A unique CD8(+) T lymphocyte signature in pediatric type 1 diabetes.

Human type 1 diabetes results from a destructive auto-reactive immune response in which CD8(+) T lymphocytes play a critical role. Given the intense ongoing efforts to develop immune intervention to prevent and/or cure the disease, biomarkers suitable for prediction of disease risk and progress, as well as for monitoring of immunotherapy are required. We undertook separate multi-parameter analyses of single naïve and activated/memory CD8(+) T lymphocytes from pediatric and adult patients, with the objective of identifying cellular profiles associated with onset of type 1 diabetes. We observe global perturbations in gene and protein expression and in the abundance of T cell populations characterizing pediatric but not adult patients, relative to age-matched healthy individuals. Pediatric diabetes is associated with a unique population of CD8(+) T lymphocytes co-expressing effector (perforin, granzyme B) and regulatory (transforming growth factor ß, interleukin-10 receptor) molecules. This population persists after metabolic normalization and is especially abundant in children with high titers of auto-antibodies to glutamic acid decarboxylase and with elevated HbA1c values. These findings highlight striking differences between pediatric and adult type 1 diabetes, indicate prolonged large-scale perturbations in the CD8(+) T cell compartment in the former, and suggest that CD8(+)CD45RA(-) T cells co-expressing effector and regulatory factors are of interest as biomarkers in pediatric type 1 diabetes.

Proteome-wide analysis of neural stem cell differentiation to facilitate transition to cell replacement therapies.

Neurodegenerative diseases are devastating disorders and the demands on their treatment are set to rise in connection with higher disease incidence. Knowledge of the spatiotemporal profile of cellular protein expression during neural differentiation and definition of a set of markers highly specific for targeted neural populations is a key challenge. Intracellular proteins may be utilized as a readout for follow-up transplantation and cell surface proteins may facilitate isolation of the cell subpopulations, while secreted proteins could help unravel intercellular communication and immunomodulation. This review summarizes the potential of proteomics in revealing molecular mechanisms underlying neural differentiation of stem cells and presents novel candidate proteins of neural subpopulations, where understanding of their functionality may accelerate transition to cell replacement therapies.

Toponostics of invasive ductal breast carcinoma: combination of spatial protein expression imaging and quantitative proteome signature analysis.

Due to enormous advances in quantitative proteomics and in immunohistochemistry (pathology), the two research areas have now reached the state to be successfully interwoven in order to tackle challenges in toponostics and to open tumor-targeted systems pathology approaches. In this study the differential expressions of candidate proteins nucleophosmin, nucleoside diphosphate kinase A/B (NDKA/B), osteoinducive factor (mimecan), and pyru-vate kinase M2 from a quantitative proteome signature for invasive ductal breast cancer were determined by immunohistochemistry on 53 tissue slices from formalin-fixed and paraffin-embedded tumor and control tissue samples from ten patients and fourteen controls. In addition, 87 images from the Human Protein Atlas representing seven tumor and nine normal breast tissue samples were investigated by computer-assisted semi-quantitative density measurements on nucleophosmin, nucleoside diphosphate kinase A/B (NDKA/B), osteoinducive factor (mimecan), pyruvate kinase M2, glyceraldehyde-3-phosphate dehydro-genase (GAP-DH), and mimecan (osteoinductive factor). Both IHC data sets match well to each other and support the quantitative proteome analysis data. Determining spatial distribution of signature protein expressions by protein imaging on morphologically intact tissue samples at the sub-cellular level and, hence, keeping all topological information, presents an added value to quantitative proteome data. Such comprehensive data sets are needed for both, pathway analyses and for "next generation clinical diagnostics" approaches.