4.7 Article

Live-cell profiling of membrane sialic acids by fluorescence imaging combined with SERS labelling

Journal

SENSORS AND ACTUATORS B-CHEMICAL
Volume 351, Issue -, Pages -

Publisher

ELSEVIER SCIENCE SA
DOI: 10.1016/j.snb.2021.130877

Keywords

Sialic acid; Proteins; Fluorescence imaging; SERS labelling

Funding

  1. National Natural Science Foundation of China [21977031, 21777041, 21974046, 22176058]
  2. Shanghai Science and Technology Committee [19ZR1472300, 19391901700, 19520744000]
  3. Shanghai Municipal Science and Technology Major Project [2018SHZDZX03]

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The study presents a highly efficient and convenient two-step tagging strategy for live-cell profiling of membrane sialic acids using fluorescence imaging and SERS labeling. By using tumor markers as model proteins, sialic acids can be partitioned into distinct space domains, allowing for classification of various types of cells. This strategy provides a robust and versatile platform for highly detailed analysis of cell surface saccharide profiles, with potential applications in optical biosensing and molecular diagnosis.
Detecting cell-surface sialic acids (SAs) is essential for cancer research, as accumulating evidence indicates that SA overexpression is closely related to unusual biopathways, such as tumorigenesis. However, it remains challenging to detect and classify membrane SAs. Here, a highly efficient and convenient two-step tagging strategy is described for live-cell profiling of membrane sialic acids with fluorescence imaging and SERS labelling. The fluorescence of SA-linked dyes indicates the SA distribution, whereas the Raman signals recognize the SA-linked proteins marked by aptamer modified SERS probes. Using the tumor markers MUC-1 and TNC as the model proteins, SAs can be partitioned into distinct space domains via Flu/SERS information on living cell membrane. Importantly, we find that SAs are highly expressed near MUC-1 and TNC on the surface of cancer cells, with subtle differences existing among various carcinoma cell lines, enabling classification of various types of cells. Together, our strategy provides a robust and versatile platform for highly detailed analysis of cell surface saccharide profiles and that it has great potential for optical biosensing and molecular diagnosis.

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