Quantification of bacterial shape using moment invariants enables distinguishing populations during cellular plasmolysis

The analysis of geometrical cell shape is fundamental to understand motility, development, and responses to external stimuli. The moment invariants framework quantifies cellular shape and size, although its applicability has not been explored for rod-shaped bacteria. In this work, we use moment invariants to evaluate the extent of cell shape change (projected area and volume) during plasmolysis, as <i>Escherichia coli</i> cells are subjected to hyperosmotic shock. The characteristic cell size descriptors width, length and area show systematic decrease as external salt (NaCl) conditions increase-except for high salt, where a small population of cells shows evidence of membrane rupture. We use these two-dimensional results to estimate cell volume during plasmolysis, finding a minimum volume that is not reduced further with increase in salt concentration. Next, we computed elongation and dispersion, metrics that quantify how cell shape is stretched out or differs from an ellipse, respectively. For dispersion, we observe the development of a long tail for the distribution at high salt. Moreover, the use of elongation-dispersion plots enables distinction of plasmolyzed and normal cells despite the presence of broad distributions. Altogether, a protocol is provided to evaluate bacterial shape, highlighting a set of metrics that help distinguish among bacterial populations.•Moment invariants enable quantitative description of bacterial morphology in two dimensions, and estimation of volume•We apply the moment invariants framework to describe changes in bacterial shape during plasmolysis•The proposed methodology shows suitability to distinguish among cellular populations.

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Journal
MethodsX
Published
2024-11-05
DOI
https://doi.org/10.1016/j.mex.2024.103036
Primary Topic
Cell Image Analysis Techniques
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article
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article

Quantification of bacterial shape using moment invariants enables distinguishing populations during cellular plasmolysis

Braulio Gutiérrez–Medina
MethodsX
Cell Image Analysis Techniques
article

Quantification of bacterial shape using moment invariants enables distinguishing populations during cellular plasmolysis

Braulio Gutiérrez–Medina
article en

Abstract

The analysis of geometrical cell shape is fundamental to understand motility, development, and responses to external stimuli. The moment invariants framework quantifies cellular shape and size, although its applicability has not been explored for rod-shaped bacteria. In this work, we use moment invariants to evaluate the extent of cell shape change (projected area and volume) during plasmolysis, as <i>Escherichia coli</i> cells are subjected to hyperosmotic shock. The characteristic cell size descriptors width, length and area show systematic decrease as external salt (NaCl) conditions increase-except for high salt, where a small population of cells shows evidence of membrane rupture. We use these two-dimensional results to estimate cell volume during plasmolysis, finding a minimum volume that is not reduced further with increase in salt concentration. Next, we computed elongation and dispersion, metrics that quantify how cell shape is stretched out or differs from an ellipse, respectively. For dispersion, we observe the development of a long tail for the distribution at high salt. Moreover, the use of elongation-dispersion plots enables distinction of plasmolyzed and normal cells despite the presence of broad distributions. Altogether, a protocol is provided to evaluate bacterial shape, highlighting a set of metrics that help distinguish among bacterial populations.•Moment invariants enable quantitative description of bacterial morphology in two dimensions, and estimation of volume•We apply the moment invariants framework to describe changes in bacterial shape during plasmolysis•The proposed methodology shows suitability to distinguish among cellular populations.

MethodsXVol. 13
Institute for Scientific and Technological Research (MX)
Zero hunger
Openalex Percentile: Top 87%
Cell Image Analysis Techniques
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