Probing low-copy-number proteins in single living cells using single-cell plasmonic immunosandwich assays

使用单细胞等离子体免疫夹层分析检测单个活细胞中的低拷贝数蛋白质

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作者:Jia Liu, Hui He, Dan Xie, Yanrong Wen, Zhen Liu

Abstract

Cellular heterogeneity is pervasive and of paramount importance in biology. Single-cell analysis techniques are indispensable for understanding the heterogeneity and functions of cells. Low-copy-number proteins (fewer than 1,000 molecules per cell) perform multiple crucial functions such as gene expression, cellular metabolism and cell signaling. The expression level of low-copy-number proteins of individual cells provides key information for the in-depth understanding of biological processes and diseases. However, the quantitative analysis of low-copy-number proteins in a single cell still remains challenging. To overcome this, we developed an approach called single-cell plasmonic immunosandwich assay (scPISA) for the quantitative measurement of low-copy-number proteins in single living cells. scPISA combines in vivo microextraction for specific enrichment of target proteins from cells and a state-of-the-art technique called plasmon-enhanced Raman scattering for ultrasensitive detection of low-copy-number proteins. Plasmon-enhanced Raman scattering detection relies on the plasmonic coupling effect (hot-spot) between silver-based plasmonic nanotags and a gold-based extraction microprobe, which dramatically enhances the signal intensity of the surface-enhanced Raman scattering of the nanotags and thereby enables sensitivity at the single-molecule level. scPISA is a straightforward and minimally invasive technique, taking only ~6-15 min (from in vivo extraction to Raman spectrum readout). It is generally applicable to all freely floating intracellular proteins provided that appropriate antibodies or alternatives (for example, molecularly imprinted polymers or aptamers) are available. The entire protocol takes ~4-7 d to complete, including material fabrication, single-cell manipulation, protein labeling, signal acquisition and data analysis.

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