Substantial Improvement in Nontuberculous Mycobacterial Identification Using ASTA MicroIDSys Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry with an Upgraded Database
- Authors
- Song, Junhyup; Yoon, Shinyoung; In, Yongha; Kim, Daewon; Lee, Hyukmin; Yong, Dongeun; Lee, Kyoungwon
- Issue Date
- May-2022
- Publisher
- KOREAN SOC LABORATORY MEDICINE
- Keywords
- Matrix-assisted laser desorption; ionization time-of-flight mass spectrometry; Nontuberculous mycobacteria; Database upgrade; Identification; Performance evaluation
- Citation
- ANNALS OF LABORATORY MEDICINE, v.42, no.3, pp.358 - 362
- Journal Title
- ANNALS OF LABORATORY MEDICINE
- Volume
- 42
- Number
- 3
- Start Page
- 358
- End Page
- 362
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/83219
- DOI
- 10.3343/alm.2022.42.3.358
- ISSN
- 2234-3806
- Abstract
- Identifying Mycobacterium using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is challenging. We evaluated the performance of MALDI-TOF MS in identifying nontuberculous mycobacteria (NTM) using the ASTA MicroIDSys system (ASTA Inc., Suwon, Korea) with the MycoDB v1.95s and upgraded MycoDB v2.0-beta databases. We tested 124 NTM isolates collected from Ogawa medium at Severance Hospital, Seoul, Korea, between January and April 2019. MicroIDSys scores were categorized into three groups: >= 140, reliable identification; 130-139, ambiguous identification; and <130, invalid identification. To validate the results, we used the reverse blot hybridization assay (Molecutech REBA MycoID, YD Diagnostics Corp., Korea). Initial analysis using MycoDB v1.95s resulted in 26.6% (33/124) reliable, 43.5% (54/124) ambiguous, and 29.8% (37/124) invalid identifications. Re-analysis using the upgraded MycoDB v2.0-beta database resulted in 94.4% (117/124) reliable, 4.0% (5/124) ambiguous, and 1.6% invalid (2/124) identifications. The percentage of reliable identifications that matched with the reference increased from 26.6% (33/124) with MycoDB v1.95s to 93.5% (116/124) with MycoDB v2.0-beta. The upgraded databases enable substantially improved NTM identification through deep learning in the inference algorithm and by considering more axes in the correlation analysis. MALDI-TOF MS using the upgraded database unambiguously identified most NTM species. Our study lays a foundation for applying MALDITOF MS for the simple and rapid identification of NTM isolated from solid media.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - ETC > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/83219)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.