A Review on Integrated ZnO-Based SERS Biosensors and Their Potential in Detecting Biomarkers of Neurodegenerative Diseases
Abstract
:1. Introduction
2. Conventional Approaches for ND Diagnosis
3. Raman and SERS-Spectroscopy-Based Monitoring of Main ND Biomarkers
- Closely monitoring signal fluctuations related to the accumulation/aggregation of specific proteins: cellular prion protein (PrPC)—a cell surface glycol protein attached to the plasma membrane—levels from cells [78], Aβ peptides, αSyn, tau protein, etc.;
4. SERS Biosensors Developed for Proteinopathies
Relevant Biomarkers Detected in SERS-Based Diagnosis of NDs
ND | Protein Targeted for Diagnosis (Biomarker) | Raman/SERS Approach | SERS Platform Used for Detection |
---|---|---|---|
Prion disease | Misfolding of cellular prion protein (PrPC) into pathogenic form (PrPSc) and PrPC aggregation β-Sheets structure more present than α-helical structure in PrP variants | Raman—Monitoring the downshift of the amide II band from 1450 to 1555 cm−1 and the decrease in intensity for the amide III band during D2O solvent exposure related to concentration of S-fibrils [127] Analyzing the presence of the 1670 cm−1 band (amide I region) as an indicator of β-sheets structure, thus PrPSc formation, with 100% accuracy [78] | Direct deep UV resonance Raman analysis of pure S-fibrils and R-fibrils samples in mixtures with the D2O Direct Raman analysis of membranous blood pellet from scrapie-infected sheep (PrPSc was found to be located in blood fraction of lymphocytes) |
Changes in Cu (II) coordination inducing the formation of Cu (II)(His)4 complexes in weakly acidic pH → formation of PrPSc alters Cu(II) binding | SERS—Monitoring the free Cu(II) and Cu(II) bound histidine (Raman bands intensities I1577 vs I1603) [84] Monitoring the PrPC–Cu(II) coordination by quantifying the dependence between Cu(II)-binding and copper concentration [84,98] | SERS analysis of cell membranes: culturing the cells onto AuNP-film-based substrates (20–70 nm thick) self-assembled on glass from spherical NPs | |
Parkinson’s disease | α-Synuclein Decrease of dopamine levels during disease progression | Raman—Monitoring the heterogeneity of α-synuclein and conformational changes during fibrillation [85] SERS—Direct dopamine monitoring from CSF samples | 300 µM concentration of protein required for thorough analysis Femtomolar levels of dopamine when using functionalized AgNPs |
Huntington’s disease | Expression of the toxic mutant Hungtingtin (Htt) protein Peripheral fibroblast modifications caused by this pathology | Glutamine residues variability for monitoring (as polyglutamine) Raman—Spectral differences for fibroblasts that were known to be from a HD patient as compared with those coming from a healthy person | Spectral data collected originated from fibroblasts—extracted plasma membrane components associated to cholesterol and phospholipids as well as proteins containing tyrosine [128] |
Alzheimer’ s disease | Amyloid-β(Aβ) and tau proteins Aggregation of the two proteins into extracellular amyloid plaques and intracellular neurofibrillary tangles Biochemical changes in the platelets collected from blood samples [106] Tears components analysis | Raman [79] and SERS [80]—Monitoring metal ions associated with amyloid protein aggregation: Zn(II) or Cu(II) bound histidine Raman bands Raman—Platelet alterations such as those related to amyloid precursor protein (APP) processing SERS—Indirect detection of tau protein by using magnetic hybrid NPs functionalized with specific antibody and Raman label (DNTB) [109] SERS—Physiologically relevant Aβ concentrations were investigated for determining the conversion from α-helical to β-sheet structure [117,129] SERS—Spectral response of tears collected from healthy subjects and known AD patients was compared in terms of protein component fingerprint; quantitative information related to pathological aggregates was assessed from the ratio I1342 vs I1243 [86] | Direct Raman analysis of metal coordinated complexes Increase in the intensity ratios for 758 cm−1 and 744 cm−1, assigned to the tryptophan sidechain and heme species specific to platelets Monitoring the SERS specific bands of DNTB at 1053 cm−1, 1332 cm−1, and 1553 cm−1 (fM levels) Microfluidic device enabling Aβ conformational biosensing as low as 10 fM [129], nanofluidic biosensor [130] Direct SERS analysis of Aβ by using chemoreceptor functionalized AuNPs-decorated polystyrene beads [108] Direct SERS analysis of tears components on a AuNPs-based substrate [86] |
5. ZnO-Based Integrated SERS Biosensors—An Overview of Their Performance
6. ZnO-Based Biosensors for the Ultrasensitive Detection of ND Biomarkers
7. Conclusions and Future Perspectives
Perspectives
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Bewernick, B.H.; Schlaepfer, T.E. Chronic depression as a model disease for cerebral aging. Dialogues Clin. Neurosci. 2013, 15, 77–85. [Google Scholar] [CrossRef]
- Jęśko, H.; Cieślik, M.; Gromadzka, G.; Adamczyk, A. Dysfunctional proteins in neuropsychiatric disorders: From neurodegeneration to autism spectrum disorders. Neurochem. Int. 2020, 141, 104853. [Google Scholar] [CrossRef] [PubMed]
- Kern, J.K.; Geier, D.A.; Sykes, L.K.; Geier, M.R. Evidence of neurodegeneration in autism spectrum disorder. Transl. Neurodegener. 2013, 2, 17. [Google Scholar] [CrossRef] [PubMed]
- Koníčková, D.; Menšíková, K.; Tučková, L.; Hényková, E.; Strnad, M.; Friedecký, D.; Stejskal, D.; Matěj, R.; Kaňovský, P. Biomarkers of Neurodegenerative Diseases: Biology, Taxonomy, Clinical Relevance, and Current Research Status. Biomedicines 2022, 10, 1760. [Google Scholar] [CrossRef] [PubMed]
- Turner, R.S.; Stubbs, T.; Davies, D.A.; Albensi, B.C. Potential New Approaches for Diagnosis of Alzheimer’s Disease and Related Dementias. Front. Neurol. 2020, 11, 496. [Google Scholar] [CrossRef]
- Nichols, E.; Steinmetz, J.D.; Vollset, S.E.; Fukutaki, K.; Chalek, J.; Abd-Allah, F.; Abdoli, A.; Abualhasan, A.; Abu-Gharbieh, E.; Akram, T.T.; et al. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: An analysis for the Global Burden of Disease Study 2019. Lancet Public Health 2022, 7, e105–e125. [Google Scholar] [CrossRef] [PubMed]
- Delaby, C.; Lehmann, S. Proteinopathies: Molecular mechanisms and diagnostic perspectives. J. Neural Transm. 2022, 129, 129–130. [Google Scholar] [CrossRef]
- Li, P.; Long, F.; Chen, W.; Chen, J.; Chu, P.K.; Wang, H. Fundamentals and applications of surface-enhanced Raman spectroscopy–based biosensors. Curr. Opin. Biomed. Eng. 2020, 13, 51–59. [Google Scholar] [CrossRef]
- Dina, N.E.; Tahir, M.A.; Bajwa, S.Z.; Amin, I.; Valev, V.K.; Zhang, L. SERS-based antibiotic susceptibility testing: Towards point-of-care clinical diagnosis. Biosens. Bioelectron. 2022, 219, 114843. [Google Scholar] [CrossRef]
- Tahir, M.A.; Dina, N.E.; Cheng, H.; Valev, V.K.; Zhang, L. Surface-enhanced Raman spectroscopy for bioanalysis and diagnosis. Nanoscale 2021, 13, 11593–11634. [Google Scholar] [CrossRef]
- Wakamatsu, K.; Chiba, Y.; Murakami, R.; Miyai, Y.; Matsumoto, K.; Kamada, M.; Nonaka, W.; Uemura, N.; Yanase, K.; Ueno, M. Metabolites and Biomarker Compounds of Neurodegenerative Diseases in Cerebrospinal Fluid. Metabolites 2022, 12, 343. [Google Scholar] [CrossRef]
- Hilal, S.; Wolters, F.J.; Verbeek, M.M.; Vanderstichele, H.; Ikram, M.K.; Stoops, E.; Ikram, M.A.; Vernooij, M.W. Plasma amyloid-β levels, cerebral atrophy and risk of dementia: A population-based study. Alzheimers Res. Ther. 2018, 10, 63. [Google Scholar] [CrossRef] [PubMed]
- Hao, N.; Wang, Z.; Liu, P.; Becker, R.; Yang, S.; Yang, K.; Pei, Z.; Zhang, P.; Xia, J.; Shen, L.; et al. Acoustofluidic multimodal diagnostic system for Alzheimer's disease. Biosens. Bioelectron. 2022, 196, 113730. [Google Scholar] [CrossRef]
- Hook, V.; Kind, T.; Podvin, S.; Palazoglu, M.; Tran, C.; Toneff, T.; Samra, S.; Lietz, C.; Fiehn, O. Metabolomics Analyses of 14 Classical Neurotransmitters by GC-TOF with LC-MS Illustrates Secretion of 9 Cell-Cell Signaling Molecules from Sympathoadrenal Chromaffin Cells in the Presence of Lithium. ACS Chem. Neurosci. 2019, 10, 1369–1379. [Google Scholar] [CrossRef]
- Moody, A.S.; Sharma, B. Multi-metal, Multi-wavelength Surface-Enhanced Raman Spectroscopy Detection of Neurotransmitters. ACS Chem. Neurosci. 2018, 9, 1380–1387. [Google Scholar] [CrossRef]
- Siek, M.; Kaminska, A.; Kelm, A.; Rolinski, T.; Holyst, R.; Opallo, M.; Niedziolka-Jonsson, J. Electrodeposition for preparation of efficient surface-enhanced Raman scattering-active silver nanoparticle substrates for neurotransmitter detection. Electrochim. Acta 2013, 89, 284–291. [Google Scholar] [CrossRef]
- Tang, L.; Li, S.; Han, F.; Liu, L.; Xu, L.; Ma, W.; Kuang, H.; Li, A.; Wang, L.; Xu, C. SERS-active Au@Ag nanorod dimers for ultrasensitive dopamine detection. Biosens. Bioelectron. 2015, 71, 7–12. [Google Scholar] [CrossRef] [PubMed]
- Gao, F.; Liu, L.; Cui, G.; Xu, L.; Wu, X.; Kuang, H.; Xu, C. Regioselective plasmonic nano-assemblies for bimodal sub-femtomolar dopamine detection. Nanoscale 2017, 9, 223–229. [Google Scholar] [CrossRef] [PubMed]
- Lu, J.; Xu, C.; Nan, H.; Zhu, Q.; Qin, F.; Manohari, A.G.; Wei, M.; Zhu, Z.; Shi, Z.; Ni, Z. SERS-active ZnO/Ag hybrid WGM microcavity for ultrasensitive dopamine detection. Appl. Phys. Lett. 2016, 109, 073701. [Google Scholar] [CrossRef]
- Lee, W.; Kang, B.-H.; Yang, H.; Park, M.; Kwak, J.H.; Chung, T.; Jeong, Y.; Kim, B.K.; Jeong, K.-H. Spread spectrum SERS allows label-free detection of attomolar neurotransmitters. Nat. Commun. 2021, 12, 159. [Google Scholar] [CrossRef]
- Wang, P.; Xia, M.; Liang, O.; Sun, K.; Cipriano, A.F.; Schroeder, T.; Liu, H.; Xie, Y.-H. Label-Free SERS Selective Detection of Dopamine and Serotonin Using Graphene-Au Nanopyramid Heterostructure. Anal. Chem. 2015, 87, 10255–10261. [Google Scholar] [CrossRef] [PubMed]
- Garre-Olmo, J. Epidemiology of Alzheimer’s disease and other dementias. Rev. Neurol. 2018, 66, 377–386. [Google Scholar] [PubMed]
- Du, Y.; Wu, H.T.; Qin, X.Y.; Cao, C.; Liu, Y.; Cao, Z.Z.; Cheng, Y. Postmortem Brain, Cerebrospinal Fluid, and Blood Neurotrophic Factor Levels in Alzheimer’s Disease: A Systematic Review and Meta-Analysis. J. Mol. Neurosci. 2018, 65, 289–300. [Google Scholar] [CrossRef] [PubMed]
- Citron, M.; Oltersdorf, T.; Haass, C.; McConlogue, L.; Hung, A.Y.; Seubert, P.; Vigo-Pelfrey, C.; Lieberburg, I.; Selkoe, D.J. Mutation of the beta-amyloid precursor protein in familial Alzheimer’s disease increases beta-protein production. Nature 1992, 360, 672–674. [Google Scholar] [CrossRef] [PubMed]
- Corder, E.H.; Saunders, A.M.; Strittmatter, W.J.; Schmechel, D.E.; Gaskell, P.C.; Small, G.W.; Roses, A.D.; Haines, J.L.; Pericak-Vance, M.A. Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer’s disease in late onset families. Science 1993, 261, 921–923. [Google Scholar] [CrossRef] [PubMed]
- Bejanin, A.; Iulita, M.F.; Vilaplana, E.; Carmona-Iragui, M.; Benejam, B.; Videla, L.; Barroeta, I.; Fernandez, S.; Altuna, M.; Pegueroles, J.; et al. Association of Apolipoprotein E ɛ4 Allele with Clinical and Multimodal Biomarker Changes of Alzheimer Disease in Adults with Down Syndrome. JAMA Neurol 2021, 78, 937–947. [Google Scholar] [CrossRef] [PubMed]
- Colautti, J.; Nagales, K. Tau and beta-amyloid in Alzheimer’s disease: Theories, treatments strategies, and future directions. The Meducator 2020, 1, 12–15. [Google Scholar] [CrossRef]
- Goldgaber, D.; Lerman, M.I.; McBride, O.W.; Saffiotti, U.; Gajdusek, D.C. Characterization and chromosomal localization of a cDNA encoding brain amyloid of Alzheimer’s disease. Science 1987, 235, 877–880. [Google Scholar] [CrossRef]
- Schmidt, S.D.; Mazzella, M.J.; Nixon, R.A.; Mathews, P.M. Aβ measurement by enzyme-linked immunosorbent assay. Methods Mol. Biol. 2012, 849, 507–527. [Google Scholar] [CrossRef]
- Bharadwaj, P.; Waddington, L.; Varghese, J.; Macreadie, I.G. A new method to measure cellular toxicity of non-fibrillar and fibrillar Alzheimer’s Aβ using yeast. J. Alzheimers Dis. 2008, 13, 147–150. [Google Scholar] [CrossRef] [PubMed]
- Russo, C.; Schettini, G.; Saido, T.C.; Hulette, C.; Lippa, C.; Lannfelt, L.; Ghetti, B.; Gambetti, P.; Tabaton, M.; Teller, J.K. Presenilin-1 mutations in Alzheimer’s disease. Nature 2000, 405, 531–532. [Google Scholar] [CrossRef]
- Larner, A.J.; Doran, M. Clinical phenotypic heterogeneity of Alzheimer’s disease associated with mutations of the presenilin-1 gene. J. Neurol. 2006, 253, 139–158. [Google Scholar] [CrossRef]
- Simon, D.K.; Tanner, C.M.; Brundin, P. Parkinson Disease Epidemiology, Pathology, Genetics, and Pathophysiology. Clin. Geriatr. Med. 2020, 36, 1–12. [Google Scholar] [CrossRef]
- Lee, A.; Gilbert, R.M. Epidemiology of Parkinson Disease. Neurol. Clin. 2016, 34, 955–965. [Google Scholar] [CrossRef]
- Armstrong, M.J.; Okun, M.S. Diagnosis and Treatment of Parkinson Disease: A Review. Jama 2020, 323, 548–560. [Google Scholar] [CrossRef]
- Mahlknecht, P.; Hotter, A.; Hussl, A.; Esterhammer, R.; Schocke, M.; Seppi, K. Significance of MRI in diagnosis and differential diagnosis of Parkinson’s disease. Neurodegener. Dis. 2010, 7, 300–318. [Google Scholar] [CrossRef]
- Smith, S.L.; Lones, M.A.; Bedder, M.; Alty, J.E.; Cosgrove, J.; Maguire, R.J.; Pownall, M.E.; Ivanoiu, D.; Lyle, C.; Cording, A.; et al. Computational approaches for understanding the diagnosis and treatment of Parkinson's disease. IET Syst. Biol. 2015, 9, 226–233. [Google Scholar] [CrossRef]
- Hotter, A.; Esterhammer, R.; Schocke, M.F.; Seppi, K. Potential of advanced MR imaging techniques in the differential diagnosis of parkinsonism. Mov. Disord. 2009, 24 (Suppl. 2), S711–S720. [Google Scholar] [CrossRef]
- Chahine, L.M.; Beach, T.G.; Brumm, M.C.; Adler, C.H.; Coffey, C.S.; Mosovsky, S.; Caspell-Garcia, C.; Serrano, G.E.; Munoz, D.G.; White, C.L.; et al. In vivo distribution of α-synuclein in multiple tissues and biofluids in Parkinson disease. Neurology 2020, 95, e1267–e1284. [Google Scholar] [CrossRef] [PubMed]
- Wasner, K.; Grünewald, A.; Klein, C. Parkin-linked Parkinson’s disease: From clinical insights to pathogenic mechanisms and novel therapeutic approaches. Neurosci. Res. 2020, 159, 34–39. [Google Scholar] [CrossRef]
- Nagatsu, T.; Nakashima, A.; Ichinose, H.; Kobayashi, K. Human tyrosine hydroxylase in Parkinson’s disease and in related disorders. J. Neural Transm. (Vienna) 2019, 126, 397–409. [Google Scholar] [CrossRef]
- Rausch, W.D.; Wang, F.; Radad, K. From the tyrosine hydroxylase hypothesis of Parkinson’s disease to modern strategies: A short historical overview. J. Neural Transm. (Vienna) 2022, 129, 487–495. [Google Scholar] [CrossRef]
- Kim, K.C.; Kim, P.; Go, H.S.; Choi, C.S.; Park, J.H.; Kim, H.J.; Jeon, S.J.; Pena, I.C.; Han, S.-H.; Cheong, J.H.; et al. Male-specific alteration in excitatory post-synaptic development and social interaction in pre-natal valproic acid exposure model of autism spectrum disorder. J. Neurochem. 2013, 124, 832–843. [Google Scholar] [CrossRef] [PubMed]
- Tyzio, R.; Nardou, R.; Ferrari, D.C.; Tsintsadze, T.; Shahrokhi, A.; Eftekhari, S.; Khalilov, I.; Tsintsadze, V.; Brouchoud, C.; Chazal, G.; et al. Oxytocin-mediated GABA inhibition during delivery attenuates autism pathogenesis in rodent offspring. Science 2014, 343, 675–679. [Google Scholar] [CrossRef] [PubMed]
- Gray, C. The Original Social Story Book; Jenison Public Schools: Jenison, MI, USA, 1993. [Google Scholar]
- Parellada, M.; Penzol, M.J.; Pina, L.; Moreno, C.; González-Vioque, E.; Zalsman, G.; Arango, C. The neurobiology of autism spectrum disorders. Eur. Psychiatry 2014, 29, 11–19. [Google Scholar] [CrossRef]
- Arndt, T.L.; Stodgell, C.J.; Rodier, P.M. The teratology of autism. Int. J. Dev. Neurosci. 2005, 23, 189–199. [Google Scholar] [CrossRef]
- Brun, L.; Auzias, G.; Viellard, M.; Villeneuve, N.; Girard, N.; Poinso, F.; Da Fonseca, D.; Deruelle, C. Localized Misfolding Within Broca’s Area as a Distinctive Feature of Autistic Disorder. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 2016, 1, 160–168. [Google Scholar] [CrossRef]
- Blackmon, K.; Ben-Avi, E.; Wang, X.; Pardoe, H.R.; Di Martino, A.; Halgren, E.; Devinsky, O.; Thesen, T.; Kuzniecky, R. Periventricular white matter abnormalities and restricted repetitive behavior in autism spectrum disorder. Neuroimage Clin. 2016, 10, 36–45. [Google Scholar] [CrossRef]
- Li, D.; Karnath, H.-O.; Xu, X. Candidate Biomarkers in Children with Autism Spectrum Disorder: A Review of MRI Studies. Neurosci. Bull. 2017, 33, 219–237. [Google Scholar] [CrossRef]
- Persico, A.M.; Napolioni, V. Autism genetics. Behav. Brain Res. 2013, 251, 95–112. [Google Scholar] [CrossRef]
- Ruggeri, B.; Sarkans, U.; Schumann, G.; Persico, A.M. Biomarkers in autism spectrum disorder: The old and the new. Psychopharmacology 2014, 231, 1201–1216. [Google Scholar] [CrossRef]
- Zafeiriou, D.I.; Ververi, A.; Dafoulis, V.; Kalyva, E.; Vargiami, E. Autism spectrum disorders: The quest for genetic syndromes. Am. J. Med. Genet. B Neuropsychiatr. Genet. 2013, 162b, 327–366. [Google Scholar] [CrossRef]
- Ford, T.C.; Crewther, D.P. A Comprehensive Review of the 1H-MRS Metabolite Spectrum in Autism Spectrum Disorder. Front. Mol. Neurosci. 2016, 9, 14. [Google Scholar] [CrossRef] [PubMed]
- Anagnostou, E.; Taylor, M.J. Review of neuroimaging in autism spectrum disorders: What have we learned and where we go from here. Mol. Autism 2011, 2, 4. [Google Scholar] [CrossRef] [PubMed]
- Ogruc Ildiz, G.; Bayari, S.; Karadag, A.; Kaygisiz, E.; Fausto, R. Fourier Transform Infrared Spectroscopy Based Complementary Diagnosis Tool for Autism Spectrum Disorder in Children and Adolescents. Molecules 2020, 25, 2079. [Google Scholar] [CrossRef] [PubMed]
- Planchez, B.; Surget, A.; Belzung, C. Animal models of major depression: Drawbacks and challenges. J. Neural Transm. 2019, 126, 1383–1408. [Google Scholar] [CrossRef] [PubMed]
- Shirayama, Y.; Yang, C.; Zhang, J.C.; Ren, Q.; Yao, W.; Hashimoto, K. Alterations in brain-derived neurotrophic factor (BDNF) and its precursor proBDNF in the brain regions of a learned helplessness rat model and the antidepressant effects of a TrkB agonist and antagonist. Eur. Neuropsychopharmacol. 2015, 25, 2449–2458. [Google Scholar] [CrossRef]
- Dionisie, V.; Ciobanu, A.M.; Toma, V.A.; Manea, M.C.; Baldea, I.; Olteanu, D.; Sevastre-Berghian, A.; Clichici, S.; Manea, M.; Riga, S.; et al. Escitalopram Targets Oxidative Stress, Caspase-3, BDNF and MeCP2 in the Hippocampus and Frontal Cortex of a Rat Model of Depression Induced by Chronic Unpredictable Mild Stress. Int. J. Mol. Sci. 2021, 22, 7483. [Google Scholar] [CrossRef] [PubMed]
- Smalheiser, N.R.; Lugli, G.; Rizavi, H.S.; Zhang, H.; Torvik, V.I.; Pandey, G.N.; Davis, J.M.; Dwivedi, Y. MicroRNA expression in rat brain exposed to repeated inescapable shock: Differential alterations in learned helplessness vs. non-learned helplessness. Int. J. Neuropsychopharmacol. 2011, 14, 1315–1325. [Google Scholar] [CrossRef] [PubMed]
- Fiori, L.M.; Kos, A.; Lin, R.; Théroux, J.-F.; Lopez, J.P.; Kühne, C.; Eggert, C.; Holzapfel, M.; Huettl, R.-E.; Mechawar, N.; et al. miR-323a regulates ERBB4 and is involved in depression. Mol. Psychiatry 2021, 26, 4191–4204. [Google Scholar] [CrossRef]
- Martins, H.C.; Schratt, G. MicroRNA-dependent control of neuroplasticity in affective disorders. Transl. Psychiatry 2021, 11, 263. [Google Scholar] [CrossRef]
- Wallensten, J.; Nager, A.; Åsberg, M.; Borg, K.; Beser, A.; Wilczek, A.; Mobarrez, F. Leakage of astrocyte-derived extracellular vesicles in stress-induced exhaustion disorder: A cross-sectional study. Sci. Rep. 2021, 11, 2009. [Google Scholar] [CrossRef]
- Flynn, S.; Leete, J.; Shahim, P.; Pattinson, C.; Guedes, V.A.; Lai, C.; Devoto, C.; Qu, B.-X.; Greer, K.; Moore, B.; et al. Extracellular vesicle concentrations of glial fibrillary acidic protein and neurofilament light measured 1 year after traumatic brain injury. Sci. Rep. 2021, 11, 3896. [Google Scholar] [CrossRef]
- Bigio, B.; Dobbin, J.; Mathe, A.; Rasgon, N.; Nasca, C.; McEwen, B. Molecular Endophenotypes of Depression: From Computational Approaches to Exosome Biology. Biol. Psychiatry 2022, 91 (Suppl. S9), S28–S29. [Google Scholar] [CrossRef]
- Osborne, L.M.; Payne, J.L.; Sherer, M.L.; Sabunciyan, S. Altered extracellular mRNA communication in postpartum depression is associated with decreased autophagy. Mol. Psychiatry 2022, 27, 4526–4535. [Google Scholar] [CrossRef]
- Kumari, S.; Shivakumar, A.B.; Mehak, S.F.; Mazumder, N.; Gangadharan, G.; Pillai, V.G. Types of Raman Scattering Techniques for Neurodegenerative Diseases. In Advances in Brain Imaging Techniques; Mazumder, N., Gangadharan, G., Kistenev, Y.V., Eds.; Springer Nature Singapore: Singapore, 2022; pp. 39–57. [Google Scholar]
- Devitt, G.; Howard, K.; Mudher, A.; Mahajan, S. Raman Spectroscopy: An Emerging Tool in Neurodegenerative Disease Research and Diagnosis. ACS Chem. Neurosci. 2018, 9, 404–420. [Google Scholar] [CrossRef]
- Fălămaș, A.; Faur, C.I.; Ciupe, S.; Chirilă, M.; Rotaru, H.; Hedeșiu, M.; Pînzaru, S.C. Rapid and noninvasive diagnosis of oral and oropharyngeal cancer based on micro-Raman and FT-IR spectra of saliva. Spectrochim. Acta Part A 2021, 252, 119477. [Google Scholar] [CrossRef]
- Faur, C.I.; Fălămaș, A.; Chirilă, M.; Roman, R.C.; Rotaru, H.; Moldovan, M.A.; Albu, S.; Baciut, M.; Robu, I.; Hedeșiu, M. Raman spectroscopy in oral cavity and oropharyngeal cancer: A systematic review br. Int. J. Oral Maxillofac. Surg. 2022, 51, 1373–1381. [Google Scholar] [CrossRef]
- Liu, K.; Zhao, Q.; Li, B.; Zhao, X. Raman Spectroscopy: A Novel Technology for Gastric Cancer Diagnosis. Front. Bioeng. Biotechnol. 2022, 10, 856591. [Google Scholar] [CrossRef] [PubMed]
- Zheng, Q.; Li, J.; Yang, L.; Zheng, B.; Wang, J.; Lv, N.; Luo, J.; Martin, F.L.; Liu, D.; He, J. Raman spectroscopy as a potential diagnostic tool to analyse biochemical alterations in lung cancer. Analyst 2020, 145, 385–392. [Google Scholar] [CrossRef]
- D’ Acunto, M.; Gaeta, R. Contribution of Raman Spectroscopy to Diagnosis and Grading of Chondrogenic Tumors. Sci. Rep. 2020, 10, 2155. [Google Scholar] [CrossRef]
- Hanna, K.; Krzoska, E.; Shaaban, A.M.; Muirhead, D.; Abu-Eid, R.; Speirs, V. Raman spectroscopy: Current applications in breast cancer diagnosis, challenges and future prospects. Br. J. Cancer 2022, 126, 1125–1139. [Google Scholar] [CrossRef]
- Kong, K.; Kendall, C.; Stone, N.; Notingher, I. Raman spectroscopy for medical diagnostics—From in vitro biofluid assays to in vivo cancer detection. Adv. Drug Del. Rev. 2015, 89, 121–134. [Google Scholar] [CrossRef] [PubMed]
- Cui, S.; Zhang, S.; Yue, S. Raman Spectroscopy and Imaging for Cancer Diagnosis. J. Healthc. Eng. 2018, 2018, 8619342. [Google Scholar] [CrossRef]
- Carota, A.G.; Campanella, B.; Del Carratore, R.; Bongioanni, P.; Giannelli, R.; Legnaioli, S. Raman spectroscopy and multivariate analysis as potential tool to follow Alzheimer’s disease progression. Anal. Bioanal. Chem. 2022, 414, 4667–4675. [Google Scholar] [CrossRef]
- Carmona, P.; Monleón, E.; Monzón, M.; Badiola, J.J.; Monreal, J. Raman analysis of prion protein in blood cell membranes from naturally affected scrapie sheep. Chem. Biol. 2004, 11, 759–764. [Google Scholar] [CrossRef]
- Miura, T.; Suzuki, K.; Kohata, N.; Takeuchi, H. Metal binding modes of Alzheimer’s amyloid beta-peptide in insoluble aggregates and soluble complexes. Biochemistry 2000, 39, 7024–7031. [Google Scholar] [CrossRef]
- Yugay, D.; Goronzy, D.P.; Kawakami, L.M.; Claridge, S.A.; Song, T.B.; Yan, Z.; Xie, Y.H.; Gilles, J.; Yang, Y.; Weiss, P.S. Copper Ion Binding Site in β-Amyloid Peptide. Nano Lett. 2016, 16, 6282–6289. [Google Scholar] [CrossRef] [PubMed]
- Ugrumov, M. Development of early diagnosis of Parkinson’s disease: Illusion or reality? CNS Neurosci. Ther. 2020, 26, 997–1009. [Google Scholar] [CrossRef] [PubMed]
- Zhou, J.; Li, J.; Papaneri, A.B.; Kobzar, N.P.; Cui, G. Dopamine Neuron Challenge Test for early detection of Parkinson’s disease. NPJ Parkinsons Dis. 2021, 7, 116. [Google Scholar] [CrossRef]
- Zhang, K.; Liu, Y.; Wang, Y.; Zhang, R.; Liu, J.; Wei, J.; Qian, H.; Qian, K.; Chen, R.; Liu, B. Quantitative SERS Detection of Dopamine in Cerebrospinal Fluid by Dual-Recognition-Induced Hot Spot Generation. ACS Appl. Mater. Interfaces 2018, 10, 15388–15394. [Google Scholar] [CrossRef] [PubMed]
- Manno, D.; Filippo, E.; Fiore, R.; Serra, A.; Urso, E.; Rizzello, A.; Maffia, M. Monitoring prion protein expression in complex biological samples by SERS for diagnostic applications. Nanotechnology 2010, 21, 165502. [Google Scholar] [CrossRef] [PubMed]
- Apetri, M.M.; Maiti, N.C.; Zagorski, M.G.; Carey, P.R.; Anderson, V.E. Secondary structure of alpha-synuclein oligomers: Characterization by Raman and atomic force microscopy. J. Mol. Biol. 2006, 355, 63–71. [Google Scholar] [CrossRef]
- Cennamo, G.; Montorio, D.; Morra, V.B.; Criscuolo, C.; Lanzillo, R.; Salvatore, E.; Camerlingo, C.; Lisitskiy, M.; Delfino, I.; Portaccio, M.; et al. Surface-enhanced Raman spectroscopy of tears: Toward a diagnostic tool for neurodegenerative disease identification. J. Biomed. Opt. 2020, 25, 1–12. [Google Scholar] [CrossRef] [PubMed]
- Stefancu, A.; Moisoiu, V.; Desmirean, M.; Iancu, S.D.; Tigu, A.B.; Petrushev, B.; Jurj, A.; Cozan, R.G.; Budisan, L.; Fetica, B.; et al. SERS-based DNA methylation profiling allows the differential diagnosis of malignant lymphadenopathy. Spectrochim. Acta Part A 2022, 264, 120216. [Google Scholar] [CrossRef] [PubMed]
- Avram, L.; Iancu, S.D.; Stefancu, A.; Moisoiu, V.; Colnita, A.; Marconi, D.; Donca, V.; Buzdugan, E.; Craciun, R.; Leopold, N.; et al. SERS-Based Liquid Biopsy of Gastrointestinal Tumors Using a Portable Raman Device Operating in a Clinical Environment. J. Clin. Med. 2020, 9, 212. [Google Scholar] [CrossRef]
- Itoh, T.; Procházka, M.; Dong, Z.-C.; Ji, W.; Yamamoto, Y.; Zhang, Y.; Ozaki, Y. Toward a New Era of SERS and TERS at the Nanometer Scale: From Fundamentals to Innovative Applications. Chem. Rev. 2023, 123, 1552–1634. [Google Scholar] [CrossRef] [PubMed]
- Langer, J.; de Aberasturi, D.J.; Aizpurua, J.; Alvarez-Puebla, R.A.; Auguié, B.; Baumberg, J.J.; Bazan, G.C.; Bell, S.E.J.; Boisen, A.; Brolo, A.G.; et al. Present and Future of Surface-Enhanced Raman Scattering. ACS Nano 2020, 14, 28–117. [Google Scholar] [CrossRef] [PubMed]
- Otto, A.; Mrozek, I.; Grabhorn, H.; Akemann, W. Surface-enhanced Raman scattering. J. Phys. Condens. Matter 1992, 4, 1143. [Google Scholar] [CrossRef]
- Huber, F.; Berwanger, J.; Polesya, S.; Mankovsky, S.; Ebert, H.; Giessibl, F.J. Chemical bond formation showing a transition from physisorption to chemisorption. Science 2019, 366, 235–238. [Google Scholar] [CrossRef]
- Ștefancu, A.; Lee, S.; Zhu, L.; Liu, M.; Lucăcel, R.C.; Cortés, E.; Leopold, N. Fermi Level Equilibration at the Metal–Molecule Interface in Plasmonic Systems. Nano Lett. 2021, 21, 6592–6599. [Google Scholar] [CrossRef] [PubMed]
- Serebrennikova, K.V.; Berlina, A.N.; Sotnikov, D.V.; Zherdev, A.V.; Dzantiev, B.B. Raman Scattering-Based Biosensing: New Prospects and Opportunities. Biosensors 2021, 11, 512. [Google Scholar] [CrossRef] [PubMed]
- Li, C.; Huang, Y.; Li, X.; Zhang, Y.; Chen, Q.; Ye, Z.; Alqarni, Z.; Bell, S.E.J.; Xu, Y. Towards practical and sustainable SERS: A review of recent developments in the construction of multifunctional enhancing substrates. J. Mater. Chem. C 2021, 9, 11517–11552. [Google Scholar] [CrossRef]
- Kneipp, K.; Kneipp, H.; Itzkan, I.; Dasari, R.R.; Feld, M.S. Ultrasensitive Chemical Analysis by Raman Spectroscopy. Chem. Rev. 1999, 99, 2957–2976. [Google Scholar] [CrossRef]
- Marsh, A.P. Molecular mechanisms of proteinopathies across neurodegenerative disease: A review. Neurol. Res. Pract. 2019, 1, 35. [Google Scholar] [CrossRef]
- Serra, A.; Manno, D.; Filippo, E.; Buccolieri, A.; Urso, E.; Rizzello, A.; Maffia, M. SERS based optical sensor to detect prion protein in neurodegenerate living cells. Sens. Actuators B Chem. 2011, 156, 479–485. [Google Scholar] [CrossRef]
- Moisoiu, V.; Socaciu, A.; Stefancu, A.; Iancu, S.D.; Boros, I.; Alecsa, C.D.; Rachieriu, C.; Chiorean, A.R.; Eniu, D.; Leopold, N.; et al. Breast Cancer Diagnosis by Surface-Enhanced Raman Scattering (SERS) of Urine. Appl. Sci. 2019, 9, 806. [Google Scholar] [CrossRef]
- Moisoiu, V.; Stefancu, A.; Iancu, S.D.; Moisoiu, T.; Loga, L.; Dican, L.; Alecsa, C.D.; Boros, I.; Jurj, A.; Dima, D.; et al. SERS assessment of the cancer-specific methylation pattern of genomic DNA: Towards the detection of acute myeloid leukemia in patients undergoing hematopoietic stem cell transplantation. Anal. Bioanal. Chem. 2019, 411, 7907–7913. [Google Scholar] [CrossRef]
- Moore, T.J.; Moody, A.S.; Payne, T.D.; Sarabia, G.M.; Daniel, A.R.; Sharma, B. In Vitro and In Vivo SERS Biosensing for Disease Diagnosis. Biosensors 2018, 8, 46. [Google Scholar] [CrossRef]
- Panikar, S.S.; Cialla-May, D.; De la Rosa, E.; Salas, P.; Popp, J. Towards translation of surface-enhanced Raman spectroscopy (SERS) to clinical practice: Progress and trends. TrAC Trends Anal. Chem. 2021, 134, 116122. [Google Scholar] [CrossRef]
- Merdalimova, A.; Chernyshev, V.; Nozdriukhin, D.; Rudakovskaya, P.; Gorin, D.; Yashchenok, A. Identification and Analysis of Exosomes by Surface-Enhanced Raman Spectroscopy. Appl. Sci. 2019, 9, 1135. [Google Scholar] [CrossRef]
- Pansieri, J.; Plissonneau, M.; Stransky-Heilkron, N.; Dumoulin, M.; Heinrich-Balard, L.; Rivory, P.; Morfin, J.-F.; Toth, E.; Saraiva, M.J.; Allémann, E.; et al. Multimodal imaging Gd-nanoparticles functionalized with Pittsburgh compound B or a nanobody for amyloid plaques targeting. Nanomedicine 2017, 12, 1675–1687. [Google Scholar] [CrossRef] [PubMed]
- Nasr, S.H.; Kouyoumdjian, H.; Mallett, C.; Ramadan, S.; Zhu, D.C.; Shapiro, E.M.; Huang, X. Detection of β-Amyloid by Sialic Acid Coated Bovine Serum Albumin Magnetic Nanoparticles in a Mouse Model of Alzheimer’s Disease. Small 2018, 14, 1701828. [Google Scholar] [CrossRef] [PubMed]
- Carmona, P.; Molina, M.; Calero, M.; Bermejo-Pareja, F.; Martínez-Martín, P.; Toledano, A. Discrimination analysis of blood plasma associated with Alzheimer’s disease using vibrational spectroscopy. J. Alzheimers Dis. 2013, 34, 911–920. [Google Scholar] [CrossRef] [PubMed]
- Carlomagno, C.; Cabinio, M.; Picciolini, S.; Gualerzi, A.; Baglio, F.; Bedoni, M. SERS-based biosensor for Alzheimer disease evaluation through the fast analysis of human serum. J. Biophotonics. 2020, 13, e201960033. [Google Scholar] [CrossRef] [PubMed]
- Guerrini, L.; Arenal, R.; Mannini, B.; Chiti, F.; Pini, R.; Matteini, P.; Alvarez-Puebla, R.A. SERS Detection of Amyloid Oligomers on Metallorganic-Decorated Plasmonic Beads. ACS Appl. Mater. Interfaces 2015, 7, 9420–9428. [Google Scholar] [CrossRef] [PubMed]
- Zengin, A.; Tamer, U.; Caykara, T. A SERS-Based Sandwich Assay for Ultrasensitive and Selective Detection of Alzheimer’s Tau Protein. Biomacromolecules 2013, 14, 3001–3009. [Google Scholar] [CrossRef] [PubMed]
- Garcia-Leis, A.; Sanchez-Cortes, S. Label-Free Detection and Self-Aggregation of Amyloid β-Peptides Based on Plasmonic Effects Induced by Ag Nanoparticles: Implications in Alzheimer’s Disease Diagnosis. ACS Appl. Nano Mater. 2021, 4, 3565–3575. [Google Scholar] [CrossRef]
- Xu, F.; Zhang, Y.; Sun, Y.; Shi, Y.; Wen, Z.; Li, Z. Silver Nanoparticles Coated Zinc Oxide Nanorods Array as Superhydrophobic Substrate for the Amplified SERS Effect. J. Phys. Chem. C 2011, 115, 9977–9983. [Google Scholar] [CrossRef]
- Sun, X.; Sun, X.; Wang, Q.; Wang, X.; Feng, L.; Yang, Y.; Jing, Y.; Yang, C.; Zhang, S. Biosensors toward behavior detection in diagnosis of Alzheimer’s disease. Front. Bioeng. Biotechnol. 2022, 10, 1031833. [Google Scholar] [CrossRef]
- Li, X.; Wu, D.; Ma, H.; Wang, H.; Wang, Y.; Fan, D.; Du, B.; Wei, Q.; Zhang, N. Ultrasensitive amyloid-β proteins detection based on curcumin conjugated ZnO nanoparticles quenching electrochemiluminescence behavior of luminol immobilized on Au@MoS2/Bi2S3 nanorods. Biosens. Bioelectron. 2019, 131, 136–142. [Google Scholar] [CrossRef] [PubMed]
- Yu, Y.; Zhang, L.; Li, C.; Sun, X.; Tang, D.; Shi, G. A Method for Evaluating the Level of Soluble β-Amyloid(1–40/1–42) in Alzheimer’s Disease Based on the Binding of Gelsolin to β-Amyloid Peptides. Angew. Chem. Int. Ed. 2014, 53, 12832–12835. [Google Scholar] [CrossRef] [PubMed]
- Zhao, C.; Wang, A.; Tang, X.; Qin, J. Electrochemical sensitive detection of amyloid-β oligomer harnessing cellular prion protein on AuNPs embedded poly (pyrrole-3-carboxylic acid) matrix. Mater. Today Adv. 2022, 14, 100250. [Google Scholar] [CrossRef]
- Chou, J.-A.; Chung, C.-L.; Ho, P.-C.; Luo, C.-H.; Tsai, Y.-H.; Wu, C.-K.; Kuo, C.-W.; Hsiao, Y.-S.; Yu, H.-h.; Chen, P. Organic Electrochemical Transistors/SERS-Active Hybrid Biosensors Featuring Gold Nanoparticles Immobilized on Thiol-Functionalized PEDOT Films. Front. Chem. 2019, 7, 281. [Google Scholar] [CrossRef] [PubMed]
- Chou, I.H.; Benford, M.; Beier, H.T.; Coté, G.L.; Wang, M.; Jing, N.; Kameoka, J.; Good, T.A. Nanofluidic Biosensing for β-Amyloid Detection Using Surface Enhanced Raman Spectroscopy. Nano Lett. 2008, 8, 1729–1735. [Google Scholar] [CrossRef]
- Proniewicza, E.; Tąta, A.; Wójcik, A.; Starowicz, M.; Pacek, J.; Molenda, M. SERS activity and spectroscopic properties of Zn and ZnO nanostructures obtained by electrochemical and green chemistry methods for applications in biology and medicine. Phys. Chem. Chem. Phys. 2020, 22, 28100–28114. [Google Scholar] [CrossRef]
- Adesoye, S.; Dellinger, K. ZnO and TiO2 nanostructures for surface-enhanced Raman scattering-based bio-sensing: A review. Sens. Biosensing Res. 2022, 37, 100499. [Google Scholar] [CrossRef]
- Proniewicza, E.; Tąta, A.; Starowicz, M.; Wójcik, A.; Pacek, J.; Molenda, M. Is the electrochemical or the “green chemistry” method the optimal method for the synthesis of ZnO nanoparticles for applications to biological material? Characterization and SERS on ZnO. Colloids Surf. A 2021, 609, 125771. [Google Scholar] [CrossRef]
- Wang, C.; Xu, X.; Qiu, G.; Ye, W.; Li, Y.; Harris, R.; Jiang, C. Group-Targeting SERS Screening of Total Benzodiazepines Based on Large-Size (111) Faceted Silver Nanosheets Decorated with Zinc Oxide Nanoparticles. Anal. Chem. 2021, 93, 3403–3410. [Google Scholar] [CrossRef]
- Maiti, N.C.; Apetri, M.M.; Zagorski, M.G.; Carey, P.R.; Anderson, V.E. Raman Spectroscopic Characterization of Secondary Structure in Natively Unfolded Proteins: α-Synuclein. JACS 2004, 126, 2399–2408. [Google Scholar] [CrossRef]
- Farah, R.; Haraty, H.; Salame, Z.; Fares, Y.; Ojcius, D.M.; Said Sadier, N. Salivary biomarkers for the diagnosis and monitoring of neurological diseases. Biomed. J. 2018, 41, 63–87. [Google Scholar] [CrossRef] [PubMed]
- Wang, G.; Hao, C.; Ma, W.; Qu, A.; Chen, C.; Xu, J.; Xu, C.; Kuang, H.; Xu, L. Chiral Plasmonic Triangular Nanorings with SERS Activity for Ultrasensitive Detection of Amyloid Proteins in Alzheimer’s Disease. Adv. Mater. 2021, 33, 2102337. [Google Scholar] [CrossRef]
- Zhang, X.; Liu, S.; Song, X.; Wang, H.; Wang, J.; Wang, Y.; Huang, J.; Yu, J. Robust and Universal SERS Sensing Platform for Multiplexed Detection of Alzheimer’s Disease Core Biomarkers Using PAapt-AuNPs Conjugates. ACS Sens. 2019, 4, 2140–2149. [Google Scholar] [CrossRef] [PubMed]
- Huefner, A.; Kuan, W.-L.; Mason, S.L.; Mahajan, S.; Barker, R.A. Serum Raman spectroscopy as a diagnostic tool in patients with Huntington’s disease. Chem. Sci. 2020, 11, 525–533. [Google Scholar] [CrossRef]
- Shashilov, V.; Xu, M.; Makarava, N.; Savtchenko, R.; Baskakov, I.V.; Lednev, I.K. Dissecting structure of prion amyloid fibrils by hydrogen-deuterium exchange ultraviolet Raman spectroscopy. J. Phys. Chem. B 2012, 116, 7926–7930. [Google Scholar] [CrossRef]
- Muratore, M. Raman spectroscopy and partial least squares analysis in discrimination of peripheral cells affected by Huntington’s disease. Anal. Chim. Acta 2013, 793, 1–10. [Google Scholar] [CrossRef] [PubMed]
- Choi, I.; Huh, Y.S.; Erickson, D. Ultra-sensitive, label-free probing of the conformational characteristics of amyloid beta aggregates with a SERS active nanofluidic device. Microfluid. Nanofluid. 2012, 12, 663–669. [Google Scholar] [CrossRef]
- Melodie, E.B.; Chou, I.H.; Hope, T.B.; Miao, W.; Jun, K.; Theresa, A.G.; Gerard, L.C. In vitro detection of beta amyloid exploiting surface enhanced Raman scattering (SERS) using a nanofluidic biosensor. In Proceedings of the SPIE, Plasmonics in Biology and Medicine V, Marseille, France, 21 February 2008; p. 68690. [Google Scholar]
- Graniel, O.; Iatsunskyi, I.; Coy, E.; Humbert, C.; Barbillon, G.; Michel, T.; Maurin, D.; Balme, S.; Miele, P.; Bechelany, M. Au-covered hollow urchin-like ZnO nanostructures for surface-enhanced Raman scattering sensing. J. Mater. Chem. C 2019, 7, 15066–15073. [Google Scholar] [CrossRef]
- Pal, A.K.; Chandra, G.K.; Umapathy, S.; Bharathi Mohan, D. Ultra-sensitive, reusable, and superhydrophobic Ag/ZnO/Ag 3D hybrid surface enhanced Raman scattering substrate for hemoglobin detection. J. Appl. Phys. 2020, 127, 164501. [Google Scholar] [CrossRef]
- Lee, S.; Kim, J.K. Surface-Enhanced Raman Spectroscopy (SERS) Based on ZnO Nanorods for Biological Applications. In Zinc Oxide Based Nano Materials and Devices; Ahmed, M.N., Ed.; IntechOpen: Rijeka, Croatia, 2019; Chapter 6. [Google Scholar]
- Kamińska, A.; Kowalska, A.A.; Snigurenko, D.; Guziewicz, E.; Lewiński, J.; Waluk, J. ZnO oxide films for ultrasensitive, rapid, and label-free detection of neopterin by surface-enhanced Raman spectroscopy. Analyst 2015, 140, 5090–5098. [Google Scholar] [CrossRef]
- Liu, C.; Xu, X.; Wang, C.; Qiu, G.; Ye, W.; Li, Y.; Wang, D. ZnO/Ag nanorods as a prominent SERS substrate contributed by synergistic charge transfer effect for simultaneous detection of oral antidiabetic drugs pioglitazone and phenformin. Sens. Actuators B Chem. 2020, 307, 127634. [Google Scholar] [CrossRef]
- Wang, X.; Shi, W.; Jin, Z.; Huang, W.; Lin, J.; Ma, G.; Li, S.; Guo, L. Remarkable SERS Activity Observed from Amorphous ZnO Nanocages. Angew. Chem. Int. Ed. 2017, 56, 9851–9855. [Google Scholar] [CrossRef] [PubMed]
- Pino, P.; Bosco, F.; Mollea, C.; Onida, B. Antimicrobial Nano-Zinc Oxide Biocomposites for Wound Healing Applications: A Review. Pharmaceutics 2023, 15, 970. [Google Scholar] [CrossRef]
- Zhang, Z.; Yu, J.; Ma, L.; Sun, Y.; Wang, P.; Wang, T.; Peng, S. Preparation of the plasmonic Ag/AgBr/ZnO film substrate for reusable SERS detection: Implication to the Z-scheme photocatalytic mechanism. Spectrochim. Acta Part A 2020, 224, 117381. [Google Scholar] [CrossRef] [PubMed]
- Sinha, G.; Depero, L.E.; Alessandri, I. Recyclable SERS Substrates Based on Au-Coated ZnO Nanorods. ACS Appl. Mater. Interfaces 2011, 3, 2557–2563. [Google Scholar] [CrossRef]
- Pimentel, A.; Araújo, A.; Coelho, B.J.; Nunes, D.; Oliveira, M.J.; Mendes, M.J.; Águas, H.; Martins, R.; Fortunato, E. 3D ZnO/Ag Surface-Enhanced Raman Scattering on Disposable and Flexible Cardboard Platforms. Materials 2017, 10, 1351. [Google Scholar] [CrossRef]
- Wang, Y.; Ruan, W.; Zhang, J.; Yang, B.; Xu, W.; Zhao, B.; Lombardi, J.R. Direct observation of surface-enhanced Raman scattering in ZnO nanocrystals. J. Raman Spectrosc. 2009, 40, 1072–1077. [Google Scholar] [CrossRef]
- Zhang, G.; Deng, C.; Shi, H.; Zou, B.; Li, Y.; Liu, T.; Wang, W. ZnO/Ag composite nanoflowers as substrates for surface-enhanced Raman scattering. Appl. Surf. Sci. 2017, 402, 154–160. [Google Scholar] [CrossRef]
- Yamada, H.; Yamamoto, Y.; Tani, N. Surface-enhanced raman scattering (SERS) of adsorbed molecules on smooth surfaces of metals and a metal oxide. Chem. Phys. Lett. 1982, 86, 397–400. [Google Scholar] [CrossRef]
- Yamada, H.; Yamamoto, Y. Surface enhanced Raman scattering (SERS) of chemisorbed species on various kinds of metals and semiconductors. Surf. Sci. 1983, 134, 71–90. [Google Scholar] [CrossRef]
- Karthick Kannan, P.; Shankar, P.; Blackman, C.; Chung, C.-H. Recent Advances in 2D Inorganic Nanomaterials for SERS Sensing. Adv. Mater. 2019, 31, 1803432. [Google Scholar] [CrossRef] [PubMed]
- Chen, X.; Zhu, L.; Ma, Z.; Wang, M.; Zhao, R.; Zou, Y.; Fan, Y. Ag Nanoparticles Decorated ZnO Nanorods as Multifunctional SERS Substrates for Ultrasensitive Detection and Catalytic Degradation of Rhodamine B. Nanomaterials 2022, 12, 2394. [Google Scholar] [CrossRef] [PubMed]
- Chou, C.-M.; Thanh Thi, L.T.; Quynh Nhu, N.T.; Liao, S.-Y.; Fu, Y.-Z.; Hung, L.V.; Hsiao, V.K.S. Zinc Oxide Nanorod Surface-Enhanced Raman Scattering Substrates without and with Gold Nanoparticles Fabricated through Pulsed-Laser-Induced Photolysis. Appl. Sci. 2020, 10, 5015. [Google Scholar] [CrossRef]
- Mei, G.S.; Menon, P.S.; Hegde, G. ZnO for performance enhancement of surface plasmon resonance biosensor: A review. Mater. Res. Express 2020, 7, 012003. [Google Scholar] [CrossRef]
- Picciolini, S.; Castagnetti, N.; Vanna, R.; Mehn, D.; Bedoni, M.; Gramatica, F.; Villani, M.; Calestani, D.; Pavesi, M.; Lazzarini, L.; et al. Branched gold nanoparticles on ZnO 3D architecture as biomedical SERS sensors. RSC Adv. 2015, 5, 93644–93651. [Google Scholar] [CrossRef]
- Yang, B.; Jin, S.; Guo, S.; Park, Y.; Chen, L.; Zhao, B.; Jung, Y.M. Recent Development of SERS Technology: Semiconductor-Based Study. ACS Omega 2019, 4, 20101–20108. [Google Scholar] [CrossRef]
- Yang, B.; Wang, Y.; Guo, S.; Jin, S.; Park, E.; Chen, L.; Jung, Y.M. Charge transfer study for semiconductor and semiconductor/ metal composites based on surface-enhanced Raman scattering. Bull. Korean Chem. Soc. 2021, 42, 1411–1418. [Google Scholar] [CrossRef]
- Krajczewski, J.; Ambroziak, R.; Kudelski, A. Substrates for Surface-Enhanced Raman Scattering Formed on Nanostructured Non-Metallic Materials: Preparation and Characterization. Nanomaterials 2020, 11, 75. [Google Scholar] [CrossRef]
- Marica, I.; Nekvapil, F.; Ștefan, M.; Farcău, C.; Fălămaș, A. Zinc oxide nanostructures for fluorescence and Raman signal enhancement: A review. Beilstein J. Nanotechnol. 2022, 13, 472–490. [Google Scholar] [CrossRef]
- Huang, J.; Chen, F.; Zhang, Q.; Zhan, Y.; Ma, D.; Xu, K.; Zhao, Y. 3D Silver Nanoparticles Decorated Zinc Oxide/Silicon Heterostructured Nanomace Arrays as High-Performance Surface-Enhanced Raman Scattering Substrates. ACS Appl. Mater. Interfaces 2015, 7, 5725–5735. [Google Scholar] [CrossRef]
- Yang, M.; Yu, J.; Lei, F.; Zhou, H.; Wei, Y.; Man, B.; Zhang, C.; Li, C.; Ren, J.; Yuan, X. Synthesis of low-cost 3D-porous ZnO/Ag SERS-active substrate with ultrasensitive and repeatable detectability. Sens. Actuators B Chem. 2018, 256, 268–275. [Google Scholar] [CrossRef]
- Doanh, T.T.; Van Hieu, N.; Quynh Trang, T.N.; Hanh Thu, V.T. In situ synthesis of hybrid zinc oxide-silver nanoparticle arrays as a powerful active platform for surface-enhanced Raman scattering detection. J. Sci. Adv. Mater. Devices 2021, 6, 379–389. [Google Scholar] [CrossRef]
- He, L.; Shi, J.; Sun, X.; Lin, M.; Yu, P.; Li, H. Gold Coated Zinc Oxide Nanonecklaces as a SERS Substrate. J. Nanosci. Nanotechnol. 2011, 11, 3509–3515. [Google Scholar] [CrossRef]
- Zhou, J.; Zhang, J.; Yang, H.; Wang, Z.; Shi, J.A.; Zhou, W.; Jiang, N.; Xian, G.; Qi, Q.; Weng, Y.; et al. Plasmon-induced hot electron transfer in Au–ZnO heterogeneous nanorods for enhanced SERS. Nanoscale 2019, 11, 11782–11788. [Google Scholar] [CrossRef]
- Li, S.; Zhang, N.; Zhang, N.; Lin, D.; Hu, X.; Yang, X. Three-dimensional ordered Ag/ZnO/Si hierarchical nanoflower arrays for spatially uniform and ultrasensitive SERS detection. Sens. Actuators B Chem. 2020, 321, 128519. [Google Scholar] [CrossRef]
- Sha, R.; Basak, A.; Maity, P.C.; Badhulika, S. ZnO nano-structured based devices for chemical and optical sensing applications. Sens. Actuators Rep. 2022, 4, 100098. [Google Scholar] [CrossRef]
- Song, C.; Que, S.; Heimer, L.; Que, L. On-Chip Detection of the Biomarkers for Neurodegenerative Diseases: Technologies and Prospects. Micromachines 2020, 11, 629. [Google Scholar] [CrossRef] [PubMed]
- Polykretis, P.; Banchelli, M.; D’Andrea, C.; de Angelis, M.; Matteini, P. Raman Spectroscopy Techniques for the Investigation and Diagnosis of Alzheimer’s Disease. Front. Biosci. 2022, 14, 22. [Google Scholar] [CrossRef]
- Payne, T.D.; Moody, A.S.; Wood, A.L.; Pimiento, P.A.; Elliott, J.C.; Sharma, B. Raman spectroscopy and neuroscience: From fundamental understanding to disease diagnostics and imaging. Analyst 2020, 145, 3461–3480. [Google Scholar] [CrossRef]
- Wu, J.; Dong, W.; Zhang, Z.; Liu, J.; Akioma, M.; Liu, J.; Liu, Y.; Pliss, A.; Zhang, X.; Luan, P. Emerging two-dimensional materials-based diagnosis of neurodegenerative diseases: Status and challenges. Nano Today 2021, 40, 101284. [Google Scholar] [CrossRef]
Techniques | Clinical Examples | Advantages | Disadvantages | Current Use |
---|---|---|---|---|
Imaging techniques | Magnetic resonance imaging (MRI) | Label based Reliable High resolution Wide collection of fluorophores or contrast agents | Incapable of differential diagnosis due to chemical contrast limitations Current need for tissue-specific labeling Toxicity due to dye usage | In vivo |
Optical coherence tomography (OCT) | In vivo | |||
Two-photon excited fluorescence (TPEF) | In vivo (limited) | |||
Photoacoustic imaging | High sensitivity Specificity Rapid output | Costs due to specific labels Specific absorption limitations of dyes in practice Toxicity due to dye usage | In vivo | |
Confocal fluorescence microscopy | ||||
Spectroscopic techniques | IR microscopy | Label free Provides chemical contrast due to molecular vibrations specific selectivity | Long wavelength → low spatial resolution Interference of water absorption specific to biological samples Tissue depth penetration is limited | Ex vivo |
Raman scattering | Label free Complex matrices as samples with minimal preparation required (biofluids, cells, tissues, etc.) No interference from water (intrinsic to samples) Selective tool for NDs differential diagnostics Imaging of (bio-)molecular distribution Specific molecular fingerprinting spectral output Clinical adapted portable setups: optical fibers with possibility to integrate with cannulas, endoscopes, catheters | Time consuming when using mapping technique of large tissular areas Low efficiency of the scattering process translated into inherently weak Raman signals Potentially destructive to the sample due to long laser exposure times | In vivo | |
Hyperspectral Raman imaging | Able to show the amyloid plaques, neuritic plaques, or neurofibrillary tangles and is able to distinguish tissue components for margin determinations | |||
Surface-enhanced Raman scattering | Label free No need for staining High chemical specificity Combined with cryo-sampling of brain tissue | Signal enhancement is local, mainly due to the molecules in close contact with the metallic nanostructures | In vivo | |
Label based When combined with spatially offset (SORS) the detection is possible within the skull Bio-barcode assays for differential PD diagnosis Reduced time Multiplexing capacity | Silver nanoparticles (AgNPs) are shown to alter neurotransmitters in in vivo conditions (rats) Reliant on costly antibodies specific to NDs | |||
Molecular Biology techniques | ELISA/Western blot | Label based High accuracy Suitable for routine protocols (predictive and diagnosis screening) | Costly due to highly specific reagents required, invasive, and laboratory dependent Time consuming when including bioinformatics protocols | Ex vivo |
Immunohistochemistry | ||||
Genomics (PCR, RT-PCR, DNA sequencing, (epi)transcriptomics) |
Analyte Neurotransmitter Biomarker | SERS Substrate | Practical Advantages | LOD/EF | Reference |
---|---|---|---|---|
Crystal violet rhodamine R6G | ZnO-based superstructure highly ordered (flower-like 3D ZnO arrays on glass substrate decorated with self-assembled Ag NPs) via induced photochemical reduction. | Multicomponent detection ability, needed for monitoring multiple metabolites in biofluids. | 10−10 M | [156] |
Crystal violet rhodamine R6G | Ag/AgBr/ZnO film prepared on a conventional glass substrate to regenerate by a visible-light-driven photocatalytic process. | High yield of reusability (up to eight times without losing the SERS efficiency). | 5.95 × 10−13 M /109 1.46 × 10−11 M/108 | [138] |
Melamine | Nanonecklaces of ZnO in combination with 45 nm Au thin films, deposited using a chemical vapor deposition technique (grown faceted and one directional ZnO nanonecklace arrays on r-plane sapphires). | Ready-to-use substrate for integrated biosensing applications. Strict control over the density and “hot spot” distribution. The faceted substrate yields higher electromagnetic enhancement and higher free energies. | 10−5 M/104 | [157] |
Methylene blue | ZnO nanorods coated with Au by means of a low-temperature hydrothermal route and sputtering deposition of Au nanoislands. | By taking advantage of the photocatalytic properties and inducing the degradation of analytes when exposed to UV, this approach provides substrates that are recyclable and affordable and have high reproducibility of SERS spectra and a long shelf life. | 10−12 M | [139] |
Melamine adenine | Superhydrophobic substrate that consists of arrays of ZnO nanorods coated with Ag NPs. | The superhydrophobic condensation effect had a massive contribution to the SERS signal (high dependency of the Raman amplification signal on the water contact angle and the water droplet volume, which are controllable). | 10−6 M/not given 10−6 M/not given | [111] |
Crystal violet melamine detection in milk | 3D substrate based on ZnO and Ag for direct SERS detection in real-life applications (Ag NPs/ZnO nanorods/Si nanomace arrays). | Increase in hot spot density by an additional expansion of the hot spot arrangement along the third dimension. Practical applications in food and environmental safety. | 10−16 M 10−10 M/ 107 | [154] |
Benzodiazepines (BZDs) in mice; serum Estazolam in urine and serum samples | Cabbage-like (111) faceted Ag nanosheets decorated with ZnO NPs | Successful group-targeting screening of 5 BZDs (estazolam, oxazepam, alprazolam, triazolam, and lorazepam) and their concentration changes during metabolic process in mice serum. | 0.5 nM | [121] |
Dopamine | Hybrid Ag NPs-decorated ZnO WGM microcavity/SiO2 Relevant SERS marker bands: 675, 780, 1150, 1280, 1350, 1450, 1497, 1580 cm−1. | Strong confinement and light enhancement inside the microcavity due to the effect of an optical resonant cavity. | 10−12 M | [19] |
Dopamine | Matchstick-shaped Au-ZnO nanorods Relevant SERS marker bands: 1363, 1583, 1765, 1904 cm−1. | Facile one-pot colloid synthesis method. High reproducibility after visible-light-assisted cleaning. Promising biocompatibility and recyclable SERS detection platforms for various molecular species. | 10−5 M/1.2 × 104 | [158] |
Bombesin | Electrochemical ZnO NPs ZnO NPs from banana skin Relevant SERS marker bands: 638, 760, 1011, 1338, 1438, 1546 cm−1 and 1062, 1129, 1295, 1438, 1462 cm−1. | Biomarkers naturally found in body fluids and indicators of malignant tumors such as glioblastoma or pancreatic, stomach, or breast cancers. | 3 × 10−5 mol/L/103 | [118,120] |
Neurotensin (neuropeptide modulator), bradykinin | ||||
Aβ samples | Multimodal (SERS and electrochemical) biosensor with integrated acustofluidics-based on ZnO nanorod @Ag NPs patterned on a Au electrode. Relevant SERS marker bands: 442,491, 569, 677, 718, 776, 793, 842, 954, 1001, 1069, 1139, 1163, 1187, 1216, 1252, 1311, 1355 cm−1 and their intensity fluctuations. | Ready-to-use, integrated clinically, accurate, sensitive, and rapid biosensor for AD biomarkers from human plasma. | 120 fM/7.5 × 105 | [13] |
Aβ from human plasma (10 AD patients and 7 healthy controls) | --/-- |
Detection Substrate | Detection Setup | Detected Biomarker | LOD | Reference |
---|---|---|---|---|
ZnO/Ag hybrid WGM microcavity | Lab RAM HR 800 micro-Raman system with an excitation of 514.5 nm | Dopamine | 1 pM | [19] |
ZnO-Ag nanoarray SERS substrate | HORIBA Jobin Yvon Raman spectrophotometer equipped with an Olympus BX41 microscope; excitation: 785 nm | AD biomarkers containing Aβ42 | 120 fM | [13] |
ZnO tetrapods decorated with branched AuNPs | Aramis Raman microscope from Horiba Jobin-Yvon equipped with a He-Ne laser: 633 nm | Apomorphine, a dopamine agonist and cancer cells | 1 μM (0.27 μg mL−1) | [149] |
Matchstick-shaped Au–ZnO heterogeneous nanorods | Renishaw Raman system inViaReflex spectrometer LabRam HR 800 spectrometer of HORIBA. (532 nm, 632.8 nm, and 785 nm) | Dopamine | 10−5 M | [158] |
3D hierarchical substrates with ZnO nanowires (NWs) on ordered vertically aligned Si NRs decorated with AgNPs | Jobin Yvon high-resolution Evolution 2 system, excitation laser: 532 nm | Conformational change of human islet amyloid polypeptides (hIAPP) specific to NDs or type II diabetes | 10−8 M (single amyloid aggregate level) | [159] |
Recent Reviews on a Similar Topic | Spectroscopies Involved | Performance and Clinical Use | Practical Key Aspects | Limitations | Originality |
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Raman Spectroscopy: An Emerging Tool in Neurodegenerative Disease Research and Diagnosis; Devitt et al. [68] |
| Through comprehensive subsections, clinically relevant spectral features are indicated for each prevalent ND along with their interpretation. |
| Indirect detection involves particular species that could interfere with the targeted spectral features or exhibit toxicity. |
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ZnO Nanostructured Based Devices for Chemical and Optical Sensing Applications; Sha et al. [160] |
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ZnO and TiO2 Nanostructures for Surface-enhanced Raman Scattering-Based Bio-sensing: A Review; Adesoye et al. [119] |
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Salivary Biomarkers for the Diagnosis and Monitoring of Neurological Diseases; Farah et al. [123] |
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On-Chip Detection of the Biomarkers for Neurodegenerative Diseases: Technologies and Prospects; Song et al. [161] | Chip-based technologies for ND biomarker detection: fluorescence, micro-cantilever deflection, chemiluminescence, surface plasmon resonance, reflectometric interference, and other optical sensing techniques |
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Raman Spectroscopy Techniques for the Investigation and Diagnosis of Alzheimer’s Disease; Polykretis et al. [162] |
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| The scope of the review is to discuss recent advancements in Raman-based investigation and diagnosis of AD, highlighting their potential in monitoring the fingerprint of AD-specific biomarkers from biological samples (even brain tissue) and distinguishing between healthy and AD patients; very promising in clinical setting |
Raman Spectroscopy and Neuroscience: from FundamentalUnderstanding to Disease Diagnostics and Imaging; Payne et al. [163] |
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Emerging Two-Dimensional Materials-Based Diagnosis of Neurodegenerative Diseases: Status and Challenges; Wu, Dong et al. [164] |
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| Challenges identified by the authors are related to material-specific performance, false positive or negative issues, stability of graphene-based materials in vivo due to biomolecular interactions and hydrophobicity of graphene, and, last but not least, the costs of such biosensors. |
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This work |
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| Practical key aspects for designing high-performance ZnO hybrid SERS integrated biosensors for the detection of ND biomarkers.Biosensing challenges in practice and possible solutions. |
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Colniță, A.; Toma, V.-A.; Brezeștean, I.A.; Tahir, M.A.; Dina, N.E. A Review on Integrated ZnO-Based SERS Biosensors and Their Potential in Detecting Biomarkers of Neurodegenerative Diseases. Biosensors 2023, 13, 499. https://doi.org/10.3390/bios13050499
Colniță A, Toma V-A, Brezeștean IA, Tahir MA, Dina NE. A Review on Integrated ZnO-Based SERS Biosensors and Their Potential in Detecting Biomarkers of Neurodegenerative Diseases. Biosensors. 2023; 13(5):499. https://doi.org/10.3390/bios13050499
Chicago/Turabian StyleColniță, Alia, Vlad-Alexandru Toma, Ioana Andreea Brezeștean, Muhammad Ali Tahir, and Nicoleta Elena Dina. 2023. "A Review on Integrated ZnO-Based SERS Biosensors and Their Potential in Detecting Biomarkers of Neurodegenerative Diseases" Biosensors 13, no. 5: 499. https://doi.org/10.3390/bios13050499
APA StyleColniță, A., Toma, V. -A., Brezeștean, I. A., Tahir, M. A., & Dina, N. E. (2023). A Review on Integrated ZnO-Based SERS Biosensors and Their Potential in Detecting Biomarkers of Neurodegenerative Diseases. Biosensors, 13(5), 499. https://doi.org/10.3390/bios13050499