Measurement of DNA Concentration as a Normalization Strategy for Metabolomic Data from Adherent Cell Lines

Silva, Leslie P.; Lorenzi, Philip L.; Purwaha, Preeti; Yong, Valeda; Hawke, David H.; Weinstein, John N.

doi:10.1021/ac401559v

Cited by 94 publications

(95 citation statements)

References 26 publications

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“…29 When we tried to measure free Gln and Glu in enzyme kinetic reactions with l -asparaginase 30 and in cancer cells by liquid chromatography (LC)-MS, 14,31 we were surprised to detect significant levels of free pGlu. When we began monitoring pGlu more closely, we found high levels of pGlu in Gln and Glu standards, an unexpected observation that prompted us to determine the source of the pGlu.…”

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confidence: 99%

An Artifact in LC-MS/MS Measurement of Glutamine and Glutamic Acid: In-Source Cyclization to Pyroglutamic Acid

et al. 2014

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Advances in metabolomics, particularly for research on cancer, have increased the demand for accurate, highly sensitive methods for measuring glutamine (Gln) and glutamic acid (Glu) in cell cultures and other biological samples. N-terminal Gln and Glu residues in proteins or peptides have been reported to cyclize to pyroglutamic acid (pGlu) during liquid chromatography (LC)-mass spectrometry (MS) analysis, but cyclization of free Gln and Glu to free pGlu during LC-MS analysis has not been well-characterized. Using an LC-MS/MS protocol that we developed to separate Gln, Glu, and pGlu, we found that free Gln and Glu cyclize to pGlu in the electrospray ionization source, revealing a previously uncharacterized artifact in metabolomic studies. Analysis of Gln standards over a concentration range from 0.39 to 200 μM indicated that a minimum of 33% and maximum of almost 100% of Gln was converted to pGlu in the ionization source, with the extent of conversion dependent on fragmentor voltage. We conclude that the sensitivity and accuracy of Gln, Glu, and pGlu quantitation by electrospray ionization-based mass spectrometry can be improved dramatically by using (i) chromatographic conditions that adequately separate the three metabolites, (ii) isotopic internal standards to correct for in-source pGlu formation, and (iii) user-optimized fragmentor voltage for acquisition of the MS spectra. These findings have immediate impact on metabolomics and metabolism research using LC-MS technologies.

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confidence: 99%

An Artifact in LC-MS/MS Measurement of Glutamine and Glutamic Acid: In-Source Cyclization to Pyroglutamic Acid

et al. 2014

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“…If the cell count information is unavailable, data can be normalized to DNA or protein concentrations post-data acquisition [16]. …”

Section: Notesmentioning

confidence: 99%

A Robust Lipidomics Workflow for Mammalian Cells, Plasma, and Tissue Using Liquid-Chromatography High-Resolution Tandem Mass Spectrometry

Ulmer

Patterson

Koelmel

et al. 2017

Methods in Molecular Biology

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Lipids have been analyzed in applications including drug discovery, disease etiology elucidation, and natural products. The chemical and structural diversity of lipids requires a tailored lipidomics workflow for each sample type. Therefore, every protocol in the lipidomics workflow, especially those involving sample preparation, should be optimized to avoid the introduction of bias. The coupling of ultra-high-performance liquid chromatography (UHPLC) with high-resolution mass spectrometry (HRMS) allows for the separation and identification of lipids based on class and fatty acid acyl chain. This work provides a comprehensive untargeted lipidomics workflow that was optimized for various sample types (mammalian cells, plasma, and tissue) to balance extensive lipid coverage and specificity with high sample throughput. For identification purposes, both data-dependent and data-independent tandem mass spectrometric approaches were incorporated, providing more extensive lipid coverage. Popular open-s ource feature detection, data processing, and identification software are also outlined.

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“…Furthermore, the use of inaccurate quantification methods may negate this step by introducing more variability. Post-analysis normalization methods are also routinely used; these include the use of internal standards (26,28), corrections for protein amount (often used for supernatant or cell-free metabolomics (29)), DNA content (an approach validated in mammalian cells (30) and applied to bacterial cells (31)), or cell number (typically used for bacterial populations (32)).…”

Section: Metabolome Interpretation Is Normalization-approach Dependenmentioning

confidence: 99%

Influential parameters for the analysis of intracellular parasite metabolomics

Carey

Covelli

Brown

et al. 2017

Preprint

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20 Due to improved instrument sensitivity and access, the use of metabolomics is gaining 21 traction for the study of many organisms and pathogens. were most appropriate for normalization as they separate sample groups and reduce 32 noise within the data set. However, these post-analysis steps did not remove the 33 contribution from the host erythrocyte, in the form of membrane rich 'ghosts', and levels 34 of technical sample variation persisted. In fact, we found that host contamination is as 35 influential on the metabolome as sample treatment. This analysis also identified 36 metabolites with potential to be used as markers to quantify host contamination levels.

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Measurement of DNA Concentration as a Normalization Strategy for Metabolomic Data from Adherent Cell Lines

Cited by 94 publications

References 26 publications

An Artifact in LC-MS/MS Measurement of Glutamine and Glutamic Acid: In-Source Cyclization to Pyroglutamic Acid

An Artifact in LC-MS/MS Measurement of Glutamine and Glutamic Acid: In-Source Cyclization to Pyroglutamic Acid

A Robust Lipidomics Workflow for Mammalian Cells, Plasma, and Tissue Using Liquid-Chromatography High-Resolution Tandem Mass Spectrometry

Influential parameters for the analysis of intracellular parasite metabolomics

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