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Updated: Jul 24, 2025

Detecting Amyloid-β Accumulation via Immunofluorescent Staining in a Mouse Model of Alzheimer's Disease
Published on: April 19, 2021
A highly sensitive hemicyanine-based near-infrared fluorescence sensor for detecting toxic amyloid aggregates in
Oshin D Warerkar1, Niyati H Mudliar1, Tanya Ahuja1
1SVKM's Shri C. B. Patel Research Centre, Vile Parle, Mumbai, Maharashtra 400056, India.
Abstract:
The development of an accurate and sensitive sensor for detecting amyloid plaques, which are responsible for many protein disorders like Alzheimer's disease, is crucial for early diagnosis. Recently, there has been a notable increase in the development of fluorescence probes that exhibit emission in the red region (>600 nm), aiming to effectively tackle the challenges encountered when working with complex biological matrices. In the current investigation, a hemicyanine-based probe, called LDS730, has been used for the sensing of amyloid fibrils, which belong to the Near-Infrared Fluorescence (NIRF) family of dyes. NIRF probes provide higher precision in detection, prevent photo-damage, and minimize the autofluorescence of biological specimens. The LDS730 sensor emits in the near-infrared region and shows a 110-fold increase in fluorescence turn-on emission when bound to insulin fibrils, making it a highly sensitive sensor. The sensor has an emission maximum of ~710 nm in a fibril-bound state, which shows a significant red shift along with a Stokes' shift of ~50 nm. The LDS730 sensor also displays excellent performance in the complicated human serum matrix, with a limit of detection (LOD) of 103 nM. Molecular docking calculations suggest that the most likely binding location of LDS730 in the fibrillar structure is the inner channels of amyloid fibrils along its long axis, and the sensor engages in several types of hydrophobic interactions with neighboring amino acid residues of the fibrillar structure. Overall, this new amyloid sensor has great potential for the early detection of amyloid plaques and for improving diagnostic accuracy.
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