Issue 6, 2023

Moving perfusion culture and live-cell imaging from lab to disc: proof of concept toxicity assay with AI-based image analysis

Abstract

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.

Graphical abstract: Moving perfusion culture and live-cell imaging from lab to disc: proof of concept toxicity assay with AI-based image analysis

Supplementary files

Article information

Article type
Paper
Submitted
21 Okt 2022
Accepted
06 Feb 2023
First published
07 Feb 2023

Lab Chip, 2023,23, 1603-1612

Moving perfusion culture and live-cell imaging from lab to disc: proof of concept toxicity assay with AI-based image analysis

L. Serioli, L. Gruzinskyte, G. Zappalà, E. T. Hwu, T. Z. Laksafoss, P. L. Jensen, D. Demarchi, A. Müllertz, A. Boisen and K. Zór, Lab Chip, 2023, 23, 1603 DOI: 10.1039/D2LC00984F

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