A visualized automatic particle counting strategy for single‐cell level telomerase activity quantification

Author:

Li Chen1,Chen Hui1,Fan Tingting1,Zhao Jingru1,Ding Zheng23,Lin Zeyu23,Sun Shuqing1,Tan Chunyan1,Liu Feng1,Jiang Hongtao23,Tan Ying1

Affiliation:

1. State Key Laboratory of Chemical Oncogenomics Shenzhen International Graduate School Tsinghua University Shenzhen China

2. Department of Urology Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology) Shenzhen China

3. Shenzhen Engineering and Technology Center of Minimally Invasive Urology Shenzhen People's Hospital Shenzhen China

Abstract

AbstractThe accurate evaluation of telomerase activity, a typical cancer biomarker, is vital for early cancer screening. In this study, we developed a dark‐field microscopy (DFM) visual single‐particle detection scheme to detect telomerase activity based on automatic counting gold nanoparticles (AuNPs). This method started with attaching the telomerase substrate (TS) primer to the magnetic beads (MBs) through streptavidin‐biotin interaction. In the presence of telomerase and dNTPs, the TS primer was expanded with (TTAGGG)n repeat units to form the telomerase extension product (MBs‐telomerase extension product), which could be hybridized with the complementary DNA (cDNA) modified with AuNPs through Au‐S bonds (AuNPs‐SH‐cDNA). After magnetic separation and DNA double‐strand unwinding, AuNPs were collected from the supernatant, and the telomerase activity was quantitatively measured by visually counting bright spots based on DFM. This strategy achieved a limit of detection as low as 1 HeLa cell and distinguished telomerase activity among different cell lines, thus verifying its excellent sensitivity and specificity. Further, two common telomerase inhibitors (BIBR1532 and curcumin) were screened with the consistent IC50 values with other methods, respectively. It is worth mentioning that this strategy can clearly identify bladder cancer among various urinary diseases. Consequently, the visualized automatic particle counting strategy is potential as a powerful tool in early and noninvasive diagnosis of bladder cancer.

Publisher

Wiley

Subject

Biomedical Engineering,Biomaterials

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