mi rna data analysis 2013

Sample & Assay Technologies

Data Analysis for the miScript miRNA PCR Arrays

Samuel J. Rulli, Ph.D.miRNA / qPCR Applications [email protected]

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

[email protected]

Questions, Comments, Concerns?Global Applications Support

[email protected]

Sample & Assay Technologies- 3 -

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.


Anatomy of a catalogued miRNA PCR Array

.84 Pathway-Specific miRNAs

.miRNA isolation control n=2

.6 “Housekeeping”

.snRNAs

.miRNA Reverse Transcription Controls (miRTC) n=2

.Positive PCR Controls (PPC) n=2

PPCMi-

RTCPPC

Mi-RTC

RNU6-2

SNORD96A

SNORD95

SNORD72

miR-142-5p

miR-16

miR-142-3p

miR-21

miR-15a

miR-29b

Let-7a

miR-126

miR-143

miR-27a

miR-9

miR-26a

Let-7b

Let-7f

miR-24

miR-30e

miR-181a

miR-29a

miR-124

miR-144

miR-30d

miR-19b

miR-22

miR-122

miR-150

miR-32

miR-155

miR-140-5p

miR-125b

miR-141

miR-92a

miR-424

miR-191

miR-17

miR-130a

miR-20a

miR-27b

miR-26b

miR-146a

miR-200c

miR-99a

miR-19a

miR-23a

miR-30a

Let-7i

miR-93

miR-106b

Let-7c

miR-101

Let-7g

miR-425

miR-15b

miR-28-5p

miR-18a

miR-25

miR-23b

miR-302a

miR-186

miR-29c

miR-7

Let-7d

miR-30c

miR-181b

miR-223

miR-320

miR-374a

miR-151-5p

Let-7e

miR-374b

miR-196b

miR-140-3p

miR-100

miR-103

miR-96

miR-302b

miR-194

miR-125a-5p

miR-423-5p

miR-376c

miR-195

miR-222

miR-28-3p

miR-128a

miR-302c

miScript miRNA PCR Array Human miFinder (MIHS-001Z)

Cel-miR-39

Cel-miR-39

SNORD61

SNORD68



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”


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”

How miScript miRNA PCR Arrays Work


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


.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 curve2) 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 andselecting house keeping genes) )

For ABI, Stratagene, Bio-Rad, and Eppendorf Real-Ti me PCR Instruments*:

*For Roche LC480: Use Second Derivative Maximum

Defining Baseline and Threshold








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










.Export Cp values to blank spread sheet (Excel).

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




Linear View

Setting Baseline


Linear View

Select “AUTO CALCULATED”

Setting Baseline


Log View

Setting Threshold


Threshold Line

Setting Threshold


Threshold Line

C(t)

Setting Threshold


Threshold Line

C(t)

Setting Threshold

Use the Same Threshold for All PCR Arrays


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)•255PCR Arrays Total





•Free!•Upload Excel spreadsheet at http://www.sabiosciences.com/pcr/arrayanalysis.php•Good for 11 Group Comparisons (Control + 10 Experimental)•255PCR Arrays Total






•Free!•Upload Excel spreadsheet athttp://pcrdataanalysis.sabiosciences.com/mirna/arrayanalysis.php•Good for 11 Group Comparisons (Control + 10 Experimental)•255PCR Arrays Total



Organizing Raw C(t) values

Download Excel Template from SABiosciences’ Web Portal…or make your own.

Cataloged Array

Row 1Sample Name

Column A:Well Location

Column B-??:

Raw C(t) Values



Cataloged Array

Row 1Sample Name


Column B-??:

Raw C(t) Values



Experiment miRNA expression Profiling during differ entiation

hMSC

Osteogenesis – Day 16

Neurogenesis – 72 hr

T1T2

T3T4

T1T2

T3T4

Differentiation protocolCollect miRNA at different time pointsRepeat experiment 3x (biological replicates)


Experiment miRNA expression Profiling during differ entiation

hMSC

Osteogenesis – Day 16

T1T2

T3T4

Differentiation protocolCollect miRNA at different time pointsRepeat experiment 3x (biological replicates)


Our Experiment-Data Analysis Overview

A A A A

Control Group 1 Group 2 Group 3

B CB CB CCB

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

hMSCs Time Point 1 Time Point 2 Time Point 3


A B C A B C A B C A B C

1 PCR Array for Each Sample

A A A A


B CB CB CCB



C(t)GOI- C(t)HKG

∆C(t)

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


A A A A


B CB CB CCB



∆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. Calculate ∆ C(t) for on each array for each GOI (Gene Of Interest)2. Calculate Average ∆∆∆∆ C(t) for each gene within a Group

∆C(t)∆C(t) ∆C(t) ∆C(t) ∆C(t) ∆C(t) ∆C(t) ∆C(t) ∆C(t) ∆C(t) ∆C(t) ∆C(t)


A A A A


B CB CB CCB



1. Calculate ∆∆∆∆ C(t) for on each array for each GOI (Gene Of Interest)2. Calculate Average ∆∆∆∆ C(t) for each gene within a Group3. Calculate ∆∆∆∆∆∆∆∆ C(t) for each gene between Groups




∆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. Calculate ∆∆∆∆ C(t) for on each array for each GOI (Gene Of Interest)2. Calculate Average ∆∆∆∆ C(t) for each gene within a Group3. Calculate ∆∆∆∆∆∆∆∆ C(t) for each gene between Groups4. Calculate Fold Change: 2(-∆∆∆∆∆∆∆∆Ct)




∆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



On-Line Data Analysis Demonstration


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.


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 sampleusing different threshold setting on machine or different machineshave different RT efficiencies


Special Cases: Serum Analysis or No HKGs

Isolation of miRNAs from Serum creates problems in normalizationNo universal endogenous HKGs

What is a good normalization strategy?�None (normalize to volume)�snRNA or miRNA in samples �All (global normalization)�spike in control


.84 Pathway-Specific miRNAs

.miRNA isolation control n=2

.6 “Housekeeping”

.snRNAs

.miRNA Reverse Transcription Controls (miRTC) n=2

.Positive PCR Controls (PPC) n=2

PPCMi-

RTCPPC

Mi-RTC

RNU6-2

SNORD96A

SNORD95

SNORD72

miR-142-5p

miR-16

miR-142-3p

miR-21

miR-15a

miR-29b

Let-7a

miR-126

miR-143

miR-27a

miR-9

miR-26a

Let-7b

Let-7f

miR-24

miR-30e

miR-181a

miR-29a

miR-124

miR-144

miR-30d

miR-19b

miR-22

miR-122

miR-150

miR-32

miR-155

miR-140-5p

miR-125b

miR-141

miR-92a

miR-424

miR-191

miR-17

miR-130a

miR-20a

miR-27b

miR-26b

miR-146a

miR-200c

miR-99a

miR-19a

miR-23a

miR-30a

Let-7i

miR-93

miR-106b

Let-7c

miR-101

Let-7g

miR-425

miR-15b

miR-28-5p

miR-18a

miR-25

miR-23b

miR-302a

miR-186

miR-29c

miR-7

Let-7d

miR-30c

miR-181b

miR-223

miR-320

miR-374a

miR-151-5p

Let-7e

miR-374b

miR-196b

miR-140-3p

miR-100

miR-103

miR-96

miR-302b

miR-194

miR-125a-5p

miR-423-5p

miR-376c

miR-195

miR-222

miR-28-3p

miR-128a

miR-302c

miScript miRNA PCR Array Human Serum and Plasma (MI HS-106Z)

Cel-miR-39

Cel-miR-39

SNORD61

SNORD68

Anatomy of a Serum miRNA PCR Array



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.


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)

15

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25

30

35

40

15 20 25 30 35 40

Raw Ct: Serum Sample A

Raw

Ct:

Ser

um S

ampl

e B

R2 = 0.9079

� Non-normalized Ct values are highly comparable� How should the data be normalized to uncover fine

differences between the two 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� Option syn-cel-miR-39 spike-in control included


� Mature miRNA expression was profiled using the Human Serum miScript miRNA PCR Array (MAH-106)

15

20

25

30

35

40

15 20 25 30 35 40


Raw

Ct:

Ser

um S

ampl

e B

R2 = 0.9079



15

20

25

30

35

40

15 20 25 30 35 40


Raw

Ct: Ser

um S

ample

B

R2 = 0.9079



.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� Option syn-cel-miR-39 spike-in control included


� Mature miRNA expression was profiled using the Human Serum miScript miRNA PCR Array (MAH-106)

15

20

25

30

35

40

15 20 25 30 35 40


Raw

Ct:

Ser

um S

ampl

e B

R2 = 0.9079



15

20

25

30

35

40

15 20 25 30 35 40


Raw

Ct: Ser

um S

ample

B

R2 = 0.9079

RAW Ct or volume normalization assumes:Same isolation efficiencySame RT efficiencySame baseline and threshold settingsSame volumetric constraints


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


Serum Sample Data Normalization (cont.)

.Step 2: Observe housekeeping gene Ct values

Position GeneCt: Sample

A Ct: Sample B

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

� 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?


Serum Sample Data Normalization (cont.)

� 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


Serum Sample Data Normalization (cont.)Option 1: Normalize to RNA Recover Control Assays

� Calculate the average Ct of the cel-miR-39 wells (H02 to H04)

� 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

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

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-20

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100

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to A

)


Serum Sample Data Normalization (cont.)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

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Serum Sample Data Normalization (cont.)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

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-20

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80

Fol

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to A

)


Serum Sample Data Normalization (cont.)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

� 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 down-regulated genes are potentially (incorrectly) identified. This suggests that there is the need for some method of normalization, other than just normalizing to ‘0’.

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miRNA Data Analysis Summary

When setting baseline and theshold with your qPCR I nstrument: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 uploa d

Organize experiment: Group Biological/Technical replicates

Focus on Sample and Experimental QualityRTCs; PPCs; Spike in Control (if applicable)

Select MOST STABLE HKGs for your experiment

Click through Fold Change Data, Export Results, Pub lish

mi rna data analysis 2013

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