脂质体
荧光
荧光寿命成像显微镜
粒子(生态学)
材料科学
荧光显微镜
膜
显微镜
荧光团
分析化学(期刊)
荧光相关光谱
生物物理学
化学
纳米技术
光学
色谱法
物理
生物化学
生物
生态学
作者
Tanner W. Young,Sarah J. Cox‐Vázquez,Ethan D. Call,Dhari Shah,Stephen C. Jacobson,Ricardo Javier Vázquez
出处
期刊:ACS Nano
[American Chemical Society]
日期:2025-01-01
被引量:1
标识
DOI:10.1021/acsnano.4c10813
摘要
Characterization of individual biological nanoparticles can be significantly improved by coupling complementary analytical methods. Here, we combine resistive-pulse sensing (RPS) with fluorescence lifetime imaging microscopy (FLIM) to differentiate liposomes at the single-particle level. RPS measures the particle volume, shape, and surface-charge density, and FLIM determines the fluorescence lifetime of the fluorophore associated with the lipid membrane. The RPS devices are fabricated in-plane on a glass substrate to facilitate coupling of RPS with FLIM measurements. For proof-of-concept, we studied liposomes containing various cholesterol concentrations with membrane-intercalated Di-8-ANEPPS, whose fluorescence lifetime is known to be sensitive to cholesterol concentrations in the membrane. RPS-FLIM revealed that increasing cholesterol concentrations in the liposome from 0% to 50% increased the fluorescence lifetimes from 2.1 ± 0.2 to 3.4 ± 0.5 ns, respectively. Moreover, RPS-FLIM discerned liposome populations with the same cholesterol concentration but labeled with dyes that have different fluorescence lifetimes (Di-8-ANEPPS and COE-S6), parsing two particle populations with statistically identical volumes, cholesterol concentration, and lipid composition. Interrogation with RPS-FLIM occurred with individual particles making a single pass through the detection region and overcomes issues with fluorescence spectral overlap that limits traditional methods. We envision RPS-FLIM as a versatile and scalable technique with the potential to differentiate biological particles at the single-particle level to simultaneously inform on particle size, surface-charge density, membrane composition, and identity.
科研通智能强力驱动
Strongly Powered by AbleSci AI