Identification of Acinetobacter Species Using MicrobialTrack

Significance of the topic

The accurate identification of pathogenic and environmental Acinetobacter species is critical due to rising antibiotic resistance and the complexity of the ACB complex.

Objectives and Study Overview

This study evaluates the ability of the cloud-based MicrobialTrack software to distinguish four species in the ACB complex (A. baumannii, A. calcoaceticus, A. nosocomialis, A. pittii) using MALDI-TOF mass spectrometry.

Methodology

Thirty-three strains were cultured on heart infusion agar and prepared by formic acid extraction and CHCA matrix application. Spectra were acquired on a MALDI-TOF MS AXIMA Performance in linear positive mode (m/z 2 000–20 000). Data were analyzed with MicrobialTrack using two genomic databases (“All” and representative “Reps”) and a default peak threshold of 0.0002.

Used Instrumentation

• AXIMA Performance MALDI-TOF mass spectrometer (Shimadzu)
• MicrobialTrack cloud-based identification software

Main Results and Discussion

Out of 159 spectra, 156 (98.1 %) were classified as “Very High” reliability, matching reference strain names at 98.7 %. Species-specific peaks, notably ribosomal proteins, were annotated by comparing measured masses to genome-predicted values, enabling clear discrimination within the ACB complex. Three spectra with fewer peaks were flagged as “Low” reliability and require further inspection.

Benefits and Practical Applications

• High-resolution species identification, even for closely related taxa
• Rich spectral annotation informed by genomic data
• Cloud-based platform eliminates the need for local software installation

Future Trends and Opportunities

Expanding genomic reference databases, integration of artificial intelligence for spectral interpretation, and real-time clinical deployment will further enhance microbial diagnostics across healthcare, food safety, and environmental monitoring.

Conclusion

MicrobialTrack leverages genome-derived protein mass predictions to deliver robust, high-confidence identification of Acinetobacter species within the challenging ACB complex, overcoming limitations of conventional MALDI-TOF MS methods.

References

1. Nithichanon A, et al. Acinetobacter nosocomialis causes as severe disease as Acinetobacter baumannii in Northeast Thailand. Microbiol Spectr. 2022;10(6):e02836-22.
2. Sekiguchi Y, et al. A large-scale genomically predicted protein mass database enables rapid and broad-spectrum identification of bacterial and archaeal isolates by mass spectrometry. Genome Biol. 2023;24:257.