Optical forward-scattering for identification of bacteria within microcolonies.
Antoine CUERJoe-Loïc KODJA
Arthur LEFEBVREFlorian LICARIRobin LOUVET
Anil NARASSIGUIN
Mathieu DUPOYPierre MARCOUX
Frédéric MALLARD
33rdrd International Conference on Bio-Sensing Technology International Conference on Bio-Sensing Technology
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P.R. Marcoux | Forward-scattering for bacterial identification | 13 May 2013 | 2
Introduction: How a bacterium species can be identified ?
Genomic analysis
Antigeniccharacteristics
ID based upon the composition of cellular
membrane
Molecular methods
Biochemical tests
Enzymatic activities
Morphological characteristics
Microscopy / staining
ID based upon theglobal cellular composition
Spectralfingerprint
API test
[P69] Non-invasive detection of [P69] Non-invasive detection of bacteria via the sensing of volatile bacteria via the sensing of volatile metabolites released by enzymatic metabolites released by enzymatic
activityactivity L.H. Guillemot, M. Vrignaud, P.R. Marcoux, T.-H. Tran-Thi.
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Introduction: Rapid methods in diagnostic
• identification tests must be performed on a much smaller amount of cells (1-103 cells), so as to reduce the time dedicated to growth• identification tests must be faster
Reference method Rapid method under investigation
24h 6h
24h a few seconds
API tests (bioMérieux):Pathogen is identified
according to the results (+ or -) of a series a biochemical tests
Raman spectroscopy:Pathogen is identified according to its Raman fingerprint
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1. Raman: inelasticinelastic scattering / Forward-scattering: elasticelastic scattering Elastic scattering yields much more photons:exposure time in Raman: 30 s (spectrum on a single cell) exposure time in forward-scattering: 1 ms (single-cell; microcolony)2. Raman: vibrational spectroscopyvibrational spectroscopy, a peak is linked with a particular vibration mode of a type of covalent bond: e.g. υ(C=O); υ(C−Η)…
Introduction: optical methods for identification
Diffraction (elastic scattering)
Raman (inelastic scattering)
Forward-scatteringForward-scattering is not a spectroscopy: it does not yield information about cell composition, but rather about morphological characteristics.
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3. As for Raman spectroscopy and intrinsic fluorescence, forward-scattering is a label-free methodlabel-free method. Non invasiveNon invasive technique, requires little or no consumable, can be automatedcan be automated.
4. In direct space: the packing of bacteria cells within microcolony induces a
periodicperiodic modulation modulation of phasephase (refraction index) and absorbanceabsorbance
in reciprocal space: it yields diffraction
fringes.
Introduction: optical methods for identification
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100806040200
15
10
5
0
-5
PC#1
PC#2
EC21HA4
strain
PCA plot for EC21 and HA4 scattering patterns
Elastic diffusion
(laser)
Projectiononto a basis
functions descriptor(n-component vector)
Multivariate statistics
classification(for ex. Principal
Component Analysis)
n ...1
Forward-scattering(543 nm)
H. alveiH. alvei
E. coliE. coli
Introduction: experimental process and setup
camera 2 camera 2 (acquisition of (acquisition of scattering scattering pattern)pattern)
camera 1 camera 1 (images in (images in direct space)direct space)
Microcolony on Microcolony on agaragar
Laser (534nm)Laser (534nm)
microcolony(6h of incubation)
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1. Scattering patternspatterns: How are they formed ?What kind of information do they give ?
2. Image analysisImage analysis: How can we compare scattering patterns quantitatively ?
3. Results: A first database of Gram- species Gram- species at 6hon TSA (Tryptic Soy Agar)
4. First results on two CNS (Coagulase-Negative StaphylococciStaphylococci) at 6h on ChromID MRSA.
Introduction
Let’s investigate the possibility of using forward-scatteringforward-scattering as an identification method on microcolonies after 6 hours of incubationafter 6 hours of incubation
(37°C), directly on agardirectly on agar medium:
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A scattering pattern contains complexcomplex phenotypicphenotypic information, information, which is a sum of various parameters, such as:
1. Refraction index:Refraction index: of nutrient agar medium, of bacteria, of extracellular matrix.
2. Cellular shapeCellular shape.
3. Geometry of bacteria stacking within microcolonystacking within microcolony (the scattering of planktonic cells, i.e. growing in liquid medium, yields a much less complex pattern).
4. Shape of the whole microcolonywhole microcolony: it acts as a micro-lens.
1. Scattering patterns : phenotypic information
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4h50
Fringes at low angles low angles: corresponds to low spatial frequencies, the whole bacterial colony scatters. More light, but less complex shape. Available from the start.
HALA
Fringes at high angles high angles: corresponds to high spatial frequencies, scattering due to the stacking of cells. Much less photons (appears after several hours of incubation), but more complex shape. Seems to yield more discrimination.
Spatial periods: a few tens of µm.
Spatial periods: 1 µm and less.
1. Scattering patterns : two kinds of fringes
LA
HA
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4h50
E. coli ATCC8739
2. Image analysis: four strains of the same species at t=6h
How can we quantitatively compare all these scattering patterns ?
E. coliE. coli ATCC25922 ATCC25922(EC10)(EC10)
E. coliE. coli ATCC8739 ATCC8739(EC11)(EC11)
E. coliE. coli ATCC35421 ATCC35421(EC21)(EC21)
E. coliE. coli ATCC11775 ATCC11775(EC28)(EC28)
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4h50
Projection Anm of scattering pattern f(r,) onto Zernike polynomial Znm
= similarity coefficient between the image f and the basis function Znm
2. Image analysis: calculation of descriptorvector (descriptor)vector (descriptor)
V=(V1,V2,…,Vn) made of Anm projections:
imageimage(scatterogram)(scatterogram)
1
0
2
0
),(),(1
ddrrrZrfn
A nmnm
The more similar f and Znm look, the higher is Anm (Zernike moment).
ff((rr,,))
ZZnmnm((rr,,))
projection
projection
projection
projection Projections Anm (Zernike moments) are calculated for the first 120 Zernike
polynomials Znm
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3. Results: a first database on Gram- species (t = 6h) Classified as
Imaged scatterogram
EC8 EC10 EC11 EC21 EC28 HA4 CF7 sum
EC8 48,8 7,9 0,0 11,0 17,3 0,0 15,0 100% (127)
EC10 6,4 49,1 14,5 0,0 25,5 2,7 1,8 100% (110)
EC11 0,0 7,3 89,1 0,0 2,7 0,9 0,0 100% (110)
EC21 12,9 0,0 0,0 81,9 0,0 0,0 5,2 100% (116)
EC28 20,5 24,2 1,5 0,8 42,4 0,0 10,6 100% (132)
HA4 0,8 0,0 0,0 0,8 0,0 86,0 12,4 100% (121)
CF7 7,8 0,0 0,0 1,7 0,0 3,5 87,0 100% (115)
Forward scattering on microcolonies (6h of
incubation, 37°C) growing on a thin layer (1mm) of TSA (Trypcase Soy Agar). Laser Laser
beam: 100µmbeam: 100µm on bacteria on bacteria.
Average classification rate (Naive Bayes) over the whole database: 69%.
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3. Results: a first database on Gram- species (t = 6h)
Principal Component Analysis
Can we discriminate the different strains of the E. coli species ?
Supervised learning(Naive Bayes Continuous)
Classification rate = 82% on average
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Second step: with commercial Petri dishes (5mm thick), scattering patterns are acquired without without opening lidsopening lids no risk of cross-contamination between samples
4. Results: distinguishing two species of Staphylococci
We chose ChromID MRSA (bioMérieux) as a nutrient medium: screening of Gram+ strains resistant to methicillinstrains resistant to methicillin.
Can we discriminate, after 6h of incubation, two species of discriminate, after 6h of incubation, two species of StaphylococciStaphylococci that grow on ChromID MRSA ?Study on Staphylococcus haemolyticus and Staphylococcus cohnii, two methicillin-resistant species (Coagulase-Negative Staphylococci).
As we obtain a significantly slower growth, we reduce the laser beam laser beam on bacteria down to 25µm on bacteria down to 25µm .
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4. Results: distinguishing two species of Staphylococci
Principal Component AnalysisSupervised learning (Naive Bayes Continuous)
Classification rates: 92% on average92% on average
Forward scattering through the whole Petri dish (including lid). 6h of incubation (37°C). 2 species of methicillin-resistant Staphylococci
growing on ChromID MRSA (bioMérieux). Laser beam: 25µm on bacteria.
S. haemolyticusS. haemolyticus
S. cohniiS. cohnii
S. haemolyticusS. haemolyticus
S. cohniiS. cohnii
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Identifying pathogenic species is not enough: a complete diagnosis must include AAntibiotic SSusceptibility TTesting (AST).
To guide the selection and modification of antimicrobial therapy
Conclusion
Currently under investigation…
Towards label-free, non invasive (without opening lids), non destructive, automated
methods.Less than 6h for identificationidentification + ASTAST
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AcknowledgmentsMathieu DUPOY
Antoine CUER, Joe-Loïc KODJAArthur LEFEVBRE, Florian LICARI
Robin LOUVET, Anil NARASSIGUINCharles-Edmond BICHOT
Frédéric MALLARDFrédéric PINSTON
http://eric.univ-lyon2.fr/~ricco/tanagra/fr/tanagra.html
http://www.cs.waikato.ac.nz/ml/weka/
optical instrumentation
optical instrumentation
microbiology
microbiology
image analysis and data mining
image analysis and data mining