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Desmin gene mutations cause myopathies and cardiomyopathies. Our previously characterised R349P desminopathy mice, which carry the ortholog of the common human desmin mutation R350P, showed marked alterations in mitochondrial morphology and function in muscle tissue. By isolating skeletal muscle myoblasts from offspring of R349P desminopathy and p53 knock-out mice, we established an immortalised cellular disease model. Heterozygous and homozygous R349P desmin knock-in and wild-type myoblasts could be well differentiated into multinucleated spontaneously contracting myotubes. The desminopathy myoblasts showed the characteristic disruption of the desmin cytoskeleton and desmin protein aggregation, and the desminopathy myotubes showed the characteristic myofibrillar irregularities. Long-term electrical pulse stimulation promoted myotube differentiation and markedly increased their spontaneous contraction rate. In both heterozygous and homozygous R349P desminopathy myotubes, this treatment restored a regular myofibrillar cross-striation pattern as seen in wild-type myotubes. High-resolution respirometry of mitochondria purified from myotubes by density gradient ultracentrifugation revealed normal oxidative phosphorylation capacity, but a significantly reduced proton leak in mitochondria from the homozygous R349P desmin knock-in cells. Consistent with a reduced proton flux across the inner mitochondrial membrane, our quantitative proteomic analysis of the purified mitochondria revealed significantly reduced levels of ADP/ATP translocases in the homozygous R349P desmin knock-in genotype. As this alteration was also detected in the soleus muscle of R349P desminopathy mice, which, in contrast to the mitochondria purified from cultured cells, showed a variety of other dysregulated mitochondrial proteins, we consider this finding to be an early step in the pathogenesis of secondary mitochondriopathy in desminopathy.

In this article, we present a data dependent acquisition (DDA) dataset which was generated as a reference and ground truth quantitative dataset. While initially used to compare samples measured with DDA and data independent acquisition (DIA) (Barkovits et al., 2020), the presented dataset holds potential value as a benchmark reference for any workflows working on DDA data. The entire dataset consists of 15 LC-MS/MS measurements composed of five distinct spike-in-states, each with three replicates. To generate the data set, a C2C12 (immortalized mouse myoblast) cell lysate was used as a complex background for five different states which were simulated by spiking 13 defined proteins at different concentrations. For this purpose, the cell lysate was used in a constant amount of 20 µg for all samples and different amounts of the 13 selected proteins ranging from 0.1 to 10 pmol were added, reflecting physiological amounts of proteins. Afterwards, all samples were tryptically digested using the same method. From each sample 200 ng tryptic peptides were measured in triplicates on a Q Exactive HF (Thermo Fisher Scientific). The mass range for MS1 was set to 350–1400 m/z with a resolution of 60,000 at 200 m/z. HCD fragmentation of the Top10 abundant precursor ions was performed at 27% NCE. The fragment analysis (MS2) was performed with a resolution of 30,000 at 200 m/z.Additionally to the raw files, the dataset contains centroided mzML files and spectrum identification results for peptide identifications performed by Mascot (Perkins et al., 1999), MS-GF+ (Kim et al., 2010) and X!Tandem (Craig and Beavis, 2004) for each separate MS analysis. The corresponding FASTA containing protein sequences as well as a combination of all identification runs performed by PIA (Uszkoreit et al., 2019, 2015) and a peptide and protein quantification performed by OpenMS (Pfeuffer et al., 2017) is included. All data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository (Perez-Riverol et al., 2018) with the dataset identifier PXD012986.

This article describes a mass spectrometric data set from rat retinae spiked with indexed Retention Time (iRT) peptides. The provided data set can be used as a spectral library to investigate for instance eye disorders as well as ocular function by data-independent acquisition (DIA) based mass spectrometry. Consequently, there is no urgent need to create an own spectral library, which requires money, time, effort as well as tissue. Besides the use as a spectral library, this data set can improve our knowledge about proteins present in the rat retina and thus the protein pathways within this tissue. The data set may also help to determine optimal parameters for peptide identification by mass spectrometry. To generate the presented data set, six rat retinae were homogenized with glass beads and pooled. The pooled sample was fractionated by SDS-PAGE (sodium dodecyl sulfate polyacrylamide gel electrophoresis) followed by tryptic in-gel digestion. The fractionation of the pooled sample was repeated for further 4 times, to end up with in total 5 technical replicates. Peptide extracts were spiked with iRT peptides and analyzed by data-dependent (DDA) nanoHPLC-ESI-MS/MS resulting in 60 files. All resulting data files are hosted in the public repository ProteomeXchange under the identifier PXD021937.

Spectral libraries generated by data dependent acquisition (DDA) are a useful tool for the analysis of data created by data independent acquisition (DIA) in mass spectrometry. The quality of DIA analysis is dependent on the quality of the spectral library. We used cerebrospinal fluid (CSF) of patients with Parkinson's disease and healthy controls to create a spectral library of human CSF proteome. To this date, there is no validated CSF biomarker for Parkinson's disease. This data set may therefore be valuable for the future analysis of CSF proteins. Part of the samples consisted of fractions that were separated by gel electrophoresis. After tryptic digestion, all samples were spiked with indexed retention time (iRT) peptides and were measured using a DDA mass spectrometry approach. The here provided data set can be used as a CSF-specific spectral library. Data files generated from the described workflow are hosted in the public repository ProteomeXchange under the identifier PXD013487.

This article describes a mass spectrometry data set generated from osteogenic differentiated bone marrow stromal cells (BMSCs) and adipose tissue derived stromal cells (ASCs) of a 24-year old healthy donor. Before osteogenic differentiation and performing mass spectrometric measurements cells have been characterized as mesenchymal stromal cells via FACS-analysis positive for CD90 and CD105 and negative for CD14, CD34, CD45 and CD11b and tri-lineage differentiation. After osteogenic differentiation, both cell types were homogenized and then fractionated by SDS gel electrophoresis, resulting in 12 fractions. The proteins underwent an in-gel digestion, spiked with iRT peptides and analysed by nanoHPLC-ESI-MS/MS, resulting in 24 data files. The data files generated from the described workflow are hosted in the public repository ProteomeXchange with identifier PXD015026. The presented data set can be used as a spectral library for analysis of key proteins in the context of osteogenic differentiation of mesenchymal stromal cells for regenerative applications. Moreover, these data can be used to perform comparative proteomic analysis of different mesenchymal stromal cells or stem cells upon osteogenic differentiation. In addition, these data can also be used to determine the optimal settings for measuring proteins and peptides of interest. Keywords: Proteome, Spectral peptide library, Mesenchymal stromal cells, Adipose derived stromal cells, Bone marrow derived stromal cells, Osteogenic differentiation, Osteogenesis

In a common proteomics analysis today, the origins of our sample in the vial are known and therefore a database dependent approach to identify the containing peptides can be used. The first YPIC challenge though provided us with 19 synthetic peptides, which together formed an English sentence. For the identification of these peptides, a de-novo approach was used, which brought us together with an internet search engine to the hidden sentence. But only having the sentence was not sufficient for us, we also wanted to identify as many as possible of the spectra in our data. Therefore, we created and refined a database approach from the de-novo method and finally could identify the peptide-sentence with a good overlap.

Metabolome analyses of body fluids are challenging due pre-analytical variations, such as pre-processing delay and temperature, and constant dynamical changes of biochemical processes within the samples. Therefore, proper sample handling starting from the time of collection up to the analysis is crucial to obtain high quality samples and reproducible results. A metabolomics analysis is divided into 4 main steps: 1) Sample collection, 2) Metabolite extraction, 3) Data acquisition and 4) Data analysis. Here, we describe a protocol for gas chromatography coupled to mass spectrometry (GC–MS) based metabolic analysis for biological matrices, especially body fluids. This protocol can be applied on blood serum/plasma, saliva and cerebrospinal fluid (CSF) samples of humans and other vertebrates. It covers sample collection, sample pre-processing, metabolite extraction, GC–MS measurement and guidelines for the subsequent data analysis. Advantages of this protocol include: • Robust and reproducible metabolomics results, taking into account pre-analytical variations that may occur during the sampling process • Small sample volume required • Rapid and cost-effective processing of biological samples • Logistic regression based determination of biomarker signatures for in-depth data analysis

This article provides a detailed dataset of human tear fluid proteins. Samples were fractionated by sodium dodecyl sulfate (SDS) gel electrophoresis resulting in 48 fractions that were spiked with an indexed retention time (iRT) peptide standard. These data are based on a data-dependent acquisition (DDA) mass spectrometric approach and can be used for example as a spectral library for tear fluid proteome analysis by data-independent acquisition (DIA). Moreover, the provided data set can be used with optimized HPLC and mass spectrometric settings for proteins/peptides of interest. Besides these aspects, this dataset can serve as a protein overview for gene ontology enrichment analysis and for modeling and benchmarking of multiple signaling pathways associated with the ocular surface in healthy or disease stages. The mass spectrometry proteomics data from the described workflow have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD011075.

This article describes a mass spectrometric data set generated from human substantia nigra tissue that was spiked with iRT peptides. The data set can be used as a spectral library for analysis of the human brain; especially for analysis of human substantia nigra, for example, in the context of Parkinson’s disease. Obtaining a sufficient amount of high-quality substantia nigra tissue is the key limiting factor for establishing a brain region-specific spectral library. Hence, combining existing spectral libraries for data-independent acquisition analysis (DIA) can overcome this major limitation. Moreover, these data can be used to map brain region-specific proteins and to model brain region-specific pathways. Both can improve our understanding of the functioning of the brain in greater depth. In addition, these data can also be used to determine the optimal settings for measuring proteins and peptides of interest. To create the substantia nigra-specific spectral library, the tissue was first homogenized and then fractionated via different types of SDS gel electrophoresis, resulting in 18 fractions. These fractions were analysed in triplicate by nanoHPLC-ESI-MS/MS, resulting in 54 data files. The data files generated from the described workflow are hosted in the public repository ProteomeXchange with the identifier PXD011076.

Phagocyte-derived production of a complex mixture of different oxidants is a major mechanism of the host defense against microbial intruders. On the protein level, a major target of these oxidants is the thiol group of the amino acid cysteine in proteins. Oxidation of thiol groups is a widespread regulatory post-translational protein modification. It is used by bacteria to respond to and to overcome oxidative stress. Numerous redox proteomic studies have shown that protein thiols in bacteria, such as Escherichia coli react towards a number of oxidants in specific ways. However, our knowledge about protein thiols in bacteria exposed to the complex mixture of oxidants encountered in the phagolysosome is still limited. In this study, we used a quantitative redox proteomic method (OxICAT) to assess the in vivo thiol oxidation status of phagocytized E. coli. The majority (65.5%) of identified proteins harbored thiols that were significantly oxidized (> 30%) upon phagocytosis. A substantial number of these proteins are from major metabolic pathways or are involved in cell detoxification and stress response, suggesting a systemic breakdown of the bacterial cysteine proteome in phagocytized bacteria. 16 of the oxidized proteins provide E. coli with a significant growth advantage in the presence of H2O2, when compared to deletion mutants lacking these proteins, and 11 were shown to be essential under these conditions.

Relative and absolute quantification of proteins in biological and clinical samples are common approaches in proteomics. Until now, targeted protein quantification is mainly performed using a combination of HPLC-based peptide separation and selected reaction monitoring on triple quadrupole mass spectrometers. Here, we show for the first time the potential of absolute quantification using a direct infusion strategy combined with single ion monitoring (SIM) on a Q Exactive mass spectrometer. By using complex membrane fractions of Escherichia coli, we absolutely quantified the recombinant expressed heterologous human cytochrome P450 monooxygenase 3A4 (CYP3A4) comparing direct infusion-SIM with conventional HPLC-SIM. Direct-infusion SIM revealed only 14.7% (±4.1 (s.e.m.)) deviation on average, compared to HPLC-SIM and a decreased processing and analysis time of 4.5 min (that could be further decreased to 30 s) for a single sample in contrast to 65 min by the LC–MS method. Summarized, our simplified workflow using direct infusion-SIM provides a fast and robust method for quantification of proteins in complex protein mixtures.