Reproducibility in Urine Proteomic Profiling by MALDI-TOF/MS

Aim: We aimed to estimate a background noise to determine the specific signal detection and evaluate different data normalization strategies to derive the best statistical approaches to utilize MALDI-TOF/MS for proteomics. Methods: We analysed a urine pooled from 10 samples with a 12.58 pmol of a 1589.9 m/z internal standard peptide. For the inter- assay variability assessment, fourteen aliquots were dialyzed by MALDI-TOF/MS. For the intra-assay study, an aliquot was divided into 20 separate sub-aliquots and analyzed by MALDI-TOF/MS. To estimate the signal detection limit (sLOD), serial dilution of a urine pool up to 1/256 were analysed in triplicate. We evaluated the sLOD and adjusted the data appropriately to reduce its variability. We investigated six data normalization approaches – the mean, median, internal standard, relative intensity, total ion current and linear rescaling normalization. Between-spectrum and the overall spectra variability were evaluated by the coefficient of variation (CV). Results: Within a mass range of 1000 m/z to 4000 m/z, we identified 129 and 122 peaks for inter-assay and intra-assay studies, respectively. Normalization methods performed almost similarly in both studies, except internal standard with an increased CV (78% and 26%, respectively). sLOD showed a marked decreasing trend with increasing m/z. With sLOD adjustment, raw data show a drastic reduction in CVs. Median and mean normalizations performed better, especially in the intra-assay study (up to 21% decrease in CV). Data dispersion was mainly reduced by mean normalization. After sLOD correction, median normalization appears as a preferable choice. Conclusion: Signal detection limit and normalization strategies can increase label-free data reproducibility, especially with sLOD correction.