New study in Nature Biotechnology: Better visualization of protein analyses and new bioinformatics tools contribute to medical research

This work has contributed to the development of new bioinformatics tools for mass spectrometry based proteomics analyses as well as emphasized the importance of correct presentation of such data. Both elements are important for the medical research and will hopefully over time contribute to increase our knowledge about diagnostics thereby enabling earlier and more precise diagnoses, e.g. in multiple sclerosis (MS). As a consequence MS patients would be offered earlier treatment that would have a favorable effect on pathogenesis, prognosis and survival.

The large number of proteins that can be expressed from the c.a. 20 000 human genes are referred to as the human proteome, and the study of entire proteomes is called proteomics. Depending on the tissue and situation the proteins will be present in different amounts and in a variety of modified versions. Furthermore, the properties of the observed proteins will often differ between healthy individuals and individuals with a disease. Such proteins, referred to as biomarkers, may contribute important information with regards to disease states. The human proteome (without the modified versions) was recently characterized in a selection of tissues and organs using mRNA transcriptomics and antibody-based immunohistochemistry in “The Human Protein Atlas” (Uhlen et al., 2015).

Proteomic analysis using mass spectrometry is an alternative sensitive and high-throughput method that can identify and quantify thousands of proteins in biological samples, including modified versions. This is usually done by an enzymatic digestion of the proteins into peptides; the peptides are then separated, ionized, fragmented and detected as spectra in a mass spectrometer. The spectra contain information about the amino acid sequence allowing its identification using bioinformatic tools.

Mass spectrometry based proteomics is increasingly being applied in medical research. For examples, the human proteome was recently characterized in a variety of organs (Kim et al., 2014, Wilhelm et al., 2014) providing important insight into organ specific protein function. The application of mass spectrometry based proteomics is also increasing in multiple sclerosis (MS) research, and researchers associated with the MS Centre and the KGJ Centre have recently reviewed the current knowledge retrieved from cerebrospinal fluid analyses of MS patients with focus on validation of biomarkers (Kroksveen et al., 2014).

At the same time as mass spectrometry based proteomics is increasingly being applied in medical research, rapid technology development has led to increases in both complexity and size of the generated data sets. As a consequence, the demand on analysis tools, quality control and presentation of data has also increased. Researchers associated with the MS Centre and the KGJ Centre have recently contributed to the development of a set of bioinformatics tools for analysis of mass spectrometry based proteomics data, PeptideShaker (Vaudel et al., 2015). Furthermore, they have discussed the importance of visualization strategies for this type of data with respect to quality control (Oveland et al., 2014).

In the article “PeptideShaker enables reanalysis of MS-derived proteomics data sets” published in Nature Biotechnology in January 2015 (Vaudel et al., 2015), a set of tools that enables re-analysis and interpretation of mass spectrometry based proteomics data shared by other research groups was presented. PeptideShaker also supports the conversion of the user's own data into standard formats for sharing in public proteomics databases. Thus, data from projects mapping the human proteome and from MS biomarker studies can easily be shared and subsequently re-analyzed in new contexts.

The article “Viewing the proteome: How to visualize proteomics data?” published in Proteomics in December 2014 is a guide to the visualization of mass spectrometry based proteomics data (Oveland et al., 2014). It focuses on important elements concerning quality control and interpretation of the numerous steps in proteomics analyses – from mass spectra to protein networks.

In summary, the researchers associated with the MS Centre and the KGJ Centre have contributed to the development of new bioinformatics tools for mass spectrometry based proteomics analyses as well as emphasized the importance of correct presentation of data. Both elements are important for the improvement of medical research and will hopefully over time increase our knowledge about diagnostics, thus contribute to the discovery of new treatment targets for MS and other diseases.

Researchers associated with the MS Centre and the KGJ Centre:
Eystein Oveland (PhD, researcher at K1, employed at KGJ)
Frode S. Berven (Professor, leader at the Proteomics Unit at UiB)
Harald Barsnes (PhD, researcher at K2, previously associated with the MS Centre)
Marc Vaudel (PhD, postdoc at the Department of Biomedicine)

References:
Kim MS, Pinto SM, Getnet D, Nirujogi RS, Manda SS, Chaerkady R, et al. A draft map of the human proteome. Nature. 2014;509(7502):575-81.

Kroksveen AC, Opsahl JA, Guldbrandsen A, Myhr KM, Oveland E, Torkildsen O, et al. Cerebrospinal fluid proteomics in multiple sclerosis. Biochimica et biophysica acta. 2014.

Oveland E, Muth T, Rapp E, Martens L, Berven FS, Barsnes H. Viewing the proteome: How to visualize proteomics data? Proteomics. 2014.

Uhlen M, Fagerberg L, Hallstrom BM, Lindskog C, Oksvold P, Mardinoglu A, et al. Proteomics. Tissue-based map of the human proteome. Science. 2015;347(6220):1260419.

Vaudel M, Burkhart JM, Zahedi RP, Oveland E, Berven FS, Sickmann A, et al. PeptideShaker enables reanalysis of MS-derived proteomics data sets. Nat Biotechnol. 2015;33(1):22-4.

Wilhelm M, Schlegl J, Hahne H, Moghaddas Gholami A, Lieberenz M, Savitski MM, et al. Mass-spectrometry-based draft of the human proteome. Nature. 2014;509(7502):582-7.