Matrix-assisted laser desorption ionization time-of-flight mass spectrometry combined with UF-5000i urine flow cytometry to directly identify pathogens in clinical urine specimens within 1 hour.

Background: Urinary tract infection (UTI) is one of the most common hospital-associated infectious. The traditional laboratory diagnosis method for UTI requires at least 24 hours, and it cannot provide the etiology basis for the clinic in time. The aim of our study is to develop a new method for pathogenic diagnosis of UTI by combining matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) and UF-5000i from urine samples directly within 1 hour.

Methods: A total of 1,503 urine samples were collected from patients suggesting symptoms of UTI from August 2018 to January 2019. Each of these samples was divided into three aliquots. The first aliquot was used for conventional cleaning mid-stream urine culture; the second one for UF-5000i analysis to screen out the bacterial counts, which were more than 1×10 bacteria/mL. The third one was processed to bacterial purification and directly identified by the MALDI-TOF MS.

Results: In our study, 296 of 1,503 urine specimens were screened out by UF-5000i (bacterial pellets counts ≥10/mL). Compared the conventional culture-dependent method, the results of our methods were consistent in 249 of 263 (94.7%) cases, and they were both single-microorganism. Among 249 credible results, species-level identification (score ≥2.0) was contained 233 (233/249. 93.6%), 16 (16/249, 6.4%) samples scored between 1.7 and 1.99, and 14 (14/249, 5.6%) samples scored <1.7 or no peaks found. When there were 2 different kinds of bacteria in the urine, the result of MALDI-TOF MS was unreliable.

Conclusions: MALDI-TOF MS combined with UF-5000i to identify the pathogenic bacteria in urine directly is a novel and reliable method and saves at least 23 hours relative to the current routine conventional method. Thus its rapid and accurate detection may provide the basis of etiology for clinical diagnosis of UTIs efficiently.