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The Recent Developments in Sample Preparation for Mass Spectrometry-Based Metabolomics.

Critical reviews in analytical chemistry

Gong ZG, Hu J, Wu X, Xu YJ.
PMID: 28631936
Crit Rev Anal Chem. 2017 Jul 04;47(4):325-331. doi: 10.1080/10408347.2017.1289836. Epub 2017 Feb 08.

Metabolomics is a critical member in systems biology. Although great progress has been achieved in metabolomics, there are still some problems in sample preparation, data processing and data interpretation. In this review, we intend to explore the roles, challenges...

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Gong ZG, Hu J, Wu X, et al. The Recent Developments in Sample Preparation for Mass Spectrometry-Based Metabolomics. Crit Rev Anal Chem. 2017;47(4):325-331doi: 10.1080/10408347.2017.1289836.
Gong, Z. G., Hu, J., Wu, X., & Xu, Y. J. (2017). The Recent Developments in Sample Preparation for Mass Spectrometry-Based Metabolomics. Critical reviews in analytical chemistry, 47(4), 325-331. https://doi.org/10.1080/10408347.2017.1289836
Gong, Zhi-Gang, et al. "The Recent Developments in Sample Preparation for Mass Spectrometry-Based Metabolomics." Critical reviews in analytical chemistry vol. 47,4 (2017): 325-331. doi: https://doi.org/10.1080/10408347.2017.1289836
Gong ZG, Hu J, Wu X, Xu YJ. The Recent Developments in Sample Preparation for Mass Spectrometry-Based Metabolomics. Crit Rev Anal Chem. 2017 Jul 04;47(4):325-331. doi: 10.1080/10408347.2017.1289836. Epub 2017 Feb 08. PMID: 28631936.
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High-throughput screening of secondary metabolites by Sorbus pohuashanensis cells under environmental stress using UHPLC-QTOF combined with AntDAS.

Physiologia plantarum

Yang J, Li Y, Li J, Yuan J, Wang S, Zhou L, Zhou L, Kang C, Guo L.
PMID: 34590719
Physiol Plant. 2021 Dec;173(4):2216-2225. doi: 10.1111/ppl.13572. Epub 2021 Oct 10.

Environment stress can promote the synthesis and accumulation of a series of secondary metabolites, which are important quality factors in medicinal plants. However, the data related to metabolites is often too large, making it difficult to screen quickly, accurately...

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Yang J, Li Y, Li J, et al. High-throughput screening of secondary metabolites by Sorbus pohuashanensis cells under environmental stress using UHPLC-QTOF combined with AntDAS. Physiol Plant. 2021;173(4):2216-2225doi: 10.1111/ppl.13572.
Yang, J., Li, Y., Li, J., Yuan, J., Wang, S., Zhou, L., Zhou, L., Kang, C., & Guo, L. (2021). High-throughput screening of secondary metabolites by Sorbus pohuashanensis cells under environmental stress using UHPLC-QTOF combined with AntDAS. Physiologia plantarum, 173(4), 2216-2225. https://doi.org/10.1111/ppl.13572
Yang, Jian, et al. "High-throughput screening of secondary metabolites by Sorbus pohuashanensis cells under environmental stress using UHPLC-QTOF combined with AntDAS." Physiologia plantarum vol. 173,4 (2021): 2216-2225. doi: https://doi.org/10.1111/ppl.13572
Yang J, Li Y, Li J, Yuan J, Wang S, Zhou L, Zhou L, Kang C, Guo L. High-throughput screening of secondary metabolites by Sorbus pohuashanensis cells under environmental stress using UHPLC-QTOF combined with AntDAS. Physiol Plant. 2021 Dec;173(4):2216-2225. doi: 10.1111/ppl.13572. Epub 2021 Oct 10. PMID: 34590719.
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Quality standards in proteomics research facilities: Common standards and quality procedures are essential for proteomics facilities and their users.

EMBO reports

Chiva C, Mendes Maia T, Panse C, Stejskal K, Douché T, Matondo M, Loew D, Helm D, Rettel M, Mechtler K, Impens F, Nanni P, Shevchenko A, Sabidó E.
PMID: 34009726
EMBO Rep. 2021 Jun 04;22(6):e52626. doi: 10.15252/embr.202152626. Epub 2021 May 19.

Proteomics research infrastructures and core facilities within the Core for Life alliance advocate for community policies for quality control to ensure high standards in proteomics services.

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Chiva C, Mendes Maia T, Panse C, et al. Quality standards in proteomics research facilities: Common standards and quality procedures are essential for proteomics facilities and their users. EMBO Rep. 2021;22(6):e52626doi: 10.15252/embr.202152626.
Chiva, C., Mendes Maia, T., Panse, C., Stejskal, K., Douché, T., Matondo, M., Loew, D., Helm, D., Rettel, M., Mechtler, K., Impens, F., Nanni, P., Shevchenko, A., & Sabidó, E. (2021). Quality standards in proteomics research facilities: Common standards and quality procedures are essential for proteomics facilities and their users. EMBO reports, 22(6), e52626. https://doi.org/10.15252/embr.202152626
Chiva, Cristina, et al. "Quality standards in proteomics research facilities: Common standards and quality procedures are essential for proteomics facilities and their users." EMBO reports vol. 22,6 (2021): e52626. doi: https://doi.org/10.15252/embr.202152626
Chiva C, Mendes Maia T, Panse C, Stejskal K, Douché T, Matondo M, Loew D, Helm D, Rettel M, Mechtler K, Impens F, Nanni P, Shevchenko A, Sabidó E. Quality standards in proteomics research facilities: Common standards and quality procedures are essential for proteomics facilities and their users. EMBO Rep. 2021 Jun 04;22(6):e52626. doi: 10.15252/embr.202152626. Epub 2021 May 19. PMID: 34009726; PMCID: PMC8183401.
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iMetaLab 1.0: a web platform for metaproteomics data analysis.

Bioinformatics (Oxford, England)

Liao B, Ning Z, Cheng K, Zhang X, Li L, Mayne J, Figeys D.
PMID: 29912378
Bioinformatics. 2018 Nov 15;34(22):3954-3956. doi: 10.1093/bioinformatics/bty466.

SUMMARY: The human gut microbiota, a complex, dynamic and biodiverse community, has been increasingly shown to influence many aspects of health and disease. Metaproteomic analysis has proven to be a powerful approach to study the functionality of the microbiota....

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Liao B, Ning Z, Cheng K, et al. iMetaLab 1.0: a web platform for metaproteomics data analysis. Bioinformatics. 2018;34(22):3954-3956doi: 10.1093/bioinformatics/bty466.
Liao, B., Ning, Z., Cheng, K., Zhang, X., Li, L., Mayne, J., & Figeys, D. (2018). iMetaLab 1.0: a web platform for metaproteomics data analysis. Bioinformatics (Oxford, England), 34(22), 3954-3956. https://doi.org/10.1093/bioinformatics/bty466
Liao, Bo, et al. "iMetaLab 1.0: a web platform for metaproteomics data analysis." Bioinformatics (Oxford, England) vol. 34,22 (2018): 3954-3956. doi: https://doi.org/10.1093/bioinformatics/bty466
Liao B, Ning Z, Cheng K, Zhang X, Li L, Mayne J, Figeys D. iMetaLab 1.0: a web platform for metaproteomics data analysis. Bioinformatics. 2018 Nov 15;34(22):3954-3956. doi: 10.1093/bioinformatics/bty466. PMID: 29912378.
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SECAT: Quantifying Protein Complex Dynamics across Cell States by Network-Centric Analysis of SEC-SWATH-MS Profiles.

Cell systems

Rosenberger G, Heusel M, Bludau I, Collins BC, Martelli C, Williams EG, Xue P, Liu Y, Aebersold R, Califano A.
PMID: 33333029
Cell Syst. 2020 Dec 16;11(6):589-607.e8. doi: 10.1016/j.cels.2020.11.006.

Protein-protein interactions (PPIs) play critical functional and regulatory roles in cellular processes. They are essential for macromolecular complex formation, which in turn constitutes the basis for protein interaction networks that determine the functional state of a cell. We and...

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Rosenberger G, Heusel M, Bludau I, et al. SECAT: Quantifying Protein Complex Dynamics across Cell States by Network-Centric Analysis of SEC-SWATH-MS Profiles. Cell Syst. 2020;11(6):589-607.e8doi: 10.1016/j.cels.2020.11.006.
Rosenberger, G., Heusel, M., Bludau, I., Collins, B. C., Martelli, C., Williams, E. G., Xue, P., Liu, Y., Aebersold, R., & Califano, A. (2020). SECAT: Quantifying Protein Complex Dynamics across Cell States by Network-Centric Analysis of SEC-SWATH-MS Profiles. Cell systems, 11(6), 589-607.e8. https://doi.org/10.1016/j.cels.2020.11.006
Rosenberger, George, et al. "SECAT: Quantifying Protein Complex Dynamics across Cell States by Network-Centric Analysis of SEC-SWATH-MS Profiles." Cell systems vol. 11,6 (2020): 589-607.e8. doi: https://doi.org/10.1016/j.cels.2020.11.006
Rosenberger G, Heusel M, Bludau I, Collins BC, Martelli C, Williams EG, Xue P, Liu Y, Aebersold R, Califano A. SECAT: Quantifying Protein Complex Dynamics across Cell States by Network-Centric Analysis of SEC-SWATH-MS Profiles. Cell Syst. 2020 Dec 16;11(6):589-607.e8. doi: 10.1016/j.cels.2020.11.006. PMID: 33333029; PMCID: PMC8034988.
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Application of Nontarget High Resolution Mass Spectrometry Data to Quantitative Source Apportionment.

Environmental science & technology

Peter KT, Wu C, Tian Z, Kolodziej EP.
PMID: 31603663
Environ Sci Technol. 2019 Nov 05;53(21):12257-12268. doi: 10.1021/acs.est.9b04481. Epub 2019 Oct 25.

High resolution mass spectrometry (HRMS) analyses provide expansive chemical characterizations of environmental samples. To date, most research efforts have developed tools to expedite labor- and time-intensive contaminant identification efforts. However, even without chemical identity, the richness of nontarget HRMS...

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Peter KT, Wu C, Tian Z, et al. Application of Nontarget High Resolution Mass Spectrometry Data to Quantitative Source Apportionment. Environ Sci Technol. 2019;53(21):12257-12268doi: 10.1021/acs.est.9b04481.
Peter, K. T., Wu, C., Tian, Z., & Kolodziej, E. P. (2019). Application of Nontarget High Resolution Mass Spectrometry Data to Quantitative Source Apportionment. Environmental science & technology, 53(21), 12257-12268. https://doi.org/10.1021/acs.est.9b04481
Peter, Katherine T, et al. "Application of Nontarget High Resolution Mass Spectrometry Data to Quantitative Source Apportionment." Environmental science & technology vol. 53,21 (2019): 12257-12268. doi: https://doi.org/10.1021/acs.est.9b04481
Peter KT, Wu C, Tian Z, Kolodziej EP. Application of Nontarget High Resolution Mass Spectrometry Data to Quantitative Source Apportionment. Environ Sci Technol. 2019 Nov 05;53(21):12257-12268. doi: 10.1021/acs.est.9b04481. Epub 2019 Oct 25. PMID: 31603663.
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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry.

Journal of visualized experiments : JoVE

Ashley SE, Meyer BA, Ellis JA, Martino DJ.
PMID: 29985321
J Vis Exp. 2018 Jun 21;(136). doi: 10.3791/57601.

Complex diseases are often underpinned by multiple common genetic variants that contribute to disease susceptibility. Here, we describe a cost-effective tag single nucleotide polymorphism (SNP) approach using a multiplexed genotyping assay with mass spectrometry, to investigate gene pathway associations...

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Ashley SE, Meyer BA, Ellis JA, et al. Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry. J Vis Exp. 2018;(136)doi: 10.3791/57601.
Ashley, S. E., Meyer, B. A., Ellis, J. A., & Martino, D. J. (2018). Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry. Journal of visualized experiments : JoVE, (136), . https://doi.org/10.3791/57601
Ashley, Sarah E, et al. "Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry." Journal of visualized experiments : JoVE vol. ,136 (2018). doi: https://doi.org/10.3791/57601
Ashley SE, Meyer BA, Ellis JA, Martino DJ. Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry. J Vis Exp. 2018 Jun 21;(136). doi: 10.3791/57601. PMID: 29985321; PMCID: PMC6101967.
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Unpicking the proteome in single cells.

Science (New York, N.Y.)

Slavov N.
PMID: 32001644
Science. 2020 Jan 31;367(6477):512-513. doi: 10.1126/science.aaz6695.

No abstract available.

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Slavov N. Unpicking the proteome in single cells. Science. 2020;367(6477):512-513doi: 10.1126/science.aaz6695.
Slavov, N. (2020). Unpicking the proteome in single cells. Science (New York, N.Y.), 367(6477), 512-513. https://doi.org/10.1126/science.aaz6695
Slavov, Nikolai. "Unpicking the proteome in single cells." Science (New York, N.Y.) vol. 367,6477 (2020): 512-513. doi: https://doi.org/10.1126/science.aaz6695
Slavov N. Unpicking the proteome in single cells. Science. 2020 Jan 31;367(6477):512-513. doi: 10.1126/science.aaz6695. PMID: 32001644; PMCID: PMC7029782.
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Chemical Class Prediction of Unknown Biomolecules Using Ion Mobility-Mass Spectrometry and Machine Learning: Supervised Inference of Feature Taxonomy from Ensemble Randomization.

Analytical chemistry

Picache JA, May JC, McLean JA.
PMID: 32628452
Anal Chem. 2020 Aug 04;92(15):10759-10767. doi: 10.1021/acs.analchem.0c02137. Epub 2020 Jul 23.

This work presents a machine learning algorithm referred to as the supervised inference of feature taxonomy from ensemble randomization (SIFTER), which supports the identification of features derived from untargeted ion mobility-mass spectrometry (IM-MS) experiments. SIFTER utilizes random forest machine...

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Picache JA, May JC, McLean JA. Chemical Class Prediction of Unknown Biomolecules Using Ion Mobility-Mass Spectrometry and Machine Learning: Supervised Inference of Feature Taxonomy from Ensemble Randomization. Anal Chem. 2020;92(15):10759-10767doi: 10.1021/acs.analchem.0c02137.
Picache, J. A., May, J. C., & McLean, J. A. (2020). Chemical Class Prediction of Unknown Biomolecules Using Ion Mobility-Mass Spectrometry and Machine Learning: Supervised Inference of Feature Taxonomy from Ensemble Randomization. Analytical chemistry, 92(15), 10759-10767. https://doi.org/10.1021/acs.analchem.0c02137
Picache, Jaqueline A, et al. "Chemical Class Prediction of Unknown Biomolecules Using Ion Mobility-Mass Spectrometry and Machine Learning: Supervised Inference of Feature Taxonomy from Ensemble Randomization." Analytical chemistry vol. 92,15 (2020): 10759-10767. doi: https://doi.org/10.1021/acs.analchem.0c02137
Picache JA, May JC, McLean JA. Chemical Class Prediction of Unknown Biomolecules Using Ion Mobility-Mass Spectrometry and Machine Learning: Supervised Inference of Feature Taxonomy from Ensemble Randomization. Anal Chem. 2020 Aug 04;92(15):10759-10767. doi: 10.1021/acs.analchem.0c02137. Epub 2020 Jul 23. PMID: 32628452.
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Proteomics Standards Initiative Extended FASTA Format.

Journal of proteome research

Binz PA, Shofstahl J, Vizcaíno JA, Barsnes H, Chalkley RJ, Menschaert G, Alpi E, Clauser K, Eng JK, Lane L, Seymour SL, Sánchez LFH, Mayer G, Eisenacher M, Perez-Riverol Y, Kapp EA, Mendoza L, Baker PR, Collins A, Van Den Bossche T, Deutsch EW.
PMID: 31081335
J Proteome Res. 2019 Jun 07;18(6):2686-2692. doi: 10.1021/acs.jproteome.9b00064. Epub 2019 May 23.

Mass-spectrometry-based proteomics enables the high-throughput identification and quantification of proteins, including sequence variants and post-translational modifications (PTMs) in biological samples. However, most workflows require that such variations be included in the search space used to analyze the data, and...

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Binz PA, Shofstahl J, Vizcaíno JA, et al. Proteomics Standards Initiative Extended FASTA Format. J Proteome Res. 2019;18(6):2686-2692doi: 10.1021/acs.jproteome.9b00064.
Binz, P. A., Shofstahl, J., Vizcaíno, J. A., Barsnes, H., Chalkley, R. J., Menschaert, G., Alpi, E., Clauser, K., Eng, J. K., Lane, L., Seymour, S. L., Sánchez, L. F. H., Mayer, G., Eisenacher, M., Perez-Riverol, Y., Kapp, E. A., Mendoza, L., Baker, P. R., Collins, A., Van Den Bossche, T., & Deutsch, E. W. (2019). Proteomics Standards Initiative Extended FASTA Format. Journal of proteome research, 18(6), 2686-2692. https://doi.org/10.1021/acs.jproteome.9b00064
Binz, Pierre-Alain, et al. "Proteomics Standards Initiative Extended FASTA Format." Journal of proteome research vol. 18,6 (2019): 2686-2692. doi: https://doi.org/10.1021/acs.jproteome.9b00064
Binz PA, Shofstahl J, Vizcaíno JA, Barsnes H, Chalkley RJ, Menschaert G, Alpi E, Clauser K, Eng JK, Lane L, Seymour SL, Sánchez LFH, Mayer G, Eisenacher M, Perez-Riverol Y, Kapp EA, Mendoza L, Baker PR, Collins A, Van Den Bossche T, Deutsch EW. Proteomics Standards Initiative Extended FASTA Format. J Proteome Res. 2019 Jun 07;18(6):2686-2692. doi: 10.1021/acs.jproteome.9b00064. Epub 2019 May 23. PMID: 31081335; PMCID: PMC6642660.
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Systematic mapping of protein-metabolite interactions with mass spectrometry-based techniques.

Current opinion in biotechnology

Li S, Shui W.
PMID: 31606719
Curr Opin Biotechnol. 2020 Aug;64:24-31. doi: 10.1016/j.copbio.2019.09.002. Epub 2019 Oct 10.

The recent rapid advance of systematic mapping of protein-metabolite interactions (PMIs) in both prokaryotic and eukaryotic cells has been catalyzed by development of innovative and effective proteomics or metabolomics strategies all based on large-scale mass spectrometry (MS) analysis of...

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Li S, Shui W. Systematic mapping of protein-metabolite interactions with mass spectrometry-based techniques. Curr Opin Biotechnol. 2019;64:24-31doi: 10.1016/j.copbio.2019.09.002.
Li, S., & Shui, W. (2020). Systematic mapping of protein-metabolite interactions with mass spectrometry-based techniques. Current opinion in biotechnology, 6424-31. https://doi.org/10.1016/j.copbio.2019.09.002
Li, Shanshan, and Shui, Wenqing. "Systematic mapping of protein-metabolite interactions with mass spectrometry-based techniques." Current opinion in biotechnology vol. 64 (2020): 24-31. doi: https://doi.org/10.1016/j.copbio.2019.09.002
Li S, Shui W. Systematic mapping of protein-metabolite interactions with mass spectrometry-based techniques. Curr Opin Biotechnol. 2020 Aug;64:24-31. doi: 10.1016/j.copbio.2019.09.002. Epub 2019 Oct 10. PMID: 31606719.
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[Current metabolomics platforms: technical composition and applications].

Yi chuan = Hereditas

Zhang FX, Wang GD.
PMID: 31549686
Yi Chuan. 2019 Sep 20;41(9):883-892. doi: 10.16288/j.yczz.19-122.

Metabolomics (defined as comprehensive small molecule chemical analysis), together with genomics, transcriptomics, proteomics and phenomics, now plays a fundamental role in system biological studies. Chromatography- mass spectrometry machines, which have the characteristics of high resolution and high sensitivity, are...

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Zhang FX, Wang GD. [Current metabolomics platforms: technical composition and applications]. Yi Chuan. 2019;41(9):883-892doi: 10.16288/j.yczz.19-122.
Zhang, F. X., & Wang, G. D. (2019). [Current metabolomics platforms: technical composition and applications]. Yi chuan = Hereditas, 41(9), 883-892. https://doi.org/10.16288/j.yczz.19-122
Zhang, Feng-Xia, and Wang, Guo-Dong. "[Current metabolomics platforms: technical composition and applications]." Yi chuan = Hereditas vol. 41,9 (2019): 883-892. doi: https://doi.org/10.16288/j.yczz.19-122
Zhang FX, Wang GD. [Current metabolomics platforms: technical composition and applications]. Yi Chuan. 2019 Sep 20;41(9):883-892. doi: 10.16288/j.yczz.19-122. Chinese. PMID: 31549686.
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Showing 181 to 192 of 212 entries