How to Quantify Western Blot Results: Complete Guide
Accurate quantification of western blot results is essential for comparing protein expression levels between samples. This comprehensive step-by-step guide provides expert methods for measuring band intensity, normalizing data, and performing accurate protein quantification, including software recommendations and best practices.
Step 1: Image Acquisition
Image Quality Requirements
- Use high-resolution images (at least 300 DPI)
- Avoid overexposed or underexposed images
- Capture images in linear range (not saturated)
- Use consistent exposure settings across samples
- Save images in lossless format (TIFF, PNG)
- Avoid JPEG compression artifacts
Step 2: Band Detection
Band Selection
- Identify correct band based on molecular weight
- Draw region of interest (ROI) around band
- Include entire band area
- Use consistent ROI size across samples
- Subtract background from same lane
Step 3: Intensity Measurement
Measurement Methods
- Volume: Integrated density (area × intensity)
- Mean intensity: Average pixel intensity
- Peak intensity: Maximum pixel value
- Subtract background intensity
- Use volume for most accurate quantification
Background Subtraction
- Measure background from same lane (near band)
- Subtract background from band intensity
- Use consistent background region
- Account for local background variations
Step 4: Normalization
Normalization Methods
- Loading control: Normalize to housekeeping protein (GAPDH, actin, tubulin)
- Total protein: Normalize to total protein stain
- Target protein: Normalize to total target protein (for PTMs)
- Calculate ratio: Target protein / Loading control
Normalization Formula
Normalized Value = (Target Band Intensity - Background) / (Loading Control Intensity - Background)
Software Tools
ImageJ / Fiji
- Free, open-source software
- Widely used for quantification
- Good for basic to advanced analysis
- Requires manual band selection
Commercial Software
- Image Lab (Bio-Rad)
- Image Studio (LI-COR)
- Quantity One
- Often includes automated analysis
Best Practices
- Use multiple biological replicates (n≥3)
- Include technical replicates when possible
- Normalize to appropriate loading control
- Ensure linear range of detection
- Document all analysis parameters
- Perform statistical analysis
- Present data with error bars