An automated method for scanning LC-MS data sets for significant peptides and proteins, including quantitative profiling and interactive confirmation.

Differential quantification of proteins and peptides by LC-MS is a promising method to acquire knowledge about biological processes, and for finding drug targets and biomarkers. However, differential protein analysis using LC-MS has been held back by the lack of suitable software tools. Large amounts of experimental data are easily generated in protein and peptide profiling experiments, but data analysis is time-consuming and labor-intensive. Here, we present a fully automated method for scanning LC-MS/MS data for biologically significant peptides and proteins, including support for interactive confirmation and further profiling. By studying peptide mixtures of known composition, we demonstrate that peptides present in different amounts in different groups of samples can be automatically screened for using statistical tests. A linear response can be obtained over almost 3 orders of magnitude, facilitating further profiling of peptides and proteins of interest. Furthermore, we apply the method to study the changes of endogenous peptide levels in mouse brain striatum after administration of reserpine, a classical model drug for inducing Parkinson disease symptoms.

Reproducibility of LC-MS-based protein identification.

Traditional analysis of liquid chromatography-mass spectrometry (LC-MS) data, typically performed by reviewing chromatograms and the corresponding mass spectra, is both time-consuming and difficult. Detailed data analysis is therefore often omitted in proteomics applications. When analysing multiple proteomics samples, it is usually only the final list of identified proteins that is reviewed. This may lead to unnecessarily complex or even contradictory results because the content of the list of identified proteins depends heavily on the conditions for triggering the collection of tandem mass spectra. Small changes in the signal intensity of a peptide in different LC-MS experiments can lead to the collection of a tandem mass spectrum in one experiment but not in another. Also, the quality of the tandem mass spectrometry experiments can vary, leading to successful identification in some cases but not in others. Using a novel image analysis approach, it is possible to achieve repeat analysis with a very high reproducibility by matching peptides across different LC-MS experiments using the retention time and parent mass over charge (m/z). It is also easy to confirm the final result visually. This approach has been investigated by using tryptic digests of integral membrane proteins from organelle-enriched fractions from Arabidopsis thaliana and it has been demonstrated that very highly reproducible, consistent, and reliable LC-MS data interpretation can be made.

Detection of artifacts and peptide modifications in liquid chromatography/mass spectrometry data using two-dimensional signal intensity map data visualization.

We demonstrate how visualization of liquid chromatography/mass spectrometry data as a two-dimensional signal intensity map can be used to assess the overall quality of the data, for the identification of polymer contaminants and artifacts, as well as for the confirmation of post-translational modifications.

Differential expression analysis of Escherichia coli proteins using a novel software for relative quantitation of LC-MS/MS data.

The study of changes in protein levels between samples derived from cells representing different biological conditions is a key to the understanding of cellular function. There are two main methods available that allow both for global scanning for significantly varying proteins and targeted profiling of proteins of interest. One method is based on 2-D gel electrophoresis and image analysis of labelled proteins. The other method is based on LC-MS/MS analysis of either unlabelled peptides or peptides derived from isotopically labelled proteins or peptides. In this study, the non-labelling approach was used involving a new software, DeCyder MS Differential Analysis Software (DeCyder MS) intended for automated detection and relative quantitation of unlabelled peptides in LC-MS/MS data. Total protein extracts of E. coli strains expressing varying levels of dihydrofolate reductase and integron integrase were digested with trypsin and analyzed using a nanoscale liquid chromatography system, Ettan MDLC, online connected to an LTQTM linear ion-trap mass spectrometer fitted with a nanospray interface. Acquired MS data were subjected to DeCyder MS analysis where 2-D representations of the peptide patterns from individual LC-MS/MS analyses were matched and compared. This approach to unlabelled quantitative analysis of the E. coli proteome resulted in relative protein abundances that were in good agreement with results obtained from traditional methods for measuring protein levels.

A neuroproteomic approach to targeting neuropeptides in the brain.

A novel universal neuropeptide display approach in the mass range of 300-5000 Da was developed to complement two-dimensional gel electrophoresis in the analysis of peptides and small proteins from brain tissue samples. For the analysis of neuropeptides we utilized on-line nanoscale capillary reversed phase liquid chromatography and electrospray ionization quadrupole-time of flight mass spectrometry. The method was employed for the analysis of a large number of peptides from three specific rat brain regions. Approximately 1500 peptides from each brain region were detected in the same analysis. Several of these peptides were sequenced using collision-induced dissociation and identified by database search tools. In addition, a method for comparing peptide elution profiles between samples was developed, to provide two- and three-dimensional computer graphics of the profiles and to pinpoint differences for statistical measurements. Among the characterized peptides were fragments from proteins such as hemoglobin, alpha-synuclein, stathmin, cyclophilin, actin, NADH dehydrogenase, cytochrome c oxidase and prosomatostatin, as well as the bioactive neuropeptides W-hemorphin-4, and LW-hemorphin-7. The present study showed that the combination of nanoscale reversed phase liquid chromatography and high-resolution tandem mass spectrometry provides a novel and powerful approach to investigate a large number of peptides and protein fragments in the brain.

Algorithms for cytoplasm segmentation of fluorescence labelled cells.

Automatic cell segmentation has various applications in cytometry, and while the nucleus is often very distinct and easy to identify, the cytoplasm provides a lot more challenge. A new combination of image analysis algorithms for segmentation of cells imaged by fluorescence microscopy is presented. The algorithm consists of an image pre-processing step, a general segmentation and merging step followed by a segmentation quality measurement. The quality measurement consists of a statistical analysis of a number of shape descriptive features. Objects that have features that differ to that of correctly segmented single cells can be further processed by a splitting step. By statistical analysis we therefore get a feedback system for separation of clustered cells. After the segmentation is completed, the quality of the final segmentation is evaluated. By training the algorithm on a representative set of training images, the algorithm is made fully automatic for subsequent images created under similar conditions. Automatic cytoplasm segmentation was tested on CHO-cells stained with calcein. The fully automatic method showed between 89% and 97% correct segmentation as compared to manual segmentation.

A novel experimental design for comparative two-dimensional gel analysis: two-dimensional difference gel electrophoresis incorporating a pooled internal standard.

The comparison of two-dimensional (2-D) gel images from different samples is an established method used to study differences in protein expression. Conventional methods rely on comparing images from at least 2 different gels. Due to the high variation between gels, detection and quantification of protein differences can be problematic. Two-dimensional difference gel electrophoresis (Ettan trade mark DIGE) is an emerging technique for comparative proteomics, which improves the reproducibility and reliability of differential protein expression analysis between samples. In the application of DIGE different samples are labelled with mass and charge matched spectrally resolvable fluorescent dyes and are then separated on the same 2-D gel. Using an Escherichia coli lysate "spiked" with varying amounts of four different known proteins, we have tested a novel experimental design that exploits the sample multiplexing capabilities of DIGE, by including a standard sample in each gel. The standard sample comprises equal amounts of each sample to be compared and was found to improve the accuracy of protein quantification between samples from different gels allowing accurate detection of small differences in protein levels between samples.