CRANAD-28, a difluoroboron curcumin analogue, has been demonstrated in earlier reports to successfully label amyloid beta (Aβ) plaques for imaging both ex vivo and in vivo. CRANAD-28’s imaging brightness, ability to penetrate the blood brain barrier, and low toxicity make the compound a potentially potent imaging tool in Alzheimer’s research. In this study, the Aβ-labeling ability of CRANAD-28 was investigated in further detail using histological staining to assess different criteria, including stained Aβ plaque brightness, Aβ plaque size, and Aβ plaque number count. The results of this study demonstrated CRANAD-28 to be superior across all criteria assessed. Furthermore, CRANAD-28 and IBA-1 antibody were used to label Aβ-plaques and microglia respectively. Statistical analysis with Spearman regression revealed a statistically significant negative correlation between the size of labeled Aβ plaques and surrounding microglia density. This finding provides interesting insight into Aβ plaque and microglia dynamism in AD pathology and corroborates the findings of previous studies. In addition, we found that CRANAD-28 provided distinct spectral signatures for Aβs in the core and periphery of the plaques. Based on the study’s results, CRANAD-28 could be considered as an alternative standard for imaging Aβ-plaques in future research studies.

Fluorescence imaging probes are indispensable tools for clinical navigation and preclinical research. However, the discovery of target‐specific probes is hampered by the scarcity of targetable fluorophore scaffolds, making the development process slow, costly, and heavily reliant on trial‐and‐error design. Here, we present a hybrid strategy that integrates AI with bioassays to accelerate the development of target‐specific fluorescent probes. We developed an AI model (PROBY) based on over one million molecule entries from nine datasets, capable of identifying fluorescent molecules and predicting seven key photophysical properties. Applying PROBY to a library of 26,416 target‐validated molecules, we identified thousands of candidates with both target affinity and favorable optical characteristics. Focusing on three clinically relevant targets (tau, BCL‐2, and TDP‐43), we validated AI‐identified candidates and discovered PE859, obatoclax, and B3, which supported applications in spectral analysis, drug screening, pathological labeling, cell imaging, and ex vivo tumor imaging. Guided by PROBY, we chemically modify PE859, yielding two optimized derivatives (859‐1 and 859‐2). With improved photophysical properties, 859‐2 enabled in vivo two‐photon imaging of tau pathology in transgenic mice. This hybrid AI‐bioassay strategy substantially broadens the accessible scaffold landscape for designing target‐specific fluorescence probes and provides a scalable, efficient, and cost‐effective framework for next‐generation probe discovery.

A screening platform based on epitope alteration for drug discovery (SPEED).

Although omics and multi-omics approaches are the most used methods to create signature arrays for liquid biopsy, the high cost of omics technologies still largely limits their wide applications for point-of-care. Inspired by the bat echolocation mechanism, we propose an “echoes” approach for creating chemiluminescence signatures via screening of a compound library, and serum samples of Alzheimer’s disease (AD) were used for our proof-of-concept study. We first demonstrated the discrepancy in physicochemical properties between AD and healthy control serums. On this basis, we developed a simple, cost-effective, and versatile platform termed UNICODE (UNiversal Interaction of Chemiluminescence echOes for Disease Evaluation). The UNICODE platform consists of a “bat” probe, which generates different chemiluminescence intensities upon interacting with various substrates, and a panel/array of “flag” molecules that are selected from library screening. The UNICODE array could enable the reflecting/“echoing” of the signatures of various serum components and intact physicochemical interactions between serum substrates. In this study, we screened a library of over 1,000 small molecules and identified 12 “flag” molecules (top 12) that optimally depict the differences between AD and healthy control serums. Finally, we employed the top 12 array to conduct tests on serum samples and utilized machine learning methods to optimize detection performance. We successfully distinguished AD serums, achieving the highest area under the curve of 90.24% with the random forest method. Our strategy could provide new insights into biofluid abnormality and prototype tools for developing liquid biopsy diagnoses for AD and other diseases.

Molecular switching plays a critical role in biological and displaying systems. Here we demonstrate the first use of peptides to operate molecular switches of donor-acceptor Stenhouse adducts (DASAs), a series of negative photochromes that are highly promising for applications ranging from smart material to biological systems. Fluorescence imaging proved Aβ40 species could make SHA-2 more stable in the linear configuration than without peptide and decrease the rate of molecular switching. According to molecular dynamics simulation, SHA-2 bound to protein resulted in substantial changes in the tertiary structure of Aβ40 monomer with the region of Glu22-Ala30 partially unfolded and being more exposed to water. This structural change is likely to impede the aggregation of Aβ40, as evidenced by fluorescence and ProteoStat ^® aggresome detection experiments. SHA-2 is able to inhibit the aggregation of Aβ40 by producing the off-pathway structures. These results open ample opportunities for optically addressable potential widely apply DASAs in the biological system based on this peptides-tailor process.