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      Evidence-based guidelines for controlling pH in mammalian live-cell culture systems

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          Abstract

          A fundamental variable in culture medium is its pH, which must be controlled by an appropriately formulated buffering regime, since biological processes are exquisitely sensitive to acid–base chemistry. Although awareness of the importance of pH is fostered early in the training of researchers, there are no consensus guidelines for best practice in managing pH in cell cultures, and reporting standards relating to pH are typically inadequate. Furthermore, many laboratories adopt bespoke approaches to controlling pH, some of which inadvertently produce artefacts that increase noise, compromise reproducibility or lead to the misinterpretation of data. Here, we use real-time measurements of medium pH and intracellular pH under live-cell culture conditions to describe the effects of various buffering regimes, including physiological CO 2/HCO 3 and non-volatile buffers (e.g. HEPES). We highlight those cases that result in poor control, non-intuitive outcomes and erroneous inferences. To improve data reproducibility, we propose guidelines for controlling pH in culture systems.

          Abstract

          Johanna Michl et al. use real-time pH measurements of cell culture media and intracellular pH data from live-cell culture conditions to explore the effects of commonly-used buffers. Based on these data, they propose guidelines for controlling pH in cell culture systems and improving reproducibility.

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          Calcium signaling.

          Calcium ions (Ca(2+)) impact nearly every aspect of cellular life. This review examines the principles of Ca(2+) signaling, from changes in protein conformations driven by Ca(2+) to the mechanisms that control Ca(2+) levels in the cytoplasm and organelles. Also discussed is the highly localized nature of Ca(2+)-mediated signal transduction and its specific roles in excitability, exocytosis, motility, apoptosis, and transcription.
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            Despite the millions of dollars spent on target validation and drug optimization in preclinical models, most therapies still fail in phase III clinical trials. Our current model systems, or the way we interpret data from them, clearly do not have sufficient clinical predictive power. Current opinion suggests that this is because the cell lines and xenografts that are commonly used are inadequate models that do not effectively mimic and predict human responses. This has become such a widespread belief that it approaches dogma in the field of drug discovery and optimization and has spurred a surge in studies devoted to the development of more sophisticated animal models such as orthotopic patient-derived xenografts in an attempt to obtain more accurate estimates of whether particular cancers will respond to given treatments. Here, we explore the evidence that has led to the move away from the use of in vitro cell lines and toward various forms of xenograft models for drug screening and development. We review some of the pros and cons of each model and give an overview of ways in which the use of cell lines could be modified to improve the predictive capacity of this well-defined model. ©2014 AACR.
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              Intracellular pH.

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                Author and article information

                Contributors
                pawel.swietach@dpag.ox.ac.uk
                Journal
                Commun Biol
                Commun Biol
                Communications Biology
                Nature Publishing Group UK (London )
                2399-3642
                26 April 2019
                26 April 2019
                2019
                : 2
                : 144
                Affiliations
                ISNI 0000 0004 1936 8948, GRID grid.4991.5, Department of Physiology, Anatomy and Genetics, , University of Oxford, ; OX1 3PT Oxford, UK
                Author information
                http://orcid.org/0000-0001-7203-0127
                http://orcid.org/0000-0002-9945-9473
                Article
                393
                10.1038/s42003-019-0393-7
                6486606
                31044169
                7ad81be1-df7c-4b93-b350-a354fed1fca9
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 20 November 2018
                : 14 March 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/100010663, EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council);
                Award ID: #723997
                Award Recipient :
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                © The Author(s) 2018

                cell culture,cancer microenvironment,cell biology,cytological techniques

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