Comparative proteomics: assessment of biological variability and dataset comparability

Area of Science:

• Microbiology
• Proteomics
• Biochemistry

Background:

• Comparative proteomics in bacteria faces challenges due to variations in dataset quality and biological differences.
• Current methods of normalizing single-sample datasets limit certainty when comparing multiple datasets.
• Existing spectral counting methods can yield inaccurate relative protein quantification.

Purpose of the Study:

• To develop a robust two-step assessment criterion for validating the comparability of liquid chromatography-tandem mass spectrometry (LC-MS/MS) datasets.
• To enhance the accuracy of calculating relative protein amounts between proteomic datasets using internal standards.
• To improve the reliability of comparative proteomics in bacterial studies.

Main Methods:

• Introduced a two-step assessment criterion: (1) relative total spectra (RTS) to determine dataset comparability, and (2) nine glycolytic enzymes as internal standards.
• Utilized Lactococcus lactis strains (HR279 and JHK24) with varying green fluorescent protein (GFP) expression levels as a model system.
• Quantified GFP abundance using spectral counting and direct fluorescence measurements, followed by statistical analysis.

Main Results:

• Datasets with an RTS value less than 1.4 accurately reflected relative differences in GFP levels between high and low expression strains.
• Without the RTS assessment and internal standards, spectral counting overestimated GFP increase (3.92 ± 1.14 fold) compared to fluorescence measurements (2.86 ± 0.42 fold, p = 0.024).
• The proposed two-step approach yielded a GFP increase of 2.88 ± 0.92 fold, showing a statistically insignificant difference (p = 0.95) from fluorescence measurements.

Conclusions:

• The developed two-step assessment criterion effectively validates the comparability of LC-MS/MS datasets.
• This method enables accurate calculation of relative protein amounts between proteomic datasets.
• The approach significantly improves the accuracy and reliability of comparative bacterial proteomics.