Comparative Urine Analysis by Liquid Chromatography−Mass Spectrometry and Multivariate Statistics: Method Development, Evaluation, and Application to Proteinuria

We describe a platform for the comparative profiling of urine using reversed-phase liquid chromatography−mass spectrometry (LC−MS) and multivariate statistical data analysis. Urinary compounds were
separated by gradient elution and subsequently detected by electrospray Ion-Trap MS. The lower limit
of detection (5.7−21 nmol/L), within-day (2.9−19%) and between-day (4.8−19%) analytical variation of
peak areas, linearity (R2:  0.918−0.999), and standard deviation for retention time (<0.52 min) of the
method were assessed by means of addition of seven 3−8 amino acid peptides (0−500 nmol/L). Relating
the amount of injected urine to the area under the curve (AUC) of the chromatographic trace at 214 nm
better reduced the coefficient of variation (CV) of the AUC of the total ion chromatogram (CV = 10.1%)
than relating it to creatinine (CV = 38.4%). LC−MS data were processed, and the common peak matrix
was analyzed by principal component analysis (PCA) after supervised classification by the nearest
shrunken centroid algorithm. The feasibility of the method to discriminate urine samples of differing
compositions was evaluated by (i) addition of seven peptides at nanomolar concentrations to blank
urine samples of different origin and (ii) a study of urine from kidney patients with and without
proteinuria. (i) The added peptides were ranked as highly discriminatory peaks despite significant
biological variation. (ii) Ninety-two peaks were selected best discriminating proteinuric from nonproteinuric samples, of which 6 were more intense in the majority of the proteinuric samples. Two of
these 6 peaks were identified as albumin-derived peptides, which is in accordance with the early rise
of albumin during glomerular proteinuria. Interestingly, other albumin-derived peptides were nondiscriminatory indicating preferential proteolysis at some cleavage sites.
Keywords: urine • proteomics • liquid chromatography−mass spectrometry • biomarker • bioinformatics •
multivariate statistical analysis