Evaluation of protein extraction methodologies on bacterial proteomic profiling: a comparative analysis.

Bacterial proteomics is a pivotal tool for elucidating microbial physiology and pathogenicity. The efficiency and reliability of proteomic analyses are highly dependent on the protein extraction methodology, which directly influences the detectable proteome. In this study, we systematically compared four protein extraction protocols-SDT lysis buffer with boiling (SDT-B), SDT lysis buffer with ultrasonication (SDT-U/S), a combination of boiling and ultrasonication (SDT-B-U/S), and SDT lysis buffer with liquid nitrogen grinding followed by ultrasonication (SDT-LNG-U/S)-to evaluate their effects on peptide and protein identification, distribution, and reproducibility in and . Both data-dependent acquisition (DDA) and data-independent acquisition (DIA) strategies were employed for comprehensive proteomic profiling. DDA analysis identified 23,912 unique peptides corresponding to 2,141 proteins in and 13,150 unique peptides corresponding to 1,511 proteins in . DIA analysis yielded slightly fewer peptides (21,027 for and 7,707 for ) but demonstrated superior reproducibility. Among the tested protocols, SDT-B-U/S outperformed the others, identifying 16,560 peptides for and 10,575 peptides for in DDA mode. It also exhibited the highest technical replicate correlation in DIA analysis ( = 0.92). This method enhanced the extraction of proteins within key molecular weight ranges (20-30 kDa for ; 10-40 kDa for ) and was particularly effective for recovering membrane proteins (e.g., OmpC). Additionally, ultrasonication-based protocols outperformed the liquid nitrogen grinding approach in extracting the proteome. These findings underscore the significant impact of protein extraction methods on bacterial proteomics. The SDT-B-U/S protocol-thermal denaturation followed by ultrasonication-proved most effective, enhancing protein recovery and reproducibility across both Gram-negative and Gram-positive bacteria. This work offers key guidance for optimizing microbial proteomic workflows.