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Methods Mol Biol. 2020;2088:119-160. doi: 10.1007/978-1-0716-0159-4_7.

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

Author information

1
Austrian Centre of Industrial Biotechnology, Vienna, Austria.
2
University of Natural Resources and Life Sciences, Vienna, Austria.
3
University of Vienna, Vienna, Austria.
4
University of Ljubljana, Ljubljana, Slovenia.
5
Leibniz Institut für Analytische Wissenschaften - e.V., Dortmund, Germany.
6
Austrian Biotech University of Applied Sciences, Tulln, Austria.
7
Austrian Centre of Industrial Biotechnology, Vienna, Austria. david.ruckerbauer@boku.ac.at.
8
University of Natural Resources and Life Sciences, Vienna, Austria. david.ruckerbauer@boku.ac.at.

Abstract

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.

KEYWORDS:

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

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