Coisolation of peptide pairs for peptide identification and MS/MS-based quantification

Abstract

SILAC-based metabolic labeling is a widely adopted proteomics approach that enables quantitative comparisons among a variety of experimental conditions. Despite its quantitative capacity, SILAC experiments analyzed with data dependent acquisition (DDA) do not fully leverage peptide pair information for identification and suffer from undersampling compared to label-free proteomic experiments. Herein, we developed a data dependent acquisition strategy that coisolates and fragments SILAC peptide pairs and uses y-ions for their relative quantification. To facilitate the analysis of this type of data, we adapted the Comet sequence database search engine to make use of SILAC peptide paired fragments and developed a tool to annotate and quantify MS/MS spectra of coisolated SILAC pairs. In an initial feasibility experiment, this peptide pair coisolation approach generally improved expectation scores compared to the traditional DDA approach. Fragment ion quantification performed similarly well to precursor quantification in the MS1 and achieved more quantifications. Lastly, our method enables reliable MS/MS quantification of SILAC proteome mixtures with overlapping isotopic distributions, which are difficult to deconvolute in MS1-based quantification. This study demonstrates the initial feasibility of the coisolation approach. Coupling this approach with intelligent acquisition strategies has the potential to improve SILAC peptide sampling and quantification.

Unified and standardized mass spectrometry data processing in Python using spectrum_utils

“…Since its introduction, spectrum_utils has been used in tools that perform spectral library searching 10 and spectrum clustering, 11 to preprocess MS/MS spectra prior to deep learning applications, 12,13 to plot MS/MS spectra from online data repositories, 14 and to assist in MS/MS processing and visualization efforts for dozens of other projects. 15–22…”

“…Since its introduction, spectrum_utils has been used in tools that perform spectral library searching 10 and spectrum clustering, 11 to preprocess MS/MS spectra prior to deep learning applications, 12,13 to plot MS/MS spectra from online data repositories, 14 and to assist in MS/MS processing and visualization efforts for dozens of other projects. [15][16][17][18][19][20][21][22] Here we present recent updates to spectrum_utils. It has now been extended with functionality for common and versatile data manipulation and visualization tasks when working with MS data in Python, including support for community data standards, updated visualization functionalities, and performance improvements.…”