A Designed Experiments Approach to Optimization of Automated Data Acquisition during Characterization of Bacteria with MALDI-TOF Mass Spectrometry

Abstract

MALDI-TOF MS has been shown capable of rapidly and accurately characterizing bacteria. Highly reproducible spectra are required to ensure reliable characterization. Prior work has shown that spectra acquired manually can have higher reproducibility than those acquired automatically. For this reason, the objective of this study was to optimize automated data acquisition to yield spectra with reproducibility comparable to those acquired manually. Fractional factorial design was used to design experiments for robust optimization of settings, in which values of five parameters (peak selection mass range, signal to noise ratio (S:N), base peak intensity, minimum resolution and number of shots summed) commonly used to facilitate automated data acquisition were varied. Pseudomonas aeruginosa was used as a model bacterium in the designed experiments, and spectra were acquired using an intact cell sample preparation method. Optimum automated data acquisition settings (i.e., those settings yielding the highest reproducibility of replicate mass spectra) were obtained based on statistical analysis of spectra of P. aeruginosa. Finally, spectrum quality and reproducibility obtained from non-optimized and optimized automated data acquisition settings were compared for P. aeruginosa, as well as for two other bacteria, Klebsiella pneumoniae and Serratia marcescens. Results indicated that reproducibility increased from 90% to 97% (p-value0.002) for P. aeruginosa when more shots were summed and, interestingly, decreased from 95% to 92% (p-value 0.013) with increased threshold minimum resolution. With regard to spectrum quality, highly reproducible spectra were more likely to have high spectrum quality as measured by several quality metrics, except for base peak resolution. Interaction plots suggest that, in cases of low threshold minimum resolution, high reproducibility can be achieved with fewer shots. Optimization yielded more reproducible spectra than non-optimized settings for all three bacteria.

Optimizing Mass Spectrometry Analyses: A Tailored Review on the Utility of Design of Experiments

“…The goal of the study performed by Zhang et al was to optimize spectrum-to-spectrum reproducibility by adjusting the input parameters that are used in the matrix assisted laser desorption ionization (MALDI) control software for automated data acquisition (Bruker FlexControl software, v3.0, Bruker Daltonics) [196]. The parameters in the design were selected according to a resolution IV fractional factorial design.…”

Use of MALDI-TOF Mass Spectrometry and a Custom Database to Characterize Bacteria Indigenous to a Unique Cave Environment (Kartchner Caverns, AZ, USA)

“…To find a method and/or procedure to optimize these parameters is of great interest to our lab. For example, one approach might involve statistical factorial design, which we used recently to optimize MALDI-TOF automated data acquisition 17 . Additional future applications and enhancement of MALDI-TOF MS-based microbial fingerprinting include construction of widely available, larger databases of environmental bacteria and/or non-bacterial microorganisms as well as characterization of mixed cultures 18 and microbial communities.…”

A Designed Experiments Approach to Optimizing MALDI-TOF MS Spectrum Processing Parameters Enhances Detection of Antibiotic Resistance in Campylobacter jejuni

“…This approach has been implemented in biomedical, veterinary and environmental routine procedures for bacterial identification at the genus, species, and sometimes at the subspecies level (e.g., Wieser et al, 2011 ; Croxatto et al, 2012 ; Koubek et al, 2012 ; Lartigue, 2013 ; Sandrin et al, 2013 ; Randall et al, 2015 ). While microbial identification using MALDI-TOF MS is rapid and reliable, the taxonomic resolution obtained from the mass spectra is not always sufficient, or the bioinformatics software pipeline is not optimized or adapted, for typing the candidate bacteria below the species level ( Sandrin et al, 2013 ; Zhang et al, 2014 ). However, some studies have shown that categorization of strains of bacteria with respect to their membership in nucleic acid-based subgroups, pathogenicity traits or antimicrobial resistance (AMR) identification is indeed feasible, but depends on the level of variability inside a given taxon as well as on the precise identification of characteristic biomarkers using bioinformatics tools ( Sandrin et al, 2013 ).…”

“…Special emphasis was put on C. jejuni resistance to beta-lactams, as this antibiotics group is considered among the most important and widespread treatment with resistance issues ( Wieser et al, 2011 ; Lartigue, 2013 ; Schubert and Kostrzewa, 2015 ). A designed experiments approach was employed ( Zhang et al, 2014 ), in which spectrum processing parameters were varied to optimize detection of AMR. Translation of the genetic and phenotypic characteristics of C. jejuni might identify useful and straightforward information collection in a global One Health context ( Maloy and Atlas, 2014 ).…”