Artistoo, a library to build, share, and explore simulations of cells and tissues in the web browser

Wortel, Inge M N; Textor, Johannes

doi:10.7554/elife.61288

Cited by 22 publications

(18 citation statements)

References 22 publications

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“…Thus, copy attempts tend to lower H because attempts with DH < 0 always succeed. The success rate of energetically unfavorable attempts instead decays exponentially with their cost depending on the temperature T (higher temperatures are more permissive for unfavorable attempts; see also the online ''Simulation 1'' at https://ingewortel.github.io/artistoosupplements/ (16,17) to explore these dynamics interactively).…”

Section: Cpmmentioning

confidence: 99%

Local actin dynamics couple speed and persistence in a cellular Potts model of cell migration

Wortel

Niculescu

Kolijn

et al. 2021

Biophysical Journal

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Section: Cpmmentioning

confidence: 99%

Local actin dynamics couple speed and persistence in a cellular Potts model of cell migration

Wortel

Niculescu

Kolijn

et al. 2021

Biophysical Journal

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“…To mimic real fluorescent histopathological images as closely as possible, we used the Cellular Potts modeling framework [13][14][15]. We placed "labelled" cells of realistic size (about 510µm in diameter) into a 3D space repre senting an unlabelled background structure, and cut out thin slices of 4µm depth (Figure 1A).…”

Section: Segmentationbased Phenotyping Fails In Dense Tissues Even When Segmentation Is Perfectmentioning

confidence: 99%

ImmuNet: A Segmentation-Free Machine Learning Pipeline for Immune Landscape Phenotyping in Tumors by Muliplex Imaging

Sultan

Gorris

Woude

et al. 2021

Preprint

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Tissue specimens taken from primary tumors or metastases contain important information for diagnosis and treatment of cancer patients. Multispectral imaging allows in situ visualization of heterogeneous cell subsets, such as lymphocytes, in tissue samples. Many image processing pipelines first segment cell boundaries and then measure marker expression to assign cell phenotypes. In dense tissue environments such as solid tumors, segmentation-based phenotyping can be inaccurate due to segmentation errors or overlapping cell boundaries. Here we introduce a machine learning pipeline design called ImmuNet that directly identifies the positions and phenotypes of immune cells without determining their exact boundaries. ImmuNet is easy to train: human annotators only need to click on immune cells and rank their expression of each marker; full annotation of tissue regions is not necessary. We demonstrate that ImmuNet is a suitable approach for immune cell detection and phenotyping in multiplex immunohistochemistry: it compares favourably to segmentation-based methods, especially in dense tissues, and we externally validate ImmuNet results by comparing them to flow cytometric measurements from the same tissue. In summary, ImmuNet performs well on diverse tissue specimens, takes relatively little effort to train and implement, and is a simpler alternative to segmentation-based approaches when only cell positions and phenotypes, but not their shapes are required for downstream analyses. We hope that ImmuNet will help cancer researchers to analyze multichannel tissue images more easily and accurately.

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“…source code in S1 Data). An interactive version in Artistoo [33] of the model running in a web browser is in S2 Data.…”

Section: Plos Computational Biologymentioning

confidence: 99%

Computational modelling of cell motility modes emerging from cell-matrix adhesion dynamics

et al. 2022

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Lymphocytes have been described to perform different motility patterns such as Brownian random walks, persistent random walks, and Lévy walks. Depending on the conditions, such as confinement or the distribution of target cells, either Brownian or Lévy walks lead to more efficient interaction with the targets. The diversity of these motility patterns may be explained by an adaptive response to the surrounding extracellular matrix (ECM). Indeed, depending on the ECM composition, lymphocytes either display a floating motility without attaching to the ECM, or sliding and stepping motility with respectively continuous or discontinuous attachment to the ECM, or pivoting behaviour with sustained attachment to the ECM. Moreover, on the long term, lymphocytes either perform a persistent random walk or a Brownian-like movement depending on the ECM composition. How the ECM affects cell motility is still incompletely understood. Here, we integrate essential mechanistic details of the lymphocyte-matrix adhesions and lymphocyte intrinsic cytoskeletal induced cell propulsion into a Cellular Potts model (CPM). We show that the combination of de novo cell-matrix adhesion formation, adhesion growth and shrinkage, adhesion rupture, and feedback of adhesions onto cell propulsion recapitulates multiple lymphocyte behaviours, for different lymphocyte subsets and various substrates. With an increasing attachment area and increased adhesion strength, the cells’ speed and persistence decreases. Additionally, the model predicts random walks with short-term persistent but long-term subdiffusive properties resulting in a pivoting type of motility. For small adhesion areas, the spatial distribution of adhesions emerges as a key factor influencing cell motility. Small adhesions at the front allow for more persistent motility than larger clusters at the back, despite a similar total adhesion area. In conclusion, we present an integrated framework to simulate the effects of ECM proteins on cell-matrix adhesion dynamics. The model reveals a sufficient set of principles explaining the plasticity of lymphocyte motility.

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Artistoo, a library to build, share, and explore simulations of cells and tissues in the web browser

Cited by 22 publications

References 22 publications

Local actin dynamics couple speed and persistence in a cellular Potts model of cell migration

Local actin dynamics couple speed and persistence in a cellular Potts model of cell migration

ImmuNet: A Segmentation-Free Machine Learning Pipeline for Immune Landscape Phenotyping in Tumors by Muliplex Imaging

Computational modelling of cell motility modes emerging from cell-matrix adhesion dynamics

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