Optimization of Automated Data Acquisition with Matrix-Assisted

Laser Desorption/Ionization Time-of-Flight Mass Spectrometry

(MALDI-TOF MS)─ A promising method for rapid

identification of bacteria

7/12/2012ASU WestStephanie Schumaker

BackgroundNumerous life-threatening infections are caused by microorganisms, particularly

bacteria. Widespread in soil, water, plants, humans or hospital environments, these pathogens mutate rapidly to become resistant to antibiotics. For example, Pseudomonas aeruginosa is one of the most serious and difficult hospital-acquired infections to treat and affects patients with cancer, ventilator-associated pneumonia, cystic fibrosis, burns, and urinary tract infections. In addition to increasing threats of emerging antibiotic-susceptible and resistant infectious diseases, bioterrorism, and microbial food and water contamination are driving forces behind studies investigating available approaches in rapid bacterial identification. Conventional well established approaches, such as microscopy and analysis of sequenced DNA targets, suffer from a number of drawbacks in that they are costly and often require days to complete (Meays et al., 2004; Cherkaoui et al., 2010). A continuing increase in demand from the medical and scientific communities for faster, less costly bacterial identification has clearly pushed the classical methods to their limits of usefulness.

An alternative method still being developed uses mass spectrometry (MS), in particular, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry-based fingerprinting (MALDI-TOF MS) (Giebel et al., 2010). MALDI-TOF MS offers the advantages of high-throughput coupled with short analysis time and reduced cost in materials and labor. For example, diverse bacteria can be identified within one minute whereas DNA-based methods can take twenty hours or more (Cherkaoui et al., 2010). The MALDI-TOF MS technique relies on the fact that when a compound breaks apart the different molecular weights produce a fragmentation pattern (mass spectrum) as unique as a fingerprint. An unknown bacterium can be identified by comparing specific characteristics within its mass spectrum to the mass spectra of known bacteria in a reference library until a match is made.

Despite the clear advantages of MALDI-TOF MS-based approaches, concerns about reproducibility and quality of fingerprints have been raised (Wunschel et al., 2005). High quality spectra are characterized by several (at least 20) peaks that have high resolution (i.e., are narrow) and high signal to noise (S:N) ratios. An example of a high quality spectrum is shown in Figure 1. It has been suggested that automating data acquisition is important when performing MALDI fingerprinting of microorganisms to increase reproducibility (Freiwald & Sauer 2009); however, our recently published findings indicated that automated data collection yielded lower quality and less reproducible fingerprints when compared to manual data acquisition (Schumaker, Sandrin, & Borror, 2012). Using multidimensional scaling (MDS), reductions in reproducibility were readily observed (Figure 2). For this reason, earlier research in our lab investigated the possibility of unidentified interactions among the computer’s programming telling it when to accept a spectrum suitable for analysis.

Figure 1. Example of a typical microbial fingerprint. Alcaligenes faecalis is represented here.

Figure 2: Effect of mode of data acquisition, automated (A) and manual (B), on fingerprint reproducibility as measured by MDS. Replicate spectra of different microorganisms are represented by different colors (Pseudomonas aeruginosa is represented in light blue). Note that replicates cluster more closely to one another when data were collected manually indicating high similarity. Present Study and Objectives

Our published research indicated that automated data acquisition yielded less reproducible spectra than manual data acquisition. We believe that this may be due to the fact that the algorithm, or logical step-by-step procedure, used to acquire data in an automated fashion seems to favor spectra with high resolution peaks. For this reason, our overarching objective is to optimize relevant parameters beyond resolution to allow automated data acquisition to yield bacterial fingerprints with reproducibility and quality comparable to those obtained manually. Working under the supervision of Dr. Todd Sandrin (a microbiologist) and in collaboration with Dr. Connie Borror (a statistician), an experimental design was created to optimize automated data acquisition by systematically altering the algorithm’s criteria for acquiring spectra of adequate quality. Preliminary data indicated that several interactions among the assigned computer parameters were capable of influencing fingerprint reproducibility and quality. Continuing the work

A B

A B C D

previously described in our lab, Pseudomonas aeruginosa was chosen as the model organism in these optimization experiments because it displayed the greatest potential for enhancement (note dispersion of data points for P. aeruginosa represented in light blue Figure 2). In order to substantiate our preliminary findings, a duplicate experiment will be performed and rigorously quantified.

Methods

Twenty-eight (28) combinations of relevant factors will be investigated in which the following metrics are varied: resolution, S:N, intensity, and number of shots. Each combination of parameters will be used to acquire 20 replicate spectra. The resulting spectra will be analyzed to assess fingerprint reproducibility and quality. Effects of these changes on spectrum quality will be quantified by metrics including resolution, S:N, intensity, and number of peaks. Reproducibility will be quantified by calculating similarity coefficients of replicate spectra. It is not known what factor or the combination of factors most profoundly influence the quality and reproducibility of spectra acquired via automation. We hypothesize that each of these parameters affects spectrum quality and reproducibility.

A previously described approach (Schumaker, Sandrin, & Borror, 2012) will be employed (Figure 3). Briefly, a culture of P. aeruginosa will be streaked on nutrient agar and incubated for 24 hours at 37 °C (A). After the incubation period, nutrient broth will be inoculated with a single colony and incubated on a 200rpm shaker platform for 24 hours at 37 °C (B). Nutrient broth will then be washed and mixed with Sinapinic Acid matrix and deposited onto a target plate (C). A UV laser will ablate the sample to generate a mass spectrum (fingerprint) (D). Replicate fingerprints will be acquired (E). The mass list of peaks in each fingerprint will be exported in BioNumerics (F). The similarity matrix will yield an average similarity coefficient to assess interreplicate reproducibility (G).  

E

F G

Figure 3: Overview of methods used to acquire microbial fingerprints and assess fingerprint similarity.

Timeline AUGUST SEPTEMBER OCTOBER NOVEMBER

Gather spectra Data analysis of spectrum quality and reproducibilityDraft manuscriptSubmit manuscript for publication; present at ANAS and/or New College Student Research Expo (Spring 2013)

SummaryBecause automated data acquisition is becoming commonplace for rapid bacterial

identification with the use of MALDI-TOF MS, a need for a widely accepted protocol is becoming more pronounced (Seng et al., 2009: van Veen et al., 2010: Cherkaoui et al., 2010; De Bruyne et al., 2011). A standard application of specific parameters used for the automated settings in MALDI-TOF MS analysis of microorganisms will be an important factor in pathogenic bacteria identification and maximizing high-throughput. Our investigation seeks to enhance existing methods of MALDI-TOF MS automated data acquisition and thereby have a significant impact on the speed and accuracy with which bacterial identification can be made. The importance of improving data acquisition will be a great benefit to the medical, environmental, and scientific communities in identifying unknown pathogens. The experience I will gain in performing this research is critical to my pursuit of my professional goals. With a strong desire to continue work in cutting edge life science research, the skills I master during this work will be invaluable as I seek employment in a specialized field of Cell and Molecular Biology Research.

Works Cited:

Cherkaoui, A. Hibbs J., Emonet S., Tangomo M., Girard M., Francois P., Schrenzel J. 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. J. Clin. Microbiol., 48(4), 1169-1175 (2010).

De Bruyne, K., Slabbinck B., Waegeman W., Vauterin P., De Baets B., Vandamme P. Bacterial species identification from MALDI-TOF mass spectra through data analysis and machine learning. Systematic and Applied Microbiology, 34(1), 20-29 (2011).

Freiwald, A. & Sauer, S. Phylogenetic classification and identification of bacteria by mass spectrometry. Nat. Protocols 4(5), 732-742 (2009).

Giebel, R., Worden, C., Rust, S. M., Kleinheinz, G. T., Robbins, M., & Sandrin, T. R. Microbial Fingerprinting using Matrix-Assisted Laser Desorption  Ionization Time-Of-Flight Mass Spectrometry (MALDI-TOF MS): Applications  and Challenges. In A. Laskin, S. Sariaslani, & G. Gadd (Eds.), Advances in Applied Microbiology, Vol 71 (Vol. 71, pp. 149–184). San Diego: Elsevier Academic Press Inc. (2010).

Meays, C.L., Broersma, K., Nordin, R. & Mazumder, A. Source tracking fecal bacteria in water: a critical review of current methods. Journal of Environmental Management 73(1), 71-79 (2004).

Schumaker, S., Borror, C., Sandrin, T.R. Automating Data Acquisition Affects Mass Spectrum Quality and Reproducibility during Bacterial Profiling using an Intact Cell Sample Preparation Method with Matrix-assisted Laser Desorption/Ionization Time-of-flight Mass Spectrometry. Rapid Commun.Mass Spectrom. (2012 ).

Seng P., Drancourt M., Gouriet F., La Scola B., Fournier P.E., Rolain JM, Raoult D. Ongoing Revolution in Bacteriology: Routine Identification of Bacteria by Matrix‐Assisted Laser Desorption Ionization Time‐of‐Flight Mass Spectrometry. Clin. Infect. Dis. 49(4), 543-551 (2009).

van Veen, S.Q., Claas, E.C.J. & Kuijper, E.J. High-Throughput Identification of Bacteria and Yeast by Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry in Conventional Medical Microbiology Laboratories. J. Clin. Microbiol. 48(3), 900-907 (2010).

Wunschel S.C., Jarman K.H., Petersen C.E., Valentine N.B., Wahl K.L., Schauki D., Jackman J., Nelson C.P., White E.V. Bacterial analysis by MALDI-TOF mass spectrometry: An inter-laboratory comparison. J. Am. Soc. Mass Spectrom. 16(4), 456-462 (2005).