Cancer-leukocyte hybrid membrane-cloaked magnetic beads for the ultrasensitive isolation, purification, and non-destructive release of circulating tumor cells.
Zhi-Min ChangRui ZhangChao YangDan ShaoYuguo TangWen-Fei DongZheng WangPublished in: Nanoscale (2021)
Most of the current circulating tumor cell (CTC) isolation techniques are based on immunomagnetic beads with antibodies or aptamers that specifically target epithelial cell adhesion molecules (EpCAMs). However, these techniques are unsuitable for the isolation and purification of circulating tumor cells because they fail to recognize EpCAM-negative CTCs and thus lead to the non-specific adsorption of background leucocytes and EpCAM-positive circulating epithelial cells. Moreover, releasing the CTCs from the capture platform without disruption is a big challenge. To address these issues, herein, we developed biomimetic magnetic beads (MBs) by cloaking a cancer cell-leukocyte hybrid membrane on the MBs. These biomimetic MBs inherited homologous CTC binding capability from the cancer cell membrane and less affinity for the background cells from the leukocyte membrane, exhibitng a higher CTC capture efficiency and separation purity than EpCAM-based MBs. Importantly, the captured CTCs could be rapidly released by a facile method i.e. co-incubation with a trypsin-EDTA solution. We demonstrated the excellent performance of these MBs for the highly pure separation and non-destructive release of CTCs in metastatic mammary carcinoma models. Our results indicate that the proposed homologous cancer-leukocyte membrane coating strategy may provide a promising method for the ultrahigh-specific and sensitive detection of CTCs.
Keyphrases
- circulating tumor cells
- circulating tumor
- papillary thyroid
- sensitive detection
- cell adhesion
- squamous cell
- quantum dots
- peripheral blood
- small cell lung cancer
- dna damage
- squamous cell carcinoma
- dna repair
- molecularly imprinted
- stem cells
- childhood cancer
- high throughput
- bone marrow
- liquid chromatography
- transcription factor
- big data
- machine learning
- dna binding
- high resolution
- mesenchymal stem cells