biomedical sensing application of raman...
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Biomedical Sensing Application of Raman Spectroscopy
Yukihiro Ozaki Kwansei Gakuin University
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Ozaki Group: Molecular Spectroscopy Lab.
• Development of Instruments
ATR-FUV/DUV spectrometer, NIR imaging sytems, High
vacuum and low temperature tip-enhanced Raman
scattering system, SPR sensors in FUV and DUV, NIR
• Developments of Spectral Analysis Chemometrics, Two-dimensional correlation spectroscopy.
• Applications of Spectroscopy
Biomedical, Nanomaterials, Polymers.
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Ozaki Group: Molecular Spectroscopy Lab.
• Far-ultraviolet (FUV) and deep-ultraviolet (DUV) spectroscopy
• Near-infrared (NIR) spectroscopy, NIR imaging
• Raman spectroscopy, Surface-enhanced Raman scattering and tip-enhanced Raman spectroscopy, Raman imaging
• Low-frequency Raman and terahertz spectroscopy
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Mika Ishigaki, Kosuke Hashimoto, Naoya Ogawa, Kana Morimoto, Yukihiro Ozaki, Hidetoshi Sato
Kwansei Gakuin University
Study of mouse embryo by Raman spectroscopy
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Health topics: Infertility
・ Infertility is a global public health problem.
Inability to become pregnant after 1 year of unprotected sexual intercourse. (WHO)
・ Japan: 10% couples are suffering from infertility.
・ In vitro fertilization (IVF) is performed to the couples.
Assessment of embryonic quality
World Health Organization www.who.int/topics/infertility/en/
・ The pregnancy rate of IVF remain about 30%. ・ Implantation and pregnancy rates after IVF have a relationship with visual inspection.
ovum collection
Ovum Sperm
fertilization incubation implantation
<Flowchart of IVF treatment>
blastocyst
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Assessment of embryonic quality
Assessment of embryonic quality: Cleavage rates, morphological feature
• The grading method is actually used in clinical practice of IVF.
• Blastomere morphology is a predictive factor for embryonic quality.
• New evaluation techniques are needed in order to improve the success rate of IVF treatment.
Grade 1 Grade 2 Grade 3 Grade 4 Grade 5
uniform 0% 30-40%
uniform <10% 20-30%
non-uniform <10% 10-20%
non-uniform 10-50% <10%
non-uniform >50% ≈0%
BL:
FR: PR:
【 Grading method 】
(BL: blastomere, FR: fragment, PR: pregnancy rate)
Hum Report 1998; 13: 1003-1013
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Evaluation of the metabolism of the embryo
Analysis of the culture medium the variation of pyruvic acid and glucose in the culture
• We want to assess the embryonic quality more directly, non-destructively, non-invasive
• Raman spectroscopy has the potential to assess the embryonic quality based on the molecular composition.
1. The relationship between the amount of Pyruvic acid intake and survival potential of human embryo.
2. The embryo with high grade intakes much glucose.
3. The metabolism evaluation through the culture medium by NIR and Raman spectroscopy.
Conaghan J. et al., J Assist Reprod Genet 1993; 10:21-30
Gardner DK. et al., Fertil Steril 2001; 76: 1175-80
Seli, Emre, et al. Fertility and Sterility 88.5 (2007): 1350-1357
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Jcl:ICR
Mouse Embryo
2-cell 8-cell unfertilized pronuclear
0.5 day 1.5 day 2.5 day
4-cell
Embryonic development
【 Early mouse embryo 】
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Objective lens +
Lens heater
CO2 gas
Electrical XY moving stage
Water bath (37℃)
Stage heater
Spectrometer (Nanofinder30; Tokyo instruments Inc.)
Polychromator +
CCD detector (-80℃)
Ti:S Laser (Ex. WL:785nm)
Beam expander
Long-pass filter
Excitation laser Raman scattering light
CO₂ incubator
Developed by H.Sato
Microscope Raman system
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No
rmal
ize
d I
nte
nsi
ty
750 1800 1000 1200 1400 1600
Raman shift [cm-1]
unfertilized (n=15)
pronuclear (n=9)
2-celled (n=26)
8-celled (n=34)
4-celled (n=21)
Averaged Raman spectra
830 85
5 939
958
1034
10
48
1083
1128
1158
11
75
1211
1256
1343
1450
1274
1309
1544
1586
16
17
1659
Applied Spectroscopy Reviews, 42: 493-541, 2007
Phe. sym. ring breath 1003
amide III =C-H bend.
CH2def. CH2def.
amide I C=C str.
Tyr
Protein Lipid
DNA/RNA
PO43⁻ sym. Str.
PO2⁻str.
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Subtraction spectra
750 1800 1000 1200 1400 1600
Raman shift [cm-1]
980 1048
1096
965
1463
Pro- UF
2 cell- UF
4 cell- UF
8 cell- UF
Secondary structure of protein Protein Lipid
DNA/RNA Protein Lipid DNA/RNA
DNA concentration increases after the fertilization.
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UF Pro 2cell 4cell 8cell
UF Pro 2cell 4cell 8cell
α-helix β-sheet
938
965
Second derivative spectra
α-helix dominant
α ̸ β (938 ̸ 965 cm-1)
0
0,0005
0,001
0 1 2 3 4 5 6 UF Pro 2-cell 4-cell 8-cell
Secondary structure of protein
UF Pro 2cell 4cell 8cell
Secondary structure of protein
・ The ration (α ̸ β) increases as the embryonic development. ・ Secondary structure of protein becomes α-helix dominant from β-sheet. ・ Raman spectroscopy can monitor the secondary structural changes of protein with the embryonic development.
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Tyrosine doublet (830 cm-1, 850 cm-1)
0
0,5
1
1,5
2
2,5
0 1 2 3 4 5 6 UF Pro 2-cell 4-cell 8-cell
UF
2-cell
4-cell 8-cell
Pro
UF Pro 2cell 4cell 8cell
Intensity ratio : RTyr=I850 / I830
Hydrogen bound behaves as drive force to form secondary structure of protein.
Rel
ati
ve in
ten
sity
Tyrosine ・ UF RTyr= 2.1 ・ Pro RTyr= 1.2 ・ 2-cell RTyr= 1.5 ・ 4-cell RTyr= 2.1 ・ 8-cell RTyr= 2.2
strong hydrogen bound acceptor
ionic state
strong hydrogen bound acceptor
Rtyr : sensitive to
OH hydrogen bound OH ionic state
The number variation of the ratio may relate with the change of the secondary stricture of proteins.
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750 1800 1000 1200 1400 1600
Raman shift [cm-1]
Unfertilized
Pronuclear
8-cell
Unfertilized Pronuclear 8-cell
-10
-5
0
5
10
15
-10 0 10 20 30
PC1(69.4%)
PC
2(21
.1%
)
-12
-8
-4
0
4
8
12
-40 -20 0 20 40 60
PC1(97.0%)
PC
2(2.
6%)
-2
-1,5
-1
-0,5
0
0,5
1
1,5
2
-5 0 5 10 15
PC1(85.6%)
PC
2(5.
7%)
Score plot
Loading plot: PC1 lipid
good
no good
PCA: Each developmental stages
・ PCAs classify the dataset of each developmental stages into two groups by lipid component. ・ The concentration of lipid components of mouse embryo with not good morphological feature is high.
Unfertilized Pronuclear 8-cell
958
1086 1272
1304
1443
1660
1741 837
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Score plot Loading plot of PC1
837
872
952
1076
1121
1266 1301
1366
1438
1653
1741
lipid
・ PCA classifies the dataset into two groups by lipids. ・ The concentration of lipid components of mouse embryo with not good morphological feature is high. ・ The differences relating to the morphological features are bigger than the ones between developmental stages. ・ Lipid are considered to be energy source. ・ Hyperlipidaemic condition reduce embryonic quality. Human reproduction 25.3 (2010): 768-778
BMC cell biology 11.1 (2010): 38
Principal Component Analysis (PCA): all stages
good
no good
【 Morphological feature 】
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-3
-2
-1
0
1
2
3
-4 -2 0 2 4 6
UF
Pro
2-cell
4-cell
8-cell
PC1(52.4%)
PC
2(2
7.2
%)
Score plot
Principal Component Analysis (PCA)
・ PC1: lipid component changes in a cyclic way ・ PC-2 classify all stage data into two groups: unfertilized and fertilized
・ In the loading plot of PC-2:
939 cm-1: α-helix 976 cm-1: β-sheet
The secondary structure of protein is changed from β-sheet to α-helix after the fertilization.
fertilized
unfertilized
750 1800 1000 1200 1400 1600
Raman shift [cm-1]
PC1
PC2 836
894
1137
1302 1438
1463 1674
871
1087
1302
1444
1659
1750
lipid
939
β-sheet C-C str.
976
C-C str. α-helix
-2
-1,5
-1
-0,5
0
0,5
1
1,5
2
UF
Pro
2-cell 4-cell
8-cell
Average of PC1 score
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Analysis of mouse embryo on early stages by Raman spectroscopy
(unfertilized・pronuclear・ 2-cell・ 4-cell・ 8-cell)
The variation of inner material with the embryonic development was detected.
Morphological features (good morphology or not) relates with the lipid concentration.
PCA result show the secondary structural changes of protein:
from β-sheet to α-helix
The structural changes of protein is also indicated by the change of tyrosine doublet ratio.
Raman spectroscopy has the potential for the in-situ monitoring of the embryonic development and to assess the embryonic quality.
Acknowledgement: MEXT KAKENHI Grant Number 25560212
Summary
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A promising tool for non-invasive, multiplexed measurement of blood constituents
KGU-MIT Collaboration
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We propose a novel analytical framework
• Vibrational spectroscopy has emerged as a promising tool for non-invasive, multiplexed measurement of blood constituents – an outstanding problem in biophotonics. Here, we propose a novel analytical framework that enables spectroscopy-based longitudinal tracking of chemical concentration without necessitating extensive a priori conocentration information. The principal idea is to employ a concentration space transformation acquired from the spectral information, where these estimates are used together with the concentration profiles generated from the system kinetic model.
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Our approach exhibits a 35% reduction in error over partial least squares regression
• Using blood glucose monitoring by Raman spectroscopy as an illustrative example, we demonstrate the efficacy of the proposed approach as compared to conventional calibration methods. Specifically, our approach exhibits a 35% reduction in error over partial least squares regression when applied to a detaset acquired from human subjects undergoing glucose tolerance tests. This method offers a new route at screening gestational diabets and opens doors for continuous process monitoring without sample perturbation at intermediate time
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A Schematic Illustration of the Raman Spectroscopic Measurement Process for in vivo
Continuous Glucose Monitoring
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Representative Raman Spectrum Acquired from a Human Subject Undergoing OGTT
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Plot of Prospective Prediction and Reference Glucose Concentration for a Representative
Human Subject
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Blood Glucose Predictions of the iCONIC Model for the Complete Human Subject Dataset
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Acknowledgement
Hidetoshi Sato (Kwansei Gakuin Univ.)
Mika Ishigaki (KGU)
Nicolas Spegazzini (KGU-MIT)
・ Dr. Yasuhiro Maeda (Riken Center, Japan) ・ Dr. Ryu Ishihara (Osaka Medical Center, Japan) ・ Dr. Bibin B. Andriana (KGU) ・ Akinori Taketani (KGU) ・ Kosuke Hashimoto (KGU) ・ Naoya Ogawa (KGU)
Prof. Aritake Mizuno (JikeiUniversity)
Kanet Wongravee (Chulalongkorn
Univ. KGU)
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Thank You Very Much!!