Issue 4, 2019

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

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

Supplementary files

Article information

Article type
Paper
Submitted
13 Aug 2018
Accepted
12 Dec 2018
First published
09 Jan 2019
This article is Open Access
Creative Commons BY-NC license

Nanoscale, 2019,11, 1737-1744

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

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. You can use material from this article in other publications, without requesting further permission from the RSC, provided that the correct acknowledgement is given and it is not used for commercial purposes.

To request permission to reproduce material from this article in a commercial publication, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party commercial publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements