benoît wattelier, phasics paris and jacques klossa, tribvn...
TRANSCRIPT
Why looking for new diagnosis and prognosis techniques in Pathology?
– specimen techniques are hard to standardize
– there are still some issues with rare events detection
– frequent need for multidisciplinary techniques to solve complex cases (multi-scale)
– microscope/eye/brain stays as the main technique and existing biophotonics techniques are mostly ignored
11th European Congress on Telepathology and 5th International Congress on Virtual Microscopy, June 8th 2012 1
An introduction to label free technologies Olivier Piot, Cyrile Gobinet and Teddy Happillon MéDIAN Reims, Benoît Wattelier, Phasics Paris and Jacques Klossa, TRIBVN Paris
Background
What is new?
– the WSI proposes a new paradigm: screen + computer can complement or replace direct microscopy vision
– the domain of biophotonic is quickly evolving making new technologies more accessible
So, we formulated the question in the following way:
could we find some innovative biophotonic technology
to match the needs of the Pathology domain
and that could be seamlessly integrated in its workflow?
11th European Congress on Telepathology and 5th International Congress on Virtual Microscopy, June 8th 2012 2
An introduction to label free technologies Olivier Piot, Cyrile Gobinet and Teddy Happillon MéDIAN Reims, Benoît Wattelier, Phasics Paris and Jacques Klossa, TRIBVN Paris
Background
vibrational spectroscopy(ies) could be an interesting answer
– it provides a full molecular signature representative from the physiopathology of the studied cells or tissue
– it does not necessitate any previous labeling
– Raman micro-spectroscopy (RMS) works at the microscopy scale and is compatible with current microscopy containers (glass slides) and optics
– RMS allows in vivo, ex vivo and in vitro studies
>> for those reasons we decided to study feasibility in pathology
11th European Congress on Telepathology and 5th International Congress on Virtual Microscopy, June 8th 2012 3
An introduction to label free technologies Olivier Piot, Cyrile Gobinet and Teddy Happillon MéDIAN Reims, Benoît Wattelier, Phasics Paris and Jacques Klossa, TRIBVN Paris
Background
Technological advances Label free technologies
Background (J. Klossa)
1. An introduction to label free technologies
– Vibrational spectroscopy, infrared and Raman (O. Piot)
– Data analysis (C. Gobinet)
– Quantitative Phase Imaging (B. Wattellier)
2. Label free technologies in Pathology
– Raman micro-spectroscopy and multispectral imaging applied to cyto-hematology (J. Klossa, T. Happillon)
– Infrared imaging applied to paraffinized microarrays tissue for colon cancer diagnosis (O. Piot)
– Infrared imaging used to highlight peritumoral areas in human skin cancers biopsies (C. Gobinet)
Discussion (J. Klossa)
11th European Congress on Telepathology and 5th International Congress on Virtual Microscopy, June 8th 2012 4
Vibrational spectroscopies:
a high potential tool for clinics
O. Piot, C. Gobinet, G. D. Sockalingum, M. Manfait
MéDIAN ”Biophotonics and Technologies for Health”
FRE CNRS 3481 MEDyC
Université de Reims Champagne-Ardenne, France
Non destructive techniques permitting:
Non invasive analysis
Direct molecular label-free analysis
Microscopic scale imaging
In depth analysis (Raman)
Interest of vibrational microspectroscopies
Molecular characterisation of cells and tissues
Probing of structural changes
Identification and monitoring of molecules
11th European Congress on Telepathology and 5th International Congress on Virtual Microscopy, June 8th 2012 6
500100015002000250030003500
W avenumber cm-1
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00
.05
0.1
00
.15
0.2
00
.25
0.3
00
.35
Ab
so
rba
nc
e U
nit
s
C-H
C-H
>CO-NH-
P=O
CO-OR
COO-
C-O
C-C
C-O-C
P=O
>C
O-N
H-
O-H
N-H
500100015002000250030003500
W avenumber cm-1
0.0
00
.05
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00
.15
0.2
00
.25
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00
.35
Ab
so
rba
nc
e U
nit
s
C-H
C-H
>CO-NH-
P=O
CO-OR
COO-
C-O
C-C
C-O-C
P=O
>C
O-N
H-
O-H
N-HInfrared absorption
1
745 624
646
701
831
855
941
1005
1033
1064 1131
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Intensity
(a.u.)
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Intensity
(a.u.)
800 1000 1200 1400 1600Wavenumber (cm-1)
Phe
Tyr
C-C
str
etc
hin
g
C-C
str
etc
hin
g o
f
ph
osp
ho
lip
ids
Sugar
contribution
Lipids C-H deformation
Amide I
Tyr
Tyr
Phe
Phe
Raman scattering
Molecular fingerprint of cells or tissues
8
Rayleigh
hn
R=hn
exc
hn
AS=
hn
exc+
hn
fin
al
hn
S=
hn
exc-
hn
fin
al
hnexc
Raman Stokes
Raman Anti-Stokes
Infrared
ABSORPTION
0 nS nAS
cm-1
Bo
nd
en
erg
y
Vibrational levels of the excited electronic state
(Singlet)
Vibrational levels of the fundamental electronic
state (Singlet)
Fluorescence Phosphorescence
Triplet State
EMISSION SCATTERING
Jablonski diagram
Vibration modes of molecular bonds
• Fundamental modes – Stretching (variation of the inter-atomic distance) – Bending (variation of the angle between two adjacent links)
– Symmetric and antisymmetric vibrations
– Other vibration modes exist: ring breathing, umbrella vibration, skeleton vibration…
• Harmonic modes (Multiple of a fundamental mode)
• Combination modes (Linear combination of several vibrations)
• Fermi resonance (ex Tyrosine doublet 830/850 cm-1): coupling between a fundamental vibration and a harmonic vibration
CH2:asymetric stretching
CH2:symetric stretching
11th European Congress on Telepathology and 5th International Congress on Virtual Microscopy, June 8th 2012 9
Infrared vs Raman
• Wavelength () range:
– (mid) Infrared: polychromatic source = 2.5 - 25 µm
– Raman: monochromatic source (visible – near infrared) ~ 0.5 µm – 1 µm
– Energy: E=h=hc/
• Rule of mutual exclusion
– In theory, molecular vibrations symmetric with regard to the centre of symmetry are forbidden in the infrared spectrum, whereas molecular vibrations which are antisymmetric to the centre of symmetry are forbidden in the Raman spectrum.
11th European Congress on Telepathology and 5th International Congress on Virtual Microscopy, June 8th 2012 10
Infrared vs Raman
Three actives vibrations
One active vibration
A Raman active vibration can be detected if the polarizability is changed during the vibration. Forms of
the polarizabilty ellipsoid and its form change during the vibration
Only the vibrations inducing a variation of the dipolar momentum are visible. Consequently, the vibration of polarized bonds will induce intense bands, while bands associated to non-polarized bonds will be not
or a little visible
Weak bands Intense bands
Infrared Raman
11
CH2:asymetric
stretching
CH2:symetric
stretching CH2: scissorbend CH3:
umbrellabend
CH2:rocking
What type of information can be extracted from a spectrum ?
Wavenumber cm-1 (~ Frequency ~ Energy) 12
13
745 624
646
701
831
855
941
1005
1033
1064 1131
1160
1210
1298
1343
1443
1654
5000
4000
3000
2000
1000
0
Intensity (a.u.)
800 1000 1200 1400 1600 Wavenumber (cm-1)
Phe
Tyr
C-C
str
etch
ing
C-C
str
etch
ing
of
ph
osp
ho
lipid
s
Sugar contribution
Lipids C-H deformation
Amide I
Tyr
Tyr
Phe
Phe
Raman spectrum of a biological tissue (skin)
13
Vibrations of disulfide bridges
Vibrations of the tyrosin Fermi doublet
Amide band
, the phenolic cycle of tyrosin is
C N
R
O R
H
Amide I
nS-S : 510 525 540 cm-1
1656 cm-1 1669 cm-1 1665 cm-1
850
830 According the ratio
buried
exposed
11th European Congress on Telepathology and 5th International Congress on Virtual Microscopy, June 8th 2012 14
• Raman spectrometer can be coupled to a conventional confocal
microscope equipped with high magnification objectives
• Dispersive gratings and multi-channels CCD detectors for analysis of Raman scattering
• Infrared spectrometer can be coupled to micro-imaging system
equipped with Cassegrain objectives
• Interferometer (Michelson type) and Fourier Transformation for recording of IR absorption
• Diffraction limit determines the spatial resolution: ~10 µm for IR, ~1 µm for Raman (Resolving power r=0.61/NA)
Specific instrumental set-up
11th European Congress on Telepathology and 5th International Congress on Virtual Microscopy, June 8th 2012 15
Acquisition conditions - IR
• Transmission mode: IR-transparent (CaF2, ZnSe) substrates
• Samples must be of limited thickness (~10 µm) and dried (strong water IR absorption)
• For biological samples, they can be fixed and paraffin-embedded (numerical dewaxing by neutralizing paraffin spectral interferences)
11th European Congress on Telepathology and 5th International Congress on Virtual Microscopy, June 8th 2012 16
• Reflection mode: glass (for visible excitation); fused-silica, CaF2 or ZnSe substrates for near infrared excitation
• Analysis of samples in water is possible
• Fiber-based probes are assessed for in vivo measurements
Acquisition conditions - Raman
11th European Congress on Telepathology and 5th International Congress on Virtual Microscopy, June 8th 2012 17
5 µm
-40 -20 0 20 40
Length X (µm)
Bands ratio: (1590-1720/1490-1590)
2 4 6 8 10 12
1
2
3
40
1
2
3 Bands ratio: (1590-1720/1490-1590)
2 4 6 8 10 12
1
2
3
40
1
2
3Bands ratio: (1590-1720/1490-1590)
2 4 6 8 10 12
1
2
3
40
1
2
3
Polarisation 00 Polarisation 450 Polarisation 900
C-N
C=O
Inte
nsi
ty
(a.u
)
C-N
C=O
Inte
nsi
ty
(a.u
)
C-N
C=O
Inte
nsi
ty
(a.u
)
Polarized Raman microspectroscopy can reveal structural changes of peritumoral dermis in basal cell carcinoma. Ly E, Piot O, Durlach A, Bernard P, Manfait M. Appl Spectrosc. 2008;62(10):1088-94. Probing tumor and peritumoral tissues in superficial and nodular basal cell carcinoma using polarized Raman microspectroscopy. Ly E, Durlach A, Antonicelli F, Bernard P, Manfait M and Piot O. Experimental Dermatology 2010;19(1):68-73
Polarized vibrational spectroscopy
Rat tail tendon
Examples of potential clinical applications
• Histopathology: spectral automated procedure for tissue characterization and diagnosis
• Cytopathology: detection of malignant cells from a smear
• “Optical biopsy”: in vivo tool to help clinicians for diagnosis and margins identification
– Real-time Raman Spectroscopy for In Vivo Skin Cancer Diagnosis. Lui H, Zhao J, McLean D, Zeng H. Cancer
Res. 2012;72(10):2491-500
11th European Congress on Telepathology and 5th International Congress on Virtual Microscopy, June 8th 2012 19
• Quality control for pharmaceutical products and detection of counterfeits
– Detection of counterfeit Viagra® by Raman microspectroscopy imaging and multivariate
analysis. Sacré PY, Deconinck E, Saerens L, De Beer T, Courselle P, Vancauwenberghe R, Chiap P, Crommen J, De Beer JO. J Pharm Biomed Anal. 2011;56(2):454-61
– Profiling of counterfeit medicines by vibrational spectroscopy. Been F, Roggo Y, Degardin K, Esseiva P, Margot P. Forensic Sci Int. 2011;211(1-3):83-100
• Analysis of excipients, drug polymorphism, packaging….
Examples of potential clinical applications
11th European Congress on Telepathology and 5th International Congress on Virtual Microscopy, June 8th 2012 20
• Early identification and typing of micro-organisms
– FTIR spectroscopy in medical mycology: applications to the differentiation and typing of
Candida.Toubas D, Essendoubi M, Adt I, Pinon JM, Manfait M, Sockalingum GD. Anal Bioanal Chem. 2007;387(5):1729-37
Hierarchical cluster analysis of infrared spectra of different species, collected on suspensions (a) and on micro-colonies (b)
Hierarchical cluster analysis of infrared spectra of strains of C. parapsilosis isolated
from 4 patients and 1 reference (ATCC 22019)
Examples of potential clinical applications
11th European Congress on Telepathology and 5th International Congress on Virtual Microscopy, June 8th 2012 21
• Screening of drugs by assessing their cellular effect
– The FTIR spectrum of prostate cancer cells allows the classification of
anticancer drugs according to their mode of action. Derenne A, Gasper R, Goormaghtigh E. Analyst. 2011;136(6):1134-41
Examples of potential clinical applications
11th European Congress on Telepathology and 5th International Congress on Virtual Microscopy, June 8th 2012 22
Drug Cell response
Intracellular follow-up
of the drug
11th European Congress on Telepathology and 5th International Congress on Virtual Microscopy, June 8th 2012 23
Improvement of the Raman detection sensibility
• Sensitivity : – Raman : 10-3 M
– Resonance Raman : 10-5 M
– SERS : 10-9 M (Surface Enhanced Raman Scattering)
P = αE
Electromagnetic effect Chemical effect
Induced dipolar momentum or Polarizability
11th European Congress on Telepathology and 5th International Congress on Virtual Microscopy, June 8th 2012 24
K562 cell treated with m-Amsacrine (10 µM,
30 min) incubated with silver colloids
SERS for intracellular imaging
Films Colloids Tips
Ra
man
inte
nsity
/ a.u
.
Wavenumber / cm-1
Ra
man
inte
nsity
/ a.u
.
Wavenumber / cm-1Wavenumber / cm-1
Metallic (silver, gold) rough surface
11th European Congress on Telepathology and 5th International Congress on Virtual Microscopy, June 8th 2012 25
Technological developments/improvements
• Raman
– SERS (Surface Enhanced Raman Scattering)
– Near Field Raman microspectroscopy (TipERS)
– Stimulated Raman scattering or CARS (non-linear phenomena)
– In depth analysis system (Spatially Offset Raman Spectroscopy)
• Infrared – ATR (Attenuated Total Reflectance): based on the use of a Ge crystal of
high refractive index, in contact with the sample Applications of ATR-FTIR spectroscopic imaging to biomedical samples. Kazarian SG,
Chan KL.Biochim Biophys Acta. 2006;1758(7):858-67
– AFM-IR (Atomic Force Microscopy-IR)
– Synchrotron radiation
11th European Congress on Telepathology and 5th International Congress on Virtual Microscopy, June 8th 2012 26
Vibrational (Raman and infrared) spectroscopy
• Promising biophotonic tool:
– Label-free: analysis of the intrinsic biomolecular composition of a sample
– High specificity: detection of subtle molecular alterations associated to malignancy
• Importance of data processing
– Identification of relevant spectroscopic markers
11th European Congress on Telepathology and 5th International Congress on Virtual Microscopy, June 8th 2012 27
11th European Congress on Telepathology and 5th International Congress on Virtual Microscopy, June 8th 2012 11th European Congress on Telepathology and 5th International Congress on Virtual Microscopy, June 8th 2012
Technological advances Label free technologies
Background (J. Klossa)
1. An introduction to label free technologies
– Vibrational spectroscopy, infrared and Raman (O. Piot)
– Data analysis (C. Gobinet)
– Quantitative Phase Imaging (B. Wattellier)
2. Label free technologies in Pathology
– Raman micro-spectroscopy and multispectral imaging applied to cyto-hematology (J. Klossa, T. Happillon)
– Infrared imaging applied to paraffinized microarrays tissue for colon cancer diagnosis (O. Piot)
– Infrared imaging used to highlight peritumoral areas in human skin cancers biopsies (C. Gobinet)
Discussion (J. Klossa)
28
Numerical processing of vibrational spectroscopy data
C. Gobinet
Team MéDIAN, “Biophotonics and Technologies for Health”, Unité MEDyC, CNRS FRE 3481, UFR de Pharmacie, Université de Reims Champagne-Ardenne, Reims, France
1/11
A spectrum = useless information + useful information
Instrumentation response/distortion
Sample fluorescence or scattering
Signal of some uninteresting biomolecules
Noise
Useless information:
Useful information: Signal of biomolecules of interest
Definition of new spectral markers
Necessity to extract useful information
Manipulate this useful information
to compare different groups
to make a diagnosis
Signal of the slide
1) Introduction
500 1000 1500
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nsi
ty
Wavenumbers (cm-1)
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Absorbance
Raman spectrum Infrared spectrum
Inte
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2/11
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-0.1
-0.05
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rban
ce
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Absorbance
Extended Multiplicative Signal Correction (EMSC)
= ai s ^
i
i
icorra
ess ˆ
900 1100 1300 1500 1700
Wavenumber cm-1
Principal components of paraffin + bi I
+ P
si
+ ei
Unknown variables
Known variables
ai, bi and P are estimated by
least squares to minimize ei
Correction of spectra:
Paraffin and baseline signals are neutralized
Error/residue to minimize
4th order polynomial function
Mean spectrum of the infrared image
2) Correction of distortions: An example in infrared spectroscopy
3/11
Extended Multiplicative Signal Correction
2) Correction of distortions: An example in infrared spectroscopy
Raw spectra
Spectra preprocessed by EMSC
Ab
sorb
ance
(a.
u.)
A
bso
rban
ce (
a. u
.)
Wavenumber (cm-1)
Wavenumber (cm-1) 4/11
Independent Component Analysis for modelization of paraffin-embedded skin biopsy spectra
3) Data reduction/feature extraction
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1
1.5
2
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x 104
Wavenumber (cm-1
)
Inten
sity
Raw spectra = 2009 spectra composed of 990 wavenumbers
Processing by ICA estimating 4 sources
Paraffin 1 Paraffin 2
Paraffin 3 Skin
Reduce/simplify a large dataset composed of: a lot of variables redundant information
5/11
Selecting a subset of relevant features for building robust learning models
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0
0.1
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4) Feature selection
Classical methods
Statistical tests
Variable selection assisted by supervised classification
Application to infrared spectroscopy: diagnosis of hepatocellular carcinoma on patients with cirrhosis
Construction of a predictive model on these 164 identified wavenumbers
80% sensitivity, 80% specificity
6/11
5) Unsupervised classification = clustering
Cluster 1 Cluster 2
No a priori information about the data: classes are not predefined
No data are needed to train the used clustering algorithm
Classes/clusters are discovered by the algorithm from the data
Different kinds of clustering methods: hierarchical clustering, K-Means, Fuzzy C-Means
Data
7/11
Examples of segmentation of infrared images acquired on human skin tumors by K-Means algorithm
Squamous cell carcinoma
100µm
Basal cell carcinoma Bowen’s disease
5) Unsupervised classification = clustering C
luster n
um
ber
Clu
ster nu
mb
er
Clu
ster nu
mb
er
H&E image K-Means image
8/11
6) Supervised classification
Classification rule/model
Class 1 Class 2 Class 1 Class 2
Classes are predefined by the user:
A learning algorithm is trained on a dataset composed of labelled objects
Prediction of the class membership of new unknown objects
Training set New data
Different kinds of classification methods:
Linear discriminant analysis, artificial neural networks, support vector machines
stratum corneum, epidermis, dermis, BCC, SCC, …
9/11
6) Supervised classification
External validation on unknown samples
Training set
Model
Validation set
1: BCC
2: SCC
7: Inflammatory stromal reaction
8: Stratum corneum
Optimization
Database
4: Epidermis
5: Reactional epidermis
6: Stroma
3: Bowen
Examples of application of linear discriminant analysis to infrared images acquired on human skin tumors
Training
Squamous cell carcinoma Basal cell carcinoma Bowen’s disease
10/11
GRAZIE MILLE
11/11
Technological advances Label free technologies
Background (J. Klossa)
1. An introduction to label free technologies
– Vibrational spectroscopy, infrared and Raman (O. Piot)
– Data analysis (C. Gobinet)
– Quantitative Phase Imaging (B. Wattellier)
2. Label free technologies in Pathology
– Raman micro-spectroscopy and multispectral imaging applied to cyto-hematology (J. Klossa, T. Happillon)
– Infrared imaging applied to paraffinized microarrays tissue for colon cancer diagnosis (O. Piot)
– Infrared imaging used to highlight peritumoral areas in human skin cancers biopsies (C. Gobinet)
Discussion (J. Klossa)
11th European Congress on Telepathology and 5th International Congress on Virtual Microscopy, June 8th 2012 40
www.phasics.com
QUANTITATIVE PHASE IMAGING BY QUADRI-WAVE INTERFEROMETRY
11th European Congress on Telepathology and 5th International Congress on Virtual Microscopy
Benoît Wattelier
A brief introduction to PHASICS
(c) 2012 - PHASICS - All rights reserved
2003: PHASICS Creation Spin-off du Laboratoire LULI (École Polytechnique - CNRS ) Patented Technology: QuadriWave Interferometry for Quantitative Phase Imaging
16 employees in 2012 (10 in R&D and 5 in microscopy applications)
1,3 M€, turnover in 2011
75 % of turnover for exportation
> 250 systems sold since 2004
Located in Palaiseau, France on Ecole Polytechnique Campus
2/18
Applications
Beam Metrology Laser beam quality assessment
Adaptive optics Laser optimization, Image Resolution Improvement
LASE
R
Surface metrology
Lens and Optics metrology OP
TIQ
UES
Quantitative Phase Imaging Biology, Pathology (smear, tissues, cultures) M
ED
(c) 2012 - PHASICS - All rights reserved 3/18
Contents • Quantitative Phase Imaging: a label-free imaging
technology for: – Contrast enhancement – Quantitative measurements on cells and tissues
• QPI with QuadriWave Interferometry
• Implementation in pathology
• Data treatment and applications
• Conclusion
(c) 2012 - PHASICS - All rights reserved 4/18
What is phase ?
(c) 2012 - PHASICS - All rights reserved
Brightfield Microscopy Image (camera)
POOR Image contrast
5/18
Phase carries propagation history
n2 > n1 n2 n1 e
vlight=c/n Dt=(n2-n1) x e / c
Phase = Optical Thickness = (n2-n1) x e (c) 2012 - PHASICS - All rights reserved
PHASE is QUANTITATIVE !
IMPROVED Image contrast
6/18
Quantitative Phase Imaging
(c) 2012 - PHASICS - All rights reserved
Zernike contrast, Hoffmann, DIC, Nomarski
Digital Holography Microscopy
Non Quantitative Easy to implement
Quantitative Need for a dedicated microscope
SID4BIO
Diffractive optics
CCD chip
Quadri-Wave Interferometry
Quantitative AND
Easy to implement
7/18
QWI in Pathology • Sample Preparation
– Works with Smears, Slices, Cultures
– No need to dye or label
– Compatible with other modalities (fluorescence, Raman, or further coloration)
– Compatible with glass or plastic slides/dishes
• Image Acquisition – SID4BIO replaces a standard digital camera
– Standard illumination (laser, halogen, LED, polychromatic)
– Any microscope objective
– Single-shot image (no scanning)
– Static or dynamic (time-lapse)
• Easy interface with workstations – TRIBVN (soon)…
– Metamorph…
(c) 2012 - PHASICS - All rights reserved 8/18
Contents • Quantitative Phase Imaging: a label-free imaging
technology for: – Contrast enhancement – Quantitative measurements on cells and tissues
• QPI with QuadriWave Interferometry
• Implementation in pathology
• Data treatment and applications
• Conclusion
(c) 2012 - PHASICS - All rights reserved 9/18
Raw image: cancer cell proliferation study
70µm
MDA-231 (human breast cancer) (acquisition 16h, 37°C, CO2 5%. X20, NA=0.3)
Cell counting
Scratch recover by migrations & mitosis
Scratch recover with MDA-231
(c) 2012 - PHASICS - All rights reserved 10/18
High pass filter: Texture detection 10µm
COS7 cell, 100x, NA=1.3
(c) 2012 - PHASICS - All rights reserved 11/18
Qua(n)titative Phase: Malaria Parasite Detection
parasite
dust
Phase Intensity
Parasites appear as low phase objects (bright), whereas dust defects disappear
(c) 2012 - PHASICS - All rights reserved 12/18
QPI guides Raman Micro-Spectroscopy
1. It is easy and fast to count the infected Red Blood Cells a Large field image 4.1% parasitemia
Close to the nominal value 2. The cell localization is recorded by the workstation for further investigations by Raman
Micro-Spectroscopy (infection confirmation, species determination) Quantitative Phase Imaging is a powerful label-free guiding technology
• HF filtering • thresholding • color scale change
Phase
Objectif x40, ON=0,6, Zoom=1
(c) 2012 - PHASICS - All rights reserved
See the poster session at the back of this room
13/18
Quantitative image: organites identification
(c) 2012 - PHASICS - All rights reserved
7nm 12nm
0
0,002
0,004
0,006
0,008
0,01
0,012
0,014
0,016
Lyso
so
me
s
Oth
er
ve
sic
les
Re
lative
re
fra
ctive
in
de
x
32 lysosomes and 38 other vesicles analyzed, in 5 cells
10 µm
COS-7, X200, NA 1.3 mitochondria (green) and lysosomes (red)
Line-out
14/18
Quantitative images: mitotic index determination
(c) 2012 - PHASICS - All rights reserved 15/18
Conclusion • The phase component in light is very rich because:
– It is a source of high contrast for cell and tissue imaging – It carries information about the cell/tissue physico-chemical
inhomogeneities
• QuadriWave Interferometry (SID4BIO) is a simple and robust way to implement Quantitative Phase Imaging – No coloration or labeling necessary – Replaces a standard camera
• It has a great potential for pathology applications: – Transparent structures imaging (cytometry, bacteria) – Cell proliferation monitoring (cancers, apoptosis) – Guide or combine with other modalities (Raman, Fluorescence) – Further image analysis leads to quantitative information to
identify cell organites or measure cell thicknesses
(c) 2012 - PHASICS - All rights reserved 16/18
Acknowledgments • MéDIAN, Université de Reims, France
– Pr. M. MANFAIT
– T. Happillon
• TRIBVN, France – J. Klossa
• Institut Fresnel, Marseille, France – S. Monneret
– J. Savatier
– P. Bon
• Centre d’Immunologie de Marseille-Luminy, Marseille, France – D. Marguet
– C. Billaudeau
– M.-C. Blache
• PHASICS – L. de Laulanié
– S. Aknoun
– M. Yonnet
Part of the work presented here has been funded through the QuITO Project by the FUI program and the Region PACA
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