MidInfrared Sensors for Pathogen Detection

Transcription

1 MidInfrared Sensors for Pathogen Detection PIs: Dr. L. Mauer, Dr. B. Reuhs, Dr. J. Irudayaraj Co-PIs: Dr. M. Cousin, Dr. C. DebRoy, Dr. S. Krishnan, Dr. J. Gore, Dr. J. Bouldin Research Associates: Dr. Reeta Davis, Dr. Sol Kim, A. Deering, Y. Burgula, D. Khali, J. Stratton, P.R.Sandeep

2 Fourier-transform infrared spectroscopy Measures absorbance of infrared light Beer s Law: A λ =lε λ c (FT-IR) Sensitive to functional groups Specific bands in fingerprint region Non-destructive technique High speed High sensitivity

3 Basic components of an FT-IR spectrometer

4 FT-IR Analysis of Bacteria Measures molecular bond vibration of compounds, excited by frequency of infrared light. FT-IR spectra display a structural and biochemical fingerprint of the bacteria (protein, lipid, carbohydrates, DNA).

5 Absorbance Absorbance Fatty acid Mixed region Amide polysaccharide Fingerprint FT-IR Analysis Wave number cm -1 Representative FT-IR spectra of L. monocytogenes collected by Continuum IR Microscope

6 FT-IR Method Development: Sample Preparation Techniques FT-IR main bench: filtration, magnetic nanoparticles FT-IR microscope: filtration, immunomagnetic separation, cellular components

7 Filtration- the apparatus Filtration Apparatus Membrane Filter Isolated colony Culture Vacuum Food Stomacher bag sample preparation

8 FT-IR and Filtration Multi-bounce flat plate ATR (MATR) Contact ATR on IR-microscope (Filtration) Contact ATR Objective Pressure Sensitive Stage Membrane Filter Contact Indicator Panel

9 FT-IR microscope Immunomagnetic separation Antibodies Cellular components (LPS, OMP) Continuµm IR-microscope (ThermoElectron)

10 Applications of FT-IR Methods in Food Microbiology 1. Detection of bacteria 2. Discrimination of viable, injured, and dead bacteria 3. Taxonomic classification of bacteria

11 FT-IR methods for Pathogen Detection in Foods Detection of E. coli O157:H7 and Salmonella typhimurium successful in: Fruit juices Skim milk Ground beef Chicken rinse water Medium aged Cheddar cheese Spinach

12 Cheddar cheese block (IR-microscope) Cheddar cheese (control) 1 CV E. coli O157:H7 on Cheddar cheese -2 E. coli K12 on Cheddar cheese CV 1 Salmonella Typhimurium on Cheddar cheese Clustering of Cheddar cheese surface treated with E. coli O157:H7, Salmonella Typhimurium, and E. coli K12 using CVA of FT-IR spectra ( cm -1 )

13 Ground Beef (IMS-FT-IR)

14 FT-IR Assay Sensitivity Detection Limit = ~40 cells in IR beam area No.of cells CFU / ml volume Infrared beam area filtration area CFU / ml 100mL 35 m No. of cells Paramete r Material cost Filtration (FT-IR) Dynabeads (FT-IR) / 4 Magnetic Nanoparticles (FT-IR) Flow cytometry Improving delivery of cells to beam area will decrease overall number of cells needed in starting sample ELISA (Eg. VIDAS) PCR (Eg. BAX) Filters Antibodies Antibodies Dye Enzymes Primers Time* < 7 h < 10 h??? 8 h 8 24 h 8-24 h Actual assay time Sensitivity at time of measurem ent < 1 h < 4 h 30 min h 1-4 h 3-4 h CFU/mL 10 4 CFU/mL CFU/mL Reference Mauer group Mauer group Irudayaraj group CFU/mL CFU/mL CFU/mL Givan, 2001 Fung, 2002 Hochberg et al., 2000

15 FT-IR Analysis of Structural Components of Bacteria

16 Development of analytical methods to discriminate between Live and Dead cells Based on spectra of growth Based on spectral differences

17 Absorbance E. coli K12 growth curve-filtration EK 24 hr Live Amide II region cm -1 EK 23 hr Live EK 22 hr Live EK 21 hr Live EK 20 hr Live EK 19 hr Live EK 18 hr Live EK 17 hr Live EK 16 hr Live EK 15 hr Live EK 14 hr Live EK 13 hr Live EK 12 hr Live EK 11 hr Live EK 10 hr Live EK 9 hr Live EK 8 hr Live EK 7 hr Live EK 6 hr Live EK 5 hr Live EK 4 hr Live EK 3 hr Live EK 2 hr Live EK 1 hr Live EK 0 hr Live EK 0 hr Dead Wave number cm -1 Mauer and Reuhs Mid-Infrared Sensors for the Rapid Analysis of Select Microbial Food Borne Pathogens. In Handbook of Homeland Security. John Wiley & Sons Publishers.

18 Detection based on spectral differences between live and dead Filtration-FT-IR Live Filtration-FT-IR Dead IMS-FT-IR Live IMS-FT-IR Dead

19 Detection based on spectral differences between live and dead Live Bacteria Dead Bacteria Dynabeads with EC Cooman plot based on spectral differences in the amide II region cm -1 and fatty acid region cm -1

20 First derivative of absorbance Absorbance Live Dead Raw FT-IR spectra of Live and dead E. coli O157:H7 Treatment: 4 min in 99 C waterbath Wavenumber cm -1 Live Dead First derivative FT-IR spectra of Live and dead E. coli O157:H Treatment: 4 min in 99 C waterbath Wavenumber cm -1

21 First derivative of absorbance 100% Live 100% Dead 99.95% Dead: 0.05% live Wavenumber cm -1 First derivative FT-IR spectra of 100% Live 100% dead, and 99.95% dead: 0.05% live of E. coli O157:H Treatment: 4 min in 99 C waterbath

22 Cooman plot Showing the differentiation of 100% live, 100% dead, and 0.05% live : 99.95% dead E. coli O157:H7 Using 6PC scores Using 10 PC scores Heat treatment: 4 min in a 99 C Water bath Region selected: cm -1 Preprocessing: Baseline correction and Normalization

23 Quantification of live cells in the presence of dead (heat-treated) cells Live cells correctly quantified by PLS in presence of dead cells

24 PLS analysis for the quantification different ratios of live and dead E. coli O157:H7 Heat treatment: 4 min in a 99 C Water bath Region selected: cm- 1, Preprocessing: first derivative, normalization

25 Effects of treatment on FT-IR classification UV 30 Salt Heat treatment CV Antibiotics Salt Alcohol UV Wave numbers cm -1 Heat Chloramphenicol Trimethoprim:Sulfamethoxazole CV 1 Alcohol Live Wave numbers cm -1 Clustering of E. coli O157:H7 (8.6 ± 0.3 log CFU/mL) spectra subject to six different processing treatments

26 Cooman Plots showing the differentiation of E. coli O157:H7 at different time intervals during heat treatment 16 min 24 min 4min 8 min Region selected: cm- 1, Preprocessing: Area normalization No of PC scores: 6

27 Dead Dead 1% Live 1% Live Live Live 4 min 8 min Dead 1% Live Live Dead 1% Live Live 16 min cm- 1, Area normalize, 6 PCs 24 min

28 FT-IR Methods for the Taxonomic Classification of Bacteria

29 Taxonomic Classification and FT-IR microscope 256 scans at 4 cm -1 resolution, cm -1

30 Genus Listeria Species L. innocua L. ivanovii L. monocytogenes L. seeligeri L. welshimeri L. grayi Lineage Lineage I Lineage II Lineage III Lineage IV Serotypes Serotypes 1/2b, 4b,4d, 3b, 4e Serotypes 1/2a, 1/2c, 3a, 3c Serotypes 4a, 4c Very rare Serotype 7 Haplotypes Lm 1.2, Lm 1.13, Lm1.1, Lm1.3, Lm 1.4, Lm1.11 Lm 1.12, Lm1.42 Lm 1.18, Lm 1.25, Lm1.26, Lm1.28, Lm 1.30, Lm1.37, Lm Strains 33291, 33287,33308, 33242,33309, 33303, 33294,33442,33464,33430,

31 Single channel First derivative of absorbance First derivative Single channel of absorbance C:\Program Files\OPUS_65\Temp\33004 a.0 Serotype Classification of L. monocytogenes 9/20/2010 4:35:40 PM 4b 1/2b 1/2a Wavenumber cm -1 Page 1 of b C:\Program Files\OPUS_65\Temp\33004 a.0 9/20/2010 4:38:11 PM 4c 1/2a 1/2b Page 1 of 1 Wavenumber cm -1

32 First derivative of absorbance E. coli O157:H7 Strain Identification Wavenumber cm -1

33 FT-IR techniques coupled with different chemometrics approaches offer a wide range of applications for: Detection, Differentiation, Quantification, and FT-IR Taxonomic level classification of bacteria from culture broth or food matrices. Thank you!