Biomedical Sensing Application of Raman Spectroscopy Yukihiro Ozaki Kwansei Gakuin University
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.
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
Study of mouse embryo by Raman spectroscopy Mika Ishigaki, Kosuke Hashimoto, Naoya Ogawa, Kana Morimoto, Yukihiro Ozaki, Hidetoshi Sato Kwansei Gakuin University
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. Ovum <Flowchart of IVF treatment> Sperm World Health Organization www.who.int/topics/infertility/en/ Assessment of embryonic quality blastocyst ovum collection fertilization incubation implantation The pregnancy rate of IVF remain about 30%. Implantation and pregnancy rates after IVF have a relationship with visual inspection.
Assessment of embryonic quality Grading method Grade 1 Grade 2 Grade 3 Grade 4 Grade 5 BL: FR: PR: uniform 0% 30-40% uniform <10% 20-30% non-uniform <10% 10-20% non-uniform 10-50% <10% (BL: blastomere, FR: fragment, PR: pregnancy rate) non-uniform >50% 0% Hum Report 1998; 13: 1003-1013 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.
Evaluation of the metabolism of the embryo Analysis of the culture medium the variation of pyruvic acid and glucose in the culture 1. The relationship between the amount of Pyruvic acid intake and survival potential of human embryo. Conaghan J. et al., J Assist Reprod Genet 1993; 10:21-30 2. The embryo with high grade intakes much glucose. Gardner DK. et al., Fertil Steril 2001; 76: 1175-80 3. The metabolism evaluation through the culture medium by NIR and Raman spectroscopy. Seli, Emre, et al. Fertility and Sterility 88.5 (2007): 1350-1357 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.
Mouse Embryo unfertilized Early mouse embryo pronuclear 2-cell 4-cell 8-cell 0.5 day 1.5 day 2.5 day Embryonic development Jcl:ICR
Microscope Raman system Spectrometer (Nanofinder30; Tokyo instruments Inc.) CO₂ incubator Polychromator + CCD detector (-80 ) Long-pass filter Objective lens + Lens heater CO 2 gas Developed by H.Sato Ti:S Laser (Ex. WL:785nm) Beam expander Water bath (37 ) Stage heater Electrical XY moving stage Excitation laser Raman scattering light
Normalized Intensity 830 855 939 958 1034 1048 1083 1128 1158 1175 1544 1211 1586 1617 1256 1274 1343 1309 Protein Lipid DNA/RNA unfertilized (n=15) Averaged Raman spectra Phe. sym. ring breath 1003 Tyr PO 4 3 sym. Str. PO 2 str. amide III =C-H bend. 1450 CH 2 def. CH 2 def. amide I C=C str. 1659 pronuclear (n=9) 2-celled (n=26) 4-celled (n=21) 8-celled (n=34) 750 1000 1200 1400 1600 1800 Raman shift [cm -1 ] Applied Spectroscopy Reviews, 42: 493-541, 2007
Protein Lipid DNA/RNA Subtraction spectra Secondary structure of protein Protein DNA/RNA Lipid Pro- UF 965 2 cell- UF 4 cell- UF 1463 8 cell- UF 980 1048 1096 750 1000 1200 1400 1600 1800 Raman shift [cm -1 ] DNA concentration increases after the fertilization.
Secondary structure of protein Secondary structure of protein Second derivative spectra UF Pro 2cell 4cell 8cell α-helix β-sheet UF Pro 2cel 4cel 8cel UF Pro 2cell 4cell 8cell 965 938 0,001 0,0005 0 α β (938 965 cm -1 ) 0 UF 1 Pro 2 2-cell 3 4-cell 4 8-cell 5 6 α-helix dominant 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.
Relative intensity Tyrosine doublet (830 cm -1, 850 cm -1 ) UF Pro 2cell 4cell 8cell 2,5 2 1,5 Intensity ratio : R Tyr =I 850 / I 830 UF 4-cell 8-cell 2-cell Pro 1 0,5 0 0 UF 1 Pro 2 2-cell 3 4-cell 4 8-cell 5 6 R tyr : sensitive to UF R Tyr = 2.1 Pro R Tyr = 1.2 2-cell R Tyr = 1.5 4-cell R Tyr = 2.1 8-cell R Tyr = 2.2 OH hydrogen bound OH ionic state strong hydrogen bound acceptor ionic state strong hydrogen bound acceptor Hydrogen bound behaves as drive force to form secondary structure of protein. Tyrosine The number variation of the ratio may relate with the change of the secondary stricture of proteins.
PC2(21.1%) PC2(2.6%) PC2(5.7%) Score plot -10 PCA: Each developmental stages Unfertilized Pronuclear 8-cell 15 10 5 0-10 0 10 20 30-5 PC1(69.4%) 12 8 4 0-40 -20 0-4 20 40 60-8 -12 PC1(97.0%) 2 1,5 1 0,5 0-5 -0,5 0 5 10 15-1 -1,5-2 PC1(85.6%) Loading plot: PC1 lipid 1443 Unfertilized Pronuclear 8-cell Unfertilized 958 837 1304 1086 1272 1660 1741 good no good Pronuclear 8-cell 750 1000 1200 1400 1600 1800 Raman shift [cm -1 ] 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.
Principal Component Analysis (PCA): all stages 837 872 952 1076 1121 1366 Score plot Loading plot of PC1 1266 1301 1438 1653 1741 lipid Morphological feature good no good 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. BMC cell biology 11.1 (2010): 38 Hyperlipidaemic condition reduce embryonic quality. Human reproduction 25.3 (2010): 768-778
PC2(27.2%) 0-4 -2 0 2 4 6 2 1,5 1 0,5 0-0,5-1 -1,5-2 UF Principal Component Analysis (PCA) Score plot 3 2 1-1 -2-3 PC1(52.4%) fertilized Average of PC1 score Pro 2-cell 4-cell 8-cell UF Pro 2-cell 4-cell 8-cell unfertilized PC1 PC2 836 871 α-helix C-C str. 894 939 976 1087 1137 β-sheet C-C str. 1302 1444 1463 1302 1438 1659 1674 1750 750 1000 1200 1400 1600 1800 Raman shift [cm -1 ] 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. lipid
Summary 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
A promising tool for non-invasive, multiplexed measurement of blood constituents KGU-MIT Collaboration
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.
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 Our approach exhibits a 35% reduction in error over partial least squares regression
A Schematic Illustration of the Raman Spectroscopic Measurement Process for in vivo Continuous Glucose Monitoring
Representative Raman Spectrum Acquired from a Human Subject Undergoing OGTT
Plot of Prospective Prediction and Reference Glucose Concentration for a Representative Human Subject
Blood Glucose Predictions of the iconic Model for the Complete Human Subject Dataset
Acknowledgement Prof. Aritake Mizuno (JikeiUniversity) Hidetoshi Sato (Kwansei Gakuin Univ.) Mika Ishigaki (KGU) Kanet Wongravee (Chulalongkorn Univ. 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)
Thank You Very Much!!