Monitoraggio e analisi di segnali e dati

nell’ingegneria tissutale Anna M. Bianchi, Sara Mariani

XXXII Scuola Annuale di Bioingegneria GNB

Approccio integrato per la medicina

rigenerativa

Anna M. Bianchi

Introduction

1. Regenerative medicine is starting its way to large-scale applications

from advanced research towards the market and towards diffusion at

clinical level.

2. From a biological and clinical point of view the procedures and the

strategies are well known and defined. Many companies are able to

produce engineered tissues: skin, bone, cartilage, among the others.

3. Although there is a need for continuing research into the

foundamentals biology there is also need for an emphasis on the

engineering and manufacturing issue

4. Need of technologies and procedures able to garantee a standards in

the products and in the production processes

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Advanced research

topic

Industrial

production

Large scale clinical

practice

• Process design

• Monitoring

• Control

• Methodology

• Instrumentation

GMP: Good Manufacturing Practice

• Traceability

• Reproducibility

• Documentation

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Regenerative medicine – Tissue engineering

Anna M. Bianchi

The lesson focuses on monitoring aspects

• Review of monitoring needs and methodologies

• Cell coltures

• Scafforld

• Bioreactors

• Tissues

• Raman spectroscopy as a promising technique

• Processing of Raman spectra

• Denoising

• Feature extraction

• Classification

• Some applications

Summary 5

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Regenerative medicine – Tissue engineering 6

cells

cell culture

scaffolds

tissue production

bioreactors

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cells

cell culture

scaffolds

tissue production

bioreactors

• culture progress

• cell differentiation

• quantity and

potency

• culture environment • number

• viability

• quality

• fabrication

• raw material

• reaction formation

• final properties

• culture conditions

• tissue growth

• Scaffold

degradation

• tissue

Regenerative medicine – Tissue engineering

Anna M. Bianchi

Many different processes

• Cellular cultures

• Scaffold production

• Cultures inside bioreactors

• Preservation and release

Monitored parameters

• Biological

• Physical

• Chemical

• Environmental

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Measurements for cell cultures:

Cell biology 9

Multiphoton

Excitation

Microscopy

Second Harmonic

Imaging

Raman

Spectroscopy

-Best noninvasive means

of fluorescence

microscopy in tissue

-Set to be more widely

used as instrumentation

becomes more affordable

-Provides additional

information to MPM,

allowing noninvasive,

spatially localized, in vivo

characterization of

subcellular properties Dyes for biological second harmonic generation imaging

J. E. Reeve, H. L. Anderson, K. Clays, 2010

-Noninvasive

-The practicalities of

incorporation into culture

environment are still to be

explored

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Measurements for cell cultures:

Physical properties of cells 10

Nuclear magnetic

resonance

microscopy

Near-field scanning

probe optical

microscopy

-Good contrast of cellular

material at high spatial

resolution, chemical

specificity and details of

structure and function

-Complex infrastructure

for generating the

magnetic fields needed

Atomic force

microscopy

-The potential of these

techniques depends on

practical factors such as

the engineering of high-

quality probes and

strategies to incorporate

them in the cell-culture

environment

Anna M. Bianchi

Measurements for cell cultures:

Physical and chemical properties of culture environment 11

Fiber optic sensors

-Low cost, easily

integrated in culture

environment

Atomic force

microscopy

-Low cost, fast

prototyping, multiple

measurements, on-chip

power sources and can

be readily integrated into

culture environments

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Measurements for scaffolds:

Physical and chemical properties 12

Scanning Electron

Microscopy

-Need for sample

preparation, limited

penetration depth, need

for large infrastructure

X-ray Computed

Tomography

-Noninvasive, no sample

preparation need, high

commercial availability of

x-ray CT systems

-Contrast may limit

discrimination of biological

material

Nuclear Magnetic

Resonance

microscopy

-Useful in the

characterization of the

scaffold structure and

functional properties

-Difficulties in handling

image artifacts

Anna M. Bianchi

Measurements for bioreactors:

Tissue biology 13

Multiphoton

microscopy

-Capable of probing

subsurface detail with low

risk of photobleaching and

phototoxicity

Confocal FRET

microscopy

-Promising thanks to the

advance in fluorescent

probe formulations and

instrumentation.

-Study of surface and

near subsurface sample

properties

Magnetic

Resonance

Imaging

-Investigation of tissues to

greater depths

-Can employ contrast

agents for improved

imaging of biological

function and molecular

dynamics

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Measurements for bioreactors:

Physical properties of tissues 14

Ultrasound

biomicroscopy

-Ability to image optically

thick samples with

submillimetre spatial

resolution without the use

of ionizing radiation or

strong magnetic fields

-Can be engineered into

culture environment

-A developing technology

-Commercial units are

likely to be available

shortly

Acoustic

techniques

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Measurements for bioreactors:

Physical and chemical properties of culture environment 15

-Low cost, easily

integrated in culture

environment

Fiber optic sensors

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Bioreactor 16

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Recent literature 17

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Raman signature 19

• The indian physicist Chandrasekhara Venkata Raman, discovered and

described this light scattering phenomenon in 1928 and was awarded the

Nobel price in 1930

• Molecular sensitivity (Raman signature)

• Used for characterizing and identifying chemical components

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Raman spectroscopy: physical principles 20

Biotechnol. J. 2013, 8, 288–297

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Biomedical applications 21

• Raman Spectroscopy has

made its way from physics and

chemistry to biomedicine

within the past two decades.

• First studies on living cells by

Puppels in 1990

• It can be employed as a non-

invasive, non-destructive, and

even non-contact monitoring

technology in numerous

biomedical fields

Biotechnol. J. 2013, 8, 288–297

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Raman Spectrometer 22

Expert. Rev. Med. Devices 3(2), (2006)

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Raman signature of typical cellular

molecules 23

Expert. Rev. Med. Devices 3(2), (2006)

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Spectral peak assignment 24

Expert. Rev. Med. Devices 3(2), (2006)

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The main drawback of Raman spectroscopy is the low efficiency of Raman

scattering; approximately only 1 in 107 - 108 will be scattered inelastically

Resonant Raman Scattering. Some molecules in cells (carotenoids, clorophyll,

hemoglobin, ..) produce enhanced Raman signal when specific wavelenghts

are used. If the wavelength corresponds to absorbtion band the Raman effect

may be amplified by a factor 102-104. Highly specific effect allow better

rejection of background noise.

Surface-enhanced Raman spectroscopy (SERS). Enhancements of 107 or

even higher (1014 -1015) are achieved when molecules are adsorbed on rough

metallic substrate (silver or gold, electromanetic enhancement)

Coherent anti-Stokes Raman Scattering (CARS). Interaction of two laser

beams, wp and ws (Stokes) generates an excitation frequency (wp – ws).

When it corresponds to vibrational frequency in the sample the CARS signal

is strongly enhanced.

Problem 25

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Pro

• Low cost

• Simple instrumentation

• Not-invasive, not-distructive, contacless

• No need of reagents or markers

• Different application levels from subcellular structures

to tissues

• Large field of applications

Con

• Low SRN due to background noise

• Peak detection

• Peack interpretation and classification

Solutions

• Hardware

• Signal processing

Pros and Cons of Raman spectroscopy 26

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• Intrinsic autofluorescence: a few orders of magnitude higher than the

Raman peacks of interest

• Culture substrate, scaffold, etc. generate background Raman

response

Shifted excitation

• By continuously changing the excitation wavelength, it is possible to continuously shift

the Raman peak, whthe the background fluorescence remains constant and can be

estimated through different methods (hardware modifications)

Fourier transform filtering

• Non automated band-limit selection, time consuming

Backgound subtraction through polynomial interpolation

• Simple and faster

• Widely used for biological applications

Spectra denoising: autofluorescence and

background subtraction 27

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Baseline subtraction (1) 28

•Polynomial order 4 – 6

•20 - 200 iterations

•Improved algorithms

Zhao J., Lui H., Mc Lean D.I., Zeng H. Appl Spectrosc. 2007;61(11): 1225-1232.

Lieber C.A., Mahadevan-Janse A. Appl Spectrosc. 2003;57(11): 1363-1367.

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Baseline subtraction (2) 29

Interferent spetrum from a known

source can be measured in isolation

to obtain its reference spectrum

Beier B.D., Berger A.J. Method for automated background subtraction form Raman spectra containing

known contaminants. Analyst. 2009;134(6): 1198-1202.

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Spectral component extraction: Classical

Least Squares (CLS) 30

• Background spectrum estimated by repeated

measures

• Polynomial for background fluorescence

• wk -> concentration of compound k

• Detection of low amplitude peaks

• Separation of overlapping peacks

• Background subtraction

A priori knowledge of the spectra of all

the compunds in the mixture and an

accurate estimation of the background

spectrum

Lutz B.R., Dentinger C.E., Nguyen L.N., Sun L., Zhang J., et al. Spectral analysis of multiplex Raman probe signature. ACS

Nano. 2008;2(11): 2306-2314.

Anna M. Bianchi

Spectral component extraction: Principal

Component Analysis (PCA) 31

Mixture matrix (measures)

Mixing matrix

(estimated)

Source matrix

(estimated)

𝑥1 𝜐 = 𝑎11𝑠1 𝜐 + 𝑎12𝑠2 𝜐 + ⋯+ 𝑎1𝑛𝑠𝑛 𝜐 𝑥2 𝜐 = 𝑎21𝑠1 𝜐 + 𝑎22𝑠2 𝜐 + ⋯+ 𝑎2𝑛𝑠𝑛 𝜐

Two or three components are usualy sufficient for spectra characterization

Need of multiple measures

Independent components not necessarily correspond to specific compounds

Anna M. Bianchi

32 Spectral component extraction: Hybrid Least Squares

and Principal Component Analysis (HLP)

The model Multiple recordings for spatial

variability in background and in

the signal of interest

Need of prior characterization experiments Van de Sompel et al., PLoS One. 2012;7(6): e38850.

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MR spectroscopy: chemical shift 33

The same proton (1H) can be in different chemical

configurations

– Locally the magnetic field is different

– The Larmor frequency is locally different

• The local variation of the resonance frequency is

called chemical shift

• Causes of chemical shift

– Electronic Shielding/deshielding

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Spectral component extraction: curve fitting Mainardi, Cerutti, 2002

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• Mixture of Lorentzian (L) and Gaussian (G) curves

• Polynomial accounting for baseline

• White noise

• Possibility of using a priori knowledge (some known parameters)

• Problem solved through the classical non-linear lest square solution (NLLS)

• Single peaks need to be combined for the compuonds of interest

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Features extraction and data analysis 35

• Compound concentrations

• Component weights

• Peak areas

• Peak heights

• PCA scores

• ...

• Statistical analysis

• Comparison in time and time trends

• Spatial comparison and spatial maps

• Cluster analysis

• Classification

• ....

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Component classification (1) 36

Owen et al., J Mater Sci: Mater Med (2006) 17:1019–1023

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Component classification (2) 37

Expert. Rev. Med. Devices 3(2), (2006)

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Raman maps: Formalin-fixed lung fibroplast 38

Photomicrograph Raman image

after

segmentation by

cluster analysis

Raman spectra representing the nucleus (1

red cluster), the cytoplasm (2 cyan cluster),

lipid vesicles (3 green cluster)

532 nm laser

120 x 150 pixels

Acquisition time: 0.2 s per pixel

Downes A., Elfick A., Raman Spectroscopy and related techniques in

biomedicine, Sensors (2010), 10: 1871-1889.

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Raman maps: In vitro living cancer cells 39

Unstrained, unlabelled, living

HeLa Cells

Light intensity 3.3 mW/mm^2

78 lines of exposure

Exposure time 5 s per line

78 x 281 pixels

Downes A., Elfick A., Raman Spectroscopy and related techniques in

biomedicine, Sensors (2010), 10: 1871-1889.

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Example 1 40

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Example 2 41

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Skin examinations

Associated with endoscopy

In vivo applications 42 Downes A., Elfick A., Raman Spectroscopy and related

techniques in biomedicine, Sensors (2010), 10: 1871-1889.

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Applications of Raman Spectroscopy in

Tissue Engineering 43

Tissue Engineering field Application example

Material Hydrogel degradation

Apatite formation

Bioactive glass structure

Cell behaviour Viability

Cell cycle

Differentiation

Phagocitosys

Tissue characterization Bone

Cartilage

Skin

Brain

Spleen

Lung

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• Raman spectroscopy a promising tool for the development of

monitoring in tissue engineering application

• Non invasive, non destructive, non contact, non contaminating

• Large field of applications

• Possible use of optical fibers

• Rapid «live» screening of different processes

• Need of technological development

• Need of better understanding and interpretation of the complex

biological spectra

• Need of interdisciplinary interaction

Conclusion (1) 44

Anna M. Bianchi

• Integrated and multidisciplinary approach for advances in

research

• Integrated and multidisciplinary approach for large scale

applications:

• Industrial production

• Clinical practice

• The required technology already exists (??). However it needs to

be finalized to the specific application.

Conclusion (2) 45