The protein mass spectrometry service market has witnessed significant growth in recent years, driven by advancements in proteomics techniques and increased funding for biomedical research. The global market is expected to reach a value of USD 2,250 million by 2033, with a CAGR of 6.7%. The growing demand for proteomics services in various applications, including drug discovery, biomarker identification, and protein characterization, is propelling the market growth. Key trends in the protein mass spectrometry service market include the increasing adoption of high-throughput platforms, advancements in data analysis tools, and the development of novel proteomics technologies. Additionally, the rising prevalence of chronic diseases, such as cancer and neurodegenerative disorders, is fueling the demand for protein mass spectrometry services for biomarker discovery and disease diagnosis. Key players in the market include Martin Control Systems, MS Bioworks LLC, Charles River Laboratories, Inc., BioPharmaSpec Inc., and PhenoSwitch Bioscience.

Accurate protein identification and quantitation are critical when interpreting the biological relevance of large-scale shotgun proteomics data sets. Although significant technical advances in peptide and protein identification have been made, accurate quantitation of high-throughput data sets remains a key challenge in mass spectrometry data analysis and is a labor intensive process for many proteomics laboratories. Here, we report a new SILAC-based proteomics quantitation software tool, named IsoQuant, which is used to process high mass accuracy mass spectrometry data. IsoQuant offers a convenient quantitation framework to calculate peptide/protein relative abundance ratios. At the same time, it also includes a visualization platform that permits users to validate the quality of SILAC peptide and protein ratios. The program is written in the C# programming language under the Microsoft .NET framework version 4.0 and has been tested to be compatible with both 32-bit and 64-bit Windows 7. It is freely available to noncommercial users at http://www.proteomeumb.org/MZw.html.

In this study discovery proteomics data was generated from five tissues (salivary gland, crop, digestive gland, style sac, and intestine) from the golden apple snail, Pomacea canaliculata. Liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) was applied to study the proteome of the five tissues. Proteins with homology to proteases were selected for relative quantitation. This collection includes IDA-MS data, ProteinPilot Reports from the IDA data, ProteinPilot reports filter, LC-MRM-MS data, R scripts used for the analysis. Lineage: The data was acquire using a liquid chromatograph-mass spectrometer (Eksigent nanoLC 415 - SCIEX 6600 QqTOF). tudies of relative quantitation were performed in Shimadzu Nexera UHPLC and analysed with a 6500 QTRAP mass spectrometer (SCIEX).

The project contains raw and result files from a comparative proteomic analysis of malignant [primary breast tumor (PT) and axillary metastatic lymph nodes (LN)] and non-tumor [contralateral (NCT) and adjacent breast (ANT)] tissues of patients diagnosed with invasive ductal carcinoma. A label-free mass spectrometry was conducted using nano-liquid chromatography coupled to electrospray ionization–mass spectrometry (LC-ESI-MS/MS) followed by functional annotation to reveal differentially expressed proteins and their predicted impacts on pathways and cellular functions in breast cancer. A total of 462 proteins was observed as differentially expressed (DEPs) among the groups of samples analyzed. Ingenuity Pathway Analysis software version 2.3 (QIAGEN Inc.) was employed to identify the most relevant signaling and metabolic pathways, diseases, biological functions and interaction networks affected by the deregulated proteins. Upstream regulator and biomarker analyses were also performed by IPA’s tools. Altogether, our findings revealed differential proteomic profiles that affected the associated and interconnected cancer signaling processes.

Mass spectrometry-based proteomics technologies are prime methods for the high-throughput identification of proteins in complex biological samples. Nevertheless, there are still technical limitations that hinder the ability of mass spectrometry to identify low abundance proteins in complex samples. Characterizing such proteins is essential to provide a comprehensive understanding of the biological processes taking place in cells and tissues. Still today, most mass spectrometry-based proteomics approaches use a data-dependent acquisition strategy, which favors the collection of mass spectra from proteins of higher abundance. Since the computational identification of proteins from proteomics data is typically performed after mass spectrometry analysis, large numbers of mass spectra are typically redundantly acquired from the same abundant proteins, and little to no mass spectra are acquired for proteins of lower abundance. We therefore propose a novel supervised learning algorithm, MealTime-MS, that identifies proteins in real-time as mass spectrometry data are acquired and prevents further data collection from confidently identified proteins to ultimately free mass spectrometry resources to improve the identification sensitivity of low abundance proteins. We use real-time simulations of a previously performed mass spectrometry analysis of a HEK293 cell lysate to show that our approach can identify 92.1% of the proteins detected in the experiment using 66.2% of the MS2 spectra. We also demonstrate that our approach outperforms a previously proposed method, is sufficiently fast for real-time mass spectrometry analysis, and is flexible. Finally, MealTime-MS’ efficient usage of mass spectrometry resources will provide a more comprehensive characterization of proteomes in complex samples.

Suppl. Table 1. Ccdc124 interactome (SILAC) in HEK293 cells stably expressing Flag-Ccdc124. Suppl. Table 2. Ccdc124 interactome (Gel Digest) in HEK293 cells stably expressing Flag-Ccdc124. Enriched Ccdc124-bound proteins after anti-Flag immunoprecipitation were excised and in-gel digest bands were further subjected to mass spectrometry.

Rheumatoid arthritis (RA) is a systemic autoimmune and inflammatory disease. Plasma biomarkers are critical for understanding disease mechanisms, treatment effects, and diagnosis. Mass spectrometry-based proteomics is a powerful tool for unbiased biomarker discovery. However, plasma proteomics is significantly hampered by signal interference from high-abundance proteins, low overall protein coverage, and high levels of missing data from data-dependent acquisition (DDA). To achieve quantitative proteomic analysis for plasma samples with a balance of throughput, performance, and cost, we developed a workflow incorporating plate-based high abundance protein depletion and sample preparation, comprehensive peptide spectral library building, and data-independent acquisition (DIA) SWATH mass spectrometry-based methodology. In this study, we analyzed plasma samples from both RA patients and healthy donors. The results showed that the new workflow performance exceeded that of the current state-of-the-art depletion-based plasma proteomic platforms in terms of both data quality and proteome coverage. Proteins from biological processes related to the activation of systemic inflammation, suppression of platelet function, and loss of muscle mass were enriched and differentially expressed in RA. Some plasma proteins, particularly acute phase reactant proteins, showed great power to distinguish between RA patients and healthy donors. Moreover, protein isoforms in the plasma were also analyzed, providing even deeper proteome coverage. This workflow can serve as a basis for further application in discovering plasma biomarkers of other diseases.

The Protein Mass Spectrometry Service market boasts a significant market size of XXX million, with a CAGR of XX%. Valued in million units, this market is driven by the increasing demand for accurate and sensitive characterization of proteins in various fields such as biochemistry, medical science, and food analysis. Key trends shaping the market include the rise of precision medicine and personalized treatments, technological advancements in mass spectrometry instrumentation, and the growing need for biomarker discovery. Despite its promising growth prospects, the market faces certain restraints. These include the high cost of instrumentation, the need for skilled professionals to operate and interpret the data, and the potential for false positives or negatives in protein identification. However, strategic alliances between leading companies and research institutions, ongoing technological innovations, and the development of novel applications in cutting-edge fields hold the potential to mitigate these challenges and drive the market forward in the coming years.

Mass spectrometry analysis (data-independent acquisition) derived intensities are reported here for all breast tumor samples (n = 75). RAW data files for these samples are accessible via ProteomeXchange with the dataset identifiers PXD032266 (S samples) and PXD037428 (V samples). Protein intensities were Log2 transformed and scaled (samples and proteins).

This dataset was used for Figure 5 in the following manuscript: "Proteogenomics decodes the evolution of human ipsilateral breast cancer". De Marchi T, Pyl PT, Sjöström M, Reinsbach SE, DiLorenzo S, Nystedt B, Tran L, Pekar G, Wärnberg F, Fredriksson I, Malmström P, Fernö M, Malmström L, Malmström J, Nimèus E. accepted for publication

The recently introduced cross-linking of isotope-labelled RNA coupled with mass spectrometry (CLIR-MS) technique enables protein-RNA cross-links to be used as precisely localized distance restraints in de novo structural modelling. The novel data type requires a bespoke data analysis approach. The new RNxQuest Python package supports this approach. Here we demonstrate the performance of the new package using a mixture of novel and published datasets.

Protein-Protein, Genetic, and Chemical Interactions for Courcelles M (2017):CLMSVault: A Software Suite for Protein Cross-Linking Mass-Spectrometry Data Analysis and Visualization. curated by BioGRID (https://thebiogrid.org); ABSTRACT: Protein cross-linking mass spectrometry (CL-MS) enables the sensitive detection of protein interactions and the inference of protein complex topology. The detection of chemical cross-links between protein residues can identify intra- and interprotein contact sites or provide physical constraints for molecular modeling of protein structure. Recent innovations in cross-linker design, sample preparation, mass spectrometry, and software tools have significantly improved CL-MS approaches. Although a number of algorithms now exist for the identification of cross-linked peptides from mass spectral data, a dearth of user-friendly analysis tools represent a practical bottleneck to the broad adoption of the approach. To facilitate the analysis of CL-MS data, we developed CLMSVault, a software suite designed to leverage existing CL-MS algorithms and provide intuitive and flexible tools for cross-platform data interpretation. CLMSVault stores and combines complementary information obtained from different cross-linkers and search algorithms. CLMSVault provides filtering, comparison, and visualization tools to support CL-MS analyses and includes a workflow for label-free quantification of cross-linked peptides. An embedded 3D viewer enables the visualization of quantitative data and the mapping of cross-linked sites onto PDB structural models. We demonstrate the application of CLMSVault for the analysis of a noncovalent Cdc34-ubiquitin protein complex cross-linked under different conditions. CLMSVault is open-source software (available at https://gitlab.com/courcelm/clmsvault.git ), and a live demo is available at http://democlmsvault.tyerslab.com/ .

Gaseous phytohormone ethylene regulates various aspects of plant development. Ethylene is perceived by ER membrane-localized receptors, which are inactivated upon binding with ethylene molecules, thereby initiating ethylene signal transduction. Here, we report that a novel E3 ligase RING finger for Ethylene receptor Degradation (RED) and its E2 partner UBC32 ubiquitinate ethylene-bound receptors for degradation through an ER associated degradation (ERAD) pathway in both Rosa hybrida and Solanum lycopersicum. The depletion of RED or UBC32 leads to hypersensitivity to ethylene, which is manifested as premature leaf abscission and petal shedding in roses, as well as the dwarf plants and accelerated fruit ripening in tomatoes. Disruption of the conserved ethylene binding site of receptors prevents RED-mediated degradation of the receptors. Our study discovers an ERAD branch that facilitates the ethylene-induced degradation of receptors, and provides insights into how the plant’s response to ethylene can be controlled by modulating the turnover of ethylene receptors

Ø There were 503 high confidence proteins identified with the filtered indicated on the previous slide. Of these, 235 proteins were found to have non-zero quantifiable values in 3 of 4 experimental repeats per group for down-stream statistical analysis.

Ø There were 30 proteins (20↓ and 10↑ in old v young) that passed both a single pairwise statistic in addition to a fold change of ><+/- 1.5. There were additionally 8 proteins (4↓ and 4↑ in old v young) that had measurable levels of protein in 3 of 4 experiments within one group vs no measurable levels in the other group, with 6 of these proteins (3 and 3 per group) that presented with average NSC’s above >1.5; therefore, these 6 proteins were added to the 30 significantly changed proteins for systems analysis.

Ø UniProtKB accession numbers were cross correlated to Gene Names and ID’s prior to systems analysis using the DAVID GO database (https://david.ncifcrf.gov/), and all proteins did cross ...

Fragmentation ion spectral analysis of chemically cross-linked proteins is an established technology in the proteomics research repertoire for determining protein interactions, spatial orientation, and structure. Here we present Kojak version 2.0, a major update to the original Kojak algorithm, which was developed to identify cross-linked peptides from fragment ion spectra using a database search approach. A substantially improved algorithm with updated scoring metrics, support for cleavable cross-linkers, and identification of cross-links between 15N-labeled homomultimers are among the newest features of Kojak 2.0 presented here. Kojak 2.0 is now integrated into the Trans-Proteomic Pipeline, enabling access to dozens of additional tools within that suite. In particular, the PeptideProphet and iProphet tools for validation of cross-links improve the sensitivity and accuracy of correct cross-link identifications at user-defined thresholds. These new features improve the versatility of the algorithm, enabling its use in a wider range of experimental designs and analysis pipelines. Kojak 2.0 remains open-source and multiplatform.

Web application and database designed for sharing, visualizing, and analyzing protein cross-linking mass spectrometry data with emphasis on structural analysis and quality control. Includes public and private data sharing capabilities, project based interface designed to ensure security and facilitate collaboration among multiple researchers. Used for private collaboration and public data dissemination.

Description from Zenodo:

"Recent studies have revealed diverse amino acid, post-translational and non-canonical modifications of proteins in diverse organisms and tissues. However, their unbiased detection and analysis remain hindered by technical limitations. Here, we present a spectral alignment method for the identification of protein modifications from high-resolution tandem peptide mass spectrometry. Termed SAMPEI for Spectral Alignment-based Modified PEptide Identification, this open-source algorithm is designed for the discovery of functional protein and peptide signaling modifications, without prior knowledge of their identities. Using synthetic standards and controlled chemical labeling experiments, we demonstrate specificity and sensitivity of SAMPEI for the discovery of protein modifications in complex cellular extracts. We then apply SAMPEI to mapping chemical protein modifications in differentiating mouse macrophage proteome. SAMPEI revealed diverse post-translational protein modifications, including distinct forms of cysteine itaconatylation which we experimentally validated. SAMPEI’s robust parameterization and versatility are expected to facilitate the discovery of biological modifications of diverse macromolecules. SAMPEI is implemented as a Python package and is available open-source from BioConda and GitHub (https://github.com/FenyoLab/SAMPEI).

The dataset is divided in 3 set of files:

1. Agnostic_discovery_benchmarking.zip file contains analyses performed to establish relative sensitivity and specificity of agnostic PTM discovery (Figure 2, Figure S3).

2. Chemoproteomics_of_LPS_stimulated_macrophages.zip file contains RAW264.7 cell proteomics identification results from X!tandem and SAMPEI (Figure 3, Figures S4-S7).

3. Itaconate_adducts_validation.zip file contains analysis to confirm cystein adducts produced by itaconic acid (Figure 5, Figures S8-S12)."

Proteomics Analytical Service Market Outlook

The global proteomics analytical service market size was valued at USD 5.2 billion in 2023 and is projected to reach USD 14.8 billion by 2032, exhibiting a CAGR of 12.4% during the forecast period. This substantial growth is driven by the increasing demand for precision medicine, advancements in mass spectrometry, and a growing focus on drug discovery and development.

One of the primary growth factors for the proteomics analytical service market is the rise in demand for personalized medicine. Personalized medicine aims to tailor medical treatment to the individual characteristics of each patient, and proteomics plays a crucial role by providing detailed insights into the protein expressions and modifications in different diseases. With increasing incidences of chronic diseases and the emphasis on early diagnosis and treatment, the need for advanced proteomics analytical services has surged, propelling market growth.

Another significant growth driver is technological advancements in proteomics. Advancements in mass spectrometry, chromatography, and bioinformatics tools have revolutionized the field of proteomics. These advanced technologies offer high-throughput, accurate, and comprehensive protein analysis, driving their adoption in research and clinical settings. Moreover, continuous innovations in proteomic technologies are expected to further accelerate market growth by improving the efficiency and scope of proteomics analytical services.

The growing investments in proteomics research and development also play a pivotal role in market expansion. Both government and private sectors are increasingly investing in proteomics projects due to the potential of proteomics in drug discovery, biomarker discovery, and clinical diagnostics. Funding for proteomics research has increased, leading to more comprehensive studies and a higher demand for analytical services that can provide detailed and accurate protein analysis.

Regionally, North America dominates the proteomics analytical service market, followed by Europe and Asia Pacific. North America's dominance is attributed to the presence of well-established pharmaceutical and biotechnology companies, leading academic institutions, and advanced healthcare infrastructure. Europe follows closely due to substantial government funding and focus on research and development. The Asia Pacific region, on the other hand, is expected to witness the highest growth rate due to increasing R&D activities, rising healthcare expenditure, and growing awareness about personalized medicine in emerging economies like China and India.

Service Type Analysis

The proteomics analytical service market is segmented by service type into protein identification, protein quantification, protein characterization, data analysis and interpretation, and others. Protein identification services hold a significant market share due to the critical role of identifying and cataloging proteins in understanding disease mechanisms and discovering potential therapeutic targets. The demand for protein identification services is further driven by advancements in mass spectrometry and electrophoresis technologies that enhance the accuracy and throughput of protein identification processes.

Protein quantification services are also experiencing substantial growth. Accurate quantification of proteins is essential for understanding protein function, interaction, and regulation within biological systems. The increasing need for quantitative proteomics in biomarker discovery, drug development, and clinical diagnostics is driving the demand for these services. Moreover, advancements in isotope labeling techniques and mass spectrometry have significantly improved the precision and sensitivity of protein quantification methods.

Protein characterization services are gaining traction due to their importance in understanding protein structure, function, and interactions. Characterizing the post-translational modifications and conformational changes of proteins is crucial for comprehending cellular processes and disease pathology. The integration of advanced techniques like X-ray crystallography, NMR spectroscopy, and cryo-electron microscopy in protein characterization is fueling the growth of this segment.

Data analysis and interpretation services are becoming increasingly vital due to the complexity and volume of proteomics data generated. Sophisticated bioinformatics tools and software are required to analyze and interpret proteomics datase

The Clinical Proteomic Tumor Analysis Consortium (CPTAC) analyzes cancer biospecimens by mass spectrometry, characterizing and quantifying their constituent proteins, or proteome. Proteomic analysis for each CPTAC study is carried out independently by Proteomic Characterization Centers (PCCs) using a variety of protein fractionation techniques, instrumentation, and workflows. Mass spectrometry and related data files are organized into datasets by study, sub-proteome, and analysis site.

Proteomics Mass Spectrometry Market Outlook

The proteomics mass spectrometry market size in 2023 is estimated to be valued at approximately USD 5.8 billion and is forecasted to reach around USD 12.1 billion by 2032, growing at a CAGR of 8.6%. The substantial growth in this market is primarily driven by advancements in mass spectrometry technology, increasing investments in proteomics research, and the rising demand for personalized medicine. The continuous evolution of proteomics technologies is enabling more precise and comprehensive analysis of proteins, which is fostering market expansion.

One of the key growth factors for the proteomics mass spectrometry market is the significant advancements in mass spectrometry technology. Techniques like Matrix-Assisted Laser Desorption/Ionization-Time of Flight (MALDI-TOF) and Electrospray Ionization (ESI) have revolutionized the field by providing high-throughput and accurate identification of proteins. Moreover, the introduction of tandem mass spectrometry has furthered this impact, allowing for more detailed structural analysis and quantification of complex protein mixtures. These technological improvements are continuously enhancing the capabilities of proteomic analyses, thereby driving market growth.

Another critical driver is the increasing investment in proteomics research by both public and private entities. Government bodies across the globe are allocating substantial funds to support proteomics studies aimed at understanding disease mechanisms and developing new treatment strategies. Similarly, pharmaceutical and biotechnology companies are investing heavily in proteomics to accelerate drug discovery and development processes. These investments are not only facilitating groundbreaking research but also expanding the application scope of proteomics, thus contributing to the market's robust growth.

The rising demand for personalized medicine is also propelling the proteomics mass spectrometry market. Personalized medicine aims to tailor treatments according to individual patient profiles, which requires comprehensive proteomic data to understand the unique protein signatures associated with diseases. Proteomics mass spectrometry provides the necessary tools to generate this data, thereby aiding in the development of personalized therapeutic strategies. As the healthcare industry continues to shift towards personalized medicine, the demand for advanced proteomic technologies is expected to increase significantly.

Regionally, North America dominates the proteomics mass spectrometry market due to the presence of leading research institutions, robust healthcare infrastructure, and significant government funding for proteomics research. Europe follows closely, driven by strong academic research activities and collaborative initiatives in the biotech sector. The Asia Pacific region is emerging as a lucrative market owing to increasing investments in healthcare infrastructure, growing focus on personalized medicine, and rising research and development activities. Latin America and the Middle East & Africa are also showing promising growth potential, supported by improving healthcare systems and increasing research collaborations.

MALDI-TOF Mass Spectrometers have become a cornerstone in the field of proteomics due to their ability to analyze large biomolecules with remarkable precision. This technique utilizes a laser to ionize samples, which are then accelerated through a time-of-flight analyzer to determine their mass-to-charge ratio. The high-throughput nature of MALDI-TOF makes it particularly valuable in clinical diagnostics and biomarker discovery, where rapid and accurate protein analysis is crucial. As researchers continue to explore the vast potential of proteomics, the role of MALDI-TOF Mass Spectrometers in facilitating groundbreaking discoveries cannot be overstated. Their integration into laboratories worldwide is a testament to their reliability and efficiency in advancing scientific research.

Technology Analysis

The proteomics mass spectrometry market is segmented by technology into MALDI-TOF, ESI, tandem mass spectrometry, and others. MALDI-TOF technology has gained significant traction due to its high-throughput capabilities and ability to analyze large biomolecules with minimal fragmentation. This technology is extensively used in clinical diagnostics and biomarker discovery because of its precision and reliability. The ongoing advancements in MALDI-TOF instrumentation

This is the raw data from Mass Spectrometry analysis of spots resolved using two-dimensional analysis from a total protein sample from Tritrichomonas foetus bovine and feline genotypes. The dataset contains data files generated following the analysis of protein spots using LC-MS/MS.

There are two types of data files.

.Wiff files were produced using the following procedure: LC−MS/MS was performed using a QSTAR Elite hybrid Q-TOF mass spectrometer (Applied Biosystems, Sciex, USA). The peptides were washed off the trap at 300 nL min-1 onto a PicoFrit column (75 μm × 100 mm) (New Objective, USA) packed with Magic C18AQ resin (Michrom Bioresources, USA), and the following protocol was used to elute peptides from the column and into the source of a QSTAR Elite hybrid Q-TOF mass spectrometer (Applied Biosystems, Sciex, USA): 5−30% MS buffer B (98% acetonitrile−0.2% formic acid) over 8 minutes, 30−80% MS buffer B over 3 minutes, 80% MS buffer B for 2 minutes, 80−85% MS buffer B for 3 minutes. MS/MS fragmentation ion scans were calibrated using fragments of Glu-Fibrinopeptide B. MS/MS spectral data was converted to Mascot Generic format using TOF/TOF extractor V2.1 (Michigan Proteome Consortium, 2003).

.raw files were produced using the following procedure: LC-MS/MS analysis was undertaken using a nanoAcquity UPLC (Waters, USA) coupled to a XevoQToF (Waters, USA) mass spectrometer. The peptides were washed off the trap at 400 nL/min on to a C18 BEH analytical column (75 μm ´ 100 mm)(Waters, USA), packed with 1.7 μm particles of pore size 130 Å and the following protocol was used to elute peptides from the column into the source of a XevoQToF mass spectrometer (Waters, USA): 1-50% MS buffer B (98% acetonitrile, 0.2% formic acid) over 30 minutes, 50-85% MS Buffer B over 2 minutes, 85% MS buffer B 3 minutes, 85-99% over 1 minute. After separation, the peptides were analysed using tandem mass spectrometry, implementing an emitter tip that tapers to 10 µm at 2300 V. A Data Directed Acquisition (DDA) experiment was performed which continuously scanned for peptides of charge state 2+ to 4+ with an intensity of more than 50 counts per second, with a maximum of three ions in any given 3 second scan.

The data can be accessed via the related website listed below.