| ```markdown | |
| # Goal/Experiment: | |
| This protocol outlines an automated procedure for estimating methylation levels using Methylation-Sensitive High-Resolution Melting (MS-HRM) analysis. The goal is to facilitate the detection and quantification of disease-related DNA methylation changes which can provide clinically relevant information in personalized patient care. | |
| # Automated Procedure for Estimation of Methylation Levels in MS-HRM Analysis | |
| **Authors:** | |
| - Sally Samsø Mathiasen | |
| - Jan Bińkowski | |
| - Tina Kjeldsen | |
| - Tomasz K Wojdacz | |
| - Lise Lotte Hansen | |
| **Affiliations:** | |
| - ¹Department of Biomedicine, Aarhus University, Aarhus DK-8000, Denmark | |
| - ²Independent Clinical Epigenetics Laboratory, Pomeranian Medical University, Szczecin, Poland | |
| - ³Department of Biomedicine, Aarhus University, Aarhus DK-8000, Denmark | |
| ## Abstract | |
| Testing for disease-related DNA methylation changes provides clinically relevant information in personalized patient care. Methylation-Sensitive High-Resolution Melting (MS-HRM) is a method used for measuring methylation changes and has already been employed in diagnostic settings. This method uses one set of primers that initiate the amplification of both methylated and non-methylated templates. Quantification of methylation levels using MS-HRM is hampered by PCR bias, leading to inaccurate calculations. This protocol utilizes the Area Under the Curve (AUC), a derivative of the HRM curves, and least square approximation (LSA) to improve accuracy. Limitations of the technique have been comprehensively evaluated, leading to a procedure that allows methylation level inference with specific measurement limitations. | |
| ## Protocol Citation | |
| ``` | |
| Sally Samsø Mathiasen, Jan Bińkowski, Tina Kjeldsen, Tomasz K Wojdacz, Lise Lotte Hansen. Automated procedure for estimation of methylation levels in MS-HRM analysis. protocols.io. https://protocols.io/view/automated-procedure-for-estimation-of-methylation-b3ptqmn | |
| ``` | |
| ## License | |
| This protocol is made available under the terms of the [Creative Commons Attribution License](https://creativecommons.org/licenses/by/4.0/), permitting unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | |
| ## Experimental Procedure | |
| ### Data Import | |
| 1. **MS-HRM Data Preparation for the Analyses (when using Light Cycler system – other PCR systems may require adjusting the data format):** | |
| 1.1 Normalize the HRM curves using the Gene Scanning software (we recommend default settings for normalization). | |
| 1.2 Generate difference plots for each normalized melting curve with the 100% methylation melting curve as the baseline/reference. | |
| 1.3 If data for any samples contain obvious outliers, consider removing them (note the name of the outlier). | |
| 1.4 Export the difference plot as a text file (example layout in supplementary materials S8-S11). | |
| > **IMPORTANT:** Calculations using the Methylation Level Calculator (MLC) require layout as described in "Plate set up" section of the MLC template. Modify columns and rows accordingly for other layouts. | |
| ### Calculation of Experiment Specific Standard Curve | |
| 2. **Methylation Levels Estimation Procedure:** | |
| 2.1 Open the Methylation Levels Calculator (MLC). | |
| 2.2 Open the exported text file from LC480 instrument (e.g., S10-S11 MGMT assay text without outliers). | |
| 2.3 Copy all data and paste into the "imported data" sheet starting in cell B3. Ensure names in rows 2 and 3 match. | |
| > **IMPORTANT:** Make sure the digital separator in the file exported from LC480 and Excel are the same. Modify the MLC for different sample layouts accordingly. | |
| 2.4 MLC will calculate and display AUC for each control and sample in row 1 of "Imported data" sheet. | |
| 2.5 Check if AUC for each replicate in row 1 is within the acceptable range. Replace outliers with 0 if necessary. | |
| > **IMPORTANT:** If MLC does not perform calculations automatically, change Excel settings to Automatic (`Formulas > Calculation options > Automatic`). | |
| 2.6 Go to sheet "0 variable": | |
| - Panel 1: AUC for each control replicate is calculated. | |
| - Panel 2: Equation 1 calculates theoretical AUC for each methylation level. | |
| - Panel 3: Theoretical and obtained AUC values for controls are plotted. | |
| 2.7 Go to sheet "1 variable": | |
| - Panel 1: AUC for each control replicate is calculated. | |
| - Panel 2: Equation 2 calculates theoretical AUC with M value set to 1. | |
| - Panel 3: Theoretical and obtained AUC values for controls are plotted. | |
| 2.8 Go to sheet "1 variable after LSA": | |
| - Solver Add-in is used. | |
| 2.9 Go to `Data > Solver > Solve`. Recalculate M value by LSA and recalculate standard curve. | |
| 2.10 Go to the sheet "2 variables": | |
| - Panel 1: AUC for each control replicate is calculated. | |
| - Panel 2: Equation 3 calculates theoretical AUC with N value set to 1. | |
| - Panel 3: Theoretical and obtained AUC values for controls are plotted. | |
| 2.11 Go to sheet "2 variables after LSA": | |
| - Solver Add-in is used. | |
| 2.12 Go to `Data > Solver > Solve`. Recalculate M and N values by LSA and recalculate standard curve. | |
| ### Estimation of Methylation Level in Unknown Samples | |
| 3. **Estimation of Methylation Level in Unknown Samples:** | |
| - MLC uses polynomial trend function for calculation. | |
| 3.1 Go to sheet "PTF": | |
| - Panel 1: Transform standard curve to describe methylation level as a function of AUC. | |
| - Panel 2: Polynomial trend function describes the standard curve. | |
| 3.2 Go to sheet "USC": | |
| - Panel 1.1: Sample name. | |
| - Panel 1.2: AUC for each replicate. | |
| - Panel 1.3: Methylation level calculated using equation 3 with M and N variables. | |
| ### Calculation of Experiment Specific Detection Window | |
| 4. **Calculation of Experiment Specific Detection Window:** | |
| 4.1 Go to sheet "Cut off (CO)": | |
| - Panel 1.1-1.2: Calculate AUC for each control replicate. | |
| - Panel 1.3-1.4: Calculate standard deviation and mean for each control replicate. | |
| - Panel 2: Plot normal distribution for each control. | |
| 4.2 Go to sheet "Detection window": | |
| - Panel 1.1-1.2: Calculate AUC for each control replicate. | |
| - Panel 1.3-1.4: Calculate standard deviation and mean for each control replicate. | |
| - Panel 2: Calculate overlap between consecutive controls. | |
| - Panel 3: Fill lower (10%) and upper limits (50%-60%) of detection window in cells P7 and Q7. | |
| ### Calculation of Methylation Levels in the Assay Specific Detection Window | |
| 5. **Calculation of Methylation Levels in the Assay Specific Detection Window:** | |
| 5.1 Go to sheet "2 variables within DW": | |
| - Solver Add-in is used. If calculations are not automatic, change Excel settings to Automatic. | |
| 5.2 Go to `Data > Solver > Solve`. Recalculate M and N values by LSA and standard curve. | |
| 5.3 Go to sheet "PTF within DW": | |
| - The same procedure is applied within the detection window. | |
| 5.4 Go to sheet "USC within DW": | |
| - Panel 1.1: Sample name. | |
| - Panel 1.2: AUC for each sample replicate. | |
| - Panel 1.3: Calculate methylation level with M and N variables within detection window. | |
| endofoutput | |
| ``` |