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Objectives Systemic sclerosis (SSc) is a rare disease requiring multicentre collaboration to reveal comprehensive details of disease-related causes for morbidity and mortality. Methods The European League Against Rheumatism (EULAR) Scleroderma Trials and Research (EUSTAR) group initiated a database to prospectively gather key data of patients with SSc using a minimal essential dataset that was reorganised in 2008 introducing new items. Baseline visit data of patients who were registered between 2004 and 2011 were analysed using descriptive statistics. Results In June 2011, 7655 patients (2838 with diffuse cutaneous (dc) and 4481 with limited cutaneous (lc) SSc who fulfilled the American College of Rheumatology diagnostic criteria had been registered in 174 centres, mainly European. The most prominent hallmarks of disease were Raynaud's phenomenon (96.3%), antinuclear antibodies (93.4%) and a typical capillaroscopic pattern (90.9%). Scleroderma was more common on fingers and hands than on any other part of the skin. Proton pump inhibitors (65.2%), calcium channel blockers (52.7%), and corticosteroids (45.3%) were most often prescribed. Among the immunosuppressant agents, cyclophosphamide was used more often in dcSSc than in lcSSc. Conclusions The EUSTAR database provides an abundance of information on the true clinical face of SSc that will be helpful in improving the classification of SSc and its subsets and for developing more specific therapeutic recommendations.

Great advances have been made in sensitivity and acquisition speed on the Orbitrap mass analyzer, enabling increasingly deep proteome coverage. However, these advances have been mainly limited to the MS2 level, whereas ion beam sampling for the MS1 scans remains extremely inefficient. Here we report a data-acquisition method, termed BoxCar, in which filling multiple narrow mass-to-charge segments increases the mean ion injection time more than tenfold as compared to that of a standard full scan. In 1-h analyses, the method provided MS1-level evidence for more than 90% of the proteome of a human cancer cell line that had previously been identified in 24 fractions, and it quantified more than 6,200 proteins in ten of ten replicates. In mouse brain tissue, we detected more than 10,000 proteins in only 100 min, and sensitivity extended into the low-attomolar range.

Physician rating websites (PRW) have been gaining in popularity among patients who are seeking a physician. However, little evidence is available on the number, distribution, or trend of evaluations on PRWs. Furthermore, there is no published evidence available that analyzes the characteristics of the patients who provide ratings on PRWs. The objective of the study was to analyze all physician evaluations that were posted on the German PRW, jameda, in 2012. Data from the German PRW, jameda, from 2012 were analyzed and contained 127,192 ratings of 53,585 physicians from 107,148 patients. Information included medical specialty and gender of the physician, age, gender, and health insurance status of the patient, as well as the results of the physician ratings. Statistical analysis was carried out using the median test and Kendall Tau-b test. Thirty-seven percent of all German physicians were rated on jameda in 2012. Nearly half of those physicians were rated once, and less than 2% were rated more than ten times (mean number of ratings 2.37, SD 3.17). About one third of all rated physicians were female. Rating patients were mostly female (60%), between 30-50 years (51%) and covered by Statutory Health Insurance (83%). A mean of 1.19 evaluations per patient could be calculated (SD 0.778). Most of the rated medical specialties were orthopedists, dermatologists, and gynecologists. Two thirds of all ratings could be assigned to the best category, \"very good\". Female physicians had significantly better ratings than did their male colleagues (P<.001). Additionally, significant rating differences existed between medical specialties (P<.001). It could further be shown that older patients gave better ratings than did their younger counterparts (P<.001). The same was true for patients covered by private health insurance; they gave more favorable evaluations than did patients covered by statutory health insurance (P<.001). No significant rating differences could be detected between female and male patients (P=.505). The likelihood of a good rating was shown to increase with a rising number of both physician and patient ratings. Our findings are mostly in line with those published for PRWs from the United States. It could be shown that most of the ratings were positive, and differences existed regarding sociodemographic characteristics of both physicians and patients. An increase in the usage of PRWs might contribute to reducing the lack of publicly available information on physician quality. However, it remains unclear whether PRWs have the potential to reflect the quality of care offered by individual health care providers. Further research should assess in more detail the motivation of patients who rate their physicians online.

Single‐cell technologies are revolutionizing biology but are today mainly limited to imaging and deep sequencing. However, proteins are the main drivers of cellular function and in‐depth characterization of individual cells by mass spectrometry (MS)‐based proteomics would thus be highly valuable and complementary. Here, we develop a robust workflow combining miniaturized sample preparation, very low flow‐rate chromatography, and a novel trapped ion mobility mass spectrometer, resulting in a more than 10‐fold improved sensitivity. We precisely and robustly quantify proteomes and their changes in single, FACS‐isolated cells. Arresting cells at defined stages of the cell cycle by drug treatment retrieves expected key regulators. Furthermore, it highlights potential novel ones and allows cell phase prediction. Comparing the variability in more than 430 single‐cell proteomes to transcriptome data revealed a stable‐core proteome despite perturbation, while the transcriptome appears stochastic. Our technology can readily be applied to ultra‐high sensitivity analyses of tissue material, posttranslational modifications, and small molecule studies from small cell counts to gain unprecedented insights into cellular heterogeneity in health and disease. Synopsis A new ultra‐high sensitivity LC‐MS workflow increases sensitivity by up to two orders of magnitude and enables true single‐cell proteome analysis. In‐depth comparison indicates that the single‐cell transcriptome is stochastic while the single‐cell proteome is complete and stable. A highly optimized data independent acquisition powered single‐cell proteomics workflow including sub‐µl sample preparation, very low flow chromatography and trapped ion mobility mass spectrometry (diaPASEF) is presented. Single‐cell proteome analysis is performed by injecting cells one‐by‐one across the cell cycle into the LC‐MS and correctly identifies cell states. Single‐cell proteome information is highly complementary to single‐cell transcriptome information. At the single‐cell level the proteome is quantitatively and qualitatively stable, while the transcriptome is stochastic. Graphical Abstract A new ultra‐high sensitivity LC‐MS workflow increases sensitivity by up to two orders of magnitude and enables true single‐cell proteome analysis. In‐depth comparison indicates that the single‐cell transcriptome is stochastic while the single‐cell proteome is complete and stable.

A comprehensive characterization of the lipidome from limited starting material remains very challenging. Here we report a high-sensitivity lipidomics workflow based on nanoflow liquid chromatography and trapped ion mobility spectrometry (TIMS). Taking advantage of parallel accumulation–serial fragmentation (PASEF), we fragment on average 15 precursors in each of 100 ms TIMS scans, while maintaining the full mobility resolution of co-eluting isomers. The acquisition speed of over 100 Hz allows us to obtain MS/MS spectra of the vast majority of isotope patterns. Analyzing 1 µL of human plasma, PASEF increases the number of identified lipids more than three times over standard TIMS-MS/MS, achieving attomole sensitivity. Building on high intra- and inter-laboratory precision and accuracy of TIMS collisional cross sections (CCS), we compile 1856 lipid CCS values from plasma, liver and cancer cells. Our study establishes PASEF in lipid analysis and paves the way for sensitive, ion mobility-enhanced lipidomics in four dimensions. Trapped ion mobility (TIMS)-mass spectrometry with parallel accumulation-serial fragmentation (PASEF) facilitates high-sensitivity proteomics experiments. Here, the authors expand TIMS and PASEF to small molecules and demonstrate fast and comprehensive lipidomics of low biological sample amounts.

Cellular functions are mediated by protein–protein interactions, and mapping the interactome provides fundamental insights into biological systems. Affinity purification coupled to mass spectrometry is an ideal tool for such mapping, but it has been difficult to identify low copy number complexes, membrane complexes and complexes that are disrupted by protein tagging. As a result, our current knowledge of the interactome is far from complete, and assessing the reliability of reported interactions is challenging. Here we develop a sensitive high-throughput method using highly reproducible affinity enrichment coupled to mass spectrometry combined with a quantitative two-dimensional analysis strategy to comprehensively map the interactome of Saccharomyces cerevisiae . Thousand-fold reduced volumes in 96-well format enabled replicate analysis of the endogenous GFP-tagged library covering the entire expressed yeast proteome 1 . The 4,159 pull-downs generated a highly structured network of 3,927 proteins connected by 31,004 interactions, doubling the number of proteins and tripling the number of reliable interactions compared with existing interactome maps 2 . This includes very-low-abundance epigenetic complexes, organellar membrane complexes and non-taggable complexes inferred by abundance correlation. This nearly saturated interactome reveals that the vast majority of yeast proteins are highly connected, with an average of 16 interactors. Similar to social networks between humans, the average shortest distance between proteins is 4.2 interactions. AlphaFold-Multimer provided novel insights into the functional roles of previously uncharacterized proteins in complexes. Our web portal ( www.yeast-interactome.org ) enables extensive exploration of the interactome dataset. A protein interaction network constructed with data from high-throughput affinity enrichment coupled to mass spectrometry provides a highly saturated yeast interactome with 31,004 interactions, including low-abundance complexes, membrane protein complexes and non-taggable protein complexes.

More than 40 years ago, the expansion joints on the Basel border bridge were constructed using corbels and dapped ends. The consoles had to be reinforced as part of the renovation measures due to damage caused by chloride entry and due to the increased loads. Diagonal rods, which were prestressed, were used. Fiber-optic sensors were additionally installed to these highly stressed rods in order to measure the strains and temperatures. This now makes it possible to measure the actual strains in the strengthening of the corbel, estimate fatigue loads, and set up a warning system in case of overstressing. This article presents the design of the measurement system and the analysis of the data. Furthermore, the reference measurements that can establish the relationship between the measured strains and the loads passed over are presented.

The size and shape of peptide ions in the gas phase are an under-explored dimension for mass spectrometry-based proteomics. To investigate the nature and utility of the peptide collisional cross section (CCS) space, we measure more than a million data points from whole-proteome digests of five organisms with trapped ion mobility spectrometry (TIMS) and parallel accumulation-serial fragmentation (PASEF). The scale and precision (CV < 1%) of our data is sufficient to train a deep recurrent neural network that accurately predicts CCS values solely based on the peptide sequence. Cross section predictions for the synthetic ProteomeTools peptides validate the model within a 1.4% median relative error ( R  > 0.99). Hydrophobicity, proportion of prolines and position of histidines are main determinants of the cross sections in addition to sequence-specific interactions. CCS values can now be predicted for any peptide and organism, forming a basis for advanced proteomics workflows that make full use of the additional information. Proteomics has been advanced by algorithms that can predict different peptide features, but predicting peptide collisional cross sections (CCS) has remained challenging. Here, the authors measure over one million CCS values of tryptic peptides and develop a deep learning model for peptide CCS prediction.

The heart is a central human organ and its diseases are the leading cause of death worldwide, but an in-depth knowledge of the identity and quantity of its constituent proteins is still lacking. Here, we determine the healthy human heart proteome by measuring 16 anatomical regions and three major cardiac cell types by high-resolution mass spectrometry-based proteomics. From low microgram sample amounts, we quantify over 10,700 proteins in this high dynamic range tissue. We combine copy numbers per cell with protein organellar assignments to build a model of the heart proteome at the subcellular level. Analysis of cardiac fibroblasts identifies cellular receptors as potential cell surface markers. Application of our heart map to atrial fibrillation reveals individually distinct mitochondrial dysfunctions. The heart map is available at maxqb.biochem.mpg.de as a resource for future analyses of normal heart function and disease. The human heart is composed of distinct regions and cell types, but relatively little is known about their specific protein composition. Here, the authors present a region- and cell type-specific proteomic map of the healthy human heart, revealing functional differences and potential cell type markers.

In common with other omics technologies, mass spectrometry (MS)-based proteomics produces ever-increasing amounts of raw data, making efficient analysis a principal challenge. A plethora of different computational tools can process the MS data to derive peptide and protein identification and quantification. However, during the last years there has been dramatic progress in computer science, including collaboration tools that have transformed research and industry. To leverage these advances, we develop AlphaPept, a Python-based open-source framework for efficient processing of large high-resolution MS data sets. Numba for just-in-time compilation on CPU and GPU achieves hundred-fold speed improvements. AlphaPept uses the Python scientific stack of highly optimized packages, reducing the code base to domain-specific tasks while accessing the latest advances. We provide an easy on-ramp for community contributions through the concept of literate programming, implemented in Jupyter Notebooks. Large datasets can rapidly be processed as shown by the analysis of hundreds of proteomes in minutes per file, many-fold faster than acquisition. AlphaPept can be used to build automated processing pipelines with web-serving functionality and compatibility with downstream analysis tools. It provides easy access via one-click installation, a modular Python library for advanced users, and via an open GitHub repository for developers. Mass spectrometry-based proteomics faces the challenge of processing vast data amounts. Here, the authors introduce AlphaPept, an open-source, Python-based framework that offers high speed analysis and easy integration for large-scale proteome analysis.