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Talanta · 2026

3D-printed spiral and contraction-expansion array cascaded microfluidic chip combined with ICP-MS for efficient sorting and online detection of circulating tumor cells.

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You Rui, Chen Beibei, He Man, Hu Bin

Аннотация

Metastasis remains the leading cause of cancer-related mortality and is closely associated with circulating tumor cells (CTCs), which serve as key biomarkers for liquid biopsy. However, their extremely low abundance in blood necessitates highly efficient and accurate isolation and detection strategies. Here, this work reports a 3D-printed cascaded inertial microfluidic platform integrating spiral and contraction-expansion (CEA) modules for direct separation and single-cell quantification of CTCs from whole blood. Benefiting from height-programmable 3D printing, the device enables precise tuning and integration of multi-stage microchannels that are difficult to achieve using conventional soft lithography. The system operates without external fields or blood pre-treatment, relying solely on size-dependent inertial focusing to achieve high-throughput separation (110 μL min-1). Coupled with single-cell inductively coupled plasma mass spectrometry (SC-ICP-MS), Eu-labeled CTCs are directly quantified via characteristic signal bursts after on-chip enrichment. The platform achieves a CTC recovery of 98.90%, white blood cell (WBC) removal efficiency of 99.37%, and red blood cell (RBC) removal efficiency of 99.70%, with high post-sorting cell viability (99.33%) and purity (72.34-99.07%). Clinical validation using blood samples from cancer patients and healthy donors demonstrates reliable CTC enumeration with counts ranging from 34 to 164 cells mL-1 in patient samples and zero detection in controls. This work provides a simple, label-enhanced, and fully integrated microfluidic chip-ICP-MS strategy for high-throughput CTC analysis, highlighting the advantages of 3D-printed height-tunable microfluidics for clinical liquid biopsy applications.

ИП Урсолов Владлен Сергеевич

ИНН 500118482352 · ОГРНИП 314500117700049

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