Mi rna data analysis 2013

Data Analysis for the
miScript miRNA PCR Arrays

Samuel J. Rulli, Ph.D.
miRNA / qPCR Applications Scientist
Samuel.Rulli@QIAGEN.com

The miRNA PCR Arrays and reagents are intended for molecular biology applications. This product
is not intended for the diagnosis, prevention or treatment of disease.
Sample & Assay Technologies

Data Analysis for the
miScript miRNA PCR Arrays

Questions, Comments, Concerns?
US Applications Support
888-503-3187
̣

Questions, Comments, Concerns?
Global Applications Support
SABio@qiagen.com

support@sabiosciences.com

Overview of Webinar
I. Brief Technology and Protocol Overview
What a miRNA PCR Array looks like
Simple Protocol
II. Calculating Fold change Values
Baseline and Threshold settings
Exporting and Organizing Data
Experimental Design
Basic Experiment with HKGs
Website Demonstration
Basic Experiment with No HKGs? What do I use?
Serum miRNA Applications
Summary
Pilot Study Promotion/ Re-Order Promotion

.

.

The miRNA PCR Arrays and reagents is intended for molecular biology applications. This product is
not intended for the diagnosis, prevention or treatment of disease.
-3-


Anatomy of a catalogued miRNA PCR Array
miScript miRNA PCR Array Human miFinder (MIHS-001Z)

84 Pathway-Specific miRNAs

.

miR- miR- miR- miR- miR142-5p 16 142-3p 21
15a

miR29b

Let7a

miR126

miR143

Let7b

miR27a

Let7f

miR- miR9
26a

miR24

miR- miR30e 181a

miR- miR- miR29a 124 144

miR- miR30d 19b

miR- miR122
22

miR- miR150
32

miR- miR- miR155 140-5p 125b

miR- miR- miR141 92a 424

miR- miR17
191

miR- miR130a 20a

miR- miR27b 26b

miR146a

miR- miR200c 99a

miR- miR- miR19a 23a 30a

Let7i

Let7c

miR101

Let7g

miR425

miR- miR15b 28-5p

miR- miR- miR18a
25
23b

miR- miR302a 186

Let7d

miR- miR30c 181b

miR- miR223
320

miR374a

miRNA isolation control n=2

.

6 “Housekeeping”
snRNAs

.

miR93

miR106b

.

miR- miR- miR103
96
302b

miR- miR29c
7

Let- miR- miR- miR- miR- miR7e 151-5p 374b 196b 140-3p 100

miR- miR- miR- miR- miR194 125a-5p423-5p 376c 195

SNORD SNORD SNORD SNORD SNORD RNU
CelCel68
72
95
96A
miR-39 miR-39 61
6-2

miR- miR- miR- miR222 28-3p 128a 302c
MiRTC

MiRTC

PPC

PPC

miRNA Reverse Transcription
Controls (miRTC) n=2

.

Positive PCR Controls
(PPC) n=2

.

-4-


How miScript miRNA PCR Arrays Work

cDNA Synthesis
universal RT Reaction
1 hours
Load Plates
2 minutes

Export Raw Ct Values

Biologically Relevant Fold Change Values
∆∆ Ct calculations
“mir-103 is up-regulated 5 fold in
experiment versus control”
-5-


How miScript miRNA PCR Arrays Work

cDNA Synthesis
universal RT Reaction
1 hours
Load Plates
2 minutes

Export Raw Ct Values

Biologically Relevant Fold Change Values
∆∆ Ct calculations
“mir-103 is up-regulated 5 fold in
experiment versus control”
-6-


Overview of Webinar
I. Brief Technology and Protocol Overview
What a miRNA PCR Array looks like
Simple Protocol
II. Calculating Fold change Values
Baseline and Threshold settings
Exporting and Organizing Data
Experimental Design
Basic Experiment with HKGs
Website Demonstration
Basic Experiment with No HKGs? What do I use?
Summary

.

.

-7-


Defining Baseline and Threshold
For ABI, Stratagene, Bio-Rad, and Eppendorf Real-Time PCR Instruments*:

Baseline
• Use Automated Baseline
-(if your instrument has Adaptive Baseline function) OR
• Manually Set Baseline
-Using Linear View:
Set to Cycle #2 or #3 up to 1 or 2 cycle values before earliest
amplification (with highest cycle being cycle #15)

.

Threshold Value
• Use Log View
• Place in
1) Linear phase of amplification curve
2) Above background signal, but within lower half to one third of curve

.

Export Ct values to blank spread sheet (Excel).

.

Threshold Must Be Same Between Runs (important for PPC and RTC and
selecting house keeping genes)
)

.

*For Roche LC480: Use Second Derivative Maximum
-8-



Baseline
-Using Linear View:

.

Threshold Value
• Use Log View
• Place in

.


.

)

.

-9-



Baseline
-Using Linear View:

.

Threshold Value
• Use Log View
• Place in

.


.

)

.

- 10 -



Baseline
-Using Linear View:

.

Threshold Value
• Use Log View
• Place in

.


.

)

.

- 11 -



Baseline
-Using Linear View:

.

Threshold Value
• Use Log View
• Place in

.


.

Threshold Must Be Same Between Runs (important for PPC and RTC
and selecting house keeping genes)
- 12 -



Baseline
-Using Linear View:

.

Threshold Value
• Use Log View
• Place in

.

Export Cp values to blank spread sheet (Excel).

.

Threshold Must Be Same Between Runs (important for PPC and RTC
and selecting house keeping genes)
- 13 -


Setting Baseline

Linear View

- 14 -


Setting Baseline
Select “AUTO CALCULATED”

Linear View

- 15 -


Setting Threshold

Log View

- 16 -


Setting Threshold

Threshold Line

- 17 -


Setting Threshold

Threshold Line

C(t)

- 18 -


Setting Threshold
Use the Same Threshold for All PCR Arrays

Threshold Line

C(t)

- 19 -


2 Ways to “CRUNCH” the Data
Excel Based Templates
•Free!
•Download from http://www.sabiosciences.com/mirnaArrayDataAnalysis.php
•Good for 2 Group Comparisons (Control + Experimental)
•10 PCR Arrays per Group

Web-Based Data Analysis
•Free!
•Upload Excel spreadsheet at
•http://pcrdataanalysis.sabiosciences.com/mirna/arrayanalysis.php

•Good for 11 Group Comparisons (Control + 10 Experimental)
•255 PCR Arrays Total

- 20 -


•Free!

•Free!
•Upload Excel spreadsheet at http://www.sabiosciences.com/pcr/arrayanalysis.php

- 21 -


•Free!

•Free!
•Upload Excel spreadsheet at
http://pcrdataanalysis.sabiosciences.com/mirna/arrayanalysis.php


- 22 -


Organizing Raw C(t) values
Download Excel Template from SABiosciences’ Web Portal…or make your own.
Cataloged Array
Row 1
Sample Name
Column A:
Well Location
Column B-??:
Raw C(t) Values

- 23 -


Cataloged Array
Row 1
Sample Name
Column A:
Well Location
Column B-??:
Raw C(t) Values

- 24 -


Cataloged Array
Row 1
Sample Name
Column A:
Well Location
Column B-??:
Raw C(t) Values

- 25 -


Cataloged Array
Row 1
Sample Name
Column A:
Well Location
Column B-??:
Raw C(t) Values

- 26 -


Experiment miRNA expression Profiling during differentiation

Osteogenesis – Day 16
T4
T3

T2
hMSC

T1

Neurogenesis – 72 hr
T1

T2
T3

T4

Differentiation protocol
Collect miRNA at different time points
Repeat experiment 3x (biological replicates)
- 27 -


Experiment miRNA expression Profiling during differentiation

Osteogenesis – Day 16
T4
T2
hMSC

T3

T1

Differentiation protocol
Collect miRNA at different time points
Repeat experiment 3x (biological replicates)
- 28 -


Our Experiment-Data Analysis Overview
Control
A

B
hMSCs

Group 1
C

A

B

Group 2
C

Time Point 1

A

B

Group 3
C

Time Point 2

A

B

C

Time Point 3

3 biological replicates that will be grouped into (3 groups + control)

- 29 -


Control
A
A

Group 1

Group 2

B

C

A

B

C

A

B

C

A

B

C

A

B
B

Group 3
A

C
C

B

A

B

C
C

1 PCR Array for Each Sample

- 30 -


Control

Group 1

Group 2

B

A

C

A

B

C

A

B

A

C

A

B

C

A

B
B

Group 3
A

C
C

B

A

B

C
C

∆C(t)
C(t)GOI- C(t)HKG

1.

Calculate ∆ C(t) for on each array for each GOI (Gene Of Interest)

- 31 -


Control

Group 1

Group 2

B

A
∆C(t)

C

A

B

C

A

B

A

C

A

B

C

A

∆C(t)

∆C(t)

∆C(t)+∆C(t)+∆C(t)
3

1.
2.

∆C(t)

∆C(t)

∆C(t)

∆C(t)

B

Group 3

B
∆C(t)

∆C(t)+∆C(t)+∆C(t)
3

∆C(t)+∆C(t)+∆C(t)
3

A

C
C
∆C(t)

B

A

B

∆C(t)

C
C

∆C(t) ∆C(t)

∆C(t)+∆C(t)+∆C(t)
3

Calculate Average ∆ C(t) for each gene within a Group

- 32 -


Control
A

B

∆C(t)

Group 1
C

∆C(t)

∆C(t)

A
∆C(t)

B
∆C(t)

Group 2
A

C
∆C(t)

∆C(t)

B

Group 3
C

∆C(t)

∆C(t)

A
∆C(t)

B

C
∆C(t) ∆C(t)

∆C(t)+∆C(t)+∆C(t)
3
∆C(t)+∆C(t)+∆C(t)
3

∆C(t)+∆C(t)+∆C(t)
3

∆C(t)+∆C(t)+∆C(t)
3

1.
2.
3.

Calculate ∆∆ C(t) for each gene between Groups

- 33 -


Control
A

B

∆C(t)

Group 1
C

∆C(t)

∆C(t)

A
∆C(t)

B
∆C(t)

Group 2
A

C
∆C(t)

∆C(t)

B

Group 3
C

∆C(t)

∆C(t)

A
∆C(t)

B

C
∆C(t) ∆C(t)

∆C(t)+∆C(t)+∆C(t)
3
∆C(t)+∆C(t)+∆C(t)
3

∆C(t)+∆C(t)+∆C(t)
3

∆C(t)+∆C(t)+∆C(t)
3

1.
2.
3.
4.

Calculate ∆∆ C(t) for each gene between Groups
∆∆Ct)
∆∆
Calculate Fold Change: 2(-∆∆
- 34 -


On-Line Data Analysis Demonstration

- 35 -


What are HKGs? Why do I need them? (Do I need them?)
House-keeping genes or Normalization genes:
Expressed in all samples and co-purify with miRNA fraction
Not changing expression levels due to disease or experimental conditions
Used to normalize for amount of sample and RT efficiency
6 small non-coding RNAs included on each array as “HKGs”
Ex: SNORD 61, SNORD 68, SNORD 72 , SNORD 95 , SNORD 96A, RNU6-2
Any other miRNA or assay on the miRNA Array can be a normalization gene
Use 1 HKG or an average of the most stable HKGs

.

Identification of stable HKGs
Prior experience / data from publication
Start with same amount of sample (RNA) and assume equal RT efficiency (actually
can measure this with miRNA RTC)
Pair-wise comparison (delta- Ct) between genes and assume genes are not changing
expression levels in the same direction.

- 36 -


Special Cases: Alternative ways to find HKGs
Pair wise Comparison:

Ct (HKG1)

22

23

26

Ct (HKG2)

18

19

22

Delta Ct

4

4

4

Useful if:
starting with different amounts of sample
using different threshold setting on machine or different machines
have different RT efficiencies

- 37 -


Special Cases: Serum Analysis or No HKGs

Isolation of miRNAs from Serum creates problems in normalization
No universal endogenous HKGs
What is a good normalization strategy?
None (normalize to volume)
snRNA or miRNA in samples
All (global normalization)
spike in control

- 38 -


Anatomy of a Serum miRNA PCR Array
miScript miRNA PCR Array Human Serum and Plasma (MIHS-106Z)

84 Pathway-Specific miRNAs

.

miR- miR- miR- miR- miR142-5p 16 142-3p 21
15a

miR29b

Let7a

miR126

miR143

Let7b

miR27a

Let7f

miR- miR9
26a

miR24

miR- miR30e 181a

miR- miR- miR29a 124 144

miR- miR30d 19b

miR- miR122
22

miR- miR150
32

miR- miR- miR155 140-5p 125b

miR- miR- miR141 92a 424

miR- miR17
191

miR- miR130a 20a

miR- miR27b 26b

miR146a

miR- miR200c 99a

miR- miR- miR19a 23a 30a

Let7i

Let7c

miR101

Let7g

miR425

miR- miR15b 28-5p

miR- miR- miR18a
25
23b

miR- miR302a 186

Let7d

miR- miR30c 181b

miR- miR223
320

miR374a

miRNA isolation control n=2

.

6 “Housekeeping”
snRNAs

.

miR93

miR106b

.

miR- miR- miR103
96
302b

miR- miR29c
7

Let- miR- miR- miR- miR- miR7e 151-5p 374b 196b 140-3p 100

miR- miR- miR- miR- miR194 125a-5p423-5p 376c 195

SNORD SNORD SNORD SNORD SNORD RNU
CelCel68
72
95
96A
miR-39 miR-39 61
6-2

miR- miR- miR- miR222 28-3p 128a 302c
MiRTC

MiRTC

PPC

PPC

miRNA Reverse Transcription
Controls (miRTC) n=2

.

Positive PCR Controls
(PPC) n=2

.

- 39 -


Serum miRNA Profiling

Human Serum miScript miRNA PCR Array (MIHS-106Z)

.

Profile the expression of mature miRNA sequences that researchers have
detected in serum and other bodily fluids
Includes miRNAs found to be present at higher levels in serum from
individuals with specific diseases
Heart and liver injury or disease, atherosclerosis, diabetes, and a number of
organ-specific cancers

What is on the array?
84 mature miRNA sequences
miRNA housekeeping gene assays
Reverse Transcription Control assays
PCR Control assays
RNA Recovery Control assays
– Works with separately purchased Syn-cel-miR-39 miScript miRNA Mimic
(MSY0000010) spiked into the sample before nucleic acid preparation to monitor
biological fluid miRNA recovery rates

The miRNA PCR Arrays and reagents is intended for molecular biology applications. This product is
not intended for the diagnosis, prevention or treatment of disease.
- 40 -


Expression Profiling of Normal Human Serum Samples
Two “normal” human serum samples (Sample A and Sample B)

.

Total RNA was isolated using the miRNeasy Mini Kit
QIAGEN Supplementary Protocol for total RNA purification from serum or plasma
Optional syn-cel-miR-39 spike-in control included

5 µl of each RNA elution was used in an miScript miRNA First Strand Kit
reverse transcription reaction
Mature miRNA expression was profiled using the Human Serum miScript
miRNA PCR Array (MIHS-106Z)

Non-normalized Ct values are highly comparable
How should the data be normalized to uncover fine
differences between the two samples?

Raw Ct: Serum Sample B

40

35

30

25

20
2

R = 0.9079
15
15

20

25

30

35

40

Raw Ct: Serum Sample A

- 41 -


Two normal human serum samples (Sample A and Sample B)
40

.

R C S
aw t: erumS ple B
am

QIAGEN Supplementary Protocol for total RNA purification from serum or plasma
Option
35 syn-cel-miR-39 spike-in control included

5 µl of each RNA elution was used in an miScript miRNA First Strand Kit
30
miRNA PCR Array (MAH-106)
40


20


25

35

30

2

R 25 0.9079
=
15

20
2

15

20

25

30

15

Raw Ct: Serum Sample 15
A
- 42 -

40

35
20

25

30

R = 0.9079
35

40



Two normal human serum samples (Sample assumes:
RAW Ct or volume normalization A and Sample B)
40
Same isolation efficiency
QIAGENSame RT efficiency total RNA purification from serum or plasma
Supplementary Protocol for
Option Same baseline and threshold
35 syn-cel-miR-39 spike-in control included settings
5 µl of each Same volumetric constraints miRNA First Strand Kit
RNA elution was used in an miScript
R C S
aw t: erumS ple B
am

.

30
miRNA PCR Array (MAH-106)
40


20


25

35

30

2

R 25 0.9079
=
15

20
2

15

20

25

30

15

Raw Ct: Serum Sample 15
A
- 43 -

40

35
20

25

30

R = 0.9079
35

40



Serum Sample Data Normalization

Step 1: Check reverse transcription control (miRTC) and PCR control (PPC) Ct values

.

Position

Control

Ct: Sample A

Ct: Sample B

H09

miRTC

18.76

18.52

H10

miRTC

18.73

18.64

H11

PPC

19.43

19.61

H12

PPC

19.61

19.76

As determined by the raw Ct values, the reverse transcription and PCR
efficiency of both samples are highly comparable
Ct values differ by less than 0.25 units

- 44 -


Serum Sample Data Normalization (cont.)

Step 2: Observe housekeeping gene Ct values

.

Position

Gene

Ct: Sample
A

H05

SNORD72

31.81

32.79

H06

SNORD95

35.00

35.00

H07

SNORD96A

35.00

35.00

H08

RNU6-2

35.00

35.00

Ct: Sample B

Housekeeping genes are either not expressed or exhibit borderline
detectable expression
As is often found with serum samples, standard housekeeping genes cannot be
used for data normalization
How should you proceed?

- 45 -


Four potential data normalization options
1.

Normalize data of each plate to its RNA Recovery Control
Assays (wells H02 to H04)
Can only be used if Syn-cel-miR-39 miScript miRNA Mimic
(MSY0000010) was spiked into the sample before nucleic acid
preparation

2.

Normalize data to Ct mean of all expressed targets (targets
with Ct < 35) for a given plate

3.

Normalize data to Ct mean of targets that are commonly
expressed in the two samples of interest

4.

Normalize data to ‘0’
Essentially you are relying on the consistency in the quantity and
quality of your original RT input

- 46 -


Option 1: Normalize to RNA Recover Control Assays
Calculate the average Ct of the cel-miR-39 wells (H02 to H04)
Position

Control

Ct: Sample A

Ct: Sample B

H02

cel-miR-39

17.84

19.37

H03

cel-miR-39

17.85

19.49

H04

cel-miR-39

17.85

19.39

Sample A: 17.85
Sample B: 19.42

Using these cel-miR-39 Ct means as normalizers, calculate ∆∆Ct values,
fold-change, and fold up/down regulation
Fold-Regulation (B to A)

100
80
60
40
20
0
-20
-40

- 47 -


Option 2: Normalize to Ct Mean of All Expressed Targets for a given plate
Determined the number of expressed targets in each plate (Ct < 35)
Sample A: 66
Sample B: 59

Calculate the Ct Mean of the expressed targets
Sample A: 28.96
Sample B: 29.70

Using these Ct means as normalizers, calculate ∆∆Ct values, fold-change,
and fold up/down regulation
NOTE: same strongly up-regulated and down-regulated miRNAs are identified


60
40
20
0
-20
-40
-60

- 48 -


Option 3: Normalize to Ct Mean of Commonly Expressed Targets
Determined the number of commonly expressed targets for the plates being
analyzed (Ct < 35 in all samples)
Commonly expressed in Sample A and Sample B: 48

Calculate the associated Ct Mean
Sample A: 27.52
Sample B: 28.86

Using these Ct means as normalizers, calculate ∆∆Ct values, fold-change,
and fold up/down regulation
NOTE: same strongly up-regulated and down-regulated miRNAs are identified


80
60
40
20
0
-20
-40

- 49 -


Option 4: Normalize to ‘0’
Normalizing to ‘0’ relies on the consistency in the quantity and quality of
your original RT input
For serum samples, this may not be the best option, as the RNA is not routinely
quantified prior to addition to a reverse transcription reaction

Normalizing the data to ‘0’, calculate ∆∆Ct values, fold-change, and fold
up/down regulation

40
20
0
-20
-40
-60
-80
-100

NOTE: These results are not completely comparable to the results achieved
with the other three normalization methods. The same strongly up-regulated
and down-regulated miRNAs are identified; however, additionally up- and downregulated genes are potentially (incorrectly) identified. This suggests that there
is the need for some method of normalization, other than just normalizing to ‘0’.
- 50 -


miRNA Data Analysis Summary
When setting baseline and theshold with your qPCR Instrument:
ABI, Stratagene, BioRad, Eppendorf
•Automatic Baseline
•Threshold in lower ½ to lower 1/3 of curves (PPC = 18 to 22)
Roche LC480:
•Second derivative maximum
Export and Collect Raw Ct values. Organize for upload
Organize experiment:
Group Biological/Technical replicates
Focus on Sample and Experimental Quality
RTCs; PPCs; Spike in Control (if applicable)
Select MOST STABLE HKGs for your experiment
Click through Fold Change Data, Export Results, Publish

- 51 -


Mi rna data analysis 2013

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