TISMorph: A tool to quantify texture, irregularity and spreading of single cells

Alizadeh, Elaheh; Xu, Wenlong; Castle, Jordan; Foss, Jacqueline; Prasad, Ashok

doi:10.1101/372755

Cited by 5 publications

(4 citation statements)

References 26 publications

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“…The sum average Haralick texture feature was discarded due to normalization concerns. (ii) Shape features (15 features) were calculated as the absolute value of the frequency coefficients of the Fourier transform of the distance to the boundary as a function of the radial angle around cell center 92 , with the sum of shape features normalized to 1. (iii) The cell environment was featurized in a similar fashion, where an indicator function with value 0 if the cell boundary was in contact with the background mask (no neighboring cell), and value 1 if in contact with the cell foreground mask.…”

Section: Methodsmentioning

confidence: 99%

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: Methodsmentioning

confidence: 99%

Morphodynamical cell state description via live-cell imaging trajectory embedding

Copperman

Gross

Chang

et al. 2021

Preprint

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“…Cell shape image dataset: The cell shape dataset contains mouse osteosarcoma 2D imaged cells [12], that were processed into a 100×2 vector of coordinates that define the cell shape contour, used as a test dataset in the Python package Geomstats [13] (for more details, see also [14] and the associated Github link). We more specifically considered the subset of "DUNN" cells (that denotes a specific lineage) from the control group (no treatment on the cells), which yields 207 cells in total.…”

Section: Synthetic Datasetsmentioning

confidence: 99%

Orthogonal outlier detection and dimension estimation for improved MDS embedding of biological datasets

Mirone

Prasad

et al. 2023

Preprint

Self Cite

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Conventional dimensionality reduction methods like Multidimensional Scaling (MDS) are sensitive to the presence of orthogonal outliers, leading to significant defects in the embedding. We introduce a robust MDS method, based on the geometry and statistics of simplices formed by data points, that allows to detect orthogonal outliers and subsequently reduce dimensionality. We validate our methods using synthetic datasets, and further show how it can be applied to a variety of large real biological datasets, including cancer image cell data and human microbiome project data.

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“…The next four features represented the mean and standard deviation of the minimal distance from the object's boundary points to the nearest neighboring object and the mean and standard deviation of the intensity values of pixels within each object. The next 30 features were computed as Fourier modes on the boundary coordinates as described in Alizadeh et al (2019). Such truncated Fourier decomposition approximates the object's shape with a smooth contour.…”

Section: Image Processing and Quantificationmentioning

confidence: 99%

Coordinated Regulation of Cdc42ep1, Actin, and Septin Filaments during Neural Crest Cell Migration

Kho

Hladyshau

Tsygankov

et al. 2022

Preprint

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The septin cytoskeleton has been demonstrated to interact with other cytoskeletal components to regulate various cellular processes, including cell migration. However, the mechanisms of how septin regulates cell migration are not fully understood. In this study, we use the highly migratory neural crest cells of frog embryos to examine the role of septin filaments in cell migration. We found that septin filaments are required for proper migration of neural crest cells by controlling both the speed and the direction of cell migration. We further determined that septin filaments regulate these features of cell migration by interacting with actin stress fibers. In neural crest cells, septin filaments co-align with actin stress fibers, and the loss of septin filaments leads to impaired stability and contractility of actin stress fibers. In addition, we showed that a partial loss of septin filaments leads to drastic changes in the orientations of newly formed actin stress fibers, suggesting that septin filaments help maintain the persistent orientation of actin stress fibers during directed cell migration. Lastly, our study revealed that these activities of septin filaments depend on Cdc42ep1, which co-localizes with septin filaments in the center of neural crest cells. Cdc42ep1 interacts with septin filaments in a reciprocal manner, with septin filaments recruiting Cdc42ep1 to the cell center and Cdc42ep1 supporting the formation of septin filaments.

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TISMorph: A tool to quantify texture, irregularity and spreading of single cells

Cited by 5 publications

References 26 publications

Morphodynamical cell state description via live-cell imaging trajectory embedding

Morphodynamical cell state description via live-cell imaging trajectory embedding

Orthogonal outlier detection and dimension estimation for improved MDS embedding of biological datasets

Coordinated Regulation of Cdc42ep1, Actin, and Septin Filaments during Neural Crest Cell Migration

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