DIA Workflow Expands Species-Specific Peptide Detection for PEP-TORCH-Based Identification of Mycobacteria

The increasing prevalence of disease caused by nontuberculous mycobacteria (NTM) highlights the need for faster and more accurate methods for mycobacterial detection, especially because many NTM species are slow growing. Liquid chromatography-tandem mass spectrometry (LC–MS/MS) provides a proteomics-based approach for detecting mycobacterial peptides and proteins from early liquid culture-derived samples. Our group previously developed PEP-TORCH, a peptide-based taxonomic algorithm for species- and subspecies-level NTM identification from LC–MS/MS data. In this study, we evaluated whether data-independent acquisition (DIA) could improve peptide identification for PEP-TORCH-based NTM identification compared with conventional data-dependent acquisition (DDA) from culture supernatants. Using Mycobacteroides abscessus and Mycobacterium avium MGIT culture supernatants, we compared DDA with DIA methods using 4, 8, and 15 m/z isolation windows. Across both species, DIA increased peptide and protein group identifications compared to DDA and expanded the number of species-specific peptides identified by PEP-TORCH. DIA also improved peptide-level coverage for protein accessions detected by both methods and identified additional DIA-exclusive accessions supported by multiple unique peptides or detection across multiple DIA window settings. When applied to clinical MGIT samples, the selected DIA workflow increased detection of M. abscessus peptides and PEP-TORCH-assigned species-specific peptides compared with DDA, supporting its utility for specimens in which pathogen-derived peptide signals are present at lower intensities. Together, these results demonstrate that DIA-MS/MS improves peptide detection from MGIT culture supernatants and strengthens the peptide evidence available for PEP-TORCH-based NTM identification.