Storage conditions differentially alter the human tooth enamel proteome

Introduction Exfoliated or extracted human teeth are increasingly used as accessible records of health history, biomarkers for predicting health risks, or analyzed for organic components to uncover the mechanisms of atypical development or disease. During formation, dental enamel incorporates and preserves biological information through mineralization, thereby creating a fossil-like record within the tooth structure as it reaches a mineral content of over 95% by weight. The key to unlocking this record depends not only on using appropriate analytical methodologies, but also on how storage conditions affect the original biogenic information. Methods To investigate the effect of storage conditions on enamel proteome, human third molars were collected upon extraction and stored for 4 months under four commonly used protocols in dental research: (1) at −80°C; (2) in 70% ethanol (EtOH) at room temperature (RT); (3) air-dried (Air) at RT; and (4) in phosphate-buffered saline with sodium azide at RT. Results Mass spectrometry-based proteomic analysis identified 454, 460, 232, and 221 proteins in the −80°C, EtOH, Air, and PBS groups, respectively. Enamel-specific proteins, such as amelogenin, ameloblastin, enamelin, kallikrein-related peptidase-4, and matrix metalloproteinase-20, were identified across all conditions, although their relative abundances varied depending on storage conditions. In addition, the preservation of specific protein families varied depending on the storage conditions. Discussion Although −80°C storage remains the gold standard for preserving organic material, storage in 70% EtOH at RT produced comparable proteomic results. This suggests that ethanol-based storage protocols may serve as a more practical alternative, easier to implement for sample collection, and help provide consistency in enamel research. Our findings underscore the importance of both storage methods and standardized protocols in enamel proteomics, as they help avoid bias in protein detection and facilitate comparisons of datasets between studies.