Capitalizing on the hydrophobic bias of electrospray ionization through chemical modification in mass spectrometry-based proteomics

Shuford, Christopher M.; Muddiman, David C.

doi:10.1586/epr.11.24

Cited by 17 publications

(20 citation statements)

References 34 publications

(46 reference statements)

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“…Also, derivatization with a hydrophobic label can increase sensitivity in ESI-MS, which was examined by Williams et al [63]. However, a hydrophobic bias, which depends on the chemical and physical properties of the analytes, is present in ESI-MS [64]. In 1993, Fenn [65] described that ion desorption from droplets is dependent on the surface activity of the analyte.…”

Section: Derivatization and Detectionmentioning

confidence: 99%

Reversed-phase separation methods for glycan analysis

2016

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Reversed-phase chromatography is a method that is often used for glycan separation. For this, glycans are often derivatized with a hydrophobic tag to achieve retention on hydrophobic stationary phases. The separation and elution order of glycans in reversed-phase chromatography is highly dependent on the hydrophobicity of the tag and the contribution of the glycan itself to the retention. The contribution of the different monosaccharides to the retention strongly depends on the position and linkage, and isomer separation may be achieved. The influence of sialic acids and fucoses on the retention of glycans is still incompletely understood and deserves further study. Analysis of complex samples may come with incomplete separation of glycan species, thereby complicating reversed-phase chromatography with fluorescence or UV detection, whereas coupling with mass spectrometry detection allows the resolution of complex mixtures. Depending on the column properties, eluents, and run time, separation of isomeric and isobaric structures can be accomplished with reversed-phase chromatography. Alternatively, porous graphitized carbon chromatography and hydrophilic interaction liquid chromatography are also able to separate isomeric and isobaric structures, generally without the necessity of glycan labeling. Hydrophilic interaction liquid chromatography, porous graphitized carbon chromatography, and reversed-phase chromatography all serve different research purposes and thus can be used for different research questions. A great advantage of reversed-phase chromatography is its broad distribution as it is used in virtually every bioanalytical research laboratory, making it an attracting platform for glycan analysis. Graphical AbstractGlycan isomer separation by reversed phase liquid chromatography

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Section: Derivatization and Detectionmentioning

confidence: 99%

Reversed-phase separation methods for glycan analysis

2016

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“…This fact becomes of particular interest when MS signal intensities are used to determine the quantitative relationship between different analytes in a sample. In this context, MS analyses come with peculiar obstacles because the chemical nature of a molecule does have a significant influence on its ionisation and/or detection strength and, thus, directly on the MS signal readout . The relative MS signal intensities do not necessarily reflect the actual relative amounts of the respective analytes present in a sample.…”

Section: Introductionmentioning

confidence: 99%

“…In electrospray ionisation (ESI), hydrophobic molecules tend to ionise significantly better compared with hydrophilic ones. This phenomenon has been explained with the increased surface activity of hydrophobic amino acid residues within an aqueous ESI droplet . The overall presence of various components in a biological sample is also well known to substantially influence ionisation efficiency, in particular for components of lower abundance .…”

Section: Introductionmentioning

confidence: 99%

Quantitative mapping of glycoprotein micro‐heterogeneity and macro‐heterogeneity: an evaluation of mass spectrometry signal strengths using synthetic peptides and glycopeptides

et al. 2013

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Mass spectrometry (MS) is used to quantify the relative distribution of glycans attached to particular protein glycosylation sites (micro-heterogeneity) and evaluate the molar site occupancy (macro-heterogeneity) in glycoproteomics. However, the accuracy of MS for such quantitative measurements remains to be clarified. As a key step towards this goal, a panel of related tryptic peptides with and without complex, biantennary, disialylated N-glycans was chemically synthesised by solid-phase peptide synthesis. Peptides mimicking those resulting from enzymatic deglycosylation using PNGase F/A and endo D/F/H were synthetically produced, carrying aspartic acid and N-acetylglucosamine-linked asparagine residues, respectively, at the glycosylation site. The MS ionisation/detection strengths of these pure, well-defined and quantified compounds were investigated using various MS ionisation techniques and mass analysers (ESI-IT, ESI-Q-TOF, MALDI-TOF, ESI/MALDI-FT-ICR-MS). Depending on the ion source/mass analyser, glycopeptides carrying complex-type N-glycans exhibited clearly lower signal strengths (10-50% of an unglycosylated peptide) when equimolar amounts were analysed. Less ionisation/detection bias was observed when the glycopeptides were analysed by nano-ESI and medium-pressure MALDI. The position of the glycosylation site within the tryptic peptides also influenced the signal response, in particular if detected as singly or doubly charged signals. This is the first study to systematically and quantitatively address and determine MS glycopeptide ionisation/detection strengths to evaluate glycoprotein micro-heterogeneity and macro-heterogeneity by label-free approaches. These data form a much needed knowledge base for accurate quantitative glycoproteomics.

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“…For example, a protein’s “proteotypic peptides”—peptides that are repeatedly and consistently identified for a given protein—can be identified using machine learning methods that take into account a wide range of physicochemical properties of amino acids, including charge, secondary structure propensity and hydrophobicity. Peptide hydrophobicity, in particular, strongly affects ionization in electrospray settings [8, 9].…”

Section: Introductionmentioning

confidence: 99%

Reducing peptide sequence bias in quantitative mass spectrometry data with machine learning

Dincer

Schweppe

et al. 2022

Preprint

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Quantitative mass spectrometry measurements of peptides necessarily incorporate sequence-specific biases that reflect the behavior of the peptide during enzymatic digestion, liquid chromatography, and in the mass spectrometer. These sequence-specific effects impair quantification accuracy, yielding peptide quantities that are systematically under- or over-estimated. We provide empirical evidence for the existence of such biases, and we use a deep neural network, called Pepper, to automatically identify and reduce these biases. The model generalizes to new proteins and new runs within a related set of MS/MS experiments, and the learned coefficients themselves reflect expected physicochemical properties of the corresponding peptide sequences. The resulting adjusted abundance measurements are more correlated with mRNA-based gene expression measurements than the unadjusted measurements. Pepper is suitable for data generated on a variety of mass spectrometry instruments, and can be used with labeled or label-free approaches, and with data-independent or data-dependent acquisition.

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Capitalizing on the hydrophobic bias of electrospray ionization through chemical modification in mass spectrometry-based proteomics

Cited by 17 publications

References 34 publications

Reversed-phase separation methods for glycan analysis

Reversed-phase separation methods for glycan analysis

Quantitative mapping of glycoprotein micro‐heterogeneity and macro‐heterogeneity: an evaluation of mass spectrometry signal strengths using synthetic peptides and glycopeptides

Reducing peptide sequence bias in quantitative mass spectrometry data with machine learning

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