Renal cell carcinoma (RCC) accounts for about 3% of all human malignancies and its incidence is increasing. There are no standard biomarkers currently used in the clinical management of patients with renal cell carcinoma. A promising strategy for new biomarker detection is comparative proteomics of urinary exosomes (UE), nanovesicles released by every epithelial cell facing the urinary space, enriched in renal proteins and excluding high-abundance plasmatic proteins, such as albumin. Aim of the work is to establish the protein profile of exosomes isolated from urines of RCC patient compared with control subjects. We enrolled 29 clear cell RCC patients and 23 control healthy subjects (CTRL), age and sex-matched, for urine collection and vesicle isolation by differential centrifugation. Such vesicles were morphologically and biochemically characterized and proved to share exosome properties. Proteomic analysis, performed on 9 urinary exosome (UE) pooled samples by gel based digestion followed by LC-MS/MS, led to the identification of 261 proteins from CTRL subject UE and 186 from RCC patient UE, and demonstrated that most of the identified proteins are membrane associated or cytoplasmic. Moreover, about a half of identified proteins are not shared between RCC and control UE. Starting from these observations, and from the literature, we selected a panel of 10 proteins, whose UE differential content was subjected to immunoblotting validation. Results show for the first time that RCC UE protein content is substantially and reproducibly different from control UE, and that these differences may provide clues for new RCC biomarker discovery.

Ischemic heart disease is a leading cause of mortality due to irreversible damage to cardiac muscle. Inspired by the post-ischemic microenvironment, we devised an extracellular matrix (ECM)–mimicking hydrogel using catalyst-free click chemistry covalent bonding between two elastin-like recombinamers (ELRs). The resulting customized hydrogel included functional domains for cell adhesion and protease cleavage sites, sensitive to cleavage by matrix metalloproteases overexpressed after myocardial infarction (MI). The scaffold permitted stromal cell invasion and endothelial cell sprouting in vitro. The incidence of non-transmural infarcts has increased clinically over the past decade, and there is currently no treatment preventing further functional deterioration in the infarcted areas. Here, we have developed a clinically relevant ovine model of non-transmural infarcts induced by multiple suture ligations. Intramyocardial injections of the degradable ELRs-hydrogel led to complete functional recovery of ejection fraction 21 days after the intervention. We observed less fibrosis and more angiogenesis in the ELRs-hydrogel–treated ischemic core region compared to the untreated animals, as validated by the expression, proteomic, glycomic, and histological analyses. These findings were accompanied by enhanced preservation of GATA4⁺ cardiomyocytes in the border zone of the infarct. We propose that our customized ECM favors cardiomyocyte preservation in the border zone by modulating the ischemic core and a marked functional recovery. The functional benefits obtained by the timely injection of the ELRs-hydrogel in a clinically relevant MI model support the potential utility of this treatment for further clinical translation.

Urinary exosomes (UE) are nanovesicles released by every epithelial cell facing the urinary space and they are considered a promising source of molecular markers for renal dysfunction and structural injury. Exosomal proteomics has emerged as a powerful tool for understanding the molecular composition of exosomes and has potential to accelerate biomarker discovery. We employed this strategy in the study of diabetic nephropathy (DN) and the consequent end stage renal disease, which represent the dramatic evolution of diabetes, often leading the patients to dialysis or kidney transplantation. The identification of DN biomarkers is likely to help monitoring the disease onset and progression. A label free LC-MS/MS approach was applied to investigate the alteration of the proteome of urinary exosomes isolated from the Zucker diabetic fatty rats (ZDF), as a model of type 2 DN. We collected 24 hour urine samples from 7 ZDF and from 7 control rats at different ages (6, 12 and 20 weeks old) to monitor the development of DN. Exosomes were isolated by ultracentrifugation and their purity assessed by immunoblotting for known exosomal markers. Exosomal proteins from urine samples of 20 week old rats were pooled and analyzed by nLC-ESI-UHR-QToF-MS/MS after pre-filtration and tryptic digestion, leading to the identification and label free quantification of 286 proteins. Subcellular localization and molecular functions were assigned to each protein by UniprotKB, showing that the majority of identified proteins were membrane-associated or cytoplasmic and involved in transport, signalling and cellular adhesion, typical functions of exosomal proteins. We further validated label free mass spectrometry results by immunoblotting, as exemplified by: Xaa-Pro dipeptidase, Major Urinary Protein 1 and Neprilysin, which resulted increased, decreased and not different, respectively, in exosomes isolated from diabetic urine samples compared to controls, by both techniques. In conclusion we show the potential of exosome proteomics for DN biomarker discovery.

Recent advancements in Matrix Assisted Laser Desorption/Ionisation (MALDI) Mass Spectrometry Imaging (MSI) technology have enabled the analysis of formalin-fixed paraffin-embedded (FFPE) tissue samples, unlocking a wealth of new proteomic information and facilitating the possibility of performing studies with higher statistical power as well as multi-centric collaborations within the field of proteomics research. However, current methods used to analyse these specimens are often time-consuming and they need to be modified when applied to human tissues of different origin. Here we present a reproducible and time-effective method that could address these aforementioned issues and widen the applicability of this technology to a number of challenging tissue types. Additionally, tissue molecular images show high spatial resolution and a strong correlation with the morphological features, enabling the identification of tissue morphology using statistically derived visualisation, without any prior knowledge.