3D shape analysis using overcomplete spherical wavelets: Application to BLEB detection in cell biology

Tournemenne, Robin; Ducroz, Christel; Olivo‐Marin, Jean‐Christophe; Dufour, Alexandre

doi:10.1109/isbi.2014.6867884

Cited by 8 publications

(4 citation statements)

References 19 publications

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“…The neuroscience community has used various techniques to segment the highly branched morphologies of neurons [62–64]. Others have used spherical harmonics or spherical wavelets to characterize 3D cell shape, but these methods have not yet been applied to biological questions [65–68]. Among the first findings linking molecular processes to 3D cell shape, one study employed cell segmentation and surface curvature analysis in combination with the measurement of local fluorescence intensity to establish roles for the molecular motor myosin II in organizing branching morphogenesis [69].…”

Section: Image Analysis Of 3d Cell Migrationmentioning

confidence: 99%

Quantifying Modes of 3D Cell Migration

Driscoll

Danuser

2015

Trends in Cell Biology

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Although it is widely appreciated that cells migrate in a variety of diverse environments in vivo, we are only now beginning to use experimental workflows that yield images with sufficient spatiotemporal resolution to study the molecular processes governing cell migration in 3D environments. Since cell migration is a dynamic process, it is usually studied via microscopy, but 3D movies of 3D processes are difficult to interpret by visual inspection. In this review, we discuss the technologies required to study the diversity of 3D cell migration modes with a focus on the visualization and computational analysis tools needed to study cell migration quantitatively at a level comparable to the analyses performed today on cells crawling on flat substrates.

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Section: Image Analysis Of 3d Cell Migrationmentioning

confidence: 99%

Quantifying Modes of 3D Cell Migration

Driscoll

Danuser

2015

Trends in Cell Biology

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“…To measure the spatial variability, we developed a new morphometric based on exponential-splines and compared embryos geometrically. While other methods have been used (Guignard et al, 2020; Kuang et al, 2022), including Fourier-shape descriptors (Agus et al, 2020; Ducroz et al, 2012; Tournemenne et al, 2014; Valizadeh and Babapour Mofrad, 2022) and dictionary-based shape-descriptors (Andrews et al, 2021; Saad et al, 2019; Tassy et al, 2006; Xiong and Sugioka, 2020), exponential splines encompass all geometrical hidden features (including volume, position, contacts, and compaction) with an arbitrary number of parameters and only a few assumptions, offering a generic tool to build developmental morphomaps. Regardless of the method, the geometrical data are ultimately reduced to fewer parameters which inherently approximate the actual cell shape.…”

Section: Discussionmentioning

confidence: 99%

Temporal variability and cell mechanics control robustness in mammalian embryogenesis

Fabrèges

Corominas-Murtra

Moghe

et al. 2023

Preprint

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How living systems achieve precision in form and function despite their intrinsic stochasticity is a fundamental yet open question in biology. Here, we establish a quantitative morphomap of pre-implantation embryogenesis in mouse, rabbit and monkey embryos, which reveals that although blastomere divisions desynchronise passively without compensation, 8-cell embryos still display robust 3D structure. Using topological analysis and genetic perturbations in mouse, we show that embryos progressively change their cellular connectivity to a preferred topology, which can be predicted by a simple physical model where noise and actomyosin-driven compaction facilitate topological transitions lowering surface energy. This favours the most compact embryo packing at the 8- and 16-cell stage, thus promoting higher number of inner cells. Impairing mitotic desynchronisation reduces embryo packing compactness and generates significantly more cell mis-allocation and a lower proportion of inner-cell-mass-fated cells, suggesting that stochasticity in division timing contributes to achieving robust patterning and morphogenesis.

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“…This approach, however, is not restricted to SPHARM and SVM. We believe that adding dynamics to other shape descriptors, such as Fourier-based descriptors 35 or wavelets 36,37 , could also improve their classification accuracy. The performance of other classifiers -such as random forest or neural networks -should also be studied in the future.…”

Section: Discussionmentioning

confidence: 99%

Dynamic spherical harmonics approach for shape classification of migrating cells

Medyukhina

Blickensdorf

Cseresnyés

et al. 2020

Sci Rep

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Cell migration involves dynamic changes in cell shape. Intricate patterns of cell shape can be analyzed and classified using advanced shape descriptors, including spherical harmonics (SPHARM). Though SPHARM have been used to analyze and classify migrating cells, such classification did not exploit SPHARM spectra in their dynamics. Here, we examine whether additional information from dynamic SPHARM improves classification of cell migration patterns. We combine the static and dynamic SPHARM approach with a support-vector-machine classifier and compare their classification accuracies. We demonstrate that the dynamic SPHARM analysis classifies cell migration patterns more accurately than the static one for both synthetic and experimental data. Furthermore, by comparing the computed accuracies with that of a naive classifier, we can identify the experimental conditions and model parameters that significantly affect cell shape. This capability should-in the future-help to pinpoint factors that play an essential role in cell migration. A cell's migration behavior depends on the state of the cell, extracellular environment, and signals from other cells 1. We can study the mechanisms of cell migration by, e.g., knocking out a certain gene or altering the structure of the extracellular matrix (ECM) and testing whether these changes affect cell migration patterns, such as cell trajectory, shape, or shape dynamics (Fig. 1). To compare migration patterns in an objective and statistically sound way, they have to be automatically analyzed and quantified 2. Whereas both cell trajectories 3,4 and cell shape 5,6 can be quantified with a multitude of available methods, the analysis of shape dynamics-especially in 3D-received considerably less attention. When analyzing cell shape in a static fashion, we look at just one snapshot of the cell's migration history. Depending on how we choose this snapshot, we may either miss important differences in cell shape-e.g., if cells transiently appear similar but have different migration patterns-or detect spurious differences-e.g., if cells occur in different phases of the same migration pattern. Even averaging cell shape descriptors over time 7 may not always be sufficient to distinguish some migration patterns, for example when all cells evolve through similar phases of cell shape but different cells do this with different frequencies (Fig. S1) 8. To distinguish such details of migration behavior we need dynamic shape analysis that takes into account relative changes in cell shape between consecutive time points. While such dynamic shape analysis has been done in 2D 8-11 , 3D shape descriptors have not been applied to characterize and compare the full dynamic migration patterns of cells. The ultimate goal, however, is to understand how cells migrate in living organisms 12. Due to advances in intravital microscopy 13-15 , we have increasingly more 3D + time data of cells migrating in vivo and we should exploit the potential of 3D methods to analyze these data 16. Although there are many simpl...

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3D shape analysis using overcomplete spherical wavelets: Application to BLEB detection in cell biology

Cited by 8 publications

References 19 publications

Quantifying Modes of 3D Cell Migration

Quantifying Modes of 3D Cell Migration

Temporal variability and cell mechanics control robustness in mammalian embryogenesis

Dynamic spherical harmonics approach for shape classification of migrating cells

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