A visualized automatic particle counting strategy for single‐cell level telomerase activity quantification
Chen Li,
Hui Chen,
Tingting Fan,
Jingru Zhao,
Zheng Ding,
Zeyu Lin,
Shuqing Sun,
Chunyan Tan,
Feng Liu,
Hongtao Jiang,
Ying Tan
Affiliations
Chen Li
State Key Laboratory of Chemical Oncogenomics Shenzhen International Graduate School Tsinghua University Shenzhen China
Hui Chen
State Key Laboratory of Chemical Oncogenomics Shenzhen International Graduate School Tsinghua University Shenzhen China
Tingting Fan
State Key Laboratory of Chemical Oncogenomics Shenzhen International Graduate School Tsinghua University Shenzhen China
Jingru Zhao
State Key Laboratory of Chemical Oncogenomics Shenzhen International Graduate School Tsinghua University Shenzhen China
Zheng Ding
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
Zeyu Lin
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
Shuqing Sun
State Key Laboratory of Chemical Oncogenomics Shenzhen International Graduate School Tsinghua University Shenzhen China
Chunyan Tan
State Key Laboratory of Chemical Oncogenomics Shenzhen International Graduate School Tsinghua University Shenzhen China
Feng Liu
State Key Laboratory of Chemical Oncogenomics Shenzhen International Graduate School Tsinghua University Shenzhen China
Hongtao Jiang
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
Ying Tan
State Key Laboratory of Chemical Oncogenomics Shenzhen International Graduate School Tsinghua University Shenzhen China
Abstract The 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.
