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

Sample to Insight
Jonathan Shaffer, Ph.D.
Jonathan.Shaffer@qiagen.com
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.

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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

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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
QIAGEN Sample to Insight solutions for miRNA research
Sample prep
Real-time
PCR assays
 QIAcube
 miRNeasy Mini
 miRNeasy Micro
 miRNeasy FFPE
 miRNeasy
Serum/Plasma
 ExoRNeasy
Serum/Plasma
Instruments
 QIAgility
 RotorGene Q
 miScript PreAMP
 miScript Primer
Assays
 GeneGlobe
Data Analysis
Center
Kits/
solutions
 Ingenuity
Pathway Analysis
 miScript Mimics
 miScript
Inhibitors

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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
 miScript Primer Assays
5. Real-time PCR reagents
 miScript SYBR Green PCR Kit

Sample to Insight
QIAGEN Sample to Insight Solutions for miRNA research
Sample prep
Real-time
PCR assays
 QIAcube
 miRNeasy Mini
 miRNeasy
Micro
 miRNeasy
FFPE
 miRNeasy
Serum/Plasma
 ExoRNeasy
Serum/Plasma
Instruments
 QIAgility
 RotorGene Q
 miScript PreAMP
 miScript Primer
Assays
 GeneGlobe
Data Analysis
Center
Kits/
solutions
 Ingenuity
Pathway Analysis
 miScript Mimics
 miScript
Inhibitors

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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 DataUsing 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 = CT
Gene - 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: Thenumber 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 plateauphase
Important: Ensure threshold settings
are the same across all PCR runs in
the same analysis to allow
comparisonof 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
CTValue
FFPE Isolation 1
FFPE Isolation 2
FFPE Isolation 3
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
CTValue
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 miRNA
on each array
ΔCT = CT
miRNA – AVG CT
SN1/2/3/4/5/6 ΔCT = CT
miRNA – 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 miRNA
on 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 miRNA
within group (N or T)
ΔCT + ΔCT + ΔCT
3
ΔCT + ΔCT + ΔCT
3

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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

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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 RNAsamples: The snoRNA/ snRNA panel
of targets does not exhibit robust expressionand 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.)
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
Option 1: CT values normalized to CT mean of expressed miRNAs
-8
-4
0
4
8
12
Fold-Regulation
 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
Option 2: CT values normalized to CT mean of invariant miRNAs
-8
-4
0
4
8
12
Fold-Regulation
Commonly ExpressedmiRNAs
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

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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
-4
0
4
8
12
Fold-Regulation
-8
-4
0
4
8
12
Fold-Regulation
 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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Agenda
1
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 valuesto results
 Using ∆∆CT Method
 Multiple analysis formats
 Scatter plot
 Volcano plot
 Multi-group plot
 Clustergram

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GeneGlobe Data Analysis Center
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

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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

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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
 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
1
42

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Live data analysis demonstration
How to use the GeneGlobe data analysis center
 MultipleAnalysis Formats
 Scatter Plot
 Volcano Plot
 Multi-Group Plot
 Clustergram

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

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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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Sample prep
Real-time
PCR assays
 QIAcube
 miRNeasy Mini
 miRNeasy Micro
 miRNeasy FFPE
 miRNeasy
Serum / Plasma
 ExoRNeasy
Serum / Plasma
Instruments
 QIAgility
 RotorGene Q
 miScript PreAMP
 miScript Primer
Assays
 GeneGlobe
Data Analysis
Center
Kits/
solutions
Analysis
 miScript Mimics

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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

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Sample prep
Real-time
PCR assays
 QIAcube
 miRNeasy Mini
 miRNeasy Micro
 miRNeasy FFPE
 miRNeasy Serum
/ Plasma
 ExoRNeasy
Serum / Plasma
Instruments
 QIAgility
 RotorGene Q
 miScript PreAMP
 miScript Primer
Assays
 GeneGlobe
Data Analysis
Center
Kits/
solutions
Analysis
 miScript Mimics

Sample to Insight
Thank you for attending today’s webinar!
Jonathan Shaffer, Ph.D.
Jonathan.Shaffer@qiagen.com
Contact QIAGEN
1-800-426-8157
BRCsupport@QIAGEN.com
Questions?

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

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