bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2024‒08‒25
fourteen papers selected by
Giovanny Rodriguez Blanco, University of Edinburgh
  1. Deep Plasma Proteomics with Data-Independent Acquisition: Clinical Study Protocol Optimization with a COVID-19 Cohort.
  2. Bottom-up Histone Post-translational Modification Analysis using Liquid Chromatography, Trapped Ion Mobility Spectrometry, and Tandem Mass Spectrometry.
  3. Native MS-guided lipidomics to define endogenous lipid microenvironments of eukaryotic receptors and transporters.
  4. Deep Learning Powers Protein Identification from Precursor MS Information.
  5. Lipid metabolism associated crosstalk: the bidirectional interaction between cancer cells and immune/stromal cells within the tumor microenvironment for prognostic insight.
  6. An Inflection Point in High-Throughput Proteomics with Orbitrap Astral: Analysis of Biofluids, Cells, and Tissues.
  7. Native metabolomics for mass spectrometry-based siderophore discovery.
  8. Proteomic landscape profiling of primary prostate cancer reveals a 16-protein panel for prognosis prediction.
  9. Using long columns to quantify over 9200 unique protein groups from brain tissue in a single injection on an Orbitrap Exploris 480 mass spectrometer.
  10. Cancer tissue of origin constrains the growth and metabolism of metastases.
  11. Signal interference between drugs and metabolites in LC-ESI-MS quantitative analysis and its evaluation strategy.
  12. Mass Spectrometry as a Quantitative Tool for SpCas9 sgRNA Quality Control.
  13. An end-to-end deep learning method for mass spectrometry data analysis to reveal disease-specific metabolic profiles.
  14. Data-independent acquisition in metaproteomics.
  1. J Proteome Res. 2024 Aug 19.
    Deep Plasma Proteomics with Data-Independent Acquisition: Clinical Study Protocol Optimization with a COVID-19 Cohort.
    Bradley Ward, Sébastien Pyr Dit Ruys, Jean-Luc Balligand, Leïla Belkhir, Patrice D Cani, Jean-François Collet, Julien De Greef, Joseph P Dewulf, Laurent Gatto, Vincent Haufroid, Sébastien Jodogne, Benoît Kabamba, Maxime Lingurski, Jean Cyr Yombi, Didier Vertommen, Laure Elens.
      Plasma proteomics is a precious tool in human disease research but requires extensive sample preparation in order to perform in-depth analysis and biomarker discovery using traditional data-dependent acquisition (DDA). Here, we highlight the efficacy of combining moderate plasma prefractionation and data-independent acquisition (DIA) to significantly improve proteome coverage and depth while remaining cost-efficient. Using human plasma collected from a 20-patient COVID-19 cohort, our method utilizes commonly available solutions for depletion, sample preparation, and fractionation, followed by 3 liquid chromatography-mass spectrometry/MS (LC-MS/MS) injections for a 360 min total DIA run time. We detect 1321 proteins on average per patient and 2031 unique proteins across the cohort. Differential analysis further demonstrates the applicability of this method for plasma proteomic research and clinical biomarker identification, identifying hundreds of differentially abundant proteins at biological concentrations as low as 47 ng/L in human plasma. Data are available via ProteomeXchange with the identifier PXD047901. In summary, this study introduces a streamlined, cost-effective approach to deep plasma proteome analysis, expanding its utility beyond classical research environments and enabling larger-scale multiomics investigations in clinical settings. Our comparative analysis revealed that fractionation, whether the samples were pooled or separate postfractionation, significantly improved the number of proteins quantified. This underscores the value of fractionation in enhancing the depth of plasma proteome analysis, thereby offering a more comprehensive landscape for biomarker discovery in diseases such as COVID-19.
    Keywords:  COVID-19; DIA-NN; biomarkers; clinical proteomics; data-independent acquisition; deep proteome analysis; fractionation; plasma proteomics
    DOI:  https://doi.org/10.1021/acs.jproteome.4c00104
  2. J Proteome Res. 2024 Aug 23.
    Bottom-up Histone Post-translational Modification Analysis using Liquid Chromatography, Trapped Ion Mobility Spectrometry, and Tandem Mass Spectrometry.
    Cassandra N Fuller, Lilian Valadares Tose, Francisca N L Vitorino, Natarajan V Bhanu, Erin M Panczyk, Melvin A Park, Benjamin A Garcia, Francisco Fernandez-Lima.
      The amino acid position within a histone sequence and the chemical nature of post-translational modifications (PTMs) are essential for elucidating the "Histone Code". Previous work has shown that PTMs induce specific biological responses and are good candidates as biomarkers for diagnostics. Here, we evaluate the analytical advantages of trapped ion mobility (TIMS) with parallel accumulation-serial fragmentation (PASEF) and tandem mass spectrometry (MS/MS) for bottom-up proteomics of model cancer cells. The study also considered the use of nanoliquid chromatography (LC) and traditional methods: LC-TIMS-PASEF-ToF MS/MS vs nLC-TIMS-PASEF-ToF MS/MS vs nLC-MS/MS. The addition of TIMS and PASEF-MS/MS increased the number of detected peptides due to the added separation dimension. All three methods showed high reproducibility and low RSD in the MS domain (<5 ppm). While the LC, nLC and TIMS separations showed small RSD across samples, the accurate mobility (1/K0) measurements (<0.6% RSD) increased the confidence of peptide assignments. Trends were observed in the retention time and mobility concerning the number and type of PTMs (e.g., ac, me1-3) and their corresponding unmodified, propionylated peptide that aided in peptide assignment. Mobility separation permitted the annotation of coeluting structural and positional isomers and compared with nLC-MS/MS showed several advantages due to reduced chemical noise.
    Keywords:  bottom-up proteomics; histones; nano liquid chromatography; parallel accumulation-serial fragmentation; post-translational modifications; tandem mass spectrometry; trapped ion mobility spectrometry
    DOI:  https://doi.org/10.1021/acs.jproteome.4c00177
  3. Nat Protoc. 2024 Aug 22.
    Native MS-guided lipidomics to define endogenous lipid microenvironments of eukaryotic receptors and transporters.
    Di Wu, Haiping Tang, Xingyu Qiu, Siyuan Song, Siyun Chen, Carol V Robinson.
      The mammalian membrane is composed of various eukaryotic lipids interacting with extensively post-translationally modified proteins. Probing interactions between these mammalian membrane proteins and their diverse and heterogeneous lipid cohort remains challenging. Recently, native mass spectrometry (MS) combined with bottom-up 'omics' approaches has provided valuable information to relate structural and functional lipids to membrane protein assemblies in eukaryotic membranes. Here we provide a step-by-step protocol to identify and provide relative quantification for endogenous lipids bound to mammalian membrane proteins and their complexes. Using native MS to guide our lipidomics strategies, we describe the necessary sample preparation steps, followed by native MS data acquisition, tailored lipidomics and data interpretation. We also highlight considerations for the integration of different levels of information from native MS and lipidomics and how to deal with the various challenges that arise during the experiments. This protocol begins with the preparation of membrane proteins from mammalian cells and tissues for native MS. The results enable not only direct assessment of copurified endogenous lipids but also determination of the apparent affinities of specific lipids. Detailed sample preparation for lipidomics analysis is also covered, along with comprehensive settings for liquid chromatography-MS analysis. This protocol is suitable for the identification and quantification of endogenous lipids, including fatty acids, sterols, glycerolipids, phospholipids and glycolipids and can be used to interrogate proteins from recombinant sources to native membranes.
    DOI:  https://doi.org/10.1038/s41596-024-01037-4
  4. J Proteome Res. 2024 Aug 21.
    Deep Learning Powers Protein Identification from Precursor MS Information.
    Yameng Dai, Yi Yang, Enhui Wu, Chengpin Shen, Liang Qiao.
      Proteome analysis currently heavily relies on tandem mass spectrometry (MS/MS), which does not fully utilize MS1 features, as many precursors remain unselected for MS/MS fragmentation, especially in the cases of low abundance samples and wide abundance dynamic range samples. Therefore, leveraging MS1 features as a complement to MS/MS has become an attractive option to improve the coverage of feature identification. Herein, we propose MonoMS1, an approach combining deep learning-based retention time, ion mobility, detectability prediction, and logistic regression-based scoring for MS1 feature identification. The approach achieved a significant increase in MS1 feature identification based on an E. coli data set. Application of MonoMS1 to data sets with wide dynamic range, such as human serum proteome samples, and with low sample abundance, such as single-cell proteome samples, enabled substantial complementation of MS/MS-based peptide and protein identification. This method opens a new avenue for proteomic analysis and can boost proteomic research on complex samples.
    Keywords:  MS1 feature; deep learning; proteomics; serum proteomics; single cell
    DOI:  https://doi.org/10.1021/acs.jproteome.4c00118
  5. Cancer Cell Int. 2024 Aug 22. 24(1): 295
    Lipid metabolism associated crosstalk: the bidirectional interaction between cancer cells and immune/stromal cells within the tumor microenvironment for prognostic insight.
    Zhongshu Lin, Guanxiang Hua, Xiaojuan Hu.
      Cancer is closely related to lipid metabolism, with the tumor microenvironment (TME) containing numerous lipid metabolic interactions. Cancer cells can bidirectionally interact with immune and stromal cells, the major components of the TME. This interaction is primarily mediated by fatty acids (FAs), cholesterol, and phospholipids. These interactions can lead to various physiological changes, including immune suppression, cancer cell proliferation, dissemination, and anti-apoptotic effects on cancer cells. The physiological modulation resulting from this lipid metabolism-associated crosstalk between cancer cells and immune/stromal cells provides valuable insights into cancer prognosis. A comprehensive literature review was conducted to examine the function of the bidirectional lipid metabolism interactions between cancer cells and immune/stromal cells within the TME, particularly how these interactions influence cancer prognosis. A novel autophagy-extracellular vesicle (EV) pathway has been proposed as a mediator of lipid metabolism interactions between cancer cells and immune cells/stromal cells, impacting cancer prognosis. As a result, different forms of lipid metabolism interactions have been described as being linked to cancer prognosis, including those mediated by the autophagy-EV pathway. In conclusion, understanding the bidirectional lipid metabolism interactions between cancer cells and stromal/immune cells in the TME can help develop more advanced prognostic approaches for cancer patients.
    DOI:  https://doi.org/10.1186/s12935-024-03481-4
  6. J Proteome Res. 2024 Aug 20.
    An Inflection Point in High-Throughput Proteomics with Orbitrap Astral: Analysis of Biofluids, Cells, and Tissues.
    Nathan G Hendricks, Santosh D Bhosale, Angel J Keoseyan, Josselin Ortiz, Aleksandr Stotland, Saeed Seyedmohammad, Chi D L Nguyen, Jonathan T Bui, Annie Moradian, Susan M Mockus, Jennifer E Van Eyk.
      This Technical Note presents a comprehensive proteomics workflow for the new combination of Orbitrap and Astral mass analyzers across biofluids, cells, and tissues. Central to our workflow is the integration of Adaptive Focused Acoustics (AFA) technology for cells and tissue lysis to ensure robust and reproducible sample preparation in a high-throughput manner. Furthermore, we automated the detergent-compatible single-pot, solid-phase-enhanced sample Preparation (SP3) method for protein digestion. The synergy of these advanced methodologies facilitates a robust and high-throughput approach for cell and tissue analysis, an important consideration in translational research. This work disseminates our platform workflow, analyzes the effectiveness, demonstrates the reproducibility of the results, and highlights the potential of these technologies in biomarker discovery and disease pathology. For cells and tissues (heart, liver, lung, and intestine) proteomics analysis by data-independent acquisition mode, identifications exceeding 10,000 proteins can be achieved with a 24 min active gradient. In 200 ng injections of HeLa digest across multiple gradients, an average of more than 80% of proteins have a CV less than 20%, and a 45 min run covers ∼90% of the expressed proteome. This complete workflow allows for large swaths of the proteome to be identified and is compatible with diverse sample types.
    Keywords:  Orbitrap Astral; PBMCs; automation; biomarker; high-throughput; mass spectrometry; missing proteins; plasma proteomics; tissue proteomics
    DOI:  https://doi.org/10.1021/acs.jproteome.4c00384
  7. Methods Enzymol. 2024 ;pii: S0076-6879(24)00327-6. [Epub ahead of print]702 317-352
    Native metabolomics for mass spectrometry-based siderophore discovery.
    Marquis T Yazzie, Zachary L Reitz, Robin Schmid, Daniel Petras, Allegra T Aron.
      Microorganisms, plants, and animals alike have specialized acquisition pathways for obtaining metals, with microorganisms and plants biosynthesizing and secreting small molecule natural products called siderophores and metallophores with high affinities and specificities for iron or other non-iron metals, respectively. This chapter details a novel approach to discovering metal-binding molecules, including siderophores and metallophores, from complex samples ranging from microbial supernatants to biological tissue to environmental samples. This approach, called Native Metabolomics, is a mass spectrometry method in which pH adjustment and metal infusion post-liquid chromatography are interfaced with ion identity molecular networking (IIMN). This rule-based data analysis workflow that enables the identification of metal-binding species based on defined mass (m/z) offsets with the same chromatographic profiles and retention times. Ion identity molecular networking connects compounds that are structurally similar by their fragmentation pattern and species that are ion adducts of the same compound by chromatographic shape correlations. This approach has previously revealed new insights into metal binding metabolites, including that yersiniabactin can act as a biological zincophore (in addition to its known role as a siderophore), that the recently elucidated lepotchelin natural products are cyanobacterial metallophores, and that antioxidants in traditional medicine bind iron. Native metabolomics can be conducted on any liquid chromatography-mass spectrometry system to explore the binding of any metal or multiple metals simultaneously, underscoring the potential for this method to become an essential strategy for elucidating biological metal-binding molecules.
    Keywords:  Direct infusion; Metallophore discovery; Native mass spectrometry; Native metabolomics; Natural product discovery; Siderophore discovery
    DOI:  https://doi.org/10.1016/bs.mie.2024.07.001
  8. Cell Rep Med. 2024 Aug 20. pii: S2666-3791(24)00400-2. [Epub ahead of print]5(8): 101679
    Proteomic landscape profiling of primary prostate cancer reveals a 16-protein panel for prognosis prediction.
    Rui Sun, Jun A, Haolan Yu, Yan Wang, Miaoxia He, Lingling Tan, Honghan Cheng, Jili Zhang, Yingrui Wang, Xiaochen Sun, Mengge Lyu, Min Qu, Lingling Huang, Zijian Li, Wenhui Zhang, Kunpeng Ma, Zhenyang Dong, Weigang Ge, Yun Zhang, Xuan Ding, Bo Yang, Jianguo Hou, Chuanliang Xu, Linhui Wang, Yi Zhu, Tiannan Guo, Xu Gao, Chenghua Yang.
      Prostate cancer (PCa) is the most common malignant tumor in men. Currently, there are few prognosis indicators for predicting PCa outcomes and guiding treatments. Here, we perform comprehensive proteomic profiling of 918 tissue specimens from 306 Chinese patients with PCa using data-independent acquisition mass spectrometry (DIA-MS). We identify over 10,000 proteins and define three molecular subtypes of PCa with significant clinical and proteomic differences. We develop a 16-protein panel that effectively predicts biochemical recurrence (BCR) for patients with PCa, which is validated in six published datasets and one additional 99-biopsy-sample cohort by targeted proteomics. Interestingly, this 16-protein panel effectively predicts BCR across different International Society of Urological Pathology (ISUP) grades and pathological stages and outperforms the D'Amico risk classification system in BCR prediction. Furthermore, double knockout of NUDT5 and SEPTIN8, two components from the 16-protein panel, significantly suppresses the PCa cells to proliferate, invade, and migrate, suggesting the combination of NUDT5 and SEPTIN8 may provide new approaches for PCa treatment.
    Keywords:  BCR-free survival; DIA-MS; NUDT5 and SEPTIN8; prognosis prediction; prostate cancer; proteomics
    DOI:  https://doi.org/10.1016/j.xcrm.2024.101679
  9. J Proteomics. 2024 Aug 17. pii: S1874-3919(24)00217-3. [Epub ahead of print]308 105285
    Using long columns to quantify over 9200 unique protein groups from brain tissue in a single injection on an Orbitrap Exploris 480 mass spectrometer.
    Xianyin Lai, Guihong Qi.
      The most exciting advancement in LC-MS/MS-based bottom-up proteomics has centered around enhancing mass spectrometers. Among these, the latest and most advanced mass spectrometer for bottom-up proteomics is the Orbitrap Astral that has the highest scan rate to accelerate throughput and the highest sensitivity to handle a very small amount of peptide samples and to achieve deeper proteomics. However, its affordability remains a challenge for most laboratories. While significant strides have been made in improving mass spectrometry, advancing liquid chromatography (LC) to achieve deeper proteomics has not achieved significant successes since the innovation of Multidimensional Protein Identification Technology (MudPIT) in 2001. To achieve deeper proteomics in a less labor-intensive and more reproducible approach while using a more cost-effective mass spectrometer, such as the Orbitrap Exploris 480, we evaluated trap columns as long as 40 cm and analytical column as long as 600 cm besides sample loading amount, gradient time, and analytical column particle size to enable a fractionation-free method for a single injection to obtain deeper proteomics. The length of trap and analytic columns is the key factor. Using a 30 cm trap column and 250 cm analytical column with other optimized LC conditions, we quantified over 9200 unique protein groups from brain tissue in a single injection using a 24-h gradient on an Orbitrap Exploris 480 mass spectrometer.
    Keywords:  Deep proteomics; Fractionation-free; Long column; Orbitrap Exploris 480; Single injection
    DOI:  https://doi.org/10.1016/j.jprot.2024.105285
  10. Nat Metab. 2024 Aug 19.
    Cancer tissue of origin constrains the growth and metabolism of metastases.
    Sharanya Sivanand, Yetis Gultekin, Peter S Winter, Sidney Y Vermeulen, Konstantine M Tchourine, Keene L Abbott, Laura V Danai, Florian Gourgue, Brian T Do, Kayla Crowder, Tenzin Kunchok, Allison N Lau, Alicia M Darnell, Alexandria Jefferson, Satoru Morita, Dan G Duda, Andrew J Aguirre, Brian M Wolpin, Nicole Henning, Virginia Spanoudaki, Laura Maiorino, Darrell J Irvine, Omer H Yilmaz, Caroline A Lewis, Dennis Vitkup, Alex K Shalek, Matthew G Vander Heiden.
      Metastases arise from subsets of cancer cells that disseminate from the primary tumour1,2. The ability of cancer cells to thrive in a new tissue site is influenced by genetic and epigenetic changes that are important for disease initiation and progression, but these factors alone do not predict if and where cancers metastasize3,4. Specific cancer types metastasize to consistent subsets of tissues, suggesting that primary tumour-associated factors influence where cancers can grow. We find primary and metastatic pancreatic tumours have metabolic similarities and that the tumour-initiating capacity and proliferation of both primary-derived and metastasis-derived cells is favoured in the primary site relative to the metastatic site. Moreover, propagating cells as tumours in the lung or the liver does not enhance their relative ability to form large tumours in those sites, change their preference to grow in the primary site, nor stably alter aspects of their metabolism relative to primary tumours. Primary liver and lung cancer cells also exhibit a preference to grow in their primary site relative to metastatic sites. These data suggest cancer tissue of origin influences both primary and metastatic tumour metabolism and may impact where cancer cells can metastasize.
    DOI:  https://doi.org/10.1038/s42255-024-01105-9
  11. J Pharm Anal. 2024 Jul;14(7): 100954
    Signal interference between drugs and metabolites in LC-ESI-MS quantitative analysis and its evaluation strategy.
    Fulin Jiang, Jingyu Liu, Yagang Li, Zihan Lu, Qian Liu, Yunhui Xing, Janshon Zhu, Min Huang, Guoping Zhong.
      Liquid chromatography-electrospray ionization tandem mass spectrometry (LC-ESI-MS) is a widely utilized technique for in vivo pharmaceutical analysis. Ionization interference within electrospray ion source, occurring between drugs and metabolites, can lead to signal variations, potentially compromising quantitative accuracy. Currently, method validation often overlooks this type of signal interference, which may result in systematic errors in quantitative results without matrix-matched calibration. In this study, we conducted an investigation using ten different groups of drugs and their corresponding metabolites across three LC-ESI-MS systems to assess the prevalence of signal interference. Such interferences can potentially cause or enhance nonlinearity in the calibration curves of drugs and metabolites, thereby altering the relationship between analyte response and concentration for quantification. Finally, we established an evaluation scheme through a step-by-step dilution assay and employed three resolution methods: chromatographic separation, dilution, and stable labeled isotope internal standards correction. The above strategies were integrated into the method establishment process to improve quantitative accuracy.
    Keywords:  Drugs; Ionization interference; Liquid chromatography-electrospray ionization tandem mass spectrometry (LC-ESI-MS); Metabolites; Quantitative analysis
    DOI:  https://doi.org/10.1016/j.jpha.2024.02.008
  12. Nucleic Acid Ther. 2024 Aug 23.
    Mass Spectrometry as a Quantitative Tool for SpCas9 sgRNA Quality Control.
    Juan Daniel Avila, Puzhou Wang.
      Mass spectrometry (MS) has long been used for quality control of oligonucleotide therapeutics, including single-guide RNAs (sgRNAs) for clustered regularly interspaced short palindromic repeats techniques. However, the application of MS is limited to qualitative assays in most cases. Here, we showed that electrospray-ionization quadrupole time-of-flight MS (ESI-QTOF-MS) assays can be quantitative for chemical species found in sgRNA samples. More specifically, using a 100-nt SpCas9 sgRNA as the example, we estimated that the limits of quantification for length variants in the range of N - 4 to N + 4 (i.e., 96-104 nucleotides) were equal to or lower than 1%. Our study highlighted the potential of ESI-QTOF in its application as a quality control method for sgRNA molecules.
    Keywords:  CRISPR; guide RNA; mass spectrometry; quality control
    DOI:  https://doi.org/10.1089/nat.2024.0043
  13. Nat Commun. 2024 Aug 20. 15(1): 7136
    An end-to-end deep learning method for mass spectrometry data analysis to reveal disease-specific metabolic profiles.
    Yongjie Deng, Yao Yao, Yanni Wang, Tiantian Yu, Wenhao Cai, Dingli Zhou, Feng Yin, Wanli Liu, Yuying Liu, Chuanbo Xie, Jian Guan, Yumin Hu, Peng Huang, Weizhong Li.
      Untargeted metabolomic analysis using mass spectrometry provides comprehensive metabolic profiling, but its medical application faces challenges of complex data processing, high inter-batch variability, and unidentified metabolites. Here, we present DeepMSProfiler, an explainable deep-learning-based method, enabling end-to-end analysis on raw metabolic signals with output of high accuracy and reliability. Using cross-hospital 859 human serum samples from lung adenocarcinoma, benign lung nodules, and healthy individuals, DeepMSProfiler successfully differentiates the metabolomic profiles of different groups (AUC 0.99) and detects early-stage lung adenocarcinoma (accuracy 0.961). Model flow and ablation experiments demonstrate that DeepMSProfiler overcomes inter-hospital variability and effects of unknown metabolites signals. Our ensemble strategy removes background-category phenomena in multi-classification deep-learning models, and the novel interpretability enables direct access to disease-related metabolite-protein networks. Further applying to lipid metabolomic data unveils correlations of important metabolites and proteins. Overall, DeepMSProfiler offers a straightforward and reliable method for disease diagnosis and mechanism discovery, enhancing its broad applicability.
    DOI:  https://doi.org/10.1038/s41467-024-51433-3
  14. Expert Rev Proteomics. 2024 Aug 21. 1-10
    Data-independent acquisition in metaproteomics.
    Enhui Wu, Guanyang Xu, Dong Xie, Liang Qiao.
      INTRODUCTION: Metaproteomics offers insights into the function of complex microbial communities, while it is also capable of revealing microbe-microbe and host-microbe interactions. Data-independent acquisition (DIA) mass spectrometry is an emerging technology, which holds great potential to achieve deep and accurate metaproteomics with higher reproducibility yet still facing a series of challenges due to the inherent complexity of metaproteomics and DIA data.AREAS COVERED: This review offers an overview of the DIA metaproteomics approaches, covering aspects such as database construction, search strategy, and data analysis tools. Several cases of current DIA metaproteomics studies are presented to illustrate the procedures. Important ongoing challenges are also highlighted. Future perspectives of DIA methods for metaproteomics analysis are further discussed. Cited references are searched through and collected from Google Scholar and PubMed.
    EXPERT OPINION: Considering the inherent complexity of DIA metaproteomics data, data analysis strategies specifically designed for interpretation are imperative. From this point of view, we anticipate that deep learning methods and de novo sequencing methods will become more prevalent in the future, potentially improving protein coverage in metaproteomics. Moreover, the advancement of metaproteomics also depends on the development of sample preparation methods, data analysis strategies, etc. These factors are key to unlocking the full potential of metaproteomics.
    Keywords:  Metaproteomics; analysis tools; data interpretation; data-independent acquisition; protein sequence database
    DOI:  https://doi.org/10.1080/14789450.2024.2394190
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