MobiLipid: A Tool for Enhancing CCS Quality Control of Ion Mobility-Mass Spectrometry Lipidomics by Internal Standardization. Hildebrand, F., Koellensperger, G., & Causon, T. March, 2024. Preprint; Submitted to Analytical Chemistry![MobiLipid: A Tool for Enhancing CCS Quality Control of Ion Mobility-Mass Spectrometry Lipidomics by Internal Standardization [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 1 download Ion mobility-mass spectrometry (IM-MS) offers benefits for lipidomics by obtaining IM-derived collision cross sections (CCS), a conditional physicochemical parameter of an ion which can enhance lipid identification. While drift tube (DT) IM-MS retains a direct link to the primary experimental method to derive CCS values, other IM technologies rely solely on external CCS calibration, posing challenges due to dissimilar chemical properties between lipids and calibrants. To address this, we introduce MobiLipid, a novel tool facilitating CCS quality control of IM-MS lipidomics workflows by internal standardization. MobiLipid utilizes a newly established DTCCSN2 library for uniformly (U)13C labeled lipids, derived from a U13C labeled yeast extract, containing 377 DTCCSN2 values. This automated open-source R markdown tool enables internal monitoring and straightforward compensation for CCSN2 biases. It supports lipid class- and adduct-specific CCS corrections, requiring only three U13C labeled lipids per lipid class-adduct combination across 10 lipid classes, without requiring additional external measurements. The applicability of MobiLipid is demonstrated for trapped IM (TIM)-MS measurements of an unlabeled yeast extract spiked with U13C labeled lipids. Monitoring the CCSN2 biases of TIMCCSN2 values compared to DTCCSN2 library entries utilizing MobiLipid resulted in mean absolute biases of 0.78% and 0.33% in positive and negative ionization mode, respectively. By applying the CCS correction integrated into the tool for the exemplary dataset, the mean absolute CCSN2 biases of 10 lipid classes could be reduced to approximately 0%.
@misc{hildebrand_mobilipid_2024,
title = {{MobiLipid}: {A} {Tool} for {Enhancing} {CCS} {Quality} {Control} of {Ion} {Mobility}-{Mass} {Spectrometry} {Lipidomics} by {Internal} {Standardization}},
copyright = {https://creativecommons.org/licenses/by-nc-nd/4.0/},
shorttitle = {{MobiLipid}},
url = {https://chemrxiv.org/engage/chemrxiv/article-details/65fd478766c138172989af91},
doi = {10.26434/chemrxiv-2024-pjwm4},
abstract = {Ion mobility-mass spectrometry (IM-MS) offers benefits for lipidomics by obtaining IM-derived collision cross sections (CCS), a conditional physicochemical parameter of an ion which can enhance lipid identification. While drift tube (DT) IM-MS retains a direct link to the primary experimental method to derive CCS values, other IM technologies rely solely on external CCS calibration, posing challenges due to dissimilar chemical properties between lipids and calibrants. To address this, we introduce MobiLipid, a novel tool facilitating CCS quality control of IM-MS lipidomics workflows by internal standardization. MobiLipid utilizes a newly established DTCCSN2 library for uniformly (U)13C labeled lipids, derived from a U13C labeled yeast extract, containing 377 DTCCSN2 values. This automated open-source R markdown tool enables internal monitoring and straightforward compensation for CCSN2 biases. It supports lipid class- and adduct-specific CCS corrections, requiring only three U13C labeled lipids per lipid class-adduct combination across 10 lipid classes, without requiring additional external measurements. The applicability of MobiLipid is demonstrated for trapped IM (TIM)-MS measurements of an unlabeled yeast extract spiked with U13C labeled lipids. Monitoring the CCSN2 biases of TIMCCSN2 values compared to DTCCSN2 library entries utilizing MobiLipid resulted in mean absolute biases of 0.78\% and 0.33\% in positive and negative ionization mode, respectively. By applying the CCS correction integrated into the tool for the exemplary dataset, the mean absolute CCSN2 biases of 10 lipid classes could be reduced to approximately 0\%.},
urldate = {2024-04-04},
author = {Hildebrand, Felina and Koellensperger, Gunda and Causon, Tim},
month = mar,
year = {2024},
note = {Preprint; Submitted to Analytical Chemistry},
}
Downloads: 1
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While drift tube (DT) IM-MS retains a direct link to the primary experimental method to derive CCS values, other IM technologies rely solely on external CCS calibration, posing challenges due to dissimilar chemical properties between lipids and calibrants. To address this, we introduce MobiLipid, a novel tool facilitating CCS quality control of IM-MS lipidomics workflows by internal standardization. MobiLipid utilizes a newly established DTCCSN2 library for uniformly (U)13C labeled lipids, derived from a U13C labeled yeast extract, containing 377 DTCCSN2 values. This automated open-source R markdown tool enables internal monitoring and straightforward compensation for CCSN2 biases. It supports lipid class- and adduct-specific CCS corrections, requiring only three U13C labeled lipids per lipid class-adduct combination across 10 lipid classes, without requiring additional external measurements. 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While drift tube (DT) IM-MS retains a direct link to the primary experimental method to derive CCS values, other IM technologies rely solely on external CCS calibration, posing challenges due to dissimilar chemical properties between lipids and calibrants. To address this, we introduce MobiLipid, a novel tool facilitating CCS quality control of IM-MS lipidomics workflows by internal standardization. MobiLipid utilizes a newly established DTCCSN2 library for uniformly (U)13C labeled lipids, derived from a U13C labeled yeast extract, containing 377 DTCCSN2 values. This automated open-source R markdown tool enables internal monitoring and straightforward compensation for CCSN2 biases. It supports lipid class- and adduct-specific CCS corrections, requiring only three U13C labeled lipids per lipid class-adduct combination across 10 lipid classes, without requiring additional external measurements. 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