PROTEOMER: A workflow-optimized laboratory information management system for 2-D electrophoresis-centered proteomics

LIMS and Clinical Data Management

In order to achieve more accurate disease prevention, diagnosis, and treatment, clinical and genetic data need extensive and systematically associated study. As one way to achieve precision medicine, a laboratory information management system (LIMS) can effectively associate clinical data in a macrocosmic aspect and genomic data in a microcosmic aspect. This chapter summarizes the application of the LIMS in a clinical data management and implementation mode. It also discusses the principles of a LIMS in clinical data management, as well as the opportunities and challenges in the context of medical informatics.

Protocol: a fast, comprehensive and reproducible one-step extraction method for the rapid preparation of polar and semi-polar metabolites, lipids, proteins, starch and cell wall polymers from a single sample

BACKGROUND

The elucidation of complex biological systems requires integration of multiple molecular parameters. Accordingly, high throughput methods like transcriptomics, proteomics, metabolomics and lipidomics have emerged to provide the tools for successful system-wide investigations. Unfortunately, optimized analysis of different compounds requires specific extraction procedures in combination with specific analytical instrumentation. However, the most efficient extraction protocols often only cover a restricted number of compounds due to the different physico-chemical properties of these biological compounds. Consequently, comprehensive analysis of several molecular components like polar primary metabolites next to lipids or proteins require multiple aliquots to enable the specific extraction procedures required to cover these diverse compound classes. This multi-parallel sample handling of different sample aliquots is therefore not only more sample intensive, it also requires more time and effort to obtain the required extracts.

RESULTS

To circumvent large sample amounts, distributed into several aliquots for the comprehensive extraction of most relevant biological compounds, we developed a simple, robust and reproducible two-phase liquid-liquid extraction protocol. This one-step extraction protocol allows for the analysis of polar-, semi-polar and hydrophobic metabolites, next to insoluble or precipitated compounds, including proteins, starch and plant cell wall components, from a single sample. The method is scalable regarding the used sample amounts but also the employed volumes and can be performed in microcentrifuge tubes, enabling high throughput analysis. The obtained fractions are fully compatible with common analytical methods, including spectroscopic, chromatographic and mass spectrometry-based techniques. To document the utility of the described protocol, we used 25 mg of Arabidopsis thaliana rosette leaves for the generation of multi-omics data sets, covering lipidomics, metabolomics and proteomics. The obtained data allowed us to measure and annotate more than 200 lipid compounds, 100 primary metabolites, 50 secondary metabolites and 2000 proteins.

CONCLUSIONS

The described extraction protocol provides a simple and straightforward method for the efficient extraction of lipids, metabolites and proteins from minute amounts of a single sample, enabling the targeted but also untargeted high-throughput analyses of diverse biological tissues and samples.

Management and dissemination of MS proteomic data with PROTICdb: example of a quantitative comparison between methods of protein extraction

High throughput MS-based proteomic experiments generate large volumes of complex data and necessitate bioinformatics tools to facilitate their handling. Needs include means to archive data, to disseminate them to the scientific communities, and to organize and annotate them to facilitate their interpretation. We present here an evolution of PROTICdb, a database software that now handles MS data, including quantification. PROTICdb has been developed to be as independent as possible from tools used to produce the data. Biological samples and proteomics data are described using ontology terms. A Taverna workflow is embedded, thus permitting to automatically retrieve information related to identified proteins by querying external databases. Stored data can be displayed graphically and a "Query Builder" allows users to make sophisticated queries without knowledge on the underlying database structure. All resources can be accessed programmatically using a Java client API or RESTful web services, allowing the integration of PROTICdb in any portal. An example of application is presented, where proteins extracted from a maize leaf sample by four different methods were compared using a label-free shotgun method. Data are available at http://moulon.inra.fr/protic/public. PROTICdb thus provides means for data storage, enrichment, and dissemination of proteomics data.