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Critical evaluation of the role of external calibration strategies for IM-MS

The major benefits of integrating ion mobility (IM) into LC–MS methods for small molecules are the additional separation dimension and especially the use of IM-derived collision cross sections (CCS) as an additional ion-specific identification parameter. Several large CCS databases are now available...

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Detalles Bibliográficos
Autores principales: Feuerstein, Max L., Hernández-Mesa, Maykel, Valadbeigi, Younes, Le Bizec, Bruno, Hann, Stephan, Dervilly, Gaud, Causon, Tim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9482903/
https://www.ncbi.nlm.nih.gov/pubmed/35960317
http://dx.doi.org/10.1007/s00216-022-04263-5
Descripción
Sumario:The major benefits of integrating ion mobility (IM) into LC–MS methods for small molecules are the additional separation dimension and especially the use of IM-derived collision cross sections (CCS) as an additional ion-specific identification parameter. Several large CCS databases are now available, but outliers in experimental interplatform IM-MS comparisons are identified as a critical issue for routine use of CCS databases for identity confirmation. We postulate that different routine external calibration strategies applied for traveling wave (TWIM-MS) in comparison to drift tube (DTIM-MS) and trapped ion mobility (TIM-MS) instruments is a critical factor affecting interplatform comparability. In this study, different external calibration approaches for IM-MS were experimentally evaluated for 87 steroids, for which (TW)CCS(N2), (DT)CCS(N2) and (TIM)CCS(N2) are available. New reference CCS(N2) values for commercially available and class-specific calibrant sets were established using DTIM-MS and the benefit of using consolidated reference values on comparability of CCS(N2) values assessed. Furthermore, use of a new internal correction strategy based on stable isotope labelled (SIL) internal standards was shown to have potential for reducing systematic error in routine methods. After reducing bias for CCS(N2) between different platforms using new reference values (95% of (TW)CCS(N2) values fell within 1.29% of (DT)CCS(N2) and 1.12% of (TIM)CCS(N2) values, respectively), remaining outliers could be confidently classified and further studied using DFT calculations and CCS(N2) predictions. Despite large uncertainties for in silico CCS(N2) predictions, discrepancies in observed CCS(N2) values across different IM-MS platforms as well as non-uniform arrival time distributions could be partly rationalized. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00216-022-04263-5.