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Multi-parametric cell profiling with a CMOS quad-modality cellular interfacing array for label-free fully automated

Jong Seok Park1, Sandra I Grijalva, Moez K Aziz

  • 1The School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30308, USA. hua.wang@ece.gatech.edu.

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This study introduces a novel quad-modality CMOS sensor array for advanced cell analysis. This multi-parametric tool enhances drug development by capturing complex cellular responses for faster screening and safety assessments.

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Area of Science:

  • Biomedical Engineering
  • Cellular Biology
  • Materials Science

Background:

  • Cells exhibit complex multi-physical responses and spatiotemporal dynamics.
  • Existing electronic sensors are often single-modality, limiting comprehensive cellular analysis.
  • There is a need for advanced tools to capture multi-parametric cellular responses for drug development.

Purpose of the Study:

  • To present a 1024-pixel CMOS quad-modality cellular interfacing array for multi-parametric cell profiling.
  • To enable comprehensive cell growth evaluation and drug screening.
  • To reduce drug screening time and cost in new drug development.

Main Methods:

  • Development of a quad-modality CMOS array integrating cellular impedance, optical detection, extracellular potential recording, and biphasic current stimulation.
  • Joint monitoring of fibroblast transparency and surface adhesion using impedance and optical sensing.
  • Simultaneous current stimulation and opto-mechanical monitoring of cardiomyocytes without dead zones.

Main Results:

  • Demonstrated comprehensive cell growth evaluation through joint impedance and optical sensing.
  • Achieved simultaneous stimulation and monitoring in cardiomyocytes.
  • Presented drug dose-dependent multi-parametric feature extraction from cardiomyocyte extracellular potentials and opto-mechanical signals.

Conclusions:

  • The quad-modality CMOS array enables multi-parametric cell profiling for drug development.
  • The system shows potential for fully automated drug screening and safety assessments.
  • This technology can significantly reduce time and cost in future drug development pipelines.