SWATH-MS proteomics data on differentially abundant proteins between normal and dark-cutting beef

Dark, firm, and dry (DFD) beef, also known as dark-cutting beef, lead to economic losses, food waste, and potential consumer rejection due to its very dark color at the point of sale. This condition is associated with a high ultimate pH, a limited blooming capacity, a redder cooked color that appears undercooked, and increased spoilage rates. Although several pre-slaughter factors have been linked to high ultimate pH, the mechanisms underlying DFD beef remain complex, multifactorial and not yet fully understood [1]. Proteomic approaches on <i>post-mortem</i> muscles have increasingly been employed to unravel the molecular mechanisms and biological pathways underlying this quality defect and to identify candidate protein biomarkers for its early prediction or better characterization using explanatory models. In this study, SWATH-MS proteomics, a data-independent acquisition strategy, was applied for the first time for an in-depth characterization and quantification of <i>post-mortem</i> muscle proteomes. The analysis was conducted using the most extensive dataset available to date on this quality defect conditions, which included 26 DFD beef samples (pH₂₄ ≥ 6.2) and 26 CONTROL samples (5.4 ≤ pH₂₄ ≤ 5.6). Muscle samples from the <i>Longissimus thoracis et lumborum</i> of <i>Asturiana de los Valles</i> yearling bulls were collected at 24 h <i>post-mortem</i> to investigate protein expression differences associated with DFD beef condition. A total of 735 proteins were quantified, among which 35 exhibited a significant difference in their abundances between the DFD condition and CONTROL samples, suggesting their potential as putative biomarkers for DFD beef. The data provided in this article can facilitate further research into beef quality defects and are available for reuse and/or reprocess and/or integration to support the development of early prediction tools for DFD beef. These data could further contribute to previous integrative studies [1], in the frame of integromics. Those approaches aimed combining multiple public proteomics datasets and DFD proteomics studies in a unique repository with the ultimate objective of refining the selection of dark-cutting beef biomarkers and deepen our understanding on the underlying biological mechanisms, hence revealing novel patterns inaccessible from individual datasets [1]. A more detailed analysis of this dataset is available in the study published by González-Blanco et al. [2]. The mass spectrometry (MS) proteomics data generated using a sequential window acquisition of all theoretical mass spectra (SWATH-MS) have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository [3] with the dataset identifier PXD059876.