNA Panels ◦ Human Signal Transduction Panel – 421 targets/10 ng total RNA Cells Treated cells RNA UMIs and SPE in action: a gene expression example Experiment: identify novel compounds that modulate known signal transduction pathways
- 19. Sample to Insight 19 6hours GSP1, GSP2 are used at different stages They never interact, which minimizes primer dimers UMIs and SPE in action: a gene expression example
- 20. Sample to Insight 20 “Leave no scientist behind…” UMIs and SPE in action: a gene expression example
- 21. Sample to Insight 21 UMIs and SPE in action: a gene expression example Included in panel kit Library Quant Kit Included in cloud Index Kit
- 22. Sample to Insight 22 UMIs and SPE in action: a gene expression example Included in panel kit Library Quant Kit Included in cloud Index Kit CLC Biomedical workbench with MT plugin
- 23. Sample to Insight Customer criteria Differential gene expression by QIAseq NGS Species coverage Human – catalog, extended, virtual and custom panels Mouse and rat – custom Biological replicates Essential for robustness of experimental design (and statistics!) Short reads for FFPE and exosomal RNA Average amplicon 97 bps’; range 95-130 bases Coverage across transcript (i.e. cover every exon) We are counting single common regions per gene. Same design philosophy as RT2 PCR Arrays Depth of sequencing High enough to infer accurate statistics determined by UMI: ~2-5 reads per UMI is enough. Role of sequencing depth Capture enough unique tags of each transcript such that statistical inferences can be made (>10 tags per gene) Stranded library prep Not required, amplicons do not overlap lncRNA Type of reads (paired or Unpaired?) Not necessary; 150 base single reads more than enough for accurate data mRNA and lncRNAs QIAseq was designed against database containing lncRNA and mRNA. Assay are specific for lncRNA or mRNA. Currently 54,881 genes from Ensembl version 81 23 UMIs and SPE in action: a gene expression example
- 24. Sample to Insight Free circulating nucleic acids RNA and DNA from dead cells shed into the bloodstream, can contain cancer-related mutations. Exosomes Tiny microvesicles found in body fluids that transport RNA between cells. Circulating tumor cells Tumor cells shed from a tumor into the bloodstream carrying genetic information. 24 Tissue samples Fresh tissue or archived FFPE samples QIAGEN’s comprehensive sample isolation portfolio is compatible with QIAseq RNA Kits and allows you to use as little as 100 pg (10 cells) to 25 ng RNA UMIs and SPE in action: a gene expression example
- 25. Sample to Insight 25 UMIs and SPE in action: a gene expression example
- 26. Sample to Insight 26 Flexible experiment design for any research interest Catalog panel options: Comprehensive panels (available for 12, 96 or 384 samples) • Cancer Transcriptome (395) • Inflammation & Immunity Transcriptome (475) • Signal Transduction PathwayFinder (406) • Stem Cell & Differentiation Markers (293) • Molecular Toxicology Transcriptome (370) • Angiogenesis & Endothelial Cell Biology (340) • Apoptosis & Cell Death (264) • ECM & Adhesion Molecules (421) UMIs and SPE in action: a gene expression example
- 27. Sample to Insight 27 lnc13 ADAM TS9 CAHM DLEU2 GAS5 GAS6- AS1 GNAS LINC00 261 MEG3 MIR31 HG MIR7- 3HG NAMA PTCSC1 PTCSC3 TERC ZFAS1 LINC00 312DLX6 NEAT GACAT1 What is the role of tumor suppressor lncRNAs? Find out! Flexible experiment design for any research interest UMIs and SPE in action: a gene expression example Extended panels: Add 25 of your favorite targets (mRNA or lncRNA) to QIAGEN’s comprehensive panel
- 28. Sample to Insight 28 Catalog panel options: QIAseq Targeted RNA Virtual Panels (available for 12, 96 or 384 samples) Each panel contains 84 genes + controls and house keeping genes Choose from over 180 panels! DiseasesPathways miRNA Targets UMIs and SPE in action: a gene expression example Flexible experiment design for any research interest
- 29. Sample to Insight 29 Online custom builder • Choose your own gene content from 54,881 human genes and lncRNAs • Easy to use online Custom Panel Builder to tailor panel to your research needs ◦ Input list of genes ◦ Select proper controls (genomic DNA contamination control, HKGs or your own) ◦ Output: list of genomic coordinates for primers designed specifically for your genes of interest UMIs and SPE in action: a gene expression example
- 30. Sample to Insight 30 Download zip file containing: • Summary file • Bed file All your custom designs are saved for easy retrieval Have questions? Contact us easily Configure and order Custom panel number UMIs and SPE in action: a gene expression example
- 31. Sample to Insight Custom builder 31 Gene ID and symbol Genome strand on which gene is located Amplicon coordinates Designated controls • Single exon (1): 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 number 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 might come from another loci
- 32. Sample to Insight Custom builder 32 Bed file Location of designed amplicon
- 33. Sample to Insight QIAGEN: Automating Sample to Insight Real time PCR + HRM PCR Fragment Analysis Pyro- sequencing Hybrid capture Bench top assay setup Integrated assay setup Low-throughput High-throughput Sample disruption Purification Assay setup Detection and analysis Medium- throughput Quality Control 33 GeneReader NGS
- 34. Sample to Insight QIAGEN: Automating Sample to Insight Purification Quality Control 34 Cells in 96 well plates RNA isolation from 96 samples RNA Integrity (96 samples done automatically while at lunch!) RNA quantification (16 samples per 90 secs)
- 35. Sample to Insight QIAGEN: Automating Sample to Insight Purification Quality Control 35 Cells in 96 well plates RNA isolation from 96 samples Sample disruption Assay setup Detection and analysis Library quantification Library integrity RNA Integrity (96 samples done automatically while at lunch!) RNA quantification (16 samples per 90 secs)
- 36. Sample to Insight 36 Small molecules/signal transduction application Cells Treated cells RNA QIAseq targeted application data Normalized, pooled libraries Indexed libraries • HEK293T cells were treated with 90 different chemical inhibitors • The 421 Signal Transduction Gene QIAseq Panel was interrogated • In one day, we went from total RNA to sequence-ready libraries for 96 samples • The final libraries were quantified, normalized, and pooled. • Prior to loading onto a NextSeq, the denatured libraries were diluted to the appropriate input concentration to generate suitable clusters on the NextSeq. • The parameters of the NextSeq sequencing run were: ◦ A single 151 bp read ◦ A custom sequencing primer (included in kit)
- 37. Sample to Insight Primary data analysis for QIAseq targeted RNA sequencing 37 QIAseq targeted RNA data analysis automated workflow • Read Mapping ◦ Identify the possible position of the read within the reference genome ◦ Align the read sequence to reference sequences • Primer Trimming ◦ Remove primer sequences from the reads • UMI Counting Go get coffee! Read mapping Primer trimming UMI count
- 38. Sample to Insight Small molecule application data 38 Primary data analysis for QIAseq targeted RNA sequencing
- 39. Sample to Insight Small molecule application data 39 Primary data analysis for QIAseq targeted RNA sequencing • QIAseq RNA quantification - read details: unique captures per target gene count Differential gene expression, inter- and intra-samples
- 40. Sample to Insight Controls: take the guesswork out of your analysis! 40 Built-in controls • Assays to control for any gDNA contamination in the RNA sample • Mean tags per target calculated and mRNA counts near this number flagged during analysis as ‘close to noise level’ • Multiple HKG assays normalize data to make sample-to-sample and run-to-run comparisons possible • Flexible – use none, one, two or any other number of genes to normalize • HKG efficacy evaluation built into secondary data analysis HKG
- 41. Sample to Insight 41 QIAseq secondary data analysis setup What kinds of things get flagged? Low tag #, high gDNA, poor normalizer performance
- 42. Sample to Insight 42 Changes in gene expression due to chemical perturbation were quantified by QIAseq RNA NGS and characterized Secondary data analysis for QIAseq targeted RNA sequencing
- 43. Sample to Insight 43 Scatter plot and clustergram (HDAC sample compared to control) Secondary data analysis for QIAseq targeted RNA sequencing
- 44. Sample to Insight HDAC mechanistic network in HEK293T cells treated with trichostatin A 44 HDAC is predicted to be inhibited by trichostatin A and drives a mechanistic network with 18 other regulators Ingenuity IPA analysis Cell cycle NHR, proliferation Transcriptional activator
- 45. Sample to Insight QIAseq sample multiplexing guidelines on NGS platforms 45 Where can you run these panels?
- 46. Sample to Insight Unparalleled efficiency and flexibility vs PCR 46 An example: 96 samples, 421 genes Parameter QIAseq Targeted RNA Panels RT-PCR Material required One pool of primers One hundred and five 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) Sample 10 ng each sample 4000 ng each sample
- 47. Sample to Insight Contents 47 Principles of unique molecular indexes (UMIs)1 Single primer extension (SPE) vs. PCR for library construction2 UMIs and SPE in action – gene expression analysis3 DNA variant analysis and novel gene fusion discoveries with UMIs and SPE4 Summary/questions5
- 48. Sample to Insight 48 Biomarkers DNA mRNA/ lncRNA Fusion miRNA QIAseq Targeted DNA Panels • Unique molecular index • Mutation /SNP analysis • CNV • Insertions/deletions • 2 ng fresh DNA QIAseq Targeted RNAscan Panels • Unique molecular index • Known fusion genes (validation) • Unknown partners (discovery) QIAseq miRNAseq Kits • UMIs • Gel free library prep • Complete miRNome QIAseq Targeted RNA Panels • UMIs • Gene expression • Start with 100 pg/10 cells DNA variant analysis and novel gene fusion discovery
- 49. Sample to Insight 49 Biomarkers DNA mRNA/ lncRNA Fusion miRNA QIAseq Targeted DNA Panels • Unique molecular index • Mutation /SNP analysis • CNV • Insertions/deletions • 2 ng fresh DNA DNA variant analysis and novel gene fusion discovery
- 50. Sample to Insight 50 PCR and sequencing errors (artifacts) limit variant calling accuracy Not possible to discern whether the mutation is: 1. A PCR or sequencing error (artifact/false positive) OR 2. A true low-frequency mutation Traditional targeted DNA sequencing EGFR exon 21 * Variant calling based on non-unique reads does not reflect the mutational status of original DNA molecules Applies to a wide range of panels DNA variant analysis and novel gene fusion discovery A mutation is seen in 1 out of 5 reads that map to EGFR exon 21
- 51. Sample to Insight 51 Digital sequencing = count and analyze each original molecule (not total reads) Not possible to discern between: 1. Five unique DNA molecules OR 2. Quintuplets of the same DNA molecule (PCR artifact) Traditional targeted DNA sequencing EGFR exon 21 DNA variant analysis and novel gene fusion discovery Five reads or library fragments that look exactly the same Five unique DNA molecules since five UMIs are detected Quintuplets of the same DNA molecule (PCR duplicates) since only one UMI is detected UMI Digital sequencing with UMIs Add UMIs before amplification UMIs
- 52. Sample to Insight 52 Digital sequencing = count and analyze each original molecule (not total reads) Traditional targeted DNA sequencing EGFR exon 21 DNA variant analysis and novel gene fusion discovery Add UMIs before amplification * A mutation is seen in 1 out of 5 reads that map to EGFR exon 21 Not possible to discern whether the mutation is: 1. A PCR or sequencing error (artifact/false positive) OR 2. A true low-frequency mutation A false variant is present in only some fragments with the same UMI A true variant is present in all fragments with the same UMI UMI * ***** Digital sequencing with UMIs UMI
- 53. Sample to Insight 53 QIAseq Targeted DNA Panel Workflow DNA variant analysis and novel gene fusion discovery
- 54. Sample to Insight 54 Biomarkers DNA mRNA/ lncRNA Fusion miRNA QIAseq Targeted RNAscan Panels • Unique molecular index • Known fusion genes (validation) • Unknown partners (discovery) DNA variant analysis and novel gene fusion discovery
- 55. Sample to Insight RPS6KB1-VMP1 ARFGEF2-SULF2 QIASeq Targeted RNAscan is a RNA target enrichment method that allows verification of known fusions and discovery of novel fusions with next-generation sequencing (NGS). DNA variant analysis and novel gene fusion discovery 55
- 56. Sample to Insight 56 DNA variant analysis and novel gene fusion discovery
- 57. Sample to Insight Contents 57 Principles of unique molecular indexes (UMIs)1 Single primer extension (SPE) vs. PCR for library construction2 UMIs and SPE in action – gene expression analysis3 DNA variant analysis and novel gene fusion discoveries with UMIs and SPE4 Summary/questions5
- 58. Sample to Insight Summary: biomarkers come in many flavors 58 Biomarkers Gene expression Copy number variants Indels Mutations miRNA expression Fusions
- 59. Sample to Insight QIAseq solutions to detect several kinds of biomarkers using NGS 59 Biomarkers Gene expression Copy number variants Indels Mutations miRNA expression Fusions
- 60. Sample to Insight 60 NGS Summary: NGS can be used for several kinds of biomarkers Biomarkers Gene expression Copy number variants Indels Mutations miRNA expression Fusions
- 61. Sample to Insight Plug & play: many flavors, same Sample-to-Insight workflow 61 Different panels can be plugged into the same targeted NGS workflow Sample Insight DNA panels for variant analysis RNA panels for differential gene expression RNA panels for fusion gene profiling miRNome panel for miRNA expression Mutations, indels, copy number variants Gene expression levels Fusions miRNA levels Sample isolation Library construction & targeted enrichment NGS run Data analysis Interpretation
- 62. Sample to Insight 62 Biomarkers Gene expression Copy number variants Indels Mutations miRNA expression Fusions QIAseq solutions to detect several kinds of biomarkers using NGS QIAseq targeted DNA Panels QIAseq-targeted RNA Panels QIAseq miRNA sequencing system QIAseq-targeted RNAscan Panels
Editor's Notes
- It is generally agreed that cancer stem cell model must coexists with other sources of tumor heterogeneity including clonal evolution, heterogeneity in micro-environment, and reversible changes in cancer-cell properties that can occur independently of hierarchical properties What is not clear is what extent is metastasis, therapy resistance and disease progression reflect the intrinsic properties of the cancer stem cells as apposed to genetic evolution or other sources of heterogeneity. What is certainly clear is that we need to develop integrated multiple experimental approach to distinguish the relative contributions of these different sources of heterogeneity to disease progression. For easier applications in real world, these have to be easy to implement, robust and
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- Some features of quantitative RNAseq Replicates (same as any proper expression experiment) Can count using a small region Depth has to allow statistical accuracy, but much shallower than transcriptome Strandedness is not needed – assays target unique regions Paired end reads not required but nice to have – must read from universal end or through it to capture barcode.
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- A variant identified in a sample represents one of two events: a true or false variant. False variants can be introduced at any step during the workflow, including sequencing reactions. This results in the inability to accurately and confidently call rare variants (those present at 1% of the sample). Due to PCR duplicates generated in amplification steps, all DNA fragments look exactly the same, and there is no way to tell whether a specific DNA fragment is a unique DNA molecule or a duplicate of a DNA molecule. With molecular barcodes, since each unique DNA molecule is barcoded before any amplification takes place, unique DNA molecules are identified by their unique barcodes, and PCR duplicates carrying the same barcode are removed, thereby increasing the sensitivity of the panel.
- A variant identified in a sample represents one of two events: a true or false variant. False variants can be introduced at any step during the workflow, including sequencing reactions. This results in the inability to accurately and confidently call rare variants (those present at 1% of the sample). Due to PCR duplicates generated in amplification steps, all DNA fragments look exactly the same, and there is no way to tell whether a specific DNA fragment is a unique DNA molecule or a duplicate of a DNA molecule. With molecular barcodes, since each unique DNA molecule is barcoded before any amplification takes place, unique DNA molecules are identified by their unique barcodes, and PCR duplicates carrying the same barcode are removed, thereby increasing the sensitivity of the panel.
- A variant identified in a sample represents one of two events: a true or false variant. False variants can be introduced at any step during the workflow, including sequencing reactions. This results in the inability to accurately and confidently call rare variants (those present at 1% of the sample). Due to PCR duplicates generated in amplification steps, all DNA fragments look exactly the same, and there is no way to tell whether a specific DNA fragment is a unique DNA molecule or a duplicate of a DNA molecule. With molecular barcodes, since each unique DNA molecule is barcoded before any amplification takes place, unique DNA molecules are identified by their unique barcodes, and PCR duplicates carrying the same barcode are removed, thereby increasing the sensitivity of the panel.