Automated Online Solid-Phase Derivatization for Sensitive Quantification of Endogenous
S-Nitrosothiols (RSNOs) constitute a circulating endogenous reservoir of nitric oxide and have important biological activities. In this study, an online coupling of solid-phase derivatization (SPD) with liquid chromatography-mass spectrometry (LC-MS) was developed and applied in the analysis of low-...
| Main Authors: | , , , , |
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| Format: | Article |
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American Chemical Society (ACS)
2019
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| Online Access: | http://hdl.handle.net/1721.1/120517 https://orcid.org/0000-0002-2325-552X |
| _version_ | 1826202907584757760 |
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| author | Wang, Xin Garcia, Carlos T. Gong, Guanyu Wishnok, John S Tannenbaum, Steven R |
| author2 | Massachusetts Institute of Technology. Department of Biological Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Biological Engineering Wang, Xin Garcia, Carlos T. Gong, Guanyu Wishnok, John S Tannenbaum, Steven R |
| author_sort | Wang, Xin |
| collection | MIT |
| description | S-Nitrosothiols (RSNOs) constitute a circulating endogenous reservoir of nitric oxide and have important biological activities. In this study, an online coupling of solid-phase derivatization (SPD) with liquid chromatography-mass spectrometry (LC-MS) was developed and applied in the analysis of low-molecular-mass RSNOs. A derivatizing-reagent-modified polymer monolithic column was prepared and adapted for online SPD-LC-MS. Analytes from the LC autosampler flowed through the monolithic column for derivatization and then directly into the LC-MS for analysis. This integration of the online derivatization, LC separation, and MS detection facilitated system automation, allowing rapid, laborsaving, and sensitive detection of RSNOs. S-Nitrosoglutathione (GSNO) was quantified using this automated online method with good linearity (R[superscript 2] = 0.9994); the limit of detection was 0.015 nM. The online SPD-LC-MS method has been used to determine GSNO levels in mouse samples, 138 ± 13.2 nM of endogenous GSNO was detected in mouse plasma. Besides, the GSNO concentrations in liver (64.8 ± 11.3 pmol/mg protein), kidney (47.2 ± 6.1 pmol/mg protein), heart (8.9 ± 1.8 pmol/mg protein), muscle (1.9 ± 0.3 pmol/mg protein), hippocampus (5.3 ± 0.9 pmol/mg protein), striatum (6.7 ± 0.6 pmol/mg protein), cerebellum (31.4 ± 6.5 pmol/mg protein), and cortex (47.9 ± 4.6 pmol/mg protein) were also successfully quantified. When the derivatization was performed within 8 min, followed by LC-MS detection, samples could be rapidly analyzed compared with the offline manual method. Other low-molecular-mass RSNOs, such as S-nitrosocysteine and S-nitrosocysteinylglycine, were captured by rapid precursor-ion scanning, showing that the proposed method is a potentially powerful tool for capture, identification, and quantification of RSNOs in biological samples. |
| first_indexed | 2024-09-23T12:25:49Z |
| format | Article |
| id | mit-1721.1/120517 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T12:25:49Z |
| publishDate | 2019 |
| publisher | American Chemical Society (ACS) |
| record_format | dspace |
| spelling | mit-1721.1/1205172022-09-28T08:00:15Z Automated Online Solid-Phase Derivatization for Sensitive Quantification of Endogenous Wang, Xin Garcia, Carlos T. Gong, Guanyu Wishnok, John S Tannenbaum, Steven R Massachusetts Institute of Technology. Department of Biological Engineering Massachusetts Institute of Technology. Department of Chemistry Wang, Xin Garcia, Carlos T. Gong, Guanyu Wishnok, John S Tannenbaum, Steven R S-Nitrosothiols (RSNOs) constitute a circulating endogenous reservoir of nitric oxide and have important biological activities. In this study, an online coupling of solid-phase derivatization (SPD) with liquid chromatography-mass spectrometry (LC-MS) was developed and applied in the analysis of low-molecular-mass RSNOs. A derivatizing-reagent-modified polymer monolithic column was prepared and adapted for online SPD-LC-MS. Analytes from the LC autosampler flowed through the monolithic column for derivatization and then directly into the LC-MS for analysis. This integration of the online derivatization, LC separation, and MS detection facilitated system automation, allowing rapid, laborsaving, and sensitive detection of RSNOs. S-Nitrosoglutathione (GSNO) was quantified using this automated online method with good linearity (R[superscript 2] = 0.9994); the limit of detection was 0.015 nM. The online SPD-LC-MS method has been used to determine GSNO levels in mouse samples, 138 ± 13.2 nM of endogenous GSNO was detected in mouse plasma. Besides, the GSNO concentrations in liver (64.8 ± 11.3 pmol/mg protein), kidney (47.2 ± 6.1 pmol/mg protein), heart (8.9 ± 1.8 pmol/mg protein), muscle (1.9 ± 0.3 pmol/mg protein), hippocampus (5.3 ± 0.9 pmol/mg protein), striatum (6.7 ± 0.6 pmol/mg protein), cerebellum (31.4 ± 6.5 pmol/mg protein), and cortex (47.9 ± 4.6 pmol/mg protein) were also successfully quantified. When the derivatization was performed within 8 min, followed by LC-MS detection, samples could be rapidly analyzed compared with the offline manual method. Other low-molecular-mass RSNOs, such as S-nitrosocysteine and S-nitrosocysteinylglycine, were captured by rapid precursor-ion scanning, showing that the proposed method is a potentially powerful tool for capture, identification, and quantification of RSNOs in biological samples. National Institutes of Health (U.S.) (Grant CA26731) National Institutes of Health (U.S.) (Grant ES-002109) 2019-02-21T15:06:43Z 2019-02-21T15:06:43Z 2018-02 2017-10 2019-02-08T13:26:35Z Article http://purl.org/eprint/type/JournalArticle 0003-2700 1520-6882 http://hdl.handle.net/1721.1/120517 Wang, Xin, Carlos T. Garcia, Guanyu Gong, John S. Wishnok, and Steven R. Tannenbaum. “Automated Online Solid-Phase Derivatization for Sensitive Quantification of Endogenous S-Nitrosoglutathione and Rapid Capture of Other Low-Molecular-Mass S-Nitrosothiols.” Analytical Chemistry 90, no. 3 (January 9, 2018): 1967–1975. © 2017 American Chemical Society https://orcid.org/0000-0002-2325-552X http://dx.doi.org/10.1021/ACS.ANALCHEM.7B04049 Analytical Chemistry Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf American Chemical Society (ACS) PMC |
| spellingShingle | Wang, Xin Garcia, Carlos T. Gong, Guanyu Wishnok, John S Tannenbaum, Steven R Automated Online Solid-Phase Derivatization for Sensitive Quantification of Endogenous |
| title | Automated Online Solid-Phase Derivatization for Sensitive Quantification of Endogenous |
| title_full | Automated Online Solid-Phase Derivatization for Sensitive Quantification of Endogenous |
| title_fullStr | Automated Online Solid-Phase Derivatization for Sensitive Quantification of Endogenous |
| title_full_unstemmed | Automated Online Solid-Phase Derivatization for Sensitive Quantification of Endogenous |
| title_short | Automated Online Solid-Phase Derivatization for Sensitive Quantification of Endogenous |
| title_sort | automated online solid phase derivatization for sensitive quantification of endogenous |
| url | http://hdl.handle.net/1721.1/120517 https://orcid.org/0000-0002-2325-552X |
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