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Standalone cell culture microfluidic device-based microphysiological system for automated cell observation and application in nephrotoxicity tests.

Hiroshi KimuraHiroko NakamuraTomomi GotoWakana UchidaTakayuki UozumiDaniel NishizawaKenta ShinhaJunko SakagamiKotaro Doi
Published in: Lab on a chip (2024)
Microphysiological systems (MPS) offer an alternative method for culturing cells on microfluidic platforms to model organ functions in pharmaceutical and medical sciences. Although MPS hardware has been proposed to maintain physiological organ function through perfusion culture, no existing MPS can automatically assess cell morphology and conditions online to observe cellular dynamics in detail. Thus, with this study, we aimed to establish a practical strategy for automating cell observation and improving cell evaluation functions with low temporal resolution and throughput in MPS experiments. We developed a versatile standalone cell culture microfluidic device (SCCMD) that integrates microfluidic chips and their peripherals. This device is compliant with the ANSI/SLAS standards and has been seamlessly integrated into an existing automatic cell imaging system. This integration enables automatic cell observation with high temporal resolution in MPS experiments. Perfusion culture of human kidney proximal tubule epithelial cells using the SCCMD improves cell function. By combining the proximal tubule MPS with an existing cell imaging system, nephrotoxicity studies were successfully performed to automate morphological and material permeability evaluation. We believe that the concept of building the ANSI/SLAS-compliant-sized MPS device proposed herein and integrating it into an existing automatic cell imaging system for the online measurement of detailed cell dynamics information and improvement of throughput by automating observation operations is a novel potential research direction for MPS research.
Keyphrases
  • single cell
  • cell therapy
  • high throughput
  • healthcare
  • machine learning
  • stem cells
  • deep learning
  • induced apoptosis
  • social media
  • health information
  • single molecule
  • cell death
  • neural network