Specter: linear deconvolution for targeted analysis of data-independent acquisition mass spectrometry proteomics

Abstract

Mass spectrometry with data-independent acquisition (DIA) has emerged as a promising method to greatly improve the comprehensiveness and reproducibility of targeted and discovery proteomics, in theory systematically measuring all peptide precursors within a biological sample. Despite the technical maturity of DIA, the analytical challenges involved in discriminating between peptides with similar sequences in convoluted spectra have limited its applicability in important cases, such as the detection of single-nucleotide polymorphisms and alternative site localizations in phosphoproteomics data. We have developed Specter, an open-source software tool that uses linear algebra to deconvolute DIA mixture spectra directly in terms of a spectral library, circumventing the problems associated with typical fragment correlation-based approaches. We validate the sensitivity of Specter and its performance relative to other methods by means of several complex datasets, and show that Specter is able to successfully analyze cases involving highly similar peptides that are typically challenging for DIA analysis methods.

Deep Learning Enable Untargeted Metabolite Extraction from High Throughput Coverage Data-Independent Acquisition

“…First, the datasets were both processed by XCMS to extract the features of the precursors. For a wider coverage, we employ a looser parameter sets: snthresh = 5, ppm = 100 and peakwidth = (5,60) to extract the EICs of the precursor ions and all of the possible fragment ions (candidate ions). The m/z bin size of the EICs were 0.1 Da and the length of the EICs were 30 s. Similarly, the linear interpolation was also used to unify retention time of the EICs of the precursor ion and the candidate ions.…”

Rapid and site-specific deep phosphoproteome profiling by data-independent acquisition (DIA) without the need for spectral libraries

“…This is achieved by co-isolating co-eluting peptide ions in predefined mass windows, fragmenting them together and analyzing all the resulting fragment ions simultaneously. However, few attempts have been reported on applying DIA to large-scale (phospho)proteomics [11][12][13][14][15] . To address this problem we developed a PTM-specific workflow for peptide-centric DIA that combines the recovery rate of library-based extraction with high confidence site localization algorithm rivaling the current gold standard based on DDA.…”

Parallel factor analysis enables quantification and identification of highly-convolved data independent-acquired protein spectra

“…Despite this, an inherent issue is related to the exhaustive fragmentation of the specific mass range using defined isolation windows or "swaths" (Gillet et al , 2012) . Due to the width of these windows, fragment signals are highly overlapped or "convolved", with multiple precursors falling in the same window, producing a set of highly overlapping ion mass spectra (Pappireddi et al , 2019;Peckner et al , 2018;Demichev et al , 2019) . A computational solution to deconvolve such data would expand the coverage and efficacy of the DIA approach.…”

“…The current standard approach for DIA analysis is targeted quantification of the acquired fragment data using spectral libraries containing fragmentation information for a particular peptide (Bruderer et al , 2015;Demichev et al , 2019;Röst et al , 2014;Peckner et al , 2018) . Library generation is however time-consuming, specific to the instrument, chromatography, and experimental condition, ideally requiring physical sample fractionation complemented with shotgun spectra acquisition (Schubert et al , 2015) .…”