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Double Spiral Chip-embedded Micro-trapezoid Filters (SMT Filters) for the Sensitive Isolation of CTCs of Prostate Cancer by Spectral Detection

Overview
Journal Nanoscale Adv
Specialty Biotechnology
Date 2022 Dec 21
PMID 36540122
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Abstract

Circulating tumor cells (CTCs) are cancer cells that are released from the original tumor and circulate in the blood vessels, carrying greatly similar constituents as the original tumor. Therefore, CTCs have a significant value in cancer prognosis, early diagnosis, and anti-cancer therapy. However, their rarity and heterogeneity make the isolation of CTCs an arduous task. In the present research, we propose a double spiral chip-embedded micro-trapezoid filter (SMT filter) for the sensitive isolation of the CTCs of prostate cancer by spectral detection. SMT filters were elongated to effectively capture CTCs and this distinctive design was conducive to their isolation and enrichment. The SMT filters were verified with tumor cells and artificial patient blood with a capture efficiency as high as 94% at a flow rate of 1.5 mL h. As a further validation, the SMT filters were validated in isolating CTCs from 10 prostate cancers and other cancers in 4 mL blood samples. Also, the CTCs tested positive for each patient blood sample, ranging from 83-114 CTCs. Significantly, we advanced hyperspectral imaging to detect the characteristic spectrum of CTCs both captured on SMT filters and enriched after isolation. The CTCs could be positively identified by hyperspectral imaging with complete integrity of the cell morphology and an improved characteristic spectrum. This represents a breakthrough in the conventional surface-enhanced Raman scattering (SERS) spectroscopy of nanoparticles. Also, the characteristic spectrum of the CTCs would be highly beneficial for distinguishing the cancer type and accurate for enumerating tumor cells with varied intensities. Furthermore, a novel integrated flower-shaped microfilter was presented with all these aforementioned merits. The success of both the SMT filters and characteristic spectral detection indicated their feasibility for further clinical analysis, the evaluation of cancer therapy, and for potential application.

Citing Articles

Systematic Meta-Analysis of Computer-Aided Detection of Breast Cancer Using Hyperspectral Imaging.

Leung J, Karmakar R, Mukundan A, Thongsit P, Chen M, Chang W Bioengineering (Basel). 2024; 11(11).

PMID: 39593720 PMC: 11591395. DOI: 10.3390/bioengineering11111060.

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