digital rnaseq technology introduction: digital rnaseq webinar part 1

Sample to Insight

QIAseq Targeted RNAseq System

Eric Lader, Ph.D.Senior Director, R&D

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Today’s agenda

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

Sample to Insight

Gene expression profiling I: the dark ages

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Northern hybridization relative quantitation with low precisionsmall dynamic rangelow assay throughput low sample throughputhigh sample requirements

Nuclease protection assay relative quantification with better precisionbetter dynamic rangehigher assay throughputhigher sample throughput

End-point RT-PCR relative quantitation with low precisionmisleading dynamic rangeeasy to do wronglow sample requirements

Filter based hybridization relative quantification with low precisionaka; the dot blot compressed dynamic range

high assay throughputlow sample throughput


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Gene expression profiling II

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qRT-PCR relative quantitation with high precisionlarge dynamic rangemoderate assay throughput >384 in parallellow throughput singleplex assayshigh sample requirements

Hybridization Array relative quantification with medium precisioncompressed dynamic rangeextremely high assay throughputlow sample throughput

Digital PCR absolute quantification broad dynamic range

moderate assay throughputlow sample throughputprice per data point can be high

Transcriptome NGS relative quantitation with high precisionhigh dynamic rangeextremely high assay throughputextremely low sample throughputprice per sample very high


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WTS – whole transcriptome sequencing

Benefits• Quantifies and characterizes all RNA

o Identifies alternative splicing eventso Detects expressed SNPs, mutations, etc.o Allele-specific expression patterns

Drawbacks• Large computational requirements

o Massive amount of data generatedo Filtering, alignment, assembly, curationo Aggressive normalization for quantificationo Not straightforwardo Requires skilled bioinformatics scientists

Costo Only runs on HT instruments

– Limits accessibility to core labso Requires large read budget = money

– Limited sample numbers in studies

5QIASeq RNA Part 1, 2/17/2016 Lader

Sample to Insight

WTS – whole transcriptome sequencing


o Identifies alternative splicing eventso Detects expressed SNPs or mutationso Allele-specific expression patterns

But what if we are only interested in gene expression ?

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o Identification of alternative splicing eventso Detects expressed SNPS or mutationso allele specific expression patterns


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Targeted expression analysis by NGS

What are the potential advantages of applying targeted gene profiling to NGS?

• Use read budget only for genes of interesto Costo Time (quick prep, run, analysis)o Sample throughput – multiplex many samples


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• Desktop platforms can be used for RNA analysiso Don’t need the core lab across campus


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• Simplified bioinformatics (no assembly required)o Don’t need that bioinformatics guy down the hall


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• Minimal sample pre-processingo No ribosomal depletion or blocking or poly A selectiono Only nanogram quantities of total RNA required


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• Minimal sample pre-processingo No ribosomal depletion or blocking or poly A selectiono 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


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• 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 bioinformaticallyo Requires no rRNA depletion or blocking or dT selectiono Makes best use of limited NGS read budgeto 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 NGSSimple to use, complex behind the scenes


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 bioinformaticallyo Requires no rRNA depletion or blocking or dT selectiono Makes best use of limited NGS read budgeto 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, Illuminao SMcounter – molecular barcoding for precise and accurate quantificationo Streamlined one-day protocol, easily automatableo Integrated controls

– GDC, reference gene controls for data normalization

o Engineered to produce results that are both Precise and Accurate

QIAseq: high-throughput digital NGSSimple to use, but complex behind the scenes


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 depthCapture enough unique barcodes of each transcript such that statistical inferences can be made (=>10 per gene)

Overall sequencing depthHigh enough to infer accurate statistics asdetermined 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


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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

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


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QIAseq Targeted RNAseq is truly a Sample to Insight solution

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 runWith platform consumables

NGS data analysis

Pathway analysis by IPA

Sample Insight


QIAseq RNA

Sample to Insight

QIAseq targeted RNA 2-stage PCR workflow

cDNA synthesis

QIAseq bead cleanup

1st stage PCR

2nd stage PCR/sample indexing

Primer extension/molecular tagging

QIAseq bead cleanup

RNA sample

6 hours96 well-plate compatible

QIAseq bead cleanup

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Everything needed to go from RNA Library in one kit, one day!


Sample to Insight

MT

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GS

RS2

GS

FS2

Boosting Primerfor 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


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


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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


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Platform agnostic precision: MiSeq vs PGM

Fold-change (HURR/HBRR) correlation – 288 gene panel


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Inter-laboratory precision on Illumina MiSeq

Fold-change (HURR/HBRR) correlation – 288 gene panel


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Reproducibility of QIAseq panel performance

Beta 1

Same samples, 2 different labs, identical results20 ng universal reference RNA and brain RNA, 384 gene panelSequenced on Illumina Nextseq, plotted fold difference in gene expression


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QIAseq profiling is highly correlated to exhaustive transcriptome NGS

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UHHR: UBHR expression ratio: QIAseq vs whole transcriptome

Whole transcriptome


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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


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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


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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


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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

QIAseq Targeted RNA Data Analysis automated workflow

Read Mapping

• Read Mappingo Identify the possible position of the read within the referenceo Align the read sequence to reference sequences

• Primer Trimmingo Remove the primer sequences from the reads

• Molecular Barcode Counting

Primer Trimming

Molecular Barcode Count


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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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QIASeq RNA Part 1, 2/17/2016 Lader 34

Read details: unique captures per target gene

Then…secondary analysisNormalization against:I. unique barcodes/total unique barcodes per sampleII. 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


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QIAseq secondary data analysis setup


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QIAseq secondary data analysis


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QIAseq Targeted RNAseq system summary

• 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 selectiono 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 NGSo Disease and pathway specific collectionso Extended panels and fully custom gene content 12–1000 genes

• Complete integrated workflow from Sample to Insighto 96-well and automation compatible o Suite of integrated performance and normalization controls

– gDNA, reference gene panel, normalization by barcodes


Sample to Insight

QIAseq targeted RNA products

QIAseq Targeted RNA Panel (12 or 96 samples) Kit containing reagents for first strand synthesis, Smcounter tagging, and gene-specific amplification for targeted RNA sequencing QIAseq Targeted RNA Extended 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 Targeted RNA Custom 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 RNA sample Indexing(12-plex or 96-plex HT) for Ion Torrent QIAseq Targeted RNA sample Indexing (12-plex or 96-plex or HT) for Illumina

Library Quant Assay/Array KitAssays 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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QIAseq sample multiplexing guidelines on NGS platformsHow many samples? How many assays?Making the best of your read budgetSample types, special handling for FFPE, cfDNAQC of sample RNA, librariesPlatform-specific special considerations


Webinar II: A deep dive into QIAseq RNA workflow and data analysis

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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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Thank You!

Technology DevelopmentYexun (Bill) Wang, Ph.D.Quan Peng, Ph.D.

BioinformaticsJohn DiCarlo. Ph.DJixin Deng, Ph.D.

Yi Rui, Ph.D.


Product Development

Eric Lader, Ph.D.

Qiong Jiang, Ph.D.

Matt Fosbrink, Ph.D.

Melanie Hussong, Ph.D.

Geoff Wilt, M.S.

digital rnaseq technology introduction: digital rnaseq webinar part 1

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