Quantitative ELISA Data Analysis Protocol

Quantitative ELISA Data Analysis Protocol

From optical density to accurate concentration — a complete guide to interpreting ELISA results

Introduction

Quantitative ELISA transforms raw absorbance readings into meaningful concentration data through standard curve fitting and sample interpolation. Accurate data analysis is critical to ensure assay reproducibility and to compare biomarker levels across experiments, treatment groups, or sample matrices.

Materials & Reagents

  • Completed ELISA plate with absorbance readings (typically at 450 nm)
  • ELISA reader and data export (CSV or Excel format)
  • Standard curve data (known concentrations)
  • Analysis software: Excel, GraphPad Prism, SoftMax Pro, or R
  • PBS or assay buffer for blanking

Step-by-Step Protocol

  1. Blank Subtraction: Subtract the average absorbance of blank wells (no antigen) from all sample and standard readings to correct for background noise.
  2. Standard Curve Preparation: Plot the mean absorbance of standards (Y-axis) against their known concentrations (X-axis). Use at least 6–8 standards covering the expected sample range.
  3. Curve Fitting: Fit the data using a four-parameter logistic (4PL) or five-parameter logistic (5PL) regression model. The equation for 4PL is:
    Y = D + (A - D) / (1 + (X/C)^B) where:
    • A = minimum asymptote
    • B = slope
    • C = inflection point (EC50)
    • D = maximum asymptote
  4. Sample Interpolation: Use the fitted curve to interpolate sample concentrations from their absorbance values. Most software performs this automatically.
  5. Dilution Correction: Multiply each calculated concentration by the dilution factor used for that sample to obtain the final value.
  6. Replicate Averaging: Average the replicate values for each sample and calculate standard deviation (SD) or coefficient of variation (CV).
  7. Validation: Ensure R² ≥ 0.99 for the standard curve and CV ≤ 15% for replicate wells to confirm data quality.

Tips / Notes

  • Always run duplicates or triplicates for each standard and sample.
  • Discard outliers only if justified by technical error (e.g., pipetting issue).
  • Use consistent curve-fitting models across experiments for comparison.
  • If using fluorescence or chemiluminescent ELISA, adjust dynamic range settings accordingly.

Troubleshooting

  • Nonlinear standard curve: Verify pipetting accuracy and ensure standards span the correct range.
  • High replicate variability: Check for inconsistent washing or plate drying between steps.
  • Negative or saturated values: Adjust dilution factors or reduce sample load.

Internal Links

Related protocols for complete assay workflows:

Keywords

ELISA data analysis, ELISA standard curve, 4PL 5PL regression, quantitative ELISA, sample interpolation, assay accuracy, biomarker quantification

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