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라만 기반 치매 모델의 뇌조직 분광 특성 측정

Ex Vivo Raman Spectroscopy Measurement of a Mouse Model of Alzheimer's Disease

  • 고관휘 (연세대학교 공과대학 전기전자공학부) ;
  • 서영희 (연세대학교 의과대학 신경외과학교실) ;
  • 임성민 (연세대학교 공과대학 전기전자공학부) ;
  • 이홍기 (연세대학교 공과대학 전기전자공학부) ;
  • 박지영 (연세대학교 의과대학 신경외과학교실) ;
  • 장원석 (연세대학교 의과대학 신경외과학교실) ;
  • 김동현 (연세대학교 공과대학 전기전자공학부)
  • Ko, Kwanhwi (School of Electrical and Electronic Engineering, College of Engineering, Yonsei University) ;
  • Seo, Younghee (Department of Neurosurgery, Brain Research Institute, College of Medicine, Yonsei University) ;
  • Im, Seongmin (School of Electrical and Electronic Engineering, College of Engineering, Yonsei University) ;
  • Lee, Hongki (School of Electrical and Electronic Engineering, College of Engineering, Yonsei University) ;
  • Park, Ji Young (Department of Neurosurgery, Brain Research Institute, College of Medicine, Yonsei University) ;
  • Chang, Won Seok (Department of Neurosurgery, Brain Research Institute, College of Medicine, Yonsei University) ;
  • Kim, Donghyun (School of Electrical and Electronic Engineering, College of Engineering, Yonsei University)
  • 투고 : 2022.10.06
  • 심사 : 2022.10.21
  • 발행 : 2022.12.25

초록

비탄성적 산란에 의한 빛의 방출 현상을 이용한 라만 분광법 기술은 무표지 방식으로 분자를 식별하는 기술로 바이오 의학 및 재료 산업에 이르기까지 다양한 분야에서 연구되고 있다. 광프로브 기반 라만 분광기는 국소 부위의 화학 분석을 최소 침습 방식으로 측정할 수 있어 수술 중 실시간 진단 기술로 적용할 수 있는 가능성을 내포하고 있다. 본 연구에서는 화학 물질의 농도별 변화에 따른 라만 신호의 변화를 살펴보아 라만 실험 장치의 캘리브레이션을 진행하였으며, 정상 쥐와 아밀로이드 베타 플라크가 축적된 5xFAD 치매성 돌연변이 모델의 대뇌 조직을 대상으로 라만 신호 특성을 측정 및 비교 분석하여 알츠하이머씨 병의 진단을 위한 가능성을 탐구하였다. 또한 대표적인 치매 원인 물질인 아밀로이드 베타에 대한 라만 신호 측정을 병행하여 치매 진단에 대한 적용을 교차 검증하였다. 본 라만 분광법 연구를 통해 치매 진단에 있어 기존문진 검사 및 뇌 영상 진단을 대체하여 정밀하게 판별할 수 있는 하나의 진단 지표로서의 가능성을 보았으며, 추후 뇌신경계뿐만 아니라 인체의 다양한 장기 및 질병에 적용시켜 물리·공학·화학 등 다양한 연구분야에서의 원천기술로 활용될 수 있을 것으로 생각된다.

Raman spectroscopy is an optical technique that can identify molecules in a label-free manner, and is therefore heavily investigated in various areas ranging from biomedical engineering to materials science. Probe-based Raman spectroscopy can perform minimally invasive chemical analysis, and thus has potential as a real-time diagnostic tool during surgery. In this study, Raman experimentation was calibrated by examining the Raman shifts with respect to the concentrations of chemical substances. Raman signal characteristics, targeted for normal mice and cerebral tissues of the 5xFAD dementia mutant model with accumulated amyloid beta plaques, were measured and analyzed to explore the possibility of diagnosis of Alzheimer's disease. The application to the diagnosis of dementia was cross-validated by measuring Raman signals of amyloid beta. The results suggest the potential of Raman spectroscopy as a diagnostic tool that may be useful in various areas of application.

키워드

과제정보

산업통상자원부 한국산업기술진흥원 산업기술혁신사업-국제공동기술개발사업(P048000064); 한국연구재단 기초연구실육성사업(2022R1A4A2000748); 범부처전주기의료기기연구개발사업단 범부처전주기의료기기연구개발사업(RS-2020-KD000088, RS-2020-KD000103).

참고문헌

  1. M. Okada, N. I. Smith, A. F. Palonpon, H. Endo, S. Kawata, M. Sodeoka, and K. Fujita, "Label-free Raman observation of cytochrome c dynamics during apoptosis," Proc. Natl. Acad. Sci. 109, 28-32 (2012). https://doi.org/10.1073/pnas.1107524108
  2. H. Lee, K. Kang, K. Mochizuki, C. Lee, K.-A. Toh, S. A. Lee, K. Fujita, and D. Kim, "Surface plasmon localization-based super-resolved Raman microscopy," Nano Lett. 20, 8951-8958 (2020). https://doi.org/10.1021/acs.nanolett.0c04219
  3. H. Lee, H. Yoo, G. Moon, K.-A. Toh, K. Mochizuki, K. Fujita, and D. Kim, "Super-resolved Raman microscopy using random structured light illumination: concept and feasibility," J. Chem. Phys. 155, 144202 (2021). https://doi.org/10.1063/5.0064082
  4. M. Paraskevaidi, C. L. M. Morais, D. E. Halliwell, D. M. A. Mann, D. Allsop, P. L. Martin-Hirsch, and F. L. Martin, "Raman spectroscopy to diagnose Alzheimer's disease and dementia with lewy bodies in blood," ACS Chem. Neurosci. 9, 2786-2794 (2018). https://doi.org/10.1021/acschemneuro.8b00198
  5. R. Michael, C. Otto, A. Lenferink, E. Gelpi, G. A. Montenegro, J. Rosandic, F. Tresserra, R. I. Barraquer, and G. F. Vrensen, "Absence of amyloid-beta in lenses of Alzheimer patients: a confocal Raman microspectroscopic study," Exp. Eye Res. 119, 44-53 (2014). https://doi.org/10.1016/j.exer.2013.11.016
  6. N. M. Ralbovsky, L. Halamkova, K. Wall, C. Anderson-Hanley, and I. K. Lednev, "Screening for Alzheimer's disease using saliva: a new approach based on machine learning and Raman hyperspectroscopy," J. Alzheimer's Dis. 71, 1351-1359 (2019). https://doi.org/10.3233/jad-190675
  7. E. Ryzhikova, N. M. Ralbovsky, V. Sikirzhytski, O. Kazakov, L. Halamkova, J. Quinn, E. A. Zimmerman, and I. K. Lednev, "Raman spectroscopy and machine learning for biomedical applications: Alzheimer's disease diagnosis based on the analysis of cerebrospinal fluid," Spectrochim. Acta A: Mol. Biomol. Spectrosc. 248, 119188 (2021). https://doi.org/10.1016/j.saa.2020.119188
  8. C. Carlomagno, M. Cabinio, S. Picciolini, A. Gualerzi, F. Baglio, and M. Bedoni, "SERS-based biosensor for Alzheimer disease evaluation through the fast analysis of human serum," J. Biophotonics 13, e201960033 (2020).
  9. R. Prucek, A. Panacek, Z. Gajdova, R. Vecerova, L. Kvitek, J. Gallo, and M. Kolar, "Specific detection of Staphylococcus aureus infection and marker for Alzheimer disease by surface enhanced Raman spectroscopy using silver and gold nanoparticle-coated magnetic polystyrene beads," Sci. Rep. 11, 6240 (2021). https://doi.org/10.1038/s41598-021-84793-7
  10. K. A. Willets and R. P. Van Duyne, "Localized surface plasmon resonance spectroscopy and sensing," Annu. Rev. Phys. Chem. 58, 267-297 (2007). https://doi.org/10.1146/annurev.physchem.58.032806.104607
  11. K. Kim, J.-W. Choi, K. Ma, R. Lee, K.-H. Yoo, C.-O. Yun, and D. Kim, "Nanoislands-based random activation of fluorescence for visualizing endocytotic internalization of adenovirus," Small 6, 1293-1299 (2010). https://doi.org/10.1002/smll.201000058
  12. K. Kim, J. Yajima, Y. Oh, W. Lee, S. Oowada, T. Nishizaka, and D. Kim, "Nanoscale localization sampling based on nanoantenna arrays for super-resolution imaging of fluorescent monomers on sliding microtubules," Small 8, 892-900 (2012). https://doi.org/10.1002/smll.201101840
  13. W. Lee, Y. Kinosita, Y. Oh, N. Mikami, H. Yang, M. Miyata, T. Nishizaka, and D. Kim, "Three-dimensional superlocalization imaging of gliding Mycoplasma mobile by extraordinary light transmission through arrayed nanoholes," ACS Nano 9, 10896-10908 (2015). https://doi.org/10.1021/acsnano.5b03934
  14. K. Kim, W. Lee, K. Chung, H. Lee, T. Son, Y. Oh, Y.-F. Xiao, D. H. Kim, and D. Kim, "Molecular overlap with optical nearfields based on plasmonic nanolithography for ultrasensitive label-free detection by light-matter colocalization," Biosens. Bioelectron. 96, 89-98 (2017). https://doi.org/10.1016/j.bios.2017.04.046
  15. T. Son, D. Lee, C. Lee, G. Moon, G. E. Ha, H. Lee, H. Kwak, E. Cheong, and D. Kim, "Superlocalized three-dimensional live imaging of mitochondrial dynamics in neurons using plasmonic nanohole arrays," ACS Nano 13, 3063-3074 (2019). https://doi.org/10.1021/acsnano.8b08178
  16. H. Lee, W. J. Rhee, G. Moon, S. Im, T. Son, J.-S. Shin, and D. Kim, "Plasmon-enhanced fluorescence correlation spectroscopy for super-localized detection of nanoscale subcellular dynamics," Biosens. Bioelectron. 184, 113219 (2021). https://doi.org/10.1016/j.bios.2021.113219
  17. M. Kirsch, G. Schackert, R. Salzer, and C. Krafft, "Raman spectroscopic imaging for in vivo detection of cerebral brain metastases," Anal. Bioanal. Chem. 398, 1707-1713 (2010). https://doi.org/10.1007/s00216-010-4116-7
  18. M. Jermyn, K. Mok, J. Mercier, J. Desroches, J. Pichette, K. Saint-Arnaud, L. Bernstein, M. C. Guiot, K. Petrecca, and F. Leblond, "Intraoperative brain cancer detection with Raman spectroscopy in humans," Sci. Transl. Med. 7, 274ra19 (2015). https://doi.org/10.1126/scitranslmed.aaa2384
  19. K. Hrubesova, M. Fouskova, L. Habartova, Z. Fisar, R. Jirak, J. Raboch, and V. Setnicka, "Search for biomarkers of Alzheimer's disease: Recent insights, current challenges and future prospects," Clin. Biochem. 72, 39-51 (2019). https://doi.org/10.1016/j.clinbiochem.2019.04.002
  20. W. G. Tharp and I. N. Sarkar, "Origins of amyloid-β," BMC Genom. 14, 290 (2013). https://doi.org/10.1186/1471-2164-14-290
  21. S. Sadigh-Eteghad, B. Sabermarouf, A. Majdi, M. Talebi, M. Farhoudi, and J. Mahmoudi, "Amyloid-beta: a crucial factor in Alzheimer's disease," Med. Princ. Pract. 24, 1-10 (2015).
  22. C. Haass and D. J. Selkoe, "Soluble protein oligomers in neurodegeneration: lessons from the Alzheimer's amyloid betapeptide," Nat. Rev. Mol. Cell Biol. 8, 101-112 (2007). https://doi.org/10.1038/nrm2101
  23. F. Panza, M. Lozupone, G. Logroscino, and B. P. Imbimbo, "A critical appraisal of amyloid-β-targeting therapies for Alzheimer disease," Nat. Rev. Neurol. 15, 73-88 (2019). https://doi.org/10.1038/s41582-018-0116-6
  24. C. S. Garcia, M. N. Abedin, S. K. Sharm, A. K. Misra, S. Ismail, U. Singh, T. F. Refaat, H. E. Ali, and S. Sandford, "Remote pulsed laser Raman spectroscopy system for detecting water, ice, and hydrous minerals," Proc. SPIE 6302, 630215 (2006).
  25. M. J. Egan, S. K. Sharma, and T. E. Acosta-Maeda, "Modified spatial heterodyne Raman spectrometer for remote-sensing analysis of organics," Proc. SPIE 10779, 107790L (2018).
  26. K. Maquelin, C. Kirschner, L. P. Choo-Smith, N. van den Braak, H. P. Endtz, D. Naumann, and G. J. Puppels, "Identification of medically relevant microorganisms by vibrational spectroscopy," J. Microbiol. Methods 51, 255-271 (2002). https://doi.org/10.1016/S0167-7012(02)00127-6
  27. E. Staniszewska-Slezak, K. Malek, and M. Baranska, "Complementary analysis of tissue homogenates composition obtained by Vis and NIR laser excitations and Raman spectroscopy," Spectrochim. Acta A: Mol. Biomol. Spectrosc. 147, 245-256 (2015). https://doi.org/10.1016/j.saa.2015.03.086
  28. B. Lochocki, B. D. C. Boon, S. R. Verheul, L. Zada, J. J. M. Hoozemans, F. Ariese, and J. F. de Boer, "Multimodal, label-free fluorescence and Raman imaging of amyloid deposits in snap-frozen Alzheimer's disease human brain tissue," Commun. Biol. 4, 474 (2021). https://doi.org/10.1038/s42003-021-01981-x
  29. G. Moon, J. Lee, H. Lee, H. Yoo, K. Ko, S. Im, and D. Kim, "Machine learning and its applications for plasmonics in biology," Cell Rep. Phys. Sci. 3, 101042 (2022). https://doi.org/10.1016/j.xcrp.2022.101042