Wash Cells& + Matrix

Results:Results:

• MALDI-TOF-MS microbial fingerprints (Figure 2), acquired in an automated (A) and manual (B) fashion, appeared similar.

• More rigorous, quantitative analysis of replicate fingerprints (Figure 3) revealed that spectra acquired manually contained base peaks with higher S:N ratios (A), while automation generally yielded spectra containing base peaks with higher resolution (B).

• Manual data acquisition yielded spectra with more peaks (Figure 4A) and broader mass ranges (Figure 4B) than automated data acquisition.

• Average pair-wise similarity coefficients (Figure 5C) indicated that automated data acquisition (Figure 5A) yielded less similar replicate spectra than manual data acquisition (Figure 5B).

References:References:

Freiwald, A. & Sauer, S. 2009, ‘Phylogenetic classification and identification of bacteria by mass spectrometry’, Nat. Protocols, vol. 4, no. 5, pp. 732-42.

Greis, K. D., Zhou, S., Burt, T. M., Carr, A. N., Dolan, E., Easwaran, V., Evdokimov, A., Kawamoto, R., Roesgen, J. & Davis, G. F. 2006, ‘MALDI-TOF MS as a Label-Free Approach to Rapid Inhibitor Screening’, J. Am. Soc. Mass Spectrom., vol. 17, pp. 815–22.

Pennanec, X., Dufour, A., Haras, D. & Re hel, K. 2010, ‘A quick and easy method to identify bacteria by matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry’, Rapid Commun. Mass Spectrom., vol. 24, pp. 384–92.

Sauer, S. & Kliem, M. 2010, ‘Mass spectrometry tools for the classification and identification of bacteria’, Nat. Rev. Micro., vol. 8, no. 1, pp. 74-82.

Seibold, E., Maier, T., Kostrzewa, M., Zeman, E. & Splettstoesser, W. 2010, ‘Identification of Francisella tularensis by Whole-Cell Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry: Fast, Reliable, Robust, and Cost-Effective Differentiation on Species and Subspecies Levels’, J. Clin. Micro., vol. 48, no. 4, pp. 1061-69.  

Discussion and Future Work:Discussion and Future Work:

• As has been shown previously (Seibold et al. 2010), MALDI-TOF MS was effective in rapidly discriminating between diverse microorganisms.

•Similar to previous reports (e.g., Sauer et al. 2010), we compared spectral reproducibility using m/z profiles; however, our more rigorous quantitative analysis revealed differences in reproducibility associated with each method of data collection.

• Although high reproducibility has been reported with automated data acquisition (Greis et al. 2006), our findings indicate that manual data acquisition yields higher reproducibility and spectrum quality, as measured by every metric employed in this study except resolution, across several species including gram positive and negative microorganisms.

• While differences in reproducibility that we observed were relatively small and unlikely to affect the ability of the method to distinguish different species, such small differences may affect the ability of the method to resolve different strains.

• Bias towards spectra with high resolution base peaks may have affected automated data acquisition. Although resolution is one standard for spectrum quality, other characteristics within a fingerprint can enhance differentiation. Future studies will seek to optimize relevant parameters to allow automated data acquisition to yield spectra with reproducibility and quality comparable to those obtained manually.

• Ensuring spectrum quality and reproducibility in MALDI-TOF-MS data acquisition methods are critical to development of a “universal protocol” that will allow more extensive and effective implementation of MALDI-TOF-MS as a rapid microbial fingerprinting tool.

MAN(UAL) VERSUS MACHINE IN MICROBIAL FINGERPRINTING USING MALDI-TOF-MS: EFFECT OF AUTOMATING DATA ACQUISITION ON FINGERPRINT

REPRODUCIBILITY AND QUALITYStephanie Schumaker (Glendale Community College, Glendale, Arizona), Susanne M. Rust, Nam Nguyen, and Todd R. Sandrin

(Arizona State University at the West campus; Glendale, Arizona)

B

Figure 3Figure 3. Effect of data acquisition method on spectrum quality as measured by (A) S:N and (B) resolution.. Effect of data acquisition method on spectrum quality as measured by (A) S:N and (B) resolution.

Figure 2Figure 2.. Replicate spectra of Serratia marcescens obtained via (A) automated and (B) manual modes of data acquisition.

Figure 4. Figure 4. Effect of data acquisition method on data richness as measured by (A) number of peaks and (B) mass range.

A

B

B

Figure 5. Figure 5. Effect of data acquisition method on fingerprint reproducibility as visualized with multidimensional scaling (MDS) (automated (A) and manual (B)) and as measured by the average similarity coefficient of 20 replicate fingerprints (C).

S. epidermidis

P. vulgaris

A. faecalisE. coliK. pneumoniae

P. aeruginosa

S. marcescens

B. cereus

A

P. vulgaris

A. faecalis

E. coli

S. marcescens B. cereus

K. pneumoniae

P. aeruginosa

S. epidermidis

B

Abstract:Abstract:

Of increasing importance is the development of faster, more cost-effective approaches to identifying microorganisms. Protecting water and food sources, preventing the spread of infectious diseases and defense against bioterrorism are driving forces behind studies investigating such approaches. Mass spectrometry (MS)-based approaches, in particular, MALDI-TOF-MS, have shown promise. Several studies have proposed “universal” protocols; however, most protocols employ manual data acquisition while a few, more recent ones employ automated data acquisition. The effect of automating data acquisition on fingerprint reproducibility and quality has not been investigated. In blinded experiments with eight microorganisms, 20 replicate fingerprints were obtained for each microorganism in both manual and automated fashions. Results suggest that automating data acquisition reduces fingerprint reproducibility. Manual data acquisition yielded intrareplicate similarity coefficients ranging from 92.7 ± 11.7 to 99.5 ± 0.3, while automated data acquisition yielded similarity coefficients ranging from 85.4 ± 8.7 to 99.5 ± 0.6. Fingerprint quality, as measured by peak number and intensity, was also reduced by automation. The effect of automation on reproducibility and fingerprint quality using MALDI-TOF-MS merits further study as efforts to standardize methods continue, particularly when the method is applied to more closely related microorganisms.

Introduction:Introduction:

As the need for rapid and accurate identification of microorganisms grows, the use of MALDI-TOF-MS to fingerprint microorganisms has garnered considerable attention. Although efforts to standardize data acquisition have been reported (Pennanec et al. 2009), the effect of how the data are gathered, through an automated or manual method, has not been studied. It has been proposed that automating data acquisition is important in performing MALDI fingerprinting of microorganisms (Freiwald & Sauer 2009); however, the effect of automating data acquisition on spectrum quality and reproducibility of the method has yet to be quantified. For this reason, our objective was to determine whether automating data acquisition affected fingerprint quality and reproducibility. Reproducibility, fingerprint quality, and data robustness were examined.  We hypothesized that the mode of data acquisition would have no effect on fingerprint reproducibility or quality. 

Methods:Methods:

Figure 1. Figure 1. A previously described approach (Freiwald & Sauer 2009) was employed with minor modifications. Briefly, each of eight microorganisms was fingerprinted repeatedly (20 replicates per microorganism) in blinded experiments. A. Each microbe was streaked for isolation onto a nutrient agar plate. B. A single colony was inoculated into nutrient broth and grown for 24 h at 37 C. C. The broth culture was washed, mixed with 1:1 sinapinic acid matrix and applied onto a MALDI target plate. D. A UV laser ablated the sample and separated bioanalytes through time-of-flight. E. Mass spectra and corresponding peak lists were obtained and exported to Bionumerics (Applied Maths; Sint-Martens-Latem, Belgium) F. Data were processed in Bionumerics using the Pearson correlation coefficient to yield a matrices of similarity coefficients. Tests of statistical significance were performed in PSAW (IBM Corporation; New York).

Automated or Manual Data Acquisition

9526.876

7265.327

4763.070

7862.47610292.176

6541.667

8869.568

7696.695

3635.479 5341.848

4361.578

* SR-60 (H12) 180\0_H12\1\1SLin, "Baseline subt."

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Inte

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.]

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9635.882

7359.622

5435.576

6418.596

4816.1857761.529

9981.2929204.490

3680.192 11214.0772606.100

5879.146 6785.728

* SR-43 (F4) 148\0_F4\1\1SLin, "Baseline subt."

0

1000

2000

3000

4000

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6000

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ns. [

a.u.

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B. cereus

E. coli