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Rapid, precise quantification of bacterial cellular dimensions across a genomic-scale knockout library
BACKGROUND: The determination and regulation of cell morphology are critical components of cell-cycle control, fitness, and development in both single-cell and multicellular organisms. Understanding how environmental factors, chemical perturbations, and genetic differences affect cell morphology req...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5320674/ https://www.ncbi.nlm.nih.gov/pubmed/28222723 http://dx.doi.org/10.1186/s12915-017-0348-8 |
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author | Ursell, Tristan Lee, Timothy K. Shiomi, Daisuke Shi, Handuo Tropini, Carolina Monds, Russell D. Colavin, Alexandre Billings, Gabriel Bhaya-Grossman, Ilina Broxton, Michael Huang, Bevan Emma Niki, Hironori Huang, Kerwyn Casey |
author_facet | Ursell, Tristan Lee, Timothy K. Shiomi, Daisuke Shi, Handuo Tropini, Carolina Monds, Russell D. Colavin, Alexandre Billings, Gabriel Bhaya-Grossman, Ilina Broxton, Michael Huang, Bevan Emma Niki, Hironori Huang, Kerwyn Casey |
author_sort | Ursell, Tristan |
collection | PubMed |
description | BACKGROUND: The determination and regulation of cell morphology are critical components of cell-cycle control, fitness, and development in both single-cell and multicellular organisms. Understanding how environmental factors, chemical perturbations, and genetic differences affect cell morphology requires precise, unbiased, and validated measurements of cell-shape features. RESULTS: Here we introduce two software packages, Morphometrics and BlurLab, that together enable automated, computationally efficient, unbiased identification of cells and morphological features. We applied these tools to bacterial cells because the small size of these cells and the subtlety of certain morphological changes have thus far obscured correlations between bacterial morphology and genotype. We used an online resource of images of the Keio knockout library of nonessential genes in the Gram-negative bacterium Escherichia coli to demonstrate that cell width, width variability, and length significantly correlate with each other and with drug treatments, nutrient changes, and environmental conditions. Further, we combined morphological classification of genetic variants with genetic meta-analysis to reveal novel connections among gene function, fitness, and cell morphology, thus suggesting potential functions for unknown genes and differences in modes of action of antibiotics. CONCLUSIONS: Morphometrics and BlurLab set the stage for future quantitative studies of bacterial cell shape and intracellular localization. The previously unappreciated connections between morphological parameters measured with these software packages and the cellular environment point toward novel mechanistic connections among physiological perturbations, cell fitness, and growth. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12915-017-0348-8) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5320674 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-53206742017-02-24 Rapid, precise quantification of bacterial cellular dimensions across a genomic-scale knockout library Ursell, Tristan Lee, Timothy K. Shiomi, Daisuke Shi, Handuo Tropini, Carolina Monds, Russell D. Colavin, Alexandre Billings, Gabriel Bhaya-Grossman, Ilina Broxton, Michael Huang, Bevan Emma Niki, Hironori Huang, Kerwyn Casey BMC Biol Software BACKGROUND: The determination and regulation of cell morphology are critical components of cell-cycle control, fitness, and development in both single-cell and multicellular organisms. Understanding how environmental factors, chemical perturbations, and genetic differences affect cell morphology requires precise, unbiased, and validated measurements of cell-shape features. RESULTS: Here we introduce two software packages, Morphometrics and BlurLab, that together enable automated, computationally efficient, unbiased identification of cells and morphological features. We applied these tools to bacterial cells because the small size of these cells and the subtlety of certain morphological changes have thus far obscured correlations between bacterial morphology and genotype. We used an online resource of images of the Keio knockout library of nonessential genes in the Gram-negative bacterium Escherichia coli to demonstrate that cell width, width variability, and length significantly correlate with each other and with drug treatments, nutrient changes, and environmental conditions. Further, we combined morphological classification of genetic variants with genetic meta-analysis to reveal novel connections among gene function, fitness, and cell morphology, thus suggesting potential functions for unknown genes and differences in modes of action of antibiotics. CONCLUSIONS: Morphometrics and BlurLab set the stage for future quantitative studies of bacterial cell shape and intracellular localization. The previously unappreciated connections between morphological parameters measured with these software packages and the cellular environment point toward novel mechanistic connections among physiological perturbations, cell fitness, and growth. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12915-017-0348-8) contains supplementary material, which is available to authorized users. BioMed Central 2017-02-21 /pmc/articles/PMC5320674/ /pubmed/28222723 http://dx.doi.org/10.1186/s12915-017-0348-8 Text en © Ursell et al. 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Software Ursell, Tristan Lee, Timothy K. Shiomi, Daisuke Shi, Handuo Tropini, Carolina Monds, Russell D. Colavin, Alexandre Billings, Gabriel Bhaya-Grossman, Ilina Broxton, Michael Huang, Bevan Emma Niki, Hironori Huang, Kerwyn Casey Rapid, precise quantification of bacterial cellular dimensions across a genomic-scale knockout library |
title | Rapid, precise quantification of bacterial cellular dimensions across a genomic-scale knockout library |
title_full | Rapid, precise quantification of bacterial cellular dimensions across a genomic-scale knockout library |
title_fullStr | Rapid, precise quantification of bacterial cellular dimensions across a genomic-scale knockout library |
title_full_unstemmed | Rapid, precise quantification of bacterial cellular dimensions across a genomic-scale knockout library |
title_short | Rapid, precise quantification of bacterial cellular dimensions across a genomic-scale knockout library |
title_sort | rapid, precise quantification of bacterial cellular dimensions across a genomic-scale knockout library |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5320674/ https://www.ncbi.nlm.nih.gov/pubmed/28222723 http://dx.doi.org/10.1186/s12915-017-0348-8 |
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