Analysis of the extracellular matrix vesicle proteome in mineralizing osteoblasts

Many key processes central to bone formation and homeostasis require the involvement of osteoblasts, cells responsible for accumulation and mineralization of the extracellular matrix (ECM). During this complex and only partially understood process, osteoblasts generate and secrete matrix vesicles (MVs) into the ECM to initiate mineralization. Although they are considered an important component of mineralization process, MVs still remain a mystery. To better understand their function and biogenesis, a proteomic analysis of MVs has been conducted. MVs were harvested by two sample preparation approaches and mass spectrometry was utilized for protein identification. A total of 133 proteins were identified in common from the two MV preparations, among which were previously known proteins, such as annexins and peptidases, along with many novel proteins including a variety of enzymes, osteoblast-specific factors, ion channels, and signal transduction molecules, such as 14-3-3 family members and Rab-related proteins. To compare the proteome of MV with that of the ECM we conducted a large-scale proteomic analysis of collagenase digested mineralizing osteoblast matrix. This analysis resulted in the identification of 1,327 unique proteins. A comparison of the proteins identified from the two MV preparations with the ECM analysis revealed 83 unique, non-redundant proteins identified in all three samples. This investigation represents the first systematic proteomic analysis of MVs and provides insights into both the function and origin of these important mineralization-regulating vesicles.     

Identifying and quantifying metabolites by scoring peaks of GC-MS data

Background

Metabolomics is one of most recent omics technologies. It has been applied on fields such as food science, nutrition, drug discovery and systems biology. For this, gas chromatography-mass spectrometry (GC-MS) has been largely applied and many computational tools have been developed to support the analysis of metabolomics data. Among them, AMDIS is perhaps the most used tool for identifying and quantifying metabolites. However, AMDIS generates a high number of false-positives and does not have an interface amenable for high-throughput data analysis. Although additional computational tools have been developed for processing AMDIS results and to perform normalisations and statistical analysis of metabolomics data, there is not yet a single free software or package able to reliably identify and quantify metabolites analysed by GC-MS.

Results

Here we introduce a new algorithm, PScore, able to score peaks according to their likelihood of representing metabolites defined in a mass spectral library. We implemented PScore in a R package called MetaBox and evaluated the applicability and potential of MetaBox by comparing its performance against AMDIS results when analysing volatile organic compounds (VOC) from standard mixtures of metabolites and from female and male mice faecal samples. MetaBox reported lower percentages of false positives and false negatives, and was able to report a higher number of potential biomarkers associated to the metabolism of female and male mice.

Conclusions

Identification and quantification of metabolites is among the most critical and time-consuming steps in GC-MS metabolome analysis. Here we present an algorithm implemented in a R package, which allows users to construct flexible pipelines and analyse metabolomics data in a high-throughput manner.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0374-2) contains supplementary material, which is available to authorized users.