The digested peptides were extracted and loaded onto a homemade trap column and an analytical column that both packed with C18 (Dr. Maisch GmbH, Germany). A 75 min gradient was used for online HPLC-MS. Thermo Fisher Orbitrap mass spectrometers were used for measuring all urine samples. LC-MS/MS data were processed using Proteome Discoverer (V1.4, Thermo Fisher) with Mascot algorithm (Mascot V2.3, Matrix Science) against human RefSeq database (2013.07.04). Tryptic peptides of 293T cell lysates were used as quality control samples for evaluation of the instrument reproducibility.
All assigned peptides were filtered with 1% false discovery rate (FDR). Only proteins with ≥2 strict peptides (1% FDR and ion score > 20) were kept for quantification. Intensity-based absolute quantification (iBAQ) algorithm was used for protein quantification. To normalize the differences in loading amounts among samples, the iBAQ value was converted to iFOT (fraction of total, iBAQ value of each protein divided by the sum of all iBAQ values of all proteins in the sample). For better visualization, iFOTs were multiplied by 105 [27 (link)]. To eliminate the difference between different groups, the iFOTs were normalized by quantile algorithm [28 (link)].
All assigned peptides were filtered with 1% false discovery rate (FDR). Only proteins with ≥2 strict peptides (1% FDR and ion score > 20) were kept for quantification. Intensity-based absolute quantification (iBAQ) algorithm was used for protein quantification. To normalize the differences in loading amounts among samples, the iBAQ value was converted to iFOT (fraction of total, iBAQ value of each protein divided by the sum of all iBAQ values of all proteins in the sample). For better visualization, iFOTs were multiplied by 105 [27 (link)]. To eliminate the difference between different groups, the iFOTs were normalized by quantile algorithm [28 (link)].
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