Mesenchymal stem cells (MSCs) have emerged as a promising means for treating degenerative or incurable diseases. Recent studies have shown that microvesicles (MVs) from MSCs (MSC-MVs) contribute to recovery of damaged tissues in animal disease models. Here, we profiled the MSC-MV proteome to investigate their therapeutic effects. LC-MS/MS analysis of MSC-MVs identified 730 MV proteins. The MSC-MV proteome included five positive and two variable known markers of MSCs, but no negative marker, as well as 43 surface receptors and signaling molecules controlling self-renewal and differentiation of MSCs. Functional enrichment analysis showed that cellular processes represented by the MSC-MV proteins include cell proliferation, adhesion, migration, and morphogenesis. Integration of MSC's self-renewal and differentiation-related genes and the proteome of MSC-conditioned media (MSC-CM) with the MSC-MV proteome revealed potential MV protein candidates that can be associated with the therapeutic effects of MSC-MVs: (1) surface receptors (PDGFRB, EGFR, and PLAUR); (2) signaling molecules (RRAS/NRAS, MAPK1, GNA13/GNG12, CDC42, and VAV2); (3) cell adhesion (FN1, EZR, IQGAP1, CD47, integrins, and LGALS1/LGALS3); and (4) MSC-associated antigens (CD9, CD63, CD81, CD109, CD151, CD248, and CD276). Therefore, the MSC-MV proteome provides a comprehensive basis for understanding the potential of MSC-MVs to affect tissue repair and regeneration.

A quality assessment algorithm for vapor-liquid equilibrium (VLE) data has been developed. The proposed algorithm combines four widely used tests of VLE consistency based on the requirements of the Gibbs-Duhem equation, with a check of consistency between the VLE binary data and the pure compound vapor pressures. A VLE data-quality criterion is proposed based on the developed algorithm, and it has been implemented in a software application in support of dynamic data evaluation. VLE predictions (NRTL and UNIFAC) were deployed to detect possible anomalies in the data sets. The proposed algorithm can be applied to VLE data sets with at least three state variables reported (pressure, temperature, plus liquid and/ or vapor composition) and is applicable to all nonreacting chemical systems at subcritical conditions. Application of the developed algorithms to identification of erroneous published VLE data sets is demonstrated.

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Microvesicles (MVs, also known as exosomes, ectosomes, microparticles) are released by various cancer cells, including lung, colorectal, and prostate carcinoma cells. MVs released from tumor cells and other sources accumulate in the circulation and in pleural effusion. Although recent studies have shown that MVs play multiple roles in tumor progression, the potential pathological roles of MV in pleural effusion, and their protein composition, are still unknown. In this study, we report the first global proteomic analysis of highly purified MVs derived from human nonsmall cell lung cancer (NSCLC) pleural effusion. Using nano-LC-MS/MS following 1D SDS-PAGE separation, we identified a total of 912 MV proteins with high confidence. Three independent experiments on three patients showed that MV proteins from PE were distinct from MV obtained from other malignancies. Bioinformatics analyses of the MS data identified pathologically relevant proteins and potential diagnostic makers for NSCLC, including lung-enriched surface antigens and proteins related to epidermal growth factor receptor signaling. These findings provide new insight into the diverse functions of MVs in cancer progression and will aid in the development of novel diagnostic tools for NSCLC.