Meeting the challenges of miRNA research: miRNA and its Role in Human Disease Webinar Series Part 2

Sample to Insight

Jonathan Shaffer, Ph.D.

[email protected] Scientist, Product Development

Meeting the challenges of miRNA research:

miRNA Function, Profiling and Data Analysis

Sample to Insight

Welcome to our 4-part webinar series on miRNA

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Part 1: Biofluid miRNA profiling: From sample to biomarker

Part 2: Meeting the challenges of miRNA research

Part 3: Advanced miRNA expression analysis

Part 4: Functional analysis of miRNA

miRNA and its role in human disease

Meeting the challenges of miRNA research

Sample to Insight

Meeting the challenges of miRNA research 3

Legal Disclaimer

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.

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Data analysis4

Agenda

miRNA background1

Sample prep2

Real-time PCR assays3

4

Interpretation5

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miRNAs: master regulators of gene expression

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miRNAs are 21-nucleotide small non-coding RNAs that are expressed in virtually all tissues Changes in miRNA expression can be correlated with gene expression changes in development,

differentiation, signal transduction, infection, aging and disease

Small but mighty!

Transcribed by RNA polymerase II as a long primary transcript (pri-miRNAs), which may contain more than one miRNA

In the nucleus, pri-miRNAs are processed to hairpin-like pre-miRNAs by the RNase III Drosha

Pre-miRNAs are then exported to the cytosol by exportin 5

In the cytosol, the RNAse III Dicer processes these precursors to mature miRNAs

These miRNAs are incorporated in RISC miRNAs with high homology to the target mRNA lead to

mRNA cleavage miRNAs with imperfect base pairing to the target mRNA

lead to translational repression and/or mRNA degradation

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How do miRNAs interact with mRNAs?

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Basis of miRNA–mRNA interaction

Seed region: nucleotides 2–7 in 5′ region of miRNA Most evolutionary conserved miRNA region Most frequently complementary to target 3′-UTRs Often sufficient to confer mRNA recognition

Beyond the seed region 3′ end also contributes (extensive pairing is rare) Some cases: central 11–12 continuous base pairs

Result of interaction Suppression of gene expression Rare cases: increase gene expression

References Grimson, A., et al, Mol. Cell 2007, 27, 91–105 Image from Bartel, D.P., Cell 2009, 136, 215–233 Guo, H., et al, Nature 2010, 466, 835–840 Thomson, D.W., et al, Nucleic Acids Res 2011, 1–9

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How do you determine miRNA–mRNA interactions?

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Step 1: Prediction algorithms!

Prediction Algorithm WebsiteTargetScan http://www.targetscan.org/

Pictar http://pictar.mdc-berlin.de/

MicroCosm Targetshttp://www.ebi.ac.uk/enright-srv/microcosm/htdocs/targets/v5/

DIANA http://diana.cslab.ece.ntua.gr/microT/

miRANDA http://www.microrna.org/microrna/home.do

TarBase (experimentally supported) http://diana.cslab.ece.ntua.gr/tarbase/

Target Prediction is based on: Bioinformatics

Seed region match Position in 3′ UTR Cross-species conservation Central sequence homology

Wet-lab research Empirical evidence from microarrays Reporter systems

Pitfalls of using prediction algorithms: Large number of candidate mRNAs for a given

miRNA May not incorporate all miRNA targeting

possibilities Different algorithms produce different target lists Potential for false positive rate of prediction

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How do you determine miRNA–mRNA interactions?

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Step 2: Experimental techniques!

miRNA target screening Gene expression analysis (inferred targets)

RNAseq Microarrays qPCR

Immunoprecipitation (direct targets) HITS-CLIP PAR-CLIP Biotin tagged miRNA

Gene-specific validation qPCR Luciferase reporter assays Western blot 5′ rapid identification of cDNA ends (5′ RLM-RACE)

Image from Chi, S.W. et al. (2009) Nature 13, 479.

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What is the role of miRNA in human disease?

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Potential events that disrupt normal miRNA activity

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Disruption of miRNA–mRNA interaction

Altered transcriptionMethylation

Histone modificationTranscription factor

Drosha processing

Genomic instabilityAmplification/deletionTranslocationInsertional mutagenesis

Loss of miRNA binding site in targetSNP or mutationAlternative splicingLoss/change of 3′-UTR

Dicer processing

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Unique signatures in human cancer

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miRNAs located in genomic regions amplified in cancers (e.g. miR-17-92 cluster) can function as oncogenes, whereas miRNAs located in portions of chromosomes deleted in cancers (e.g. miR-15a-miR-16-1 cluster) can function as tumor suppressors

Abnormal expression of miRNAs has been found in both solid and hematopoietic tumors

miRNA expression fingerprints correlate with clinical and biological characteristics of tumors including tissue type, differentiation, aggression and response to therapy

In the last 15 years, a substantial number of studies and reviews have associated the presence of various miRNAs with cell proliferation,

resistance to apoptosis, invasiveness and differentiation in cancer cells

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12Webinar 4: Functional Analysis of miRNA

QIAGEN Sample to Insight solutions for miRNA research

Sample prep Real-time PCR assays Data analysis Interpretation

QIAcube

miRNeasy Mini miRNeasy Micro

miRNeasy FFPE

miRNeasy Serum/Plasma

ExoRNeasy Serum/Plasma

Instruments

QIAgility RotorGene Q Compatibility with all Real-time PCR instruments

miScript PCR System miScript PreAMP miScript Microfluidics miScript PCR Arrays miScript Primer Assays

GeneGlobe Data Analysis Center

Kits/solutions

Ingenuity Pathway Analysis

miScript Mimics miScript Inhibitors

Sample to Insight

Data analysis4

Agenda

miRNA background1

Sample prep2


13

Interpretation5

Sample to Insight



Sample prep Real-time PCR assays Data analysis Interpretation

QIAcube


miRNeasy FFPE



Instruments




Kits/solutions Ingenuity

Pathway Analysis miScript Mimics miScript Inhibitors

Sample to Insight


Sample to Insight: sample prep

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A “total RNA solution” for every sample type

Cells, fresh tissue, frozen tissue

miRNeasy Mini Kit

miRNeasy Micro Kit

miRNeasy 96 Kit

FFPE tissue

miRNeasy FFPE Kit

Fluids (serum, plasma, urine, CSF, saliva etc.)

miRNeasy Serum / Plasma Kit

Exosome enrichment/isolation from serum/plasma

ExoRNeasy Serum/Plasma Maxi Kit

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Data analysis4

Agenda

miRNA background1

Sample prep2


16

Interpretation5

Sample to Insight



Sample Prep Real-time PCR assays Data analysis Interpretation

QIAcube


miRNeasy FFPE



Instruments




Kits/solutions



Sample to Insight


miScript PCR System

Complete miRNA quantification system

Reverse transcription miScript II RT Kit

Preamplification for limiting RNA amounts miScript PreAMP PCR Kit miScript PreAMP Primer Mixes

High-throughput expression analysis miScript miRNA PCR Arrays

Low-throughput miRNA quantification miScript Primer Assays

Real-time PCR reagents miScript SYBR Green PCR Kit

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miScript PCR System

Basic workflow

1. Isolate total RNA

2. Perform reverse-transcription

3. Perform PreAMP (optional)

4. Prepare PCR pre-mix

5. Load PCR arrays or plates

6. Perform real-time PCR

7. Analyze data

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Reverse transcription: miScript II RT Kit

Two buffers = Total RNA discovery!

miScript II RT Kit

Biogenesis studies?Mature miRNA

quantification and profiling?

HiFlex Buffer HiSpec Buffer

Flexible detection of all RNA molecules

Patent-pending technology for the specific detection of mature miRNAs

Note: Only HiSpec Buffer is recommended for use with miScript miRNA PCR Arrays

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miScript II RT Kit

Reverse transcription theory

HiFlex Buffer HiSpec Buffer

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Preamplification for limiting samples: miScript PreAMP Kit

miRNome profiling from as little as 1 ng total RNA or biofluids with limited RNA

Highly multiplex, PCR-based preamplification Compatible with all miScript miRNA PCR Arrays and miScript Primer Assays Enables miRNA profiling experiments using very limited amounts of starting material

Cell or tissues: 1 ng total RNA Fluids:

Serum/plasma: 50 µl or less Urine: Any amount CSF: Any amount Aqueous humor: Any amount When in doubt, ‘miScript PreAMP’ it!

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High-throughput expression analysis: miScript PCR Arrays

What are miScript PCR Arrays? Wet-lab verified miRNA primer assays pre-dried in PCR plates

miRNome arrays Most species Broadest content

Targeted miRNome arrays “sub-miRNomes”

Focused arrays Biological pathways and diseases

Formats 96-well, 384-well, Fluidigm® BioMarkTM

Compatible with virtually all mainstream real-time instruments Fully customizable

Prep your PCR reaction mix Load your plate Run your real-time experiment!

No pipetting of individual primers!

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Anatomy of a miScript miRNA PCR Array

96-well format: 84 miRNA + 12 controls

cel-miR-39

miScript PCR controls for normalization

miRTC PPCSNORD61; SNORD68; SNORD72 SNORD95; SNORD96A; RNU6-2

RTcontrol

PCRcontrol

Spike in control

84 miRNAs

cel-miR-39 Alternative data normalization using exogenously spiked Syn-cel-miR-39 miScript miRNA Mimic

miScript PCR controls Data normalization using the ∆∆CT method of relative quantification

miRNA reverse transcription control (miRTC) Assessment of reverse transcription performance

Positive PCR control (PPC) Assessment of PCR performance

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miRNome arrays

The most complete, validated miRNome available!

Human Coverage through miRBase v21 2402 primer assays!

Mouse Coverage through miRBase v21 1765 primer assays!

Rat Dog Rhesus macaque Cow

100% validated assays Each assay is bench validated Each array is quality controlled

Leading miRNome coverage

Completely scalable! Choose as many plates as you

want … profile the v21 miRNome … profile only the v16 miRNome

Contact product development if there is interest in other species!

miRBase Profiler miRNome Arrays Benefits of miRBase Profiler Arrays

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Targeted miRNome arrays

Manageable “sub-miRNomes”

miFinder 384HC: 372 best expressed, best characterized miRNAs

Serum and Plasma 384HC: 372 best expressed miRNAs in serum / plasma

Cancer PathwayFinder 384HC: 372 miRNA most widely associated with cancer

Liver miFinder 384HC: 372 best expressed miRNAs in liver tissue

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Focused arrays

miRNA panels based on biological pathways and diseases

miFinder Cancer PathwayFinder Liver miFinder Brain cancer Breast cancer Ovarian cancer Prostate cancer Cancer stem cells Apoptosis Cardiovascular disease Cell differentiation and development Diabetes Fibrosis Hypoxia signaling pathway Immunopathology Inflammatory response and autoimmunity Neurological development and disease Pain: neuropathic and inflammatory T-cell and B-cell activation Tumor suppressor miRNAs Serum and plasma

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PCR arrays or individual miScript Primer Assays?

What option is best for your experiment?

It’s a balance of samples and assays

miRNome screening: always PCR arrays More than 24 assays: always PCR arrays Less than 24 assays: depends on the number of samples

Limited samples (16 or less): individual PCR assays Extensive number of samples (16, 50, 100, etc.): PCR arrays

or

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miScript PCR System performance

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-2 -1 0 1 2 3 4Log (ng) of RNA in cDNA synthesis using the HiFlex Buffer

Mea

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miR-16miR-20amiR-21Linear (miR-16)Linear (miR-20a)Linear (miR-21)

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1 2 3 4 5 6Log copy number of miRNA using the HiFlex Buffer

Mea

n C T

miR-21Linear (miR-21)

Linearity of six logs of input RNADetection of 10 copies to >106 copies of miRNA

Exceptional linearity and specificity

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miScript PCR System: exceptional specificity

Excellent discrimination between closely related miRNA family members

Relative detection (as % of perfect match)

cDNA used in PCR

miScript Primer Assay used

Let-7b Let-7c miR-98 Let-7d Let-7e Let-7a Let-7f Let-7g Let-7i

Let-7b 100.0 1.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0Let-7c 0.5 100.0 0.0 0.0 1.0 0.1 0.0 0.0 0.0miR-98 0.0 0.2 100.0 0.1 0.0 0.1 0.0 0.0 0.1Let-7d 0.1 0.0 0.0 100.0 0.0 0.4 0.0 0.0 0.0Let-7e 0.1 0.0 0.0 0.0 100.0 0.2 0.0 0.0 0.0Let-7a 0.1 0.6 0.0 0.5 3.9 100.0 0.1 0.0 0.0Let-7f 0.6 0.1 0.0 0.1 0.0 1.1 100.0 0.1 0.1Let-7g 0.6 0.2 0.0 0.1 0.0 0.0 0.0 100.0 0.2Let-7i 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.1 100.0

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miScript PCR System: detailed workflows

Standard workflowNon-limiting samples

(cells / tissues, 200 µl serum / plasma)

Fluidigm® workflowAny sample type

PreAMP workflowLimiting samples

(FFPE, urine, CSF, 50 µl serum / plasma)

Isolate total RNAmiRNeasy kits

Dilute cDNADilution is array-dependent

Prepare PCR pre-mixand load array

Perform reverse-transcriptionmiScript II RT Kit, 20 µl rxn, HiSpec

Perform real-time PCR


Dilute cDNA10 µl cDNA + 40 µl H2O

Perform PreAMPmiScript PreAMP, array-specific


Dilute amplified DNADilution is sample input-dependent

Prepare PCR pre-mixand load array



Dilute cDNA10 µl cDNA + 40 µl H2O

Perform microfluidics PreAMPmiScript PreAMP, array-specific


Dilute amplified DNADilution is sample input-dependent

Load Fluidigm Dynamic Array


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Data analysis4

Agenda

miRNA background1

Sample prep2


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Interpretation5

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Sample prep

Real-time PCR assays

Data analysis

Interpretation

QIAcube

miRNeasy Mini

miRNeasy Micro

miRNeasy FFPE

miRNeasy Serum / Plasma

ExoRNeasy Serum / Plasma

Instruments QIAgility RotorGene Q Compatibility with all Real-time PCR instruments

miScript PCR System

miScript PreAMP miScript Microfluidics

miScript PCR Arrays miScript Primer Assays


Kits/solutions



Sample to Insight


Real-time PCR data analysis

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(1) Schmittgen TD, Livak KJ.(2008):Analyzing real-time PCR data by the comparative C(T) method. Nat Protoc.;3(6):1101-8

(2) Livak, KJ, and Schmittgen, TD.(2001): Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the 2-∆∆CT Method METHODS 25, 402–408

(3) www.Gene-Quantification.info

CT = 23.8

Absolute quantification Absolute input copies, based on a standard curve

Relative quantification Comparative CT method: also known as the 2-∆∆CT method Selection of internal control Selection of calibrator (e.g., untreated control or normal

sample) Assumes that the PCR efficiency of the target gene is

similar to the internal control gene (and that the efficiency of the PCR is close to 100%)

Fold change = 2-∆∆CT

∆CT = CTGene - CT

Normalizer ∆∆CT = ∆CT (sample 2) – ∆CT (sample 1) where sample 1

is the control sample and sample 2 is the experimental sample

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Data analysis workflow

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Steps 1 and 2: Set baseline and threshold to determine CT values

Step 3:Export CT values

Step 4: Analyze data using ΔΔCT method of relative quantification

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Data analysis step 1: Set your baseline

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Baseline Definition: Noise level in early cycles where there is no detectable increase in

fluorescence due to PCR products How to set:

Observe amplification plot using the “Linear View” Determine the earliest visible amplification Set the baseline from cycle 2 (or 3) to two cycles before the earliest visible amplification Note: The number of cycles used to calculate the baseline can be changed and should

be reduced if high template amounts are used

Important: Ensure baseline settings are the same across all PCR runs in the same analysis to allow comparison of results

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Data analysis step 2: Set your threshold

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Threshold Purpose: Used to determine the CT (threshold cycle) value. The point at which

the amplification curve intersects with the threshold line is called the CT

How to set: Observe amplification plot using the “Log View” Place the threshold in the lower half of the log-linear range of the amplification plot,

above the background signal Note: Never set the threshold in the plateau phase

Important: Ensure threshold settings are the same across all PCR runs in the same analysis to allow comparison of results

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Data analysis step 3: Export CT values

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One 5 µm FFPE section used per FFPE isolation Each isolation is from a different section On average, each isolation provided enough total RNA for:

Two full human miRNome profiles Ten pathway-focused PCR arrays

RT: 125 ng total RNA, HiSpec Buffer qPCR: Human miFinder miScript miRNA PCR Array (0.5 ng cDNA per well)

4

8

1216

2024

2832

3640

1 7 13 19 25 31 37 43 49 55 61 67 73Human miFinder miRNA Assay

C T V

alue

FFPE Isolation 1FFPE Isolation 2

FFPE Isolation 34

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1 7 13 19 25 31 37 43 49 55 61 67 73Human miFinder miRNA Assay

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FFPE Isolation 1

FFPE Isolation 2

FFPE Isolation 3

Normal lung Lung tumor

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Data analysis step 4: Analyze data

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ΔΔCT method of relative quantification

Normal (N) lung total RNA Lung tumor (T) total RNA

N cDNA (Iso. 1) N cDNA (Iso. 2) N cDNA (Iso. 3) T cDNA (Iso. 1) T cDNA (Iso. 2) T cDNA (Iso. 3)

Exported CT values Exported CT valuesCalculate ΔCT

for each miRNAon each array

ΔCT = CTmiRNA – AVG CT

SN1/2/3/4/5/6 ΔCT = CTmiRNA – AVG CT

SN1/2/3/4/5/6

Tip for choosing an appropriate snoRNA / snRNA controls for normalization Make sure that the selected controls are not influenced by the experimental conditions

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Data analysis step 4: Analyze data (cont.)

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ΔΔCT method of relative quantification

Normal (N) lung Lung tumor (T)Calculate ΔCT

for each miRNAon each array

ΔCT ΔCTΔCT ΔCTΔCT ΔCT

Calculate ΔΔCT for each miRNA

between groups(T – N)

ΔΔCT = Avg. ΔCT (T) – Avg. ΔCT (N)

Calculate fold-change for each miRNA (T vs. N)

2-(ΔΔCT)

Calculate average ΔCT for each miRNAwithin group (N or T)

ΔCT + ΔCT + ΔCT 3

ΔCT + ΔCT + ΔCT 3

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Data analysis example 1

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Two conditions: Normal and Tumor miRNA (hsa-miR-21-5p)

Normal CT = 21 Tumor CT = 15

Normalizer (RNU6-2) Normal CT = 16 Tumor CT = 14

Analysis1. Calculate ΔCT for each condition (i.e., normalize your miRNA CT values)

Normal: 21 – 16 = 5 Tumor: 15 – 14 = 1

2. Calculate ΔΔCT (tumor relative to normal) using the equation ΔCT (T) – ΔCT (N) ΔΔCT (tumor relative to normal): 1 – 5 = – 4

3. Calculate fold-change (tumor relative to normal) using the equation 2-ΔΔCT

2-ΔΔCT (tumor relative to normal): 2-(-4) = 164. Calculate fold-regulation

If the fold-change is greater than 1, the result may be reported as a fold upregulation

Increased expression in a tumor sample

Compared to the normal sample, hsa-miR-21-5p is 16-fold upregulated in the tumor sample

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Data analysis example 2

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Two conditions: Normal and Tumor miRNA (hsa-miR-16-5p)

Normal CT = 15 Tumor CT = 16

Normalizer (RNU6-2) Normal CT = 16 Tumor CT = 14

Analysis1. Calculate ΔCT for each condition (i.e. normalize your miRNA CT values)

Normal: 15 – 16 = –1 Tumor: 16 – 14 = 2

2. Calculate ΔΔCT (tumor relative to normal) using the equation ΔCT (T) – ΔCT (N) ΔΔCT (tumor relative to normal): 2 – (–1) = 3

3. Calculate fold-change (tumor relative to normal) using the equation 2-ΔΔCT

2-ΔΔCT (tumor relative to normal): 2–(3) = 0.1254. Calculate fold-regulation:

If the fold-change is less than 1, the negative inverse of the result may be reported as a fold downregulation (–1 / 0.125 = –8)

Decreased expression in a tumor sample

Compared to the normal sample, hsa-miR-16-5p is 8-fold downregulated in the tumor sample

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Serum and plasma data analysis

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Serum and plasma samples (cont.)

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Special data analysis case

Serum or plasma total RNA samples: The snoRNA / snRNA panel of targets does not exhibit robust expression and therefore should not be selected as normalization controls

Control Serum sample 1 Serum sample 2 Serum sample 3

SNORD61 36.3 34.3 35.8

SNORD68 34.6 35.0 35.3

SNORD72 35.0 35.0 35.0

SNORD95 31.1 39.3 33.5

SNORD96A 33.6 34.5 35.4

RNU6-2 37.9 39.1 35.0

Typical CT values for miScript PCR Controls in serum samples

Step 1: Calibrate samples using cel-miR-39-3p CT mean Step 2: Normalize serum or plasma sample data

Option 1: Normalize CT values to CT mean of all commonly expressed miRNAs Option 2: Normalize CT values to CT mean of invariant miRNAs

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Serum and plasma samples (cont.)

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Calibrate data using cel-miR-39-3p CT mean Uncalibrated

Assay Sample 1 Sample 2

hsa-miR-16 16.0 19.0

hsa-miR-21 20.0 24.0

hsa-miR-192 23.0 26.0

hsa-miR-103 23.0 23.0

hsa-miR-25 22.0 25.0

cel-miR-39-3p 18.0 21.0

Compared to sample 1, all assays in sample 2 appear to have delayed CT values Compared to sample 1, cel-miR-39-3p in sample 2 also has a delayed CT value Conclusion: calibrate samples (cel-miR-39-3p CT values indicate a differential recovery)

Calibrated (Sample 2 CT values -3)Assay Sample 1 Sample 2

hsa-miR-16 16.0 16.0

hsa-miR-21 20.0 21.0

hsa-miR-192 23.0 23.0

hsa-miR-103 23.0 20.0

hsa-miR-25 22.0 22.0

cel-miR-39-3p 18.0 18.0

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Serum and plasma sample data normalization options

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Option 1: CT values normalized to CT mean of expressed miRNAs

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-4

0

4

8

12Fo

ld-R

egul

atio

n

Calculate the CT mean for commonly expressed miRNAs Those miRNAs with CT values < 30 (or 32 or 35) in all assessed samples

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Serum and plasma sample data normalization options

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Option 2: CT values normalized to CT mean of invariant miRNAs

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-4

0

4

8

12

Fold

-Reg

ulat

ion

Commonly expressed miRNAshsa-let-7a hsa-miR-92a

hsa-let-7c hsa-miR-93

hsa-miR-21 hsa-miR-103a

hsa-miR-22 hsa-miR-126

hsa-miR-23a hsa-miR-145

hsa-miR-24 hsa-miR-146a

hsa-miR-25 hsa-miR-191

hsa-miR-26a hsa-miR-222

hsa-miR-26b hsa-miR-423-5p

Calculate the CT mean for invariant miRNAs Choose at least four to six miRNAs that exhibit little CT variation

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Serum and plasma sample data normalization options (cont.)

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Comparison of normalization methods

Option 1:Commonly expressed miRNAs

(miRNome, 384HC, pathway)

Option 2:Invariant panel of miRNAs

(small panel screening, single assays)

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-4

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Fold

-Reg

ulat

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-8

-4

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Note 1: In this example, fold-regulation looks highly similar, irrespective of the chosen normalization method. This is correct, as your results should be independent of the chosen normalization method

Note 2: For small panel screening, do not use a CT mean of all miRNAs, as this array is biased (miRNA assays included on this array are not random)

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miScript’s straightforward data analysis solution

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Incorporating the free GeneGlobe Data Analysis Center

Steps 1 and 2: Set baseline and threshold to determine CT values

Step 3:Export CT values

Step 4: Access the free data analysis software at

www.qiagen.com/GeneGlobe

Step 5 and on:Automatic data using ΔΔCT method of relative quantification

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Data analysis step 5: GeneGlobe Data Analysis Center

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Analyze your miScript miRNA PCR Array and miScript Primer Assay results!

Web-based software No installation needed Tailored for each array

Raw CT values to results Using ∆∆CT Method

Multiple analysis formats Scatter plot Volcano plot Multi-group plot Clustergram

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Step 1

What should you do at this page?1. Choose format2. Choose array3. Choose instrument4. Confirm catalog number5. Click Start Analysis

1. Format

3. Instr.

2. Array

4. CatNo

5. Click Start Analysis

Sample to Insight


GeneGlobe Data Analysis Center (cont.)

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Step 2

Upload data tab

What should you do at this tab?1. Verify technology2. Verify catalog number3. Verify plate format4. Upload exported CT values

5. Click Upload

4. Upload exported CT values

5. Click Upload

1. Technology

3. Plate Format2. Cat. No.

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Step 3

Analysis setup tab Uploaded Data

Key features: All miRNAs and controls found on

chosen array All CT data uploaded to software

What should you do at this tab? Verify that your data has been uploaded

correctly

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Step 4

Analysis setup tab Sample Manager

Key features Define groups Integrate preamplification into analysis Allows you to choose whether your sample

is a serum, plasma, other body fluid or cell-free source

If your sample is a cell-free source, you can choose to calibrate your data based on the miRNeasy Serum / Plasma spike-in control

Allows you to set the lower limit of detection

What should you do at this tab?1. Define groups2. Select preamplification status3. Select sample type4. Select calibration5. Set lower limit of detection6. Click Update

1. Groups

3. Sample type

2. PreAMP

4. Calibration

5. LOD

6. Update

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Step 5 (ONLY IF YOU CALIBRATE YOUR DATA)

Analysis setup tab Processed Data

Key features: Shows data that has been calibrated

using the miRNeasy Serum / Plasma spike-in control assay (cel-miR-39-3p) CT values

What should you do at this tab? Verify that your data has been calibrated

correctly

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Step 6

Analysis setup tab Data QC

Key features: Display results of quality checks:

PCR efficiency RT efficiency

What should you do at this tab? Verify that your samples have

passed the QC checks

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Step 7

Analysis setup tab Select Normalization Method

Key features: Provides four common methods

for data normalization Manual selection of HKG Auto selection of HKG Auto selection from Full Plate Global CT mean of expressed

miRNAs

What should you do at this tab? Choose how you want your data

to be normalized

Choose normalization method

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Step 7 (cont.)

Analysis setup tab Select Normalization Method

Key features: Provides four common methods

for data normalization Manual selection of HKG Auto selection of HKG Auto selection from Full Plate Global CT mean of expressed

miRNAs

What should you do at this tab? Click Perform Normalization

Click Perform Normalization

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Step 8

Analysis setup tab Data Overview

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Step 9Analysis tab: this tab provides an overview of all ΔΔCT related calculations and provides a guide for you regarding the trust that you should place in your data

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Step 10

Scatter Plot, Volcano Plot, Clustergram, and Multigroup Plot tabs: When clicked, these tabs provide various statistical outputs that will open as new windows. The scatter plot is included as an example

Plots & charts tab Plot Home

Key features: Provides five common plots or

charts to visualize your data What should you do at this tab?

Click on plot or chart of interest

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Step 11

Export data tab

Key features: Allows you to export your analysis results of choice

What should you do at this tab?1. Select analysis results to export2. Click Export

1. Select results

2. Export

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Step 12 (optional)

What’s next tab

Key features: Assists in determining how to further assess your miRNAs of interest Assists in determining which genes are predicted to be regulated by your miRNAs of

interest Provides contact information for help in interpreting results and data

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Step 13 (optional)

What’s next tab miRNA Expression

Key features: Assists in determining

how to further assess your miRNAs of interest

Sample to Insight

Data analysis4

Agenda

miRNA background1

Sample prep2


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Interpretation5

Sample to Insight



Sample prep


Data analysis

Interpretation

QIAcube

miRNeasy Mini

miRNeasy Micro

miRNeasy FFPE

miRNeasy Serum / Plasma

ExoRNeasy Serum / Plasma

Instruments QIAgility RotorGene Q Compatibility with all Real-time PCR instruments

miScript PCR System

miScript PreAMP miScript Microfluidics

miScript PCR Arrays miScript Primer Assays


Kits/solutions Ingenuity

Pathway Analysis

miScript Mimics

miScript Inhibitors

Sample to Insight


Ingenuity Pathway Analysis (IPA)

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Asking “what’s next?” by modeling, analyzing and understanding complex 'omics data

Analysis of gene expression / miRNA / SNP microarray data Deeper understanding of metabolomics, proteomics and RNAseq data Identification of upstream regulators Insight into molecular and chemical interactions and cellular phenotypes Discoveries about disease processes

Sample to Insight


Testing “what’s next?”

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Manipulating miRNA function

Sample to Insight


Where can I find the products discussed today?

www.qiagen.com

www.qiagen.com/GeneGlobe

Sample to Insight



What we have covered today

miRNAs are master regulators of gene expression

QIAGEN has a Sample to Insight solution specifically tailored for you!

Sample prep


Data analysis

Interpretation

Choose QIAGEN and turn your hypotheses into actionable insights!

Sample to Insight


Thank you for attending today’s webinar!

Jonathan Shaffer, [email protected]

Contact [email protected]

Questions?

Meeting the challenges of miRNA research: miRNA and its Role in Human Disease Webinar Series Part 2

Healthcare

Transcript of Meeting the challenges of miRNA research: miRNA and its Role in Human Disease Webinar Series Part 2