Reviews
Clinical Chemistry 61:1
100–111 (2015)
MALDI-TOF MS for the Diagnosis of Infectious
Diseases
Robin Patel1,2*
BACKGROUND: First introduced into clinical microbiology laboratories in Europe, MALDI-TOF MS is being
rapidly embraced by laboratories around the globe. Although it has multiple applications, its widespread
adoption in clinical microbiology relates to its use as
an inexpensive, easy, fast, and accurate method for
identification of grown bacteria and fungi based on
automated analysis of the mass distribution of bacterial proteins.
CONTENT: This review provides a historical perspective
on this new technology. Modern applications in the clinical microbiology laboratory are reviewed with a focus on
the most recent publications in the field. Identification of
aerobic and anaerobic bacteria, mycobacteria, and fungi are
discussed, as are applications for testing urine and positive
blood culture bottles. The strengths and limitations of
MALDI-TOF MS applications in clinical microbiology are
also addressed.
SUMMARY: MALDI-TOF MS is a tool for rapid, accurate,
and cost-effective identification of cultured bacteria and
fungi in clinical microbiology. The technology is automated, high throughput, and applicable to a broad range
of common as well as esoteric bacteria and fungi.
MALDI-TOF MS is an incontrovertibly beneficial
technology for the clinical microbiology laboratory.
© 2014 American Association for Clinical Chemistry
MALDI-TOF MS is being rapidly embraced by laboratories around the globe. This review, based in part on
previously published work (1, 2 ), provides a historical
perspective on this new technology. Modern applications
in the clinical microbiology laboratory are reviewed with
a focus on the most recent publications in the field. Identification of aerobic and anaerobic bacteria, mycobacteria, and fungi are discussed, as are applications for testing
1
Division of Clinical Microbiology, Department of Laboratory Medicine and Pathology,
and 2 Division of Infectious Diseases, Department of Medicine, Mayo Clinic, Rochester,
MN.
* Address correspondence to the author at: Mayo Clinic, 200 First St SW, Rochester, MN
55905. Fax 507-284-4272; e-mail patel.robin@mayo.edu.
Received June 11, 2014; accepted September 16, 2014.
Previously published online at DOI: 10.1373/clinchem.2014.221770
© 2014 American Association for Clinical Chemistry
100
urine samples and positive blood culture bottles. The
strengths and limitations of MALDI-TOF MS applications in clinical microbiology are also addressed.
History and Development of Commercial
Systems
The notion of using mass spectrometry for identification of bacteria was proposed in 1975 (3 ), though at
the time it was not possible to analyze intact proteins
because they would have been fragmented in the process.
Technology for the analysis of intact macromolecules was
invented in the 1980s, allowing intact protein analysis. In
1985, Koichi Tanaka described a “soft desorption ionization” method using ultrafine metal powder and glycerol
that enabled mass spectrometric analysis of biological
macromolecules, for which he was awarded the Nobel
Prize in Chemistry (4 ). Around the same time, Franz
Hillenkamp and Michael Karas reported soft desorption
ionization using an organic compound matrix (5 ), for
which the term matrix-assisted laser desorption ionization (MALDI) was coined. Advances in information
technology/computer science and development of wellvalidated comprehensive databases of mass spectra representing diverse types of bacteria and fungi ultimately enabled automation of MALDI-TOF MS and associated
data analysis, delivering a tool for the clinical microbiology laboratory.
The most recent developments have been the
commercialization and regulatory approval of MALDITOF MS for routine bacterial and fungal identification
in clinical microbiology laboratories. In the past year, 2
systems, the VITEK® MS (bioMérieux Inc.) and the
MALDI Biotyper CA System (Bruker Daltonics Inc.),
have been cleared by the US Food and Drug Administration (FDA) for identification of cultured bacteria and, in
the case of the former system, yeast. Although each includes a mass spectrometer, software, and database, all 3
components, including the list of microorganisms
cleared for identification, are unique to each system.
Bruker’s mass spectrometer is a desktop instrument,
whereas bioMérieux’s is a larger floor model. Numeric
scores are reported on different scales and are not directly
comparable.
Bruker’s system is the most studied in terms of publications and evaluations in multiple laboratories. Development began in 2005; in 2008, Mellmann et al. pub-
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MALDI-TOF for the Diagnosis of Infectious Disease
lished a study demonstrating that by constructing their
own database of mass spectra, they could accurately identify nonfermenting gram-negative bacterial isolates (6 ).
A year later, Seng et al. published a prospective evaluation
of Bruker’s commercial mass spectral database for identification of 1660 bacteria routinely isolated in their laboratory, reporting correct identification of 95% and 84%
to the genus and species levels, respectively, in 6 min/
isolate (7 ). Over the past 5 years, upgraded analysis software and increasingly inclusive and better curated mass
spectral libraries have become available. Although the
FDA-cleared list of organisms is currently limited to
gram-negative bacteria, RUO libraries (e.g., RUO
MALDI Biotyper Reference Library v3.3.1.2, Security
Relevant library v1.0) are available.
In 1998, AnagnosTec began development of a microbial mass spectral database called SARAMIS, used
with Shimadzu’s AXIMA Assurance mass spectrometer
(Shimadzu). In 2010, bioMérieux acquired AnagnosTec
and changed the system’s name to VITEK MS RUO.
bioMérieux developed a new database, operating software, and analysis algorithm referred to as VITEK MS.
The FDA-cleared list of organisms includes aerobic
and anaerobic gram-negative and gram-positive bacteria, as well as yeast. Lastly, bioMérieux developed the
VITEK MS Plus system that includes use of the FDAcleared database in conjunction with the more comprehensive VITEK MS RUO software and database.
MALDI-TOF MS has been rapidly developed for
use in clinical microbiology laboratories; iterative improvements continue to improve its performance for microbial identification. These changes can make it confusing to interpret the literature. Software, databases, and
mass spectrometers must be considered, along with cutoff values applied for accepting identifications, sample
preparations, comparators, and organism types studied.
MALDI-TOF MS systems have, in general, initially
focused on identification of commonly isolated gramnegative and gram-positive bacteria; current databases
may be less comprehensive vis-à-vis less common organism types, such as certain anaerobes, mycobacteria, and
fungi. Efforts are underway to enhance and/or build databases for identification of esoteric organisms, and with
some exceptions (8 ), MALDI-TOF MS is proving useful
for their identification. Although databases are becoming
progressively more complete, due to changing nomenclature and ongoing description of new species/genera, regular and ongoing database updates will be imperative to
provide quality microbial identification into the foreseeable future.
Users have the option to construct their own mass
spectral databases by generating mass spectra using locally important strains and/or those not well represented
in the commercial databases. User-constructed databases
may be searched alongside commercial databases. Aca-
demic laboratories have engaged in database building to
enhance the performance of commercially available systems. The Mayo Clinic Custom MALDI-TOF MS Library, for example, currently contains 1599 mass spectral
entries representing organisms not adequately addressed
by commercially available databases.
How Does MALDI-TOF MS Work?
In clinical microbiology, there are several preparatory
methods for MALDI-TOF MS, including moving an
intact bacterial colony onto the MALDI plate (with and
without the addition of a formic acid solution), and preparatory protein extraction. The last method was historically used but, compared to direct colony testing, is not
as user friendly for clinical microbiology applications and
thus is generally reserved for processing hazardous or
difficult-to-lyse organisms. Direct colony processing is
easiest, fastest, and least expensive. A colony is “picked”
from a culture plate to a “spot” on a MALDI-TOF–MS
target plate. The addition of a formic acid solution to the
MALDI plate may be used to improve the quality of the
generated mass spectrum, which may be particularly
helpful for certain types of organisms, such as yeasts (Fig.
1). After drying, the target plate is placed in the mass
spectrometer’s ionization chamber (Fig. 2).
In MALDI (matrix-assisted laser desorption ionization), a matrix (e.g., ␣-cyano-4-hydroxycinnamic acid
dissolved in 50% acetonitrile and 2.5% trifluoroacetic
acid) assists in the desorption and ionization of microbial
analytes through the energy of a laser. The matrix isolates
analyzed molecules and protects them from fragmentation by the laser. As a result of being “shot” by the laser,
microbial and matrix molecules are desorbed, with the
majority of energy being absorbed by the matrix, converting it to an ionized state. Through random collision in
the gas phase, charge is transferred from matrix to microbial molecules. The ionized microbial molecules are accelerated, based on mass-to-charge ratio, into a TOF
(time-of-flight) mass analyzer, a tube under vacuum. Ions
travel toward an ion detector, with smaller analytes reaching the detector first, followed by progressively larger
analytes. A mass spectrum is generated, representing the
number of ions of a given mass impacting the ion detector over time.
How Does the Analysis Work?
For clinical microbiology applications, highly abundant
microbial proteins such as ribosomal proteins are the
main contributors to the generated mass spectrum, although specific proteins are not identified and their mass
and abundance are merely profiled. In general, mass spectra are unique to individual organism-types, with peaks
specific to genera, species, and strains. The mass specClinical Chemistry 61:1 (2015) 101
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Fig. 1. MALDI-TOF MS process.
Using a plastic or wooden stick, loop, or pipette tip, a colony is picked from a culture plate to a spot on a MALDI-TOF MS target plate (a reusable
or disposable plate with a number of test spots). One or many isolates may be tested at a time. In this example, cells are treated with formic acid
on the target plate. The spot is overlain with 1–2 μL of matrix and dried. The plate is placed in the ionization chamber of the mass spectrometer
(see Fig. 2). A mass spectrum is generated and compared against a database of mass spectra by the software, resulting in identification of the
organism (Candida parapsilosis in position A4 in the example). Reproduced by permission of Mayo Foundation for Medical Education and
Research. All rights reserved.
trum of the test isolate is compared to a database of reference spectra or deconvoluted spectra to determine relatedness to spectra in the database; the most closely
related organisms are identified with a value provided as
to the level of confidence in identification. Depending on
how high the value is, the organism may be identified at
the family, genus, or species level. A variety of algorithms
are used for database comparison. For example, the Biotyper system uses pattern recognition and generates a
score ranging from 0 to 3.000 on the basis of the presence
or absence of mass spectral signals (peaks) in the test
isolate matching database entries, and the presence of
102
Clinical Chemistry 61:1 (2015)
peaks in the test isolate not present in database entries.
According to the manufacturer’s criteria for their RUO application, a score of ⱖ2.000 represents species-level, a score
of 1.700 –1.999 a genus-level, and a score of ⱕ1.700 no
identification. System users have found that lower score cutoffs may be adequate for identification of certain groups of
organisms; extensive verification by the end user is required
before adopting this approach. For the MALDI Biotyper
CA System, a score of ⱕ1.999 represents no identification.
The VITEK MS RUO system database is searched using 1
of 2 algorithms. One is a pattern matching process, referred
to as a reference spectra approach. In the second approach,
MALDI-TOF for the Diagnosis of Infectious Disease
Reviews
Fig. 2. MALDI-TOF mass spectrometer. The target plate is placed into the chamber of the mass spectrometer.
Spots to be analyzed are shot by a laser, desorbing and ionizing microbial and matrix molecules from the target plate. The cloud of ionized
molecules is accelerated into the TOF mass analyzer, toward a detector. Lighter molecules travel faster, followed by progressively heavier
analytes. A mass spectrum is generated, representing the number of ions hitting the detector over time. Separation is by mass-to-charge ratio,
but because the charge is typically single for this application, separation is effectively by molecular weight. Reproduced by permission of Mayo
Foundation for Medical Education and Research. All rights reserved.
mass spectral entries are grouped together as “SuperSpectra,” representing spectra of conserved mass signals derived from multiple individual isolates of a particular organism group, with those signals weighted according to their
specificity for species, genera, and/or families. The output is
a confidence value, ranging from 0% to 100%. With the
VITEK MS system, analysis is not directly based on pattern recognition comparing mass spectra. The mass spectral entries of individual isolates are computationally
sorted into weighted bin matrices generating “Advanced
Spectra Classifiers.” The bin pattern of test isolates is
compared against the commercial weighted bin matrix.
The weighting algorithm weights bins according to their
importance. For example, bins that are highly specific for
individual species receive a higher weight than do those
that are moderately species specific. All 3 strategies function well. Closely related species with similar patterns and
a small number of distinguishing peaks, such as Streptococcus pneumoniae and Streptococcus mitis groups, may be
better (though not perfectly) distinguished using the
VITEK MS strategy than the others (9, 10 ). Nevertheless, spectral profiles of some organisms are so similar to
one another (e.g., Escherichia coli and Shigella species)
that their differentiation using available algorithms is unreliable with any of the aforementioned strategies. This is
a particular challenge because E. coli is one of the most
frequent organisms encountered in clinical microbiology
laboratories; MALDI-TOF MS users must use alternate
or additional strategies (e.g., lactose fermentation, quick
indole) to distinguish E. coli from Shigella species.
MALDI-TOF MS does not currently accurately differentiate all species within certain groups of organisms,
such as the Enterobacter cloacae complex (11 ), Burkholderia cepacia complex, and Streptococcus bovis species
group. Likewise, accurate differentiation of all species of
Acinetobacter may be challenging. Neisseria polysaccharea
may be misidentified as Neisseria meningitidis using the
Biotyper system (12 ). It is not yet known whether discrimination of some closely related organisms will be possible with improved databases and/or software. Misidentifications may occur if such details are not considered
during data analysis. The aforementioned limitations
aside, most organisms are either correctly identified or
yield a low score/percent, indicating that identification
Clinical Chemistry 61:1 (2015) 103
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has not been achieved; the latter typically implies no
“match” in the database, but can occur due to technically
poor preparation. In the next section, select studies of
MALDI-TOF MS for bacterial and fungal identification
are reviewed, with a focus on those recently published.
Performance of MALDI-TOF MS for
Identification of Aerobic Bacteria
MALDI-TOF MS performs at least as well as, if not
better than, automated biochemical identification for
commonly encountered bacteria and yeast. We initially
compared the Biotyper system (v2.0 software and library)
and the BD Phoenix Automated Microbiology System
for identification of 440 common and unusual gramnegative bacilli. The MALDI-TOF MS correctly identified 93% and 82%, whereas the biochemical system correctly identified 83% and 75% of these gram-negative
bacilli to the genus and species levels, respectively (13 ).
We further evaluated the Biotyper system (v2.0 software
and library) using 217 clinical isolates of staphylococci,
streptococci, and enterococci, 98% of which were correctly identified to the genus level (14 ). McElvania
Tekippe et al. evaluated the Biotyper for identification of
239 aerobic gram-positive organisms using direct onplate testing with formic acid. Using a score cutoff of
ⱖ1.700 for genus-level identification, 92% of the grampositive organisms studied were identified (15 ). Lau et al.
evaluated the Biotyper system (v3.0 software and library
V.3.1.2.0) for identification of 67 “difficult-to-identify”
bacteria; 75% and 45% of the difficult-to-identify bacteria studied were correctly identified to the genus and
species levels, respectively, with 4 misidentified (16 ).
Hsueh et al. evaluated the Biotyper system for identification of 147 isolates of aerobically growing gram-positive
organisms, including Nocardia species, Listeria monocytogenes, Kocuria species, Rhodococcus species, Gordonia species, and Tsukamurella species (17 ). All 8 Rhodococcus
equi were correctly identified. All 15 Kocuria species
yielded a top match to the correct species, either Kocuria
kristinae or Kocuria marina, although due to low scores
only 27% of Kocuria species were identifiable to the species level. Likewise, all 39 L. monocytogenes isolates studied yielded a top match to L. monocytogenes, but due to
low scores only 90% were identifiable to the species level.
With the exception of Nocardia nova and Nocardia otitidiscaviarum, Nocardia species were not identified, nor
were Tsukamurella or Gordonia species. Schulthess et al.
evaluated the Biotyper system (database v3.1.2.0) for
identification of gram-positive bacilli (18 ). Using the
manufacturer’s interpretative criteria and an initial collection of 190 isolates, 85% and 87% of the grampositive bacilli studied were identified to the genus level
with on-plate formic acid testing and formal extraction,
respectively, compared to 72% with direct colony test104
Clinical Chemistry 61:1 (2015)
ing. For some isolates, multiple species had scores
ⱖ2.000; these included Corynebacterium aurimucosum
and Corynebacterium minutissimum, Corynebacterium
simulans and Corynebacterium striatum, Lactobacillus gasseri and Lactobacillus johnsonii, and L. monocytogenes, Listeria ivanovii, and Listeria innocua. Reducing the species
cutoff to 1.700 increased the percentage of isolates identified. These investigators also compared identification of
215 clinical isolates of gram-positive bacilli by MALDITOF MS using on-plate formic acid testing and a species
and genus cutoff of 1.700; 87% and 79% of the grampositive bacilli studied were identified to the genus and
species levels, respectively. The authors proposed an algorithm combining MALDI-TOF MS with nucleic acid
sequence analysis for identification of gram-positive bacilli that includes score cutoff values lower than those
recommended by the manufacturer and covers the most
frequently found genera and species. In our own work,
we found that 87% of 92 non-diphtheriae Corynebacterium species could be identified using a species-level cutoff of ⱖ1.700, with the exception of C. aurimucosum
being misidentified as C. minutissimum (19 ).
Multiple evaluations of the VITEK MS system
(database v2.0) have been published. Richter et al. evaluated 965 routinely encountered Enterobacteriaceae and
showed that VITEK MS correctly identified 97% and
84% of the Enterobacteriaceae studied to the genus
and species levels, respectively (20 ). Manji et al. showed
that the VITEK MS correctly identified 91% and 78% of
558 non-Enterobacteriaceae gram-negative bacilli to the
genus and species levels, respectively (21 ). Branda et al.
evaluated the VITEK MS system for identification of
226 isolates of fastidious gram-negative bacteria; 97%
and 96% of the fastidious gram-negative bacteria studied
were accurately identified to the genus and species levels,
respectively (22 ). Rychert et al. evaluated the Vitek MS
system for identification of 1146 aerobic gram-positive
bacteria and showed that 93% were correctly identified
to the species level, with an additional 3% identified only
to the genus level because of a split identification with
multiple matching species within the same genus (including Listeria monocytogenes with matches to L. monocytogenes, Listeria ivanovii, and Listeria welshimeri) (23 ). Karpanoja et al. compared the MALDI Biotyper and VITEK
MS IVD systems for identification of viridans group
streptococci using 54 type strains and 97 blood culture
isolates (24 ). The Biotyper and VITEK MS systems
yielded correct species-level identification for 94% and
69% of the type strains and correctly classified 89% and
93% of the blood culture isolates to the group level,
respectively.
Although MALDI-TOF MS is able to accurately
identify Francisella tularensis and Brucella species, the
Biotyper library does not contain them and so will not
identify these organisms; use of Bruker’s “Security Rele-
MALDI-TOF for the Diagnosis of Infectious Disease
vant” database enables Brucella species and F. tularensis
identification (8 ).
Performance of MALDI-TOF MS for
Identification of Anaerobic Bacteria
MALDI-TOF MS has become the method of choice for
identification of anaerobic bacteria, replacing 16S rRNA
gene sequencing and gas–liquid chromatography. Many
clinical microbiology laboratories have not had tools
available to identify anaerobic bacteria; MALDI-TOF
MS provides a tool to do this, which enables increased
routine identification of anaerobic bacteria, likely informing interpretation of the clinical significance and
predicted susceptibility of anaerobes.
There have been several recent studies examining
MALDI-TOF MS for identification of anaerobic bacteria. Jamal et al. reported species-level identification of
89% and 100% of 274 routinely isolated anaerobic bacteria (enriched in Bacteroides fragilis) using the Biotyper
DB Update–v3.3 with direct on-plate testing and the
VITEK MS v1 system/v1.1 database, respectively (25 ).
Hsu et al. evaluated the Biotyper (v3.0 software) for identification of 101 anaerobic bacteria and showed that using on-plate formic acid testing and a cutoff of ⱖ1.700
improved the rate of accurate identification compared to
direct on-plate testing and manufacturer-recommended
cutoff scores (26 ). We evaluated a diverse collection of
253 clinical anaerobic isolates using the Biotyper system
(v3.0 software and library v3.3.1.0), on-plate formic acid
testing, and a user-supplemented database; 92% and
71% of anaerobic bacteria were correctly identified to the
genus and species levels, respectively (27 ). Barreau et al.
analyzed 1325 anaerobes using MALDI Biotyper (v3.0
software) and a species cutoff of ⱖ1.900 with direct onplate testing and showed that 100% and 93% of the
isolates were correctly identified at the genus and species
levels, respectively (28 ). Finally, Garner et al. evaluated
the VITEK MS system (database v2.0) using 651 anaerobic isolates and reported that 93% and 91% of anaerobic bacteria were correctly identified to the genus and
species levels, respectively (29 ).
Performance of MALDI-TOF MS for
Identification of Mycobacteria
As with identification of anaerobic bacteria, identification of mycobacteria has been a challenge; historically it
has been done using biochemical testing, DNA probes,
high-performance liquid or gas liquid chromatography,
and/or gene sequencing. MALDI-TOF MS is proving a
useful tool for identification of mycobacteria with a few
limitations, such as the inability to differentiate members
of the Mycobacterium tuberculosis complex and some
closely related species such as Mycobacterium chimaera
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and Mycobacterium intracellulare or Mycobacterium mucogenicum and Mycobacterium phocaicum. Although Mycobacterium abscessus and Mycobacterium massiliense may
be challenging to differentiate, Teng et al. did so using
cluster analysis of spectra generated by the Biotyper system (30 ). MALDI-TOF MS is easier, less expensive,
faster, and more accessible to routine clinical microbiology laboratories than traditional strategies for mycobacterial identification, which will likely make it the method
of choice for their identification in the near future.
Testing of mycobacteria by MALDI-TOF MS requires special processing to kill tested organisms, disrupt
clumped cells, and break open cell envelopes. Databases
for mycobacterial identification are under development.
Balada-Llasat et al. evaluated 178 mycobacterial isolates
grown on solid and/or broth medium and processed using heat inactivation, treatment with ethanol, and mechanical disruption, as recommended by Bruker, and
showed that the Biotyper system (Mycobacteria database
v1 and software v3.0) was able to identify 98% and 94%
of the Mycobacterium species studied to the genus and
species levels, respectively (31 ). Mather et al. tested 2
protein extraction protocols using vortex mixing with
silica beads and heat or ethanol killing preparatory to
testing using the MALDI Biotyper and Vitek MS RUO
platforms; the Biotyper database was augmented with
mass spectral entries from 123 clinical Mycobacterium
strains (32 ). Of 198 clinical isolates tested, 95% of the
Mycobacterium species studied were correctly identified
with the Biotyper system and augmented database, 79%
with the Bruker Mycobacterium Library 2.0 database, and
94% with the Vitek MS RUO system.
Performance of MALDI-TOF MS for
Identification of Fungi
In addition to bacteria, MALDI-TOF MS can identify
yeasts. Although older studies used preparatory extraction for yeasts, many laboratories use the same direct
on-plate formic acid testing strategy used for bacteria
(Fig. 1) (33 ).
Dhiman et al. evaluated the Biotyper system (library
v3.0) using a species level cutoff of ⱖ1.800 for identification of 138 common and 103 unusual yeast isolates
and reported 96% and 85% accurate genus- and specieslevel identification, respectively, using protein extraction
(34 ). Lacroix et al. showed that the Biotyper system with
protein extraction and the manufacturer’s species-level
cutoff identified 97% of 1383 routinely isolated Candida
isolates (35 ). Westblade et al. evaluated the Vitek MS
system (database v2.0) for identification of 852 yeast isolates and found that 97% and 96% were identified to the
genus and species levels, respectively (36 ). Pence et al.
compared the VITEK MS (IVD Knowledgebase v.2.0)
and Biotyper (software v3.1) for identification of 117
Clinical Chemistry 61:1 (2015) 105
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yeast isolates using on-plate formic acid testing (37 ).
VITEK MS correctly identified 95% of the isolates,
whereas the Biotyper correctly identified 83% of the isolates using a species-level cutoff of ⱖ1.700. Hamprecht
et al. compared the Biotyper (v3.0 software, v3.0.10.0
database, species-level cutoff ⱖ2.000) and VITEK MS
(V2.0 knowledge base) systems for identification of 210
yeasts using on-plate formic acid testing (38 ). The
VITEK MS system identified 96% of the isolates; the
Biotyper system identified 91% of isolates. De Carolis et
al. created an in-house library using spectra from 156
reference and clinical yeast isolates, generated with a fast
sample preparation procedure involving suspending a
single colony of yeast in 50 ␮L of 10% formic acid,
vortex mixing, and using 1 ␮L of the lysate for analysis
(39 ). Using their database and processing, they identified
96% of 4232 routinely isolated yeasts using a specieslevel cutoff of ⱖ2.000 and the Biotyper system (software
v3.0). Rosenvinge et al. showed that the Biotyper system
identified 88% of 200 yeast isolates to the species level
using on-plate formic acid testing and a species cutoff of
ⱖ1.700 (40 ). Mancini et al. compared the Biotyper system (library v3.0) with protein extraction and Vitek MS
(v1.2.0) with on-plate formic acid testing for the identification of 197 yeast isolates (41 ). The rate of correct
identifications at the species level was comparable using
the commercial databases (90% vs 84%) and improved
to 100% using the Biotyper system with an in-house–
developed database.
Schulthess et al. evaluated Bruker’s Filamentous
Fungi Library 1.0 (42 ). Molds were grown in a broth
medium for 24 – 48 h and subjected to protein extraction.
First, they studied a clinical strain collection of 83 nondermatophyte, nondematiaceous molds and showed that 78%
and 54% were identified to the genus and species levels,
respectively. Reducing the species cutoff to ⱖ1.700 improved identification to the species level to 71%. They then
prospectively tested 200 consecutive clinical mold isolates and were able to identify 84% and 79% to the genus
and species levels, respectively, using a species cutoff of
ⱖ1.700. Lau et al. developed an alternate extraction procedure for molds and constructed their own database
comprising 294 individual isolates representing 76 genera and 152 species (43 ). To extract proteins, a small
piece of mold was excised and subjected to protein extraction with the addition of zirconia-silica bead processing. They then challenged their database with 421
clinical isolates using the Biotyper software, and demonstrated accurate genus- and species-level identifications
in 94% and 90% of isolates, respectively, with no
misidentifications.
Theel et al. developed a dermatophyte library and
showed that when used in conjunction with the Biotyper
library v3.0 and protein extraction, it identified 93% and
60% of 171 isolates to the genus and species levels, re106
Clinical Chemistry 61:1 (2015)
spectively (44 ). De Respinis et al. developed an in-house
database and performed MALDI-TOF MS with an
AXIMA Confidence mass spectrometer (Shimadzu Biotech) and MALDI-TOF MS Launchpad 2.8 software
(Shimadzu Biotech) for identification of dermatophytes;
of 141 clinical isolates tested, 96% were correctly identified (45 ).
Laboratory Work Flow and Cost Associated
with MALDI-TOF MS
In the past, identification of bacteria and fungi has been a
challenging, multistep process, individualized by organism type. Students of clinical microbiology have been
meticulously trained to interpret colony and Gram-stain
morphology of bacteria growing on solid media as a prelude to selecting appropriate further testing using quick
biochemical tests such as catalase and oxidase, manual
biochemical tests, automated biochemical tests, and
broad-range sequencing. With MALDI-TOF MS, colonies are accurately identified in minutes, without a priori
knowledge of microorganism type. And because it
doesn’t matter whether a bacterium or yeast is tested,
with the exception of safe handling of microorganisms
that can be hazardous in the laboratory, the complex
decision-making process classically surrounding identification of bacteria or fungi growing on solid media is
obviated. Because only a small amount of organism is
required, testing can be performed from single colonies
on primary culture plates. Tests for screening for some
enteric pathogens may be eliminated. Gram-staining colonies may not always be needed because this information
is no longer required. For some organisms (e.g., Staphylococcus aureus), rapid tests performed at the bench, will
remain. DNA sequencing expenses are avoided for esoteric organisms, waste disposal is decreased, and QC and
laboratory technologist training and labor for replaced
and retired tests eliminated.
Seng et al. recently published over a decade of experience in routine identification of clinical bacterial isolates, including 40 months using MALDI-TOF MS and
91 months using phenotypic identification (46 ).
MALDI-TOF MS and phenotypic identification identified 36 and 19 species/10 000 isolates, respectively. Additional phenotypic identification was required for 4.5
and 35.2/10 000 isolates with MALDI-TOF MS and primary phenotypic identification, respectively. Additionally, compared with phenotypic identification and sequencing, MALDI-TOF MS reduced the time for
identification by 55- and 169-fold and cost by 5- and
96-fold, respectively.
Our experience has been that following implementation of MALDI-TOF MS into our reference laboratory for bacterial identification, we eliminated automated biochemical-based microbial identification
MALDI-TOF for the Diagnosis of Infectious Disease
and have so far reduced the number of tubed biochemical set– based identifications from 4668 to 987/year.
To date, we have halved the number of isolates requiring 16S rRNA gene sequencing; through progressive
database improvements, this number continues to decrease. Following implementation of MALDI-TOF
MS for yeast identification, the associated supply costs
decreased from $30 525 to $5400/year as a result of
the elimination of germ tube and rapid assimilation of
trehalose and API (analytical profile index) strip tests;
this was associated with a decrease in turnaround time for
identification by 1–5 days, depending on the species. Implementation of MALDI-TOF MS also reduced the number
of tests on which our staff must maintain competency and
therefore our yearly competency evaluation burden. Following implementation of MALDI-TOF MS for dermatophyte identification, the associated supply costs decreased from $20 020 to $2340/year, with a turnaround
time savings of 1.5 days.
Antimicrobial Susceptibility Testing Using
MALDI-TOF MS
Antimicrobial susceptibility is not directly determined
with the above-described strategy. By rapid organism
identification, intrinsic antimicrobial resistance characteristics of particular species (or typical susceptibility of
the identified species based on local antibiograms) may
guide therapy. Because some resistance-associated factors
(e.g., ␤-lactamases) are proteins, and MALDI-TOF MS
detects proteins, it might be intuited that antimicrobial
resistance–associated proteins would be detected directly.
Unfortunately, at least as currently performed, this has
been challenging. For example, although ␤-lactamases
are highly active, they are expressed at low concentrations; further, their molecular weights are similar to those
of other bacterial proteins and there are hundreds of types
of ␤-lactamases of similar masses. Because MALDI-TOF
MS may provide insight into strain typing, and some
strains are more or less likely to be resistant to certain
antimicrobial agents, strain typing may infer an association with antimicrobial resistance. MALDI-TOF MS
may be applied as part of a functional assay to measure
␤-lactam degradation by ␤-lactamases; in this case, it is
not proteins but antimicrobial compounds and their
chemically modified counterparts that are measured
(47 ). This approach requires specific system configuration and incubation to allow antibiotic hydrolysis, and it
applies only to resistance mechanisms associated with antibiotic degradation (48 –50 ). Another strategy to detect antimicrobial resistance is to grow an organism for
a short period of time (e.g., ⱕ3 h) in the presence of an
antibiotic of interest and isotopically labeled amino
acids (51 ). If the organism is resistant to the antibiotic,
it will incorporate isotopically labeled amino acids,
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increasing protein masses and leading to mass shifts of
their corresponding peaks in the profiled spectra (52 ).
Further details on detection of antibiotic resistance
using MALDI-TOF MS can be found in the recent
review by Hrabak et al. (53 ).
Direct Testing of Clinical Samples
Although direct testing of clinical samples is not generally
feasible because of the high limit of detection of MALDITOF MS, urine may be tested directly owing to the high
numbers of bacteria present in clinically infected urine
(54 ). Urine flow cytometry may be used to screen out
negative urines, with MALDI-TOF MS reserved to test
the positive urine samples (55, 56 ). Urine must be processed before MALDI-TOF MS testing. Limitations include the inability to reliably identify polymicrobial infection (55 ) and impairment of database matching caused by
urinary proteins such as ␣-defensins (57 ). Demarco et al.
recently described a diafiltration method using desalting,
fractionation, and concentration for preparing urine samples before MALDI-TOF MS analysis (58 ). Other sample
types, such as cerebrospinal fluid (59, 60 ), may also be
tested directly.
Testing of Positive Blood Culture Bottles
MALDI-TOF MS can be used for rapid identification of
microorganisms growing in blood culture bottles. The
positive bottles may be subcultured to solid media and
growth tested by MALDI-TOF MS after a short incubation period (e.g., 2– 4 h). Alternatively, blood culture
bottles may be tested directly. This application requires
preparatory processing because blood culture bottles contain macromolecules from blood and growth media. Processing may be accomplished using differential centrifugation and washings, selective lysis of blood cells, serum
separator tubes, or filtration; commercial processing using the Sepsityper (Bruker Daltonics) is available (61–
64 ). Although results are valid when obtained, yield is
generally not as good as direct colony testing, with a
higher percentage of gram-negative than gram-positive
organisms being identified in most studies (65 ). As with
direct testing of urine, not all organisms present in polymicrobial infections are necessarily identified (66 ). Both the
Biotyper and VITEK MS systems have been applied to positive blood culture bottles. Chen et al. used the Sepsityper to
evaluate the Vitek MS IVD and Biotyper systems for bacterial identification in 181 monomicrobial blood culture bottles (67 ). Genus/species level identifications were provided
using the Biotyper and VITEK MS systems in 98%/82%
and 93%/81% of cases, respectively. Twenty-one blood cultures were composed of 2 bacterial species. The Vitek MS
IVD system identified only the majority species in all 21,
whereas the Biotyper system identified both of the 2 species
Clinical Chemistry 61:1 (2015) 107
Reviews
with ⬎1.6 scores in 5 mixed cultures in the “Top 10
matched pattern choices” (67 ).
MALDI-TOF MS performed on positive blood culture bottles can rapidly identify probable contaminants
or suggest complementary diagnostic testing in the case
of pathogen detection (68 ). By testing of positive blood
culture bottles using MALDI-TOF MS, time to organism identification is reduced by a day or more (69 ). A
limitation is that antimicrobial susceptibility is not provided. To overcome this limitation, MALDI-TOF MS
has been tied with direct antimicrobial susceptibility
testing of positive blood culture bottles (70 ), improving time to optimal therapy (71 ). MALDI-TOF MS
on positive blood culture bottles is rapid (estimated at
30 – 45 min (72, 73 ) (albeit not as fast as direct colony
testing); such testing is typically batched with testing
performed on a limited number of occasions throughout the day.
Typing Using MALDI-TOF MS
Because MALDI-TOF MS can provide accurate specieslevel identification, using it for subspecies level identification, and therefore strain typing, has been proposed. For
example, Mencacci et al. used MALDI-TOF MS for typing
of Acinetobacter baumannii (74 ), and Josten et al. used
MALDI-TOF MS for typing of S. aureus (75 ). Such applications remain to be fully explored.
Limitations of MALDI-TOF MS
MALDI-TOF MS has limitations. Unlike publically available sequence databases such as GenBank, MALDITOF MS databases are proprietary. Low identification
percentages for some organisms may be improved by addition of mass spectral entries of underrepresented species or strains (to cover intraspecies variability), but doing
so may be beyond the capability of some laboratories.
Because of low scores/percentages, repeat testing may be
needed. Growth on some media may be associated with
low scores/percentages. Tiny or mucoid colonies may fail
identification; tiny colonies may be more rapidly identified using 16S rRNA gene sequencing than MALDITOF MS. Refined criteria are needed to distinguish
closely related species and differentiate them from the
next best taxon match. For certain species, genus- or
species-specific (including lowered) cutoff values may be
appropriate. Newer mass spectrometry methods may enable separation of microorganisms at deeper taxonomic
levels than MALDI-TOF MS (76 ). Laboratory errors
may occur as a result of colony inoculation in erroneous
target plate locations, testing of impure colonies, smearing between spots, failure to clean target plates, or erroneous data entry into laboratory information systems.
There is a learning curve involved in applying ideal col108
Clinical Chemistry 61:1 (2015)
ony amounts to target plates. Instrument cost must be
considered; laser, software, hardware, or mass spectrometer failure can occur, necessitating an appropriate service
plan. Although MALDI-TOF MS is generally reproducible, there are sources of variability, including the mass
spectrometer (different types as well as individual instruments), matrix and solvent composition, preparation
methods, technologist training and competence, culture
conditions (such as media, colony age, and temperature),
and biological variability; QC strategies are being defined. Well-isolated colonies must be tested; if colonies
are not well isolated, they may represent more than one
organism, and the minority species may be undetected. A
CLSI guideline on MALDI-TOF MS is being prepared.
Because of the ease of use of this technology, technologists
may be tempted to work up everything, including clinically
nonsignificant isolates, leading to confusion on the part of
clinicians receiving such reports and the potential for inappropriate patient treatment. At the same time, because of the
ease of use of MALDI-TOF MS, technologists may lose
their skills in visually identifying colonies. Because MALDITOF MS may yield different (generally more accurate)
identification than current systems, reporting of organisms
with which healthcare providers are unfamiliar may lead
them to ignore potentially clinically significant results or to
over treat clinically insignificant results.
Future Perspectives
MALDI-TOF MS is enabling adoption of total laboratory automation in clinical microbiology laboratories.
Total laboratory automation features automated sample
processing, plating, incubation, plate reading using digital imaging, and spotting to MALDI-TOF MS plates.
Early growth detection by digital imaging combined with
MALDI-TOF MS results in faster detection of microorganisms compared to conventional techniques (77 ). We
can anticipate new applications of MALDI-TOF MS,
such as using antibodies to capture analytes of interest
(78 ). Finally, MALDI-TOF MS will have profound effects
on microbiology education of medical technologists, clinical
microbiology laboratory directors, and other management
staff, medical students, residents, and fellows. For medical
students, residents, and fellows who don’t practice in the
laboratory, it is time to deemphasize conventional
biochemical-based microbial identification and to focus instead on interpretation of results of MALDI-TOF MSbased identification alongside quick biochemical tests and
biochemical reactions important to microbial pathogenesis.
Author Contributions: All authors confirmed they have contributed to the
intellectual content of this paper and have met the following 3 requirements: (a)
significant contributions to the conception and design, acquisition of data, or
Reviews
MALDI-TOF for the Diagnosis of Infectious Disease
analysis and interpretation of data; (b) drafting or revising the article for intellectual content; and (c) final approval of the published article.
Authors’ Disclosures or Potential Conflicts of Interest: Upon manuscript submission, all authors completed the author disclosure form. Disclosures and/or potential conflicts of interest:
Employment or Leadership: None declared.
Consultant or Advisory Role: R. Patel, Curetis and Thermo Fisher.
Stock Ownership: None declared.
Honoraria: None declared.
Research Funding: None declared.
Expert Testimony: None declared.
Patents: None declared.
Acknowledgments: I thank Dr. Nancy L. Wengenack, Scott A. Cunningham, and Brenda L. Dylla and Jill M. Mainella for their thoughtful
reviews of this manuscript. I thank Sherry M. Ihde and Sherri L. Wohlfiel for providing the cost, volume, and turnaround-time data for Mayo
Clinic Rochester.
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