Data Analysis for the
miScript miRNA PCR Arrays

Samuel J. Rulli, Ph.D.
miRNA / qPCR Applications Scientist
Samuel.Rulli@Q...
Data Analysis for the
miScript miRNA PCR Arrays

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

Qu...
Overview of Webinar
I. Brief Technology and Protocol Overview
What a miRNA PCR Array looks like
Simple Protocol
II. Calcul...
Anatomy of a catalogued miRNA PCR Array
miScript miRNA PCR Array Human miFinder (MIHS-001Z)

84 Pathway-Specific miRNAs

....
How miScript miRNA PCR Arrays Work

cDNA Synthesis
universal RT Reaction
1 hours
Load Plates
2 minutes

Export Raw Ct Valu...
How miScript miRNA PCR Arrays Work

cDNA Synthesis
universal RT Reaction
1 hours
Load Plates
2 minutes

Export Raw Ct Valu...
Overview of Webinar
I. Brief Technology and Protocol Overview
What a miRNA PCR Array looks like
Simple Protocol
II. Calcul...
Defining Baseline and Threshold
For ABI, Stratagene, Bio-Rad, and Eppendorf Real-Time PCR Instruments*:

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

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

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

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

Baseline
• Use Au...
Defining Baseline and Threshold
*For Roche LC480: Use Second Derivative Maximum

Baseline
• Use Automated Baseline
-(if yo...
Setting Baseline

Linear View

- 14 -

Sample & Assay Technologies
Setting Baseline
Select “AUTO CALCULATED”

Linear View

- 15 -

Sample & Assay Technologies
Setting Threshold

Log View

- 16 -

Sample & Assay Technologies
Setting Threshold

Threshold Line

- 17 -

Sample & Assay Technologies
Setting Threshold

Threshold Line

C(t)

- 18 -

Sample & Assay Technologies
Setting Threshold
Use the Same Threshold for All PCR Arrays

Threshold Line

C(t)

- 19 -

Sample & Assay Technologies
2 Ways to “CRUNCH” the Data
Excel Based Templates
•Free!
•Download from http://www.sabiosciences.com/mirnaArrayDataAnalysi...
2 Ways to “CRUNCH” the Data
Excel Based Templates
•Free!
•Download from http://www.sabiosciences.com/mirnaArrayDataAnalysi...
2 Ways to “CRUNCH” the Data
Excel Based Templates
•Free!
•Download from http://www.sabiosciences.com/mirnaArrayDataAnalysi...
Organizing Raw C(t) values
Download Excel Template from SABiosciences’ Web Portal…or make your own.
Cataloged Array
Row 1
...
Organizing Raw C(t) values
Download Excel Template from SABiosciences’ Web Portal…or make your own.
Cataloged Array
Row 1
...
Organizing Raw C(t) values
Download Excel Template from SABiosciences’ Web Portal…or make your own.
Cataloged Array
Row 1
...
Organizing Raw C(t) values
Download Excel Template from SABiosciences’ Web Portal…or make your own.
Cataloged Array
Row 1
...
Experiment miRNA expression Profiling during differentiation

Osteogenesis – Day 16
T4
T3

T2
hMSC

T1

Neurogenesis – 72 ...
Experiment miRNA expression Profiling during differentiation

Osteogenesis – Day 16
T4
T2
hMSC

T3

T1

Differentiation pr...
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 ...
Our Experiment-Data Analysis Overview
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...
Our Experiment-Data Analysis Overview
Control

Group 1

Group 2

B

A

C

A

B

C

A

B

A

C

A

B

C

A

B
B

Group 3
A
...
Our Experiment-Data Analysis Overview
Control

Group 1

Group 2

B

A
∆C(t)

C

A

B

C

A

B

A

C

A

B

C

A

∆C(t)

∆C...
Our Experiment-Data Analysis Overview
Control
A

B

∆C(t)

Group 1
C

∆C(t)

∆C(t)

A
∆C(t)

B
∆C(t)

Group 2
A

C
∆C(t)

...
Our Experiment-Data Analysis Overview
Control
A

B

∆C(t)

Group 1
C

∆C(t)

∆C(t)

A
∆C(t)

B
∆C(t)

Group 2
A

C
∆C(t)

...
On-Line Data Analysis Demonstration

- 35 -

Sample & Assay Technologies
What are HKGs? Why do I need them? (Do I need them?)
House-keeping genes or Normalization genes:
Expressed in all samples ...
Special Cases: Alternative ways to find HKGs
Pair wise Comparison:

Ct (HKG1)

22

23

26

Ct (HKG2)

18

19

22

Delta Ct...
Special Cases: Serum Analysis or No HKGs

Isolation of miRNAs from Serum creates problems in normalization
No universal en...
Anatomy of a Serum miRNA PCR Array
miScript miRNA PCR Array Human Serum and Plasma (MIHS-106Z)

84 Pathway-Specific miRNAs...
Serum miRNA Profiling

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

.

Profile the expression of mature miRNA sequenc...
Expression Profiling of Normal Human Serum Samples
Two “normal” human serum samples (Sample A and Sample B)

.

Total RNA ...
Expression Profiling of Normal Human Serum Samples
Two normal human serum samples (Sample A and Sample B)
40

.

R C S
aw ...
Expression Profiling of Normal Human Serum Samples
Two normal human serum samples (Sample assumes:
RAW Ct or volume normal...
Serum Sample Data Normalization

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

.

P...
Serum Sample Data Normalization (cont.)

Step 2: Observe housekeeping gene Ct values

.

Position

Gene

Ct: Sample
A

H05...
Serum Sample Data Normalization (cont.)
Four potential data normalization options
1.

Normalize data of each plate to its ...
Serum Sample Data Normalization (cont.)
Option 1: Normalize to RNA Recover Control Assays
Calculate the average Ct of the ...
Serum Sample Data Normalization (cont.)
Option 2: Normalize to Ct Mean of All Expressed Targets for a given plate
Determin...
Serum Sample Data Normalization (cont.)
Option 3: Normalize to Ct Mean of Commonly Expressed Targets
Determined the number...
Serum Sample Data Normalization (cont.)
Option 4: Normalize to ‘0’
Normalizing to ‘0’ relies on the consistency in the qua...
miRNA Data Analysis Summary
When setting baseline and theshold with your qPCR Instrument:
ABI, Stratagene, BioRad, Eppendo...
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Mi rna data analysis 2013

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Mi rna data analysis 2013

  1. 1. 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
  2. 2. 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 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
  3. 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. -3- Sample & Assay Technologies
  4. 4. 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 . 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. -4- Sample & Assay Technologies
  5. 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” -5- Sample & Assay Technologies
  6. 6. 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- Sample & Assay Technologies
  7. 7. 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- Sample & Assay Technologies
  8. 8. 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- Sample & Assay Technologies
  9. 9. 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 -9- Sample & Assay Technologies
  10. 10. 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 - 10 - Sample & Assay Technologies
  11. 11. 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 - 11 - Sample & Assay Technologies
  12. 12. 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 - 12 - Sample & Assay Technologies
  13. 13. Defining Baseline and Threshold *For Roche LC480: Use Second Derivative Maximum 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 Cp values to blank spread sheet (Excel). . Threshold Must Be Same Between Runs (important for PPC and RTC and selecting house keeping genes) - 13 - Sample & Assay Technologies
  14. 14. Setting Baseline Linear View - 14 - Sample & Assay Technologies
  15. 15. Setting Baseline Select “AUTO CALCULATED” Linear View - 15 - Sample & Assay Technologies
  16. 16. Setting Threshold Log View - 16 - Sample & Assay Technologies
  17. 17. Setting Threshold Threshold Line - 17 - Sample & Assay Technologies
  18. 18. Setting Threshold Threshold Line C(t) - 18 - Sample & Assay Technologies
  19. 19. Setting Threshold Use the Same Threshold for All PCR Arrays Threshold Line C(t) - 19 - Sample & Assay Technologies
  20. 20. 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 - Sample & Assay Technologies
  21. 21. 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://www.sabiosciences.com/pcr/arrayanalysis.php •Good for 11 Group Comparisons (Control + 10 Experimental) •255 PCR Arrays Total - 21 - Sample & Assay Technologies
  22. 22. 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 - 22 - Sample & Assay Technologies
  23. 23. 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 - Sample & Assay Technologies
  24. 24. 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 - 24 - Sample & Assay Technologies
  25. 25. 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 - 25 - Sample & Assay Technologies
  26. 26. 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 - 26 - Sample & Assay Technologies
  27. 27. 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 - Sample & Assay Technologies
  28. 28. 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 - Sample & Assay Technologies
  29. 29. 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 - Sample & Assay Technologies
  30. 30. Our Experiment-Data Analysis Overview 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 - Sample & Assay Technologies
  31. 31. Our Experiment-Data Analysis Overview 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 - Sample & Assay Technologies
  32. 32. Our Experiment-Data Analysis Overview 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 ∆ C(t) for on each array for each GOI (Gene Of Interest) Calculate Average ∆ C(t) for each gene within a Group - 32 - Sample & Assay Technologies
  33. 33. Our Experiment-Data Analysis Overview 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 on each array for each GOI (Gene Of Interest) Calculate Average ∆ C(t) for each gene within a Group Calculate ∆∆ C(t) for each gene between Groups - 33 - Sample & Assay Technologies
  34. 34. Our Experiment-Data Analysis Overview 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 on each array for each GOI (Gene Of Interest) Calculate Average ∆ C(t) for each gene within a Group Calculate ∆∆ C(t) for each gene between Groups ∆∆Ct) ∆∆ Calculate Fold Change: 2(-∆∆ - 34 - Sample & Assay Technologies
  35. 35. On-Line Data Analysis Demonstration - 35 - Sample & Assay Technologies
  36. 36. 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 - Sample & Assay Technologies
  37. 37. 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 - Sample & Assay Technologies
  38. 38. 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 - Sample & Assay Technologies
  39. 39. 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 . 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. - 39 - Sample & Assay Technologies
  40. 40. 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 - Sample & Assay Technologies
  41. 41. 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 - Sample & Assay Technologies
  42. 42. Expression Profiling of Normal Human Serum Samples Two normal human serum samples (Sample A and Sample B) 40 . R C S aw t: erumS ple B am Total RNA was isolated using the miRNeasy Mini Kit 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 reverse transcription reaction Mature miRNA expression was profiled using the Human Serum miScript 30 miRNA PCR Array (MAH-106) 40 Non-normalized Ct values are highly comparable How should the data be normalized to uncover fine differences between the two samples? 20 Raw Ct: Serum Sample B 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 Raw Ct: Serum Sample A Sample & Assay Technologies
  43. 43. Expression Profiling of Normal Human Serum Samples Two normal human serum samples (Sample assumes: RAW Ct or volume normalization A and Sample B) 40 Total RNA was isolated using the miRNeasy Mini Kit 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 . reverse transcription reaction Mature miRNA expression was profiled using the Human Serum miScript 30 miRNA PCR Array (MAH-106) 40 Non-normalized Ct values are highly comparable How should the data be normalized to uncover fine differences between the two samples? 20 Raw Ct: Serum Sample B 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 Raw Ct: Serum Sample A Sample & Assay Technologies
  44. 44. 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 - Sample & Assay Technologies
  45. 45. 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 - Sample & Assay Technologies
  46. 46. 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 - 46 - Sample & Assay Technologies
  47. 47. 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) 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 - Sample & Assay Technologies
  48. 48. 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 Fold-Regulation (B to A) 60 40 20 0 -20 -40 -60 - 48 - Sample & Assay Technologies
  49. 49. 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 Fold-Regulation (B to A) 80 60 40 20 0 -20 -40 - 49 - Sample & Assay Technologies
  50. 50. 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 Fold-Regulation (B to A) 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 - Sample & Assay Technologies
  51. 51. 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 - Sample & Assay Technologies

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