Surface Adjustment of Reverse Phase Protein Arrays using Positive Control Spots

Area of Science:

• Biotechnology
• Proteomics
• Bioinformatics

Background:

• Reverse phase protein arrays (RPPA) are crucial for quantifying relative protein expression across numerous samples.
• RPPA signal is influenced by true biological signal, additive background noise, and spatial variations across the array.
• Standard background subtraction methods are insufficient to eliminate all non-biological signal variations.

Purpose of the Study:

• To develop and validate a novel surface adjustment method for RPPA data.
• To correct for spatial trends and non-biological variations on RPPA slides.
• To improve the accuracy and reproducibility of protein expression measurements.

Main Methods:

• A surface adjustment technique was developed utilizing positive control spots on the array.
• Generalized additive models were employed to estimate a smoothed surface correcting for spatial effects.
• A nested surface adjustment was implemented for intensity-based correction when positive controls were in a dilution series.

Main Results:

• The surface adjustment method successfully removed spatial trends from RPPA data.
• This method demonstrated superior performance compared to background subtraction alone in correcting non-biological trends.
• Within-slide replicate agreement was significantly increased by the surface adjustment technique.

Conclusions:

• Surface adjustment is a vital step for accurate RPPA data analysis.
• The inclusion of positive control spots is essential for implementing effective surface adjustment.
• This approach enhances the reliability and precision of protein expression quantification in high-throughput studies.