title: Comparative proteomics: assessment of biological variability and dataset comparability.
abstract: BACKGROUND
  Comparative proteomics in bacteria are often hampered by the differential nature of dataset quality and/or inherent biological deviations. Although common practice compensates by reproducing and normalizing datasets from a single sample, the degree of certainty is limited in comparison of multiple dataset. To surmount these limitations, we introduce a two-step assessment criterion using: (1) the relative number of total spectra (R TS ) to determine if two LC-MS/MS datasets are comparable and (2) nine glycolytic enzymes as internal standards for a more accurate calculation of relative amount of proteins. Lactococcus lactis HR279 and JHK24 strains expressing high or low levels (respectively) of green fluorescent protein (GFP) were used for the model system. GFP abundance was determined by spectral counting and direct fluorescence measurements. Statistical analysis determined relative GFP quantity obtained from our approach matched values obtained from fluorescence measurements.
  
  
  RESULTS
  L. lactis HR279 and JHK24 demonstrates two datasets with an R TS value less than 1.4 accurately reflects relative differences in GFP levels between high and low expression strains. Without prior consideration of R TS and the use of internal standards, the relative increase in GFP calculated by spectral counting method was 3.92 ± 1.14 fold, which is not correlated with the value determined by the direct fluorescence measurement (2.86 ± 0.42 fold) with the p = 0.024. In contrast, 2.88 ± 0.92 fold was obtained by our approach showing a statistically insignificant difference (p = 0.95).
  
  
  CONCLUSIONS
  Our two-step assessment demonstrates a useful approach to: (1) validate the comparability of two mass spectrometric datasets and (2) accurately calculate the relative amount of proteins between proteomic datasets.
authors.affiliation: Department of Food and Nutrition, Chungnam National University, Daejeon, 305-764, South Korea. ksr7744@cnu.ac.kr.
authors.forename: Sa Rang
authors.initials: SR
authors.lastname: Kim
authors.affiliation: Department of Food and Nutrition, Chungnam National University, Daejeon, 305-764, South Korea. t.vi.nguyen@gmail.com.
authors.forename: Tuong Vi
authors.initials: TV
authors.lastname: Nguyen
authors.affiliation: Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon, 305-764, South Korea. snr85@cnu.ac.kr.
authors.forename: Na Ri
authors.initials: NR
authors.lastname: Seo
authors.affiliation: Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon, 305-764, South Korea. shjeong0512@gmail.com.
authors.forename: Seunghup
authors.initials: S
authors.lastname: Jung
authors.affiliation: Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon, 305-764, South Korea. hjan@cnu.ac.kr.
authors.forename: Hyun Joo
authors.initials: HJ
authors.lastname: An
authors.affiliation: Robert Mondavi Institute for Wine and Food Science, Department of Food Science, University of California, Davis, CA, 95616, USA. damills@ucdavis.edu.
authors.forename: David A
authors.initials: DA
authors.lastname: Mills
authors.affiliation: Department of Food and Nutrition, Chungnam National University, Daejeon, 305-764, South Korea. jaykim@cnu.ac.kr.
authors.forename: Jae Han
authors.initials: JH
authors.lastname: Kim
mesh_terms: D001426:Bacterial Proteins / Q000378:metabolism*; D002853:Chromatography, Liquid; D030562:Databases, Protein; D049452:Green Fluorescent Proteins / Q000378:metabolism; D007778:Lactobacillus / Q000254:growth & development / Q000378:metabolism*; D040901:Proteomics / Q000379:methods*; D012015:Reference Standards; D053719:Tandem Mass Spectrometry
chemical_list: D001426:Bacterial Proteins; D049452:Green Fluorescent Proteins