Witold Wolski

OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data.

Hannes L. Rost, 2, ∗ George Rosenberger, 2, ∗ Pedro Navarro, Ludovic Gillet, Sasa M. Miladinovic, 3 Olga T. Schubert, 2 Witold Wolski, Ben C. Collins, Johan Malmstrom, Lars Malmstrom, and Ruedi Aebersold 6, 7, † Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, CH-8093 Zurich, Switzerland Ph.D. Program in Systems Biology, University of Zurich and ETH Zurich, CH-8057 Zurich, Switzerland Biognosys AG, CH-8952 Schlieren, Switzerland SyBIT project of SystemsX.ch, ETH Zurich, CH-8092 Zurich, Switzerland Department of Immunotechnology, Lund University, S-22100 Lund, Sweden Competence Center for Systems Physiology and Metabolic Diseases, CH-8093 Zurich, Switzerland Faculty of Science, University of Zurich, CH-8057 Zurich, Switzerland (Dated: October 19, 2015)

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.

Systems proteomics of liver mitochondria function

Expanded proteomic analysis of metabolism Combined analysis of large data sets characterizing genes, transcripts, and proteins can elucidate biological functions and disease processes. Williams et al. report an exceptionally detailed characterization of mitochondrial function in a genetic reference panel of recombinant inbred mice. They measured the metabolic function of nearly 400 mice under various environmental conditions and collected detailed quantitative information from livers of the animals on over 25,000 transcripts. These data were integrated with quantitation of over 2500 proteins and nearly 1000 metabolites. Such analysis showed a frequent lack of correlation of transcript and protein abundance, enabled the identification of genomic variants of mitochondrial enzymes that caused inborn errors in metabolism, and revealed two genes that appear to function in cholesterol metabolism. Science, this issue p. 10.1126/science.aad0189 Advances in mass spectrometry yield insights into mitochondrial function. INTRODUCTION Over the past two decades, continuous improvements in “omics” technologies have driven an ever-greater capacity to define the relationships between genetics, molecular pathways, and overall phenotypes. Despite this progress, the majority of genetic factors influencing complex traits remain unknown. This is exemplified by mitochondrial supercomplex assembly, a critical component of the electron transport chain, which remains poorly characterized. Recent advances in mass spectrometry have expanded the scope and reliability of proteomics and metabolomics measurements. These tools are now capable of identifying thousands of factors driving diverse molecular pathways, their mechanisms, and consequent phenotypes and thus substantially contribute toward the understanding of complex systems. RATIONALE Genome-wide association studies (GWAS) have revealed many causal loci associated with specific phenotypes, yet the identification of such genetic variants has been generally insufficient to elucidate the molecular mechanisms linking these genetic variants with specific phenotypes. A multitude of control mechanisms differentially affect the cellular concentrations of different classes of biomolecules. Therefore, the identification of the causal mechanisms underlying complex trait variation requires quantitative and comprehensive measurements of multiple layers of data—principally of transcripts, proteins, and metabolites and the integration of the resulting data. Recent technological developments now support such multiple layers of measurements with a high degree of reproducibility across diverse sample or patient cohorts. In this study, we applied a multilayered approach to analyze metabolic phenotypes associated with mitochondrial metabolism. RESULTS We profiled metabolic fitness in 386 individuals from 80 cohorts of the BXD mouse genetic reference population across two environmental states. Specifically, this extensive phenotyping program included the analysis of metabolism, mitochondrial function, and cardiovascular function. To understand the variation in these phenotypes, we quantified multiple, detailed layers of systems-scale measurements in the livers of the entire population: the transcriptome (25,136 transcripts), proteome (2622 proteins), and metabolome (981 metabolites). Together with full genomic coverage of the BXDs, these layers provide a comprehensive view on overall variances induced by genetics and environment regarding metabolic activity and mitochondrial function in the BXDs. Among the 2600 transcript-protein pairs identified, 85% of observed quantitative trait loci uniquely influenced either the transcript or protein level. The transomic integration of molecular data established multiple causal links between genotype and phenotype that could not be characterized by any individual data set. Examples include the link between D2HGDH protein and the metabolite D-2-hydroxyglutarate, the BCKDHA protein mapping to the gene Bckdhb, the identification of two isoforms of ECI2, and mapping mitochondrial supercomplex assembly to the protein COX7A2L. These respective measured variants in these mitochondrial proteins were in turn associated with varied complex metabolic phenotypes, such as heart rate, cholesterol synthesis, and branched-chain amino acid metabolism. Of note, our transomics approach clarified the contested role of COX7A2L in mitochondrial supercomplex formation and identified and validated Echdc1 and Mmab as involved in the cholesterol pathway. CONCLUSION Overall, these findings indicate that data generated by next-generation proteomics and metabolomics techniques have reached a quality and scope to complement transcriptomics, genomics, and phenomics for transomic analyses of complex traits. Using mitochondria as a case in point, we show that the integrated analysis of these systems provides more insights into the emergence of the observed phenotypes than any layer can by itself, highlighting the complementarity of a multilayered approach. The increasing implementation of these omics technologies as complements, rather than as replacements, will together move us forward in the integrative analysis of complex traits. Model of the transomics analysis. A transomics approach was taken to analyze genetic and environmental variation in metabolic and mitochondrial phenotypes by measuring five distinct layers of biology in a diverse population of BXD mice. The combined analysis of all layers together provides additional information not yielded by any single omics approach. Recent improvements in quantitative proteomics approaches, including Sequential Window Acquisition of all Theoretical Mass Spectra (SWATH-MS), permit reproducible large-scale protein measurements across diverse cohorts. Together with genomics, transcriptomics, and other technologies, transomic data sets can be generated that permit detailed analyses across broad molecular interaction networks. Here, we examine mitochondrial links to liver metabolism through the genome, transcriptome, proteome, and metabolome of 386 individuals in the BXD mouse reference population. Several links were validated between genetic variants toward transcripts, proteins, metabolites, and phenotypes. Among these, sequence variants in Cox7a2l alter its protein’s activity, which in turn leads to downstream differences in mitochondrial supercomplex formation. This data set demonstrates that the proteome can now be quantified comprehensively, serving as a key complement to transcriptomics, genomics, and metabolomics—a combination moving us forward in complex trait analysis.

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.

Multilayered genetic and omics dissection of mitochondrial activity in a mouse reference population

The manner by which genotype and environment affect complex phenotypes is one of the fundamental questions in biology. In this study, we quantified the transcriptome--a subset of the metabolome--and, using targeted proteomics, quantified a subset of the liver proteome from 40 strains of the BXD mouse genetic reference population on two diverse diets. We discovered dozens of transcript, protein, and metabolite QTLs, several of which linked to metabolic phenotypes. Most prominently, Dhtkd1 was identified as a primary regulator of 2-aminoadipate, explaining variance in fasted glucose and diabetes status in both mice and humans. These integrated molecular profiles also allowed further characterization of complex pathways, particularly the mitochondrial unfolded protein response (UPR(mt)). UPR(mt) shows strikingly variant responses at the transcript and protein level that are remarkably conserved among C. elegans, mice, and humans. Overall, these examples demonstrate the value of an integrated multilayered omics approach to characterize complex metabolic phenotypes.

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.

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

BackgroundPeptide 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.ResultsWe 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.ConclusionThe 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%.

Epigenetics and Proteomics Join Transcriptomics in the Quest for Tuberculosis Biomarkers

ABSTRACT An estimated one-third of the worlds 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. 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.

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.

Analytical model of peptide mass cluster centres with applications

BackgroundThe 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.ResultsWe 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.ConclusionThe 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.