Sample to Insight

Welcome to 3 part webinar series on Digital RNA sequencing

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, NGSJean-Noel Billaud, Ph.D. Principal Scientist. Bioinformatics

Date: March 1st, 1 pm EST, 10 am PST, 6 pm GMT

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QIAseq Targeted RNA Panels for gene expression profiling using Digital RNA sequencing Raed N. Samara, Ph.D.

Global Product Manager

Molecular barcodes enablingDigital RNAseq

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Agenda

• Introduction

• Digital sequencing with QIAseq targeted RNA panels

• Application data

• Summary

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Gene expression profiling

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

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Gene expression profiling

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 dataLow per-sample cost

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Gene expression profiling

Disadvantages of current technologies and methodologies

Current technology Advantages Disadvantages

PCR-based Accuracy Limited sample & assay throughputRequires a lot of RNA

Whole transcriptome sequencing (WTS) Throughput power ExpensiveComplex data

Microarrays Easy data analysis High background noiseRequires a lot of RNA

Traditional targeted RNA sequencing Manageable dataLow per-sample cost Amplification bias

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Gene expression profiling

Ideal methodology should combine all advantages and overcome all disadvantages

Current technology Advantages Disadvantages

PCR-based Accuracy Limited sample & assay throughputRequires a lot of RNA

Whole transcriptome sequencing (WTS) Throughput power ExpensiveComplex data

Microarrays Easy data analysis High background noiseRequires a lot of RNA

Traditional targeted RNA sequencing Manageable dataLow per-sample cost Amplification bias

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QIAseq targeted RNA panels

Digital RNAseq for gene expression profiling

A complete set of reagents for cDNA synthesis, enrichment of genes and library construction

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QIAseq targeted RNA panels

How do they address current limitations?

Current technology Disadvantages

How QIAseq targeted RNA panels address disadvantages of current technologies

PCR-basedLimited sample & assay throughput

Requires a lot of RNA

Profile 1000 genes in 96 samples simultaneouslyRequires 25 ng RNA

Whole transcriptome sequencing (WTS) ExpensiveComplex data

Cost-effectiveEasy data analysis

Microarrays High background noiseRequires a lot of RNA

Highly specific assaysRequires 25 ng RNA

Traditional targeted RNA sequencing Amplification bias Digital sequencing removes amplification bias

AccuracyNGS power

Valuable insight

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QIAseq targeted RNA panels

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

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Digital sequencing by Molecular barcodes for accuracy

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 basedon reads[reads (ratio)]

12 (2)

6 (1)

Ratio based on reads

Gene ASample 1

Gene ASample 2

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Digital sequencing by Molecular barcodes for accuracy

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 ASample 1

Gene ASample 2

Ratio of genes basedon barcodes

4

1

Tag each gene with unique molecular barcodes

Count unique barcodes, not reads

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QIASeq Targeted RNA Panels overcome traditional targeted RNAseq challenges

• Digital sequencing using molecular barcode technologyo Tagging each cDNA template with a unique barcode o Counting the number of barcodes to correct any amplification artifactso Providing unparalleled value: accurate and unbiased gene expression analysis with NGS

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Simple Procedure6

hour

s

GSP1, GSP2 never see other,

thereby minimizing primer dimers

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Sample indexes for increased throughput

Indexes

• Illuminao 12 indexeso 96 indexes

– Tube and array formats

• Ion Torrento 12 indexeso 96 indexes

− Array format

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Sample indexes for increased throughput

Indexes

• Illuminao 12 indexeso 96 indexes

– Tube and array formats

• Ion Torrento 12 indexeso 96 indexes

– Array format

Sample 63

Sample 1

Sample 17

Sample 24Sample 30

Sample 96

Sample 48

Sample 71

Sample 56

Sample 35

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Outstanding performance metrics

- 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

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170+ pathway and disease focused panels for human

- Cancer Transcriptome- Inflammation & Immunity Transcriptome- Signal Transduction PathwayFinder- Stem Cell & Differentiation Markers- Molecular Toxicology Transcriptome- Angiogenesis & Endothelial Cell Biology- Apoptosis & Cell Death- Extracellular Matrix & Cell Adhesion Molecules

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Take guessing out of your analysis

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

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Custom panels

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

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Extended panels

Extend an existing panel

Extend an existing panel by adding up to 25 additional genes

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Custom builder

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

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Custom builder

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

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Custom builder

Bed file

Location of designed amplicon

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QIAseq targeted RNA panels

• 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

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Small Molecules – 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 concentration to 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

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Small Molecule Application Data

• FASTQ files were generated and uploaded into the Primary Analysis Molecular Tag Counting Portal – QIAseq RNA Quantification

• Overview of the Summary Page

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Small Molecule Application Data

• QIAseq RNA Quantification - Read Details: Unique Captures per Target Gene Count

Differential gene expression inter- and intra-samples

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Small Molecule Application Data

• Changes in gene expression by these treatments were measured by QIAseq RNA NGS, and fold-changes in gene expression due to chemical perturbation were characterized.

Upload data to secondary data analysis portal and IPA

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Fold changes display

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Scatter plot and clustergram (HDAC Sample compared to the Control Sample)

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HDAC Mechanistic Network in HEK293T Cells Treated with Trichostatin A

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HDAC is predicted to be inhibited by Trichostatin A and drives a Mechanistic Network along with 18 other regulators.

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Unparalleled efficiency and flexibility

96 samples, 421 genes

Parameter QIAseq targeted RNA panels

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

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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

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QIAseq targeted RNA panels overcome challenges of existing gene expression profiling methods

Current technology Disadvantages

How QIAseq targeted RNA panels address disadvantages of current technologies

PCR-basedLimited sample & assay throughput

Requires a lot of RNA

Profile 1000 genes in 96 samples simultaneouslyRequires 25 ng RNA

Whole transcriptome sequencing (WTS) ExpensiveComplex data

Cost-effectiveEasy data analysis

Microarrays High background noiseRequires a lot of RNA

Highly specific assaysRequires 25 ng RNA

Traditional targeted RNA sequencing Amplification bias Digital sequencing removes amplification bias

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QIASeq Targeted RNA Panel: Sample to Insight workflow

• Integrated library preparation• Works with any RNA sample type• Compatible with most sequencers • Complementary data analysis tool for fold change analysis

Comprehensive suite of tools to gain valuable insight

Attend next week’s webinar

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Thank you

Raed N. Samara, PhD

Global Product Manager

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