Note 9). This should always be prepared fresh on the day of
27. Metabolite identification is based on the retention times closely matching and the ratios of a primary (quantifying) fragment
Acknowledgment
We gratefully acknowledge the financial support of UK BBSRC and TSB. We are also grateful to Dr. Chris Sellick from MedImmune, who was instrumental in developing the methodology for this GC–
MS-based technique.
References
1. Sellick CA, Croxford AS, Maqsood AR, Stephens G, Westerhoff HV, Goodacre R, Dickson AJ (2011) Metabolite profiling of recombinant CHO cells: designing tailored feeding regimes that enhance recombinant antibody production.
Biotechnol Bioeng 108(12):3025–3031
2. Browne SM, Al-Rubeai M (2011) Defining viability in mammalian cell cultures. Biotechnol Lett 33:1745–1749
3. Jayapal KP, Wlaschin KF, Hu W-S, Yap MGS (2007) Recombinant protein therapeutics from CHO cells-20 years and counting. Chem Eng Prog 103(10):40–47
4. Dickson AJ, Pogson CI (1977) The metabolic integrity of hepatocytes in sustained incuba- tions. FEBS Lett 83(1):27–32
5. Stoddart MJ (2011) Cell viability assays: intro- duction. Methods Mol Biol. 740:1–6.
doi:10.1007/978-1-61779-108-6_1
6. Ahn WS, Antoniewicz MR (2012) Towards dynamic metabolic flux analysis in CHO cell cultures. Biotechnol J 7(1):61–74
7. Kildegaard HF, Baycin-Hizal D, Lewis NE, Betenbaugh MJ (2013) The emerging CHO systems biology era: harnessing the omics revoloution for biotechnology. Current Opin Biotech 24(6):1–6
8. Luo J, Vijayasankaran N, Austen J, Santuray R, Hudson T, Amanullah A, Li F (2012) Comparative metabolite analysis to understand lactate metabolism shift in Chinese hamster ovary cell culture process. Biotechnol Bioeng 109(1):146–156
9. Chong WPK, Reddy SG, Yusufi FNZ, Lee DY, NSC W, Heng CK, MGS Y, Ho YS (2010) Metabolomics driven approach for the
improvement of Chinese hamster ovary cell growth: overexpression of malate dehydrogenase II. J Biotechnol 147(2):116–121
10. Braasch K, Nikolic-Jaric M, Cabel T, Salimi E, Brdiges GE, Thompson DJ, Butler M (2013) The changing dielectric properties of CHO cells can be used to determine early apoptotic events in a bioprocess. Biotechnol Bioeng 110(11):2902–2914
11. Dickson AJ (2014) Enhancement of produc- tion of protein biopharmaceuticals by mamma- lian cell cultures: the metabolomics perspective.
Current Opin Biotech 30:73–79
12. Chong WPK, Yusufi FNK, Lee D-Y, Reddy SG, Wong NSC, Heng CK, Yap MGS, Ho YS (2011) Metabolomics-based identification of apoptosis-inducing metabolites in recombinant fed-batch CHO culture media. J Biotechnol 151(2):218–224
13. Majors BS, Betenbaugh MJ, Pederson NE, Chiang GC (2008) Enhancement of transient gene expression and culture viability using Chinese hamster ovary cells overexpressing Bcl-x(L). Biotech Bioeng 101(3):567–578 14. Chong WPK, Thng SH, Hiu AP, Lee D-Y,
Chan ECY, Ho YS (2012) LC-MS-based met- abolic characterization of high monoclonal antibody-producing chinese hamster ovary cells. Biotech Bioeng 109(12):3103–3111 15. Dunn WB, Broadhurst D, Begley P, Zelena E,
Francis-McIntyre S, Anderson N, Brown M, Knowles JD, Halsall A, Nicholls AW, Wilson ID, Douglas BK, Goodacre R, and The Human Serum Metabolome (HUSERMET) Consor- tium (2011) Procedures for large scale meta- bolic profiling of serum and plasma using gas GC-MS Metabolite Profiling to Assess Cellular Viability
chromatography and liquid chromatography coupled to mass spectrometry. Nature 6(7):
1060–1083
16. Kind T, Wohlgemuth G, Lee DY, Lu Y, Palazoglu M, Shahbaz S, Fiehn O (2009) FiehnLib-mass spectral and retention index libraries for metabo- lomics based on quadrupole and time-of-flight gas chromatography/mass spectrometry. Anal Chem 81(24):10038–10048
17. Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M (2012) KEGG for integration and interpretation of large-scale molecular data sets. NAR 40(1):109–114
18. Sellick CA, Hansen R, Stephens GM, Goodacre R, Dickson AJ (2011) Metabolite extraction from suspension-cultured mammalian cells for global metabolite profiling. Nat Protoc 8(6):
1241–1249
19. Sellick CA, Knight D, Croxford AS, Maqsood AR, Stephens GM, Goodacre R, Dickson AJ (2010) Evaluation of extraction processes for intracellular metabolite profiling of mammalian cells: matching extraction approaches to cell type and metabolite targets. Metabolomics 6:427–438
20. Sellick CA, Hansen R, Maqsood AR, Dunn WB, Stephens GM, Goodacre R, Dickson AJ (2009) Effective quenching processes for phys- iologically valid metabolite profiling of sus- pension cultured mammalian cells. Anal Chem 8:174–183
21. Agilent Technologies (2007) Considerations for selecting GC/MS or LC/MS for meta- bolomics [pdf]. USA: Agilent Technologies.
https://www.chem.agilent.com/Library/
selec5onguide/Public/5989C6328EN.pdf
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Chapter 13
Assaying Spontaneous Network Activity and Cellular Viability Using Multi-well Microelectrode Arrays
Jasmine P. Brown, Brittany S. Lynch, Itaevia M. Curry-Chisolm, Timothy J. Shafer, and Jenna D. Strickland
Abstract
Microelectrode array (MEA) technology is a neurophysiological method that allows for the spontaneous measure of activity in neural cultures and determination of drug and chemical effects thereon. Recent introduction of multi-well MEA (mwMEA) formats have dramatically increased the throughput of this technology, allowing more efficient compound screening. Rapid characterization of compounds for neu- roactivity or neurotoxicity hazard evaluation following acute, chronic, or developmental exposures ideally would also consider compound effects on cell health, and to do so in the same well requires a multiplexed approach. Procedures describing the multiplexed method to acute and developmental screening are described in this chapter.
Key words Microelectrode array (MEA), Neurophysiological method, mwMEA, Developmental screening
1 Introduction
Multi-well microelectrode array (mwMEA) systems have increased throughput of traditional MEAs, making them an effective in vitro screening tool to prioritize large sets of compounds. This pheno- typic approach can be applied to both drug discovery and drug safety screening, as well as acute [1, 2], chronic, and developmen- tal [3–5] screening for cardiac and neurotoxicity. For example, cur- rently, thousands of compounds lack sufficient toxicity data, espe cially as it relates to the nervous system and its development, and mwMEAs have been proposed as one approach to address this issue [6]. However, discrimination of compound effects on cell function versus cell health remains challenging as some formats of higher throughput mwMEA plates are opaque. Thus, determina- tion of cell health was often completed in “sister” cultures at dif- ferent densities. This method proved to be both suboptimal and inefficient, because it required the preparation of additional culture
materials and dosing solutions. The advancement of a multiplexed approach allowing for simple and rapid characterization of compound effects on both neurophysiological and cellular viability endpoints within the same network provides a method to differen- tiate between compound-induced changes in neural activity and overall reductions in cell health [7]. The ability to determine if endpoint specific effects of a compound (e.g., changes in network firing rates) and changes in viability occur simultaneously remains an important aspect of in vitro screening for neurotoxicity. The methods demonstrated in this chapter utilize a mixed primary cor- tical culture comprising both inhibitory and excitatory neurons and glial cells. However, these methods may be adapted for other MEA platforms (i.e., 96-well platforms) or cell types (i.e., cardiac cells).
Using a multiplexed screening approach we demonstrate a simple and rapid method for simultaneous determination of com- pound effects on both neural network function and cellular health.
Here we present comprehensive methods for assessment of compound effects on neural network function following acute or developmental exposure. These methods include culturing, record- ing, and viability assessment on mwMEA plates. Specifically, to assess changes in function, primary cortical cultures were grown on mwMEA plates (48 wells). Changes in spontaneous network activ- ity in response to treatment were monitored for both acute (~40 min on day in vitro 13) and developmental exposures (~15 mins on days in vitro 2, 5, 7, 9, and 12). Effects on cell health were assessed from the same well following recording by measur- ing both lactate dehydrogenase (LDH) release and metabolic activity (CellTiter Blue; CTB). Modifications to each of the viabil- ity assays were needed for both acute and developmental exposures (see Subheading 3.7 for details). While facilitating rapid determina- tion of both changes in neural network function and viability, these methods also serve to reduce cost, time, and animal use. These methods could easily be adapted to a variety of other cell types and exposure conditions.
2 Materials
The materials listed below are referenced in multiple procedures.
Those specific to a certain procedure are listed in their respective materials section. All procedures in this chapter use sterile tech- nique and are done in a laminar flow hood with the exception of the viability assays described later in this chapter.