A targeted proteomics approach to the rapid identification of bacterial cell mixtures by matrix‐assisted laser desorption/ionization mass spectrometry

Warscheid, Bettina; Fenselau, Catherine

doi:10.1002/pmic.200400911

Cited by 69 publications

(27 citation statements)

References 57 publications

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“…In the described method it is assumed that the spectra at the 96DWP scale contains information that allows the prediction of the productivity of the cell line at the 10 L bioreactor scale and makes no assumptions as to whether the MALDI-ToF fingerprint changes or is the same between the two scales for a given cell line. This approach has similarities to the use of intact cell MALDI-ToF profiling of bacterial strains that is used clinically, in biodefence and in the academic environment to classify and identify bacterial strains (De Bruyne et al, 2011;Lundquist et al, 2005;Panda et al, 2013;Veloo et al, 2011;Warscheid and Fenselau, 2004). The proposed approach is straightforward and rapid and based around the development of a historical database of MALDI-ToF spectra associated with subsequent productivity data at the 10 L scale to predict the performance of cell lines from the 96 DWP scale.…”

Section: Discussionmentioning

confidence: 99%

“…see (De Bruyne et al, 4 2011;Franco et al, 2010;Lundquist et al, 2005; Munteanu and Hopf, 2013;Panda et al, 2013;Veloo et al, 2011;Warscheid and Fenselau, 2004) and has led to a fundamental shift in the characterisation of bacteria in the clinical microbiology setting (Clark et al, 2013). Commercial software has been developed that allows the comparison of test spectra with a library of known spectra to identify bacterial strains with high precision (Bright et al, 2002;Sogawa et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

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Rapid high-throughput characterisation, classification and selection of recombinant mammalian cell line phenotypes using intact cell MALDI-ToF mass spectrometry fingerprinting and PLS-DA modelling

Povey

O'Malley

Root

et al. 2014

Journal of Biotechnology

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Despite many advances in the generation of high producing recombinant mammalian cell lines over the last few decades, cell line selection and development is often slowed by the inability to predict a cell line's phenotypic characteristics (e.g. growth or recombinant protein productivity) at larger scale (large volume bioreactors) using data from early cell line construction at small culture scale. Here we describe the development of an intact cell MALDI-ToF mass spectrometry fingerprinting method for mammalian cells early in the cell line construction process whereby the resulting mass spectrometry data is used to predict the phenotype of mammalian cell lines at larger culture scale using a Partial Least Squares Discriminant Analysis (PLS-DA) model. Using MALDI-ToF mass spectrometry, a library of mass spectrometry fingerprints was generated for individual cell lines at the 96 deep well plate stage of cell line development. The growth and productivity of these cell lines were evaluated in a 10 L bioreactor model of Lonza's large-scale (up to 20,000 L) fed-batch cell culture processes.Using the mass spectrometry information at the 96 deep well plate stage and phenotype information at the 10 L bioreactor scale a PLS-DA model was developed to predict the productivity of unknown cell lines at the 10 L scale based upon their MALDI-ToF fingerprint at the 96 deep well plate scale. This approach provides the basis for the very early prediction of cell lines' performance in cGMP manufacturing-scale bioreactors and the foundation for methods and models for predicting other mammalian cell phenotypes from rapid, intact-cell mass spectrometry based measurements.Keywords: Cell line development, Chinese hamster ovary cells, whole cell MALDI-ToF mass spectrometry, PLS-DA modelling, cell line prediction 3 IntroductionThe majority of recombinant protein biopharmaceuticals are produced from cultured mammalian cells (Walsh, 2010), with the most commonly used industrial mammalian cell host being the Chinese hamster ovary (CHO) cell (Kim et al., 2012). Despite the development of high throughput methods that allow the screening of many recombinant cell lines to isolate those with desirable phenotypes (e.g. high growth and productivity), the ability of such methods to select or predict the performance of a given cell line at manufacturing scale remains limited with the best cell lines at a manufacturing scale often distributed across the phenotypic performance of cell lines at smaller-scale (Porter et al., 2010a;Porter et al., 2010b). As a result, early use of a simple productivity based approach does not necessarily allow the identification of high producers in the population of cell lines, and some potentially high producers are discarded early in the process (Porter et al., 2010b). In order to address this issue, a number of proteomic, transcriptomic and metabolic based studies have now been undertaken and the subsequent data used to develop models to predict the phenotype of a given cell line (e.g. Clarke et al., 2011;Clarke...

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rapid high-throughput characterisation, classification and selection of recombinant mammalian cell line phenotypes using intact cell MALDI-ToF mass spectrometry fingerprinting and PLS-DA modelling

Povey

O'Malley

Root

et al. 2014

Journal of Biotechnology

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“…Mass spectrometry selectively identifies components of a sample by molecular weight analysis. The technique has been used to identify bacterial and viral proteins (72,301,316) and intact bacterial cells (28,142,147,300,302,308) and to distinguish aerosolized spores of Bacillus thuringiensis and Bacillus atrophaeus (94). VOL.…”

Section: Chemical and Physical Detectionmentioning

confidence: 99%

Current and Developing Technologies for Monitoring Agents of Bioterrorism and Biowarfare

et al. 2005

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SUMMARY Recent events have made public health officials acutely aware of the importance of rapidly and accurately detecting acts of bioterrorism. Because bioterrorism is difficult to predict or prevent, reliable platforms to rapidly detect and identify biothreat agents are important to minimize the spread of these agents and to protect the public health. These platforms must not only be sensitive and specific, but must also be able to accurately detect a variety of pathogens, including modified or previously uncharacterized agents, directly from complex sample matrices. Various commercial tests utilizing biochemical, immunological, nucleic acid, and bioluminescence procedures are currently available to identify biological threat agents. Newer tests have also been developed to identify such agents using aptamers, biochips, evanescent wave biosensors, cantilevers, living cells, and other innovative technologies. This review describes these current and developing technologies and considers challenges to rapid, accurate detection of biothreat agents. Although there is no ideal platform, many of these technologies have proved invaluable for the detection and identification of biothreat agents.

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“…For top-down analysis, bacterial differentiation is accomplished through the comparison of the MS data of intact proteins to those of an experimental mass spectral database containing the mass spectral fingerprints of the studied microorganisms (6,7). Conversely, bacterial differentiation using the product ion mass spectral data of digested peptide sequences is accomplished through the utilization of search engines for publicly available sequence databases to infer identification (25,29). Several peptide-searching algorithms (i.e., SEQUEST and MASCOT) have been developed to address peptide identification using proteomics databases that were generated from either fully or partially genome-sequenced organisms (6,11,19).…”

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confidence: 99%

Double-Blind Characterization of Non-Genome-Sequenced Bacteria by Mass Spectrometry-Based Proteomics

Jabbour

Deshpande

Wade

et al. 2010

Appl Environ Microbiol

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Due to the possibility of a biothreat attack on civilian or military installations, a need exists for technologies that can detect and accurately identify pathogens in a near-real-time approach. One technology potentially capable of meeting these needs is a high-throughput mass spectrometry (MS)-based proteomic approach. This approach utilizes the knowledge of amino acid sequences of peptides derived from the proteolysis of proteins as a basis for reliable bacterial identification. To evaluate this approach, the tryptic digest peptides generated from double-blind biological samples containing either a single bacterium or a mixture of bacteria were analyzed using liquid chromatography-tandem mass spectrometry. Bioinformatic tools that provide bacterial classification were used to evaluate the proteomic approach. Results showed that bacteria in all of the double-blind samples were accurately identified with no false-positive assignment. The MS proteomic approach showed strain-level discrimination for the various bacteria employed. The approach also characterized doubleblind bacterial samples to the respective genus, species, and strain levels when the experimental organism was not in the database due to its genome not having been sequenced. One experimental sample did not have its genome sequenced, and the peptide experimental record was added to the virtual bacterial proteome database. A replicate analysis identified the sample to the peptide experimental record stored in the database. The MS proteomic approach proved capable of identifying and classifying organisms within a microbial mixture.The detection and accurate identification of pathogens of biological origin are of great importance to the armed forces and civilian sectors. Achieving these tasks is vital in the response to manmade or natural biothreat attacks in a proper and efficient manner to minimize the outbreak of epidemic cases. Several approaches reported in the literature have addressed the detection and identification of microorganisms based on the characterization of metabolites (1, 17) and genomic contents of bacterial cells (16). In these studies, the genomic sequence similarities generated from PCR were used to group bacteria at the genus/species level (27). Prior knowledge of the sample, or the targeting of one or a group of biological substances, is required in PCR techniques for proper primer utilization. However, proteins constitute greater than 60% of the dry weight of microorganism cellular components (4,8,12,13,22) and could provide in-depth information for the bacterial differentiation of species and their strains. Moreover, advancements in mass spectrometry (MS) ionization, detection methods, and data processing make MS a suitable analytical technique for the differentiation of microorganisms (5-7).Using MS techniques for bacterial differentiation relies on the comparison of the proteomic information generated from the analysis of either intact protein profiles (top down) or the product ion mass spectra of digested peptide sequences ...

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A targeted proteomics approach to the rapid identification of bacterial cell mixtures by matrix‐assisted laser desorption/ionization mass spectrometry

Cited by 69 publications

References 57 publications

Rapid high-throughput characterisation, classification and selection of recombinant mammalian cell line phenotypes using intact cell MALDI-ToF mass spectrometry fingerprinting and PLS-DA modelling

Rapid high-throughput characterisation, classification and selection of recombinant mammalian cell line phenotypes using intact cell MALDI-ToF mass spectrometry fingerprinting and PLS-DA modelling

Current and Developing Technologies for Monitoring Agents of Bioterrorism and Biowarfare

Double-Blind Characterization of Non-Genome-Sequenced Bacteria by Mass Spectrometry-Based Proteomics

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