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BMC Biology

, 15:17

First Online: 21 February 2017Received: 18 August 2016Accepted: 06 January 2017DOI: 10.1186-s12915-017-0348-8

Cite this article as: Ursell, T., Lee, T.K., Shiomi, D. et al. BMC Biol 2017 15: 17. doi:10.1186-s12915-017-0348-8

Abstract

BackgroundThe 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.

ResultsHere 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.

ConclusionsMorphometrics 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.

KeywordsMicrobiology Cell biology Cell morphology Cell shape Imaging Chemical genomics Principal component analysis Segmentation Microscopy Computer vision Electronic supplementary materialThe online version of this article doi:10.1186-s12915-017-0348-8 contains supplementary material, which is available to authorized users.

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Autor: Tristan Ursell - Timothy K. Lee - Daisuke Shiomi - Handuo Shi - Carolina Tropini - Russell D. Monds - Alexandre Colavin -

Fuente: https://link.springer.com/







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