Robust Analytical Methods for the Accurate Quantification of the Total Biomass Composition of Mammalian Cells

  • Diana Széliová
  • Harald Schoeny
  • Špela Knez
  • Christina Troyer
  • Cristina Coman
  • Evelyn Rampler
  • Gunda Koellensperger
  • Robert Ahrends
  • Stephen Hann
  • Nicole Borth
  • Jürgen Zanghellini
  • David E. RuckerbauerEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 2088)


Biomass composition is an important input for genome-scale metabolic models and has a big impact on their predictive capabilities. However, researchers often rely on generic data for biomass composition, e.g. collected from similar organisms. This leads to inaccurate predictions, because biomass composition varies between different cell lines, conditions, and growth phases. In this chapter we present protocols for the determination of the biomass composition of Chinese Hamster Ovary (CHO) cells. These methods can easily be adapted to other types of mammalian cells. The protocols include the quantification of cell dry mass and of the main biomass components, namely protein, lipid, DNA, RNA, and carbohydrates. Cell dry mass is determined gravimetrically by weighing a defined number of cells. Amino acid composition and protein content are measured by gas chromatography mass spectrometry. Lipids are quantified by shotgun mass spectrometry, which provides quantities for the different lipid classes and also the distribution of fatty acids. RNA is purified and then quantified spectrophotometrically. The methods for DNA and carbohydrates are simple fluorometric and colorimetric assays adapted to a 96-well plate format. To ensure quantitative results, internal standards or spike-in controls are used in all methods, e.g. to account for possible matrix effects or loss of material. Finally, the last section provides a guide on how to convert the measured data into biomass equations, which can then be integrated into a metabolic model.

Key words

Biomass composition DNA RNA Amino acids Lipids Carbohydrates Chinese Hamster Ovary cells 



DS, SH, NB, JZ, and DER acknowledge support by the Federal Ministry for Digital and Economic Affairs (bmwd), the Federal Ministry for Transport, Innovation and Technology (bmvit), the Styrian Business Promotion Agency SFG, the Standortagentur Tirol, Government of Lower Austria and ZIT—Technology Agency of the City of Vienna through the COMET-Funding Program managed by the Austrian Research Promotion Agency FFG. DS received additional funding from the PhD program BioToP (Biomolecular Technology of Proteins) of the Austrian Science Fund (FWF Project W1224). The funding agencies had no influence on the conduct of this research.

Supplementary material

465987_1_En_7_MOESM1_ESM.csv (1 kb)
Caption of the data object (CSV 1 kb)
465987_1_En_7_MOESM2_ESM.csv (0 kb)
Caption of the data object (CSV 1 kb)
465987_1_En_7_MOESM3_ESM.csv (12 kb)
Caption of the data object (CSV 12 kb)


  1. 1.
    Kyriakopoulos S, Kontoravdi C (2013) Analysis of the landscape of biologically-derived pharmaceuticals in Europe: dominant production systems, molecule types on the rise and approval trends. Eur J Pharm Sci 48:428–441CrossRefGoogle Scholar
  2. 2.
    Mathias S, Fischer S, Handrick R, Fieder J, Schulz P, Bradl H, Gorr I, Gamer M, Otte K (2018) Visualisation of intracellular production bottlenecks in suspension-adapted CHO cells producing complex biopharmaceuticals using fluorescence microscopy. J Biotechnol 271:47–55CrossRefGoogle Scholar
  3. 3.
    Lewis NE, Nagarajan H, Palsson BO (2012) Constraining the metabolic genotype–phenotype relationship using a phylogeny of in silico methods. Nat Rev Microbiol 10:291–305CrossRefGoogle Scholar
  4. 4.
    Rejc Ž, Magdevska L, Tršelič T, Osolin T, Vodopivec R, Mraz J, Pavliha E, Zimic N, Cvitanović T, Rozman D, Moškon M, Mraz M (2017) Computational modelling of genome-scale metabolic networks and its application to CHO cell cultures. Comput Biol Med 88:150–160CrossRefGoogle Scholar
  5. 5.
    Calmels C, McCann A, Malphettes L, Andersen MR (2019) Application of a curated genome-scale metabolic model of CHO DG44 to an industrial fed-batch process. Metab Eng 51:9–19CrossRefGoogle Scholar
  6. 6.
    Hefzi H, Ang KS, Hanscho M, Bordbar A, Ruckerbauer D, Lakshmanan M, Orellana CA, Baycin-Hizal D, Huang Y, Ley D, Martinez VS, Kyriakopoulos S, Jiménez NE, Zielinski DC, Quek L-E, Wulff T, Arnsdorf J, Li S, Lee JS, Paglia G, Loira N, Spahn PN, Pedersen LE, Gutierrez JM, King ZA, Lund AM, Nagarajan H, Thomas A, Abdel-Haleem AM, Zanghellini J, Kildegaard HF, Voldborg BG, Gerdtzen ZP, Betenbaugh MJ, Palsson BO, Andersen MR, Nielsen LK, Borth N, Lee D-Y, Lewis NE (2016) A consensus genome-scale reconstruction of chinese hamster ovary cell metabolism. Cell Syst 3:434–443.e8CrossRefGoogle Scholar
  7. 7.
    Baart GJE, Martens DE (2012) Genome-scale metabolic models: reconstruction and analysis. In: Christodoulides M (ed) Neisseria meningitidis: advanced methods and protocols. Methods in molecular biology. Humana Press, Totowa, pp 107–126CrossRefGoogle Scholar
  8. 8.
    Dikicioglu D, Kırdar B, Oliver SG (2015) Biomass composition: the “elephant in the room” of metabolic modelling. Metabolomics 11:1690–1701CrossRefGoogle Scholar
  9. 9.
    Széliová D (2019) Manuscript in preparationGoogle Scholar
  10. 10.
    Selvarasu S, Ho YS, Chong WPK, Wong NSC, Yusufi FNK, Lee YY, Yap MGS, Lee D-Y (2012) Combined in silico modeling and metabolomics analysis to characterize fed-batch CHO cell culture. Biotechnol Bioeng 109:1415–1429CrossRefGoogle Scholar
  11. 11.
    Pan X, Dalm C, Wijffels RH, Martens DE (2017) Metabolic characterization of a CHO cell size increase phase in fed-batch cultures. Appl Microbiol Biotechnol 101:8101–8113CrossRefGoogle Scholar
  12. 12.
    Zhang Y, Baycin-Hizal D, Kumar A, Priola J, Bahri M, Heffner KM, Wang M, Han X, Bowen MA, Betenbaugh MJ (2017) High-throughput lipidomic and transcriptomic analysis to compare SP2/0, CHO, and HEK-293 mammalian cell lines. Anal Chem 89:1477–1485CrossRefGoogle Scholar
  13. 13.
    Chen A, Leith M, Tu R, Tahim G, Sudra A, Bhargava S (2017) Effects of diluents on cell culture viability measured by automated cell counter. PLoS One 12:e0173375CrossRefGoogle Scholar
  14. 14.
    Fountoulakis M, Lahm H-W (1998) Hydrolysis and amino acid composition analysis of proteins. J Chromatogr A 826:109–134CrossRefGoogle Scholar
  15. 15.
    Sandra K, Vandenheede I, Sandra P (2014) Modern chromatographic and mass spectrometric techniques for protein biopharmaceutical characterization. J Chromatogr A 1335:81–103CrossRefGoogle Scholar
  16. 16.
    Hoofnagle AN, Whiteaker JR, Carr SA, Kuhn E, Liu T, Massoni SA, Thomas SN, Townsend RR, Zimmerman LJ, Boja E, Chen J, Crimmins DL, Davies SR, Gao Y, Hiltke TR, Ketchum KA, Kinsinger CR, Mesri M, Meyer MR, Qian W-J, Schoenherr RM, Scott MG, Shi T, Whiteley GR, Wrobel JA, Wu C, Ackermann BL, Aebersold R, Barnidge DR, Bunk DM, Clarke N, Fishman JB, Grant RP, Kusebauch U, Kushnir MM, Lowenthal MS, Moritz RL, Neubert H, Patterson SD, Rockwood AL, Rogers J, Singh RJ, Eyk JV, Wong SH, Zhang S, Chan DW, Chen X, Ellis MJ, Liebler DC, Rodland KD, Rodriguez H, Smith RD, Zhang Z, Zhang H, Paulovich AG (2016) Recommendations for the generation, quantification, storage, and handling of peptides used for mass spectrometry-based assays. Clin Chem 62:48–69PubMedPubMedCentralGoogle Scholar
  17. 17.
    Weiss M, Manneberg M, Juranville J-F, Lahm H-W, Fountoulakis M (1998) Effect of the hydrolysis method on the determination of the amino acid composition of proteins. J Chromatogr A 795:263–275CrossRefGoogle Scholar
  18. 18.
    Poole CF (2013) Alkylsilyl derivatives for gas chromatography. J Chromatogr A 1296:2–14CrossRefGoogle Scholar
  19. 19.
    Rampler E, Dalik T, Stingeder G, Hann S, Koellensperger G (2012) Sulfur containing amino acids – challenge of accurate quantification. J Anal At Spectrom 27(6):1018CrossRefGoogle Scholar
  20. 20.
    Coman C, Solari FA, Hentschel A, Sickmann A, Zahedi RP, Ahrends R (2016) Simultaneous metabolite, protein, lipid extraction (SIMPLEX): a combinatorial multimolecular omics approach for systems biology. Mol Cell Proteomics 15:1453–1466CrossRefGoogle Scholar
  21. 21.
    Schuhmann K, Almeida R, Baumert M, Herzog R, Bornstein SR, Shevchenko A (2012) Shotgun lipidomics on a LTQ orbitrap mass spectrometer by successive switching between acquisition polarity modes. J Mass Spectrom 47:96–104CrossRefGoogle Scholar
  22. 22.
    Yang K, Han X (2011) Accurate quantification of lipid species by electrospray ionization mass spectrometry – meets a key challenge in lipidomics. Metabolites 1:21–40CrossRefGoogle Scholar
  23. 23.
    Herzog R, Schuhmann K, Schwudke D, Sampaio JL, Bornstein SR, Schroeder M, Shevchenko A (2012) LipidXplorer: a software for consensual cross-platform lipidomics. PLoS One 7:e29851CrossRefGoogle Scholar
  24. 24.
    Trevelyan WE, Harrison JS (1952) Studies on yeast metabolism. 1. Fractionation and microdetermination of cell carbohydrates. Biochem J 50:298–303PubMedPubMedCentralGoogle Scholar
  25. 25.
    Joseph H (1955) The determination of sugar in blood and spinal fluid with anthrone reagent. J Biol Chem 212:335–343Google Scholar
  26. 26.
    Laurentin A, Edwards CA (2003) A microtiter modification of the anthrone-sulfuric acid colorimetric assay for glucose-based carbohydrates. Anal Biochem 315:143–145CrossRefGoogle Scholar
  27. 27.
    Beck A, Hunt K, Carlson R (2018) Measuring cellular biomass composition for computational biology applications. Processes 6:38CrossRefGoogle Scholar
  28. 28.
    Sheikh K, Förster J, Nielsen LK (2008) Modeling hybridoma cell metabolism using a generic genome-scale metabolic model of Mus musculus. Biotechnol Prog 21:112–121CrossRefGoogle Scholar
  29. 29.
    Rutherfurd SM, Gilani GS (2009) Amino acid analysis. Curr Protoc Protein Sci 58(1):11.9.1–11.9.37CrossRefGoogle Scholar
  30. 30.
    Southam AD, Weber RJM, Engel J, Jones MR, Viant MR (2017) A complete workflow for high-resolution spectral-stitching nanoelectrospray direct-infusion mass-spectrometry-based metabolomics and lipidomics. Nat Protoc 12:255–273CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  • Diana Széliová
    • 1
    • 2
  • Harald Schoeny
    • 3
  • Špela Knez
    • 4
  • Christina Troyer
    • 5
  • Cristina Coman
    • 6
  • Evelyn Rampler
    • 3
  • Gunda Koellensperger
    • 3
  • Robert Ahrends
    • 6
  • Stephen Hann
    • 1
    • 2
  • Nicole Borth
    • 1
    • 2
  • Jürgen Zanghellini
    • 5
    • 7
    • 8
  • David E. Ruckerbauer
    • 1
    • 2
    Email author
  1. 1.Austrian Centre of Industrial BiotechnologyViennaAustria
  2. 2.University of Natural Resources and Life SciencesViennaAustria
  3. 3.University of ViennaViennaAustria
  4. 4.University of LjubljanaLjubljanaSlovenia
  5. 5.University of Natural Resources and Life SciencesViennaAustria
  6. 6.Leibniz Institut für Analytische Wissenschaften - e.V.DortmundGermany
  7. 7.Austrian Biotech University of Applied SciencesTullnAustria
  8. 8.Austrian Centre of Industrial BiotechnologyViennaAustria

Personalised recommendations