Calibration of mass spectrometric peptide mass fingerprint data without specific external or internal calibrants

BACKGROUND: Peptide Mass Fingerprinting (PMF) is a widely used mass spectrometry (MS) method of analysis of proteins and peptides. It relies on the comparison between experimentally determined and theoretical mass spectra. The PMF process requires calibration, usually performed with external or internal calibrants of known molecular masses. RESULTS: We have introduced two novel MS calibration methods. The first method utilises the local similarity of peptide maps generated after separation of complex protein samples by two-dimensional gel electrophoresis. It computes a multiple peak-list alignment of the data set using a modified Minimum Spanning Tree (MST) algorithm. The second method exploits the idea that hundreds of MS samples are measured in parallel on one sample support. It improves the calibration coefficients by applying a two-dimensional Thin Plate Splines (TPS) smoothing algorithm. We studied the novel calibration methods utilising data generated by three different MALDI-TOF-MS instruments. We demonstrate that a PMF data set can be calibrated without resorting to external or relying on widely occurring internal calibrants. The methods developed here were implemented in R and are part of the BioConductor package mscalib available from . CONCLUSION: The MST calibration algorithm is well suited to calibrate MS spectra of protein samples resulting from two-dimensional gel electrophoretic separation. The TPS based calibration algorithm might be used to correct systematic mass measurement errors observed for large MS sample supports. As compared to other methods, our combined MS spectra calibration strategy increases the peptide/protein identification rate by an additional 5 – 15%

Analytical model of peptide mass cluster centres with applications

BACKGROUND: The elemental composition of peptides results in formation of distinct, equidistantly spaced clusters across the mass range. The property of peptide mass clustering is used to calibrate peptide mass lists, to identify and remove non-peptide peaks and for data reduction. RESULTS: We developed an analytical model of the peptide mass cluster centres. Inputs to the model included, the amino acid frequencies in the sequence database, the average length of the proteins in the database, the cleavage specificity of the proteolytic enzyme used and the cleavage probability. We examined the accuracy of our model by comparing it with the model based on an in silico sequence database digest. To identify the crucial parameters we analysed how the cluster centre location depends on the inputs. The distance to the nearest cluster was used to calibrate mass spectrometric peptide peak-lists and to identify non-peptide peaks. CONCLUSION: The model introduced here enables us to predict the location of the peptide mass cluster centres. It explains how the location of the cluster centres depends on the input parameters. Fast and efficient calibration and filtering of non-peptide peaks is achieved by a distance measure suggested by Wool and Smilansky

Modic type 2 changes are fibroinflammatory changes with complement system involvement adjacent to degenerated vertebral endplates

Background Vertebral endplate signal intensity changes visualized by magnetic resonance imaging termed Modic changes (MC) are highly prevalent in low back pain patients. Interconvertibility between the three MC subtypes (MC1, MC2, MC3) suggests different pathological stages. Histologically, granulation tissue, fibrosis, and bone marrow edema are signs of inflammation in MC1 and MC2. However, different inflammatory infiltrates and amount of fatty marrow suggest distinct inflammatory processes in MC2. Aims The aims of this study were to investigate (i) the degree of bony (BEP) and cartilage endplate (CEP) degeneration in MC2, (ii) to identify inflammatory MC2 pathomechanisms, and (iii) to show that these marrow changes correlate with severity of endplate degeneration. Methods Pairs of axial biopsies (n = 58) spanning the entire vertebral body including both CEPs were collected from human cadaveric vertebrae with MC2. From one biopsy, the bone marrow directly adjacent to the CEP was analyzed with mass spectrometry. Differentially expressed proteins (DEPs) between MC2 and control were identified and bioinformatic enrichment analysis was performed. The other biopsy was processed for paraffin histology and BEP/CEP degenerations were scored. Endplate scores were correlated with DEPs. Results Endplates from MC2 were significantly more degenerated. Proteomic analysis revealed an activated complement system, increased expression of extracellular matrix proteins, angiogenic, and neurogenic factors in MC2 marrow. Endplate scores correlated with upregulated complement and neurogenic proteins. Discussion The inflammatory pathomechanisms in MC2 comprises activation of the complement system. Concurrent inflammation, fibrosis, angiogenesis, and neurogenesis indicate that MC2 is a chronic inflammation. Correlation of endplate damage with complement and neurogenic proteins suggest that complement system activation and neoinnervation may be linked to endplate damage. The endplate-near marrow is the pathomechanistic site, because MC2 occur at locations with more endplate degeneration. Conclusion MC2 are fibroinflammatory changes with complement system involvement which occur adjacent to damaged endplates

Epigenetics and proteomics join transcriptomics in the quest for tuberculosis biomarkers

CITATION: Esterhuyse, M. M. et al. 2015. Epigenetics and proteomics join transcriptomics in the quest for tuberculosis biomarkers. mBio, 6(5):e01187-15, doi:10.1128/mBio.01187-15.The original publication is available at http://mbio.asm.orgAn estimated one-third of the world’s population is currently latently infected with Mycobacterium tuberculosis. Latent M. tuberculosis infection (LTBI) progresses into active tuberculosis (TB) disease in ~5 to 10% of infected individuals. Diagnostic and prognostic biomarkers to monitor disease progression are urgently needed to ensure better care for TB patients and to decrease the spread of TB. Biomarker development is primarily based on transcriptomics. Our understanding of biology combined with evolving technical advances in high-throughput techniques led us to investigate the possibility of additional platforms (epigenetics and proteomics) in the quest to (i) understand the biology of the TB host response and (ii) search for multiplatform biosignatures in TB. We engaged in a pilot study to interrogate the DNA methylome, transcriptome, and proteome in selected monocytes and granulocytes from TB patients and healthy LTBI participants. Our study provides first insights into the levels and sources of diversity in the epigenome and proteome among TB patients and LTBI controls, despite limitations due to small sample size. Functionally the differences between the infection phenotypes (LTBI versus active TB) observed in the different platforms were congruent, thereby suggesting regulation of function not only at the transcriptional level but also by DNA methylation and microRNA. Thus, our data argue for the development of a large-scale study of the DNA methylome, with particular attention to study design in accounting for variation based on gender, age, and cell type.http://mbio.asm.org/content/6/5/e01187-15.abstract?sid=fe0ea1c7-6da2-4e53-b4a4-5cd8233777c7Publisher's versio

The SysteMHC Atlas project.

Mass spectrometry (MS)-based immunopeptidomics investigates the repertoire of peptides presented at the cell surface by major histocompatibility complex (MHC) molecules. The broad clinical relevance of MHC-associated peptides, e.g. in precision medicine, provides a strong rationale for the large-scale generation of immunopeptidomic datasets and recent developments in MS-based peptide analysis technologies now support the generation of the required data. Importantly, the availability of diverse immunopeptidomic datasets has resulted in an increasing need to standardize, store and exchange this type of data to enable better collaborations among researchers, to advance the field more efficiently and to establish quality measures required for the meaningful comparison of datasets. Here we present the SysteMHC Atlas (https://systemhcatlas.org), a public database that aims at collecting, organizing, sharing, visualizing and exploring immunopeptidomic data generated by MS. The Atlas includes raw mass spectrometer output files collected from several laboratories around the globe, a catalog of context-specific datasets of MHC class I and class II peptides, standardized MHC allele-specific peptide spectral libraries consisting of consensus spectra calculated from repeat measurements of the same peptide sequence, and links to other proteomics and immunology databases. The SysteMHC Atlas project was created and will be further expanded using a uniform and open computational pipeline that controls the quality of peptide identifications and peptide annotations. Thus, the SysteMHC Atlas disseminates quality controlled immunopeptidomic information to the public domain and serves as a community resource toward the generation of a high-quality comprehensive map of the human immunopeptidome and the support of consistent measurement of immunopeptidomic sample cohorts

prolfqua: A Comprehensive R-Package for Proteomics Differential Expression Analysis

Mass spectrometry is widely used for quantitative proteomics studies, relative protein quantification, and differential expression analysis of proteins. There is a large variety of quantification software and analysis tools. Nevertheless, there is a need for a modular, easy-to-use application programming interface in R that transparently supports a variety of well principled statistical procedures to make applying them to proteomics data, comparing and understanding their differences easy. The prolfqua package integrates essential steps of the mass spectrometry-based differential expression analysis workflow: quality control, data normalization, protein aggregation, statistical modeling, hypothesis testing, and sample size estimation. The package makes integrating new data formats easy. It can be used to model simple experimental designs with a single explanatory variable and complex experiments with multiple factors and hypothesis testing. The implemented methods allow sensitive and specific differential expression analysis. Furthermore, the package implements benchmark functionality that can help to compare data acquisition, data preprocessing, or data modeling methods using a gold standard data set. The application programmer interface of prolfqua strives to be clear, predictable, discoverable, and consistent to make proteomics data analysis application development easy and exciting. Finally, the prolfqua R-package is available on GitHub https://github.com/fgcz/ prolfqua, distributed under the MIT license. It runs on all platforms supported by the R free software environment for statistical computing and graphics.ISSN:1535-3893ISSN:1535-390

Targeted proteomics coming of age - SRM, PRM and DIA performance evaluated from a core facility perspective

Quantitative mass spectrometry is a rapidly evolving methodology applied in a large number of omics-type research projects. During the past years, new designs of mass spectrometers have been developed and launched as commercial systems while in parallel new data acquisition schemes and data analysis paradigms have been introduced. Core facilities provide access to such technologies, but also actively support the researchers in finding and applying the best-suited analytical approach. In order to implement a solid fundament for this decision making process, core facilities need to constantly compare and benchmark the various approaches. In this article we compare the quantitative accuracy and precision of current state of the art targeted proteomics approaches single reaction monitoring (SRM), parallel reaction monitoring (PRM) and data independent acquisition (DIA) across multiple liquid chromatography mass spectrometry (LC-MS) platforms, using a readily available commercial standard sample. All workflows are able to reproducibly generate accurate quantitative data. However, SRM and PRM workflows show higher accuracy and precision compared to DIA approaches, especially when analyzing low concentrated analytes