All-in-One Microfluidic Chip for Online Labeling, Separating, and Focusing Rare Circulating Tumor Cells from Blood Samples Followed by Inductively Coupled Plasma Mass Spectrometry Detection

化学 循环肿瘤细胞 免疫磁选 电感耦合等离子体质谱法 磁选 去唾液酸糖蛋白受体 微流控 单细胞分析 检出限 细胞 色谱法 纳米技术 质谱法 癌症 材料科学 生物化学 内科学 医学 冶金 体外 肝细胞 转移
作者
Yan Xu,Beibei Chen,Man He,Zewei Cui,Bin Hu
出处
期刊:Analytical Chemistry [American Chemical Society]
卷期号:95 (37): 14061-14067 被引量:4
标识
DOI:10.1021/acs.analchem.3c02680
摘要

Circulating tumor cell (CTC) detection is essential for early cancer diagnosis and evaluating treatment efficacy. Despite the growing interest in isolating CTCs and further quantifying surface biomarkers at the single-cell level, highly efficient separation of rare CTCs from massive blood cells is still a big challenge. Here, we developed an all-in-one microfluidic chip system for the immunolabeling, magnetic separation, and focusing of HepG2 cells (as a CTC model) and online combined it with single cell-inductively coupled plasma mass spectrometry (SC-ICP–MS) for quantitative analysis of the asialoglycoprotein receptor (ASGPR) on single HepG2 cells. Lanthanide-labeled anti-ASGPR monoclonal antibody and antiepithelial cell adhesion molecule-modified magnetic beads were prepared as signal and magnetic probes, respectively. Target cells were highly efficiently labeled with signal and magnetic probes in the mixing zone of the microfluidic chip and then focused and sorted in the separation zone by specific magnetic separation techniques to avoid matrix contamination. The average cell recovery of HepG2 cells was derived to be 94.1 ± 5.7% with high separation efficiency and purity. The sorted cells with signal probes were detected for enumeration and quantification of ASGPR on their surface by SC-ICP–MS. The developed method showed good specificity and high sensitivity, detecting an average of (1.0 ± 0.2) × 105 ASGPR molecules per cell surface. This method can be used for absolute quantitative analysis of ASGPR on the surface of single hepatocellular carcinoma cells in real-world samples, providing a highly efficient analytical platform for studying targeted drug delivery in cancer therapy.
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