Interrogation of an ovine serum peptide spectral library to annotate ambiguous clinicopathological biomarkers using data-independent acquisition

(2022) Interrogation of an ovine serum peptide spectral library to annotate ambiguous clinicopathological biomarkers using data-independent acquisition. F1000Research, 11, Article number: 1433.

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Description

Background: The use of data-independent data acquisition mass spectrometry (DIA-MS) on biological samples from domestic animals is still uncommon. Here, sequential window acquisition of all theoretical mass spectra (SWATH-MS) – a variant of DIA-MS was used to analyse serum peptides of healthy sheep as compared with serum of sick sheep by interrogating a novel peptide spectral library (PSL). This approach enabled the detection and annotation of a wide range of proteins, than conventional clinical pathology protein assays. Methods: Serum samples from healthy sheep were obtained from a commercial source and normalised to represent a healthy sheep proteome background and then compared with serum samples of sheep suffering from a range of naturally-acquired illnesses submitted to The University of Queensland, Australia. Purified tryptic peptides were subjected to liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) on a quadrupole time-of-flight instrument (TripleTOF 5600+, SCIEX) set in a cyclic data-independent acquisition (DIA) mode using a generic (SWATH™, SCIEX) acquisition method. Data were processed using PeakView® v2.2 software with SWATH™ Acquisition MicroApp 2.0 (SCIEX) and MarkerView™ v1.3 software (SCIEX) pipeline to generate protein lists for downstream gene ontology annotation and pathway analysis of identified proteins. Results: There were distinct differences in peptide chromatographic features of sick sheep samples compared to those from healthy sheep. Healthy and sick sheep serum samples yielded 335 and 236 protein identifications (IDs), respectively. There were 96 protein IDs unique to sick sheep serum. A total of 431 protein IDs were annotated by combining healthy control and sick sheep protein IDs. Conclusions: SWATH analysis successfully aided in the detection some established clinicopathological serum biochemical analytes. This approach enabled the distinction of protein profiles of sick sheep samples from a healthy control sample, thereby providing a promising future perspective for the application of SWATH analysis in veterinary clinical use.

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ID Code: 241981
Item Type: Contribution to Journal (Journal Article)
Refereed: No
Additional Information: Funding Information: This work was undertaken as part of author’s PhD which was financially supported by an Australian Postgraduate Award scholarship through The University of Queensland and personal resources. All the facilities for the experimental work were funded by a collaborative arrangement with Queensland University of Technology Central Analytical Research Facility (QUT-CARF) arranged by the author. Academic supervisors, laboratory personnel and other personnel involved in this work were paid employees of their respective institutions.
Measurements or Duration: 17 pages
Keywords: annotation of proteins in serum, healthy sheep, nanoLC-nanoESI-MS/MS, peptide spectral library, sheep serum proteomics, sick sheep, TICs, Veterinary SWATH analysis
DOI: 10.12688/f1000research.128316.1
ISSN: 2046-1402
Pure ID: 140106495
Divisions: Current > QUT Faculties and Divisions > Academic Division
Funding Information: This work was undertaken as part of author’s PhD which was financially supported by an Australian Postgraduate Award scholarship through The University of Queensland and personal resources. All the facilities for the experimental work were funded by a collaborative arrangement with Queensland University of Technology Central Analytical Research Facility (QUT-CARF) arranged by the author. Academic supervisors, laboratory personnel and other personnel involved in this work were paid employees of their respective institutions.
Copyright Owner: 2022 Chemonges S.
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Deposited On: 31 Jul 2023 04:54
Last Modified: 29 Feb 2024 13:42