Whole cell peptide mass finger-printing: a simple, fast and reliable method for the identification of organisms using MALDI-TOF MS

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1 Whole cell peptide mass finger-printing: a simple, fast and reliable method for the identification of organisms using MALDI-TOF MS cccta Les Diablerets September 17, 2010 Guido Vogel

2 Current Identification Methods morphology Microscopy, smell, color etc molecular specific-pcr, 16S-Seq., RT-PCR SNP, MLST, RFLP, VNTR immunological mono- or polyclonal antibodies ID biochemical metabolic capacities and resistance to antibiotics ?

3 Modern microbial taxonomy: proteomics & genomics Voyager Spec #1=>AdvBC(32,0.5,1.0)=>NF0.7[BP = , 2117] Mass (m/z) protein bacterial cell DNA proteome species definition & determination genome % Intensity mass fingerprint DNA fingerprint

4 MALDI-TOF MS: history Matrix Assisted Laser Desorption/Ionization Time-Of-Flight Mass Spectrometry developed in 1980 s by Karas & Hillenkamp and Tanaka et al. first commercial apparatus in 1991 Nobel Prize for Chemistry to K. Tanaka in 2002

5 MALDI-TOF MS MALDI-TOF MS Detector for linear mode ~ 200 cm Axima Confidence

6 MALDI-TOF MS: basic principles Detector for linear mode ion detector in vacuum < 10-7 Torr analyte molecules incorporated in matrix crystals

7 MALDI-TOF MS: basic principles Detector for linear mode ion detector laser pulse: desorption of matrix and analyte molecules,ionization by charge transfer

8 MALDI-TOF MS: basic principles Detector for linear mode ion detector build-up of an electromagnetic field acceleration of ions

9 MALDI-TOF MS: basic principles Detector for linear mode ion detector separation of ions in a field free drift range of a fixed length by velocity (Time Of Flight) L 1 m no further acceleration!

10 MALDI-TOF MS: basic principles detection separation acceleration ionization desorption ion detector field-free drift range grid electrode acceleration zone matrix/analyte crystals m z = 2eU L² t² m: mass z: charge U: accelaration voltage L: path length t: time e: elemetary charge template

11 IC-MALDI-TOF MS mass ranges and peaks Fusarium proliferatum (ascomycota): proteins and cell wall compartiments Intact Cell MALDI-TOF MS Late 90`s Direct application of whole cells Mass (m/z) metabolites enzymes & enzyme complexes matrix structural proteins & polymers 0 m/z reflector linear 9590

12 IC-MALDI-TOF MS of bacteria differences in peak patterns Pantoea agglomerans Acinetobacter lwoffi Burkholderia cepacia Raoultella ornithinolytica Staphylococcus aureus Escherichia coli 4000 m/z 8000 distinctly different peak patterns of different taxa

13 MALDI-TOF MS of whole/intact cells: detected cell components What are the detected cellular compounds? Mostly proteins, but also complex lipids and polysaccharides Which proteins are detected? proteins that are extractable, soluble, moderately hydrophilic, stable, and abundant What determines a proteins mass signal intensity? High signal intensity is favored by abundance, stability, amino acid composition (esp. Arg and Lys)

14 Streptococcus pneumoniae identifying mass signals % Intensity 4419 L L L L L L L L S L L L S S S S L7/L m/z mass signals matching to ribosomal proteins (considering possible methionine cleavage and/or methylation) are marked in red ~50% of peaks unambiguously represent ribosomal proteins protein masses obtained from the Swiss-Prot/TrEMBL sequence database

15 MALDI-TOF MS studies of diverse microbial taxa proofs of principle Overview of taxa analysed by ICMS in peer-reviewed publications (continued) Taxon comment reference Bacillus sp. mass spectral typing after inactivation of spores with TFA Lasch et al Bacillus cereus pretreatment with corona plasma discharge Ryzhov et al Listeria sp. differentiation clonal lineages possible Barbuddhe et al Staphylococcus 23 species and subspecies were discriminated Carbonnelle et al Staphylococcus The discrimination potential of MRSA of IC-MS and MSSA for identification Du et al Staphylococcus discrimination of MRSA and MSSA Edwards-Jones et al Streptococcus viridans gr. identification and typing of clinical has isolates been excluding shown Str. pneumoniae for Friedrichs et al Streptococcus mutans gr. identification of species tested on clinical isolates Rupf et al Streptococcus pyogenes differentiation of invasive and non-invasive isolates Moura et al Enterococcus sp. source bacteria, tracking of environmental fungi, and isolates metazoa: Giebel et al Lactobacillus plantarum identification of ribosomal proteins as biomarkers Sun et al IC-MS for identification can be applied to a Nonfermenter identification of clinical isolates Mellmann 2008 Eukaryotes Fungi Fusarium sp. discrimination wide range of Fusarium of microorganisms species Seyfarth et al Serpula sp. identification of indoor wood-decay fungi Schmidt & Kallow 2005 dermatophytes identifcation of species by mass fingerprints Erhard et al fungal spores reproducible mass spectra in filamentous fungi Welham et al Protists Plasmodium sp. detection in processed blood samples Demirev et al cryptosporidia specific biomarkers for two species Magnuson et al Giardia lamblia discrimination of species Villegas et al dinoflagellates identification of HAB species Lee et al Metazoa nematoda identification of plant parasites Perera et al. 2005a aphids insecta discrimination of species Perera et al. 2005b

16 SARAMIS by Anagnostec Spectral ARchive And Microbial Identification System database containing SuperSpectra, Reference Spectra, user s spectra software package for archiving, analysis, comparison of spectra SARAMIS can identify bacteria, fungi, yeasts with confidence values... compare sample spectra to reference spectra... allows typing of strains on a sub-specific level... add new spectral data to the database... handle large numbers of spectral data

17 Workflow of Step 1 Sample preparation: Smear-method FlexiMass-Target with 48 positions colony selection and transfer of cells addition of 0.5 µl Matrix solution suitable for bacteria, yeasts and filamentous fungi

18 Workflow of Step 2 Measurement loading samples to AXIMIA FlexiMass target holder 4 X automated spectrum acquisition ~20 sec [c].2B m/z

19 Workflow of Step 3 Identification with SuperSpectra matching to SuperSpectra computing sum of peak weights ranking matching SuperSpectra check for conflicting significant results delivering result with confidence value m/z

20 Workflow of Step 4 Comparison tools m/z comparison to mass spectral patterns of reference spectra in the database cluster analysis of selected reference and sample mass spectra

21 Current projects at mabritec Differentiation of cell-lines NK3.3 Homo sapiens WIL2S Raji Hela CMT93 RAW264 SF21 Mus musculus Spodoptera frungiperda Identification of specific marker proteins on the species level Independent of passage number and medium supplements SF9 H5 Trichoplusia ni Drosophila melanogaster Differentiation of lineages within the same species CRL1963

22 Biting midges of the genus Culicoides among the smallest haematophagous insects Vectors of orbivirusses such as bluetongue virus and African horse sickness virus causative agents of chronic insect bite hypersensitivity over 1400 species described so far species identification is difficult, laborious and expensive using morphological features using PCR-based genetic methods

23 Dendrogram of MALDI-TOF mass spectra of three types of blood sucking insects

24 Thank you for your attention