title: Evaluation of Spectral Counting for Relative Quantitation of Proteoforms in Top-Down Proteomics.
abstract: Spectral counting is a straightforward label-free quantitation strategy used in bottom-up proteomics workflows. The application of spectral counting in label-free top-down proteomics workflows can be similarly straightforward but has not been applied as widely as quantitation by chromatographic peak areas or peak intensities. In this study, we evaluate spectral counting for quantitative comparisons in label-free top-down proteomics workflows by comparison with chromatographic peak areas and intensities. We tested these quantitation approaches by spiking standard proteins into a complex protein background and comparing relative quantitation by spectral counts with normalized chromatographic peak areas and peak intensities from deconvoluted extracted ion chromatograms of the spiked proteins. Ratio estimates and statistical significance of differential abundance from each quantitation technique are evaluated against the expected ratios and each other. In this experiment, spectral counting was able to detect differential abundance of spiked proteins for expected ratios ≥2, with comparable or higher sensitivity than normalized areas and intensities. We also found that while ratio estimates using peak areas and intensities are usually more accurate, the spectral-counting-based estimates are not substantially worse. Following the evaluation and comparison of these label-free top-down quantitation strategies using spiked proteins, spectral counting, along with normalized chromatographic peak areas and intensities, were used to analyze the complex protein cargo of exosomes shed by myeloid-derived suppressor cells collected under high and low conditions of inflammation, revealing statistically significant differences in abundance for several proteoforms, including the active pro-inflammatory proteins S100A8 and S100A9.
authors.affiliation: University of Maryland , College Park, Maryland 20742, United States.
authors.forename: Lucía
authors.initials: L
authors.lastname: Geis-Asteggiante
authors.affiliation: University of Maryland Baltimore County , Baltimore, Maryland 21250, United States.
authors.forename: Suzanne
authors.initials: S
authors.lastname: Ostrand-Rosenberg
authors.affiliation: University of Maryland , College Park, Maryland 20742, United States.
authors.forename: Catherine
authors.initials: C
authors.lastname: Fenselau
authors.affiliation: Georgetown University Medical Center , Washington DC 20007, United States.
authors.forename: Nathan J
authors.initials: NJ
authors.lastname: Edwards
mesh_terms: D000818:Animals; D040501:Calgranulin A / Q000032:analysis*; D040502:Calgranulin B / Q000032:analysis*; D045744:Cell Line, Tumor; D002853:Chromatography, Liquid; D019295:Computational Biology; D013058:Mass Spectrometry; D051379:Mice; D040901:Proteomics
chemical_list: D040501:Calgranulin A; D040502:Calgranulin B; C576329:S100A9 protein, mouse; C086145:S100a8 protein, mouse