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Robust estimation of bacterial cell count from optical density

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Beal_2020_CommsBio_Robust_estimation_CC.pdf (1.391Mb)
Date
17/09/2020
Author
iGEM Interlab Study Contributors
Melo Czekster, Clarissa
Powis, Simon John
Keywords
QR Microbiology
DAS
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Abstract
Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals  <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data.
Citation
iGEM Interlab Study Contributors , Melo Czekster , C & Powis , S J 2020 , ' Robust estimation of bacterial cell count from optical density ' , Communications Biology , vol. 3 , 512 . https://doi.org/10.1038/s42003-020-01127-5
Publication
Communications Biology
Status
Peer reviewed
DOI
https://doi.org/10.1038/s42003-020-01127-5
ISSN
2399-3642
Type
Journal article
Rights
Copyright © The Author(s) 2020. 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.
Description
Partial support for this work was provided by NSF Expeditions in Computing Program Award #1522074 as part of the Living Computing Project, and by the Engineering and Physical Sciences Research Council [EP/R034915/1] and EU H2020 [820699].
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  • University of St Andrews Research
URL
https://doi.org/10.1038/s42003-020-01371-9
URI
http://hdl.handle.net/10023/20971

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