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Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) is widely used in clinical microbiology laboratories because it is cost-effective, reliable, and fast. This study is aimed at comparing the identification performance of the recently developed Autof ms1000 (Autobio, China) with that of the Bruker Biotyper (Bruker Daltonics, Germany). From January to June 2020, 205 preserved strains and 302 clinical isolates were used for comparison. Bacteria were tested with duplicates of the direct transfer method, and formic acid extraction was performed if the results were not at the species level. Fungi were tested with formic acid extraction followed by ethanol extraction methods. 16S rRNA or ITS region sequence analysis was performed on isolates that could not be identified by any of the instruments and on isolates that showed inconsistent results. The time to result of each instrument was also compared. Among preserved strains, species-level identification results were obtained in 202 (98.5%) strains by the Autof ms1000 and 200 (97.6%) strains by the Bruker Biotyper. Correct identification at the species/complex level was obtained for 200 (97.6%) strains by the Autof ms1000 and for 199 (97.1%) strains by the Bruker Biotyper. Among clinical isolates, species-level identification results were obtained in 301 (99.7%) strains and 300 (99.3%) strains by the Autof ms1000 and Bruker Biotyper, respectively. Correct identification at the species/complex level was achieved for 299 (99.0%) strains by the Autof ms1000 and for 300 (99.3%) strains by the Bruker Biotyper. The time to analyze 96 spots was approximately 14 min for the Autof ms1000 and approximately 27 min for the Bruker Biotyper. The two instruments showed comparable performance for the routine identification of clinical microorganisms. In addition, the Autof ms1000 has a short test time, making it convenient for use in clinical microbiology laboratories.

Comparison of Autof ms1000 and Bruker Biotyper MALDI-TOF MS Platforms for Routine Identification of Clinical Microorganisms