Advanced mirna expression analysis

Advanced mirna expression analysis Data Analysis Tutorial Jonathan Shaffer, Ph.D. Jonathan.Shaffer@qiagen.com Senior Scientist, Product Development

Legal Disclaimer 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 productspecific 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. Advanced mirna expression analysis 2

Welcome to our 4-part webinar series on mirnas mirna and its role in human disease 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 Advanced mirna expression analysis 3

Advanced mirna expression analysis Agenda Calculating fold-change using the C T method of relative quantification Setting the Baseline and Threshold Data analysis example 1: Basic experiment Data analysis example 2: Serum mirna experiment Using the free GeneGlobe Data Analysis Center Live data analysis demonstrations Summary of QIAGEN s mirna detection portfolio Questions Advanced mirna expression analysis 4

QIAGEN Solutions for mirna Research Instruments QIAcube QIAgility RotorGene Q Compatibility with all Real-Time PCR instruments Sample Prep Real-time PCR Assays Data Analysis Interpretatio n Kits/ Solutions mirneasy Mini mirneasy Micro mirneasy FFPE mirneasy Serum/Plasma ExoRNeasy Serum/Plasma miscript PCR System miscript PreAMP miscript Microfluidics miscript PCR Arrays miscript Primer Assays GeneGlobe Data Analysis Center Ingenuity Pathway Analysis miscript Mimics miscript Inhibitors Advanced mirna expression analysis 5

QIAGEN Solutions for mirna Research Instruments QIAcube QIAgility RotorGene Q Compatibility with all Real-Time PCR instruments Sample Prep Real-time PCR Assays Data Analysis Interpretatio n Kits/ Solutions mirneasy Mini mirneasy Micro mirneasy FFPE mirneasy Serum/Plasma ExoRNeasy Serum/Plasma miscript PCR System miscript PreAMP miscript Microfluidics miscript PCR Arrays miscript Primer Assays GeneGlobe Data Analysis Center Ingenuity Pathway Analysis miscript Mimics miscript Inhibitors Advanced mirna expression analysis 6

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 Advanced mirna expression analysis 7

QIAGEN Solutions for mirna Research Instruments QIAcube QIAgility RotorGene Q Compatibility with all Real-Time PCR instruments Sample Prep Real-time PCR Assays Data Analysis Interpretatio n Kits/ Solutions mirneasy Mini mirneasy Micro mirneasy FFPE mirneasy Serum/Plasma ExoRNeasy Serum/Plasma miscript PCR System miscript PreAMP miscript Microfluidics miscript PCR Arrays miscript Primer Assays GeneGlobe Data Analysis Center Ingenuity Pathway Analysis miscript Mimics miscript Inhibitors Advanced mirna expression analysis 8

Real-time PCR data analysis Absolute quantification Absolute input copies, based on a standard curve C T = 23.8 Relative quantification Comparative C T 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 C T = C T Gene - C T Normalizer C T = C T (sample 2) C T (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 Advanced mirna expression analysis 9

Data analysis workflow Steps 1 & 2: Set Baseline and Threshold to determine C T values Step 3: Export C T values Step 4: Analyze data using C T method of relative quantification Advanced mirna expression analysis 10

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 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 Important: Ensure baseline settings are the same across all PCR runs in the same analysis to allow comparison of results. Advanced mirna expression analysis 11

Data analysis step 2: Set your threshold Threshold Purpose: Used to determine the C T (threshold cycle) value. The point at which the amplification curve intersects with the threshold line is called the C T. 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 threshold settings are the same across all PCR runs in the same analysis to allow comparison of results. Advanced mirna expression analysis 12

Example: Setting the Baseline and Threshold 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. Advanced mirna expression analysis 13

Data analysis step 3: Export C T values 40 Normal Lung 40 Lung Tumor 36 36 32 32 28 28 CT Value 24 20 16 CT Value 24 20 16 12 FFPE Isolation 1 12 FFPE Isolation 1 8 4 FFPE Isolation 2 FFPE Isolation 3 8 4 FFPE Isolation 2 FFPE Isolation 3 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) Advanced mirna expression analysis 14

Data analysis step 4: Analyze data C T 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 C T values C T = C T mirna AVG C T SN1/2/3/4/5/6 Calculate C T for each mirna on each array Exported C T values C T = C T mirna AVG C T 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 Advanced mirna expression analysis 15

Data analysis step 4: Analyze data (cont.) C T method of relative quantification Normal (N) Lung Lung Tumor (T) Calculate C T for each mirna on each array C T C T C T Calculate Average C T for each mirna within group (N or T) C T C T C T C T + C T + C T C T + C T + C T 3 Calculate C T for each mirna between groups (T N) 3 C T = Avg. C T (T) Avg. C T (N) Calculate fold-change for each mirna (T vs. N) 2 -( CT) Advanced mirna expression analysis 16

Data analysis example 1 Increased expression in a tumor sample Two conditions: Normal and Tumor mirna (hsa-mir-21-5p) Normal C T = 21 Tumor C T = 15 Normalizer (RNU6-2) Normal C T = 16 Tumor C T = 14 Analysis 1. Calculate C T for each condition (i.e. normalize your mirna C T values) Normal: 21 16 = 5 Tumor: 15 14 = 1 2. Calculate C T (tumor relative to normal) using the equation C T (T) C T (N) C T (tumor relative to normal): 1 5 = 4 3. Calculate fold-change (tumor relative to normal) using the equation 2 - CT 2 - CT (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 up-regulation. Compared to the normal sample, hsa-mir-21-5p is 16-fold up-regulated in the tumor sample Advanced mirna expression analysis 17

Data analysis example 2 Decreased expression in a tumor sample Two conditions: Normal and Tumor mirna (hsa-mir-16-5p) Normal C T = 15 Tumor C T = 16 Normalizer (RNU6-2) Normal C T = 16 Tumor C T = 14 Analysis 1. Calculate C T for each condition (i.e. normalize your mirna C T values) Normal: 15 16 = 1 Tumor: 16 14 = 2 2. Calculate C T (tumor relative to normal) using the equation C T (T) C T (N) C T (tumor relative to normal): 2 ( 1) = 3 3. Calculate fold-change (tumor relative to normal) using the equation 2 - CT 2 - CT (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) Compared to the normal sample, hsa-mir-16-5p is 8-fold down-regulated in the tumor sample Advanced mirna expression analysis 18

Serum and plasma data analysis Advanced mirna expression analysis 19

Serum and plasma samples (cont.) Special data analysis case 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 C T 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-39-3p C T mean Step 2: Normalize serum or plasma sample data Option 1: Normalize C T values to C T mean of all commonly expressed mirnas Option 2: Normalize C T values to C T mean of invariant mirnas Advanced mirna expression analysis 20

Serum and plasma samples (cont.) Calibrate data using cel-mir-39-3p C T 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 C T values Compared to sample 1, cel-mir-39-3p in sample 2 also has a delayed C T value Conclusion: calibrate samples (cel-mir-39-3p C T values indicate a differential recovery) Calibrated (Sample 2 C T 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 Advanced mirna expression analysis 21

Serum and plasma sample data normalization options Option 1: C T values normalized to C T mean of expressed mirnas Calculate the C T mean for commonly expressed mirnas Those mirnas with C T values < 30 (or 32 or 35) in all assessed samples 12 8 Fold- Regulation 4 0-4 -8 Advanced mirna expression analysis 22

Serum and plasma sample data normalization options Option 2: C T values normalized to C T mean of invariant mirnas Calculate the C T mean for invariant mirnas Choose at least 4 to 6 mirnas that exhibit little C T variation Commonly Expressed mirnas 12 hsa-let-7a hsa-mir-92a 8 hsa-let-7c hsa-mir-93 hsa-mir-21 hsa-mir-22 hsa-mir-23a hsa-mir-24 hsa-mir-103a hsa-mir-126 hsa-mir-145 hsa-mir-146a Fold- Regulation 4 0 hsa-mir-25 hsa-mir-191 hsa-mir-26a hsa-mir-222-4 hsa-mir-26b hsa-mir-423-5p -8 Advanced mirna expression analysis 23

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) 12 12 8 8 Fold- Regulation 4 0 Fold- Regulation 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 C T mean of all mirnas, as this array is biased (mirna assays included on this array are not random) Advanced mirna expression analysis 24

miscript s straightforward, data analysis solution Incorporating the free GeneGlobe Data Analysis Center Steps 1 & 2: Set Baseline and Threshold to determine C T values Step 3: Export C T values Step 4: Access the free data analysis software at www.qiagen.com/geneglobe Step 5 & on: Automatic data using C T method of relative quantification Advanced mirna expression analysis 25

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 C T values to results Using C T Method Multiple Analysis Formats Scatter Plot Volcano Plot Multi-Group Plot Clustergram Advanced mirna expression analysis 26

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 2. Array 3. Instr. 4. CatNo 5. Click Start Analysis Advanced mirna expression analysis 27

GeneGlobe Data Analysis Center (cont.) Step 2 1. Technology Upload data tab 2. Cat. No. 3. Plate Format What should you do at this tab? 1. Verify technology 2. Verify catalog number 3. Verify plate format 4. Upload exported C T values 5. Click Upload 4. Upload exported C T values 5. Click Upload Advanced mirna expression analysis 28

GeneGlobe Data Analysis Center (cont.) Step 3 Analysis setup tab Uploaded Data Key features: All mirnas and controls found on chosen array All C T data uploaded to software What should you do at this tab? Verify that your data has been uploaded correctly Advanced mirna expression analysis 29

GeneGlobe Data Analysis Center (cont.) 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 What should you do at this tab? 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 2. PreAMP 3. Sample type 4. Calibration 5. LOD 6. Update Advanced mirna expression analysis 30

GeneGlobe Data Analysis Center (cont.) 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) C T values What should you do at this tab? Verify that your data has been calibrated correctly Advanced mirna expression analysis 31

GeneGlobe Data Analysis Center (cont.) Step 6 Analysis setup tab Data QC Key features: Display results of Quality Checks: PCR efficiency RT efficiency What should you do at this tab? Verify that your samples have passed the QC checks Advanced mirna expression analysis 32

GeneGlobe Data Analysis Center (cont.) Step 7 Choose normalization method Analysis setup tab Select Normalization Method Key features: Provides 4 common methods for data normalization Manual selection of HKG Auto selection of HKG Auto selection from Full Plate Global C T Mean of expressed mirnas What should you do at this tab? Choose how you want your data to be normalized Advanced mirna expression analysis 33

GeneGlobe Data Analysis Center (cont.) Step 7 (cont.) Click Perform Normalization Analysis setup tab Select Normalization Method Key features: Provides 4 common methods for data normalization Manual selection of HKG Auto selection of HKG Auto selection from Full Plate Global C T Mean of expressed mirnas What should you do at this tab? Click Perform Normalization Advanced mirna expression analysis 34

GeneGlobe Data Analysis Center (cont.) Step 8 Analysis setup tab Data Overview Advanced mirna expression analysis 35

Step 9 GeneGlobe Data Analysis Center (cont.) Analysis tab: this tab provides an overview of all C T related calculations and provides a guide for you regarding the trust that you should place in your data. Advanced mirna expression analysis 36

GeneGlobe Data Analysis Center (cont.) 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 5 common plots or charts to visualize your data What should you do at this tab? Click on plot or chart of interest Advanced mirna expression analysis 37

GeneGlobe Data Analysis Center (cont.) Step 11 Export data tab Key features: Allows you to export your analysis results of choice What should you do at this tab? 1. Select analysis results to export 2. Click Export 1. Select results 2. Export Advanced mirna expression analysis 38

Step 12 (optional) GeneGlobe Data Analysis Center (cont.) 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 Advanced mirna expression analysis 39

Step 13 (optional) GeneGlobe Data Analysis Center (cont.) What s next tab mirna Expression Key features: Assists in determining how to further assess your mirnas of interest Advanced mirna expression analysis 40

Live data analysis demonstration How to use the GeneGlobe data analysis center Web-based software No installation needed Tailored for each array Raw C T values to results Using C T Method Multiple Analysis Formats Scatter Plot Volcano Plot Multi-Group Plot Clustergram Advanced mirna expression analysis 41

Live data analysis example 1 Is mirna expression altered under hypoxic conditions? Experimental workflow Seed Hep G2 cells 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 Advanced mirna expression analysis 42

Live data analysis demonstration: serum sample data How to use the GeneGlobe data analysis center Web-based software No installation needed Tailored for each array Raw C T values to results Using C T Method Multiple Analysis Formats Scatter Plot Volcano Plot Multi-Group Plot Clustergram Advanced mirna expression analysis 43

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) PreAMP: miscript PreAMP PCR Kit and miscript PreAMP Primer Mix qpcr: Serum & Plasma 384HC miscript mirna PCR Array 70 nl serum equivalents for an entire plate Free data analysis Advanced mirna expression analysis 44

QIAGEN Solutions for mirna Research Instruments QIAcube QIAgility RotorGene Q Compatibility with all Real-Time PCR instruments Sample Prep Real-time PCR Assays Data Analysis Interpretatio n Kits/ Solutions mirneasy Mini mirneasy Micro mirneasy FFPE mirneasy Serum/Plasma ExoRNeasy Serum/Plasma miscript PCR System miscript PreAMP miscript Microfluidics miscript PCR Arrays miscript Primer Assays GeneGlobe Data Analysis Center Ingenuity Pathway Analysis miscript Mimics miscript Inhibitors Advanced mirna expression analysis 45

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 Advanced mirna expression analysis 46

Where can I find the products discussed today? www.qiagen.com www.qiagen.com/geneglobe Advanced mirna expression analysis 47

QIAGEN Solutions for mirna Research Instruments QIAcube QIAgility RotorGene Q Compatibility with all Real-Time PCR instruments Sample Prep Real-time PCR Assays Data Analysis Interpretatio n Kits/ Solutions mirneasy Mini mirneasy Micro mirneasy FFPE mirneasy Serum/Plasma ExoRNeasy Serum/Plasma miscript PCR System miscript PreAMP miscript Microfluidics miscript PCR Arrays miscript Primer Assays GeneGlobe Data Analysis Center Ingenuity Pathway Analysis miscript Mimics miscript Inhibitors Advanced mirna expression analysis 48

Welcome to our 4-part webinar series on mirnas mirna and its role in human disease 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 Advanced mirna expression analysis 49

Questions? Thank you for attending today s webinar! Jonathan Shaffer, Ph.D. Jonathan.Shaffer@qiagen.com Contact QIAGEN 1-800-426-8157 BRCsupport@QIAGEN.com Advanced mirna expression analysis 50