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Simple, Affordable, and Modular Patterning of Cells using DNA
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DNA Framework-Programmed Cell Capture via Topology-Engineered Receptor-Ligand Interactions.

Min Li1, Hongming Ding2, Meihua Lin3

  • 1Institute of Molecular Medicine, State Key Laboratory of Oncogenes and Related Genes, Renji Hospital, School of Medicine and School of Chemistry and Chemical Engineering , Shanghai Jiao Tong University , Shanghai 200127 , China.

Journal of the American Chemical Society
|November 7, 2019
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Summary
This summary is machine-generated.

Researchers engineered cell surface receptor-ligand interactions using tetrahedral DNA frameworks for high-affinity capture of circulating tumor cells (CTCs). This novel approach enhances cancer cell detection and diagnosis.

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Area of Science:

  • Biotechnology
  • Molecular Biology
  • Nanotechnology

Background:

  • Receptor-ligand interactions (RLIs) are crucial in biology but complex on cell surfaces due to dynamic receptor distribution.
  • The traditional keys-and-locks model inadequately describes these complex cell surface RLIs.

Purpose of the Study:

  • To develop a tetrahedral DNA framework (TDF)-programmed platform for topologically engineering cell surface RLIs.
  • To enable high-affinity capture of circulating tumor cells (CTCs) by actively recruiting clustered receptors.

Main Methods:

  • Synthesized n-simplexes using TDFs with 1-3 aptamers targeting epithelial cell adhesion molecule (EpCAM).
  • Utilized the orthogonal anchoring of ligands on TDF vertices for spatial organization.
  • Employed 2-simplex probes for CTC capture and evaluated their binding affinity and effect on endocytosis.

Main Results:

  • The 2-simplex with three EpCAM aptamers demonstrated a ~19-fold increase in binding affinity.
  • Engineered RLIs prevented cancer cell endocytosis.
  • Achieved high-efficiency capture of CTCs from clinical breast cancer patient samples.

Conclusions:

  • TDF-programmed platform offers a powerful method for engineering cell surface RLIs.
  • This approach facilitates studying RLIs in physiological contexts.
  • The platform shows significant potential for advancing cancer diagnosis, particularly for CTC detection.