Isobaric labeling-based proteomics is widely applied in deep proteome quantification. Among the platforms for isobaric labeled proteomic data analysis, the commercial software Proteome Discoverer (PD) is widely used, incorporating the search engine CHIMERYS, while FragPipe (FP) is relatively new, free for noncommercial purposes, and integrates the engine MSFragger. Here, we compared PD and FP over three public proteomic data sets labeled using 6plex, 10plex, and 16plex tandem mass tags. Our results showed the protein abundances generated by the two software are highly correlated. PD quantified more proteins (10.02%, 15.44%, 8.19%) than FP with comparable NA ratios (0.00% vs. 0.00%, 0.85% vs. 0.38%, and 11.74% vs. 10.52%) in the three data sets. Using the 16plex data set, PD and FP outputs showed high consistency in quantifying technical replicates, batch effects, and functional enrichment in differentially expressed proteins. However, FP saved 93.93%, 96.65%, and 96.41% of processing time compared to PD for analyzing the three data sets, respectively. In conclusion, while PD is a well-maintained commercial software integrating various additional functions and can quantify more proteins, FP is freely available and achieves similar output with a shorter computational time. Our results will guide users in choosing the most suitable quantification software for their needs.

The technological advances in mass spectrometry allow us to collect more comprehensive data with higher quality and increasing speed. With the rapidly increasing amount of data generated, the need for streamlining analyses becomes more apparent. Proteomic data is known to be often affected by systemic bias from unknown sources, and failing to adequately normalize the data can lead to erroneous conclusions. To allow researchers to easily evaluate and compare different normalization methods via a user-friendly interface, we have developed “proteiNorm”. The current implementation of proteiNorm accommodates preliminary filter on peptide and sample level, followed by an evaluation of several popular normalization methods and visualization of missing value. The user then selects an adequate normalization method and one of several imputation methods used for the subsequent comparison of different differential abundance/expression methods and estimation of statistical power. The application of proteiNorm and interpretation of its results is demonstrated on a Tandem Mass Tag mass spectrometry example data set, where the proteome of three different breast cancer cell lines was profiled with and without hydroxyurea treatment. With proteiNorm, we provide a user-friendly tool to identify an adequate normalization method and to select an appropriate method for a differential abundance/expression analysis.

Mass spectrometry (MS) has become an accessible tool for whole proteome quantitation with the ability to characterize protein expression across thousands of proteins within a single experiment. A subset of MS quantification methods (e.g., SILAC and label-free) monitor the relative intensity of intact peptides, where thousands of measurements can be made from a single mass spectrum. An alternative approach, isobaric labeling, enables precise quantification of multiple samples simultaneously through unique and sample specific mass reporter ions. Consequently, in a single scan, the quantitative signal comes from a limited number of spectral features (≤11). The signal observed for these features is constrained by automatic gain control, forcing codependence of concurrent signals. The study of constrained outcomes primarily belongs to the field of compositional data analysis. We show experimentally that isobaric tag proteomics data are inherently compositional and highlight the implications for data analysis and interpretation. We present a new statistical model and accompanying software, which improves estimation accuracy and the ability to detect changes in protein abundance. Finally, we demonstrate a unique compositional effect on proteins with infinite changes. We conclude that many infinite changes will appear small and that the magnitude of these estimates is highly dependent on experimental design.

Multiplexed isobaric labeling methods, such as tandem mass tags (TMT), remarkably improve the throughput of quantitative mass spectrometry. Here, we present a 27-plex TMT method coupled with two-dimensional liquid chromatography (LC/LC) for extensive peptide fractionation and high-resolution tandem mass spectrometry (MS/MS) for peptide quantification and then apply the method to profile the complex human brain proteome of Alzheimer’s disease (AD). The 27-plex method combines multiplexed capacities of the 11-plex and the 16-plex TMT, as the peptides labeled by the two TMT sets display different mass and hydrophobicity, which can be well separated in LC-MS/MS. We first systematically optimized the protocol for the newly developed 16-plex TMT, including labeling reaction, desalting, and MS conditions, and then directly compared the 11-plex and 16-plex methods by analyzing the same human AD samples. Both methods yielded similar proteome coverage, analyzing >100 000 peptides in >10 000 human proteins. Furthermore, the 11-plex and 16-plex samples were mixed for a 27-plex assay, resulting in more than 8000 protein measurements within the same MS time. The 27-plex results are highly consistent with those of the individual 11-plex and 16-plex TMT analyses. We also used these proteomics data sets to compare the AD brain with the nondementia controls, discovering major AD-related proteins and revealing numerous novel protein alterations enriched in the pathways of amyloidosis, immunity, mitochondrial, and synaptic functions. Overall, our data strongly demonstrate that this new 27-plex strategy is highly feasible for routine large-scale proteomic analysis.

Effective extension of mass spectrometry-based proteomics to single cells remains challenging. Herein we combined microfluidic nanodroplet technology with tandem mass tag (TMT) isobaric labeling to significantly improve analysis throughput and proteome coverage for single mammalian cells. Isobaric labeling facilitated multiplex analysis of single cell-sized protein quantities to a depth of ∼1 600 proteins with a median CV of 10.9% and correlation coefficient of 0.98. To demonstrate in-depth high throughput single cell analysis, the platform was applied to measure protein expression in 72 single cells from three murine cell populations (epithelial, immune, and endothelial cells) in

Single cell transcriptomics have revolutionized fundamental understanding of basic biology and disease. Since transcripts often do not correlate with protein expression, it is paramount to complement transcriptomics approaches with proteome analysis at single cell resolution. Despite continuous technological improvements in sensitivity, mass spectrometry-based single cell proteomics ultimately faces the challenge of reproducibly comparing the protein expression profiles of thousands of individual cells. Here, we combine two hitherto opposing analytical strategies, DIA and Tandem-Mass-Tag (TMT)-multiplexing, to generate highly reproducible, quantitative proteome signatures from ultra-low input samples. While conventional, data-dependent shotgun proteomics (DDA) of ultra-low input samples critically suffers from the accumulation of missing values with increasing sample-cohort size, data-independent acquisition (DIA) strategies do usually not to take full advantage of isotope-encoded sample multiplexing. We also developed a novel, identification-independent proteomics data analysis pipeline to quantitatively compare DIA-TMT proteome signatures across hundreds of samples independent of their biological origin to identify cell types and single protein knockouts. We validate our approach using integrative data analysis of different human cell lines and standard database searches for knockouts of defined proteins. These data establish a novel and reproducible approach to markedly expand the numbers of proteins one detects from ultra-low input samples, such as single cells.

The quantification of proteoforms, i.e., all molecular forms in which proteins can be present, by top-down proteomics provides essential insights into biological processes at the molecular level. Isobaric labeling-based quantification strategies are suitable for multi-dimensional separation strategies and allow for multiplexing of the samples. Here, we investigated cysteine-directed isobaric labeling by iodoTMT in combination with a gel- and gas-phase fractionation (GeLC-FAIMS-MS) for in-depth quantitative proteoform analysis. We optimized the acquisition workflow (i.e., the FAIMS compensation voltages, isolation windows, acquisition strategy, and fragmentation method) using a two-proteome mix to increase the number of quantified proteoforms and reduce ratio compression. Additionally, we implemented a mass feature-based quantification strategy in the widely used deconvolution algorithm FLASHDeconv, which improves and facilitates data analysis. The optimized iodoTMT GeLC-FAIMS-MS workflow was applied to quantitatively analyze the proteome of Escherichia coli grown under glucose or acetate as the sole carbon source, resulting in the identification of 726 differentially abundant proteoforms.

The files serve as input and intermediate results for a MaxQuant and MsstatsTMT training on lysine methyl transferase 9 knockdown and control cell proteomics (https://doi.org/10.1186/s12935-020-1141-2) in the Galaxy training network (https://training.galaxyproject.org).

Input files: human FASTA protein database for Maxquant. MaxQuant experimental design template, MSstatsTMT annotation file

Intermediate result files: MaxQuant protein groups and evidence

Cancer is currently considered as the end point of numerous genomic and epigenomic mutations and as the result of the interaction of transformed cells within the stromal microenvironment. The present work focuses on breast cancer, one of the most common malignancies affecting the female population in industrialized countries. In this study, we perform a proteomic analysis of bioptic samples from human breast cancer, namely, interstitial fluids and primary cells, normal vs disease tissues, using tandem mass tags (TmT) quantitative mass spectrometry combined with the MudPIT technique. To the best of our knowledge, this work, with over 1700 proteins identified, represents the most comprehensive characterization of the breast cancer interstitial fluid proteome to date. Network analysis was used to identify functionally active networks in the breast cancer associated samples. From the list of differentially expressed genes, we have retrieved the associated functional interaction networks. Many different signaling pathways were found activated, strongly linked to invasion, metastasis development, proliferation, and with a significant cross-talking rate. This pilot study presents evidence that the proposed quantitative proteomic approach can be applied to discriminate between normal and tumoral samples and for the discovery of yet unknown carcinogenesis mechanisms and therapeutic strategies.

To determine the proteins in the membrane raft, we used the TMT-labeling and nano-liquid chromatography mass spectrometry (nano-LC-MS/MS) analysis by Creative Proteomics (NY, USA; https://www.creative-proteomics.com/). Rat primary microglia were treated with IL-6 (25 ng/ml) for 15 min. Membrane rafts were obtained by flotation assay. Samples were prepared from three independent experiments. Proteins in equal volumes of raft fractions were digested with trypsin, desalted, and labeled with a TMT reagent (Thermo Fisher Science). The TMT-labeled peptides were fractionated and analyzed by nano-LC-MS/MS. The resulting MS/MS data were analyzed and searched against the rat protein database using Proteome Discoverer 2.1.

Fertility depends, in part, on interactions between male and female reproductive proteins inside the female reproductive tract (FRT) that mediate postmating changes in female behavior, morphology, and physiology. Coevolution between interacting proteins within species may drive reproductive incompatibilities between species, yet the mechanisms underlying postmating-prezygotic isolating barriers remain poorly resolved. Here, we used quantitative proteomics in sibling Drosophila species to investigate the molecular composition of the FRT environment and its role in mediating species-specific postmating responses. We found that (1) FRT proteomes in D. simulans and D. mauritiana virgin females express unique combinations of secreted proteins and are enriched for distinct functional categories, (2) mating induces substantial changes to the FRT proteome in D. mauritiana but not in D. simulans, and (3) the D. simulans FRT pr...

TMT Labeling Reagents Market Outlook

According to our latest research, the global TMT Labeling Reagents market size reached USD 543.2 million in 2024 and is anticipated to grow at a robust CAGR of 13.7% during the forecast period, with the market expected to achieve USD 1,569.5 million by 2033. This impressive growth trajectory is driven by the increasing adoption of tandem mass tag (TMT) labeling reagents in advanced proteomics research, clinical diagnostics, and drug discovery applications. The surge in demand for high-throughput and multiplexed quantitative proteomics, coupled with technological advancements in mass spectrometry, continues to accelerate market expansion in both established and emerging economies.

The primary growth factor propelling the TMT Labeling Reagents market is the rapid advancement in proteomics technologies, which are crucial for understanding protein function, disease mechanisms, and biomarker discovery. The ability of TMT labeling reagents to enable simultaneous quantification of multiple protein samples in a single experiment has revolutionized the workflow of proteomics research. This multiplexing capability not only enhances throughput and data quality but also significantly reduces experimental variability and cost per sample. As academic and research institutions, pharmaceutical companies, and contract research organizations increasingly invest in precision medicine and translational research, the demand for highly sensitive and reliable quantification methods like TMT labeling continues to rise, fueling market growth.

Another significant driver for the TMT Labeling Reagents market is the expanding applications of quantitative proteomics in clinical diagnostics and drug discovery. The pharmaceutical and biotechnology industries are leveraging TMT labeling reagents for biomarker validation, target identification, and drug efficacy studies, which are essential steps in the development of personalized therapies. The integration of TMT-based approaches with cutting-edge mass spectrometry platforms allows for comprehensive protein profiling, facilitating early disease detection and therapeutic monitoring. Additionally, regulatory bodies are increasingly recognizing the value of proteomic data in clinical trials, further incentivizing the adoption of TMT labeling reagents in regulated environments and driving market penetration across clinical and translational research settings.

Technological innovations and product enhancements have also played a pivotal role in the growth of the TMT Labeling Reagents market. Leading manufacturers are continuously developing new TMT reagents with higher multiplexing capabilities, improved labeling efficiency, and compatibility with a broader range of mass spectrometry instruments. The introduction of reagents like TMTpro, which allows for the simultaneous analysis of up to 18 samples, has set new benchmarks for throughput and scalability in quantitative proteomics. These advancements not only cater to the evolving needs of high-throughput laboratories but also open up new avenues for large-scale biomarker discovery and systems biology research. As a result, the market is witnessing an influx of investments from both public and private sectors, further accelerating innovation and adoption rates.

From a regional perspective, North America continues to dominate the TMT Labeling Reagents market owing to its well-established research infrastructure, strong presence of leading biotechnology and pharmaceutical companies, and significant government funding for life sciences research. However, the Asia Pacific region is emerging as a lucrative market, driven by increasing investments in healthcare infrastructure, growing academic research activities, and rising awareness about advanced proteomics techniques. Europe also holds a substantial market share, supported by robust collaborations between academia and industry, as well as favorable regulatory frameworks. The Middle East & Africa and Latin America, while currently representing smaller market shares, are expected to witness steady growth due to improving healthcare systems and expanding research capabilities.

Product Type Analysis

The TMT Labeling Reagents market is segmented by product type into TMTzero, TMTsixplex, TMTtenplex, TMT11plex, TMTpro, and others. Among these, TMTpro has emerged as a game-changer due to its unparalleled multiplexing capability, allowing researchers to analyze up to 18 samples simul

Haematococcus pluvialis is a green microalga of commercial interests due to its ability to produce a high value ketocarotenoid, astaxanthin. As a non-model species that lacks a well annotated genome, omics analyses such as transcriptomics and proteomics analysis have often been used together with physiological and biochemical analysis to explore pathways of interest. However, interpretation of these datasets remains challenging. In this work, TMT-based proteomics and phosphoproteomics analyses were conducted on Haematococcus cells grown under favorable conditions (green stage biomass) and high-light stress conditions (red stage biomass). Phosphoproteins were enriched using titanium dioxide before LC-MS/MS analysis. Our proteomics and phosphoproteomics analyses identified 1394 proteins and 569 phosphosites on 366 phosphoproteins, respectively. Of these, 1315 proteins and 396 phosphosites on 314 phosphoproteins were quantifiable, among which 370 proteins and 121 phosphosites on 94 phosphoproteins were differentially expressed. Using an improved analysis pipeline that combines Blast2GO, KEGG, and DAVID to analyze differentially expressed proteins and phosphoproteins, total identified proteins increased from 255 to 322 and total identified phosphoproteins increased from 59 to 70, which were 26.28% and 18.64%, respectively, higher than with the UniProt analysis alone. Using this pipeline, a previously uncharacterized protein and phosphoprotein were identified as an ATPase subunit B and a phosphofructokinase, respectively, and further confirmed with translated genomic and transcriptomic data. This work provides the first example of phosphoproteomics analysis in H. pluvialis, while the proteomics and phosphoproteomics analysis pipelines described here may be useful to analyze omics data from other non-model algal species.

To further explore the underlying mechanism of the α-lactalbumin peptides Gly-Ile-Asn-Tyr (GINY) and Asp-Gln-Trp (DQW) in alleviating hepatic steatosis, TMT-based quantitative proteomics was performed. Overall, a total of 3808 quantifiable proteins were identified from the four groups, and the quantitative results were visualized in Table S1. Among them, 213 proteins were identified as DEPs (Tables S2-4). Further analysis showed that 37 proteins were the mutual DEPs among the four groups (Table S5). As shown in Fig. 2b and Table S2, 155 proteins were identified as DEPs in the FFAs group (versus the control group). Compared with the FFAs group, 99 proteins were identified as DEPs in the GINY group (Table S3), and 89 proteins were identified as DEPs in the DQW group ( Table S4).

This dataset consists of semi-quantitative 10-plex TMT proteomics analysis of FACS-separated bone marrow cells obtained from 3 healthy bone marrow donors and 3 MDS-RS patients. The objective of this data collection was to assess several parameters on how the bone marrow of MDS-RS patients differs from that of healthy donors. The samples included in this analyses were 3 samples of sorted GPA+ erythroblasts pooled from 5 healthy donors (2+2+1) and 3 samples of sorted GPA+ erythroblasts plus 3 samples of purified ring sideroblats from 3 MDS-RS patients. This dataset includes the raw mass spectra, QC and processed results. Processing: In short, FACS-separated samples were snap frozen in liquid nitrogen. Cell pellets were lysed with 4 % SDS lysis buffer and prepared for mass spectrometry using a modified version of the SP3 protein clean up and digestion protocol published by Moggridge S. et al 2018. The following files are included in the dataset: - Processed data and analysis.xlsx (2.63 MB) - Variable_list_Processed_data_and_analysis.xlsx (23 KB) - Readme for raw data .zip file.txt (562 bytes) - Readme for Processed data.xlsx file (1.07 KB) - 13553_STD_EAEHPM1_10_TMT10_HiRIEF_3-10pH_20210504_07.15.zip (7.68 GB)

Aneuploidy causes severe developmental defects and is a near universal feature of tumor cells. Despite its profound effects, the cellular processes affected by aneuploidy are not well characterized. Here, we examined the consequences of aneuploidy on the proteome of aneuploid budding yeast strains. We show that although protein levels largely scale with gene copy number, subunits of multi-protein complexes are notable exceptions. Posttranslational mechanisms attenuate their expression when their encoding genes are in excess. Our proteomic analyses further revealed a novel aneuploidy-associated protein expression signature characteristic of altered metabolism and redox homeostasis. Indeed aneuploid cells harbor increased levels of reactive oxygen species (ROS). Interestingly, increased protein turnover attenuates ROS levels and this novel aneuploidy-associated signature and improves the fitness of most aneuploid strains. Our results show that aneuploidy causes alterations in metabolism and redox homeostasis. Cells respond to these alterations through both transcriptional and posttranscriptional mechanisms.

We performed in a rat pancreatitic model a quantitative shotgun proteomic profiling of the soluble and whole membrane fractions from the pancreas during the early phase of cerulein (Cer)-induced AP compared to controls. Three control and three pancreatitic samples from both the soluble and the whole membrane fractions were analysed in two independent TMT6plex experiments. After trypsin digestion and TMT labelling, the peptide mixtures were fractionated into 12 fractions by off-gel electrophoresis. ESI LTQ-OT MS was performed on a LTQ Orbitrap velos equipped with a NanoAcquity system from Waters. MS data were analysed for protein identifications using EasyProt (v2.3) and peak lists were generated into .mgf format with EasyProtConv (CID/HCD merging was used to improve peptide identification and quantification). Searches were conducted against UniProt Swiss-Prot database (UniProtKB release 2014_10 of Oct, 29, 2014), specifying Rattus norvegicus taxonomy. Only proteins with at least two unique peptide sequences and a false discovery rate (FDR) ≤ 1 % were selected for further quantification. Proteins were clustered based on shared peptides indistinguishable by MS. Quantification was conducted using Isobar R package (v.1.9.3.2). We identified 997 proteins, of which 353 were significantly differentially expressed (22, 256 or 55 in both, the soluble or the membrane fractions, respectively).

Selenoprotein T (SELENOT, SelT), a thioredoxin-like enzyme, exerts an essential oxidoreductase activity in the endoplasmic reticulum. However, its precise function remains unknown. To gain more understanding of SELENOT function, a conventional SELENOT knockout (KO) mouse model was constructed for the first time by CRISPR/Cas9 technique. TMT proteomics analysis was conducted to explore the differentially expressed proteins (DEPs) in the liver, revealing 60 up-regulated and 94 down-regulated DEPs in KO mice. The results of proteomics were validated by western blot of 3 selected DEPs (Gys2, DIO1, Gsta2). Furthermore, the bioinformatics analysis showed that SELENOT KO-induced DEPs were mainly related to lipid metabolism, cancer and PPAR signaling pathway. Overall, these findings provide a holistic perspective into SELENOT KO-induced DEPs in the liver and novel insights into the role of SELENOT in glucose and lipid metabolism, and thus enhance our understanding of SELENOT function.

We evaluated the degree of over-labeling for data generated by the Single-Cell ProtEomics by Mass Spectrometry (SCoPE2) protocol. We performed a MaxQuant search in which TMT Pro labels were specified as a variable modification on histidine, serine, threonine, and tyrosine, and as a fixed modifications on primary amines (N-terminus and lysine). We found very few peptides with TMT labeling on histidine, serine, threonine, or tyrosine. After applying standard filters used by the SCoPE2 data pipeline (keeping peptides that have PEP = 0.9 and eliminating reverse hits and potential contaminants), we find a total of 225 unique over labeled sequences, amounting to about 3% of all peptide sequences. When evaluating each SCoPE2 run individually, the amount of over-labeled and confidently identified peptides ranges from 0.1% to 2.92% of the total run.

Host kinases play essential roles in the host cell cycle, innate immune signaling, the stress response to viral infection, and inflammation. Previous work has demonstrated coronaviruses specifically target kinase cascades to subvert host cell responses to infection and rely upon host kinase activity to phosphorylate viral proteins to enhance replication. Given the number of kinase inhibitors that are already FDA approved to treat cancers, fibrosis, and other human disease, they represent an attractive class of compounds to repurpose for host targeted therapies against emerging coronavirus infections. To further understand the host kinome response to betacoronavirus infection we employed multiplex inhibitory bead mass spectrometry (MIB-MS) following MERS-CoV and SARS-CoV-2 infection of human lung epithelial cell lines. To determine signaling pathways altered over the SARS-CoV-2 time-course, global quantitative phosphoproteomic and proteomic analysis of SARS-CoV-2 infected A549-hACE2 was performed on paired cell lysates (n=3) from the MIB-MS analysis. This PRIDE Submission contains the proteome and phosphoproteome data. The MIB-MS data were uploaded as a separate PRIDE Submission. Over 7,200 proteins and 14,300 phosphosites were quantified over the 24 hr infection time course. Further analysis of the phosphoproteome indicated activation of MAPK, PI3K, and mTOR signaling.