Benoît Wattelier, Phasics Paris and Jacques Klossa, TRIBVN ...mitel.dimi.uniud.it/tp2012/presentations/A9-1.pdf · So, we formulated the question in the following way: ... vibrational

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?



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



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)


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


500100015002000250030003500

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P=O

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C-O

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C-O-C

P=O

>C

O-N

H-

O-H

N-H

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P=O

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COO-

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C-O-C

P=O

>C

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H-

O-H

N-HInfrared absorption

1

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(a.u.)

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Amide I

Tyr

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Phe

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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


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.


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

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ing

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of

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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


• 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


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)


• 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


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


• 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….



• 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)



• 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



Drug Cell response

Intracellular follow-up

of the drug


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


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


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


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 11th European Congress on Telepathology and 5th International Congress on Virtual Microscopy, June 8th 2012












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

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Absorbance

Raman spectrum Infrared spectrum

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2/11

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rban

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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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)

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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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













www.phasics.com

[email protected]

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


What is phase ?


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


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…


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


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


High pass filter: Texture detection 10µm

COS7 cell, 100x, NA=1.3


Qua(n)titative Phase: Malaria Parasite Detection

parasite

dust

Phase Intensity

Parasites appear as low phase objects (bright), whereas dust defects disappear


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


See the poster session at the back of this room

13/18

Quantitative image: organites identification


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


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


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


17/18

Thank you

for your attention!

www.phasics.com

[email protected]


http://www.phasics.com/

http://www.phasics.com/

Documents

Benoît Wattelier, Phasics Paris and Jacques Klossa, TRIBVN ...mitel.dimi.uniud.it/tp2012/presentations/A9-1.pdf · So, we formulated the question in the following way: ... vibrational