Gas chromatography-mass spectrometry has been widely used to analyze hundreds of organic acids in urine to provide a diagnostic basis for organic acidemia. However, it is difficult to operate in clinical laboratories on a daily basis due to sample pretreatment processing. Therefore, we aimed to develop a fully automated system for quantifying serum organic acids using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The pretreatment CLAM-2030 device was connected to an LC-MS/MS system for processing serum under optimized conditions, which included derivatizing serum organic acids using 3-Nitrophenylhydrazine. The derivatized organic acids were separated on a reverse-phase Sceptor HD-C column and detected using negative-ion electrospray ionization multiple reaction monitoring MS. The automated pretreatment-LC-MS/MS system processed serum in less than 1 h and analyzed 19 serum organic acids, which are used to detect organic acidemias. The system exhibited high quantitative sensitivity ranging from approximately 2 to 100 µM with a measurement reproducibility of 10.4% CV. Moreover, a proof-of-concept validation of the system was performed using sera from patients with propionic acidemia (n = 5), methylmalonic acidemia (n = 2), and 3-methylcrotonylglycinuria (n = 1). The levels of marker organic acids specific to each disease were significantly elevated in the sera of the patients compared to those in control samples. The automated pretreatment-LC-MS/MS system can be used as a rapid in-hospital system to measure organic acid levels in serum for the diagnosis of organic acidemias.
Fully Automated Quantitative Measurement of Serum Organic Acids via LC-MS/MS for the Diagnosis of Organic Acidemias: Establishment of an Automation System and a Proof-of-Concept Validation.
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作者:Ueyanagi Yasushi, Setoyama Daiki, Kawakami Daisuke, Mushimoto Yuichi, Matsumoto Shinya, Hotta Taeko, Kang Dongchon
| 期刊: | Diagnostics | 影响因子: | 3.300 |
| 时间: | 2021 | 起止号: | 2021 Nov 25; 11(12):2195 |
| doi: | 10.3390/diagnostics11122195 | ||
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