Literature DB >> 31893373

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

Diana Széliová1,2, Harald Schoeny3, Špela Knez4, Christina Troyer2, Cristina Coman5, Evelyn Rampler3, Gunda Koellensperger3, Robert Ahrends5, Stephen Hann1,2, Nicole Borth1,2, Jürgen Zanghellini2,6,1, David E Ruckerbauer7,8.   

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.

Entities:  

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

Year:  2020        PMID: 31893373     DOI: 10.1007/978-1-0716-0159-4_7

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  2 in total

1.  Experimental determination of Escherichia coli biomass composition for constraint-based metabolic modeling.

Authors:  Vetle Simensen; Christian Schulz; Emil Karlsen; Signe Bråtelund; Idun Burgos; Lilja Brekke Thorfinnsdottir; Laura García-Calvo; Per Bruheim; Eivind Almaas
Journal:  PLoS One       Date:  2022-01-27       Impact factor: 3.240

2.  Inclusion of maintenance energy improves the intracellular flux predictions of CHO.

Authors:  Diana Széliová; Jerneja Štor; Isabella Thiel; Marcus Weinguny; Michael Hanscho; Gabriele Lhota; Nicole Borth; Jürgen Zanghellini; David E Ruckerbauer; Isabel Rocha
Journal:  PLoS Comput Biol       Date:  2021-06-11       Impact factor: 4.779

  2 in total

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