荧光
计算机科学
选择(遗传算法)
纳米技术
生物分子
吸收(声学)
亮度
过程(计算)
生物系统
化学
生化工程
材料科学
人工智能
生物
物理
工程类
光学
操作系统
复合材料
作者
Daniela Cavazos‐Elizondo,Alan Aguirre‐Soto
标识
DOI:10.1002/anse.202200004
摘要
Abstract Fluorescent labels have been paramount in the advancement of molecular biology and related fields, for they unveil Nature's unknowns. Numerous labels have been developed that can be attached to most biomolecules via various labeling chemistries. Practitioners are expected to navigate through the growing libraries of fluorescent probes to select the best one for their target application. It is precisely this vast collection of labels that makes the selection process challenging, often leading to non‐ideal choices. Here, we present a meta‐analysis of fluorescent labels data with the intention of facilitating the selection process. We classify tags into molecular (dyes), macromolecular (proteins), and nanoparticles. Groups are then organised into subgroups by chemical structure. Entries were structured based on cost, absorption and emission wavelengths, Stokes shifts, molar extinction coefficients, quantum yields, lifetimes, and brightness. We correlate chemical composition to available photophysical properties of the fluorophores in their bound‐whenever possible‐and unbound states. The limited access to complete, comparable, and meaningful photophysical data for fluorescent labels and to reliable fluorescence standards, where calibration is still challenging and benchmarks are still ill‐defined, are highlighted to raise critical awareness of the current issues hampering further development of the field.
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