Advanced miRNA Expression Analysis: miRNA and its Role in Human Disease Webinar Series Part 3

Sample to Insight

Jonathan Shaffer, Ph.D.

[email protected]

Senior Scientist, Product Development

Advanced miRNA expression analysis

data analysis tutorial

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Legal disclaimer

Advanced miRNA expression analysis 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.

Sample to Insight

Welcome to our four-part webinar series on miRNAs


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

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Live data analysis demonstrations4

Agenda

1

Data analysis: the ΔΔCT method2

Using the GeneGlobe Data Analysis Center3

4

QIAGEN solutions for miRNA detection

Sample to Insight


QIAGEN Sample to Insight Solutions for miRNA research

Sample prepReal-time

PCR assaysData analysis Interpretation

QIAcube

miRNeasy Mini

miRNeasy Micro

miRNeasy FFPE

miRNeasySerum/Plasma

ExoRNeasySerum/Plasma

Instruments

QIAgilityRotorGene QCompatibility 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 MimicsmiScript Inhibitors

Sample to Insight


QIAGEN Sample to Insight solutions for miRNA research



QIAcube

miRNeasy Mini

miRNeasy Micro

miRNeasy FFPE



Instruments


miScript PCR System

miScript PreAMP


miScript PCR Arrays



Kits/solutions


miScript MimicsmiScriptInhibitors

Sample to Insight

miScript PCR System


Complete miRNA quantification system

1. Reverse transcription

miScript II RT Kit

2. Preamplification for limiting RNA amounts

miScript PreAMP PCR Kit

miScript PreAMP Primer Mixes

3. High-throughput expression analysis

miScript miRNA PCR Arrays

4. Low-throughput miRNA quantification


5. Real-time PCR reagents

miScript SYBR Green PCR Kit

Sample to Insight


QIAGEN Sample to Insight Solutions for miRNA research



QIAcube

miRNeasy Mini

miRNeasy Micro

miRNeasy FFPE



Instruments


miScript PCR System

miScript PreAMP


miScript PCR Arrays



Kits/solutions


miScript MimicsmiScriptInhibitors

Sample to Insight


Agenda

1



9


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Real-time PCR data analysis


(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


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


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


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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Example: Setting the baseline and threshold


Applied Biosystems® 7900HT

Threshold: 0.2

Baseline: From cycle 2 (or 3) to two cycles before the earliest visible amplification

Threshold: Place in the lower half of the log-linear range of the amplification plot, above the background signal

Baseline: Cycles 3 to 15

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Data analysis step 3: Export CT values


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

12

16

20

24

28

32

36

40

1 7 13 19 25 31 37 43 49 55 61 67 73

Human miFinder miRNA Assay

CT V

alu

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FFPE Isolation 1

FFPE Isolation 2

FFPE Isolation 3

4

8

12

16

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Human miFinder miRNA Assay

CT V

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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


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.)


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

Sample to Insight

Data analysis example 1


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

Analysis

1. 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) = 16

4. 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

Sample to Insight

Data analysis example 2


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

Analysis

1. 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) = 0.125

4. 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.)


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

Sample to Insight

Serum and plasma samples (cont.)


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

Sample to Insight

Serum and plasma sample data normalization options


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

-8

-4

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Calculate the CT mean for commonly expressed miRNAs

Those miRNAs with CT values < 30 (or 32 or 35) in all assessed samples

Sample to Insight

Serum and plasma sample data normalization options


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

-8

-4

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Commonly Expressed miRNAs

hsa-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

Sample to Insight

Serum and plasma sample data normalization options (cont.)


Comparison of normalization methods

Option 1:

Commonly expressed miRNAs(miRNome, 384HC, pathway)

Option 2:

Invariant panel of miRNAs(small panel screening, single assays)

-8

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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)

Sample to Insight


Agenda

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26


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miScript’s straightforward data analysis solution


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


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

Sample to Insight



Step 1

What should you do at this page?

1. Choose format

2. Choose array

3. Choose instrument

4. Confirm catalog number

5. Click Start Analysis

1. Format

3. Instr.

2. Array

4. CatNo

5. Click Start Analysis

Sample to Insight

GeneGlobe Data Analysis Center (cont.)


Step 2

Upload data tab

What should you do at this tab?

1. Verify technology

2. Verify catalog number

3. Verify plate format

4. Upload exported CT values

5. Click Upload

4. Upload exported CT values

5. Click Upload

1. Technology

3. Plate Format

2. 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


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


1. Define groups

2. Select preamplification status

3. Select sample type

4. Select calibration

5. Set lower limit of detection

6. Click Update

1. Groups

3. Sample type

2. PreAMP

4. Calibration

5. LOD

6. Update

Sample to Insight



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


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


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


Choose how you want your data

to be normalized

Choose

normalization

method

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Step 8

Analysis setup tab Data Overview

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Step 9

Analysis 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


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


1. Select analysis results to export

2. 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

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Agenda

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42


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Live data analysis demonstration


How to use the GeneGlobe data analysis center

Web-based software





Multiple Analysis Formats

Scatter Plot

Volcano Plot

Multi-Group Plot

Clustergram

Sample to Insight

Live data analysis example 1


Is miRNA expression altered under hypoxic conditions?

Treatment: ± deferoxamine (DFO) for 24 hr

Isolation: miRNeasy Mini Kit

RT: miScript II RT Kit (HiSpec)

qPCR: miFinder 384HC

miScript miRNA PCR Array

Free data analysis

Experimental workflow

Seed Hep G2 cells

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Live data analysis demonstration: serum sample data


How to use the GeneGlobe data analysis center

Web-based software





Multiple analysis formats

Scatter plot

Volcano plot

Multi-group plot

Clustergram

Sample to Insight

Live data analysis example 2


Is serum miRNA expression altered in colorectal cancer?

Experimental workflow:

5 µl serum input

Serum samples: Normal (n=3) and

colorectal cancer (n=3)

Isolation: miRNeasy Serum / Plasma Kit

RT: miScript II RT Kit (HiSpec)

qPCR: Serum & Plasma 384HC

miScript miRNA PCR Array

70 nl serum equivalents for an entire plate

Free data analysis

PreAMP: miScript PreAMP PCR Kit and

miScript PreAMP Primer Mix

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QIAcube

miRNeasy Mini

miRNeasy Micro

miRNeasy FFPE

miRNeasySerum / Plasma

ExoRNeasySerum / Plasma

Instruments


miScript PCR System

miScript PreAMP


miScript PCR Arrays



Kits/solutions



Sample to Insight

Ingenuity Pathway Analysis (IPA)


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

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Where can I find the products discussed today?


www.qiagen.com

www.qiagen.com/GeneGlobe

Sample to Insight





QIAcube

miRNeasy Mini

miRNeasy Micro

miRNeasy FFPE

miRNeasy Serum / Plasma

ExoRNeasySerum / Plasma

Instruments


miScript PCR System

miScript PreAMP


miScript PCR Arrays



Kits/solutions



Sample to Insight


Thank you for attending today’s webinar!

Jonathan Shaffer, Ph.D.

[email protected]

Contact QIAGEN

1-800-426-8157

[email protected]

Questions?

Advanced miRNA Expression Analysis: miRNA and its Role in Human Disease Webinar Series Part 3

Healthcare

Transcript of Advanced miRNA Expression Analysis: miRNA and its Role in Human Disease Webinar Series Part 3