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. selected reaction monitoring (SRM) have been developed in order to guarantee the measurement of low-abundance analytes or the observation of pre-specified targets across multiple samples 2 , but this gain in specificity comes at the cost of vastly limiting the range of observable precursors. Data-independent acquisition (DIA) is a relatively new approach that combines the reproducibility of SRM with the breadth of DDA by simultaneously fragmenting all precursors whose mass-to-charge ( m/z ) ratios fall into one of a small number of wide windows that traverse the entire m/z range. This results in convoluted MS2 spectra whose fragment ion intensities may be comprised of contributions from multiple peptide precursors and which are far more complex to analyze than their DDA counterparts. The new challenges posed by DIA demand specialized software tools for downstream analysis 3-7 . Most of these are targeted methods that require a user-provided spectral library defining the search space of peptides (and, in turn, proteins) that can be identified and quantified in the acquired data. These targeted tools are for the most part derived heuristically from corresponding methods for DDA and PRM analysis, in which library members are scored against acquired MS2 spectra based on characteristics such as normalized dot product, fragment ion correlation, and chromatographic peak shape. Although these scores typically penalize assignments to library spectra whose annotated b or y fragment ions are judged to exhibit interferences, these methods do not rigorously account for the confounding effects of precursor cofragmentation, resulting in a limited ability to distinguish precursors with shared spectral features. Alternatively, untargeted methods 4,5,8 aim to deconvolute the data directly without the use of spectral libraries based on the grouping of fragment ions with correlated elution profiles. This analysis takes into account both MS1 and MS2 intensity information in order to score correlated peak groups but implicitly discards fragments with significant interferences due to their poor correlation with a precursor's elution profile. Although a promising route for the discovery of previously unobserved analytes, the fact that this approach uses no prior information as provided by a spectral library may cause it to suffer from a high false negative rate in complex samples 9 and makes it more susceptible to missing data than targeted methods when attempting to quantify analytes across multiple conditions. Here we describe Specter, an algorithm for the identification and quantification of spectral library members within DIA data. It is based on recognizing and formalizing the fundamental distinction between DIA and DDA, namely the cofragmentation of potentially large numbers of precursors, some of which may share fragment ion m / z ratios. Specter is based on a principled mathematical formulation of the cofragmentation problem, which is then solved by means of linear algebra. As opposed to the usual approach involving detection of correlated chromatographic profiles of selected precursor fragment ions, spectral deconvolution takes place purely at the MS2 level and involves the entire sequence of m/z coordinates and relative intensities of library spectra peaks. This allows for the direct calculation of extracted ion chromatograms of fractional contributions of library precursors to MS2 spectra, which can then be visualized and analyzed using traditional chromatographic approaches 10 . This approach is neither spectrum-centric (it does not match acquired MS2 spectra to those in a database) nor peptide-centric (it does not assess the evidence for individual library members within the acquired data), in contrast to all other existing methods. Rather, it is "combination-centric", in that it identifies and quantifies the single combination of library spectra that best explains an acquired MS2 spectrum. This approach removes the need to reduce spectra to curated fragment ions, carries an intrinsically low false discovery
The method Specter REF uses regression models to describe mixtures peptides, but it acts at the MS2 level and requires library spectra for every modeled peptide; it does not use MS1 data.
Specter: linear deconvolution as a new paradigm for targeted analysis of data-independent acquisition mass spectrometry proteomics
bioRxiv
Biology Computer Science