Declaration of Conflicting Interests
The authors, LLC, KM, and SK, declare competing financial inter- ests. The work performed in this chapter is for reporting on prod- uct performance of Nexcelom Bioscience, LLC. The performed experiments were to demonstrate cell viability detection methods using image cytometry.
References
1. Cook JA, Mitchell JB (1989) Viability mea- surements in mammalian cell systems. Anal Biochem 179:1–7
2. Oh H, Livingston R, Smith K et al (2004) Comparative study of the time dependency of cell death assays. MURJ 11:53–62
3. Chan LL, Zhong XM, Qiu J et al (2011) Cellometer vision as an alternative to flow cytometry for cell cycle analysis, mitochondrial potential, and immunophenotyping. Cytometry Part A 79A:507–517
4. Chan LL, Wilkinson AR, Paradis BD et al (2012) Rapid image-based cytometry for com- parison of fluorescent viability staining meth- ods. J Fluoresc 22:1301–1311
5. Saldi S, Driscoll D, Kuksin D et al (2014) Image-based cytometric analysis of fluorescent viability and vitality staining methods for ale and lager fermentation yeast. J Am Soc Brew Chem 72:253–260
6. Han X, Liu Z, Mc J et al (2015) CRISPR-Cas9 delivery to hard-to-transfect cells via mem- brane deformation. Sci Adv 1:1–8
7. Shah D, Naciri M, Clee P et al (2006) NucleoCounter—an efficient technique for the determination of cell number and viability in animal cell culture processes. Cytotechnology 51:39–44
8. Al-Rubeai M, Welzenbach K, Lloyd DR et al (1997) A rapid method for evaluation of cell number and viability by flow cytometry.
Cytotechnology 24:161–168
9. Strober W (2001) Monitoring cell growth. In:
Current protocols in immunology, vol APPENDIX 3A
10. Shapiro HM (2004) “Cellular Astronomy” - a foreseeable future in cytometry. Cytometry Part A 60A:115–124
11. Stoddart M (2011) Cell viability assays: intro- duction. Methods Mol Biol 740:1–6
12. Davey HM, Kell DB (1996) Flow cytometry and cell sorting of heterogeneous microbial populations: the importance of single-cell anal- yses. Microbiol Rev 60:641–696
13. Michelson AD (1996) Flow cytometry: a clinical test of platelet function. Blood 87:4925–4936 14. Tibbe AGJ, de Grooth BG, Greve J et al (2002)
Imaging technique implemented in CellTracks system. Cytometry Part A 47:248–255 15. Shapiro HM, Perlmutter NG (2006) Personal
cytometers: slow flow or no flow? Cytometry Part A 69A:620–630
16. Gerstner AOH, Mittag A, Laffers W et al (2006) Comparison of immunophenotyping by slide-based cytometry and by flow cytome- try. J Immunol Methods 311:130–138
17. Mital J, Schwarz J, Taatjes DJ et al (2005) Laser scanning cytometer-based assays for mea- suring host cell attachment and invasion by the human pathogen Toxplasma gondii. Cytometry Part A 69A:13–19
18. Hall A, Wu L-P, Parhamifar L et al (2015) Differential modulation of cellular bioenerget-
41 ics by poly(l-lysine)s of different molecular
weights. Biomacromolecules 16:2119–2126 19. Siqueira-Neto JL, Moon S, Jang J et al (2012)
An image-based high-content screening assay for compounds targeting intracellular Leishmania donovani amastigotes in human macrophages. PLoS Negl Trop Dis 6:e1671 20. Zanella F, Lorens JB, Link W (2010) High
content screening: seeing is believing. Trends Biotechnol 28:237–245
21. Schepers K, Pietras EM, Reynaud D et al (2013) Myeloproliferative neoplasia remodels the endosteal bone marrow niche into a self- reinforcing leukemic niche. Cell Stem Cell 13:285–299
22. Szabo SE, Monroe SL, Fiorino S et al (2004) Evaluation of an automated instrument for viability and concentration measurements of cryopreserved hematopoietic cells. Lab Hematol 10:109–111
23. Macfarlane RG, Payne AM-M, Poole JCF et al (1959) An automatic apparatus for counting red blood cells. Br J Haemacytol 5:1–15 24. Verso ML (1971) Some nineteenth-century
pioneers of haematology. Med Hist 15:55–67 25. Falzone N, Huyser C, Franken D (2010)
Comparison between propidium iodide and 7-amino-actinomycin-D for viability assessment during flow cytometric analyses of the human sperm acrosome. Andrologia 42:20–26
26. Gordon KM, Duckett L, Daul B et al (2003) A simple method for detecting up to five immuno- fluorescent parameters together with DNA stain- ing for cell cycle or viability on a benchtop flow cytometer. J Immunol Methods 275:113–121 27. Jarnagin JL, Luchsinger DW (1980) The use
of fluorescein diacetate and ethidium bromide as a stain for evaluating viability of mycobacte- ria. Biotech Histochem 55:253–258
28. Roth B, Poot M, Yue S et al (1997) Bacterial viability and antibiotic susceptibility testing
with SYTOX green nucleic acid stain. Appl Environ Microbiol 63:2421–2431
29. Wlodkowic D, Skommer J, Faley S et al (2009) Dynamic analysis of apoptosis using cyanine SYTO probes: from classical to microfluidic cytometry. Exp Cell Res 315:1706–1714 30. Bratosin D, Mitrofan L, Palii C et al (2005)
Novel fluorescence assay using calcein-AM for the determination of human erythrocyte viabil- ity and aging. Cytometry Part A 66A:78–84 31. Jones KH, Senft JA (1985) An improved
method to determine cell viability by simulta- neous staining with fluorescein diacetate- propidium iodide. J Histochem Cytochem 33:77–79
32. Donoghue AM, Garner DL, Donoghue DJ et al (1995) Viability assessment of Turkey sperm using fluorescent staining and flow cytometry. Poult Sci 74:1191–1200
33. Mascotti K, McCullough J, Burger SR (2000) HPC viability measurement: trypan blue versus acridine orange and propidium iodide.
Transfusion 40:693–696
34. Cai K, Yang J, Guan M et al (2005) Single UV excitation of Hoechst 33342 and propidium iodide for viability assessment of rhesus mon- key spermatozoa using flow cytometry. Arch Androl 51:371–383
35. Smith PJ, Wiltshire M, Davies S et al (1999) A novel cell permeant and far red-fluorescing DNA probe, DRAQ5, for blood cell discrimi- nation by flow cytometry. J Immunol Methods 229:131–139
36. Akagi J, Kordon M, Zhao H et al (2013) Real- time cell viability assays using a new anthracy- cline derivative DRAQ7®. Cytometry Part A 83A:227–234
37. Sutkeviciene N, Andersson MA, Zilinskas H et al (2005) Assessment of boar semen quality in relation to fertility with special reference to methanol stress. Theriogenology 63:739–747 Assessment of Cell Viability Using Image Cytometry
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Chapter 4
High-Throughput Spheroid Screens Using Volume, Resazurin Reduction, and Acid Phosphatase Activity
Delyan P. Ivanov, Anna M. Grabowska, and Martin C. Garnett
Abstract
Mainstream adoption of physiologically relevant three-dimensional models has been slow in the last 50 years due to long, manual protocols with poor reproducibility, high price, and closed commercial plat- forms. This chapter describes high-throughput, low-cost, open methods for spheroid viability assessment which use readily available reagents and open-source software to analyze spheroid volume, metabolism, and enzymatic activity. We provide two ImageJ macros for automated spheroid size determination—for both single images and images in stacks. We also share an Excel template spreadsheet allowing users to rapidly process spheroid size data, analyze plate uniformity (such as edge effects and systematic seeding errors), detect outliers, and calculate dose-response. The methods would be useful to researchers in pre- clinical and translational research planning to move away from simplistic monolayer studies and explore 3D spheroid screens for drug safety and efficacy without substantial investment in money or time.
Key words Alamar blue, Viability assays, Overlay culture, Hanging drop, FiJi ImageJ, Image analysis, Three-dimensional cell culture, In vitro model, Preclinical screening, Drug sensitivity
1 Introduction
The purpose of this chapter is to describe the detailed practical procedures behind three complementary techniques (volume, resazurin reduction, and acid phosphatase activity) for spheroid viability assessment in high-throughput 96-well format [1].
Spheroids are self-organized three-dimensional (3D) aggregates of cells displaying physiologically relevant gradients of oxygen, nutri- ents, and cell–cell and cell matrix interactions [2, 3]. Aggregate cultures were first described in the 1950s by Moscona [4], and the advantages of using spheroids in cancer research were recognized in the 1970s by Sutherland [5]. However, poor reproducibility, large variation in spheroid size, lengthy hand-operated manipula- tions, and low throughput precluded the introduction of spheroids in mainstream drug screens. The introduction of plate-based plat- forms for spheroid culture in hanging drop [6, 7] or liquid overlay
Daniel F. Gilbert and Oliver Friedrich (eds.), Cell Viability Assays: Methods and Protocols, Methods in Molecular Biology, vol. 1601, DOI 10.1007/978-1-4939-6960-9_4, © Springer Science+Business Media LLC 2017
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[8, 9] has enabled researchers to produce a single spheroid per well and control spheroid size in a high-throughput format. This has stimulated the adoption of spheroid screens for drugs targeting dormant tumor cells [10], spheroid assay modeling chemo- and radio-resistance [11, 12] strategies for sensitizing hypoxic cells in the tumor core [13, 14], and drug safety assays [15, 16].
We present a suite of three multiplexable methods to assess spheroid health in overlay spheroid cultures. The plate format used is compatible with standard plate readers, and the methods rely on generic reagents available at lower costs compared to ready-made kits. The techniques use open-source software for image analysis (Fiji distribution of ImageJ [17, 18]) and do not require invest- ments in new equipment or software. The practical application of these methods and the characterization of linearity and sensitivity have been discussed in our recent publication [1]. These methods are suitable for monocultures (one cell type) and cocultures (mixed cell types). In cocultures, the volume or metabolic activity of the coculture spheroid can be used as a proxy measure for the total number of cells; subsequently the proportion of each cell type can be quantified with microscopy or flow cytometry [19].
Manual spheroid size measurements and morphological charac- terization of 3D aggregates have been extensively used from the earliest days of spheroid research [4, 5, 20]. With the development of overlay and hanging-drop plates, many proprietary platforms (such as Celigo, Zeiss, Perkin-Elmer) have been employed in mea- suring spheroid size and estimating volume in high throughput [8, 21]. These platforms require substantial investment in equipment and image analysis software, which may not be available to all research labs and may slow down the adoption of spheroid screens.
We have written two macros which automate spheroid size analysis on the bioimaging Fiji [17] distribution of the open-source image analysis platform ImageJ [18]. The first macro works on manually acquired images from simple setups of camera-equipped bright- field microscopes, where spheroid images are recorded as separate files in a folder. It is compatible with computers with less than 2GB of RAM which may struggle to load all images in a single image sequence.
The second macro is substantially faster and works with image sequences (stacks), for example images taken with automated- stage microscopes, which often produce stacks of multiple images in one file. It can also be used on separate images imported as an image sequence in ImageJ (see Note 10). Estimating spheroid viability solely based on spheroid size can be misleading because: