Targeted Metabolomics - Usage Guide

Targeted Metabolomics - Usage Guide

Overview

Targeted metabolomics quantifies a predefined set of metabolites using selected reaction monitoring (SRM/MRM). This approach provides absolute quantification with high sensitivity and reproducibility.

Prerequisites

# Skyline (free, comprehensive)
# Download from: https://skyline.ms/

# R packages
install.packages(c("calibrate", "ggplot2"))

# Python
pip install pandas numpy scipy scikit-learn

Quick Start

Tell your AI agent what you want to do:

• "Build calibration curves and quantify metabolites from my MRM data"
• "Validate my targeted assay with QC sample statistics"

Example Prompts

Calibration

"Build calibration curves for my standard dilution series with 1/x weighting" "Calculate R-squared and back-calculated accuracy for each analyte" "Determine LOD and LOQ from calibration curve residuals"

Quantification

"Calculate absolute concentrations using my calibration curves" "Normalize to internal standards before quantification" "Apply dilution factors and report final concentrations"

Validation

"Calculate accuracy and precision from QC samples at low, medium, and high levels" "Check if QC accuracy is within 85-115% acceptance criteria" "Report CV% for replicate measurements"

Quality Assessment

"Flag samples with concentrations outside the calibration range" "Check for carryover using blank samples after high concentration samples" "Assess matrix effects using post-extraction spike"

What the Agent Will Do

1. Import peak areas/heights and standard concentrations
2. Build calibration curves with appropriate weighting
3. Calculate regression statistics and LOD/LOQ
4. Quantify unknowns using calibration
5. Validate with QC sample statistics
6. Export concentrations with quality flags

Tips

• Use weighted regression (1/x or 1/x^2) for wide concentration ranges
• Include at least 6 calibration points spanning expected sample range
• QC samples at 3 levels (low, medium, high) track assay performance
• Accept calibrators with 85-115% back-calculated accuracy (80-120% at LLOQ)
• Use stable isotope-labeled internal standards when available

Acceptance Criteria

LOD/LOQ Calculation

• LOD: 3.3 x (residual SD) / slope
• LOQ: 10 x (residual SD) / slope

Standard Curve Levels

Software Options

• Skyline - Free, comprehensive
• TraceFinder - Thermo
• MassHunter - Agilent
• MultiQuant - SCIEX

References

• FDA Bioanalytical Method Validation Guidance (2018)
• EMA Guideline on bioanalytical method validation
• Skyline: doi:10.1093/bioinformatics/btq054