Comparison of Bruker Biotyper Sirius, VITEK MS PRIME, and ASTA MicroIDSys MALDI-TOF MS Systems for Cryptococcus Species Identification Across Protein Extraction Protocols.

BACKGROUND

Accurate and rapid identification of Cryptococcus spp. is critical for patient care. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is increasingly used for yeast identification; however, its performance varies with instrument and database coverage. We compared the performance of three MALDI-TOF MS systems-Bruker Biotyper Sirius (Sirius; Bruker Daltonics), bioMérieux VITEK MS PRIME (PRIME; bioMérieux), and ASTA MicroIDSys (ASTA; ASTA Inc.)-for detecting Cryptococcus spp. across protein-extraction protocols.

METHODS

We analyzed 51 C. neoformans, two C. deuterogattii, five Vanrija humicola, and one Naganishia liquefaciens clinical isolates. Each isolate was tested after on-plate formic acid extraction; isolates failing identification were retested after repeat on-plate and intube extractions. The results were categorized as correct, incomplete, incorrect, or no identification against ribosomal DNA sequencing results.

RESULTS

After initial on-plate extraction, correct identification rates were 86.4%, 88.1%, and 69.5% for Sirius, PRIME, and ASTA, respectively. After repeat on-plate extraction, ASTA accuracy increased to 81.4% (P =0.016). Additional in-tube extraction did not significantly improve identification across platforms, although PRIME outperformed ASTA (P =0.012). All platforms reliably classified C. neoformans (correct identification rates: 94%-96%) after additional on-plate extraction; discrepancies occurred mainly in uncommon species, possibly owing to gaps in database coverage.

CONCLUSIONS

Sirius and PRIME demonstrated robust performance for C. neoformans identification and C. gattii complex differentiation, outperforming ASTA for relatively rare species. Single on-plate extraction is sufficient in clinical practice. However, continued database expansion is essential for maximizing MALDI-TOF MS performance and accurately identifying Cryptococcus spp., thereby informing antifungal therapy and improving patient outcomes.