Microrobotic Swarms for Intracellular Measurement with Enhanced Signal-to-Noise Ratio.

Journal: ACS nano
Published Date:

Abstract

In cell biology, fluorescent dyes are routinely used for biochemical measurements. The traditional global dye treatment method suffers from low signal-to-noise ratios (SNR), especially when used for detecting a low concentration of ions, and increasing the concentration of fluorescent dyes causes more severe cytotoxicity. Here, we report a robotic technique that controls how a low amount of fluorescent-dye-coated magnetic nanoparticles accurately forms a swarm and increases the fluorescent dye concentration in a local region inside a cell for intracellular measurement. Different from existing magnetic micromanipulation systems that generate large swarms (several microns and above) or that cannot move the generated swarm to an arbitrary position, our system is capable of generating a small swarm (e.g., 1 μm) and accurately positioning the swarm inside a single cell (position control accuracy: 0.76 μm). In experiments, the generated swarm inside the cell showed an SNR 10 times higher than the traditional global dye treatment method. The high-SNR robotic swarm enabled intracellular measurements that had not been possible to achieve with traditional global dye treatment. The robotic swarm technique revealed an apparent pH gradient in a migrating cell and was used to measure the intracellular apparent pH in a single oocyte of living . With the position control capability, the swarm was also applied to measure calcium changes at the perinuclear region of a cell before and after mechanical stimulation. The results showed a significant calcium increase after mechanical stimulation, and the calcium increase was regulated by the mechanically sensitive ion channel, PIEZO1.

Authors

  • Xian Wang
    Wenzhou Medical University, Wenzhou, China.
  • Tiancong Wang
    Department of Mechanical and Industrial Engineering, University of Toronto, Toronto M5S 3G8, Canada.
  • Xin Chen
    University of Nottingham, Nottingham, United Kingdom.
  • Junhui Law
    Department of Mechanical and Industrial Engineering, University of Toronto, Toronto M5S 3G8, Canada.
  • Guanqiao Shan
    Department of Mechanical and Industrial Engineering, University of Toronto, Toronto M5S 3G8, Canada.
  • Wentian Tang
    Department of Mechanical and Industrial Engineering, University of Toronto, Toronto M5S 3G8, Canada.
  • Zheyuan Gong
    School of Mechanical Engineering and Automation, Beihang University, Beijing, China.
  • Peng Pan
    Department of Gastroenterology, Changhai Hospital, Second Military Medical University/Naval Medical University, Shanghai 200433, China.
  • Xinyu Liu
    Institute of Medical Technology, Peking University Health Science Center, Beijing, China.
  • Jiangfan Yu
    Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong.
  • Changhai Ru
  • Xi Huang
    Institute of Computing Technology(ICT), Chinese Academy of Sciences(CAS), Beijing, China.
  • Yu Sun
    Department of Neurology, China-Japan Friendship Hospital, Beijing, China.