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Issue 4, 2019
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Quantitative profiling of CD13 on single acute myeloid leukemia cells by super-resolution imaging and its implication in targeted drug susceptibility assessment

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Abstract

Quantitative profiling of membrane proteins on the cell surface is of great interest in tumor targeted therapy and single cell biology. However, the existing technologies are either of insufficient resolution, or unable to provide precise information on the localization of individual proteins. Here, we report a new method that combines the use of quantum dot labeling, super-resolution microscopy (structured illumination microscopy, SIM) and software modeling. In this proof-of-principle study, we assessed the biological effects of Bestatin on individual cells from different AML cell lines expressing CD13 proteins, a potential target for tumor targeted therapy. Using the proposed method, we found that the different AML cell lines exhibit different CD13 expression densities, ranging from 0.1 to 1.3 molecules per μm2 cell surface, respectively. Importantly, Bestatin treatment assays shows that its effects on cell growth inhibition, apoptosis and cell cycle change are directly proportional to the density of CD13 on the cell surface of these cell lines. The results suggest that the proposed method advances the quantitative analysis of single cell surface proteins, and that the quantitative profiling information of the target protein on single cells has potential value in targeted drug susceptibility assessment.

Graphical abstract: Quantitative profiling of CD13 on single acute myeloid leukemia cells by super-resolution imaging and its implication in targeted drug susceptibility assessment

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Publication details

The article was received on 13 Aug 2018, accepted on 12 Dec 2018 and first published on 09 Jan 2019


Article type: Paper
DOI: 10.1039/C8NR06526H
Nanoscale, 2019,11, 1737-1744
  • Open access: Creative Commons BY-NC license
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    Quantitative profiling of CD13 on single acute myeloid leukemia cells by super-resolution imaging and its implication in targeted drug susceptibility assessment

    Y. Xi, D. Wang, T. Wang, L. Huang and X. Zhang, Nanoscale, 2019, 11, 1737
    DOI: 10.1039/C8NR06526H

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