Sensor-based cellular microphysiometry is a technique that allows non-invasive, label-free, real-time monitoring of living cells that can greatly improve the predictability of toxicology testing by removing the influence of biochemical labels. In this work, the Intelligent Mobile Lab for In Vitro Diagnostics (IMOLA-IVD) was utilized to perform cellular microphysiometry on 3D multicellular spheroids. Using a commercial 3D printer, 3 × 3 microwell arrays were fabricated to maintain nine previously cultured HepG2 spheroids on a single BioChip. Integrated layers above and under the spheroids allowed fluidic contact between spheroids in microwells and BioChip sensors while preventing wash out from medium perfusion. Spheroid culturing protocols were optimized to grow spheroids to a diameter of around 620 μm prior to transfer onto BioChips. An ON/OFF pump cycling protocol was developed to optimize spheroid culture within the designed microwells, intermittently perfuse spheroids with fresh culture medium, and measure the extracellular acidification rate (EAR) and oxygen uptake rate (OUR) with the BioChips of the IMOLA-IVD platform. In a proof-of-concept experiment, spheroids were perfused for 36 h with cell culture medium before being exposed to medium with 1% sodium dodecyl sulphate (SDS) to lyse cells as a positive control. These microphysiometry studies revealed a repeatable pattern of extracellular acidification throughout the experiment, indicating the ability to monitor real-time metabolic activity of spheroids embedded in the newly designed tissue encapsulation. After perfusion for 36 h with medium, SDS exposure resulted in an instant decrease in EAR and OUR signals from 37 mV/h (± 5) to 8 mV/h (± 8) and from 308 mV/h (± 21) to −2 mV/h (± 13), respectively. The presented spheroid monitoring system holds great potential as a method to automate screening and analysis of pharmaceutical agents using 3D multicellular spheroid models.
This is a preview of subscription content, log in to check access
F. Alexander would like to thank the Whitaker International Program for their financial support of this work. The authors would also like to thank the Deutscher Tierschutzbund—Akademie für Tierschutz (German Animal Welfare Federation—Animal Welfare Academy).
Alépée N, Alepee N, Bahinski A et al (2014) State-of-the-art of 3D cultures (organs-on-a-chip) in safety testing and pathophysiology. Altex 31:441–477. doi:10.14573/altex1406111Google Scholar
Alexander F, Wiest J (2016) Automated transepithelial electrical resistance measurements of the EpiDerm reconstructed human epidermis model. Conf Proc IEEE Eng Med Biol Soc 2016:469–472. doi:10.1109/EMBC.2016.7590741Google Scholar
Brischwein M, Grothe H, Wiest J, Zottmann M, Ressler J, Wolf B (2009) Planar ruthenium oxide sensors for cell-on-a-chip metabolic studies. Chem Anal-Wars 54:1193–1201Google Scholar
Edmondson R, Broglie JJ, Adcock AF, Yang L (2014) Three-dimensional cell culture systems and their applications in drug discovery and cell-based biosensors. Assay Drug Dev Technol 12:207–218. doi:10.1089/adt.2014.573CrossRefGoogle Scholar
Eklund SE, Taylor D, Kozlov E et al (2004) A microphysiometer for simultaneous measurement of changes in extracellular glucose, lactate, oxygen, and acidification rate. Anal Chem 76:519–527. doi:10.1021/ac034641zCrossRefGoogle Scholar
Hartung T, Bruner L, Curren R et al (2010) First alternative method validated by a retrospective weight-of-evidence approach to replace the Draize eye test for the identification of non-irritant substances for a defined applicability domain. Altex 27:43–51. doi:10.14573/altex.2010.1.43CrossRefGoogle Scholar
Lee M-Y, Park CB, Dordick JS, Clark DS (2005) Metabolizing enzyme toxicology assay chip (MetaChip) for
high-throughput microscale toxicity analyses. Proc Natl Acad Sci USA 102:983–987. doi:10.1073/pnas.0406755102CrossRefGoogle Scholar
Marx U, Andersson TB, Bahinski A et al (2016) Biology-inspired microphysiological system approaches to solve the prediction dilemma of substance testing. Altex 33:272–321. doi:10.14573/altex.1603161Google Scholar
Ramaiahgari SC, den Braver MW, Herpers B et al (2014) A 3D in vitro model of differentiated HepG2 cell spheroids with improved liver-like properties for repeated dose high-throughput toxicity studies. Arch Toxicol 88:1083–1095. doi:10.1007/s00204-014-1215-9Google Scholar
Waring MJ, Arrowsmith J, Leach AR et al (2015) An analysis of the attrition of drug candidates from four major pharmaceutical companies. Nat Rev Drug Discov 14:475–486. doi:10.1038/nrd4609CrossRefGoogle Scholar
Weltin A, Slotwinski K, Kieninger J et al (2014) Cell culture monitoring for drug screening and cancer research: a transparent, microfluidic, multi-sensor microsystem. Lab Chip 14:138–146. doi:10.1039/c3lc50759aCrossRefGoogle Scholar
Wilkening S, Stahl F, Bader A (2003) Comparison of primary human hepatocytes and hepatoma cell line HepG2 with regard to their biotransformation properties. Drug Metab Dispos 31:1035–1042. doi:10.1124/dmd.31.8.1035CrossRefGoogle Scholar