This work addresses the challenge of accurately identifying living circulating tumor cell (CTC) from contaminating leukocytes by developing a novel, fixation-free dual-marker sensing strategy that preserves cell viability and biomolecular integrity for downstream analysis. Our strategy utilizes telomerase-responsive gold nanoparticles (polyA-TSP-AuNPs) to increase intracellular negative charge, which in turn enhances the electrostatic accumulation of a custom-synthesized, mitochondria-targeting aggregation-induced emission probe (DSA-PPh3). This dual-marker identification system was then integrated with our rVAR2-FETCH enrichment method, and the resulting CTC counts were combined with hematological parameters in a supervised machine learning model for diagnosis. The dual-marker system amplified the tumor-to-leukocyte signal ratio to 10.03 and showed excellent concordance with the CellSearch reagent (R = 0.97) while preserving RNA integrity. When integrated with rVAR2-FETCH enrichment, our platform detected CTC in 83.67% (41/49) of non-small cell lung cancer patients, outperforming the complete CellSearch kit. Furthermore, machine learning models integrating CTC counts with hematological biomarkers achieved excellent diagnostic performance for lung cancer, with support vector machine demonstrating the best results (AUC = 0.977).
He et al. (2026) studied this question.