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Details

Autor(en) / Beteiligte
Titel
Moving perfusion culture and live-cell imaging from lab to disc: proof of concept toxicity assay with AI-based image analysis
Ist Teil von
  • Lab on a chip, 2023-03, Vol.23 (6), p.163-1612
Ort / Verlag
England: Royal Society of Chemistry
Erscheinungsjahr
2023
Quelle
MEDLINE
Beschreibungen/Notizen
  • In vitro , cell-based assays are essential in diagnostics and drug development. There are ongoing efforts to establish new technologies that enable real-time detection of cell-drug interaction during culture under flow conditions. Our compact (10 × 10 × 8.5 cm) cell culture and microscope on disc (CMoD) platform aims to decrease the application barriers of existing lab-on-a-chip (LoC) approaches. For the first time in a centrifugal device, (i) cells were cultured for up to six days while a spindle motor facilitated culture medium perfusion, and (ii) an onboard microscope enabled live bright-field imaging of cells while the data wirelessly transmitted to a computer. The quantification of cells from the acquired images was done using artificial intelligence (AI) software. After optimization, the obtained cell viability data from the AI-based image analysis proved to correlate well with data collected from commonly used image analysis software. The CMoD was also suitable for conducting a proof-of-concept toxicity assay with HeLa cells under continuous flow. The half-maximal inhibitory time (IT50) for various concentrations of doxorubicin (DOX) in the case of HeLa cells in flow, was shown to be lower than the IT50 obtained from a static cytotoxicity assay, indicating a faster onset of cell death in flow. The CMoD proved to be easy to handle, enabled cell culture and monitoring without assistance, and is a promising tool for examining the dynamic processes of cells in real-time assays. We developed a compact perfusion cell culture with integrated wireless detection device for real-time optical monitoring. The platform enables long-term cell growth and cytotoxicity assay where cell viability is quantified using AI software.
Sprache
Englisch
Identifikatoren
ISSN: 1473-0197
eISSN: 1473-0189
DOI: 10.1039/d2lc00984f
Titel-ID: cdi_rsc_primary_d2lc00984f

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