Nano-Brush Structure for Rapid Label-Free Differentiation of Alzheimer’s Disease Stages and Direct Capture of Neuron-Derived Exosomes from Human Blood Plasma
Abstract
The measurement of the neurofilament light chain (NFL) in human blood plasma/serum is a promising liquid biopsy for Alzheimer's disease (AD) diagnosis, offering advantages over conventional neuroimaging techniques recommended in clinical guidelines. Here, a controllable nano-brush structure comprising upstanding silicon nanowires coated with indium tin oxide was employed as the sensing substrate. This nano-brush structure was modified with an NFL antibody (NFLAb) via silane coupling and then further connected as the extended gate in a field-effect transistor (EGFET). Notable signal differences emerged within a 2 min timeframe, enabling the label-free differentiation in human blood plasmas among four distinct cohorts: healthy controls, subjective cognitive decline, mild cognitive impairment, and dementia due to AD. Our study indicates that achieving a surface roughness exceeding 400 nm on the modified nano-brush structure enables the effective electrical sensing in our EGFETs. These distinct electrical responses measured via the NFLAb-modified nano-brush EGFETs can be attributed to the combined effects of the captured NFLs and NFL-specific neuron-derived exosomes (NDEs) found in dementia patients, as confirmed by electron spectroscopy for chemical analysis, atomic force microscopy, and scanning electron microscopy. Finally, the potential of quantitatively detecting NDEs on the NFLAb-modified nano-brush structure was demonstrated using spiked solutions containing NFL-specific NDEs from IMR-32 neuroblast cells, wherein concentration-dependent changes were observed in the EGFETs output signal. Our findings show that the NFLAb-modified nano-brush EGFET enables rapid, label-free differentiation between healthy individuals and patients at varying stages of AD.
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Lin et al. developed a transistor sensor to measure NFL-specific neuron-derived exosomes in human blood plasma/serum.
“Lin et al. have developed a new transistor sensor to measure NFL-specific NDEs in human blood plasma/serum [238].”
From Extracellular vesicles as nanotheranostic platforms for targeted neurological disorder interventions (2024) · cites this paper as [238]
Cites
Preclinical Alzheimer’s Disease: Definition, Natural History, and Diagnostic Criteria
Dubois, B., Hampel, H., Feldman, H. H., Scheltens, P., Aisen, P., Andrieu, S., Bakardjian, H., Benali, H., Bertram, L., Blennow, K., et al. (2016) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2016.02.002 · Cited by 1,925 · see citing works
Alzheimer’s disease β-amyloid peptides are released in association with exosomes
Rajendran, L., Honsho, M., Zahn, T. R., Keller, P., Geiger, K. D., Verkade, P., & Simons, K. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0603838103 · Cited by 1,343 · see citing works
Label-free detection and molecular profiling of exosomes with a nano-plasmonic sensor
Im, H., Shao, H., Park, Y. I., Peterson, V. M., Castro, C. M., Weissleder, R., & Lee, H. (2014) · Nature Biotechnology · doi:10.1038/nbt.2886 · Cited by 1,324 · see citing works
Alzheimer disease: Epidemiology, diagnostic criteria, risk factors and biomarkers
Reitz, C., & Mayeux, R. P. (2014) · Biochemical Pharmacology · doi:10.1016/j.bcp.2013.12.024 · Cited by 1,180 · see citing works
Fiandaca, M. S., Kapogiannis, D., Mapstone, M., Boxer, A., Eitan, E., Schwartz, J. B., Abner, E. L., Petersen, R. C., Federoff, H. J., Miller, B. L., et al. (2015) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2014.06.008 · Cited by 959 · see citing works
Dominantly Inherited Alzheimer Network, Preische, O., Schultz, S. A., Apel, A., Kuhle, J., Kaeser, S. A., Barro, C., Gräber, S., Kuder-Buletta, E., LaFougere, C., et al. (2019) · Nature Medicine · doi:10.1038/s41591-018-0304-3 · Cited by 927 · see citing works
Veitch, D. P., Weiner, M. W., Aisen, P. S., Beckett, L. A., Cairns, N. J., Green, R. C., Harvey, D., Jack, C. R., Jagust, W., Morris, J. C., et al. (2019) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2018.08.005 · Cited by 454 · see citing works
Jia, L., Qiu, Q., Zhang, H., Chu, L., Du, Y., Zhang, J., Zhou, C., Liang, F., Shi, S., Wang, S., et al. (2019) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2019.05.002 · Cited by 376 · see citing works
Self-citationThe emerging role of exosomes in mental disorders
Saeedi, S., Israel, S. A., Nagy, C., & Turecki, G. (2019) · Translational Psychiatry · doi:10.1038/s41398-019-0459-9 · Cited by 367 · see citing works
Ghoneim, M. T., Nguyen, A. N., Dereje, N., Huang, J., Moore, G. C., Murzynowski, P. J., & Dağdeviren, C. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00655 · Cited by 319 · see citing works
Kim, K., Kim, M., Kim, D. W., Kim, S. Y., Park, S., & Park, C. B. (2020) · Nature Communications · doi:10.1038/s41467-019-13901-z · Cited by 258 · see citing works
Lin, Y.-S., Lee, W.-J., Wang, S.-J., & Fuh, J.-L. (2018) · Scientific Reports · doi:10.1038/s41598-018-35766-w · Cited by 251 · see citing works
Self-citationChu, C.-H., Sarangadharan, I., Regmi, A., Chen, Y.-W., Hsu, C.-P., Chang, W.-H., Lee, G.-Y., Chyi, J., Chen, C.-C., Shiesh, S., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-05426-6 · Cited by 248 · see citing works
CNS-Derived Blood Exosomes as a Promising Source of Biomarkers: Opportunities and Challenges
Hornung, S., Dutta, S., & Bitan, G. (2020) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2020.00038 · Cited by 239 · see citing works
Goetzl, E. J., Abner, E. L., Jicha, G. A., Kapogiannis, D., & Schwartz, J. B. (2018) · The FASEB Journal · doi:10.1096/fj.201700731r · Cited by 219 · see citing works
Subtyping of circulating exosome-bound amyloid β reflects brain plaque deposition
Lim, C. Z. J., Zhang, Y., Chen, Y., Zhao, H., Stephenson, M. C., Ho, N. R. Y., Chen, Y., Chung, J., Reilhac, A., Loh, T. P., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-09030-2 · Cited by 209 · see citing works
Doldán, X., Fagúndez, P., Cayota, A., Laíz, J., & Tosar, J. P. (2016) · Analytical Chemistry · doi:10.1021/acs.analchem.6b02421 · Cited by 206 · see citing works
Diagnostic and therapeutic potential of exosomes in Alzheimer’s disease
Martins, T. S., Trindade, D., Vaz, M., Campelo, I., Almeida, M., Trigo, G., Silva, O. A. B. D. C. E., & Henriques, A. G. (2020) · Journal of Neurochemistry · doi:10.1111/jnc.15112 · Cited by 174 · see citing works
Sarangadharan, I., Regmi, A., Chen, Y.-W., Hsu, C.-P., Chen, P., Chang, W.-H., Lee, G.-Y., Chyi, J., Shiesh, S., Lee, G., et al. (2017) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2017.09.018 · Cited by 169 · see citing works
A Calibration Method for Nanowire Biosensors to Suppress Device-to-Device Variation
Ishikawa, F. N., Curreli, M., Chang, H.-K., Chen, P.-C., Zhang, R., Côté, R. J., Thompson, M. E., & Zhou, C. (2009) · ACS Nano · doi:10.1021/nn9011384 · Cited by 148 · see citing works
Highly sensitive palladium oxide thin film extended gate FETs as pH sensor
Das, A., Ko, D. H., Chen, C.-H., Chang, L., Lai, C., Chu, F.-C., Chow, L., & Lin, R. (2014) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2014.08.057 · Cited by 142 · see citing works
Chen, C., Ganguly, A., Lu, C.-Y., Chen, T., Kuo, C.-C., Chen, R.-S., Tu, W.-H., Fischer, W. B., Chen, K., & Chen, L. (2011) · Analytical Chemistry · doi:10.1021/ac102489y · Cited by 141 · see citing works
Sun, Y., Jin, H. Z., Jiang, X., & Gui, R. (2020) · Analytical Chemistry · doi:10.1021/acs.analchem.9b05583 · Cited by 128 · see citing works
Extracellular Vesicle as a Source of Alzheimer’s Biomarkers: Opportunities and Challenges
Lee, S., Mankhong, S., & Kang, J. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20071728 · Cited by 125 · see citing works
Huang, X., Bhattacharjee, S., & Hoek, E. M. V. (2009) · Langmuir · doi:10.1021/la9028113 · Cited by 114 · see citing works
Extracellular Vesicles in Brain Tumors and Neurodegenerative Diseases
Ciregia, F., Urbani, A., & Palmisano, G. (2017) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2017.00276 · Cited by 110 · see citing works
A reversible surface functionalized nanowire transistor to study protein–protein interactions
Lin, S., Pan, C., Tseng, K.-C., Lin, M.-C., Chen, C.-D., Tsai, C.-C., Yu, S.-H., Sun, Y., Lin, T., & Chen, Y. (2009) · Nano Today · doi:10.1016/j.nantod.2009.04.005 · Cited by 88 · see citing works
Self-citationMishra, A. K., Jarwal, D. K., Mukherjee, B., Kumar, A., Ratan, S., & Jit, S. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.2966585 · Cited by 87 · see citing works
Adam, T., & Hashim, U. (2014) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2014.10.005 · Cited by 81 · see citing works
Concentration-Normalized Electroanalytical Assaying of Exosomal Markers
Li, Q., Tofaris, G. K., & Davis, J. J. (2017) · Analytical Chemistry · doi:10.1021/acs.analchem.6b05037 · Cited by 75 · see citing works
Kumar, A., Kim, S., Su, Y., Sharma, M., Kumar, P., Singh, S., Lee, J., Furdui, C. M., Singh, R. N., Hsu, F., et al. (2021) · EBioMedicine · doi:10.1016/j.ebiom.2020.103192 · Cited by 75 · see citing works
Dual-Selective Magnetic Analysis of Extracellular Vesicle Glycans
Wang, Z., Sun, X., Natalia, A., Tang, C., Ang, C. B. T., Ong, C. J., Teo, M. C. C., So, J. B. Y., & Shao, H. (2019) · Matter · doi:10.1016/j.matt.2019.10.018 · Cited by 71 · see citing works
Coaxial-structured ZnO/silicon nanowires extended-gate field-effect transistor as pH sensor
Li, H.-H., Yang, C.-E., Kei, C., Su, C.-Y., Dai, W.-S., Tseng, J.-K., Yang, P.-Y., Chou, J. C., & Cheng, H. (2012) · Thin Solid Films · doi:10.1016/j.tsf.2012.05.045 · Cited by 63 · see citing works
Palit, S., Singh, K., Lou, B., Her, J.-L., Pang, S., & Pan, T.-M. (2020) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2020.127850 · Cited by 58 · see citing works
TiO2 Nano Flowers Based EGFET Sensor for pH Sensing
Yang, C.-C., Chen, K., & Su, Y. (2019) · Coatings · doi:10.3390/coatings9040251 · Cited by 56 · see citing works
Peripheral Biomarkers for Early Detection of Alzheimer’s and Parkinson’s Diseases
Htike, T. T., Mishra, S., Kumar, S., Padmanabhan, P., & Gulyás, B. Z. (2018) · Molecular Neurobiology · doi:10.1007/s12035-018-1151-4 · Cited by 54 · see citing works
Exosome-based approaches in the management of Alzheimer’s disease
Kandimalla, R., Saeed, M., Tyagi, N., Gupta, R. C., & Aqil, F. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.104974 · Cited by 43 · see citing works
ZnO nano-array-based EGFET biosensor for glucose detection
Qi, J., Zhang, H., Ji, Z., Xu, M., & Zhang, Y. (2015) · Applied Physics A · doi:10.1007/s00339-015-9122-3 · Cited by 42 · see citing works
Suthar, J., Prieto‐Simón, B., Williams, G. R., & Guldin, S. (2022) · Analytical Chemistry · doi:10.1021/acs.analchem.1c04282 · Cited by 36 · see citing works
Neurofilament light chain level in plasma extracellular vesicles and Parkinson’s disease
Chung, C., Chan, L., Chen, J., Bamodu, O. A., & Hong, C. (2020) · Therapeutic Advances in Neurological Disorders · doi:10.1177/1756286420975917 · Cited by 35 · see citing works
Tian, T., Qiao, S., & Tannous, B. A. (2023) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.2c07981 · Cited by 27 · see citing works
Rao, L., Wang, P., Qian, Y., Zhou, G., & Nötzel, R. (2020) · ACS Omega · doi:10.1021/acsomega.0c05364 · Cited by 27 · see citing works
Wang, C., Wang, H.-I., Tang, W.-T., Luo, C., Kobayashi, T., & Leu, J. (2011) · Optics Express · doi:10.1364/oe.19.024286 · Cited by 27 · see citing works
Dumbbell Aptamer Sensor Based on Dual Biomarkers for Early Detection of Alzheimer’s Disease
Zhou, J., Sun, Y., Zhang, J., Luo, F., Ma, H., Guan, M., Feng, J., & Dong, X. (2023) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.2c21379 · Cited by 26 · see citing works
Lin, S., Vinzons, L. U., Kang, Y.-S., & Lai, T. (2015) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.5b01878 · Cited by 26 · see citing works
Self-citationLin, S., Huang, S.-Y., Chen, S.-F., Vinzons, L. U., Ciou, J.-Y., & Wong, P.-J. (2014) · ACS Applied Materials & Interfaces · doi:10.1021/am503323y · Cited by 24 · see citing works
Self-citationWide linear sensing sensors using ZnO:Ta extended-gate field-effect-transistors
Chiu, Y.-S., Lee, C.-T., Lou, L., Ho, S., & Chuang, C.-T. (2013) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2013.07.107 · Cited by 23 · see citing works
ZnO-Nanowire-Based Extended-Gate Field-Effect-Transistor pH Sensors Prepared on Glass Substrate
Chang, S.-P., Li, C.-W., Chen, K., Chang, S., Hsu, C., Hsueh, T., & Hsueh, H.-T. (2012) · Science of Advanced Materials · doi:10.1166/sam.2012.1410 · Cited by 23 · see citing works
Hajian, R., DeCastro, J., Parkinson, J., Kane, A., Camelo, A. F. R., Chou, P. P., Yang, J., Wong, N., Hernandez, E. D. O., Goldsmith, B. R., et al. (2021) · Advanced Biology · doi:10.1002/adbi.202000594 · Cited by 21 · see citing works
Investigation into the Effect of Varied Functional Biointerfaces on Silicon Nanowire MOSFETs
Lin, S., Chi, T.-Y., Lai, T., & Liu, M.-C. (2012) · Sensors · doi:10.3390/s121216867 · Cited by 18 · see citing works
Self-citationDevelopment of EGFET-based ITO pH sensors using epoxy free membrane
Ahmed, N. M., Sabah, F. A., Al-Hardan, N. H., Almessiere, M. A., Mohammad, S. M., Lim, W. F., Jumaah, M., Islam, A. K. M. S., Hassan, Z., Quah, H. J., et al. (2021) · Semiconductor Science and Technology · doi:10.1088/1361-6641/abe914 · Cited by 17 · see citing works
Yang, Y., Lu, Y. M., Gupta, A. K., & Lin, S. (2021) · Materials Letters · doi:10.1016/j.matlet.2021.131575 · Cited by 7 · see citing works
Self-citation
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Choi, H. K., Chen, M., Goldston, L. L., & Lee, K.-B. (2024) · Nano Convergence · doi:10.1186/s40580-024-00426-5 · Cited by 26 · see citing works
Biosensor-based methods for exosome detection with applications to disease diagnosis
Ge, W., Mu, Z., Yang, S., Zeng, Y., Deng, Y., Lin, Y., Xie, P., & Li, G. (2025) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2025.117362 · Cited by 24 · see citing works
Self-citationExtracellular vesicles‐based theranostics for neurodegenerative diseases
Zhao, H., Zhu, L., Wang, C., & Yang, Y. (2024) · Wiley Interdisciplinary Reviews Nanomedicine and Nanobiotechnology · doi:10.1002/wnan.1993 · Cited by 14 · see citing works
Self-citationPei, J., Perumal, N. C., Meng, P., Long, Q., & Palanisamy, C. P. (2025) · Ageing Research Reviews · doi:10.1016/j.arr.2025.102904 · Cited by 10 · see citing works
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Lim, J., Lee, H. J., Kim, J., Moon, C., Lee, J., Kang, Y., Kim, R., Mun, B., Ji, Y. W., & Haam, S. (2024) · Advanced Functional Materials · doi:10.1002/adfm.202413723 · Cited by 7 · see citing works
Zhao, Y., Chang, W., Li, X., Su, P., Zhang, H., & Zhang, J. (2025) · ACS Sensors · doi:10.1021/acssensors.5c00287 · Cited by 6 · see citing works
Zhu, A.-M., An, Y., Xu, B., Li, Z., & Dong, S. (2025) · Microchemical Journal · doi:10.1016/j.microc.2025.113720 · Cited by 4 · see citing works
Advanced nanostructured biosensors enabled by rational surface engineering for bacterial detection
Kumar, N., Vinzons, L. U., Shia, W.-Y., Chu, P.-H., Liao, Y., Liu, C., & Lin, S. (2025) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2025.118112 · Cited by 4 · see citing works
Self-citationShe, J.-W., Lin, L., Janardhanan, J. A., Wang, I.-C., Hsu, F.-C., Tseng, H.-S., Hsiao, Y., & Yu, H. (2026) · ACS Applied Bio Materials · doi:10.1021/acsabm.5c02404 · Cited by 3 · see citing works
Xie, P., Wang, X., Wang, H., Xue, H., He, S., Li, L., Wong, M. S., & Qiao, X. G. (2026) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2026.118430 · Cited by 2 · see citing works
Chang, M.-H., Chou, L., Liu, Y.-J., Hsu, C.-H., Yang, Y.-Y., Juan, B.-C., Lin, G. L., Lin, Y.-J., Huang, Y., Kumar, N., et al. (2026) · Digest of technical papers/Digest of technical papers - IEEE International Solid-State Circuits Conference · doi:10.1109/isscc49663.2026.11409179
Self-citation
- Authors
- Lin, S.-P., Lee, W.-J., Sun, M.-C., Yang, Y.-H., Vinzons, L. U., Lin, Y.-M., & Wei, Y.-T.
- Affiliations
- National Chung Hsing University (TW)
- Taichung Veterans General Hospital (TW)
- Graduate Institute of Biomedical Engineering, National Chung Hsing University, Taichung, Taiwan 40227, Republic of China
- Corresponding author
- Lin, Shu-Ping
- Venue
- ACS Applied Materials & Interfaces
- Publisher
- American Chemical Society (ACS)
- Published
- 2023-11-23
- Volume
- 15
- Issue
- 48
- Pages
- 56478-56489
- ISSN
- 1944-8244 (ISSN-L)
- 1944-8252
- OpenAlex
- W4388946033
- Cited by
- 11
- References
- 52
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 3
- FWCI
- 0.82
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- ACS Applied Materials & Interfaces (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Chung-Hsing University (TCVGH-NCHU109DCA0100022)
- Ministry of Science and Technology, Taiwan (MOST 111-2628-E-005-005-MY3, MOST 109-2221-E-005-011-)
- Taichung Veterans General Hospital (TCVGH-NCHU109DCA0100022)
- Engineering in Agriculture Biotech Leadership Center, National Chung Hsing University
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electrochemical sensors and biosensors
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Advanced biosensing and bioanalysis techniques
- Physical Sciences › Chemical Engineering › Bioengineering › Analytical Chemistry and Sensors
- Keywords
- Alzheimer's disease, neurofilament light chain, liquid biopsy, silicon nanowires, indium tin oxide, extended-gate field-effect transistor, label-free biosensing, plasma biomarkers, mild cognitive impairment, dementia, surface roughness, exosome capture
- MeSH terms
- Alzheimer Disease, Humans, Neurons, Plasma, Biomarkers, Exosomes, Cognitive Dysfunction
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