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Human mesenchymal stem cells (MSC) have been on the focus of intense clinical-oriented research due to their multilineage differentiation potential and immunomodulatory properties. However, to reach the clinically meaningful cell numbers for cellular therapy and tissue engineering applications, MSC ex-vivo expansion is mandatory but sequential cell passaging results in loss of proliferative, clonogenic and differentiation potential. To get clues into the molecular mechanisms underlying cellular senescence resulting from extended ex-vivo cultivation of bone marrow (BM) MSC, we explored a two-dimensional gel electrophoresis (2-DE) based quantitative proteomics to compare the expression programs of Passage 3 cells (P3), commonly used in clinical studies with expanded MSC, and Passage 7 (P7) cells, which already demonstrated significant signs of culture-induced senescence. Proteins of the functional categories "Structural components and cellular cytoskeleton" and "Folding and stress response proteins" are less abundant in P7 cells, compared to P3, while proteins involved in "Energy metabolism", "Cell cycle regulation and aging" and "Apoptosis" are more abundant. The large number of multiple size and charge isoforms with an altered content that were identified in this study in P7 versus P3, namely the cytoskeleton components β-actin (7 forms) and vimentin (24 forms), also emphasizes the importance of post-transcriptional modification upon long-term cultivation. The differential protein expression registered suggests that cellular senescence occurring during ex-vivo expansion of BM MSC is associated with the impairment of cytoskeleton remodeling and/or organization and the repair of damaged proteins resulting from cell exposure to culture stress. The genome-wide expression approach used in this study has proven useful for getting mechanistic insights into the observed decrease on the proliferative and clonogenic potential of P7 versus P3 cells and paves the way to set up a proteome profiling strategy for quality control to assure safe and clinically effective expanded MSC.

With the completion of the human genome sequence, biomedical sciences have entered in the "omics" era, mainly due to high-throughput genomics techniques and the recent application of mass spectrometry to proteomics analyses. However, there is still a time lag between these technological advances and their application in the clinical setting. Our work is designed to build bridges between high-performance proteomics and clinical routine. Protein extracts were obtained from fresh frozen normal lung and non-small cell lung cancer samples. We applied a phosphopeptide enrichment followed by LC-MS/MS. Subsequent label-free quantification and bioinformatics analyses were performed. We assessed protein patterns on these samples, showing dozens of differential markers between normal and tumor tissue. Gene ontology and interactome analyses identified signaling pathways altered on tumor tissue. We have identified two proteins, PTRF/cavin-1 and MIF, which are differentially expressed between normal lung and non-small cell lung cancer. These potential biomarkers were validated using western blot and immunohistochemistry. The application of discovery-based proteomics analyses in clinical samples allowed us to identify new potential biomarkers and therapeutic targets in non-small cell lung cancer.

Protein expression studies based on the two major intra-abdominal human fat depots, the subcutaneous and the omental fat, can shed light into the mechanisms involved in obesity and its co-morbidities. Here we address, for the first time, the identification and validation of reference proteins for data standardization, which are essential for accurate comparison of protein levels in expression studies based on fat from obese and non-obese individuals.To uncover adipose tissue proteins equally expressed either in omental and subcutaneous fat depots (study 1) or in omental fat from non-obese and obese individuals (study 2), we have reanalyzed our previously published data based on two-dimensional fluorescence difference gel electrophoresis. Twenty-four proteins (12 in study 1 and 12 in study 2) with similar expression levels in all conditions tested were selected and identified by mass spectrometry. Immunoblotting analysis was used to confirm in adipose tissue the expression pattern of the potential reference proteins and three proteins were validated: PARK7, ENOA and FAA. Western Blot analysis was also used to test customary loading control proteins. ENOA, PARK7 and the customary loading control protein Beta-actin showed steady expression profiles in fat from non-obese and obese individuals, whilst FAA maintained steady expression levels across paired omental and subcutaneous fat samples.ENOA, PARK7 and Beta-actin are proper reference standards in obesity studies based on omental fat, whilst FAA is the best loading control for the comparative analysis of omental and subcutaneous adipose tissues either in obese and non-obese subjects. Neither customary loading control proteins GAPDH and TBB5 nor CALX are adequate standards in differential expression studies on adipose tissue. The use of the proposed reference proteins will facilitate the adequate analysis of proteins differentially expressed in the context of obesity, an aim difficult to achieve before this study.

BACKGROUND:Proteomics is expected to play a key role in cancer biomarker discovery. Although it has become feasible to rapidly analyze proteins from crude cell extracts using mass spectrometry, complex sample composition hampers this type of measurement. Therefore, for effective proteome analysis, it becomes critical to enrich samples for the analytes of interest. Despite that one-third of the proteins in eukaryotic cells are thought to be phosphorylated at some point in their life cycle, only a low percentage of intracellular proteins is phosphorylated at a given time. METHODOLOGY/PRINCIPAL FINDINGS:In this work, we have applied chromatographic phosphopeptide enrichment techniques to reduce the complexity of human clinical samples. A novel method for high-throughput peptide profiling of human tumor samples, using Parallel IMAC and MALDI-TOF MS, is described. We have applied this methodology to analyze human normal and cancer lung samples in the search for new biomarkers. Using a highly reproducible spectral processing algorithm to produce peptide mass profiles with minimal variability across the samples, lineal discriminant-based and decision tree-based classification models were generated. These models can distinguish normal from tumor samples, as well as differentiate the various non-small cell lung cancer histological subtypes. CONCLUSIONS/SIGNIFICANCE:A novel, optimized sample preparation method and a careful data acquisition strategy is described for high-throughput peptide profiling of small amounts of human normal lung and lung cancer samples. We show that the appropriate combination of peptide expression values is able to discriminate normal lung from non-small cell lung cancer samples and among different histological subtypes. Our study does emphasize the great potential of proteomics in the molecular characterization of cancer.