Mass-spectrometry data, MaxQuant ProteinGroups output.

This bundle contains microfilm scans of formatted outputs of all data acquired by the Apollo 15 Orbital Mass Spectrometer from lunar orbit during 30 July to 07 August 1971, along with relevant documentation.

Comprehensive, reproducible and precise analysis of large sample cohorts is one of the key objectives of quantitative proteomics. Here, we present an implementation of data-independent acquisition using its parallel acquisition nature that surpasses the limitation of serial MS2 acquisition of data-dependent acquisition on a quadrupole ultra-high field Orbitrap mass spectrometer. In deep single shot data-independent acquisition, we identified and quantified 6,383 proteins in human cell lines using 2-or-more peptides/protein and over 7,100 proteins when including the 717 proteins that were identified on the basis of a single peptide sequence. 7,739 proteins were identified in mouse tissues using 2-or-more peptides/protein and 8,121 when including the 382 proteins that were identified on the basis of a single peptide sequence. Missing values for proteins were within 0.3 to 2.1% and median coefficients of variation of 4.7 to 6.2% among technical triplicates. In very complex mixtures, we could quantify 10,780 proteins and 12,192 proteins when including the 1,412 proteins that were identified on the basis of a single peptide sequence. Using this optimized DIA, we investigated large-protein networks before and after the critical period for whisker experience-induced synaptic strength in the murine somatosensory cortex 1 barrel field. This work shows that parallel mass spectrometry enables proteome profiling for discovery with high coverage, reproducibility, precision and scalability.

Effective analysis of protein samples by mass spectrometry (MS) requires careful selection and optimization of a range of experimental parameters. As the output from the primary detection device, the “raw” MS data file can be used to gauge the success of a given sample analysis. However, the closed-source nature of the standard raw MS file can complicate effective parsing of the data contained within. To ease and increase the range of analyses possible, the RawQuant tool was developed to enable parsing of raw MS files derived from Thermo Orbitrap instruments to yield meta and scan data in an openly readable text format. RawQuant can be commanded to export user-friendly files containing MS1, MS2, and MS3 metadata as well as matrices of quantification values based on isobaric tagging approaches. In this study, the utility of RawQuant is demonstrated in several scenarios: (1) reanalysis of shotgun proteomics data for the identification of the human proteome, (2) reanalysis of experiments utilizing isobaric tagging for whole-proteome quantification, and (3) analysis of a novel bacterial proteome and synthetic peptide mixture for assessing quantification accuracy when using isobaric tags. Together, these analyses successfully demonstrate RawQuant for the efficient parsing and quantification of data from raw Thermo Orbitrap MS files acquired in a range of common proteomics experiments. In addition, the individual analyses using RawQuant highlights parametric considerations in the different experimental sets and suggests targetable areas to improve depth of coverage in identification-focused studies and quantification accuracy when using isobaric tags.

Nuclear export of mRNAs requires loading the mRNP to the transporter Mex67/Mtr2 in the nucleoplasm, controlled access to the pore by the basket-localised TREX-2 complex and mRNA release at the cytoplasmic site by the DEAD-box RNA helicase Dbp5. Asymmetric localisation of nucleoporins (NUPs) and transport components as well as the ATP dependency of Dbp5 ensure unidirectionality of transport. Trypanosomes possess homologues of the mRNA transporter Mex67/Mtr2, but not of TREX-2 or Dbp5. Instead, nuclear export is likely fuelled by the GTP/GDP gradient created by the Ran GTPase. However, it remains unclear, how directionality is achieved since the current model of the trypanosomatid pore is mostly symmetric. We have revisited the architecture of the trypanosome nuclear pore complex using a novel combination of expansion microscopy, proximity labelling and streptavidin imaging. We could confidently assign the NUP76 complex, a known Mex67 interaction platform, to the cytoplasmic site of the pore and the NUP64/NUP98/NUP75 complex to the nuclear site. Having defined markers for both sites of the pore, we set out to map all 75 trypanosome proteins with known nuclear pore localisation to a subregion of the pore using mass spectrometry data from proximity labelling. This approach defined several further proteins with a specific localisation to the nuclear site of the pore, including proteins with predicted structural homology to TREX-2 components. We mapped the components of the Ran-based mRNA export system to the nuclear site (RanBPL), the cytoplasmic site (RanGAP, RanBP1) or both (Ran, MEX67). Lastly, we demonstrate, by deploying an auxin degron system, that NUP76 holds an essential role in mRNA export consistent with a possible functional orthology to NUP82/88. Altogether, the combination of proximity labelling with expansion microscopy revealed an asymmetric architecture of the trypanosome nuclear pore supporting inherent roles for directed transport. Our approach delivered novel nuclear pore associated components inclusive positional information, which can now be interrogated for functional roles to explore trypanosome-specific adaptions of the nuclear basket, export control, and mRNP remodelling.

Rib bone (< 10 g) were collected post-mortem from five male, and five female deceased subjects. Soft tissue was removed from each rib using a scalpel and a 20 mg block of cortical bone was removed. The sample processing workflow on samples included protein extraction and peptide digestion using a modified acidic demineralization protocol. Data acquisition was performed using Thermo Scientific Q Exactive Plus Orbitrap mass spectrometer coupled to an Easy-nLC 1000 liquid chromatograph (Thermo Scientific; Waltham, MA; USA). Mass spectral data were obtained using a “top-10” data-dependent collection strategy in which an initial MS scan over the range of m/z 380-1800 and a resolution of 70,000 was used to select 10 precursor ions for subsequent MSMS scans. RAW data was exported and analyzed in parallel using two protein identification software programs: PEAKS 7.5 (Bioinformatics Solutions Inc., Waterloo, Ontario, Canada).RAW datafiles were directly imported into software. Oxidation of methionine, carbamidomethylation of cysteine, deamidation of asparagine and glutamine, and hydroxylation of proline were included in the search settings as partial post-translational modifications. Precursor mass error of 15 ppm using monoisotopic mass was used for parent ion identifications and 0.05 Da for fragment ions masses. Protein identifications (IDs) were filtered by a 1% FDR.

This bundle contains microfilm scans of formatted outputs of all data acquired by the Apollo 16 Orbital Mass Spectrometer from lunar orbit during 20-24 April 1972, along with relevant documentation.

The large-scale analysis of thousands of proteins under various experimental conditions or in mutant lines has gained more and more importance in hypothesis-driven scientific research and systems biology in the past years. Quantitative analysis by large scale proteomics using modern mass spectrometry usually results in long lists of peptide ion intensities. The main interest for most researchers, however, is to draw conclusions on the protein level. Postprocessing and combining peptide intensities of a proteomic data set requires expert knowledge, and the often repetitive and standardized manual calculations can be time-consuming. The analysis of complex samples can result in very large data sets (lists with several 1000s to 100 000 entries of different peptides) that cannot easily be analyzed using standard spreadsheet programs. To improve speed and consistency of the data analysis of LC–MS derived proteomic data, we developed cRacker. cRacker is an R-based program for automated downstream proteomic data analysis including data normalization strategies for metabolic labeling and label free quantitation. In addition, cRacker includes basic statistical analysis, such as clustering of data, or ANOVA and t tests for comparison between treatments. Results are presented in editable graphic formats and in list files.

This upload contains processed chromatogram matrices for 206 CF-MS experiments. Database search and protein quantitation was performed with MaxQuant (version 1.6.5.0), and potential contaminants, reverse hits, and proteins identified only by modified peptides were filtered out.

Multiple chromatograms are provided for each experiment, corresponding to different protein quantitation strategies. These include iBAQ, MaxLFQ, MS1 intensity, and spectral counts, for label-free datasets, and the isotopologue ratio for SILAC or dimethyl labelling datasets. Each directory also contains a metadata file, which contains further information about each row in the corresponding chromatogram matrices output by MaxQuant.

In addition to processed chromatograms, which are provided in the "Chromatograms" directory, the upload also contains the raw output files (proteinGroups.txt) from each MaxQuant search. These files are provided within the "Protein groups" directory.

Complete MaxQuant outputs for each experiment are available from the PRIDE repository under the accession PXD022048.

The purpose of this study was to generate a basis for the decision of what protein quantities are reliable and find a way for accurate and precise protein quantification. To investigate this we have used thousands of peptide measurements to estimate variance and bias for quantification by iTRAQ (isobaric tags for relative and absolute quantification) mass spectrometry in complex human samples. A549 cell lysate was mixed in the proportions 2:2:1:1:2:2:1:1, fractionated by high resolution isoelectric focusing and liquid chromatography and analyzed by three mass spectrometry platforms; LTQ Orbitrap Velos, 4800 MALDI-TOF/TOF and 6530 Q-TOF. We have investigated how variance and bias in the iTRAQ reporter ions data are affected by common experimental variables such as sample amount, sample fractionation, fragmentation energy, and instrument platform. Based on this, we have suggested a concept for experimental design and a methodology for protein quantification. By using duplicate samples in each run, each experiment is validated based on its internal experimental variation. The duplicates are used for calculating peptide weights, unique to the experiment, which is used in the protein quantification. By weighting the peptides depending on reporter ion intensity, we can decrease the relative error in quantification at the protein level and assign a total weight to each protein that reflects the protein quantitation confidence. We also demonstrate the usability of this methodology in a cancer cell line experiment as well as in a clinical data set of lung cancer tissue samples. In conclusion, we have in this study developed a methodology for improved protein quantification in shotgun proteomics and introduced a way to assess quantification for proteins with few peptides. The experimental design and developed algorithms decreased the relative protein quantification error in the analysis of complex biological samples. Data analysis: LTQ Orbitrap Velos Proteome discoverer 1.1 with Mascot 2.2 (Matrix Science) was used for protein identification. Precursor mass tolerance was set to 10 ppm and for fragments 0.8 Da and 0.015 Da were used for detection in the linear iontrap and the orbitrap, respectively. Oxidized methionine was set as dynamic modification and carbamidomethylation, N-terminal 8plex iTRAQ, and lysyl 8plex iTRAQ as fixed modifications. 4800 MALDI TOF/TOF Peptide identification from the Maldi-TOF/TOF data was carried out using the Paragon algorithm in the ProteinPilot 2.0 software package (Applied Biosystems). Default settings for a 4800 instrument were used (i.e. no manual settings for mass tolerance was given). The following parameters were selected in the analysis method: iTRAQ 8plex peptide labeled as sample type, IAA as alkylating agent of cysteine, trypsin as digesting enzyme, 4800 as instrument, gel based ID and Urea denaturation as special factors, biological modifications as ID focus, and thorough ID as search effort. 6530 QTOF Peptide identification from the QTOF data was carried out using the Spectrum Mill Protein Identification software (Agilent). Data was extracted between MH+ 600 and 4000 Da (Agilent’s definition). Trypsin was used as digesting enzyme, and parent and daughter ion tolerance was set to 25 and 50 ppm, respectively. IAA for cysteine and iTRAQ partial-mix (N-term, K) were set as fixed modifications while oxidized methionine was set as variable modification. Database and peptide cut-off for all searches Searches were performed against the IPI database (build 3.64) limited to human sequences allowing 2 missed cleavages. False discovery rate (FDR) was estimated by searching the data against a database consisting of both forward and reversed sequences and set to < 1 % at the protein level using MAYU. Peptides corresponding to a <1% protein FDR rate was used in the calculations. Peptide and protein identification using Mascot for comparison between instruments Peptide identifications were performed using Mascot Daemon 2.3.2 with Mascot 2.4 for fractions 32 to 36 from IPG-IEF with 400 ug loaded peptides. Carbamidomethylation (CAM) for cysteine was set as fixed modification, oxidized methionine as variable modification and iTRAQ 8plex was set as quantification for all searches. MALDI-TOF/TOF search settings: Parent and daughter ion tolerance was set to 150 ppm and 0.2 Da, respectively. LTQ Orbitrap search settings: Precursor mass tolerance was set to 10 ppm and for fragments 0.8 Da and 0.015 Da were used for data generated in the linear ion trap and the orbitrap, respectively. QTOF search settings: Parent and daughter ion tolerance was set to 25 and 50 ppm, respectively.

Top-down mass spectrometry has become an important technique for the identification of proteins and characterisation of chemical and posttranslational modifications. However, as the molecular mass of proteins increases intact mass determination and top-down fragmentation efficiency become more challenging due to the partitioning of the mass spectral signal into many isotopic peaks. In large proteins, this results in reduced sensitivity and increased spectral complexity and signal overlap. This phenomenon is a consequence of the natural isotopic heterogeneity of the elements which comprise proteins (notably 13C). Here we present a bacterial recombinant expression system for the production of proteins depleted in 13C and 15N and use this strategy to prepare a range of isotopically depleted proteins. High resolution MS of isotope depleted proteins reveal dramatically reduced isotope distributions, which results in increases in sensitivity and deceased spectral complexity. We demonstrate that the monoisotopic signal is observed in mass spectra of proteins up to ~50 kDa. This allows confidant assignment of accurate molecular mass, and facile detection of low mass modifications (such as deamidation). We outline the benefits of this isotope depletion strategy for top-down fragmentation. The reduced spectral complexity alleviates problems of signal overlap; the presence of monoisotopic signals allow more accurate assignment of fragment ions; and the dramatic increase in single-to-noise ratio (up to 7-fold increases) permits vastly reduced data acquisition times. Together, these compounding benefits allow the assignment of ca. 3-fold more fragment ions than analysis of proteins with natural isotopic abundances. Thus, more comprehensive sequence coverage can be achieved; we demonstrate near single amino-acid resolution of the 29 kDa protein carbonic anhydrase from a single top-down MS experiment. Finally, we demonstrate that the ID-MS strategy allows far greater sequence coverage to be obtained in time limited top-down data acquisitions - highlighting potential advantages for top-down LC-MS/MS workflows and top-down proteomics.

Explore Mass Spectrometry import export trade data. Find top buyers, suppliers, HS codes, ports, & market trends to make smarter, data-driven trade decisions.

Logistic regression classification models were fit to manually classified quality control (QC) LC-MS/MS datasets to develop a model that can predict whether a dataset is in or out of control. Model parameters were optimized by minimizing a loss function that accounts for the tradeoff between false positive and false negative errors. In addition to the 1152 training/testing datasets, we are including 2662 additional datasets, all of the same QC sample (whole cell lysate of Shewanella oneidensis). Datasets originate from 6 Thermo instrument platforms: Exactive, LTQ, VelosPro, Orbitrap, Q-Exactive, and Velos Orbitrap.

A total of 206 co-fractionation mass spectrometry (CF-MS) experiments were downloaded from public proteomics repositories such as PRIDE and MASSIVE, then re-analyzed with MaxQuant.

This upload contains processed chromatogram matrices for all 206 experiments. Database search and protein quantitation was performed with MaxQuant (version 1.6.5.0), and potential contaminants, reverse hits, and proteins identiifed only by modified peptides were filtered out.

Multiple chromatograms are provided for each experiment, corresponding to different protein quantitation strategies. These include iBAQ, MaxLFQ, MS1 intensity, and spectral counts, for label-free datasets, and the isotopologue ratio for SILAC or dimethyl labelling datasets. Each directory also contains a metadata file, which contains further information about each row in the corresponding chromatogram matrices output by MaxQuant.

Complete MaxQuant outputs for each experiment are available from the PRIDE repository under the accession PXD022048.

This data package is associated with the publication “Investigating the impacts of solid phase extraction on dissolved organic matter optical signatures and the pairing with high-resolution mass spectrometry data in a freshwater system” submitted to “Limnology and Oceanography: Methods.” This data is an extension of the River Corridor and Watershed Biogeochemistry SFA’s Spatial Study 2021 (https://doi.org/10.15485/1898914). Other associated data and field metadata can be found at the link provided. The goal of this manuscript is to assess the impact of solid phase extraction (SPE) on the ability to pair ultra-high resolution mass spectrometry data collected from SPE extracts with optical properties collected on ambient stream samples. Forty-seven samples collected from within the Yakima River Basin, Washington were analyzed dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC), absorbance, and fluorescence. Samples were subsequently concentrated with SPE and reanalyzed for each measurement. The extraction efficiency for the DOC and common optical indices were calculated. In addition, SPE samples were subject to ultra-high resolution mass spectrometry and compared with the ambient and SPE generated optical data. Finally, in addition to this cross-platform inter-comparison, we further performed and intra-comparison among the high-resolution mass spectrometry data to determine the impact of sample preparation on the interpretability of results. Here, the SPE samples were prepared at 40 milligrams per liter (mg/L) based on the known DOC extraction efficiency of the samples (ranging from ~30 to ~75%) compared to the common practice of assuming the DOC extraction efficiency of freshwater samples at 60%. This data package folder consists of one main data folder with one subfolder (Data_Input). The main data folder contains (1) readme; (2) data dictionary (dd); (3) file-level metadata (flmd); (4) final data summary output from processing script; and (5) the processing script. The R-markdown processing script (SPE_Manuscript_Rmarkdown_Data_Package.rmd) contains all code needed to reproduce manuscript statistics and figures (with the exception of that stated below). The Data_Input folder has two subfolders: (1) FTICR and (2) Optics. Additionally, the Data_Input folder contains dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) data (SPS_NPOC_Summary.csv) and relevant supporting Solid Phase Extraction Volume information (SPS_SPE_Volumes.csv). Methods information for the optical and FTICR data is embedded in the header rows of SPS_EEMs_Methods.csv and SPS_FTICR_Methods.csv, respectively. In addition, the data dictionary (SPS_SPE_dd.csv), file level metadata (SPS_SPE_flmd.csv), and methods codes (SPS_SPE_Methods_codes.csv) are provided. The FTICR subfolder contains all raw FTICR data as well as instructions for processing. In addition, post processed FTICR molecular information (Processed_FTICRMS_Mol.csv) and sample data (Processed_FTICRMS_Data.csv) is provided that can be directly read into R with the associated R-markdown file. The Optics subfolder contains all Absorbance and Fluorescence Spectra. Fluorescence spectra have been blank corrected, inner filter corrected, and undergone scatter removal. In addition, this folder contains Matlab code used to make a portion of Figure 1 within the manuscript, derive various spectral parameters used within the manuscript, and used for parallel factor analysis (PARAFAC) modeling. Spectral indices (SPS_SpectralIndices.csv) and PARAFAC outputs (SPS_PARAFAC_Model_Loadings.csv and SPS_PARAFAC_Sample_Scores.csv) are directly read into the associated R-markdown file.

Next Generation Mass Spectrometer Sales Market Outlook

The Next Generation Mass Spectrometer Sales Market is poised to experience a significant expansion, with the global market size anticipated to grow from approximately $2.5 billion in 2023 to an estimated $4.9 billion by 2032, reflecting a robust compound annual growth rate (CAGR) of 7.7%. This impressive growth is driven by continuous advancements in technology, increasing applications across various sectors, and the rising need for precise and accurate analytical tools. As industries strive for more comprehensive data analysis and higher precision in product testing and research, the demand for next-generation mass spectrometers is set to surge, positioning the market for substantial growth throughout the forecast period.

The growth of the next-generation mass spectrometer market is majorly propelled by technological innovations which have exponentially increased the sensitivity, accuracy, and speed of mass spectrometers. These advancements have resulted in more reliable and detailed analytical results, which are crucial for sectors such as pharmaceuticals and biotechnology that require precision in molecular analysis. Moreover, the integration of AI and machine learning with mass spectrometry has further enhanced the capabilities of these instruments, allowing for more complex data processing and interpretation, thus expanding their application scope. The push towards more personalized medicine is also a significant factor, as mass spectrometers are essential tools in the development of tailored therapies based on specific molecular insights.

Another pivotal factor contributing to the growth of the mass spectrometer market is the expanding demand within environmental testing and food and beverage testing sectors. As global regulations tighten on environmental pollutants and food safety, there is a heightened need for advanced analytical tools that can detect and quantify trace levels of contaminants swiftly and accurately. Mass spectrometers offer unmatched sensitivity and selectivity, making them indispensable in compliance testing and ensuring public safety. Furthermore, increasing awareness and governmental initiatives towards environmental preservation and food safety are expected to drive further investments and research in these sectors, thereby propelling market growth.

The healthcare sector, particularly clinical diagnostics, is another key growth driver for the mass spectrometer market. The adoption of next-generation mass spectrometry in clinical laboratories is on the rise due to its ability to provide accurate results in detecting and quantifying biomarkers for various diseases. This has become increasingly important in the era of personalized healthcare, where precise diagnostic tools are critical for effective treatment planning and monitoring. As the prevalence of chronic diseases and the aging population continues to grow, the demand for advanced mass spectrometry solutions in clinical diagnostics is anticipated to witness a substantial increase, further fueling market expansion.

Regionally, North America continues to hold a dominant position in the next-generation mass spectrometer market, attributed to its well-established healthcare infrastructure, significant R&D investments, and the presence of major market players. However, the Asia Pacific region is expected to witness the fastest growth during the forecast period, driven by increasing industrialization, rising healthcare needs, and supportive government initiatives for research and development activities. Additionally, the growing emphasis on quality assurance in manufacturing and environmental safety in regions such as Europe and Latin America will also contribute to the overall growth of the mass spectrometer market globally.

Product Type Analysis

The next-generation mass spectrometer market is segmented into various product types, including hybrid mass spectrometers, single mass spectrometers, and others. Hybrid mass spectrometers, which combine the functionalities of different spectrometers, are witnessing a substantial demand due to their enhanced capabilities in delivering comprehensive and high-resolution analyses. The combination of different mass spectrometry techniques allows for improved accuracy and sensitivity, making them ideal for complex sample analyses in pharmaceutical and biotechnology applications. As research demands become more intricate, the preference for hybrid systems is expected to grow significantly.

Single mass spectrometers, on the other hand, continue to maintain a strong presence in the market, part

This software pack, together with the figures it produces, presents supplementary material in support of the publication J. De Keyser, F. Dhooghe, K. Altwegg, M. Rubin, N. Hänni, S. A. Fuselier, J.-J. Berthelier, E. Neefs. Mass calibration of Rosetta’s ROSINA/DFMS mass spectrometer. International Journal of Mass Spectrometry, 2024.

This data set holds MATLAB software, in particular routines for computing the mass calibration of mass spectra obtained with the DFMS mass spectrometer onboard the European Space Agency’s Rosetta spacecraft, for the neutral high-resolution modes and commanded mass-over-charge 13-69 Da/e.
The software was developed with MATLAB R2022a. This data set also holds the output files and the figures obtained by running that program, as well as a mass calibration file that can be used by DFMS data analysis programs.

The inspection results of the mass spectrometry rapid screening technique (mass spectrometry fast screening) at the Taoyuan fruit and vegetable market in its 113th year.

Liquid chromatography mass spectrometry is a popular technique for high throughput analysis of biological samples. Identification and quantification of molecular species via mass spectrometry output requires postexperimental computational analysis of the raw instrument output. While tandem mass spectrometry remains a primary method for identification and quantification, species-resolved precursor data provides a rich source of unexploited information. Several algorithms have been proposed to resolve raw precursor signals into species-resolved isotopic envelopes. Many methods are particularly dependent on user parameters, and because they lack a means to optimize parameters, tend to perform poorly. To this end we present XNet, a parameter-less Bayesian machine learning approach to isotopic envelope extraction through the clustering of extracted ion chromatograms. We evaluate the performance of XNet and other prevalent methods on a quantitative ground truth data set. XNet is publicly available with an Apache license.

These data consist of the raw mass spectrometry files collected as part of an effort to understand the proteomic response of the marine diatom Thalassiosira pseudonana to varied CO2 concentration. High and low CO2 acclimated cells were grown with 15N-nitrate or natural abundance nitrate and harvested for proteomic analysis.

Data consist of 37 gigabytes of .raw files produced by the Thermo Scientific mass spectrometer. To obtain the data, please contact BCO-DMO.

Description of the data files:
The data files (37 GB in total size) are in .raw format, as produced by the Thermo Scientific mass spectrometer. The investigators used Proteome Discoverer software to analyze the .raw files. The mass spectrometry files are stored under in folders named MS3027 and MS3184, which were collected for the first and second biological replicates, respectively. In cases where individual LC fractions were subject to mass spectrometry multiple times, file names for the repeated analyses are the VLS number followed by \"_