BioMed Research International
Volume 2015 (2015), Article ID 516410, 18 pages
http://dx.doi.org/10.1155/2015/516410

Research Article

Performances and Reliability of Bruker Microflex LT and VITEK MS MALDI-TOF Mass Spectrometry Systems for the Identification of Clinical Microorganisms

Kivanc Bilecen,¹ Gorkem Yaman,² Ugur Ciftci,³ and Yahya Rauf Laleli³

¹Department of Research & Development, Duzen Laboratories Group, 06680 Ankara, Turkey
²Department of Clinical Microbiology, Duzen Laboratories Group, 34387 Istanbul, Turkey
³Department of Clinical Microbiology, Duzen Laboratories Group, 06680 Ankara, Turkey

Received 1 September 2015; Revised 14 November 2015; Accepted 16 November 2015

Academic Editor: Qaisar Mahmood

Copyright © 2015 Kivanc Bilecen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

In clinical microbiology laboratories, routine microbial identification is mostly performed using culture based methodologies requiring 24 to 72 hours from culturing to identification. Matrix assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) technology has been established as a cost effective, reliable, and faster alternative identification platform. In this study, we evaluated the reliability of the two available MALDI-TOF MS systems for their routine clinical level identification accuracy and efficiency in a clinical microbiology laboratory setting. A total of 1,341 routine phenotypically identified clinical bacterial and fungal isolates were selected and simultaneously analyzed using VITEK MS (bioMérieux, France) and Microflex LT (Bruker Diagnostics, Germany) MALDI-TOF MS systems. For any isolate that could not be identified with either of the systems and for any discordant result, 16S rDNA gene or ITS1/ITS2 sequencing was used. VITEK MS and Microflex LT correctly identified 1,303 (97.17%) and 1,298 (96.79%) isolates to the species level, respectively. In 114 (8.50%) isolates initial phenotypic identification was inaccurate. Both systems showed a similar identification efficiency and workflow robustness, and they were twice as more accurate compared to routine phenotypic identification in our sample pool. MALDITOF systems with their accuracy and robustness offer a good identification platform for routine clinical microbiology laboratories.

1. Introduction

Rapid and accurate identification of bacteria and yeasts from clinical specimens is crucial for the effective management of infections. In the clinical microbiology laboratories, microbial identification is conventionally done by phenotypic and biochemical analyses mostly using automated systems. These analyses require time ranging from a few hours to several days depending on microbial species in question. Workload and cost requirements for molecular methods, although they provide faster and accurate results, limit their routine use in clinical microbiology laboratories.

MALDI-TOF MS technology makes generation of unique mass spectral fingerprints of microorganisms possible, which are mostly a snapshot of ribosomal proteins ideal for an accurate microbial identification at the species level [1]. MALDI-TOF MS can rapidly and accurately identify a wide range of microorganisms at a reasonable cost using only a portion or the entire colony and a drop of matrix solution. While the MALDI-TOF MS analysis for the identification of intact bacterial cells was demonstrated 17 years ago [2, 3] and was extended to eukaryotic fungal cells 13 years ago [4, 5], not until recently has its potential for routine use been assessed for identification of a wide spectrum of bacteria, yeasts, molds, and mycobacteria that can be isolated in the clinical laboratories [6–13]. The ability of MALDI-TOF MS to directly identify bacteria in positive blood cultures is also important for the effective management of bloodstream infections [14, 15].

By this mean, two different CE marked in vitro diagnostic (IVD) MALDI TOF-MS systems were implemented to our laboratories in Ankara and Istanbul in March, 2012, and since then they have been used as the main identification tool for routine clinical bacterial and yeast isolates. In this study we evaluated performances of Bruker Microflex LT (Bruker Daltonics, Germany) and VITEK MS (bioMérieux, France) MALDI TOF-MS systems for identification of routine clinical microbiology isolates.

2. Materials and Methods

2.1. Clinical Isolates and Collection Strains

A total of 1,341 routine clinical isolates including 1,181 bacterial and 160 fungal specimens were analyzed using VITEK MS and Bruker Microflex LT. The isolates were obtained from clinical specimens including urine, blood, tissue, wounds, bronchoalveolar lavage, tracheal aspirate, sputum and wounds, which had been sent to our laboratory from various intensive care units, health facilities, and laboratories between April 2012 and December 2013. No selection criteria was applied for the isolates in order to obtain a comprehensive diversity.

Both MALDI-TOF MS systems were verified using 18 certified reference microorganisms obtained from reference culture collections.

2.2. Culture Conditions and Identification of Microorganisms

The specimens were routinely inoculated to appropriate media like, Columbia agar with 5% sheep blood, eosin methylene blue (EMB) agar, chromogenic urinary tract infection (CUTI) medium, chocolate agar, oxacillin resistant screening agar (ORSA), Salmonella-shigella agar, Thayer-Martin agar, Candida chromogenic agar, and blood culture bottles at 37°C depending on specimen type.

All of the specimens were incubated overnight at 37°C. The isolates were then Gram stained and identified using the reciprocate cards of VITEK II (bioMérieux, France) automated microbial identification system. The cultures and phenotypic identification results were transported to Ankara Duzen Laboratory Microbiology Department for VITEK MS analyses.

The strains were prepared and identified with VITEK MS system in Ankara and the results were documented. Following these initial identifications, culture plates were transported to our Clinical Laboratory Department located in İstanbul under appropriate conditions for the next day analysis with Bruker Microflex LT system.

2.3. VITEK MS MALDI-TOF System

From the overnight appropriate agar plates a portion or the whole colony was smeared onto the spots of VITEK MS-DS disposable target slides and then the spot was covered with 1 μL of α-cyano-4-hidroxycinnamic acid (CHCA) matrix solution. Spots were completely air dried, and, then, the slides were placed on an adapter and inserted to VITEK MS instrument. Spectra were generated using the MYLA software (bioMérieux, France) and the identification was automatically done with the VITEK MS IVD version 2.4.0-5 software. This software version contains >25,000 spectra, covering 586 species consisting of 508 bacteria and 78 fungi in its database. The software compares the spectrum obtained to the expected spectrum of each organism or organism group. Then the percent probability, a quantitative value, is calculated for each sample. The range of percent probabilities for a correct identification is from 60 to 99 with values closer to 99.9 indicating a closer match. When the obtained percent probability is under 60, then it was considered as no-identification. Confidence level is determined with percent probability and number of choices. During our analyses, although there was a unique identification pattern within the good confidence level, a list of possible organisms was given for certain samples. In such cases if both organisms belonged to the same genus then this result was accepted as a reliable identification to the genus level. In some other cases, when the strain belonged to a complex (e.g., Enterobacter cloacae complex) these results were documented as a correct result according to the cutoff value of the system; however the identification to the complex level was documented in order to compare the performances of the two systems accurately.

2.4. Bruker Microflex LT System

A portion or the whole colony was directly transferred onto the 96 spotted polished steel target plate. The spot was covered with 1 μL of CHCA matrix solution. After completely air drying, the plate was loaded to the Microflex LT instrument. The spectra were generated in linear positive ion mode with delayed extraction in a mass range of 4 to 10 kDa using a 337 nm nitrogen laser with a frequency of 60 Hz. The automated data analysis was processed with MALDI Biotyper Realtime Classification and Biotyper software version 3.0 (Bruker Daltonics). The obtained spectra were analyzed by standard pattern-matching algorithm, which compared the raw spectra with the spectra of the Bruker library by using the standard setting, and the results were listed in a ranking table. The results were expressed as log (score) values, which ranged from 0 to 3 as recommended by the manufacturer. Score values of >1.7 generally indicated relationships at the genus level, and values of >2.0 generally indicated relationships at the species level. The highest score was used for species identification. The Bruker library contains > 80,000 spectra covering 2,048 species and 385 genera. If the result was below 1.7 then the extraction method was performed where the colony was transferred into 1.5-mL screw cap tubes and mixed thoroughly in 0.3 mL of double-distilled ultrapure water. 0.9 mL of pure ethanol was added to tubes and after vortexing they were centrifuged at 13,000 ×g for 2 min. The supernatant was discarded and the pellet was mixed thoroughly with 50 μL of 70% aqueous formic acid. After the addition of 50 μL of acetonitrile, the mixture was centrifuged at 13,000 ×g for 2 min. One microliter of the microorganism extract supernatant was placed onto the polished steel and covered with 1 μL of CHCA matrix solution and loaded to the instrument.

2.5. Discordant Results

Results obtained from VITEK MS and Microflex LT were compared to initial onsite phenotypic identification results. The decision matrix used for the comparison and classification of results is given in Table 1. Briefly, when MS and the onsite phenotypic identification results agreed at the species level, then this was accepted as a correct result at the species level. When any MS equipment gave a correct result but as a species complex, then this was considered as a correct result and specified in the result table. For any discordant with the onsite phenotypic identification result we performed a second phenotypic identification. If this second phenotypic identification agreed with the MS result then this second result is accepted depending on correctness level, species, or genus. Otherwise a 16S or ITS1/2 sequencing analysis was performed depending on the nature of microorganism (prokaryote or eukaryote).

Table 1: Decision table used for the classification of results during the study.

Type
On-site phenotypic identification
MALDI-TOF MS analysis
Secondary phenotypic identification or molecular identification (16S or ITS1/2 sequencing)
Accepted
Number of m/o’s ( = 1341)

VITEK
Bruker

A
Identified as
Identified as
Identified as
N/A
Accepted as
1203

B
Identified as or no-identification
Identified as
Identified as
Identified as
Accepted as
97

C
Identified as
Identified as
Identified as
Identified as (or )
Accepted as (or )
16

Identified as
Identified as

D
Identified as
Identified as
Identified as
Identified as (or ) (sequencing only)
Accepted as (or )
6

E
Identified as
Identified as
Identified as
Identified as (sequencing only)
Accepted as
3

F
Identified as
No-identification
Identified as (or )
Identified as (or or )
Accepted as (or or )
12

Identified as (or )
No-identification

G
Identified as
No-identification
No-identification
Identified as (or ) (sequencing only)
Accepted as (or )
3

Shigella boydii was not included.

2.6. Extraction and Purification of Genomic DNA

Three to four colonies were selected from agar plates, resuspended in 500 μL 1x PBS (pH 7.4), and used for genomic DNA isolation. Genomic DNA isolation from Gram-negative bacteria was performed using DNA4U Bacterial Genomic DNA Isolation Kit (NANObiz, Turkey) as instructed by the manufacturer. DNA isolation from Gram-positive bacteria and other microorganisms were performed using conventional phenol/chloroform extraction method. Briefly, samples in 500 μL 1x PBS were centrifuged at 12,500 g for 5 minutes, resuspended in sterile water, recentrifuged, and resuspended in 500 μL TES buffer (50 mM Tris-HCl, pH 8.0; 1 mM EDTA; 6.7% sucrose). For Gram-positive bacteria 75 μL of 10 mg/mL lysozyme was added and samples were incubated at 37°C for 1 hour. After the incubation 125 μL of 20% SDS was added into each sample and incubated at 37°C for 30 minutes. Later 700 μL of phenol (SIGMA, USA) was added and samples were gently mixed using a vortex. Samples were then centrifuged at 2,000 rpm for 5 minutes and aqueous phases were removed into clean microcentrifuge tubes. Equal amount of 24 : 1 (v/v) chloroform : isoamyl alcohol was added and centrifuged at 2,000 rpm for 5 minutes. Aqueous phase was again removed into a clean microcentrifuge tube, and first 0.1 volume of ice cold 3 M potassium acetate, pH 5.5, and then 2 volumes of ice cold absolute ethanol were added. Samples were then incubated at −80°C for 15 minutes. Ethanol was removed through centrifugation at 10,000 rpm for 15 minutes, and this ethanol wash was repeated once more. Obtained pellets were then incubated at 50°C for approximately 15 minutes. Dry pellets were resuspended in 50 μL of sterile water. Purified genomic DNA was either visualized via agarose gel electrophoresis or directly measured for absorbance at 260 nm.

2.7. PCR Amplification of 16S Regions

The forward primer S-D-Bact-0008-a-S-20 (5′-AGA GTT TGA TCC TGG CTC AG-3′) [16] and the reverse primer rP2 (5′-ACG GCT ACC TTG TTA CGA CTT-3′) [17], which target the domain Bacteria, were used to amplify bacterial 16S rDNA sequences by PCR. Reaction mixture for PCR contained 1x PCR buffer (Fermentas), 2.0 mM MgCl2, 200 μM dNTP (each), 200 ng template genomic DNA, 0.5 μM of each primer, and 1.0 U Taq polymerase (Fermentas). DNA denaturation and amplification cycles were performed as described [16] using GeneAmp PCR System 9700 thermocycler (Applied Biosystems). Amplification products were checked via agarose gel electrophoresis.

2.8. PCR Amplification of ITS Regions

ITS1 (5′-TCCGTAGGTGAACCTGCGG-3′) and ITS4 (5′-TCCTCCGCTTATTGATATGC-3′) primers were used to amplify internal transcribed spacer regions 1 and 2 of microorganisms [18]. Reaction mixture for PCR contained 1x PCR buffer (Fermentas), 1.5 mM MgCl₂, 100 μM dNTP (each), 0.5 μM of each primer, and 1.0 U Taq polymerase (Fermentas) to a final concentration of 15 μL. DNA denaturation and amplification cycles were performed as described [18].

2.9. DNA Sequencing and Sequence Analyses

PCR products were sequenced via Sanger sequencing using 3130 Genetic Analyzer (Applied Biosystems). Obtained sequences were compared to 16S rDNA and/or ITS sequences that are found in public databases: NCBI (http://blast.ncbi.nlm.nih.gov/) and Green Genes 16S rDNA (http://greengenes.lbl.gov/).

3. Results

3.1. Reference Microorganisms

Eighteen reference bacterial strains were tested (Table 2) for verification and comparison of both MALDI-TOF MS systems. Salmonella typhimurium (ATCC 202165), Salmonella enteritidis (DM10), and Salmonella enterica subsp. enterica serovar typhimurium (ATCC 14028) were identified only to the genus level as Salmonella spp. These were all expected as they are within the current MALDI-TOF system limitations [1]. The rest of the reference microorganisms could all be accurately identified to the species level (Table 2).

Table 2: Results of 18 reference bacterial strains that were analyzed by VITEK MS and Bruker Microflex LT for an initial system setup and comparison.

Microorganism
VITEK
Bruker

S. pyogenes 
ATCC 19615
✓
✓

E. coli 
ATCC 25922
✓
✓

K. pneumoniae 
ATCC 13883
✓
✓

P. aeruginosa 
ATCC 27853
✓
✓

S. aureus 
ATCC 25923
✓
✓

S. pneumoniae 
ATCC 49619
✓
✓

P. vulgaris 
ATCC 13315
P. vulgaris/penneri
P. hauseri 
(new nomenclature)

S. typhimurium 
ATCC 14028
Salmonella group
Salmonella spp.

Y. enterocolitica 
ATCC 9610
✓
✓

V. cholera 
RSKK 913
✓
✓ 
(V. cholerae biovar albensis)

G. vaginalis 
DM 108
✓
✓

S. saprophyticus 
DM 17
✓
✓

S. agalactiae 
DM 20
✓
✓

MRSA 
DM 66
S. aureus
S. aureus

S. epidermidis 
DM 19
✓
✓

S. enteritidis 
DM 10
Salmonella group
Salmonella spp.

A. hydrophila 
DM 110
A. hydrophila/caviae
A. caviae

P. shigelloides 
DM 111
✓
✓

3.2. Routine Clinical Bacterial Isolates

Our results indicated that both MS systems had high accuracy in the identification of routine clinical bacterial isolates that cover a wide range of species known to cause human infections (Tables 3 and 4). VITEK MS correctly identified 916 (77.56%) clinical bacterial isolates to the species and 231 (19.56%) isolates to the clinically relevant species group, totaling 1,147 (97.12%) correct identification on the species level. From the same pool, Bruker Microflex LT identified 1,142 (96.70%) clinical isolates to the species level.

Table 3: Summary of high-confidence identifications by VITEK MS and Bruker Microflex LT MS systems.

Microorganism groups
Number of isolates
Species content
Correct Identification
Misidentification
No-identification

Species
Genus only

VITEK
Bruker
VITEK
Bruker
VITEK
Bruker
VITEK
Bruker

Nonfermentatives
356 (30.1%)
16
352 (98.8%)
350 (98.4%)
—
3 (0.8%)
2 (0.6%)
—
2 (0.6%)
3 (0.8%)

Enterobacteriaceae
309 (26.2%)
23
292 (94.5%)
288 (93.2%)
15 (4.9%)
19 (6.2%)
1 (0.3%)
1 (0.3%)
1 (0.3%)
1 (0.3%)

Staphylococci
273 (23.1%)
10
271 (99.3%)
270 (98.9%)
2 (0.7%)
—
—
2 (0.7%)
—
1 (0.4%)

Streptococci
29 (2.5%)
11
28 (96.6%)
27 (93.2%)
—
1 (3.4%)
—
1 (3.4%)
1 (3.4%)
—

Enterococci and other Gr(+) cocci
160 (13.6%)
5
160 (100.0%)
160 (100.0%)
—
—
—
—
—
—

Coryneforms and other Gr(+) bacilli
30 (2.5%)
14
21 (70.00%)
25 (83.3%)
2 (6.7%)
4 (13.4%)
2 (6.7%)
—
5 (16.6%)
1 (3.3%)

HACEK and
other Gr(−) cocci
18 (1.5%)
8
18 (100.0%)
18 (100.0%)
—
—
—
—
—
—

Anaerobes
2 (0.2%)
2
2 (100.0%)
2 (100.0%)
—
—
—
—
—
—

Others
4 (0.3%)
4
3 (75.0%)
2 (50.0%)
—
1 (25.0%)
—
—
1 (25.0%)
1 (25.0%)

TOTAL (bacteria)
1,181
93
1,147 (97.1%)
1,142 (96.7%)
19 (1.6%)
28 (2.4%)
5 (0.4%)
4 (0.3%)
10 (0.9%)
7 (0.6%)

Fungi
160
14
156 (97.5%)
156 (97.5%)
3 (1.9%)
4 (2.5%)
—
—
1 (0.6%)
—

TOTAL (bacteria + fungi)
1,341
107
1,303 (97.2%)
1,298 (96.8%)
22 (1.6%)
32 (2.4%)
5 (0.4%)
4 (0.3%)
11 (0.8%)
7 (0.5%)

Table 4: High-confidence identifications by VITEK MS and Bruker Microflex LT MS systems.

Microorganism groups
# of
isolates
Correct Identification
Misidentification
No-identification

Species
Genus only

VITEK
Bruker
VITEK
Bruker
VITEK
Bruker
VITEK
Bruker

Species
Species Complex

Nonfermentatives (16 species)

Achromobacter xylosoxidans
6
4
2
6
—
—
—
—
—
—

Acinetobacter baumannii
194
—
192
192
—
—
—
—
2
(F × 2)
2
(F × 2)

Acinetobacter guillouiae
1
—
1
1
—
—
—
—
—
—

Acinetobacter johnsonii
1
1
—
1
—
—
—
—
—
—

Acinetobacter lwoffii
1
1
—
1
—
—
—
—
—
—

Acinetobacter radioresistens
1
1
—
1
—
—
—
—
—
—

Aeromonas hydrophila
3
—
3
2
—
1 (C)
—
—
—
—

Aeromonas sobria
1
—
1
—
—
—
—
—
—
1
(F)

Alcaligenes faecalis
2
2
—
2
—
—
—
—
—
—

Burkholderia cepacia
2
—
2
—
—
2 (C × 2)
—
—
—
—

Comamonas aquatica
1
—
—
1
—
—
1 (D)
—
—
—

Delftia acidovorans
3
3
—
3
—
—
—
—
—
—

Elizabethkingia meningoseptica
1
1
—
1
—
—
—
—
—
—

Pseudomonas aeruginosa
125
124
—
125
—
—
1 (C)
—
—
—

Pseudomonas fluorescens
1
1
—
1
—
—
—
—
—
—

Stenotrophomonas maltophilia
13
13
—
13
—
—
—
—
—
—

Total
356
151 (42.4%)
201 (56.4%)
350 (98.4%)
—
3 (0.8%)
2 (0.6%)
—
2 
(0.6%)
3 
(0.8%)

Enterobacteriaceae (23 species)

Chryseobacterium indologenes
1
1
—
1
—
—
—
—
—
—

Citrobacter braakii
3
—
3
3
—
—
—
—
—
—

Citrobacter freundii
5
3
2
5
—
—
—
—
—
—

Citrobacter sedlakii
1
1
—
1
—
—
—
—
—
—

Citrobacter youngae
2
—
2
1
—
1 (C)
—
—
—
—

Enterobacter aerogenes
4
4
—
4
—
—
—
—
—
—

Enterobacter asburiae
2
—
2
1
—
1 (C)
—
—
—
—

Enterobacter cancerogenous
1
1
—
1
—
—
—
—
—
—

Enterobacter cloacae
11
—
11
9
—
2 (C × 2)
—
—
—
—

Escherichia coli
123
123
—
123
—
—
—
—
—
—

Klebsiella oxytoca
5
5
—
5
—
—
—
—
—
—

Klebsiella pneumonia
64
64
—
64
—
—
—
—
—
—

Kluyvera cryocrescens
1
—
—
—
—
—
—
—
1
(G)
1 (G)

Morganella morganii
12
12
—
12
—
—
—
—
—
—

Proteus mirabilis
28
28
—
28
—
—
—
—
—
—

Providencia rettgeri
3
3
—
3
—
—
—
—
—
—

Providencia stuartii
1
1
—
1
—
—
—
—
—
—

Raoultella ornithinolytica
3
2
1
3
—
—
—
—
—
—

Raoultella planticola
1
—
1
1
—
—
—
—
—
—

Salmonella spp.
15
—
—
—
15
15
—
—
—
—

Serratia liquefaciens
1
1
—
1
—
—
—
—
—
—

Serratia marcescens
21
21
—
21
—
—
—
—
—
—

Shigella boydii
1
—
—
—
—
—
1 (N/A)
1
(N/A)
—
—

Total
309
270 (87.4%)
22 (7.1%)
288 (93.3%)
15 (4.9%)
19 (6.1%)
1 (0.3%)
1 
(0.3%)
1 
(0.3%)
1 
(0.3%)

Staphylococci (10 species)

Staphylococcus aureus
52
52
—
52
—
—
—
—
—
—

Staphylococcus capitis
8
8
—
8
—
—
—
—
—
—

Staphylococcus caprae
1
1
—
1
—
—
—
—
—
—

Staphylococcus epidermidis
68
68
—
65
—
—
—
2
(C × 2)
—
1 (F)

Staphylococcus haemolyticus
54
54
—
54
—
—
—
—
—
—

Staphylococcus hominis
82
82
—
82
—
—
—
—
—
—

Staphylococcus lugdunensis
1
1
—
1
—
—
—
—
—
—

Staphylococcus pettenkoferi
1
—
—
1
1 (C)
—
—
—
—
—

Staphylococcus sciuri
2
2
—
2
—
—
—
—
—
—

Staphylococcus simulans
4
3
—
4
1 (D)
—
—
—
—
—

Total
273
271 (99.3%)
—
270 (98.9%)
2 (0.7%)
—
—
2 
(0.7%)
—
1 
(0.4%)

Streptococci (11 species)

Leuconostoc mesenteroides
1
—
—
—
—
—
—
1 (F)
1
(F)
—

Streptococcus agalactiae
9
9
—
9
—
—
—
—
—
—

Streptococcus anginosus
1
1
—
1
—
—
—
—
—
—

Streptococcus dysgalactiae
3
3
—
3
—
—
—
—
—
—

Streptococcus gallolyticus
1
1
—
1
—
—
—
—
—
—

Streptococcus mitis
1
—
1
—
—
1 (C)
—
—
—
—

Streptococcus oralis
1
—
1
1
—
—
—
—
—
—

Streptococcus parasanguinis
1
1
—
1
—
—
—
—
—
—

Streptococcus pneumonia
4
4
—
4
—
—
—
—
—
—

Streptococcus pyogenes
6
6
—
6
—
—
—
—
—
—

Streptococcus salivarius
1
1
—
1
—
—
—
—
—
—

Total
29
26 (89.7%)
2 (6.9%)
27 (93.2%)
—
1 (3.4%)
—
1 
(3.4%)
1 
(3.4%)
—

Enterococci and other Gr(+) cocci (5 species)

Aerococcus viridans
4
4
—
4
—
—
—
—
—
—

Enterococcus avium
4
4
—
4
—
—
—
—
—
—

Enterococcus faecalis
54
54
—
54
—
—
—
—
—
—

Enterococcus faecium
84
84
—
84
—
—
—
—
—
—

Enterococcus gallinarum
14
14
—
14
—
—
—
—
—
—

Total
160
160 (100%)
—
160 (100%)
—
—
—
—
—
—

Coryneforms and other Gr(+) bacilli (14 species)

Actinomyces naeslundii
1
—
—
—
—
1 (F)
—
—
1
(F)
—

Bacillus cereus
1
—
1
1
—
—
—
—
—
—

Bacillus simplex
1
1
—
1
—
—
—
—
—
—

Corynebacterium amycolatum
4
—
4
4
—
—
—
—
—
—

Corynebacterium appendicis
1
—
—
—
1 (E)
1 (E)
—
—
—
—

Corynebacterium coyleae
1
—
—
1
—
—
1 (D)
—
—
—

Corynebacterium glaucum
1
—
—
—
—
—
—
—
1
(G)
1 (G)

Corynebacterium jeikeium
2
2
—
2
—
—
—
—
—
—

Corynebacterium mucifaciens
3
—
—
3
—
—
1 (D)
—
2
(F)
—

Corynebacterium striatum
10
10
—
10
—
—
—
—
—
—

Corynebacterium urealyticum
1
1
—
1
—
—
—
—
—
—

Corynebacterium ureicelerivorans
2
—
—
—
1 (E)
2 (E, F)
—
—
1
(F)
—

Lactobacillus paracasei
1
—
1
1
—
—
—
—
—
—

Listeria monocytogenes
1
1
—
1
—
—
—
—
—
—

Total
30
15 (50.0%)
6 (20.0%)
25 (83.4%)
2 (6.7%)
4 (13.3%)
2 (6.7%)
—
5 
(16.6%)
1 
(3.3%)

HACEK and other Gr(−) cocci (8 species)

Haemophilus haemolyticus
1
1
—
1
—
—
—
—
—
—

Haemophilus influenza
3
3
—
3
—
—
—
—
—
—

Haemophilus parahaemolyticus
2
2
—
2
—
—
—
—
—
—

Haemophilus parainfluenzae
1
1
—
1
—
—
—
—
—
—

Legionella pneumophila
7
7
—
7
—
—
—
—
—
—

Moraxella catarrhalis
1
1
—
1
—
—
—
—
—
—

Neisseria meningitides
1
1
—
1
—
—
—
—
—
—

Pasteurella multocida
2
2
—
2
—
—
—
—
—
—

Total
18
18 (100%)
—
18 (100%)
—
—
—
—
—
—

Anaerobes (2 species)

Clostridium perfringens
1
1
—
1
—
—
—
—
—
—

Clostridium sporogenes
1
1
—
1
—
—
—
—
—
—

Total
2
2 (100%)
—
2 (100%)
—
—
—
—
—
—

Others (4 species)

Bergeyella zoohelcum
1
1
—
1
—
—
—
—
—
—

Myroides odoratimimus
1
1
—
—
—
1 (C)
—
—
—
—

Rhodococcus equi
1
1
—
1
—
—
—
—
—
—

Wautersiella falsenii
1
—
—
—
—
—
—
—
1 (G)
1 (G)

Total
4
3 (75.0%)
—
2 (50.0%)
—
1 (C) (25.0%)
—
—
1 (G) 
(25.0%)
1 (G) (25.0%)

Total (bacteria)
1,181
916 (77.6%)
231 (19.6%)
1,142 (96.7%)
19 (1.6%)
28 (2.4%)
5 (0.4%)
4 
(0.3%)
10 
(0.8%)
7 
(0.6%)

Fungi (14 species)

Aspergillus brasiliensis
1
—
—
1
—
—
—
—
1 (F)
—

Candida albicans
43
43
—
41
—
2 (C × 2)
—
—
—
—

Candida dubliniensis
1
1
—
1
—
—
—
—
—
—

Candida glabrata
44
44
—
44
—
—
—
—
—
—

Candida kefyr
11
11
—
11
—
—
—
—
—
—

Candida krusei
4
4
—
4
—
—
—
—
—
—

Candida lusitaniae
2
2
—
2
—
—
—
—
—
—

Candida norvegensis
1
1
—
1
—
—
—
—
—
—

Candida orthopsilosis
2
—
—
2
2 (D × 2)
—
—
—
—
—

Candida parapsilosis
14
14
—
13
—
1 (C)
—
—
—
—

Candida tropicalis
32
31
1
32

Cryptococcus neoformans
1
1
—
1
—
—
—
—
—
—

Meyerozyma caribbica
1
—
—
—
1 (E)
1 (E)
—
—
—
—

Trichosporon asahii
3
3
—
3
—
—
—
—
—
—

Total (fungi)
160
155 (96.9%)
1 (0.6%)
156 (97.5%)
3 (1.9%)
4 (2.5%)
—
—
1 
(0.6%)
—

TOTAL (bacteria + fungi)
1,341
1,071 (79.9%)
232 (17.3%)
1,298 (96.8%)
22 (1.6%)
32 (2.4%)
5 (0.4%)
4 
(0.3%)
11 (0.8%)
7 
(0.5%)

1,303 (97.2%)

Identification on genus only level was also similar for both systems. VITEK MS could identify 19 (1.61%) and Bruker Microflex LT could identify 28 (2.37%) isolates only to the genus level. Genus level, but not to species level identification of Salmonella spp. () accounts for the main decrease in the percentage for correct species level identification for both systems, which is a known system limitation.

Misidentification numbers were similar for VITEK MS and Bruker Microflex LT with 5 (0.42%) and 4 (0.33%) isolates, respectively. Shigella boydii was incorrectly identified as E. coli by both systems, but this is also a known system limitation.

Except one Leuconostoc mesenteroides isolate, the remaining misidentified isolates were identified by at least one system. L. mesenteroides was misidentified as Staphylococcus pettenkoferi by Bruker Microflex LT, whereas it could not be identified by VITEK MS.

A total of three isolates, Wautersiella falsenii, Kluyvera cryocrescens, and Corynebacterium glaucum, could not be identified by both systems (Table 4).

3.3. Fungal Samples

We have observed a very similar identification accuracy in both systems. Out of 160 fungal samples VITEK MS correctly identified 155 (96.88%) isolates to the species, 1 (0.62%) isolate to the species complex, and 3 (1.88%) isolates to the genus level. Similarly, Bruker Microflex LT identified 156 (97.50%) fungal isolates to the species level and 4 (2.50%) isolates to the genus level.

There was no misidentification in both systems. VITEK MS could not identify an Aspergillus brasiliensis isolate as this microorganism is not present in the system’s library, whereas Bruker Microflex LT identified this isolate to the species level.

4. Discussion

Our results indicated that, using any of the two available MALDI-TOF MS systems, a species level correct identification accuracy of over 96% could be achieved without any prior information about the tested samples. We have reached this high accuracy level using the regular sample extraction methods without any strain specific pretreatment in order to determine the efficiency of MS systems in the routine clinical microbiology settings.

In our laboratory, conventional biochemistry based phenotypic microbial identification methods costs about $15 and typically takes from 6 to 18 hours depending on the tested microorganisms. MALDI-TOF MS system, on the other hand, requires only 5 to 30 minutes for the identification and it costs less than $1 per sample.

For this study we collected various clinical specimens which were sent to our laboratory with an initial on-site identification that had been carried out using a VITEK II Compact phenotypic identification platform. During our analyses; for 97 (7.23%) isolates both MS systems gave a different result than this initial on-site phenotypic identification. Our further molecular (16S or ITS1/2 sequencing) or phenotypic analysis resulted in favor of MS systems for all of these 97 isolates. Additional 17 (0.01%) isolates were also confirmed to be different than the on-site phenotypic identification and these isolates were misidentified at least by one of the MS systems. As a result, a total of 114 (8.50%) isolates were misidentified by phenotypic methods, whereas an average of 37 (2.76%) isolates were misidentified or could not be identified by MS systems used in this study, making them at least twice as more accurate (Table 6). Using the wrong ID card and in some few instances wrong labeling might account for these incorrect on-site identifications. MS systems, as they do not need prior strain information, proved themselves more accurate and efficient for this instance.

Identification of microorganisms using a MALDI-TOF system is based on prerecorded protein spectra that are present in the system library [19]. These spectra are mainly based on ribosomal proteins and therefore MALDI-TOF systems have intrinsic limitations to differentiate closely related species or strains such as Salmonella spp. In our study the number of identification on the genus only level was 19 (1.61%) for VITEK MS and 28 (2.37%) for Bruker Microflex LT, mainly due to this systems’ limitations. Genus level, but not to species level, identification of Salmonella spp. () accounts for the main decrease in the percentage for species level correct identification for both systems.

When we compared the identification accuracies of the two systems we have seen similar results. However we have observed a relatively higher number of correct genera but wrong species count for the Bruker Microflex LT especially with closely related genera or complex members. VITEK MS prefer to give multiple results as species complexes leading to fewer errors while dealing with close species like Streptococcus parasanguinis/mitis/oralis, Enterobacter cloacae/asburiae, Citrobacter freundii/youngae, Aeromonas hydrophila/caviae, and Burkholderia cepacia/vietnamiensis. However, Bruker Microflex LT tends to report single species and that was the reason of correct genus but wrong species results in 8 cases. This grouping style favors VITEK MS during the analysis of clinical samples because distinguishing these closely related genera is clinically irrelevant; however, it can cause certain limitations for research studies and for the analysis of environmental, food, and industrial samples.

Two Corynebacterium spp. (C. ureicelerivorans and C. appendicis) isolates confirmed with 16S rDNA sequencing could only be correctly identified to the genus level by both systems. Both systems identified this isolate as C. pseudodiphtericum. In general, these are rare clinical isolates where molecular identification is needed mainly due to the MALDI-TOF MS library limitations [20]. One C. ureicelerivorans and one Actinomyces naeslundii isolates, which could not be identified by VITEK MS, were identified to the genus only level by Bruker Microflex LT. Two Staphylococcus species S. simulans and S. pettenkoferi were identified to the genus level by VITEK MS; however, Bruker Microflex LT could identify both of them to the species level. On the other hand, one Myroides odoratimimus isolate could only be identified to genus level by Bruker Microflex LT, whereas it was identified to species level by VITEK MS.

VITEK MS had a relatively higher no-identification rate compared to Bruker Microflex LT in the Coryneforms and other Gram-positive bacilli group (Table 5). It has already been shown that probably due to the thick peptidoglycan layer, as it might interfere with the laser ionization, better results had been obtained with a pretreatment prior to MS run for Gram-positive bacteria [21].

Table 5: Results summary of MALDI-TOF MS systems for the microorganisms that either were misidentified or could not be identified.

Microorganism groups
Number of isolates
Species content
Misidentification
No-identification

VITEK
Bruker
VITEK
Bruker

Nonfermentatives

Acinetobacter baumannii
194

—
—
2 (F × 2)
2 (F × 2)

Aeromonas sobria
1

—
—
—
1 (F)

Total
356
16
2
—
2
3

Enterobacteriaceae

Kluyvera cryocrescens
1

—
—
1 (G)
1 (G)

Shigella boydii
1

1 (N/A)
1 (N/A)
—
—

Total
309
23
1
1
1
1

Staphylococci

Staphylococcus epidermidis
68

—
2 (C × 2)
—
1 (F)

Total
273
10
—
2
—
1

Streptococci

Leuconostoc mesenteroides
1

—
1 (F)
1 (F)
—

Total
29
11
—
1
1
—

Enterococci and other Gr(+) cocci

Total
160
5
—
—
—
—

Coryneforms and other Gr(+) bacilli

Actinomyces naeslundii
1

—
—
1 (F)
—

Corynebacterium coyleae
1

1 (D)
—
—
—

Corynebacterium glaucum
1

—
—
1 (G)
1 (G)

Corynebacterium mucifaciens
3

1 (D)
—
2 (F)
—

Corynebacterium ureicelerivorans
2

—
—
1 (F)
—

Total
30
14
2
—
5
1

HACEK and other Gr(−) cocci

Total
18
8
—
—
—
—

Anaerobes

Total
2
2
—
—
—
—

Others

Wautersiella falsenii
1

—
—
1 (G)
1 (G)

Total
4
4
—
—
1 (G)
1 (G)

Total (bacteria)
1,181
93
5
4
10
7

Fungi

Aspergillus brasiliensis
1

—
—
1 (F)
—

Total (fungi)
160
14
—
—
1
—

Total (bacteria + fungi)
1,341
107
5
4
11
7

Table 6: Detailed results of microorganisms that were either misidentified or could not be identified by at least one MALDI-TOF system or phenotypic identification.

Correct ID
Number of isolates
On-site phenotypic identification
Secondary phenotypic identification or molecular identification  (16S or ITS1/2 sequencing)
VITEK
Bruker

Nonfermentatives

Achromobacter xylosoxidans
1
Achromobacter denitrificans
Achromobacter xylosoxidans
Achromobacter denitrificans/xylosoxidans
Achromobacter xylosoxidans

Acinetobacter baumannii
1
Cedecea lapegei
Acinetobacter baumannii
Acinetobacter baumannii complex
Acinetobacter baumannii

Acinetobacter baumannii
1
Escherichia coli
Acinetobacter baumannii complex
Acinetobacter baumannii complex
Acinetobacter baumannii

Acinetobacter baumannii
1
Klebsiella pneumoniae
Acinetobacter baumannii
Acinetobacter baumannii complex
Acinetobacter baumannii

Acinetobacter baumannii
1
Pseudomonas aeruginosa
Acinetobacter baumannii
Acinetobacter baumannii complex
Acinetobacter baumannii

Acinetobacter baumannii
1
Staphylococcus hominis
Acinetobacter baumannii complex
Acinetobacter baumannii complex
Acinetobacter baumannii

Acinetobacter baumannii
2
Acinetobacter baumannii
Acinetobacter baumannii
No identification
Acinetobacter baumannii

Acinetobacter baumannii
2
Acinetobacter baumannii
Acinetobacter baumannii
Acinetobacter baumannii complex
No identification

Acinetobacter guillouiae
1
Acinetobacter lwoffii
Acinetobacter guillouiae
Acinetobacter baumannii complex
Acinetobacter guillouiae

Acinetobacter johnsonii
1
Acinetobacter lwoffii
Acinetobacter johnsonii
Acinetobacter johnsonii
Acinetobacter johnsonii

Acinetobacter radioresistens
1
Acinetobacter lwoffii
Acinetobacter radioresistens
Acinetobacter radioresistens
Acinetobacter radioresistens

Aeromonas hydrophila
1
Aeromonas sobria
Aeromonas hydrophila
Aeromonas hydrophila/caviae
Aeromonas hydrophila

Aeromonas hydrophila
1
Aeromonas hydrophila/caviae
Aeromonas hydrophila
Aeromonas hydrophila/caviae
Aeromonas caviae

Aeromonas sobria
1
Aeromonas sobria
Aeromonas sobria
Aeromonas hydrophila/caviae, sobria
No identification

Alcaligenes faecalis
1
Chromobacterium violaceum
Alcaligenes faecalis
Alcaligenes faecalis
Alcaligenes faecalis

Burkholderia cepacia
2
Burkholderia cepacia
Burkholderia cepacia
Burkholderia cepacia/vietnamiensis
Burkholderia cenocepacia

Comamonas aquatica
1
Comamonas testosteroni
Comamonas aquatic
Delftia acidovorans
Comamonas aquatica

Delftia acidovorans
1
No identification
Delftia acidovorans
D. acidovorans
D. acidovorans

Delftia acidovorans
1
No identification
Delftia acidovorans
Delftia acidovorans
Delftia acidovorans

Pseudomonas aeruginosa
3
Acinetobacter baumannii
Pseudomonas aeruginosa
Pseudomonas aeruginosa
Pseudomonas aeruginosa

Pseudomonas aeruginosa
1
Enterobacter aerogenes
Pseudomonas aeruginosa
Pseudomonas aeruginosa
Pseudomonas aeruginosa

Pseudomonas aeruginosa
1
Pseudomonas aeruginosa
Pseudomonas aeruginosa
Porphyromonas gingivalis
Pseudomonas aeruginosa

Stenotrophomonas maltophilia
1
Pseudomonas aeruginosa
Stenotrophomonas maltophilia
Stenotrophomonas maltophilia
Stenotrophomonas maltophilia

Enterobacteriaceae

Chryseobacterium indologenes
1
No identification
Chryseobacterium indologenes
Chryseobacterium indologenes
Chryseobacterium indologenes

Citrobacter braakii
1
Citrobacter freundii
Citrobacter braakii
Citrobacter braakii/farmeri/freundii
Citrobacter braakii

Citrobacter freundii
1
No identification
Citrobacter freundii
Citrobacter freundii
Citrobacter freundii

Citrobacter sedlakii
1
Citrobacter amalonaticus
Citrobacter sedlakii
Citrobacter sedlakii
Citrobacter sedlakii

Citrobacter youngae
1
No identification
Citrobacter youngae
Citrobacter freundii/youngae
Citrobacter youngae

Citrobacter youngae
1
Citrobacter youngae
Citrobacter youngae
Citrobacter freundii/youngae
Citrobacter freundii

Enterobacter asburiae
1
Escherichia coli
Enterobacter cloacae complex
Enterobacter cloacae/asburiae
Enterobacter asburiae

Enterobacter asburiae
1
Enterobacter cloacae complex
Enterobacter asburiae
Enterobacter cloacae/asburiae
Enterobacter cloacae

Enterobacter cancerogenous
1
Enterobacter aerogenes
Enterobacter cancerogenous
Enterobacter cancerogenous
Enterobacter cancerogenous

Enterobacter cloacae
1
Enterobacter cancerogenus
Enterobacter cloacae complex
Enterobacter cloacae/asburiae
Enterobacter cloacae

Enterobacter cloacae
1
No identification
Enterobacter cloacae complex
Enterobacter cloacae/asburiae
Enterobacter cloacae

Enterobacter cloacae
1
No identification
Enterobacter cloacae
Enterobacter cloacae/asburiae
Enterobacter cloacae

Enterobacter cloacae
1
Enterobacter cloacae
Enterobacter cloacae
Enterobacter cloacae/asburiae
Enterobacter asburiae

Enterobacter cloacae
1
Enterobacter cloacae
Enterobacter cloacae
Enterobacter cloacae/asburiae
Enterobacter asburiae

Enterobacter cloacae
1
No identification
Enterobacter cloacae
Enterobacter cloacae/asburiae
Enterobacter cloacae

Klebsiella oxytoca
1
Escherichia coli
Klebsiella oxytoca
Klebsiella oxytoca
Klebsiella oxytoca

Klebsiella pneumoniae
1
Acinetobacter baumannii
Klebsiella pneumonia
Klebsiella pneumoniae
Klebsiella pneumoniae

Klebsiella pneumoniae
1
Enterobacter aerogenes
Klebsiella pneumonia
Klebsiella pneumoniae
Klebsiella pneumoniae

Klebsiella pneumoniae
1
Enterobacter aerogenes
Klebsiella pneumoniae
Klebsiella pneumoniae
Klebsiella pneumoniae

Klebsiella pneumoniae
1
Escherichia coli
Klebsiella pneumonia
Klebsiella pneumoniae
Klebsiella pneumoniae

Kluyvera cryocrescens
1
Raoultella planticola
Kluyvera cryocrescens
No identification
No identification

Proteus mirabilis
1
Stenotrophomonas maltophilia
Proteus mirabilis
Proteus mirabilis
Proteus mirabilis

Raoultella ornithinolytica
1
No identification
Raoultella ornithinolytica
Raoultella ornithinolytica/planticola
Raoultella ornithinolytica

Shigella boydii
1
Shigella boydii
Shigella boydii
Escherichia coli
Escherichia coli

Staphylococci

Staphylococcus aureus
1
Acinetobacter baumannii
Staphylococcus aureus
Staphylococcus aureus
Staphylococcus aureus

Staphylococcus hominis
1
Acinetobacter baumannii
Staphylococcus hominis
Staphylococcus hominis
Staphylococcus hominis

Staphylococcus epidermidis
1
Enterococcus faecium
Staphylococcus epidermidis
Staphylococcus epidermidis
Staphylococcus epidermidis

Staphylococcus simulans
1
Granulicatella elegans
Staphylococcus simulans
Staphylococcus haemolyticus
Staphylococcus simulans

Staphylococcus haemolyticus
1
Staphylococcus aureus
Staphylococcus haemolyticus
Staphylococcus haemolyticus
Staphylococcus haemolyticus

Staphylococcus pettenkoferi
1
Staphylococcus auricularis
Staphylococcus pettenkoferi
Staphylococcus auricularis/capitis
Staphylococcus pettenkoferi

Staphylococcus hominis
1
Staphylococcus epidermidis
Staphylococcus hominis
Staphylococcus hominis
Staphylococcus hominis

Staphylococcus aureus
1
Staphylococcus haemolyticus
Staphylococcus aureus
Staphylococcus aureus
Staphylococcus aureus

Staphylococcus capitis
1
Staphylococcus haemolyticus
Staphylococcus capitis
Staphylococcus capitis
Staphylococcus capitis

Staphylococcus aureus
1
Staphylococcus hominis
Staphylococcus aureus
Staphylococcus aureus
Staphylococcus aureus

Staphylococcus epidermidis
1
Staphylococcus hominis
Staphylococcus epidermidis
Staphylococcus epidermidis
Staphylococcus epidermidis

Staphylococcus epidermidis
1
Staphylococcus lentus
Staphylococcus epidermidis
Staphylococcus epidermidis
Staphylococcus epidermidis

Staphylococcus hominis
1
Staphylococcus saprophyticus
Staphylococcus hominis
Staphylococcus hominis
Staphylococcus hominis

Staphylococcus hominis
1
Staphylococcus warneri
Staphylococcus hominis
Staphylococcus hominis
Staphylococcus hominis

Staphylococcus hominis
2
Staphylococcus warneri
Staphylococcus hominis
Staphylococcus hominis
Staphylococcus hominis

Staphylococcus haemolyticus
1
Staphylococcus warneri
Staphylococcus haemolyticus
Staphylococcus haemolyticus
Staphylococcus haemolyticus

Staphylococcus epidermidis
1
Staphylococcus epidermidis
Staphylococcus epidermidis
Staphylococcus epidermidis
Bacillus pumilus

Staphylococcus epidermidis
1
Staphylococcus epidermidis
Staphylococcus epidermidis
Staphylococcus epidermidis
Dermobacter hominis

Staphylococcus aureus
1
No identification
Staphylococcus aureus
Staphylococcus aureus
Staphylococcus aureus

Staphylococcus lugdunensis
1
No identification
Staphylococcus lugdunensis
Staphylococcus lugdunensis
Staphylococcus lugdunensis

Staphylococcus epidermidis
1
Staphylococcus epidermidis
Staphylococcus epidermidis
Staphylococcus epidermidis
No identification

Streptococci

Leuconostoc mesenteroides
1
Staphylococcus capitis
Leuconostoc mesenteroides
No identification
Staphylococcus pettenkoferi

Streptococcus agalactiae
3
No identification
Streptococcus agalactiae
Streptococcus agalactiae
Streptococcus agalactiae

Streptococcus dysgalactiae
1
No identification
Streptococcus dysgalactiae
Streptococcus dysgalactiae
Streptococcus dysgalactiae

Streptococcus mitis
1
Streptococcus mitis
Streptococcus mitis
Streptococcus parasanguinis/mitis/oralis
Streptococcus pneumoniae

Streptococcus salivarius
1
Streptococcus lentus
Streptococcus salivarius
Streptococcus salivarius
Streptococcus salivarius

Enterococci & other Gr(+) cocci

Enterococcus faecalis
1
Escherichia coli
Enterococcus faecalis
Enterococcus faecalis
Enterococcus faecalis

Enterococcus faecium
1
Enterococcus faecalis
Enterococcus faecium
Enterococcus faecium
Enterococcus faecium

Enterococcus faecalis
2
Enterococcus faecium
Enterococcus faecalis
Enterococcus faecalis
Enterococcus faecalis

Enterococcus gallinarum
1
Enterococcus faecium
Enterococcus gallinarum
Enterococcus gallinarum
Enterococcus gallinarum

Enterococcus faecium
2
Enterococcus gallinarum
Enterococcus faecium
Enterococcus faecium
Enterococcus faecium

Enterococcus faecalis
1
Serratia fonticola
Enterococcus faecalis
Enterococcus faecalis
Enterococcus faecalis

Enterococcus faecalis
1
Staphylococcus hominis
Enterococcus faecalis
Enterococcus faecalis
Enterococcus faecalis

Coryneforms & other Gr(+) bacilli

Corynebacterium striatum
1
Actinomyces spp.
Corynebacterium striatum
Corynebacterium striatum
Corynebacterium striatum

Corynebacterium urealyticum
1
Brucella spp.
Corynebacterium urealyticum
Corynebacterium urealyticum
Corynebacterium urealyticum

Corynebacterium appendicis
1
Corynebacterium jeikeium
Corynebacterium appendicis
Corynebacterium pseudodiphtericum
Corynebacterium pseudodiphtericum

Corynebacterium ureicelerivorans
1
Corynebacterium jeikeium
Corynebacterium ureicelerivorans
Corynebacterium afermentans
Corynebacterium afermentans

Corynebacterium mucifaciens
2
Corynebacterium jeikeium
Corynebacterium mucifaciens
No identification
Corynebacterium mucifaciens

Corynebacterium ureicelerivorans
1
Corynebacterium jeikeium
Corynebacterium ureicelerivorans
No identification
Corynebacterium coyleae

Corynebacterium glaucum
1
Corynebacterium jeikeium
Corynebacterium glaucum
No identification
No identification

Corynebacterium mucifaciens
1
Clostridium spp.
Corynebacterium mucifaciens
Rhizobium radiobacter
Corynebacterium mucifaciens

Corynebacterium coyleae
1
Dermococcus/Kytococcus/ 
Micrococcus
Corynebacterium coyleae
Arthrobacter cumminsii
Corynebacterium coyleae

Lactobacillus paracasei
1
Pediococcus pentosaceus
Lactobacillus paracasei
Lactobacillus casei/paracasei
Lactobacillus paracasei

Corynebacterium striatum
1
Propionibacterium acnes
Corynebacterium striatum
Corynebacterium striatum
Corynebacterium striatum

Bacillus simplex
1
Sphingomonas paucimobilis
Bacillus simplex
Bacillus simplex
Bacillus simplex

Corynebacterium amycolatum
1
Bacillus spp.
Corynebacterium amycolatum
Corynebacterium amycolatum/xerosis
Corynebacterium amycolatum

Corynebacterium jeikeium
2
Bacillus spp.
Corynebacterium jeikeium
Corynebacterium jeikeium
Corynebacterium jeikeium

Corynebacterium striatum
2
Bacillus spp.
Corynebacterium striatum
Corynebacterium striatum
Corynebacterium striatum

Actinomyces naeslundii
1
Actinomyces naeslundii
Actinomyces naeslundii
No identification
Actinomyces spp.

HACEK & other Gr(−) cocci

Moraxella catarrhalis
1
Arcanobacterium hemoliticum
Moraxella catarrhalis
Moraxella catarrhalis
Moraxella catarrhalis

Haemophilus influenzae
1
Bacillus spp.
Haemophilus influenzae
Haemophilus influenzae
Haemophilus influenzae

Pasteurella multocida
1
Bacillus spp.
Pasteurella multocida
Pasteurella multocida
Pasteurella multocida

Others

Bergeyella zoohelcum
1
No identification
Bergeyella zoohelcum
Bergeyella zoohelcum
Bergeyella zoohelcum

Wautersiella falsenii
1
Weeksella virosa
Wautersiella falsenii
No identification
No identification

Myroides odoratimimus
1
Myroides spp.
Myroides odoratimimus
Myroides odoratimimus
Myroides spp.

Fungi

Candida glabrata
2
Candida albicans
Candida glabrata
Candida glabrata
Candida glabrata

Candida albicans
1
Candida famata
Candida albicans
Candida albicans
Candida albicans

Candida tropicalis
1
Candida famata
Candida tropicalis
Candida tropicalis/glabrata
Candida tropicalis

Candida tropicalis
1
Candida famata
Candida tropicalis
Candida tropicalis
Candida tropicalis

Candida albicans
2
Candida glabrata
Candida albicans
Candida albicans
Candida albicans

Candida glabrata
1
Candida lusitaniae
Candida glabrata
Candida glabrata
Candida glabrata

Candida tropicalis
2
Candida parapsilosis
Candida tropicalis
Candida tropicalis
Candida tropicalis

Candida glabrata
1
Candida rugose
Candida glabrata
Candida glabrata
Candida glabrata

Candida kefyr
1
Candida sphaerica
Candida kefyr
Candida kefyr
Candida kefyr

Candida orthopsilosis
1
Candida tropicalis
Candida orthopsilosis
Candida parapsilosis
Candida orthopsilosis

Candida parapsilosis
2
Candida spp.
Candida parapsilosis
Candida parapsilosis
Candida parapsilosis

Candida orthopsilosis
1
Candida spp.
Candida orthopsilosis
Candida parapsilosis
Candida orthopsilosis

Meyerozyma caribbica
1
Candida spp.
Meyerozyma caribbica
Candida guilliermondii
Candida guilliermondii

Candida albicans
1
Stephanoascus ciferrii
Candida albicans
Candida albicans
Candida albicans

Candida krusei
1
No identification
Candida krusei
Candida krusei
Candida krusei

Candida albicans
1
Candida albicans
Candida albicans
Candida albicans
Candida tropicalis

Candida albicans
1
Candida albicans
Candida albicans
Candida albicans
Candida glabrata

Candida parapsilosis
1
Candida parapsilosis
Candida parapsilosis
Candida parapsilosis
Candida orthopsilosis

Aspergillus brasiliensis
1
Aspergillus brasiliensis
Aspergillus brasiliensis
No identification
Aspergillus brasiliensis

Molecular identification (16S or ITS1/2 sequencing).

Among the 3 species that could not be identified by both systems (Table 4) W. falsenii was not included in the VITEK MS library both in genus and in species level. C. glaucum was represented on the genus level but not on the species level in the VITEK MS library. K. cryocrescens, on the other hand, was already in the library. As we had only one isolate for the MALDI-TOF analysis, it could have been a strain specific issue. Bruker had spectra for all these three species in its library. W. falsenii and C. glaucum are represented with 2 spectra, whereas K. cryocrescens is represented with only 1 spectrum in Bruker BioTyper library. It is likely that this low number of spectral representation in the library was the reason for no identification. In the upcoming updates once their spectral representation is increased or included in the library, we believe both systems will perform better in the identification.

Evaluating all results together, we have observed that most of the species with incorrect and/or no identifications were not common microorganisms encountered in clinical settings and also they are either not represented in systems’ libraries or represented with low number of spectra. We believe that these issues will be solved once systems’ libraries are updated in the near future.

VITEK MS’ species complex approach (reporting the closely related species together), which is a guaranteed attitude, increases the rate of correct identification and it is an advantage for the identifications from the routine clinical isolates. Bruker Microflex LT, however, confidently acts and reports single species, which can provide an advantage in research studies as well as in analyses of environmental, food, and industrial samples. However, consequently, both systems correctly identified 97% of routine clinical isolates making them reliable tools for clinical microbiology laboratories.

Comparing final confirmed identifications, MALDI-TOF MS systems showed a better identification accuracy compared to phenotypic identification. In addition to a better accuracy, a faster workflow and considerable lower cost provide clinical laboratories with a better identification tool [22, 23].

Conflict of Interests

The authors declare that they have no conflict of interests.

References

A. Croxatto, G. Prod'hom, and G. Greub, “Applications of MALDI-TOF mass spectrometry in clinical diagnostic microbiology,” FEMS Microbiology Reviews, vol. 36, no. 2, pp. 380–407, 2012.

M. A. Claydon, S. N. Davey, V. Edwards-Jones, and D. B. Gordon, “The rapid identification of intact microorganisms using mass spectrometry,” Nature Biotechnology, vol. 14, no. 11, pp. 1584–1586, 1996.

R. D. Holland, J. G. Wilkes, F. Rafii et al., “Rapid identification of intact whole bacteria based on spectral patterns using matrix-assisted laser desorption/ionization with time-of- flight mass spectrometry,” Rapid Communications in Mass Spectrometry, vol. 10, no. 10, pp. 1227–1232, 1996.

T.-Y. Li, B.-H. Liu, and Y.-C. Chen, “Characterization of Aspergillus spores by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry,” Rapid Communications in Mass Spectrometry, vol. 14, no. 24, pp. 2393–2400, 2000.

K. J. Welham, M. A. Domin, K. Johnson, L. Jones, and D. S. Ashton, “Characterization of fungal spores by laser desorption/ionization time-of-flight mass spectrometry,” Rapid Communications in Mass Spectrometry, vol. 14, no. 5, pp. 307–310, 2000.

P. Seng, M. Drancourt, F. Gouriet et al., “Ongoing revolution in bacteriology: routine identification of bacteria by matrix-assisted laser desorption ionization time-of-flight mass spectrometry,” Clinical Infectious Diseases, vol. 49, no. 4, pp. 543–551, 2009.

D. Parisi, M. Magliulo, P. Nanni, M. Casale, M. Forina, and A. Roda, “Analysis and classification of bacteria by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and a chemometric approach,” Analytical and Bioanalytical Chemistry, vol. 391, no. 6, pp. 2127–2134, 2008.

G. Yaman, I. Akyar, and S. Can, “Evaluation of the MALDI TOF-MS method for identification of Candida strains isolated from blood cultures,” Diagnostic Microbiology and Infectious Disease, vol. 73, no. 1, pp. 65–67, 2012.

J. Qian, J. E. Cutler, R. B. Cole, and Y. Cai, “MALDI-TOF mass signatures for differentiation of yeast species, strain grouping and monitoring of morphogenesis markers,” Analytical and Bioanalytical Chemistry, vol. 392, no. 3, pp. 439–449, 2008.

M.-C. Wang, W.-H. Lin, J.-J. Yan et al., “Early identification of microorganisms in blood culture prior to the detection of a positive signal in the BACTEC FX system using matrix-assisted laser desorption/ionization-time of flight mass spectrometry,” Journal of Microbiology, Immunology and Infection, vol. 48, no. 4, pp. 419–424, 2015.

E. Bessède, M. Angla-gre, Y. Delagarde, S. Sep Hieng, A. Ménard, and F. Mégraud, “Matrix-assisted laser-desorption/ionization BIOTYPER: experience in the routine of a University hospital,” Clinical Microbiology and Infection, vol. 17, no. 4, pp. 533–538, 2011.

A. Alanio, J.-L. Beretti, B. Dauphin et al., “Matrix-assisted laser desorption ionization time-of-flight mass spectrometry for fast and accurate identification of clinically relevant Aspergillus species,” Clinical Microbiology and Infection, vol. 17, no. 5, pp. 750–755, 2011.

M. L. Demarco and C.-A. D. Burnham, “Diafiltration MALDI-TOF mass spectrometry method for culture-independent detection and identification of pathogens directly from urine specimens,” American Journal of Clinical Pathology, vol. 141, no. 2, pp. 204–212, 2014.

M. Drancourt, “Detection of microorganisms in blood specimens using matrix-assisted laser desorption ionization time-of-flight mass spectrometry: a review,” Clinical Microbiology and Infection, vol. 16, no. 11, pp. 1620–1625, 2010.

Y. Hoyos-Mallecot, C. Riazzo, C. Miranda-Casas, M. Rojo-Martín, J. Gutiérrez-Fernández, and J. Navarro-Marí, “Rapid detection and identification of strains carrying carbapenemases directly from positive blood cultures using MALDI-TOF MS,” Journal of Microbiological Methods, vol. 105, pp. 98–101, 2014.

A. Suau, R. Bonnet, M. Sutren et al., “Direct analysis of genes encoding 16S rRNA from complex communities reveals many novel molecular species within the human gut,” Applied and Environmental Microbiology, vol. 65, no. 11, pp. 4799–4807, 1999.

W. G. Weisburg, S. M. Barns, D. A. Pelletier, and D. J. Lane, “16S ribosomal DNA amplification for phylogenetic study,” Journal of Bacteriology, vol. 173, no. 2, pp. 697–703, 1991.

M. Korabecna, “The variability in the fungal ribosomal DNA (ITS1, ITS2, and 5.8 S rRNA gene): its biological meaning and application in medical mycology,” in Communicating Current Research and Educational Topics and Trends in Applied Microbiology, pp. 783–787, Formatex, 2007.

A. Wieser, L. Schneider, J. Jung, and S. Schubert, “MALDI-TOF MS in microbiological diagnostics-identification of microorganisms and beyond (mini review),” Applied Microbiology and Biotechnology, vol. 93, no. 3, pp. 965–974, 2012.

P. Seng, C. Abat, J. M. Rolain et al., “Identification of rare pathogenic bacteria in a clinical microbiology laboratory: impact of matrix-assisted laser desorption ionization-time of flight mass spectrometry,” Journal of Clinical Microbiology, vol. 51, no. 7, pp. 2182–2194, 2013.

A. C. M. Veloo, M. Knoester, J. E. Degener, and E. J. Kuijper, “Comparison of two matrix-assisted laser desorption ionisation-time of flight mass spectrometry methods for the identification of clinically relevant anaerobic bacteria,” Clinical Microbiology and Infection, vol. 17, no. 10, pp. 1501–1506, 2011.

D. Martiny, L. Busson, I. Wybo, R. Ait El Haj, A. Dediste, and O. Vandenberg, “Comparison of the Microflex LT and Vitek MS systems for routine identification of bacteria by matrix-assisted laser desorption ionization-time of flight mass spectrometry,” Journal of Clinical Microbiology, vol. 50, no. 4, pp. 1313–1325, 2012.

A. Cherkaoui, J. Hibbs, S. Emonet et al., “Comparison of two matrix-assisted laser desorption ionization-time of flight mass spectrometry methods with conventional phenotypic identification for routine identification of bacteria to the species level,” Journal of Clinical Microbiology, vol. 48, no. 4, pp. 1169–1175, 2010.