Joint modeling of cell and nuclear shape variation

Johnson, Gregory R.; Buck, Taraz E.; Sullivan, Devin P.; Rohde, Gustavo K.; Murphy, Robert F.

doi:10.1091/mbc.e15-06-0370

Cited by 32 publications

(41 citation statements)

References 31 publications

(45 reference statements)

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“…A nonrigid image registration method like large distance diffeomorphic mapping (LDDMM) (46) can determine for each voxel of the segmentation image the corresponding position in the space of a given template shape. We used a method that is an approximation to LDDMM, specifically an extension to the Christensen algorithm (46–48). The amount of morphing required on average for each sensor and time point is shown in table S3.…”

Section: Methodsmentioning

confidence: 99%

Computational spatiotemporal analysis identifies WAVE2 and cofilin as joint regulators of costimulation-mediated T cell actin dynamics

et al. 2016

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Fluorescence microscopy is one of the most important tools in cell biology research and it provides spatial and temporal information to investigate regulatory systems inside cells. This technique can generate data in the form of signal intensities at thousands of positions resolved inside individual live cells; however, given extensive cell-to-cell variation, methods do not currently exist to assemble these data into three- or four-dimensional maps of protein concentration that can be compared across different cells and conditions. Here, we have developed one such method and applied it to investigate actin dynamics in T cell activation. Antigen recognition in T cells by the T cell receptor (TCR) is amplified by engagement of the costimulatory receptor CD28 and we have determined how CD28 modulates actin dynamics. We imaged actin and eight core actin regulators under conditions where CD28 in the context of a strong TCR signal was engaged or blocked to yield over a thousand movies. Our computational analysis identified diminished recruitment of the activator of actin nucleation WAVE2 and the actin severing protein cofilin to F-actin as the dominant difference upon costimulation blockade. Reconstitution of WAVE2 and cofilin activity restored the defect in actin signaling dynamics upon costimulation blockade. Thus we have developed and validated an approach to quantify protein distributions in time and space for analysis of complex regulatory systems.

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Section: Methodsmentioning

confidence: 99%

Computational spatiotemporal analysis identifies WAVE2 and cofilin as joint regulators of costimulation-mediated T cell actin dynamics

et al. 2016

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“…The joint model approach has so far been only been developed using a non-parametric, diffeomorphic approach [33]. An advantage of the diffeomorphic approach is that it can be applied simultaneously to multi-channel images.…”

Section: Constructing Modelsmentioning

confidence: 99%

“…For example, the time required to calculate a diffeomorphic distance for a pair of 144 × 144 × 14 voxel 3D image was more than ten times the time required to calculated descriptive features for them [33]. This increased time is due to the need to iteratively solve a differential equation to find the path that morphs each into the other.…”

Section: Constructing Modelsmentioning

confidence: 99%

“…Under the assumption that the direction of each movement only depends on the previous position (i.e., that the cell follows a first-order Markov model), a movie with arbitrary temporary resolution and arbitrary length can be synthesized even if only pairs of frames are available (assuming enough pairs are available to make good estimates throughout the shape space). This approach has been applied to learn dynamics shape models for a number of different cultured cell lines [33]. …”

Section: Constructing Modelsmentioning

confidence: 99%

“…A shape for position 5 is then found by morphing the shape at position 3 towards position 4. From Johnson et al [33]. …”

Section: Figurementioning

confidence: 99%

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Building cell models and simulations from microscope images

Murphy

2016

Methods

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The use of fluorescence microscopy has undergone a major revolution over the past twenty years, both with the development of dramatic new technologies and with the widespread adoption of image analysis and machine learning methods. Many open source software tools provide the ability to use these methods in a wide range of studies, and many molecular and cellular phenotypes can now be automatically distinguished. This article presents the next major challenge in microscopy automation, the creation of accurate models of cell organization directly from images, and reviews the progress that has been made towards this challenge.

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Cell shape characterization and classification with discrete Fourier transforms and self‐organizing maps

et al. 2017

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Cells in their natural environment often exhibit complex kinetic behavior and radical adjustments of their shapes. This enables them to accommodate to short- and long-term changes in their surroundings under physiological and pathological conditions. Intravital multi-photon microscopy is a powerful tool to record this complex behavior. Traditionally, cell behavior is characterized by tracking the cells' movements, which yields numerous parameters describing the spatiotemporal characteristics of cells. Cells can be classified according to their tracking behavior using all or a subset of these kinetic parameters. This categorization can be supported by the a priori knowledge of experts. While such an approach provides an excellent starting point for analyzing complex intravital imaging data, faster methods are required for automated and unbiased characterization. In addition to their kinetic behavior, the 3D shape of these cells also provide essential clues about the cells' status and functionality. New approaches that include the study of cell shapes as well may also allow the discovery of correlations amongst the track- and shape-describing parameters. In the current study, we examine the applicability of a set of Fourier components produced by Discrete Fourier Transform (DFT) as a tool for more efficient and less biased classification of complex cell shapes. By carrying out a number of 3D-to-2D projections of surface-rendered cells, the applied method reduces the more complex 3D shape characterization to a series of 2D DFTs. The resulting shape factors are used to train a Self-Organizing Map (SOM), which provides an unbiased estimate for the best clustering of the data, thereby characterizing groups of cells according to their shape. We propose and demonstrate that such shape characterization is a powerful addition to, or a replacement for kinetic analysis. This would make it especially useful in situations where live kinetic imaging is less practical or not possible at all. © 2017 International Society for Advancement of Cytometry.

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Joint modeling of cell and nuclear shape variation

Cited by 32 publications

References 31 publications

Computational spatiotemporal analysis identifies WAVE2 and cofilin as joint regulators of costimulation-mediated T cell actin dynamics

Computational spatiotemporal analysis identifies WAVE2 and cofilin as joint regulators of costimulation-mediated T cell actin dynamics

Building cell models and simulations from microscope images

Cell shape characterization and classification with discrete Fourier transforms and self‐organizing maps

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