2. Goslar, 09/10/2007 Identification of Microorganisms using MALDI-TOF MS profiling: Adopted sample preparation methods and bioinformatic approaches Dr. Markus Kostrzewa Bruker Daltonik GmbH, Leipzig

3. Goslar, 09/10/2007 MALDI-TOF MS microorganism identification Identified species Select a colony Prepare onto a MALDI target plate Unknown microrganism ? Data interpretation Generate MALDI-TOF profile spectrum

4. Goslar, 09/10/2007 Target/Acceleration Time-of-FlightMolecular MassDesorption/Ionisation DetectorDrift Region m/z a.i. Mass Spectra Laser MALDI-TOF mass spectrometry

5. Goslar, 09/10/2007 Sample preparation Direct “cell smear“ method most simple method, applicable to many bacteria Organic solvent extraction improved quality for difficult bacteria, yeast, fungi Mechanical cell disruption (e.g. sonication) In case of very ridgid cell walls Compatibility of different procedures Protocols for inactivation and shipment of microorganisms are available

6. Goslar, 09/10/2007 E.coli MALDI-TOF MS profile spectrum Positive linear mode Mass range 2-20 kDa

7. Goslar, 09/10/2007 Improved quality by adopted sample preparation Identification score 2.445 1.997

8. Goslar, 09/10/2007 Psdm. oleovorans B396_Medium 360 0 1000 Psdm. oleovorans B396_Medium 464 0 1000 Psdm. oleovorans B396_Medium 53 0 1000 Psdm. oleovorans B396_Medium 65 0 1000 Psdm. oleovorans B396_Medium 98 0 500 1000 Psdm. oleovorans B396_MRS10 0 1000 2000 Psdm. oleovorans B396_YPD 0 1000 2000 4000500060007000800090001000011000 m/z Pseudomonas oleovorans grown on different media Low influence of culture conditions

9. Goslar, 09/10/2007 Arthrobacter, effect of storage products Taken from: Vargha M et al. J Microbiol Methods. 2006 Possible influence of growth state 16h 24h 64h 0 1000 2000 3000 4000 3000400050006000700080009000100001100012000 m/z Clostridium butyricum, effect of sporulation Coop. with Prof. Krüger, Dr. Grosse-Herrenthey, Leipzig, Germany

10. Goslar, 09/10/2007 MALDI BioTyper 2.0 - GUI Unknown samples Match against microbial reference database Identification result

11. Goslar, 09/10/2007 MALDI BioTyper 2.0 – realtime analysis Wizard guiding through setup from measurement to data analysis BioTyper Automation Control Wizard Define Project Analyte Placement Select Methods Start MALDI BioTyper 2.0: realtime Analysis Start Automation Control Wizard. Use a SOP and do not bother with instrument settings. Results available directly after measurement

12. Goslar, 09/10/2007 MALDI BioTyper – Algorithms Pattern matching weighted pattern matching Principle component analysis Cluster analysis Correlation analysis

13. Goslar, 09/10/2007 Score based pattern matching Calculation of a matching score based on: Rel Score % matches of the reference spectrum (e.g. 6 / 10 = 0.6) Rel P-Num. % matches of the unknown spectrum (e.g. 6 / 20 = 0.3) I-Corr. value of intensity correlation Unknown microorganism is matched against each Main spectrum in a library. Ranking according to matching score, threshold for ID Robust standard method for species ID

14. Goslar, 09/10/2007 Neisseria meningitidis serotypes A W135 X Y How about subtyping?

15. Goslar, 09/10/2007 Incorrect hirachical clustering of three Neisseria meningitidis serogroups after PCA Principle component analysis PCA is looking for the largest variations in a given group. If measurement variations are larger than inter-species/ subspecies variations it may fail!  Depending strongly on standardization of measurement!

16. Goslar, 09/10/2007 Weighted pattern matching Batch weighting: Automated generation of a weighted main spectra library; every main spectrum of a library is compared with all the other main spectra Manual weighting: Weight of each peak in a main spectrum can be edited manually Combination of both procedures is possible Hierachical approach in combination with standard pattern matching Characteristic peaks are selected and weighted by occurence in subgroups, intensity, and frequency

17. Goslar, 09/10/2007 Weighted pattern matching Identification Results weighted Detected Species log(Score) -------------------------------------------------------------- Sp. 1 Serogruppe_A 2.677 Ser.A Serogruppe_Y 2.150 Serogruppe_W135 2.044 Serogruppe_X 2.026 Sp. 2 Serogruppe_W135 2.339 Ser.W135 Serogruppe_Y 2.123 Serogruppe_X 1.784 Serogruppe_A 1.571 Sp. 3 Serogruppe_X 2.665 Ser.X Serogruppe_W135 2.033 Serogruppe_Y 1.902 Serogruppe_A 1.136 Sp. 4 Serogruppe_Y 2.294 Ser.Y Serogruppe_W135 2.126 Serogruppe_X 1.958 Serogruppe_A 1.617 Neisseria meningitidis serogroups Correct identification of subspecies through weighting of specific peaks. Expansion of pattern matching towards subspecies detection.

18. Goslar, 09/10/2007 Correlation analysis Color code: dark red – highest correlation dark blue – lowest correlation Correlation analysis of different Salmonella enterica serovars: Correlation analysis according to Arnold & Reilly, RCMS, 1998, modified 1. 1849_Hadar_VAB 2. 371_enteritidis_VAB 3. 042_typhimurium_O5_VAB 4. 104_enteritidis_VAB 5. 123_typhimurium_O5_VAB 6. 163_Virchow_VAB 7. 188_Dublin_VAB 8. 202_Infantis_VAB 9. 242_Infantis_VAB 10. 285_Virchow_VAB 11. 506_Hadar_VAB 12. 754_Agona_VAB

19. Goslar, 09/10/2007 Microorganism databases Acetobacter aceti subsp. aceti Acetobacter pasteurianus subsp.lovaniensis Acetobacter pasteurianus subsp.pasteurianus Actinomadura aurantiaca Actinomadura libanotica Actinomadura livida Agrobaterium tumefaciens Arthrobacter globiformis Arthrobacter oxydans Arthrobacter pyridinolis Arthrobacter sulfureus Bacillus alcalophilus Bacillus cohnii Bacillus sphaericus Brevibacillus brevis Brevibacterium linens Cellulomonas flavigena Cellulomonas turbata Corynebacterium glutamicum Comamonas testosteronii Gluconobacter oxydans subsp. oxydans Gordonia amarae Gordonia rubropertincta Gordonia terrae Halomonas denitrificans Halomonas elongata Halomonas halmophila Hydrogenophaga flava Hydrogenophaga pseudoflava Methylobacterium mesophilicum Methylobacterium organophilum Methylobacterium radiotolerans Methylobacterium rhodesianum Paracoccus versutus Pseudomonas balearica Pseudomonas fluorescens Pseudomonas oleovorans Pseudomonas putida Pseudomonas stutzeri Pseudonocardia hydrocarbonoxydans Rhizobium leguminosarum Rhodococcus coprophilus Rhodococcus fascians Rhodococcus globerulus Rhodococcus rhodnii Rhodococcus rhodochrous Rhodococcus ruber Sinorhizobium meliloti Starkaya novella Streptomyces albus Streptomyces avidinii Streptomyces azureus Streptomyces badius Streptomyces griseus Streptomyces hirsutus Streptomyces lavendulae Streptomyces phaeochromogenes Streptomyces violaceoruber Streptomyces viridisporus Libraries: Generation of reference pattern for new microorganisms by users Ready-to-use libraries with microbial strains for direct identification

20. Goslar, 09/10/2007 Minimal sample preparation Powerful bioinformatic approaches Species to strain resolution, mixture detection High throughput at low costs per analysis Non-expert identification possible Dedicated databases of high quality Conclusions

21. Goslar, 09/10/2007 The BDAL BioTyper team: Thomas Maier Kristina Schlosser Thomas Wenzel Thorsten Mieruch Stefan Klepel Uwe Renner Jan-Henner Wurmbach Karl-Otto Kräuter Alexander Rueegg Thanks to: … and many cooperation partners! In particular: Prof. Stackebrandt, Dr. Schumann