Extracellular vesicles (EVs) have a great potential in clinical applications. However, their isolation from different bodily fluids and their characterisation are currently not optimal or standardised. Here, we report the results of examining the performance of ultrafiltration combined with size exclusion chromatography (UF-SEC) to isolate EVs from urine. The results reveal that UF-SEC is an efficient method and provides high purity. Furthermore, we introduce asymmetrical-flow field-flow fractionation coupled with a UV detector and multi-angle light-scattering detector (AF4/UV-MALS) as a characterisation method and compare it with current methods. We demonstrate that AF4/UV-MALS is a straightforward and reproducible method for determining size, amount and purity of isolated urinary EVs.

Naturally occurring peptides, including growth factors, hormones, and neurotransmitters, represent an important class of biomolecules and have crucial roles in human physiology. The study of these peptides in clinical samples is therefore as relevant as ever. Compared to more routine proteomics applications in clinical research, peptidomics research questions are more challenging and have special requirements with regard to sample handling, experimental design, and bioinformatics. In this review, we describe the issues that confront peptidomics in a clinical context. After these hurdles are (partially) overcome, peptidomics will be ready for a successful translation into medical practice.

Bio-active peptides are involved in the regulation of most physiological processes in the body. Classical bio-active peptides (CBAPs) are cleaved from a larger precursor protein and stored in secretion vesicles from which they are released in the extracellular space. Recently, another non-classical type of bio-active peptides (NCBAPs) has gained interest. These typically are not secreted but instead appear to be translated from short open reading frames (sORF) and released directly into the cytoplasm. In contrast to CBAPs, these peptides are involved in the regulation of intra-cellular processes such as transcriptional control, calcium handling and DNA repair. However, bio-chemical evidence for the translation of sORFs remains elusive. Comprehensive analysis of sORF-encoded polypeptides (SEPs) is hampered by a number of methodological and biological challenges: the low molecular mass (many 4-10 kDa), the low abundance, transient expression and complications in data analysis. We developed a strategy to address a number of these issues. Our strategy is to exclude false positive identifications. In total sample, we identified 926 peptides originated from 37 known (neuro)peptide precursors in mouse striatum. In addition, four SEPs were identified including NoBody, a SEP that was previously discovered in humans and three novel SEPS from 5' untranslated transcript regions (UTRs).

Introduction: Antibody-mediated rejection (ABMR) impacts kidney allograft outcome. The diagnosis is made based on findings from invasive kidney transplant biopsy specimens. The aim of this study was to identify a noninvasive urinary protein biomarker for ABMR after kidney transplantation. Methods: We performed a multicenter case-control study to identify a urinary biomarker for ABMR (training cohort, n ¼ 249) and an independent, prospective multicenter cohort study for validation (n ¼ 391). We used concomitant biopsies to classify the samples according to the Banff classification. After untargeted protein identification and quantification, we used a support vector machine to train the model in the training cohort. The primary endpoint was the diagnostic accuracy of the urinary biomarker for ABMR in the validation cohort. Results: We identified a set of 10 urinary proteins that accurately discriminated patients with (n ¼ 60) and without (n ¼ 189) ABMR in the training cohort with an area under the curve (AUC) of 0.98 (95% confidence interval [CI], 0.96-1.00). The diagnostic accuracy was maintained in the validation cohort (AUC, 0.88; 95% CI, 0.8-0.93) for discriminating the presence (n ¼ 43) from the absence (n ¼ 348) of ABMR. The negative predictive value of the 10-protein marker set for exclusion of ABMR was 0.99, and the positive predictive value was 0.33. The diagnostic accuracy was independent of the reason for performing the biopsy, time after transplantation, and better than the accuracy of gross proteinuria (AUC, 0.76). Conclusions: We identified and validated a urinary protein biomarker set that can be used to exclude ABMR.