Mass Spectrometry Imaging Used to Process Biologic “Big Data”

The vast amounts of data that are generated by the latest scientific instruments are leading to significant scientific discoveries, but only when scientists can effectively process that data. Big data represents both an opportunity and a challenge for scientists. This is especially the case for analytic scientists studying biologic materials using a fairly new technique known as mass spectrometry imaging (MSI). Help has arrived from a recent project, however, called Computis, which has a range of new tools for effectively processing MSI data.

MSI is a form of mass spectrometry (MS) that enables both spatial and mass spectrometric analysis by measuring the masses of compounds at specific locations on the surface of a solid sample. MSI can, therefore, construct a visual image of its chemical composition. Various MSI techniques are available, including matrix-assisted laser desorption/ionization (MALDI), secondary ion MS (SIMS) and desorption electrospray ionization (DESI), but they all generate huge amounts of data.

Although software tools are available for processing this information, they tend to be produced by the manufacturers of the MSI instruments and therefore each uses diverse data formats, making it difficult to combine and process data generated by different instruments. Computis was set up by the European Commission in 2006 to overcome this hurdle by developing a new generation of flexible and efficient tools for processing any MSI data. It involved academic and industrial teams from across Europe, including the French Atomic Energy Commission and the Swiss pharmaceutical giant Novartis (Basel, Switzerland). The project was completed in 2010 and the main outputs have now been described November 2014 in an article in the European Journal of Mass Spectrometry (EJMS).

The project members rapidly figured out that their first task should be to develop a common format for MSI data that all the other data formats could be converted into, which led to the imzML data format. This format divides MSI data into two separate files: the mass data are stored in a binary file to ensure proficient storage, while metadata such as instrumental parameters and sample details are stored in an XML file. The members also developed several tools for translating MSI data in other formats into the imzML format.

The project then developed several tools specially designed to work with the imzML format. This included two tools for processing and displaying MSI data, Data Cube Explorer and SpectViewer, and a tool called EasyReg2D for incorporating MSI data with image data from other analytic instruments, including microscopes. Furthermore, the researchers adapted an existing MSI processing tool known as BioMap to work with data in the imzML format.

According to the investigators, this project has been successful in greatly expanding the tools available for processing MSI data, helping to ensure big data becomes an opportunity for scientists instead of remaining a challenge.

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