A Protocol for a High-Throughput Multiplex Cell Viability Assay

Part of the Methods in Molecular Biology book series (MIMB, volume 1470)


High-throughput cell viability assays are broadly used in RNAi and small molecule screening experiments to identify compounds that selectively kill cancer cells or as counter screens to exclude the compounds that have a generic effect on cell growth. While there are several assaying techniques available, cellular fitness is often assessed on the basis of one single and often rather indirect physiological indicator. This can lead to inconsistencies and poor correspondence between cell viability screening experiments, conducted under comparable conditions but with different viability indicators. Multiplexing, i.e., the combination of different individual assaying techniques in one experiment and subsequent comparative analysis of multiparametric data can decrease inter-assay variability and increase dataset concordance. Here, we describe a protocol for a multiplexing approach for high-throughput cell viability screening to address the issues encountered in the classical strategy using a single fitness indicator described above. The method combines a biochemical, luminescence-based approach and two fluorescence-based assay types. The biochemical method assesses cellular fitness by quantifying intracellular ATP concentration. Calcein labeling reflects cell fitness through membrane integrity and indirect measurement of ATP-dependent enzymatic esterase activity. Hoechst DNA stain correlates cell fitness with cellular DNA content. The presented multiplexing approach is suitable for low, medium and high-throughput screening and has the potential to decrease inter-assay variability and increase dataset concordance as well as reproducibility of experimental results.

Key words

High-throughput screening Cell-based assays Multiplexing Cell viability Cell fitness RNAi screening Drug discovery Target validation In vitro toxicity screening 


  1. 1.
    Sekhon BK, Roubin RH, Tan A et al (2008) High-throughput screening platform for anticancer therapeutic drug cytotoxicity. Assay Drug Dev Technol 6:711–721CrossRefPubMedGoogle Scholar
  2. 2.
    Cai XY, Xiong LM, Yang SH et al (2014) Comparison of toxicity effects of ropivacaine, bupivacaine, and lidocaine on rabbit intervertebral disc cells in vitro. Spine J 14:483–490CrossRefPubMedGoogle Scholar
  3. 3.
    Laurenza I, Pallocca G, Mennecozzi M et al (2013) A human pluripotent carcinoma stem cell-based model for in vitro developmental neurotoxicity testing: effects of methylmercury, lead and aluminum evaluated by gene expression studies. Int J Dev Neurosci 31:679–691CrossRefPubMedGoogle Scholar
  4. 4.
    Wiezorek C (1984) Cell cycle dependence of Hoechst 33342 dye cytotoxicity on sorted living cells. Histochemistry 81:493–495CrossRefPubMedGoogle Scholar
  5. 5.
    Haibe-Kains B, El-Hachem N, Birkbak NJ et al (2013) Inconsistency in large pharmacogenomic studies. Nature 504:389–393CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Barretina J, Caponigro G, Stransky N et al (2012) The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483:603–607CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Garnett MJ, Edelman EJ, Heidorn SJ et al (2012) Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 483:570–575CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Gilbert DF, Erdmann G, Zhang X et al (2011) A novel multiplex cell viability assay for high-throughput RNAi screening. PLoS One 6, e28338CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Womac AD, Burkeen JF, Neuendorff N et al (2009) Circadian rhythms of extracellular ATP accumulation in suprachiasmatic nucleus cells and cultured astrocytes. Eur J Neurosci 30:869–876CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Ataullakhanov FI, Vitvitsky VM (2002) What determines the intracellular ATP concentration. Biosci Rep 22:501–511CrossRefPubMedGoogle Scholar
  11. 11.
    das Neves RP, Jones NS, Andreu L et al (2010) Connecting variability in global transcription rate to mitochondrial variability. PLoS Biol 8, e1000560CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Braut-Boucher F, Pichon J, Rat P et al (1995) A non-isotopic, highly sensitive, fluorimetric, cell-cell adhesion microplate assay using calcein AM-labeled lymphocytes. J Immunol Methods 178:41–51CrossRefPubMedGoogle Scholar
  13. 13.
    Graca da Silveira M, Vitoria San Romao M, Loureiro-Dias MC et al (2002) Flow cytometric assessment of membrane integrity of ethanol-stressed Oenococcus oeni cells. Appl Environ Microbiol 68:6087–6093CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Larsson R, Nygren P (1989) A rapid fluorometric method for semiautomated determination of cytotoxicity and cellular proliferation of human tumor cell lines in microculture. Anticancer Res 9:1111–1119PubMedGoogle Scholar
  15. 15.
    Mollapour M, Tsutsumi S, Neckers L (2010) Hsp90 phosphorylation, Wee1 and the cell cycle. Cell Cycle 9:2310–2316CrossRefPubMedGoogle Scholar
  16. 16.
    Horn T, Arziman Z, Berger J et al (2007) GenomeRNAi: a database for cell-based RNAi phenotypes. Nucleic Acids Res 35:D492–D497CrossRefPubMedGoogle Scholar
  17. 17.
    Gilbert DF, Wilson JC, Nink V et al (2009) Multiplexed labeling of viable cells for high-throughput analysis of glycine receptor function using flow cytometry. Cytometry A 75:440–449CrossRefPubMedGoogle Scholar
  18. 18.
    Talwar S, Lynch JW, Gilbert DF (2013) Fluorescence-based high-throughput functional profiling of ligand-gated ion channels at the level of single cells. PLoS One 8, e58479CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Gebhardt FM, Mitrovic AD, Gilbert DF et al (2010) Exon-skipping splice variants of excitatory amino acid transporter-2 (EAAT2) form heteromeric complexes with full-length EAAT2. J Biol Chem 285:31313–31324CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Gilbert D, Esmaeili A, Lynch JW (2009) Optimizing the expression of recombinant alphabetagamma GABAA receptors in HEK293 cells for high-throughput screening. J Biomol Screen 14:86–91CrossRefPubMedGoogle Scholar
  21. 21.
    Boutros M, Bras LP, Huber W (2006) Analysis of cell-based RNAi screens. Genome Biol 7:R66CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Pelz O, Gilsdorf M, Boutros M (2010) web cellHTS2: a web-application for the analysis of high-throughput screening data. BMC Bioinformatics 11:185CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Institute of Medical Biotechnology and Erlangen Graduate School in Advanced Optical Technologies (SAOT)Friedrich-Alexander Universität Erlangen-NürnbergErlangenGermany
  2. 2.Division of Signaling and Functional GenomicsGerman Cancer Research Center (DKFZ)HeidelbergGermany
  3. 3.Department of Cell and Molecular Biology, Medical Faculty MannheimHeidelberg UniversityHeidelbergGermany

Personalised recommendations