Quantification and assessment of detection capability in imaging mass spectrometry using a revised mimetic tissue model

Abstract

Aim: A revised method of preparing the mimetic tissue model for quantitative imaging mass spectrometry (IMS) is evaluated. Concepts of assessing detection capability are adapted from other imaging or mass spectrometry (MS)-based technologies to improve upon the reliability of IMS quantification. Materials & methods: The mimetic tissue model is prepared by serially freezing spiked-tissue homogenates into a cylindrical mold to create a plug of tissue with a stepped concentration gradient of matrix-matched standards. Weighted least squares (WLS) linear regression is applied due to the heteroscedastisity (change in variance with intensity) of most MS data. Results & conclusions: Imaging poses several caveats for quantification which are unique compared with other MS-based methods. Aspects of the design, construction, application, and evaluation of the matrix-matched standard curve for the mimetic tissue model are discussed. In addition, the criticality of the ion distribution in the design of a purposeful liquid chromatography coupled to mass spectrometry (LC-MS) validation is reviewed.