Optimization of protein extraction and digestion workflows for proteomic analysis of saliva, salivary stones and kidney stones

TL;DR

This paper develops optimized methods for extracting and analyzing proteins from saliva and stone samples to better understand calcification diseases.

Contribution

The study introduces optimized workflows for protein extraction and digestion in complex biological matrices like saliva and kidney stones.

Findings

Optimized workflows improved peptide and protein identification in saliva and stone samples.

Sonication-assisted protocols enhanced proteomic analysis of stone-derived materials.

The methods showed variability in optimal conditions depending on the sample type.

Abstract

Salivary and kidney stone diseases are associated with pathological calcification, yet their molecular composition remains incompletely characterized. Proteomic analysis of saliva and stone material may provide valuable insights into these processes; however, such analyses are technically challenging and strongly dependent on efficient and reproducible sample preparation workflows.

In this study, we systematically evaluated and optimised protein extraction and digestion conditions for proteomic analysis of saliva, salivary stones and kidney stones. Different lysis buffers, sample amounts, sonication parameters and digestion strategies were tested. Additional biological materials, including salivary gland tissue and Raoultella ornithinolytica, were analysed to assess the broader applicability of the developed workflows.

Optimised workflows significantly improved peptide and protein identification, reduced the proportion of missed cleavages and enhanced sequence coverage across multiple biological matrices. Sonication-assisted protocols proved particularly effective for stone-derived materials, although optimal conditions varied depending on the sample type.

This pilot study demonstrates that careful optimisation of protein extraction and digestion is essential for reliable proteomic analysis of challenging biomaterials such as saliva and pathological calcifications. The workflows established here provide a robust methodological foundation for future large-scale and biomarker-oriented proteomic studies of stone disease and related calcification disorders.

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