High-Throughput Fluorescence Profiling of Remodeling Dynamics at Surface-Deposited Amyloid Interfaces
Quantitative sensing of remodeling dynamics at surface-deposited amyloid interfaces remains challenging with conventional analytical platforms which are largely optimized for bulk solution readouts and therefore struggle to capture spatially heterogeneous remodeling of surface deposits. To address these limitations, we report a 96-zone amyloid-deposited paper plate (96-ADPP) that allows reproducible formation of amyloid deposits on a cellulose matrix. By controlling both deposition and readout geometry, the platform provides matched fluorescence quantification and spatial imaging across identical assay zones, establishing a reproducible interface-based sensing format. Using this platform, we profile the remodeling responses of deposited amyloid to five amyloid-modulating agents (EPPS, ibuprofen, puerarin, pepsin, and trypsin), capturing both signal amplification and reduction. The platform further facilitates analysis of combined and sequential compound treatments, revealing how compound pairing and treatment order govern biointerface remodeling dynamics. Notably, sequential treatment with puerarin followed by EPPS produced a 5.6-fold greater reduction in deposited amyloid signal compared with their combined administration, accompanied by a 2.7-fold decrease in signal deviation. Overall, the 96-ADPP provides a high-throughput platform for fluorescence-based profiling of remodeling dynamics at surface-deposited amyloid interfaces, enabling systematic analysis of pairwise interaction effects and order-dependent responses to compound combinations.
Authors
- Taeha Lee (ORCID: https://orcid.org/0000-0003-3271-5097)
- Gyudo Lee (ORCID: https://orcid.org/0000-0001-7895-5112)
- Seokbeom Roh (ORCID: https://orcid.org/0000-0002-0069-7691)
- Hyungbeen Lee (ORCID: https://orcid.org/0000-0003-3418-8518)
- Sungjin Kim (ORCID: https://orcid.org/0000-0002-2068-9189)
- Dain Kang
- Minwoo Bae
- Wonjun Hwang
- Sohyun Choi
- Yeongjun Seo
Institutions
- Digital Science (United States) (US)
- Sejong Institute (KR)
- Artificial Intelligence in Medicine (Canada) (CA)
Publication Details
- Journal
- Analytical Chemistry
- Published
- 2026-06-16
- DOI
- https://doi.org/10.1021/acs.analchem.6c02272
- Primary Topic
- Alzheimer's disease research and treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Ministry of Health and Welfare
- National Research Foundation of Korea
- Ministry of Science and ICT, South Korea