The Phosphate Source Influences Gene Expression and Quality of Mineralization during In Vitro Osteogenic Differentiation of Human Mesenchymal Stem Cells

For in vitro differentiation of bone marrow-derived mesenchymal stem cells/mesenchymal stromal cells into osteoblasts by 2-dimensional cell culture a variety of protocols have been used and evaluated in the past. Especially the external phosphate source used to induce mineralization varies considerably both in respect to chemical composition and concentration. In light of the recent findings that inorganic phosphate directs gene expression of genes crucial for bone development, the need for a standardized phosphate source in in vitro differentiation becomes apparent. We show that chemical composition (inorganic versus organic phosphate origin) and concentration of phosphate supplementation exert a severe impact on the results of gene expression for the genes commonly used as markers for osteoblast formation as well as on the composition of the mineral formed. Specifically, the intensity of gene expression does not necessarily correlate with a high quality mineralized matrix. Our study demonstrates advantages of using inorganic phosphate instead of beta-glycerophosphate and propose colorimetric quantification methods for calcium and phosphate ions as cost-and time-effective alternatives to X-ray diffraction and Fourier-transform infrared spectroscopy for determination of the calcium phosphate ratio and concentration of mineral matrix formed under in vitro-conditions. We critically discuss the different assays used to assess in vitro bone formation in respect to specificity and provide a detailed in vitro protocol that could help to avoid contradictory results due to variances in experimental design.

EMMA 2--a MAGE-compliant system for the collaborative analysis and integration of microarray data

Background: Understanding transcriptional regulation by genome-wide microarray studies can contribute to unravel complex relationships between genes. Attempts to standardize the annotation of microarray data include the Minimum Information About a Microarray Experiment (MIAME) recommendations, the MAGE-ML format for data interchange, and the use of controlled vocabularies or ontologies. The existing software systems for microarray data analysis implement the mentioned standards only partially and are often hard to use and extend. Integration of genomic annotation data and other sources of external knowledge using open standards is therefore a key requirement for future integrated analysis systems. Results: The EMMA 2 software has been designed to resolve shortcomings with respect to full MAGE-ML and ontology support and makes use of modern data integration techniques. We present a software system that features comprehensive data analysis functions for spotted arrays, and for the most common synthesized oligo arrays such as Agilent, Affymetrix and NimbleGen. The system is based on the full MAGE object model. Analysis functionality is based on R and Bioconductor packages and can make use of a compute cluster for distributed services. Conclusion: Our model-driven approach for automatically implementing a full MAGE object model provides high flexibility and compatibility. Data integration via SOAP-based web-services is advantageous in a distributed client-server environment as the collaborative analysis of microarray data is gaining more and more relevance in international research consortia. The adequacy of the EMMA 2 software design and implementation has been proven by its application in many distributed functional genomics projects. Its scalability makes the current architecture suited for extensions towards future transcriptomics methods based on high-throughput sequencing approaches which have much higher computational requirements than microarrays.