Multiparameter resonant imaging for studying cell interactions.

Multiparameter resonant imaging for studying cell interactions.
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用于研究细胞相互作用的多参数共振成像。

DOI:
10.1038/s41377-018-0032-y
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发表时间:
2018
期刊:
Light, science & applications
影响因子:
--
通讯作者:
Juan-Colás J
Juan-Colás J
中科院分区:
--
文献类型:
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作者:
Juan-Colás J

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The development of optical microscopy techniques has provided major insights into the machinery of life, from molecules to cells to tissues and organs. Fluorescent techniques have been particularly successful in this respect by elucidating biological pathways and helping us understand the complex relationships that underpin all living organisms. For example, the first use of fluorescence as an analytical tool in 1864 1 paved the way for the study of biological entities and interactions, but it was not until nearly 60 years later, in the 1930s, that fluorophores were first employed to perform biological investigations into specific tissue components and bacteria that do not autofluoresce 2. These advances led to the first labeling of an antibody with a visible label in 1941 3, which gave birth to the field of immunofluorescence and allowed us obtain unprecedented insight into antibody structure.Nevertheless, fluorescence techniques require the addition of labels, which complicate the procedure and may interfere with the very pathways that we are trying to understand 4, 5. As a result, new methods have been developed that do not require the addition of fluorescent labels but use photonic resonances to enhance the weak interaction between electromagnetic fields and the biological objects of interest. Moreover, it is important to detect multiple parameters in parallel since information about the density, adhesion, or motility of a living body and properties such as the stiffness and impedance of biological objects all provide insights into our understanding of biological pathways. An important step toward this goal of multiparameter characterization has now been taken with the introduction of a resonant labelfree microscopy technique that offers insights into both