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.

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.

Proteomic identification of proliferation and progression markers in human polycythemia vera stem and progenitor cells.

Polycythemia vera (PV) is a stem cell disorder characterized by hyperproliferation of the myeloid lineages and the presence of an activating JAK2 mutation. To elucidate mechanisms controlling PV stem and progenitor cell biology, we applied a recently developed highly sensitive data-independent acquisition mass spectrometry workflow to purified hematopoietic stem and progenitor cell (HSPC) subpopulations of patients with chronic and progressed PV. We integrated proteomic data with genomic, transcriptomic, flow cytometry, and in vitro colony formation data. Comparative analyses revealed added information gained by proteomic compared with transcriptomic data in 30% of proteins with changed expression in PV patients. Upregulated biological pathways in hematopoietic stem and multipotent progenitor cells (HSC/MPPs) of PV included mammalian target of rapamycin (MTOR), STAT, and interferon signaling. We further identified a prominent reduction of clusterin (CLU) protein expression and a corresponding activation of nuclear factor-κB (NF-κB) signaling in HSC/MPPs of untreated PV patients compared with controls. Reversing the reduction of CLU and inhibiting NF-κB signaling decreased proliferation and differentiation of PV HSC/MPPs in vitro. Upon progression of PV, we identified upregulation of LGALS9 and SOCS2 protein expression in HSC/MPPs. Treatment of patients with hydroxyurea normalized the expression of CLU and NF-κB2 but not of LGALS9 and SOCS2. These findings expand the current understanding of the molecular pathophysiology underlying PV and provide new potential targets (CLU and NF-κB) for antiproliferative therapy in patients with PV.

APOE2, E3, and E4 differentially modulate cellular homeostasis, cholesterol metabolism, and inflammatory response in isogenic iPSC-derived astrocytes.

The apolipoprotein E4 (APOE4) variant is the strongest genetic risk factor for Alzheimer disease (AD), while the APOE2 allele is protective. A major question is how different APOE genotypes affect the physiology of astrocytes, the main APOE-producing brain cells. Here, we differentiated human APOE-isogenic induced pluripotent stem cells (iPSCs) (APOE4, E3, E2, and APOE knockout [APOE-KO]) to functional "iAstrocytes". Mass-spectrometry-based proteomic analysis showed genotype-dependent reductions of cholesterol and lipid metabolic and biosynthetic pathways (reduction: APOE4 >E3 >E2). Cholesterol efflux and biosynthesis were reduced in APOE4 iAstrocytes, while subcellular localization of cholesterol in lysosomes was elevated. An increase in immunoregulatory proteomic pathways (APOE4 >E3 >E2) was accompanied by elevated cytokine release in APOE4 cells (APOE4 >E3 >E2 >KO). Activation of iAstrocytes exacerbated proteomic changes and cytokine secretion mostly in APOE4 iAstrocytes, while APOE2 and APOE-KO iAstrocytes were least affected. Taken together, APOE4 iAstrocytes reveal a disease-relevant phenotype, causing dysregulated cholesterol/lipid homeostasis, increased inflammatory signaling, and reduced ß-amyloid uptake, while APOE2 iAstrocytes show opposing effects.

rawDiag: An R Package Supporting Rational LC-MS Method Optimization for Bottom-up Proteomics.

Optimizing methods for liquid chromatography coupled to mass spectrometry (LC-MS) is a nontrivial task. Here we present rawDiag, a software tool supporting rational method optimization by providing MS operator-tailored diagnostic plots of scan-level metadata. rawDiag is implemented as an R package and can be executed on the R command line or through a graphical user interface (GUI) for less experienced users. The code runs platform-independent and can process 100 raw files in <3 min on current consumer hardware, as we show in our benchmark. As a demonstration of the functionality of our package we include a real-world example taken from our daily core facility business.

Aggregatibacter actinomycetemcomitans H-NS promotes biofilm formation and alters protein dynamics of other species within a polymicrobial oral biofilm.

Aggregatibacter actinomycetemcomitans is a Gram-negative organism, strongly associated with aggressive forms of periodontitis. An important virulence property of A. actinomycetemcomitans is its ability to form tenacious biofilms that can attach to abiotic as well as biotic surfaces. The histone-like (H-NS) family of nucleoid-structuring proteins act as transcriptional silencers in many Gram-negative bacteria. To evaluate the role of H-NS in A. actinomycetemcomitans, hns mutant derivatives of serotype a strain D7S were generated. Characteristics of the hns mutant phenotype included shorter and fewer pili, and substantially lower monospecies biofilm formation relative to the wild type. Furthermore, the D7S hns mutant exhibited significantly reduced growth within a seven-species oral biofilm model. However, no apparent difference was observed regarding the numbers and proportions of the remaining six species regardless of being co-cultivated with D7S hns or its parental strain. Proteomics analysis of the strains grown in monocultures confirmed the role of H-NS as a repressor of gene expression in A. actinomycetemcomitans. Interestingly, proteomics analysis of the multispecies biofilms indicated that the A. actinomycetemcomitans wild type and hns mutant imposed different regulatory effects on the pattern of protein expression in the other species, i.e., mainly Streptococcus spp., Fusobacterium nucleatum, and Veillonella dispar. Gene ontology analysis revealed that a large portion of the differentially regulated proteins was related to translational activity. Taken together, our data suggest that, apart from being a negative regulator of protein expression in A. actinomycetemcomitans, H-NS promotes biofilm formation and may be an important factor for survival of this species within a multispecies biofilm.

Precise Temporal Profiling of Signaling Complexes in Primary Cells Using SWATH Mass Spectrometry.

Spatiotemporal organization of protein interactions in cell signaling is a fundamental process that drives cellular functions. Given differential protein expression across tissues and developmental stages, the architecture and dynamics of signaling interaction proteomes is, likely, highly context dependent. However, current interaction information has been almost exclusively obtained from transformed cells. In this study, we applied an advanced and robust workflow combining mouse genetics and affinity purification (AP)-SWATH mass spectrometry to profile the dynamics of 53 high-confidence protein interactions in primary T cells, using the scaffold protein GRB2 as a model. The workflow also provided a sufficient level of robustness to pinpoint differential interaction dynamics between two similar, but functionally distinct, primary T cell populations. Altogether, we demonstrated that precise and reproducible quantitative measurements of protein interaction dynamics can be achieved in primary cells isolated from mammalian tissues, allowing resolution of the tissue-specific context of cell-signaling events.

OpenMS: a flexible open-source software platform for mass spectrometry data analysis.

High-resolution mass spectrometry (MS) has become an important tool in the life sciences, contributing to the diagnosis and understanding of human diseases, elucidating biomolecular structural information and characterizing cellular signaling networks. However, the rapid growth in the volume and complexity of MS data makes transparent, accurate and reproducible analysis difficult. We present OpenMS 2.0 (http://www.openms.de), a robust, open-source, cross-platform software specifically designed for the flexible and reproducible analysis of high-throughput MS data. The extensible OpenMS software implements common mass spectrometric data processing tasks through a well-defined application programming interface in C++ and Python and through standardized open data formats. OpenMS additionally provides a set of 185 tools and ready-made workflows for common mass spectrometric data processing tasks, which enable users to perform complex quantitative mass spectrometric analyses with ease.

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.

Epigenetics and Proteomics Join Transcriptomics in the Quest for Tuberculosis Biomarkers.

UNLABELLED: An 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. IMPORTANCE: DNA methylation modifies the transcriptional program of cells. We have focused on two major populations of leukocytes involved in immune response to infectious diseases, granulocytes and monocytes, both of which are professional phagocytes that engulf and kill bacteria. We have interrogated how DNA methylation, gene expression, and protein translation differ in these two cell populations between healthy individuals and patients suffering from TB. To better understand the underlying biologic mechanisms, we harnessed a statistical enrichment analysis, taking advantage of predefined and well-characterized gene sets. Not only were there clear differences on various levels between the two populations, but there were also differences between TB patients and healthy controls in the transcriptome, proteome, and, for the first time, DNA methylome in these cells. Our pilot study emphasizes the value of a large-scale study of the DNA methylome taking into account our findings.

Rapid mass spectrometric conversion of tissue biopsy samples into permanent quantitative digital proteome maps.

Clinical specimens are each inherently unique, limited and nonrenewable. Small samples such as tissue biopsies are often completely consumed after a limited number of analyses. Here we present a method that enables fast and reproducible conversion of a small amount of tissue (approximating the quantity obtained by a biopsy) into a single, permanent digital file representing the mass spectrometry (MS)-measurable proteome of the sample. The method combines pressure cycling technology (PCT) and sequential window acquisition of all theoretical fragment ion spectra (SWATH)-MS. The resulting proteome maps can be analyzed, re-analyzed, compared and mined in silico to detect and quantify specific proteins across multiple samples. We used this method to process and convert 18 biopsy samples from nine patients with renal cell carcinoma into SWATH-MS fragment ion maps. From these proteome maps we detected and quantified more than 2,000 proteins with a high degree of reproducibility across all samples. The measured proteins clearly distinguished tumorous kidney tissues from healthy tissues and differentiated distinct histomorphological kidney cancer subtypes.

Building high-quality assay libraries for targeted analysis of SWATH MS data.

Targeted proteomics by selected/multiple reaction monitoring (S/MRM) or, on a larger scale, by SWATH (sequential window acquisition of all theoretical spectra) MS (mass spectrometry) typically relies on spectral reference libraries for peptide identification. Quality and coverage of these libraries are therefore of crucial importance for the performance of the methods. Here we present a detailed protocol that has been successfully used to build high-quality, extensive reference libraries supporting targeted proteomics by SWATH MS. We describe each step of the process, including data acquisition by discovery proteomics, assertion of peptide-spectrum matches (PSMs), generation of consensus spectra and compilation of MS coordinates that uniquely define each targeted peptide. Crucial steps such as false discovery rate (FDR) control, retention time normalization and handling of post-translationally modified peptides are detailed. Finally, we show how to use the library to extract SWATH data with the open-source software Skyline. The protocol takes 2-3 d to complete, depending on the extent of the library and the computational resources available.

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.

Transformation and other factors of the peptide mass spectrometry pairwise peak-list comparison process.

BACKGROUND: Biological Mass Spectrometry is used to analyse peptides and proteins. A mass spectrum generates a list of measured mass to charge ratios and intensities of ionised peptides, which is called a peak-list. In order to classify the underlying amino acid sequence, the acquired spectra are usually compared with synthetic ones. Development of suitable methods of direct peak-list comparison may be advantageous for many applications. RESULTS: The pairwise peak-list comparison is a multistage process composed of matching of peaks embedded in two peak-lists, normalisation, scaling of peak intensities and dissimilarity measures. In our analysis, we focused on binary and intensity based measures. We have modified the measures in order to comprise the mass spectrometry specific properties of mass measurement accuracy and non-matching peaks. We compared the labelling of peak-list pairs, obtained using different factors of the pairwise peak-list comparison, as being the same or different to those determined by sequence database searches. In order to elucidate how these factors influence the peak-list comparison we adopted an analysis of variance type method with the partial area under the ROC curve as a dependent variable. CONCLUSION: The analysis of variance provides insight into the relevance of various factors influencing the outcome of the pairwise peak-list comparison. For large MS/MS and PMF data sets the outcome of ANOVA analysis was consistent, providing a strong indication that the results presented here might be valid for many various types of peptide mass measurements.

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 http://www.bioconductor.org. 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%.