Transitioning from Targeted to Comprehensive Mass Spectrometry Using Genetic Algorithms.

J Am Soc Mass Spectrom
Authors
Abstract

Targeted proteomic assays are becoming increasingly popular because of their robust quantitative applications enabled by internal standardization, and they can be routinely executed on high performance mass spectrometry instrumentation. However, these assays are typically limited to 100s of analytes per experiment. Considerable time and effort are often expended in obtaining and preparing samples prior to targeted analyses. It would be highly desirable to detect and quantify 1000s of analytes in such samples using comprehensive mass spectrometry techniques (e.g., SWATH and DIA) while retaining a high degree of quantitative rigor for analytes with matched internal standards. Experimentally, it is facile to port a targeted assay to a comprehensive data acquisition technique. However, data analysis challenges arise from this strategy concerning agreement of results from the targeted and comprehensive approaches. Here, we present the use of genetic algorithms to overcome these challenges in order to configure hybrid targeted/comprehensive MS assays. The genetic algorithms are used to select precursor-to-fragment transitions that maximize the agreement in quantification between the targeted and the comprehensive methods. We find that the algorithm we used provided across-the-board improvement in the quantitative agreement between the targeted assay data and the hybrid comprehensive/targeted assay that we developed, as measured by parameters of linear models fitted to the results. We also found that the algorithm could perform at least as well as an independently-trained mass spectrometrist in accomplishing this task. We hope that this approach will be a useful tool in the development of quantitative approaches for comprehensive proteomics techniques. Graphical Abstract ᅟ.

Year of Publication
2016
Journal
J Am Soc Mass Spectrom
Volume
27
Issue
11
Pages
1745-1751
Date Published
2016 Nov
ISSN
1879-1123
DOI
10.1007/s13361-016-1465-2
PubMed ID
27562500
PubMed Central ID
PMC5061621
Links
Grant list
U01 CA164186 / CA / NCI NIH HHS / United States
U54 HG008097 / HG / NHGRI NIH HHS / United States