Mi rna part ii_2013

Sample & Assay Technologies

Advanced miRNA Expression Analysis:
From Experimental Design through Data Analysis

Jonathan Shaffer, Ph.D.
Jonathan.Shaffer@qiagen.com
microRNA Technologies, R&D Americas

The products described in this webinar are intended for molecular biology applications.
These products are not intended for the diagnosis, prevention or treatment of disease.


Three part webinar series
miRNA and its role in human disease

Webinar 1 :
Speaker:

Webinar 2 :
Speaker:

Webinar 3 :
Speaker:

Meeting the challenges of miRNA research:
An introduction to microRNA biogenesis, function, and analysis

Advanced microRNA expression analysis:
From experimental design through data analysis

Profiling miRNA expression in Cells, FFPE, and serum:
On the road to biomarker development

Webinar 2: Advanced miRNA Expression Analysis

2


Advanced miRNA Expression Analysis
Agenda

Overview
miRNA Background
miRNA expression profiling using a miScript miRNA PCR Array

How to calculate fold-change using the ∆∆CT method of relative quantification
Setting the Baseline and Threshold
Data analysis example 1: Basic Experiment
Using the free miScript miRNA PCR Array data analysis tools
Data analysis example 2: Serum miRNA Experiment

Summary of QIAGEN’s miRNA detection portfolio
Current Promotion


3


Canonical pathway of miRNA biogenesis

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 RNAse III-like enzyme
Drosha.
Pre-miRNAs are then exported to the cytosol by
exportin 5.
In the cytosol RNAse III-like 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.


4


Why quantify miRNAs?

HighWire + PubMed Publications

miRBase Entries

Virtually every publication includes characterization by quantification.
Changes in miRNA can be correlated with gene expression changes in development,
differentiation, signal transduction, infection, aging, and disease.


5


miRNA expression profiling applications

Mechanisms of gene regulation
Developmental biology
Novel miRNA discovery
Studying miRNA–mRNA and miRNA–protein interactions
Integrative analyses of miRNAs in the context of gene
regulatory networks
Biomarkers
Tissue-based miRNA biomarkers
Tissues of unknown origin
Circulating biomarkers
Forensics
From Pritchard, C.C., et al, Nature Rev. Genet 2012, 13, 358-369

6


miRNA quantification approaches

Northern blotting
Deep sequencing
Microarrays
Real-time PCR based approaches


7


miRNA quantification by real-time PCR

Real-time PCR quantification of miRNAs
Has been the gold standard for gene quantification
Is the method of choice to confirm next-generation
sequencing and microarray results
Simple and easy to carry out
High sensitivity, specificity
High throughput compatible, automatable
Needs very low amounts of template


8


miRNA expression profiling using real-time PCR
Key considerations

Scientific question
Well-defined and testable
Experimental sample set
Statistically meaningful number of replicates (biological replicates)
A minimum of three replicates recommended
Additional replicates may enhance statistical power
Inclusion of proper controls
Experimental testing platform
Simple, straightforward workflow
High sensitivity and specificity
When profiling expression: a variety of PCR arrays to meet your
experimental needs
Easy and simple data analysis tools
Customizable solutions
Availability of companion research tools


9


miScript PCR System
Fully integrated, complete miRNA quantification system
1.

miScript II RT Kit
HiFlex Buffer: Unparalleled flexibility for quantification of
miRNA and mRNA from a single cDNA preparation
HiSpec Buffer: Unmatched specificity for mature
miRNA profiling

2.

miScript miRNA PCR Arrays
miRNome
Pathway-focused

3.

miScript PreAMP Kit
Optional step for small or precious samples
Full miRNome profiling from as little as 1 ng RNA

4.

Assays
miScript Primer Assays
miScript Precursor Assays
QuantiTect Primer Assays

5.

miScript SYBR Green PCR Kit
QuantiTect SYBR Green PCR Master Mix
Universal Primer

6.

miScript miRNA PCR Array data analysis software
Straightforward, free data analysis


10


miScript II RT Kit
A complete miRNA quantification solution

miScript II RT Kit

Mature miRNA
quantification and
profiling?

Biogenesis studies?

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

11


miScript II RT Kit
Reverse transcription using HiSpec Buffer


12


miRNome miScript miRNA PCR Arrays
Leading coverage and validated assays

miRNome Arrays
Human
Mouse
Rat
Dog
Rhesus macaque
Species

Assays
(miRBase V16)

Human

940

Rat

653

Dog

277

Rhesus macaque

469 (V18)

100% validated assays
Each assay is bench validated
Each array is quality controlled
Leading miRNome coverage
Customizable

1066

Mouse

Benefits of miRNome Arrays


miRBase V17 and V18 assays are
available!
Contact us if you are interest in a
different species!

13


Focused miScript miRNA PCR Arrays
Biologically relevant, intelligently designed

Focused Arrays
miFinder
Cancer PathwayFinder
Brain Cancer
Breast Cancer
Ovarian Cancer
Liver miFinder – New!
Apoptosis
Cell Differentiation & Development
Immunopathology
Inflammatory Response & Autoimmunity
Diabetes
Neurological Development & Disease
T-Cell & B-Cell Activation
Prostate Cancer
Cardiovascular Disease
Serum & Plasma


Benefits of Focused Arrays

100% validated assays
Each assay is bench validated
Each array is quality controlled
Biological relevant and up-to-date
Customizable
Contact us if you are interest in a
different species!

14


High Content (HC) miScript miRNA PCR Arrays
Targeted miRNome profiling
miFinder 384HC
Serum & Plasma 384HC
Cancer PathwayFinder 384HC
Liver miFinder 384HC – New!


15


Anatomy of a miScript miRNA PCR Array
96-well Format: 84 miRNA + 12 controls

84 miRNAs

Cel-miR-39

SNORD61; SNORD68; SNORD72
SNORD95; SNORD96A; RNU6-2

miRTC

PPC

Spike in
Control

miScript PCR Controls for
Normalization

RT
Control

PCR
Control

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


16


Stable expression and excellent amplification efficiencies
Tissue Expression

35

Mean CT

30

SNORD61
SNORD68

25

SNORD72

20

SNORD95

15

SNORD96A
RNU6-2

10
5
Brain

Kidney

Liver

Lung

Skeletal
Muscle

Testis

Thymus

Amplification Efficiencies
Primer

Hsa

Mmu

Rno

Cfa

Mml

SNORD61

103

99

95

99

101

SNORD68

101

100

93

97

99

SNORD72

94

100

93

91

97

SNORD95

102

103

98

100

106

SNORD96A

102

95

95

92

104

RNU6-2

98

98

95

99

103

Highly conserved across multiple species
Human, Mouse, Rat, Dog, Rhesus macaque
Relatively stable expression in many tissues
Amplification efficiencies of these assays are 100%
Consistent performance in both HiSpec and HiFlex Buffers
Ideal normalizers for ∆∆CT method of relative quantification


17


Formats built to fit your cycler AND your experiment

96-well

384-well

384-well (4 x 96)

Rotor-Disc 100


18


Compatible with a wide range of instruments

96-Well: 7000, 7300, 7500, 7700, 7900HT, ViiA 7
FAST 96-Well: 7500, 7900HT, Step One Plus, ViiA 7
FAST 384-Well: 7900HT, ViiA 7

iCycler, MyiQ, MyiQ2, iQ5, CFX96, CFX384
Opticon, Opticon 2, Chromo 4

Mastercycler ep realplex 2/2S/4/4S

LightCycler 480

Mx3000p, Mx3005p, Mx4000p

TP-800

RotorGene Q


19


QIAGEN PCR Array Service Core

Send your samples and receive results!

Total RNA Isolation: miRNeasy Kits
Reverse Transcription: miScript II RT Kit
qPCR: miScript miRNA PCR Arrays
Data analysis included!


20

Rapid Workflow = Robust, Reproducible Performance
1st Time Array User
1 hour
HiSpec Buffer

2 minutes

30
Mean CT: Biological Replicate 2


25

20

y = 1.0075x + 0.2891

2 hours

2

R = 0.989
15
15

20

25

30

Mean CT: Biological Replicate 1

15 minutes


Total HeLa S3 (miRNeasy)
Pellet 1: Frozen June 2010
Pellet 2: Frozen April 2011
HiSpec Buffered cDNA
miScript real-time PCR
miFinder miScript miRNA PCR Array
21


Real-time PCR data analysis

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 = 23.8

– ∆CT = CTGene - CTNormalizer
– ∆∆CT = ∆CT (sample 2) – ∆CT (sample 1) where sample 1
is the control sample and sample 2 is the experimental
sample

(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


22


Data analysis workflow

Steps 1 & 2:
Set Baseline and Threshold to determine CT values

Step 3:
Export CT values

Step 4:
Analyze data using ∆∆CT method of relative quantification


23


Steps 1 & 2

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 to 2 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


24


Steps 1 & 2

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


25


Applied Biosystems® 7900HT

Baseline: 3 to 15

Threshold: 0.2

Baseline: From cycle 2 (or 3) to 2 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.

26


QIAGEN Rotor-Gene® Q

Baseline

Threshold: 0.02

Always select “Dynamic Tube”
Potentially select “Slope Correct”
and/or “Ignore First”


27


miScript miRNA PCR Arrays on Roche® LightCycler® 480
Use the Second Derivative Maximum analysis method

Select Second Derivative Maximum
analysis method


Obtain CT Values

28


Step 3: Export CT values

Normal Lung

40

36

32

32

28

28
CT Value

36

CT Value

Lung Tumor

40

24
20

24
20
16

16
12

FFPE Isolation 1

12

FFPE Isolation 2

8

FFPE Isolation 1
FFPE Isolation 2

8

FFPE Isolation 3

FFPE Isolation 3

4

4
1

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

1

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

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)


29


Step 4: Analyze data
∆∆CT method of relative quantification

Normal (N) Lung Total RNA
N cDNA (Iso. 1)

N cDNA (Iso. 2)

Lung Tumor (T) total RNA

N cDNA (Iso. 3)

Exported CT values

T cDNA (Iso. 1)

Calculate ∆CT
for each miRNA
on each array

∆CT = CTmiRNA – AVG CTSN1/2/3/4/5/6

T cDNA (Iso. 2)

T cDNA (Iso. 3)

Exported CT values

∆CT = CTmiRNA – AVG CTSN1/2/3/4/5/6

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


30


Step 4: Analyze data (cont.)

Normal (N) Lung

Lung Tumor (T)
Calculate ∆CT
for each miRNA
on each array

∆CT

∆CT

∆CT

∆CT

∆CT

∆CT

Calculate Average
∆CT for each miRNA
within group (N or T)

∆CT + ∆CT + ∆CT

∆CT + ∆CT + ∆CT

3

3
Calculate ∆∆CT for
each miRNA
between groups
(T – N)

∆∆CT = Avg. ∆CT (T) – Avg. ∆CT (N)
Calculate fold-change for each miRNA (T vs. N)

2-(∆∆CT)

31


Step 4: Analyze data (cont.)
Fold-Regulation: Tumor vs. Normal

Log2 Fold-Regulation (Tumor vs. Normal)

14
10
6
2
-2
-6
-10
-14

Significant differences exist between the mature miRNA
expression levels of the two tissue types


32


miScript’s straightforward, data analysis workflow
Incorporating the free miScript miRNA PCR Array Data
Analysis Software

Steps 1 & 2:

Step 3:
Export CT values

Step 4:
Access the free data analysis tools at
http://pcrdataanalysis.sabiosciences.com/mirna

Step 5 & on:
Automatic data using ∆∆CT method of relative quantification


33


Steps 1 & 2

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 to 2 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

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 that baseline and threshold settings are the same across all PCR
runs in the same analysis to allow comparison of results.


34


Steps 3 & 4
Export CT values and access free analysis tools

Export CT values according to the manual supplied with the real-time PCR
instrument
Access the free miScript miRNA PCR Array Data Analysis Tools
Website: http://sabiosciences.com/mirnaArrayDataAnalysis.php
– Web-based

– Excel-based


35


Step 5: Automatic data analysis
miScript miRNA PCR Array Data Analysis Tools

Web-based software
No installation needed
Tailored for each array

Raw CT values to fold-change results
Using ∆∆CT Method

Multiple Analysis Formats
Scatter Plot
Volcano Plot
Multi-Group Plot
Clustergram

Downloadable Excel analysis templates are also available

36


miScript miRNA PCR Array Data Analysis
Step 5A

Website dedicated for array data analysis
http://pcrdataanalysis.sabiosciences.com/mirna

1. Choose Catalog Number

2. Upload exported CT values
3. Click Upload

Upload Readout Tab
This tab includes:
Uploading instructions
Data normalization instructions
Instructions that walk you through the data analysis
(right side of ‘Upload Readout’ Tab – not shown on
this image)
You can also Take a Test Run or Play Movie
Guide for help with using the software

What should you do at this page?
1.
2.
3.

Choose array catalog number from ‘Catalog
Number’ dropdown menu
Upload exported CT values from computer
Click Upload

37


Step 5B

Readout Tab

Basic Setup

3. Click Update All Changes
1. Choose ‘Control Genes’

This tab includes:
All miRNAs and controls found on
chosen array
Column where Normalization RNA
(control gene) can be selected
All CT data uploaded to software

2. Designate CT values
column groups

What should you do at this page?
1.

2.
3.

Click boxes next to desired ‘Control
Genes’ (miScript PCR Controls are
pre-selected)
Designate columns of CT values as
Control, Group 1, Group 2, etc.
Click ‘Update All Changes’


38


Step 5C
Readout Tab

View Housekeeping Genes

Choose ‘Normalized By’

Readout Tab
View Housekeeping
Genes, Data Overview, & Data QC
View Housekeeping Genes Tab
Selected ‘Control Genes’ can be
visualized and method of normalization
can be chosen

Data Overview Tab
This tab provides an overview of all
∆∆CT calculations performed by the
software

Readout Tab

Data QC

Data QC Tab
This tab provides an overview of QC
data associated with the miRTC
(reverse transcription control) and PPC
(positive PCR control)


39


Step 5D

Analysis Result Tab: this tab provides an overview of all ∆∆CT related calculations and
provides a guide for you regarding the trust that you should place in your data (see red arrow)

Comments on
data quality


40


Step 5E

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.

Scatter Plot


41


Step 5F

Export Data Tab: When clicked, results calculated by the miScript miRNA PCR
Array Data Analysis software can be exported to Microsoft Excel.


42


Serum Sample analysis using the miScript PCR System


43


Workflow: Serum & plasma miRNA expression profiling

1. Collect whole blood and separate serum or plasma
2. Recommended starting amount of serum or plasma: 100–200 µl
Note: If starting with 50 µl (or less) of serum or plasma, incorporate preamplification
3. Isolate RNA: miRNeasy Serum/Plasma Kit + miRNeasy Serum/Plasma Spike-in Control
4. Reverse transcription: Perform miScript II RT reaction (using HiSpec Buffer)
Per intended 384-well plate, reverse transcribe 1.5 µl RNA eluate (1/10th prep)
5. (Optional) Preamplification: If the starting amount of serum or plasma is 50 µl (or less),
perform preamplification using the miScript PreAMP PCR Kit
6. Real-time PCR: Profile miRNA expression using chosen miScript miRNA PCR Array
Array recommendations:
– miRNome miScript miRNA PCR Array
– Serum & Plasma 384HC miScript miRNA PCR Array
– Serum & Plasma miScript miRNA PCR Array


44


Biomarker discovery workflow
Concept
Phase I (determined expressed miRNAs):
Pooled sample profiling,
Maximal Assays (miRNome or 384HC)

Phase II (determine differentially expressed miRNAs):
Individual profiling of samples (that went into pools),
Only Expressed miRNAs (< 384-well plate)

Phase III (statistical power):
Individual profiling of all samples in study,
Differentially Expressed miRNAs from Phase II (multiple samples per 384-well plate)


45


Special data analysis case
Serum & Plasma Samples

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.
Typical CT Values for miScript PCR Controls in Serum Samples
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

Step 1: Calibrate samples using cel-miR-39 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


46


Anatomy of a miScript miRNA PCR Array
96-well Format: 84 miRNA + 12 controls

84 miRNAs

Cel-miR-39

SNORD61; SNORD68; SNORD72
SNORD95; SNORD96A; RNU6-2

miRTC

PPC

Spike in
Control

miScript PCR Controls for
Normalization

RT
Control

PCR
Control

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

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


47

Serum or plasma sample data normalization


Calibrate samples using cel-miR-39 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

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 in sample 2 also has a delayed CT value
Conclusion: calibrate samples (cel-miR-39 CT values indicate a differential recovery)

Calibrated (Sample 1 CT values +3)
Assay

Sample 1

Sample 2

hsa-miR-16

19.0

19.0

hsa-miR-21

23.0

24.0

hsa-miR-192

26.0

26.0

hsa-miR-103

26.0

23.0

hsa-miR-25

25.0

25.0

cel-miR-39

21.0

21.0


48


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

Calculate the CT mean for commonly expressed miRNAs
Those miRNAs with CT values < 30 (or 32, or 35) in all assessed samples
12

Fold-Regulation

8

4

0

-4

-8


49


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

Calculate the CT mean for invariant miRNAs
Choose at least 4 to 6 miRNAs that exhibit little CT variation
12

Commonly Expressed miRNAs
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

8
Fold-Regulation

hsa-let-7a

hsa-miR-423-5p

4

0

-4

-8


50


Comparison of normalization methods

Option 1:
Commonly Expressed miRNAs

Option 2:
Invariant Panel of miRNAs

(miRNome, 384HC)

(small panel screening)

8
Fold-Regulation

12

8
Fold-Regulation

12

4

0

4

0

-4

-4

-8

-8

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)


51


miRNA expression profiling
2-∆CT: Breast Cancer vs. Normal

Normal Serum

1.E+01

30
26
22
Serum Isolation 1

18

Serum Isolation 2
Serum Isolation 3

1.E-01
1.E-02
miR-34a

1.E-03
1.E-04
1.E-05
1.E+01

200 µl serum
50 µl total RNA 5 µl total RNA, HiSpec Buffer
miFinder miScript miRNA PCR Array

1.E+00

Workflow

-∆CT

1.E-01

2

1.E-02

1.E-03

miRNA

1.E-04

6 11 16 21 26 31 36 41 46 51 56 61

1.E-05

14
1

1.E+00

2

CT Value

34

-∆CT

: Breast Cancer Serum

38

: Normal Serum

one-half of cDNA used for 96-well

High reproducibility can be achieved using the miRNeasy Supplementary Protocol
Each isolation was from a different normal serum donor

Significant differences exist between the mature miRNA expression levels of the two tissue
types
± 2-fold [red lines] used as a cutoff for significance

52


Where can I find miScript miRNA PCR Arrays?
www.sabiosciences.com/mirna_pcr_array.php

miRNA Overview
miScript PCR System
Products for functional studies
miRNA purification options

53


Where can I find miScript Primer Assays?
www.qiagen.com/GeneGlobe


54


QIAGEN’s miRNA portfolio
Your miRNA workflow, from sample to results!

Quantification
and profiling

miRNeasy Mini Kit, miRNeasy Micro Kit
miRNeasy 96 Kit
miRNeasy FFPE Kit
miRNeasy Serum/Plasma Kit

Profiling

Isolation

Functionalization

miScript II RT Kit and PreAMP Kit

HiPerFect Transfection Reagent

miScript SYBR Green PCR Kit

Attractene Transfection Reagent


miScript miRNA Mimics

miScript miRNA Data Analysis Tool

miScript miRNA Inhibitors

PAXgene Tissue miRNA Kit

miScript Primer Assay

Custom miScript miRNA Mimics

PAXgene Blood miRNA Kit

miScript Precursor Assay

miScript Target Protector

Supplementary protocol for miRNA from
Plasma and Serum

miScript PCR Starter Kit

miScript miRNA Inhibitor 96 and 384
Plates and Sets

QIAGEN Service Core

QIAcube

QIAgility

Rotor-Gene Q
55


Upcoming webinars
Experimental Setup and miRNA Profiling webinars

Webinar 3: Profiling miRNA expression:
on the road to biomarker
development
Date:

April 24, 2013, 9:30 am EDT

Speaker:



56


Thank you for attending

Technical Support Contact Information
Monday through Friday
8:00AM to 6:00PM EST
Direct Phone: 888-503-3187
E-Mail: support@sabiosciences.com

http://www.sabiosciences.com/promotion/miscriptdemo.php
57


Questions?
Thank you for attending today’s webinar!

Jonathan.Shaffer@qiagen.com
microRNA Technologies, R&D Americas


58

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