Biophysical Measurements of Bacterial Cell Shape

Nguyen, Jeffrey P.; Bratton, Benjamin P.; Shaevitz, Joshua W.

doi:10.1007/978-1-4939-3676-2_17

Cited by 4 publications

(2 citation statements)

References 10 publications

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“…Alternative approaches have directly defined a force based on the distance to the local maximum intensity in the image [45,69]. Others have defined a proper energy functional as a distance between a microscopy image and an artificial image created from a mesh, as in [23,54]. The artificial image is generated by creating a thin boolean mask from the mesh, that is then convolved with a PSF, that is assumed to be known.…”

Section: Energy Based Segmentationmentioning

confidence: 99%

Differentiable Rendering for 3D Fluorescence Microscopy

Ichbiah¹,

Delbary²,

Turlier³

2023

Preprint

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Differentiable rendering is a growing field that is at the heart of many recent advances in solving inverse graphics problems, such as the reconstruction of 3D scenes from 2D images. By making the rendering process differentiable, one can compute gradients of the output image with respect to the different scene parameters efficiently using automatic differentiation. Interested in the potential of such methods for the analysis of fluorescence microscopy images, we introduce deltaMic, a microscopy renderer that can generate a 3D fluorescence microscopy image from a 3D scene in a fully differentiable manner. By convolving the meshes in the scene with the point spread function (PSF) of the microscope, that characterizes the response of its imaging system to a point source, we emulate the 3D image creation process of fluorescence microscopy. This is achieved by computing the Fourier transform (FT) of the mesh and performing the convolution in the Fourier domain. Naive implementation of such mesh FT is however slow, inefficient, and sensitive to numerical precision. We solve these difficulties by providing a memory and computationally efficient fully differentiable GPU implementation of the 3D mesh FT. We demonstrate the potential of our method by reconstructing complex shapes from artificial microscopy images. Eventually, we apply our renderer to real confocal fluorescence microscopy images of embryos to accurately reconstruct the multicellular shapes of these cell aggregates.

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Section: Energy Based Segmentationmentioning

confidence: 99%

Differentiable Rendering for 3D Fluorescence Microscopy

Ichbiah¹,

Delbary²,

Turlier³

2023

Preprint

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“…While high-resolution 3D images are the key input to this method, the method does not return a resolution-enhanced image. Rather, this method reconstructs the 3D surface coordinates and shape of the cell using a forward convolution algorithm using active contours and the apparent blurring function of the microscope 3 (Figure 1). It has been used to study the bacterial actin homolog MreB in E. coli 4,5,6,7 , the novel periskeletal element CrvA in V. cholerae 8 , and the putative bactofilin CcmA in Helicobacter pylori 9 (Figure 2).…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional Imaging of Bacterial Cells for Accurate Cellular Representations and Precise Protein Localization

Bratton¹,

Barton²,

Morgenstein³

2019

JoVE

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The shape of a bacterium is important for its physiology. Many aspects of cell physiology such as cell motility, predation, and biofilm production can be affected by cell shape. Bacterial cells are three-dimensional (3D) objects, although they are rarely treated as such. Most microscopy techniques result in two-dimensional (2D) images leading to the loss of data pertaining to the actual 3D cell shape and localization of proteins. Certain shape parameters, such as Gaussian curvature (the product of the two principal curvatures), can only be measured in 3D because 2D images do not measure both principal curvatures. Additionally, not all cells lie flat when mounting and 2D imaging of curved cells may not accurately represent the shapes of these cells. Accurately measuring protein localization in 3D can help determine the spatial regulation and function of proteins. A forward convolution technique has been developed that uses the blurring function of the microscope to reconstruct 3D cell shapes and to accurately localize proteins. Here, a protocol for preparing and mounting samples for live cell imaging of bacteria in 3D both to reconstruct an accurate cell shape and to localize proteins is described. The method is based on simple sample preparation, fluorescent image acquisition, and MATLAB-based image processing. Many high-quality fluorescent microscopes can be simply modified to take these measurements. These cell reconstructions are computationally intensive and access to high-throughput computational resources is recommended, although not necessary. This method has been successfully applied to multiple bacterial species and mutants, fluorescent imaging modalities, and microscope manufacturers.

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MreB polymers and curvature localization are enhanced by RodZ and predict E. coli's cylindrical uniformity

et al. 2018

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The actin-like protein MreB has been proposed to coordinate the synthesis of the cell wall to determine cell shape in bacteria. MreB is preferentially localized to areas of the cell with specific curved geometries, avoiding the cell poles. It remains unclear whether MreB’s curvature preference is regulated by additional factors, and which specific features of MreB promote specific features of rod shape growth. Here, we show that the transmembrane protein RodZ modulates MreB curvature preference and polymer number in E. coli, properties which are regulated independently. An unbiased machine learning analysis shows that MreB polymer number, the total length of MreB polymers, and MreB curvature preference are key correlates of cylindrical uniformity, the variability in radius within a single cell. Changes in the values of these parameters are highly predictive of the resulting changes in cell shape (r2 = 0.93). Our data thus suggest RodZ promotes the assembly of geometrically-localized MreB polymers that lead to the growth of uniform cylinders.

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Biophysical Measurements of Bacterial Cell Shape

Cited by 4 publications

References 10 publications

Differentiable Rendering for 3D Fluorescence Microscopy

Differentiable Rendering for 3D Fluorescence Microscopy

Three-dimensional Imaging of Bacterial Cells for Accurate Cellular Representations and Precise Protein Localization

MreB polymers and curvature localization are enhanced by RodZ and predict E. coli's cylindrical uniformity

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