2023 article

Achieving Sub-Parts-per-Million Mass Measurement Accuracy on an Orbitrap Mass Spectrometry Imaging Platform without Automatic Gain Control

Kibbe, R. R., & Muddiman, D. C. (2023, April 25). JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY.

By: R. Kibbe n & D. Muddiman n 

co-author countries: United States of America πŸ‡ΊπŸ‡Έ
author keywords: centroid; mass measurement accuracy; IR-MALDESI; mass spectrometry imaging; Orbitrap; automatic gain control
MeSH headings : Animals; Mice; Mass Spectrometry / methods; Data Accuracy; Lipids
Source: Web Of Science
Added: May 15, 2023

The collection of profile data is standard practice within the field of mass spectrometry (MS). However, profile data collection often results in large data files that require extensive processing times, especially in mass spectrometry imaging (MSI) studies where thousands of high-resolution scans are recorded. Natively collecting centroid MS data is an alternative that effectively reduces both the resulting file size and the data processing time. Herein, high-resolution accurate mass (HRAM) Orbitrap MSI data on mouse liver tissue sections without automatic gain control (AGC) were natively collected in both profile and centroid modes and compared based on the file size and processing time. Additionally, centroid data were evaluated against the profile data with regard to the spectra integrity, mass measurement accuracy (MMA), and the number of lipid annotations to ensure that centroid data did not compromise the data quality. For both native and postacquisition centroided data, the variation in mass measurement accuracy decreased relative to the profile data collection. Furthermore, centroid data collection increased the number of METASPACE database annotations indicating higher sensitivity and greater accuracy for lipid annotation compared to native profile data collection. Profile MSI data was shown to have a higher likelihood of false positive identifications due to an increased number of data points on either side of the peaks, whereas the same trend was not observed in data collected in native centroid data collection. This publication explores and explains the importance in properly centroiding MSI data, either natively or by adequate centroiding methods, to obtain the most accurate information and come to the best conclusions. These data support that natively collecting centroid data significantly improves MMA to sub-ppm levels without AGC and reduces false positive annotations.