QIAseq RNA is a revolutionary turnkey solution for digital gene expression analysis by NGS. From 10 genes to 1000, from one sample to 100, QIAseq RNA delivers precise results on both ION and Illumina sequencing platforms. The data from QIAseq RNA is directly comparable to expression analysis derived from whole transcriptome sequencing or by qRTPCR, only better, cheaper, faster, and more flexible. This webinar will describe the principles of digital expression analysis by NGS, and review the features and benefits of the QIAseq system, options available, and the integrated data analysis package.

1. Sample to Insight
QIAseq Targeted RNAseq System
Eric Lader, Ph.D.
Senior Director, R&D

2. Sample to Insight
Today’s agenda
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Expression profiling – a historical prospective
Whole transcriptome sequencing
Principle of QIAseq Targeted RNAseq
QIAseq RNA performance
What comes next? Webinar II and III
Targeted expression analysis
QIAseq RNA NGS workflow
QIAseq primary and secondary data analysis
QIASeq RNA Part 1, 2/17/2016 Lader
QIAseq random molecular barcodes

3. Sample to Insight
Gene expression profiling I: the dark ages
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Northern hybridization relative quantitation with low precision
small dynamic range
low assay throughput
low sample throughput
high sample requirements
Nuclease protection assay relative quantification with better precision
better dynamic range
higher assay throughput
higher sample throughput
End-point RT-PCR relative quantitation with low precision
misleading dynamic range
easy to do wrong
low sample requirements
Filter based hybridization relative quantification with low precision
aka; the dot blot compressed dynamic range
high assay throughput
low sample throughput
QIASeq RNA Part 1, 2/17/2016 Lader

4. Sample to Insight
Gene expression profiling II
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qRT-PCR relative quantitation with high precision
large dynamic range
moderate assay throughput >384 in parallel
low throughput singleplex assays
high sample requirements
Hybridization Array relative quantification with medium precision
compressed dynamic range
extremely high assay throughput
low sample throughput
Digital PCR absolute quantification
broad dynamic range
moderate assay throughput
low sample throughput
price per data point can be high
Transcriptome NGS relative quantitation with high precision
high dynamic range
extremely high assay throughput
extremely low sample throughput
price per sample very high
QIASeq RNA Part 1, 2/17/2016 Lader

5. Sample to Insight
WTS – whole transcriptome sequencing
Benefits
• Quantifies and characterizes all RNA
o Identifies alternative splicing events
o Detects expressed SNPs, mutations, etc.
o Allele-specific expression patterns
Drawbacks
• Large computational requirements
o Massive amount of data generated
o Filtering, alignment, assembly, curation
o Aggressive normalization for quantification
o Not straightforward
o Requires skilled bioinformatics scientists
Cost
o Only runs on HT instruments
– Limits accessibility to core labs
o Requires large read budget = money
– Limited sample numbers in studies
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6. Sample to Insight
WTS – whole transcriptome sequencing
Benefits
• Quantifies and characterizes all RNA
o Identifies alternative splicing events
o Detects expressed SNPs or mutations
o Allele-specific expression patterns
But what if we are only interested
in gene expression?
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Benefits
• Quantifies and characterizes all RNA
o Identification of alternative splicing events
o Detects expressed SNPS or mutations
o allele specific expression patterns
QIASeq RNA Part 1, 2/17/2016 Lader

7. Sample to Insight
Targeted expression analysis by NGS
What are the potential advantages of applying targeted gene profiling to NGS?
• Use read budget only for genes of interest
o Cost
o Time (quick prep, run, analysis)
o Sample throughput – multiplex many samples
7QIASeq RNA Part 1, 2/17/2016 Lader

8. Sample to Insight
Targeted expression analysis by NGS
What are the potential advantages of applying targeted gene profiling to NGS?
• Use read budget only for genes of interest
o Cost
o Time (quick prep, run, analysis)
o Sample throughput – multiplex many samples
• Desktop platforms can be used for RNA analysis
o Don’t need the core lab across campus
8QIASeq RNA Part 1, 2/17/2016 Lader

9. Sample to Insight
Targeted expression analysis by NGS
What are the potential advantages of applying targeted gene profiling to NGS?
• Use read budget only for genes of interest
o Cost
o Time (quick prep, run, analysis)
o Sample throughput – multiplex many samples
• Desktop platforms can be used for RNA analysis
o Don’t need the core lab across campus
• Simplified bioinformatics (no assembly required)
o Don’t need that bioinformatics guy down the hall
9QIASeq RNA Part 1, 2/17/2016 Lader

10. Sample to Insight
Targeted expression analysis by NGS
What are the potential advantages of applying targeted gene profiling to NGS?
• Use read budget only for genes of interest
o Cost
o Time (quick prep, run, analysis)
o Sample throughput – multiplex many samples
• Desktop platforms can be used for RNA analysis
o Don’t need the core lab across campus
• Simplified bioinformatics (no assembly required)
o Don’t need that bioinformatics guy down the hall
• Minimal sample pre-processing
o No ribosomal depletion or blocking or poly A selection
o Only nanogram quantities of total RNA required
10QIASeq RNA Part 1, 2/17/2016 Lader

11. Sample to Insight
Targeted expression analysis by NGS
What are the potential advantages of applying targeted gene profiling to NGS?
• Use read budget only for genes of interest
o Cost
o Time (quick prep, run, analysis)
o Sample throughput – multiplex many samples
• Desktop platforms can be used for RNA analysis
o Don’t need the core lab across campus
• Simplified bioinformatics (no assembly required)
o Don’t need that bioinformatics guy down the hall
• Minimal sample pre-processing
o No ribosomal depletion or blocking or poly A selection
o Only nanogram quantities of total RNA required
When? Who? Why?
• Scientists with known gene list or pathway
• Follow up on broader experiment, such as WTS or microarray
11QIASeq RNA Part 1, 2/17/2016 Lader

12. Sample to Insight
• Complete, integrated system from Sample to Insight
o Sensitive and highly specific
o Extremely flexible in experimental design (n samples x n assays)
o Simple for end user to address bioinformatically
o Requires no rRNA depletion or blocking or dT selection
o Makes best use of limited NGS readbudget
o Flexible content
– Leverage Qiagen content know-how
– Disease and pathway focused panels
– Ready to use, easy to modify, and fully custom panel content
QIAseq: high-throughput digital NGS
Simple to use, complex behind the scenes
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13. Sample to Insight
• Complete, integrated system; sample to insight
o Sensitive and highly specific
o Extremely flexible in experimental design (n samples x n assays)
o Simple for end user to address bioinformatically
o Requires no rRNA depletion or blocking or dT selection
o Makes best use of limited NGS readbudget
o Flexible content
– Leverage Qiagen content know-how
– Disease and pathway focused panels
– Ready to use, easy to modify, and fully custom panel content
• Features
o NGS platform agnostic – Ion, Illumina
o SMcounter – molecular barcoding for precise and accurate quantification
o Streamlined one-day protocol, easily automatable
o Integrated controls
– GDC, reference gene controls for data normalization
o Engineered to produce results that are both Precise and Accurate
QIAseq: high-throughput digital NGS
Simple to use, but complex behind the scenes
13QIASeq RNA Part 1, 2/17/2016 Lader

14. Sample to Insight
Criteria
Biological replicates Essential for robustness of experimental design
Technical replicates Generally not required
Coverage across the
transcript
Not important; we are counting genes by common
regions
Role of sequencing depth
Capture enough unique barcodes of each transcript
such that statistical inferences can be made (=>10
per gene)
Overall sequencing depth
High enough to infer accurate statistics as
determined by Smcounter - >1 reads per unique
barcode
Stranded library prep Not required; amplicons do not overlap lncRNA
Paired-end reads
Not required; 150-base single-ended reads more
than enough (platform independent)
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QIAseq considerations
QIASeq RNA Part 1, 2/17/2016 Lader

15. 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.
Access RNA from any sample
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Tissue samples
Fresh or FFPE tissue samples of tumor extracted
from the patient’s body
QIAGEN’s comprehensive sample isolation portfolio compatible with QIAseq RNA
QIASeq RNA Part 1, 2/17/2016 Lader

16. Sample to Insight
QIAseq Targeted RNAseq is truly a Sample to Insight solution
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Easy enough for first-time NGS users
Advanced enough for power users
Any samples  any genes  any platform
Sample
isolation
Targeted
enrichment
Library
construction
NGS run
With platform
consumables
NGS data
analysis
Pathway
analysis
by IPA
Sample Insight
QIASeq RNA Part 1, 2/17/2016 Lader
QIAseq RNA

17. Sample to Insight
QIAseq targeted RNA 2-stage PCR workflow
cDNA synthesis
QIAseq beadcleanup
1st stage PCR
2nd stage PCR/sample indexing
Primer extension/moleculartagging
QIAseq beadcleanup
RNA sample
6 hours
96 well-plate compatible
QIAseq beadcleanup
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Everything needed to go from RNA  Library in one kit, one day!
QIASeq RNA Part 1, 2/17/2016 Lader

18. Sample to Insight
MT
2
1
GS
RS2
GS
FS2
Boosting Primer
for amplicon 1
QIAseq targeted RNA sequencing principle
Universal PCR adding NGS adaptors and sample indexes
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MT RS2GS
MT = 12-base unique barcode
GS = gene specific
RS2 universal binding
These are quite different
QIASeq RNA Part 1, 2/17/2016 Lader
cDNA – random and dT primed
Limited PCR

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Sequencing libraries were prepared using 1.25, 5, or 20 ng universal reference RNA
Gene panels ranging from 12-plex to 1000-plex.
Sequencing was performed on the Illumina MiSeq, dedicating 1 million reads per sample.
Specificity is calculated as percent of trimmed and mapped reads that map to intended target.
Specificity of QIAseq RNA sequencing
QIASeq RNA Part 1, 2/17/2016 Lader

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Wang, et al. BMC Genomics (2015) 16:589
Smcounter barcodes deliver far superior CV than raw reads
ERCC standards spiked into UHRR, triplicate samples
Counting RNA transcripts rather than PCR copies
QIASeq RNA Part 1, 2/17/2016 Lader

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Platform agnostic precision: MiSeq vs PGM
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Fold-change (HURR/HBRR)correlation – 288 gene panel
QIASeq RNA Part 1, 2/17/2016 Lader

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Inter-laboratory precision on Illumina MiSeq
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Fold-change (HURR/HBRR)correlation – 288 gene panel
QIASeq RNA Part 1, 2/17/2016 Lader

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Reproducibility of QIAseq panel performance
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Beta 1
Same samples, 2 different labs, identical results
20 ng universal reference RNAand brain RNA, 384 gene panel
Sequenced on Illumina Nextseq, plotted fold difference in gene expression
QIASeq RNA Part 1, 2/17/2016 Lader

24. Sample to Insight
QIAseq profiling is highly correlatedto exhaustive transcriptome NGS
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UHHR: UBHR expression ratio: QIAseq vs whole transcriptome
Whole
transcriptome
QIASeq RNA Part 1, 2/17/2016 Lader

25. Sample to Insight
Comparison of gene expression: qPCR vs qRNASeq
• Relative gene expression changes between UHHR and UHBR RNA samples
(determined by multiplex NGS vs singleplex real-time qRTPCR assays
1. qPCR was normalized by CT (GOI-HKG)
2. qRNAseq was normalized to total number of QIAseq SMcounter barcodes
3. Fold change (Log 2) compared between two reference RNA samples
4. NGS required 5 ng total RNA, qPCR requires1200 ng (384-well PCR in triplicate)
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 Fold change between reference brain and universal reference RNA
determined by both qPCR and qQIAseq
Excellent correlation of relative gene expression changes by
real-time qPCR and QIAseq RNA sequencing
Comparison of gene expression: qPCR vs QIAseq
QIASeq RNA Part 1, 2/17/2016 Lader

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 ERCC standards spiked into samples at 86 to 705,500 copies
 ERCC assays added to 384-plex gene panel
 Three technical replicates of complete workflow were performed (RNA to data)
A) Measuring sensitivity with calibrated standards. Under standard conditions (20 ng input UHRR, 500 K
MiSeq reads), the reliable limit of sensitivity to detect ERCC transcripts was ~100 copies. Greater read
budget would increase sensitivity to ~10 copies.
B) Precision of technical triplicates at various concentrations. At >10 barcodes/gene, CV was less than 5% for
all targets, indicating high technical reproducibility. This corresponds to ~ 100 copies target RNA in the
sample.
In summary, accurate quantification is possible down to ~100 copies of an RNA target in 20 ng
total RNA, which is the equivalent of ~0.2 copies per cell
Benchmarking sensitivity with ERCC calibrated RNA standards
QIASeq RNA Part 1, 2/17/2016 Lader

28. Sample to Insight
Effect of sequencing depth on sensitivity – 384-plex
Low read depth caused “dropping out” of low expressing genes (<10 tags/gene)
that recommended read depth is able to capture and quantify.
The majority of expression analysis is unaffected by variations in read depth
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Analyzed by unique tags per gene
QIASeq RNA Part 1, 2/17/2016 Lader

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QIAseq primary data analysis
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QIAseq primary data analysis interface
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Data Analysis for QIAseq Targeted RNA Sequencing
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QIAseq Targeted RNA Data Analysis automated workflow
Read
Mapping
• Read Mapping
o Identify the possible position of the read within the reference
o Align the read sequence to reference sequences
• Primer Trimming
o Remove the primer sequences from the reads
• Molecular Barcode Counting
Primer
Trimming
Molecular
Barcode
Count
QIASeq RNA Part 1, 2/17/2016 Lader

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QIAseq primary data analysis output
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Sample by sample, gene by gene, unique barcode (and total) counts
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Read details: unique captures per target gene
QIASeq RNA Part 1, 2/17/2016 Lader 34
Then…secondary analysis
Normalization against:
I. unique barcodes/total unique barcodes per sample
II. housekeeping genes (one, some, all)
III. genes of your choice
Calculate – fold change, p-values, generate heat maps, volcano plots

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QIAseq secondary data analysis setup
QIASeq RNA Part 1, 2/17/2016 Lader

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QIAseq secondary data analysis setup
QIASeq RNA Part 1, 2/17/2016 Lader

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QIAseq secondary data analysis
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QIAseq Targeted RNAseq system summary
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• Extremely sensitive expression profiling, >1 copy per cell
• Highly flexible experimental design, from 12–1000 or more targets, 1 to 96 samples
• High specificity, ~97-99%maintained through all panels
• Extremely high read uniformity ~0.98 at 20% mean
• Smcounter – random molecular barcoding for quantification
• Requires no rRNA depletion or blocking or dT selection
o Only requires ~1–20 ng total RNA
• Makes best use of limited NGS read budget
• System optimized for best possible performance with FFPE samples
• Leverage QIAGEN content know-how for NGS
o Disease and pathway specific collections
o Extended panels and fully custom gene content 12–1000 genes
• Complete integrated workflow fromSample to Insight
o 96-well and automation compatible
o Suite of integrated performance and normalization controls
– gDNA, reference gene panel, normalization by barcodes
QIASeq RNA Part 1, 2/17/2016 Lader

39. Sample to Insight
QIAseq targeted RNA products
QIAseq Targeted RNAPanel (12 or 96 samples)
Kit containing reagents for first strand synthesis, Smcounter tagging, and gene-specific amplification for
targeted RNA sequencing
QIAseq Targeted RNAExtended Panel (12 or 96 samples) (up to 25 additional targets)
Kit containing reagents for first strand synthesis, Smcounter tagging, and gene-specific amplification for
targeted RNA sequencing;
QIAseq TargetedRNACustom Panel (12, 96 or 384 samples)
Kit containing reagents for first strand synthesis, Smcounter tagging, and gene-specific amplification for
targeted RNA sequencing
QIAseq Targeted RNAsample Indexing(12-plex or 96-plex HT) for Ion Torrent
QIAseq Targeted RNAsample Indexing (12-plex or 96-plex or HT) for Illumina
Library Quant Assay/Array Kit
Assays and master mix for library quantification prior to NGS
Initial content: comprehensive 250–500 gene panels
and ALL human RT2
panel content (200 panels)
Immunity and Inflammation Angiogenesis and Endothelial
Cell Death Cancer Pathway
Signal Transduction ECM and Cell Adhesion
Molecular Toxicology Stem Cells
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40. Sample to Insight
QIAseq sample multiplexing guidelines on NGS platforms
How many samples? How many assays?
Making the best of your read budget
Sample types, special handling for FFPE, cfDNA
QC of sample RNA, libraries
Platform-specific special considerations
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Webinar II: A deep dive into QIAseq RNA workflow and data analysis

41. Sample to Insight
Webinar III: A Sample to Insight application
QIAseq NGS and Ingenuity IPA
• Cancer Scoring
• Hereditary Disease Scoring
• Causal Network Analysis
• Druggable Pathways
• Disease Model-based Analysis
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42. Sample to Insight
Thank You!
Technology Development
Yexun (Bill) Wang, Ph.D.
Quan Peng, Ph.D.
Bioinformatics
John DiCarlo. Ph.D
Jixin Deng, Ph.D.
Yi Rui, Ph.D.
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Product Development
Eric Lader, Ph.D.
Qiong Jiang, Ph.D.
Matt Fosbrink, Ph.D.
Melanie Hussong, Ph.D.
Geoff Wilt, M.S.