Joël Vandekerckhove

MS-driven proteomics has evolved over the past two decades to a high tech and high impact research field. Two distinct factors clearly influenced its expansion: the rapid growth of an arsenal of instrument and proteomic techniques that led to an explosion of high quality data and the development of software tools to analyze and interpret these data which boosted the number of scientific discoveries. In analogy with the benchmarking of new instruments and proteomic techniques, such software tools must be thoroughly tested and analyzed. Recently, new tools were developed for automatic peptide quantification in quantitative proteomic experiments. Here we present a case study where the most recent and frequently used tools are analyzed and compared.

Proteins are reckoned to be the key actors in a living organism. By studying proteins, one engages into deciphering a complex series of events occurring during a protein’s life span. This starts at the creation of a protein, which is tightly controlled on both a transcriptional (Williams and Tyler, 2007, Curr Opin Genet Dev 17, 88–93) and a translational level (Van Der Kelen et al., 2009, Crit Rev Biochem Mol Biol 44, 143–168). During translation, a primary strand of amino acids undergoes a complex folding process in order to obtain a native three-dimensional protein structure (Gross et al., 2003, Cell 115, 739–750). Proteins take on a plethora of functions, such as complex formation, receptor activity, and signal transduction, which ultimately adds up to a cellular phenotype. Consequently, protein analysis is of major interest in molecular biology and involves annotating their presence and localization, as well as their modification state and biochemical context. To accomplish this, many methods have been developed over the last decades, and their general principles and important recent advances in large-scale protein analysis or proteomics are discussed in this review.

The Enabled/Vasodilator stimulated phosphoprotein (Ena/VASP) gene family comprises three genes in vertebrates: Vasp, Enabled homologue (Enah) and Ena-VASP like (Evl). Enah has the most complex gene structure. It has extra alternatively included exons compared to Vasp and Evl, and possibly one alternatively excluded intron S. The aim of this mapping study was to probe the occurrence of combinations of exon usage in Enah thereby identifying possible vertebrate ENAH splice variants. We investigated this via an in silico analysis and by performing a reverse transcription-polymerase chain reaction (RT-PCR) screen on mouse samples. We further probed the expression pattern of mouse Enah splice variants during development and in a selection of mouse adult tissues and mouse cell lines.

Acetylation of nascent protein N^α-termini is a common modification among archae and eukaryotes and can influence the structure and function of target proteins. This modification has been studied on an individual protein or (synthetic) peptide level or on a proteome scale using two-dimensional polyacrylamide gel electrophoresis. We recently developed mass spectrometry driven proteome analytical approaches specifically targeting the amino (N) terminus of proteins based on the concept of diagonal reverse-phase chromatography. We here review how this so-called combined fractional diagonal chromatography (COFRADIC) technique can be used in combination with differential mass-tagging strategies as to both qualitatively and quantitatively assess protein N^α-acetylation in whole proteomes.

Nemaline myopathy is a neuromuscular disorder characterized by the presence of nemaline bodies in patient muscles. 20% of the cases are associated with α-skeletal muscle actin mutations. We previously showed that actin mutations can cause four different biochemical phenotypes and that expression of NM associated actin mutants in fibroblasts, myoblasts and myotubes induces a range of cellular defects.

Labeling of proteins and peptides with stable heavy isotopes (deuterium, carbon‐13, nitrogen‐15, and oxygen‐18) is widely used in quantitative proteomics. These are either incorporated metabolically in cells and small organisms, or postmetabolically in proteins and peptides by chemical or enzymatic reactions. Only upon measurement with mass spectrometers holding sufficient resolution, light, and heavy labeled peptide ions or reporter peptide fragment ions segregate and their intensity values are subsequently used for quantification. Targeted use of these labels or mass tags further leads to specific monitoring of diverse aspects of dynamic proteomes. In this review article, commonly used isotope labeling strategies are described, both for quantitative differential protein profiling and for targeted analysis of protein modifications.

Migration is an important aspect of cellular behaviour and is therefore widely studied in cell biology. Numerous components are known to participate in this process in a highly dynamic manner. In order to obtain a better insight in cell migration, mutants or drugs are used and their motive phenotype is then linked with the disturbing factors. One of the typical approaches to study motion paths of individual cells relies on fitting mean square displacements to a persistent random walk function. Since the numerous calculations involved often rely on diverse commercial software packages, the analysis can be expensive, labour-intensive and error-prone work. Additionally, due to the nature of algorithms employed the calculations involved are not readily reproducible without access to the exact software package(s) used. We here present the cell_motility software, an open source Java application under the GNU-GPL license that provides a clear and concise analysis workbench for large amounts of cell motion data. Apart from performing the necessary calculations, the software also visualizes the original motion paths as well as the results of the calculations to help the user interpret the data. The application features an intuitive graphical user interface as well as full user and developer documentation and both source and binary files can be freely downloaded from the project website at http://genesis.UGent.be/cell_motility . In providing a free, open source software solution for the automated processing of cell motion data, we aim to achieve two important goals: labs can greatly simplify their data analysis pipeline as switching between different computational software packages becomes obsolete (thus reducing the chances for human error during data manipulation and transfer) and secondly, to provide scientists in the field with a freely available common platform to perform their analyses, enabling more efficient data quality control through peer reviewing.