Protemic characterization of peripheral blood mononuclear cells

Peripheral blood mononuclear cells (PBMCs) play quite diverse and important roles in monitoring immune homeostasis. Thus, these subset of blood cells may provide access to potential physiological relevant biomolecules, namely proteins. For this reason, PBMCs represent a promising biological sample in scientific research, particularly as a source of potential biological markers discovery of the most diverse diseases. Prior studies of proteomic characterization of PBMCs from healthy individuals lack either the identification of a large number of proteins or its quantification in a way that is compatible with the search of potential biomarker candidates. Therefore, this study aimed to provide a comprehensive PBMCs proteome characterisation as well as to create a SWATH library. It was also evaluated if by using the BD Vacutainer® CPT™ tubes for PBMCs isolation, it would be possible to identify a larger number of immunologically relevant proteins in comparison to plasma samples. The enrichment test assay revealed that it is possible to identify more immune-related proteins from isolated PBMCs than from plasma. Moreover, the majority of the quantified proteins with an “immune system” GO term assigned is present in higher amounts in PBMCs samples. 2D LC-MS/MS proved to be the best approach to use in qualitative analysis of PBMCs and in the construction of a SWATH library, since it resulted in an increase of both identified and quantified proteins (66.3% and 16.9%, respectively) in comparison to 1D LC-MS/MS. A total of 2071 proteins were identified and it was possible to quantify 922 different proteins among six distinct samples. From these proteins, 445 were commom between all individuals. In conclusion, this work provides a comprehensive PBMCs proteome dataset that will be useful in further studies that focus on the search for potential biological markers of various pathologies in these cells. Additionally, SWATH-MS proved to be a reproducible and effective acquisition method to quantify PBMCs proteins.

Metabolic signature of lung cancer: a metabolomic study of human tissues and biofluids

This thesis reports the application of metabolomics to human tissues and biofluids (blood plasma and urine) to unveil the metabolic signature of primary lung cancer. In Chapter 1, a brief introduction on lung cancer epidemiology and pathogenesis, together with a review of the main metabolic dysregulations known to be associated with cancer, is presented. The metabolomics approach is also described, addressing the analytical and statistical methods employed, as well as the current state of the art on its application to clinical lung cancer studies. Chapter 2 provides the experimental details of this work, in regard to the subjects enrolled, sample collection and analysis, and data processing. In Chapter 3, the metabolic characterization of intact lung tissues (from 56 patients) by proton High Resolution Magic Angle Spinning (HRMAS) Nuclear Magnetic Resonance (NMR) spectroscopy is described. After careful assessment of acquisition conditions and thorough spectral assignment (over 50 metabolites identified), the metabolic profiles of tumour and adjacent control tissues were compared through multivariate analysis. The two tissue classes could be discriminated with 97% accuracy, with 13 metabolites significantly accounting for this discrimination: glucose and acetate (depleted in tumours), together with lactate, alanine, glutamate, GSH, taurine, creatine, phosphocholine, glycerophosphocholine, phosphoethanolamine, uracil nucleotides and peptides (increased in tumours). Some of these variations corroborated typical features of cancer metabolism (e.g., upregulated glycolysis and glutaminolysis), while others suggested less known pathways (e.g., antioxidant protection, protein degradation) to play important roles. Another major and novel finding described in this chapter was the dependence of this metabolic signature on tumour histological subtype. While main alterations in adenocarcinomas (AdC) related to phospholipid and protein metabolisms, squamous cell carcinomas (SqCC) were found to have stronger glycolytic and glutaminolytic profiles, making it possible to build a valid classification model to discriminate these two subtypes. Chapter 4 reports the NMR metabolomic study of blood plasma from over 100 patients and near 100 healthy controls, the multivariate model built having afforded a classification rate of 87%. The two groups were found to differ significantly in the levels of lactate, pyruvate, acetoacetate, LDL+VLDL lipoproteins and glycoproteins (increased in patients), together with glutamine, histidine, valine, methanol, HDL lipoproteins and two unassigned compounds (decreased in patients). Interestingly, these variations were detected from initial disease stages and the magnitude of some of them depended on the histological type, although not allowing AdC vs. SqCC discrimination. Moreover, it is shown in this chapter that age mismatch between control and cancer groups could not be ruled out as a possible confounding factor, and exploratory external validation afforded a classification rate of 85%. The NMR profiling of urine from lung cancer patients and healthy controls is presented in Chapter 5. Compared to plasma, the classification model built with urinary profiles resulted in a superior classification rate (97%). After careful assessment of possible bias from gender, age and smoking habits, a set of 19 metabolites was proposed to be cancer-related (out of which 3 were unknowns and 6 were partially identified as N-acetylated metabolites). As for plasma, these variations were detected regardless of disease stage and showed some dependency on histological subtype, the AdC vs. SqCC model built showing modest predictive power. In addition, preliminary external validation of the urine-based classification model afforded 100% sensitivity and 90% specificity, which are exciting results in terms of potential for future clinical application. Chapter 6 describes the analysis of urine from a subset of patients by a different profiling technique, namely, Ultra-Performance Liquid Chromatography coupled to Mass Spectrometry (UPLC-MS). Although the identification of discriminant metabolites was very limited, multivariate models showed high classification rate and predictive power, thus reinforcing the value of urine in the context of lung cancer diagnosis. Finally, the main conclusions of this thesis are presented in Chapter 7, highlighting the potential of integrated metabolomics of tissues and biofluids to improve current understanding of lung cancer altered metabolism and to reveal new marker profiles with diagnostic value.