Progress and Perspective of Solid-State Organic Fluorophores for Biomedical Applications

• Published in: Journal of the American Chemical Society Vol. 143; no. 50; pp. 21143 - 21160
• Main Authors: Li, Ke, Ren, Tian-Bing, Huan, Shuangyan, Yuan, Lin, Zhang, Xiao-Bing
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 22.12.2021
• Subjects: Alzheimer Disease - diagnosis; Alzheimer Disease - metabolism; Amyloid beta-Peptides - chemistry; Amyloid beta-Peptides - metabolism; Animals; Biosensing Techniques - methods; Cell Line; Contrast Media - chemistry; Fluorescent Dyes - chemistry; Humans; Infrared Rays; Mice; Microscopy, Fluorescence; Optical Imaging - methods; Photosensitizing Agents - chemistry; Phototherapy; Quinazolinones - chemistry; Reactive Oxygen Species - chemistry; Reactive Oxygen Species - metabolism; Theranostic Nanomedicine
• ISSN: 0002-7863; 1520-5126
• DOI: 10.1021/jacs.1c10925

Fluorescent organic dyes have been extensively used as raw materials for the development of versatile imaging tools in the field of biomedicine. Particularly, the development of solid-state organic fluorophores (SSOFs) in the past 20 years has exhibited an upward trend. In recent years, studies on SSOFs have focused on the development of advanced tools, such as optical contrast agents and phototherapy agents, for biomedical applications. However, the practical application of these tools has been hindered owing to several limitations. Thus, in this Perspective, we have provided insights that could aid researchers to further develop these tools and overcome the limitations such as limited aqueous dispersibility, low biocompatibility, and uncontrolled emission. First, we described the inherent photophysical properties and fluorescence mechanisms of conventional, aggregation-induced emissive, and precipitating SSOFs with respect to their biomedical applications. Subsequently, we highlighted the recent development of functionalized SSOFs for bioimaging, biosensing, and theranostics. Finally, we elucidated the potential prospects and limitations of current SSOF-based tools associated with biomedical applications.