ABSTRACT
Hepatocellular carcinoma (HCC)-related mortality is high because early detection modalities are hampered by inaccuracy, expense and inherent procedural risks. Thus there is an urgent need for minimally invasive, highly specific and sensitive biomarkers that enable early disease detection when therapeutic intervention remains practical. Successful therapeutic intervention is predicated on the ability to detect the cancer early. Similar unmet medical needs abound in most fields of medicine and require novel methodological approaches. Proteomic profiling of body fluids presents a sensitive diagnostic tool for early cancer detection. Here we describe such a strategy of comparative proteomics to identify potential serum-based biomarkers to distinguish high-risk chronic hepatitis C virus infected patients from HCC patients. In order to compensate for the extraordinary dynamic range in serum proteins, enrichment methods that compress the dynamic range without surrendering proteome complexity can help minimize the problems associated with many depletion methods. The enriched serum can be resolved using 2D-difference in-gel electrophoresis and the spots showing statistically significant changes selected for identification by liquid chromatography-tandem mass spectrometry. Subsequent quantitative verification and validation of these candidate biomarkers represent an obligatory and rate-limiting process that is greatly enabled by selected reaction monitoring (SRM). SRM is a tandem mass spectrometry method suitable for identification and quantitation of target peptides within complex mixtures independent on peptide-specific antibodies. Ultimately, multiplexed SRM and dynamic multiple reaction monitoring can be utilized for the simultaneous analysis of a biomarker panel derived from support vector machine learning approaches, which allows monitoring a specific disease state such as early HCC. Overall, this approach yields high probability biomarkers for clinical validation in large patient cohorts and represents a strategy extensible to many diseases.
ABSTRACT
Diabetes mellitus is a challenging autoimmune disease. Biomedical researchers are currently exploring efficient and effective ways to solve this challenge. The potential of stem cell therapies for treating diabetes represents one of the major focuses of current research on diabetes treatment. Here, we have attempted to differentiate adult stem cells from umbilical cord blood-derived mesenchymal cells (UCB-MSC), Wharton's jelly-derived mesenchymal stem cells (WJ-MSC) and amniotic epithelial stem cells (AE-SC) into insulin-producing cells. The serum-free protocol developed in this study resulted in the differentiation of cells into definitive endoderm, pancreatic foregut, pancreatic endoderm and, finally, pancreatic endocrine cells, which expressed the marker genes SOX17, PDX1, NGN3, NKX6.1, INS, GCG, and PPY, respectively. Detection of the expression of the gap junction-related gene connexin-36 (CX36) using RT-PCR provided conclusive evidence for insulin-producing cell differentiation. In addition to this RT-PCR result, insulin and C-peptide protein were detected by immunohistochemistry and ELISA. Glucose stimulation test results showed that significantly greater amounts of C-peptide and insulin were released from differentiated cells than from undifferentiated cells. In conclusion, the methods investigated in this study can be considered an effective and efficient means of obtaining insulin-producing cells from adult stem cells within a week.
