madman
Super Moderator
Despite ongoing concerns regarding its clinical application, mass spectrometry (MS)-based steroid assay represents a promising tool in endocrine research. Recent studies indicate that monitoring the blood levels of individual sterols provides improved diagnostic insight into hyperlipidemia compared with immunoassays routinely used in clinical practice. Hypercortisolism and hyperaldosteronism can also be easily evaluated along with successful subtyping of adrenal diseases using MS-based methods, while metabolic signatures of sex steroids provide experimental evidence of abnormal puberty and male infertility. Many MS-based biological and clinical studies are based on liquid chromatography-mass spectrometry (LC-MS) coupled to electrospray ionization and tandem MS scan modes. However, gas chromatography-mass spectrometry (GC-MS) provides better chromatographic separation. Improved chromatographic resolution enables largescale steroid profiling to allow a bird-eye view and increase the chances of identifying potent biomarkers in endocrine research. In addition to the technical advantages of MS-based assays over immunoassays, minimizing the sample amounts with acceptable analytical sensitivity and standardization of surrogate materials provides cutting-edge tools for precision and personalized medicine.
Introduction
Immunoaffinity techniques are easy to use in clinical practice with higher analytical detectability. However, the lack of specificity for steroids of a similar chemical structure and standardization across assays for accurate quantification of individual steroid hormones limits their applicability in precision or personalized medicine (Lee et al. 2006; Hsing et al. 2007; Middle 2007; Wood et al. 2008; Taylor et al. 2015). Mass spectrometry (MS) is currently recognized as a promising tool in omics-based clinical studies, and is preferred over antibody-based radioimmunoassay (RIA) and enzyme-linked immunoassay (ELISA) in steroid analysis (Faupel-Badger et al. 2010; Taylor et al. 2015). The Endocrine Society announced that biological levels of sex steroids measured using MS-based assays are important endpoints in clinical study and practice (Handelsman and Wartofsky 2013).
Several clinical studies mainly focus on the biochemical mechanisms of specific biomolecules based on the known relevance of receptor expression in disease behavior, such as glucocorticoids in Cushing’s syndrome and estrogens in both breast cancer and osteoporosis (Jordan et al. 2001; Moreira et al. 2018; Page-Wilson et al. 2019). However, metabolic changes involving other steroids such as cholesterols and androgens in pathophysiology and subtyping of Cushing’s syndrome have been demonstrated (Barbetta et al. 2001; Arnaldi et al. 2010). Breast cancer and osteoporosis are also closely associated with cholesterol oxidation (Nelson et al. 2011; Kloudova et al. 2017). The use of cutting-edge MS-based steroid profiling enables the assessment of metabolic signatures of steroid hormones in diseases, and this birds-eye view of complex steroid cascades may be used to identify novel steroid metabolic pathways (Choi and Chung 2015; Keevil 2016; Rege et al. 2018).
In general, MS-based metabolite profiling yields multiple data sets derived from complex biological specimens. A single biomarker may lack sensitivity and specificity for predictive/prognostic detection of disease as well as therapeutic evaluation, which is not adequate to improve patient management (Shin et al. 2013; Xia et al. 2013). To enhance and ensure an integrated understanding of the steroid metabolome and its relationship with other disease pathways, the whole metabolism along with precursors and metabolites related to the steroids of interest should be monitored collectively, together with an interpretation of their metabolic consequences. Such extensive information could be useful in determining metabolic signatures for precision medicine.
This review presents the current status of MS-based steroid profiling techniques applied to both individual and whole steroid metabolomes with a focus on biomarker discovery, clinical application, and technical advances. To address the unmet clinical needs, the role of high-throughput analysis with less-invasive and reduced sampling procedure, improved analytical sensitivity for pathological confirmation, evaluating local concentrations using both snap frozen and paraffin-embedded tissues, and standardization with surrogate materials are also discussed.
*A Brief Introduction to MS Analysis
*Steroidomics in Biomarker Studies
-Cholesterol homeostasis
-Adrenal corticosteroids
-Sex steroids
Conclusion
Despite its wide-ranging industrial and scientific applications, the MS-based assay is a relatively new clinical and laboratory technique and is an emerging and promising tool to effectively address the healthcare needs of patients. MS-based multiplexed panels can efficiently support the diagnosis and monitoring of different clinical outcomes. The LC-MS assay is preferred in the routine analysis based on simple sampling procedures, such as dilution and protein precipitation, while GC-MS requires minimal purification steps. Both GC- and LC-MS are complementary because the advantages of one may offset the limitations of the other technique. Chemical derivatization can overcome potential drawbacks in the GC-MS assay, and provide better volatility and stability in GC separation, as well as enhance the ionization efficiency and MS interpretation in both quantitative and qualitative GC- and LC-MS analyses (Moon et al. 2011; Marcos and Pozo 2015; Wang et al. 2015). In particular, MS-based assay was expressed as the MVP of endocrine research (Endocrine News, March 2015); however, it can be improved to ensure superior detection via optimal sample purification and chromatographic separation methods to overcome the challenge due to structurally similar steroid hormones in the body (Moon et al. 2016; Choi 2018).
Metabolomics can be used to assess multiple metabolites in various clinical fields and offer potential biomarkers with diagnostic sensitivity and specificity. The analytical techniques used in metabolomics include non-targeted and targeted metabolite profiling approaches through qualitative and quantitative analyses, respectively. In general, non-targeted metabolite profiling increases the probability of identifying unknown biomarkers. However, most steroid hormones exist at trace levels, which are insufficient to be identified and semi-quantified. To address this issue, database-dependent metabolite profiling of 232 steroids was introduced (Jung et al. 2010), and recent advances in largescale steroid profiling have been developed, which are not just focused on specific functional groups of steroids alone (Moon et al. 2009; Hána et al. 2019). For example, hypertensive physiology may be closely associated with adrenal and sex steroids, and not merely cholesterol metabolism (Muller et al. 2003; Suzuki et al. 2003; Walker 2007). Therefore, a large-scale overview of steroid metabolism may provide comprehensive insights to identify potential biomarkers as well as develop patient screening programs in addition to the currently used clinical steroid protocols.
MS-based analytical platforms in clinical practice are limited by the reduced sample size for automated high-throughput systems, suggesting the need for improved analytical sensitivity for pathological confirmation using biopsy specimens. Surrogate materials for reproducible quantification should be further developed to provide cutting-edge technology for precision and personalized medicine. In addition to biomarker discovery based on MS-based profiling, immunoassays and other technical advances (Hong et al. 2017; Lee et al. 2019) should be used in parallel as complementary tools for large-scale population screening in the future.
Introduction
Immunoaffinity techniques are easy to use in clinical practice with higher analytical detectability. However, the lack of specificity for steroids of a similar chemical structure and standardization across assays for accurate quantification of individual steroid hormones limits their applicability in precision or personalized medicine (Lee et al. 2006; Hsing et al. 2007; Middle 2007; Wood et al. 2008; Taylor et al. 2015). Mass spectrometry (MS) is currently recognized as a promising tool in omics-based clinical studies, and is preferred over antibody-based radioimmunoassay (RIA) and enzyme-linked immunoassay (ELISA) in steroid analysis (Faupel-Badger et al. 2010; Taylor et al. 2015). The Endocrine Society announced that biological levels of sex steroids measured using MS-based assays are important endpoints in clinical study and practice (Handelsman and Wartofsky 2013).
Several clinical studies mainly focus on the biochemical mechanisms of specific biomolecules based on the known relevance of receptor expression in disease behavior, such as glucocorticoids in Cushing’s syndrome and estrogens in both breast cancer and osteoporosis (Jordan et al. 2001; Moreira et al. 2018; Page-Wilson et al. 2019). However, metabolic changes involving other steroids such as cholesterols and androgens in pathophysiology and subtyping of Cushing’s syndrome have been demonstrated (Barbetta et al. 2001; Arnaldi et al. 2010). Breast cancer and osteoporosis are also closely associated with cholesterol oxidation (Nelson et al. 2011; Kloudova et al. 2017). The use of cutting-edge MS-based steroid profiling enables the assessment of metabolic signatures of steroid hormones in diseases, and this birds-eye view of complex steroid cascades may be used to identify novel steroid metabolic pathways (Choi and Chung 2015; Keevil 2016; Rege et al. 2018).
In general, MS-based metabolite profiling yields multiple data sets derived from complex biological specimens. A single biomarker may lack sensitivity and specificity for predictive/prognostic detection of disease as well as therapeutic evaluation, which is not adequate to improve patient management (Shin et al. 2013; Xia et al. 2013). To enhance and ensure an integrated understanding of the steroid metabolome and its relationship with other disease pathways, the whole metabolism along with precursors and metabolites related to the steroids of interest should be monitored collectively, together with an interpretation of their metabolic consequences. Such extensive information could be useful in determining metabolic signatures for precision medicine.
This review presents the current status of MS-based steroid profiling techniques applied to both individual and whole steroid metabolomes with a focus on biomarker discovery, clinical application, and technical advances. To address the unmet clinical needs, the role of high-throughput analysis with less-invasive and reduced sampling procedure, improved analytical sensitivity for pathological confirmation, evaluating local concentrations using both snap frozen and paraffin-embedded tissues, and standardization with surrogate materials are also discussed.
*A Brief Introduction to MS Analysis
*Steroidomics in Biomarker Studies
-Cholesterol homeostasis
-Adrenal corticosteroids
-Sex steroids
Conclusion
Despite its wide-ranging industrial and scientific applications, the MS-based assay is a relatively new clinical and laboratory technique and is an emerging and promising tool to effectively address the healthcare needs of patients. MS-based multiplexed panels can efficiently support the diagnosis and monitoring of different clinical outcomes. The LC-MS assay is preferred in the routine analysis based on simple sampling procedures, such as dilution and protein precipitation, while GC-MS requires minimal purification steps. Both GC- and LC-MS are complementary because the advantages of one may offset the limitations of the other technique. Chemical derivatization can overcome potential drawbacks in the GC-MS assay, and provide better volatility and stability in GC separation, as well as enhance the ionization efficiency and MS interpretation in both quantitative and qualitative GC- and LC-MS analyses (Moon et al. 2011; Marcos and Pozo 2015; Wang et al. 2015). In particular, MS-based assay was expressed as the MVP of endocrine research (Endocrine News, March 2015); however, it can be improved to ensure superior detection via optimal sample purification and chromatographic separation methods to overcome the challenge due to structurally similar steroid hormones in the body (Moon et al. 2016; Choi 2018).
Metabolomics can be used to assess multiple metabolites in various clinical fields and offer potential biomarkers with diagnostic sensitivity and specificity. The analytical techniques used in metabolomics include non-targeted and targeted metabolite profiling approaches through qualitative and quantitative analyses, respectively. In general, non-targeted metabolite profiling increases the probability of identifying unknown biomarkers. However, most steroid hormones exist at trace levels, which are insufficient to be identified and semi-quantified. To address this issue, database-dependent metabolite profiling of 232 steroids was introduced (Jung et al. 2010), and recent advances in largescale steroid profiling have been developed, which are not just focused on specific functional groups of steroids alone (Moon et al. 2009; Hána et al. 2019). For example, hypertensive physiology may be closely associated with adrenal and sex steroids, and not merely cholesterol metabolism (Muller et al. 2003; Suzuki et al. 2003; Walker 2007). Therefore, a large-scale overview of steroid metabolism may provide comprehensive insights to identify potential biomarkers as well as develop patient screening programs in addition to the currently used clinical steroid protocols.
MS-based analytical platforms in clinical practice are limited by the reduced sample size for automated high-throughput systems, suggesting the need for improved analytical sensitivity for pathological confirmation using biopsy specimens. Surrogate materials for reproducible quantification should be further developed to provide cutting-edge technology for precision and personalized medicine. In addition to biomarker discovery based on MS-based profiling, immunoassays and other technical advances (Hong et al. 2017; Lee et al. 2019) should be used in parallel as complementary tools for large-scale population screening in the future.
