Integrated analysis of cell shape and movement in moving frame

Heryanto, Yusri Dwi; Cheng, Chin-Yi; Uchida, Yutaka; Mimura, Kazushi; Ishii, Masaru; Yamada, Ryo

doi:10.1242/bio.058512

Cited by 5 publications

(3 citation statements)

References 35 publications

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“…This workflow of live-cell imaging, segmentation, featurization, and tracking has been used to describe cell state as a continuum 22 and to develop a cell trajectory-based description of an epithelial-mesenchymal transition (EMT) 23 in the space of single-timepoint snapshot features. Heryanto et al utilized 3D shape descriptors to explore the relationship between 3D shape and cell motility 24 . Notably, trajectory information, including combined motility and morphological features computed as averages over single-cell trajectories, have been used to define and identify cell state space in microglia and neural progenitor cells.…”

Section: Introductionmentioning

confidence: 99%

“…Heryanto et al utilized 3D shape descriptors to explore the relationship between 3D shape and cell motility 24 . Notably, trajectory information, including combined motility and morphological features computed as averages over single-cell trajectories, have been used to define and identify cell state space in microglia and neural progenitor cells.…”

Section: Introductionmentioning

confidence: 99%

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Morphodynamical cell state description via live-cell imaging trajectory embedding

Copperman

Gross

Chang

et al. 2021

Preprint

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Time-lapse imaging provides powerful insight into the dynamical response of cells to perturbation, but the quantitative analysis of morphological changes over time is a challenge. Here, we exploit the concept of "morphodynamical trajectories" to analyze cellular behavior using morphological feature trajectory histories, rather than the common practice of examining morphological feature time courses in the space of single-timepoint (snapshot) morphological features. Our morphodynamical trajectory embedding analysis yielded quantitative and descriptive models of future time points based on the extended history information of MCF10A mammary epithelial cells treated with a panel of ligands. The trajectory analysis constructs a shared morphodynamical cell-state landscape, where the response of MCF10A cells induced by various extracellular signals is characterized by ligand-specific regulation of state transitions. Additionally, we show that including trajectories in single-cell morphological analysis enables (i) systematic characterization of cell state trajectories, and (ii) better separation of phenotypes and more descriptive models of ligand-induced differences as compared to snapshot-based analysis. This morphodynamical trajectory embedding is broadly applicable for the quantitative analysis of cell responses via live-cell imaging across many biological and biomedical applications.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Morphodynamical cell state description via live-cell imaging trajectory embedding

Copperman

Gross

Chang

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…Such questions in three-dimensional necessitate automated analysis even more than in two dimensions, both because such datasets are inherently harder to visualize and interpret, and because three-dimensional environments typically induce a richer variety of morphodynamics [ 18 ]. Spherical harmonic descriptors (SPHARM), a three-dimensional analogue of Fourier descriptors, are a promising method for quantifying three-dimensional cell shape and connecting with motion [ 19 ]. However, the representations are typically too uninterpretable for exploring morphodynamics with high precision, and their use so far is primarily limited to classification or the detection of established shape changes [ 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

T cell morphodynamics reveal periodic shape oscillations in three-dimensional migration

Cavanagh

Kempe

Mazalo

et al. 2022

J. R. Soc. Interface.

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T cells use sophisticated shape dynamics (morphodynamics) to migrate towards and neutralize infected and cancerous cells. However, there is limited quantitative understanding of the migration process in three-dimensional extracellular matrices (ECMs) and across timescales. Here, we leveraged recent advances in lattice light-sheet microscopy to quantitatively explore the three-dimensional morphodynamics of migrating T cells at high spatio-temporal resolution. We first developed a new shape descriptor based on spherical harmonics, incorporating key polarization information of the uropod. We found that the shape space of T cells is low-dimensional. At the behavioural level, run-and-stop migration modes emerge at approximately 150 s, and we mapped the morphodynamic composition of each mode using multiscale wavelet analysis, finding ‘stereotyped’ motifs. Focusing on the run mode, we found morphodynamics oscillating periodically (every approx. 100 s) that can be broken down into a biphasic process: front-widening with retraction of the uropod, followed by a rearward surface motion and forward extension, where intercalation with the ECM in both of these steps likely facilitates forward motion. Further application of these methods may enable the comparison of T cell migration across different conditions (e.g. differentiation, activation, tissues and drug treatments) and improve the precision of immunotherapeutic development.

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Morphodynamical cell state description via live-cell imaging trajectory embedding

et al. 2023

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Time-lapse imaging is a powerful approach to gain insight into the dynamic responses of cells, but the quantitative analysis of morphological changes over time remains challenging. Here, we exploit the concept of “trajectory embedding” to analyze cellular behavior using morphological feature trajectory histories—that is, multiple time points simultaneously, rather than the more common practice of examining morphological feature time courses in single timepoint (snapshot) morphological features. We apply this approach to analyze live-cell images of MCF10A mammary epithelial cells after treatment with a panel of microenvironmental perturbagens that strongly modulate cell motility, morphology, and cell cycle behavior. Our morphodynamical trajectory embedding analysis constructs a shared cell state landscape revealing ligand-specific regulation of cell state transitions and enables quantitative and descriptive models of single-cell trajectories. Additionally, we show that incorporation of trajectories into single-cell morphological analysis enables (i) systematic characterization of cell state trajectories, (ii) better separation of phenotypes, and (iii) more descriptive models of ligand-induced differences as compared to snapshot-based analysis. This morphodynamical trajectory embedding is broadly applicable to the quantitative analysis of cell responses via live-cell imaging across many biological and biomedical applications.

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Integrated analysis of cell shape and movement in moving frame

Cited by 5 publications

References 35 publications

Morphodynamical cell state description via live-cell imaging trajectory embedding

Morphodynamical cell state description via live-cell imaging trajectory embedding

T cell morphodynamics reveal periodic shape oscillations in three-dimensional migration

Morphodynamical cell state description via live-cell imaging trajectory embedding

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