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Rappold BA. Review of the Use of Liquid Chromatography-Tandem Mass Spectrometry in Clinical Laboratories: Part II-Operations. Ann Lab Med 2022; 42:531-557. [PMID: 35470272 PMCID: PMC9057814 DOI: 10.3343/alm.2022.42.5.531] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 02/08/2022] [Accepted: 04/13/2022] [Indexed: 11/19/2022] Open
Abstract
Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is increasingly utilized in clinical laboratories because it has advantages in terms of specificity and sensitivity over other analytical technologies. These advantages come with additional responsibilities and challenges given that many assays and platforms are not provided to laboratories as a single kit or device. The skills, staff, and assays used in LC-MS/MS are internally developed by the laboratory, with relatively few exceptions. Hence, a laboratory that deploys LC-MS/MS assays must be conscientious of the practices and procedures adopted to overcome the challenges associated with the technology. This review discusses the post-development landscape of LC-MS/MS assays, including validation, quality assurance, operations, and troubleshooting. The content knowledge of LC-MS/MS users is quite broad and deep and spans multiple scientific fields, including biology, clinical chemistry, chromatography, engineering, and MS. However, there are no formal academic programs or specific literature to train laboratory staff on the fundamentals of LC-MS/MS beyond the reports on method development. Therefore, depending on their experience level, some readers may be familiar with aspects of the laboratory practices described herein, while others may be not. This review endeavors to assemble aspects of LC-MS/MS operations in the clinical laboratory to provide a framework for the thoughtful development and execution of LC-MS/MS applications.
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Affiliation(s)
- Brian A Rappold
- Laboratory Corporation of America Holdings, Research Triangle Park, NC, USA
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Han X, Ye H. Overview of Lipidomic Analysis of Triglyceride Molecular Species in Biological Lipid Extracts. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2021; 69:8895-8909. [PMID: 33606510 PMCID: PMC8374006 DOI: 10.1021/acs.jafc.0c07175] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
Triglyceride (TG) is a class of neutral lipids, which functions as an energy storage depot and is important for cellular growth, metabolism, and function. The composition and content of TG molecular species are crucial factors for nutritional aspects in food chemistry and are directly associated with several diseases, including atherosclerosis, diabetes, obesity, stroke, etc. As a result of the complexities of aliphatic moieties and their different connections/locations to the glycerol backbone in TG molecules, accurate identification of individual TG molecular species and quantitative assessment of TG composition and content are particularly challenging, even at the current stage of lipidomics development. Herein, methods developed for analysis of TG species, such as liquid chromatography-mass spectrometry with a variety of columns and different mass spectrometric techniques, shotgun lipidomics approaches, and ion-mobility-based analysis, are reviewed. Moreover, the potential limitations of the methods are discussed. It is our sincere hope that the overviews and discussions can provide some insights for researchers to select an appropriate approach for TG analysis and can serve as the basis for those who would like to establish a methodology for TG analysis or develop a new method when novel tools become available. Biologically accurate analysis of TG species with an enabling method should lead us toward improving the nutritional quality, revealing the effects of TG on diseases, and uncovering the underlying biochemical mechanisms related to these diseases.
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Affiliation(s)
- Xianlin Han
- Barshop Institute for Longevity and Aging Studies, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229 USA
- Departments of Medicine - Diabetes, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229 USA
- To whom correspondence should be addressed: Xianlin Han, Ph.D., Barshop Institute for Longevity and Aging Studies, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229 USA, , Tele: (210) 562-4104
| | - Hongping Ye
- Department of Medicine - Nephrology, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229 USA
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Saydmohammed M, Jha A, Mahajan V, Gavlock D, Shun TY, DeBiasio R, Lefever D, Li X, Reese C, Kershaw EE, Yechoor V, Behari J, Soto-Gutierrez A, Vernetti L, Stern A, Gough A, Miedel MT, Lansing Taylor D. Quantifying the progression of non-alcoholic fatty liver disease in human biomimetic liver microphysiology systems with fluorescent protein biosensors. Exp Biol Med (Maywood) 2021; 246:2420-2441. [PMID: 33957803 PMCID: PMC8606957 DOI: 10.1177/15353702211009228] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Metabolic syndrome is a complex disease that involves multiple organ systems including a critical role for the liver. Non-alcoholic fatty liver disease (NAFLD) is a key component of the metabolic syndrome and fatty liver is linked to a range of metabolic dysfunctions that occur in approximately 25% of the population. A panel of experts recently agreed that the acronym, NAFLD, did not properly characterize this heterogeneous disease given the associated metabolic abnormalities such as type 2 diabetes mellitus (T2D), obesity, and hypertension. Therefore, metabolic dysfunction-associated fatty liver disease (MAFLD) has been proposed as the new term to cover the heterogeneity identified in the NAFLD patient population. Although many rodent models of NAFLD/NASH have been developed, they do not recapitulate the full disease spectrum in patients. Therefore, a platform has evolved initially focused on human biomimetic liver microphysiology systems that integrates fluorescent protein biosensors along with other key metrics, the microphysiology systems database, and quantitative systems pharmacology. Quantitative systems pharmacology is being applied to investigate the mechanisms of NAFLD/MAFLD progression to select molecular targets for fluorescent protein biosensors, to integrate computational and experimental methods to predict drugs for repurposing, and to facilitate novel drug development. Fluorescent protein biosensors are critical components of the platform since they enable monitoring of the pathophysiology of disease progression by defining and quantifying the temporal and spatial dynamics of protein functions in the biosensor cells, and serve as minimally invasive biomarkers of the physiological state of the microphysiology system experimental disease models. Here, we summarize the progress in developing human microphysiology system disease models of NAFLD/MAFLD from several laboratories, developing fluorescent protein biosensors to monitor and to measure NAFLD/MAFLD disease progression and implementation of quantitative systems pharmacology with the goal of repurposing drugs and guiding the creation of novel therapeutics.
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Affiliation(s)
- Manush Saydmohammed
- University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Anupma Jha
- University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Vineet Mahajan
- University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Dillon Gavlock
- University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Tong Ying Shun
- University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Richard DeBiasio
- University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Daniel Lefever
- University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Xiang Li
- University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Celeste Reese
- University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Erin E Kershaw
- Department of Medicine, Division of Endocrinology and Metabolism, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Vijay Yechoor
- Department of Medicine, Division of Endocrinology and Metabolism, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Jaideep Behari
- Department of Medicine, Division of Gastroenterology, Hepatology and Nutrition, Pittsburgh, PA 15261, USA
- UPMC Liver Clinic, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Alejandro Soto-Gutierrez
- Department of Pathology, University of Pittsburgh, Pittsburgh, PA 15261, USA
- Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Larry Vernetti
- University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Andrew Stern
- University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Albert Gough
- University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Mark T Miedel
- University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - D Lansing Taylor
- University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA
- Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA 15261, USA
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15261, USA
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