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

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Abstract

Cells are complex systems with concurrent multi-physical responses, and cell physiological signals are often encoded with spatiotemporal dynamics and further coupled with multiple cellular activities. However, most existing electronic sensors are only single-modality and cannot capture multi-parametric cellular responses. In this paper, a 1024-pixel CMOS quad-modality cellular interfacing array that enables multi-parametric cell profiling for drug development is presented. The quad-modality CMOS array features cellular impedance characterization, optical detection, extracellular potential recording, and biphasic current stimulation. The fibroblast transparency and surface adhesion are jointly monitored by cellular impedance and optical sensing modalities for comprehensive cell growth evaluation. Simultaneous current stimulation and opto-mechanical monitoring based on cardiomyocytes are demonstrated without any stimulation/sensing dead-zone. Furthermore, drug dose-dependent multi-parametric feature extractions in cardiomyocytes from their extracellular potentials and opto-mechanical signals are presented. The CMOS array demonstrates great potential for fully automated drug screening and drug safety assessments, which may substantially reduce the drug screening time and cost in future new drug development.

Graphical abstract: Multi-parametric cell profiling with a CMOS quad-modality cellular interfacing array for label-free fully automated drug screening

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Publication details

The article was received on 08 Feb 2018, accepted on 06 Jun 2018 and first published on 31 Aug 2018


Article type: Paper
DOI: 10.1039/C8LC00156A
Citation: Lab Chip, 2018, Advance Article
  • Open access: Creative Commons BY license
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    Multi-parametric cell profiling with a CMOS quad-modality cellular interfacing array for label-free fully automated drug screening

    J. S. Park, S. I. Grijalva, M. K. Aziz, T. Chi, S. Li, M. N. Sayegh, A. Wang, H. C. Cho and H. Wang, Lab Chip, 2018, Advance Article , DOI: 10.1039/C8LC00156A

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