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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
+
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
Anna M. Bianchi
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
Anna M. Bianchi
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
Anna M. Bianchi
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
Anna M. Bianchi
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
Anna M. Bianchi
Raman spectroscopy: physical principles 20
Biotechnol. J. 2013, 8, 288–297
Anna M. Bianchi
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
Anna M. Bianchi
Raman Spectrometer 22
Expert. Rev. Med. Devices 3(2), (2006)
Anna M. Bianchi
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)
Anna M. Bianchi
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
Anna M. Bianchi
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
Anna M. Bianchi
• 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
Anna M. Bianchi
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.
Anna M. Bianchi
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.
Anna M. Bianchi
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.
Anna M. Bianchi
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
Anna M. Bianchi
Spectral component extraction: curve fitting Mainardi, Cerutti, 2002
34
• 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
Anna M. Bianchi
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
• ....
Anna M. Bianchi
Component classification (1) 36
Owen et al., J Mater Sci: Mater Med (2006) 17:1019–1023
Anna M. Bianchi
Component classification (2) 37
Expert. Rev. Med. Devices 3(2), (2006)
Anna M. Bianchi
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.
Anna M. Bianchi
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.
Anna M. Bianchi
Example 1 40
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Example 2 41
Anna M. Bianchi
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.
Anna M. Bianchi
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
Anna M. Bianchi
• 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