In vitro and in vivo properties of distinct populations of amniotic fluid mesenchymal progenitor cells

Human mesenchymal progenitor cells (MPCs) are considered to be of great promise for use in tissue repair and regenerative medicine. MPCs represent multipotent adherent cells, able to give rise to multiple mesenchymal lineages such as osteoblasts, adipocytes or chondrocytes. Recently, we identified and characterized human second trimester amniotic fluid (AF) as a novel source of MPCs. Herein, we found that early colonies of AF-MPCs consisted of two morphologically distinct adherent cell types, termed as spindle-shaped (SS) and round-shaped (RS). A detailed analysis of these two populations showed that SS-AF-MPCs expressed CD90 antigen in a higher level and exhibited a greater proliferation and differentiation potential. To characterize better the molecular identity of these two populations, we have generated a comparative proteomic map of SS-AF-MPCs and RS-AF-MPCs, identifying 25 differentially expressed proteins and 10 proteins uniquely expressed in RS-AF-MPCs. Furthermore, SS-AF-MPCs exhibited significantly higher migration ability on extracellular matrices, such as fibronectin and laminin in vitro, compared to RS-AF-MPCs and thus we further evaluated SS-AF-MPCs for potential use as therapeutic tools in vivo. Therefore, we tested whether GFP-lentiviral transduced SS-AF-MPCs retained their stem cell identity, proliferation and differentiation potential. GFP-SS-AF-MPCs were then successfully delivered into immunosuppressed mice, distributed in different tissues and survived longterm in vivo. In summary, these results demonstrated that AF-MPCs consisted of at least two different MPC populations. In addition, SS-AF-MPCs, isolated based on their colony morphology and CD90 expression, represented the only MPC population that can be expanded easily in culture and used as an efficient tool for future in vivo therapeutic applications

AF-MSCs fate can be regulated by culture conditions

Human mesenchymal stem cells (hMSCs) represent a population of multipotent adherent cells able to differentiate into many lineages. In our previous studies, we isolated and expanded fetal MSCs from second-trimester amniotic fluid (AF) and characterized them based on their phenotype, pluripotency and proteomic profile. In the present study, we investigated the plasticity of these cells based on their differentiation, dedifferentiation and transdifferentiation potential in vitro. To this end, adipocyte-like cells (AL cells) derived from AF-MSCs can regain, under certain culture conditions, a more primitive phenotype through the process of dedifferentiation. Dedifferentiated AL cells derived from AF-MSCs (DAF-MSCs), gradually lost the expression of adipogenic markers and obtained similar morphology and differentiation potential to AF-MSCs, together with regaining the pluripotency marker expression. Moreover, a comparative proteomic analysis of AF-MSCs, AL cells and DAF-MSCs revealed 31 differentially expressed proteins among the three cell populations. Proteins, such as vimentin, galectin-1 and prohibitin that have a significant role in stem cell regulatory mechanisms, were expressed in higher levels in AF-MSCs and DAF-MSCs compared with AL cells. We next investigated whether AL cells could transdifferentiate into hepatocyte-like cells (HL cells) directly or through a dedifferentiation step. AL cells were cultured in hepatogenic medium and 4 days later they obtained a phenotype similar to AF-MSCs, and were termed as transdifferentiated AF-MSCs (TRAF-MSCs). This finding, together with the increase in pluripotency marker expression, indicated the adaption of a more primitive phenotype before transdifferentiation. Additionally, we observed that AF-, DAF- and TRAF-MSCs displayed similar clonogenic potential, secretome and proteome profile. Considering the easy access to this fetal cell source, the plasticity of AF-MSCs and their potential to dedifferentiate and transdifferentiate, AF may provide a valuable tool for cell therapy and tissue engineering applications

Assaying Rho GTPase–dependent processes in Dictyostelium discoideum

The model organism D. discoideum is well-suited to investigate basic questions of molecular and cell biology, particularly those related to the structure, regulation and dynamics of the cytoskeleton, signal transduction, cell-cell adhesion and development. D. discoideum cells make use of Rho-regulated signaling pathways to reorganize the actin cytoskeleton during chemotaxis, endocytosis and cytokinesis. In this organism the Rho family encompasses 20 members, several belonging to the Rac subfamily, but there are no representatives of the Cdc42 and Rho subfamilies. Here we present protocols suitable for monitoring the actin polymerization response and the activation of Rac upon stimulation of aggregation competent cells with the chemoattractant cAMP, and for monitoring the localization and dynamics of Rac activity in live cells

Proteomics analysis of bladder cancer invasion: targeting EIF3D for therapeutic intervention

Patients with advanced bladder cancer have poor outcomes, indicating a need for more efficient therapeutic approaches. This study characterizes proteomic changes underlying bladder cancer invasion aiming for the better understanding of disease pathophysiology and identification of drug targets. High resolution liquid chromatography coupled to tandem mass spectrometry analysis of tissue specimens from patients with non-muscle invasive (NMIBC, stage pTa) and muscle invasive bladder cancer (MIBC, stages pT2+) was conducted. Comparative analysis identified 144 differentially expressed proteins between analyzed groups. These included proteins previously associated with bladder cancer and also additional novel such as PGRMC1, FUCA1, BROX and PSMD12, which were further confirmed by immunohistochemistry. Pathway and interactome analysis predicted strong activation in muscle invasive bladder cancer of pathways associated with protein synthesis e.g. eIF2 and mTOR signaling. Knock-down of eukaryotic translation initiation factor 3 subunit D (EIF3D) (overexpressed in muscle invasive disease) in metastatic T24M bladder cancer cells inhibited cell proliferation, migration, and colony formation in vitro and decreased tumor growth in xenograft models. By contrast, knocking down GTP-binding protein Rheb (which is upstream of EIF3D) recapitulated the effects of EIF3D knockdown in vitro, but not in vivo. Collectively, this study represents a comprehensive analysis of NMIBC and MIBC providing a resource for future studies. The results highlight EIF3D as a potential therapeutic target

SILAC-based proteomic quantification of chemoattractant-induced cytoskeleton dynamics on a second to minute timescale

Cytoskeletal dynamics during cell behaviours ranging from endocytosis and exocytosis to cell division and movement is controlled by a complex network of signalling pathways, the full details of which are as yet unresolved. Here we show that SILAC-based proteomic methods can be used to characterize the rapid chemoattractant-induced dynamic changes in the actin–myosin cytoskeleton and regulatory elements on a proteome-wide scale with a second to minute timescale resolution. This approach provides novel insights in the ensemble kinetics of key cytoskeletal constituents and association of known and novel identified binding proteins. We validate the proteomic data by detailed microscopy-based analysis of in vivo translocation dynamics for key signalling factors. This rapid large-scale proteomic approach may be applied to other situations where highly dynamic changes in complex cellular compartments are expected to play a key role

Clinical decision modeling system

<p>Abstract</p> <p>Background</p> <p>Decision analysis techniques can be applied in complex situations involving uncertainty and the consideration of multiple objectives. Classical decision modeling techniques require elicitation of too many parameter estimates and their conditional (joint) probabilities, and have not therefore been applied to the problem of identifying high-performance, cost-effective combinations of clinical options for diagnosis or treatments where many of the objectives are unknown or even unspecified.</p> <p>Methods</p> <p>We designed a Java-based software resource, the Clinical Decision Modeling System (CDMS), to implement Naïve Decision Modeling, and provide a use case based on published performance evaluation measures of various strategies for breast and lung cancer detection. Because cost estimates for many of the newer methods are not yet available, we assume equal cost. Our use case reveals numerous potentially high-performance combinations of clinical options for the detection of breast and lung cancer.</p> <p>Results</p> <p>Naïve Decision Modeling is a highly practical applied strategy which guides investigators through the process of establishing evidence-based integrative translational clinical research priorities. CDMS is not designed for clinical decision support. Inputs include performance evaluation measures and costs of various clinical options. The software finds trees with expected emergent performance characteristics and average cost per patient that meet stated filtering criteria. Key to the utility of the software is sophisticated graphical elements, including a tree browser, a receiver-operator characteristic surface plot, and a histogram of expected average cost per patient. The analysis pinpoints the potentially most relevant pairs of clinical options ('critical pairs') for which empirical estimates of conditional dependence may be critical. The assumption of independence can be tested with retrospective studies prior to the initiation of clinical trials designed to estimate clinical impact. High-performance combinations of clinical options may exist for breast and lung cancer detection.</p> <p>Conclusion</p> <p>The software could be found useful in simplifying the objective-driven planning of complex integrative clinical studies without requiring a multi-attribute utility function, and it could lead to efficient integrative translational clinical study designs that move beyond simple pair wise competitive studies. Collaborators, who traditionally might compete to prioritize their own individual clinical options, can use the software as a common framework and guide to work together to produce increased understanding on the benefits of using alternative clinical combinations to affect strategic and cost-effective clinical workflows.</p

An Introspective Comparison of Random Forest-Based Classifiers for the Analysis of Cluster-Correlated Data by Way of RF++

Many mass spectrometry-based studies, as well as other biological experiments produce cluster-correlated data. Failure to account for correlation among observations may result in a classification algorithm overfitting the training data and producing overoptimistic estimated error rates and may make subsequent classifications unreliable. Current common practice for dealing with replicated data is to average each subject replicate sample set, reducing the dataset size and incurring loss of information. In this manuscript we compare three approaches to dealing with cluster-correlated data: unmodified Breiman's Random Forest (URF), forest grown using subject-level averages (SLA), and RF++ with subject-level bootstrapping (SLB). RF++, a novel Random Forest-based algorithm implemented in C++, handles cluster-correlated data through a modification of the original resampling algorithm and accommodates subject-level classification. Subject-level bootstrapping is an alternative sampling method that obviates the need to average or otherwise reduce each set of replicates to a single independent sample. Our experiments show nearly identical median classification and variable selection accuracy for SLB forests and URF forests when applied to both simulated and real datasets. However, the run-time estimated error rate was severely underestimated for URF forests. Predictably, SLA forests were found to be more severely affected by the reduction in sample size which led to poorer classification and variable selection accuracy. Perhaps most importantly our results suggest that it is reasonable to utilize URF for the analysis of cluster-correlated data. Two caveats should be noted: first, correct classification error rates must be obtained using a separate test dataset, and second, an additional post-processing step is required to obtain subject-level classifications. RF++ is shown to be an effective alternative for classifying both clustered and non-clustered data. Source code and stand-alone compiled versions of command-line and easy-to-use graphical user interface (GUI) versions of RF++ for Windows and Linux as well as a user manual (Supplementary File S2) are available for download at: http://sourceforge.org/projects/rfpp/ under the GNU public license