Processing strategies and software solutions for data‐independent acquisition in mass spectrometry

Pena, Aivett Bilbao; Varesio, Emmanuel; Luban, Jeremy; Strambio‐De‐Castillia, Caterina; Hopfgartner, Gérard; Müller, Markus; Lisacek, Frédérique

doi:10.1002/pmic.201400323

Cited by 167 publications

(140 citation statements)

References 53 publications

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“…Saturation correction is also relevant to data-independent acquisition (DIA) MS analyses, which are continuous MS/MS maps for all eluting LC peaks [30,31]. The datasets of the BSA tryptic digest spiked at different concentration in the yeast background were acquired in DIA mode by alternating low and high collision energy to generate MS and MS/MS spectra.…”

Section: Resultsmentioning

confidence: 99%

An algorithm to correct saturated mass spectrometry ion abundances for enhanced quantitation and mass accuracy in omic studies

Pena

Gibbons

Slysz

et al. 2018

International Journal of Mass Spectrometry

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The mass accuracy and peak intensity of ions detected by mass spectrometry (MS) measurements are essential to facilitate compound identification and quantitation. However, high concentration species can yield erroneous results if their ion intensities reach beyond the limits of the detection system, leading to distorted and non-ideal detector response (e.g. saturation), and largely precluding the calculation of accurate m/z and intensity values. Here we present an open source computational method to correct peaks above a defined intensity (saturated) threshold determined by the MS instrumentation such as the analog-to-digital converters or time-to-digital converters used in conjunction with time-of-flight MS. In this method, the isotopic envelope for each observed ion above the saturation threshold is compared to its expected theoretical isotopic distribution. The most intense isotopic peak for which saturation does not occur is then utilized to re-calculate the precursor m/z and correct the intensity, resulting in both higher mass accuracy and greater dynamic range. The benefits of this approach were evaluated with proteomic and lipidomic datasets of varying complexities. After correcting the high concentration species, reduced mass errors and enhanced dynamic range were observed for both simple and complex omic samples. Specifically, the mass error dropped by more than 50% in most cases for highly saturated species and dynamic range increased by 1–2 orders of magnitude for peptides in a blood serum sample.

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Section: Resultsmentioning

confidence: 99%

An algorithm to correct saturated mass spectrometry ion abundances for enhanced quantitation and mass accuracy in omic studies

Pena

Gibbons

Slysz

et al. 2018

International Journal of Mass Spectrometry

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“…A critical point in DIA is to match the fragment ion spectra to the corresponding precursor ions. This can be done by superimposing the chromatographic elution profiles of peptide precursor and fragment ions (socalled spectral deconvolution) or by matching fragment ion spectra to spectral libraries [22][23][24]. Multiple peptides may co-elute within the selection window of DDA resulting in mixed or "chimeric" fragment ion spectra notably when analyzing highly complex peptide mixtures that exceed the separation capacity of the chromatographic column.…”

Section: The Mainstream Shotgun Bottom-up Approachmentioning

confidence: 99%

Genomic variability and protein species — Improving sequence coverage for proteogenomics

Bischoff

Permentier

Guryev

et al. 2016

Journal of Proteomics

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“…8 Because DIA systematically parallelizes the fragmentation of all detectable ions within a wide m/z range regardless of intensity, it is particularly suitable to perform more consistent and accurate quantification by extracted ion chromatograms (XICs) at the fragment ion level. 9 Indeed, Venable et al 10 observed that the selectivity afforded by XICs at the MS/MS level resulted in reduced noise with an average signal-to-noise improvement of 3.5-fold, as well as a larger dynamic range (by at least a factor of 2) compared to precursor ion XICs from MS spectra. Along the same line, Gillet et al 11 illustrated how SWATH, in combination with targeted data extraction using information from spectral libraries (generated experimentally by surveying the sample of interest by DDA or in silico from public repositories), allows consistent quantification of proteins/peptides and displays highly correlated values compared to SRM.…”

Section: Introductionmentioning

confidence: 99%

Ranking Fragment Ions Based on Outlier Detection for Improved Label-Free Quantification in Data-Independent Acquisition LC–MS/MS

et al. 2015

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Data-independent acquisition LC-MS/MS techniques complement supervised methods for peptide quantification. However, due to the wide precursor isolation windows, these techniques are prone to interference at the fragment ion level, which in turn is detrimental for accurate quantification. The “non-outlier fragment ion” (NOFI) ranking algorithm has been developed to assign low priority to fragment ions affected by interference. By using the optimal subset of high priority fragment ions these interfered fragment ions are effectively excluded from quantification. NOFI represents each fragment ion as a vector of four dimensions related to chromatographic and MS fragmentation attributes and applies multivariate outlier detection techniques. Benchmarking conducted on a well-defined quantitative dataset (i.e. the SWATH Gold Standard), indicates that NOFI on average is able to accurately quantify 11-25% more peptides than the commonly used Top-N library intensity ranking method. The sum of the area of the Top3-5 NOFIs produces similar coefficients of variation as compared to the library intensity method but with more accurate quantification results. On a biologically relevant human dendritic cell digest dataset, NOFI properly assigns low priority ranks to 85% of annotated interferences, resulting in sensitivity values between 0.92 and 0.80 against 0.76 for the Spectronaut interference detection algorithm.

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Processing strategies and software solutions for data‐independent acquisition in mass spectrometry

Cited by 167 publications

References 53 publications

An algorithm to correct saturated mass spectrometry ion abundances for enhanced quantitation and mass accuracy in omic studies

An algorithm to correct saturated mass spectrometry ion abundances for enhanced quantitation and mass accuracy in omic studies

Genomic variability and protein species — Improving sequence coverage for proteogenomics

Ranking Fragment Ions Based on Outlier Detection for Improved Label-Free Quantification in Data-Independent Acquisition LC–MS/MS

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