In-bone protein digestion followed by LC-MS/MS peptide analysis as a new way towards the routine proteomic characterization of human maxillary and mandibular bone tissue in oral surgery.
Radovan HynekIva MichalusPavel CejnarJiří ŠantrůčekSabina SeidlováŠtěpánka KučkováPetra SázelováVáclav KašičkaPublished in: Electrophoresis (2021)
Proteomic characterization of alveolar bones in oral surgery represents an analytical challenge due to their insoluble character. The implementation of a straightforward technique could lead to the routine use of proteomics in this field. This work thus developed a simple technique for the characterization of bone tissue for human maxillary and mandibular bones. It is based on the direct in-bone tryptic digestion of proteins in both healthy and pathological human maxillary and mandibular bone samples. The released peptides were then identified by the LC-MS/MS. Using this approach, a total of 1120 proteins were identified in the maxillary bone and 1151 proteins in the mandibular bone. The subsequent partial least squares-discrimination analysis (PLS-DA) of protein data made it possible to reach 100% discrimination between the samples of healthy alveolar bones and those of the bone tissue surrounding the inflammatory focus. These results indicate that the in-bone protein digestion followed by the LC-MS/MS and subsequent statistical analysis can provide a deeper insight into the field of oral surgery at the molecular level. Furthermore, it could also have a diagnostic potential in the differentiation between the proteomic patterns of healthy and pathological alveolar bone tissue. Data are available via ProteomeXchange with the identifier PXD026775.
Keyphrases
- bone mineral density
- soft tissue
- bone loss
- endothelial cells
- postmenopausal women
- primary care
- cone beam computed tomography
- healthcare
- machine learning
- oxidative stress
- risk assessment
- body composition
- coronary artery bypass
- protein protein
- atrial fibrillation
- induced pluripotent stem cells
- percutaneous coronary intervention
- data analysis
- liquid chromatography
- deep learning