Assessment of data-independent acquisition mass spectrometry for the identification of single amino acid variants

Assessment of data-independent acquisition mass spectrometry for the identification of single amino acid variants.

Introduction:

Data-independent acquisition mass spectrometry (DIA-MS) holds promise for proteogenomic analysis, particularly in identifying single amino acid variants (SAAVs) crucial for understanding diseases like cancer or neurodegeneration. Here, we evaluate the performance of different commonly used DIA-MS search tools regarding number of correct SAAV identifications and error rates using HeLa cell lysates. We further show a comparison to DDA and confirm the identity of the confidently identified SAAV peptides using SID-PRM targeted MS.

Methods:

We used HeLa cells and devised a custom sequence search database integrating 233 SAAV peptides derived from HeLa cell single nucleotide polymorphisms (SNPs). We employed DIA-MS with search engines DIA-NN, Spectronaut (SN), and Fragpipe-MSFragger-DIA (FP-DIA). Our evaluation focused on identifying true positive SAAV peptides and false positives through entrapment databases to assess false discovery match ratio (FDMR) for SAAV peptide identifications. We performed stable isotope dilution and parallel reaction monitoring (SID-PRM) to validate the identification of SAAV peptides.

Results:

FP-DIA showed the lowest FDMR among DIA search engines. DIA-MS demonstrated increased FDMR with larger entrapment databases, yet maintained relatively constant true positive SAAV peptide counts. In addition, integrating DIA and data-dependent acquisition (DDA) MS data enhanced SAAV peptide identification, with a lower false discovery rate (FDR) observed in DDA searches. DIA-MS outperformed DDA-MS in identifying true positive HeLa SAAV peptides albeit displaying greater mean FDMR. Validation using stable isotope dilution and parallel reaction monitoring (SID-PRM) confirmed SAAV peptides identified by DIA-MS and DDA-MS searches, highlighting the reliability of our approach.

Conclusion:

Our study underscores the capability of DIA-MS for extensive and confident SAAV identification, offering insights into its potential for proteogenomic analyses. The comparative assessment of DIA search engines makes FP-DIA the tool with the least error, suggesting its utility for comprehensive SAAV profiling in disease contexts.