Local cellular neighborhood controls proliferation in cell competition

Bove, Anna; Gradeci, Daniel; Fujita, Yasuyuki; Banerjee, Shiladitya; Charras, Guillaume; Lowe, Alan R.

doi:10.1091/mbc.e17-06-0368

Cited by 69 publications

(139 citation statements)

References 36 publications

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“…We also assessed performance on the detection of the number of neighbours as this feature can be particularly relevant when studying collective organisation of the cells in various contexts (Blin et al, 2018;Bove et al, 2017;Mesa et al, 2017;Schmitz et al, 2017;Shaya et al, 2017;Toth et al, 2018). Again, only Ilastik and Nessys resulted in an accurate neighbour count with a standard deviation of 2.2 neighbours from GT.…”

Section: Quantification Of Performance Metrics Of Nessys In Comparisomentioning

confidence: 99%

“…Although excellent tools exist for the automated live tracking of 2D cultures (Bove et al, 2017;Hilsenbeck et al, 2016;Piccinini et al, 2016;Roccio et al, 2013;Winter et al, 2015) , the situation described here would not be possible to address with these tools because of the propensity of differentiating mES cells to squeeze underneath each other (Movie S3) making it necessary to perform analysis in three dimensions.…”

Section: Tracking Cell-cell Interactions During Differentiation Of Plmentioning

confidence: 99%

“…One possible approach to tackle this challenging situation would be to perform mosaic labelling to make it easier to track a few fluorescent cells among predominantly unlabelled cells. This would be sufficient to extract a subset of cell lineages but would fail to capture local cell-cell interaction or the broader multicellular context surrounding each cell, both of which are central to understanding the emergent properties of differentiating populations (Bove et al, 2017;Toth et al, 2018). For this reason we decided to label all the Sox1-GFP cells with our NE-mKate2 construct in order to analyse the collective behaviours of differentiating cells in 3D.…”

Section: Tracking Cell-cell Interactions During Differentiation Of Plmentioning

confidence: 99%

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Nessys: a novel method for accurate nuclear segmentation in 3D

Blin

Sadurska

Migueles

et al. 2018

Preprint

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Methods for measuring the properties of individual cells within their native 3D environment will enable a deeper understanding of embryonic development, tissue regeneration, and tumorigenesis. However current methods for segmenting nuclei in 3D tissues are not designed for situations where nuclei are densely packed, non-spherical, heterogeneous in shape, size, or texture, all of which are true of many embryonic and adult tissue types as well as in many cases for cells differentiating in culture.Here we overcome this bottleneck by devising a novel method based on labelling the nuclear envelope (NE) and automatically distinguishing individual nuclei using a tree structured ridge tracing method followed by shape ranking according to a trained classifier. The method is fast and makes it possible to process images that are larger than the computer's memory. We consistently obtain accurate segmentation rates of >90% even for challenging images such as mid-gestation embryos or 3D cultures. We provide a 3D editor and inspector for the manual curation of the segmentation results as well as a program to assess the accuracy of the segmentation.We have also generated a live reporter of the NE that can be used to track live cells in three dimensions over time. We use this to monitor the history of cell interactions and occurrences of neighbour exchange within cultures of pluripotent cells during differentiation.We provide these tools in an open-access user-friendly format. 2 35 40 45 50 2012; Carpenter et al., 2013; Meijering et al., 2016).Here, we report a new approach to overcome these bottlenecks in quantitative image analysis of individual cells in 3D. Rather than relying on staining for nuclear content (for example DAPI or Hoescht staining), we instead detect the nuclear envelope (NE). This makes it easier to identify individual nuclei that are in close contact with each other and does not suffer from segmentation problems associated with textured nuclear staining, unusually shaped nuclei, or cell debris. Furthermore, the NE of individual nuclei are easily discernable by eye in crowded tissues, and so manual correction of any mis-segmented nuclei becomes easier than is the case for DAPI stained nuclei. We provide a user-friendly 3D-4D editing tool to rapidly correct any

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Section: Quantification Of Performance Metrics Of Nessys In Comparisomentioning

confidence: 99%

Section: Tracking Cell-cell Interactions During Differentiation Of Plmentioning

confidence: 99%

Section: Tracking Cell-cell Interactions During Differentiation Of Plmentioning

confidence: 99%

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Nessys: a novel method for accurate nuclear segmentation in 3D

Blin

Sadurska

Migueles

et al. 2018

Preprint

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“…The more rigorous application of the technique can involve parameter estimation and uncertainty analysis, as well as larger experimental datasets (as compared with the number of model parameters); however, this is beyond the scope of the current study. [4] to expression (1), performed using the Matlab fminsearch function. Note the log Y scale.…”

Section: Figure 10mentioning

confidence: 99%

“…One of the early but in-depth examples of nonlinear Ordinary Differential Equations (ODE) systems applications to immunology is the work of Perelson et al [1], which actually treats coupled rate equations for the mixture of different cellular and molecular components in blood. More recently, the ODE approach was applied, in particular, to model abnormal regimes in haematopoiesis with multiple phenotypes [2,3], and competition-mediated proliferation in two-phenotype cell culture [4]. It is well-known, however, that the right-hand sides of such ODEs are the subject of meticulous designs bordering with art, as ODEs, being a general method, are not specially suited for cell proliferation modelling.…”

mentioning

confidence: 99%

Modelling mitosis with multiple phenotypes: relation to Haeckel’s recapitulation law

Alexandrov

2018

Preprint

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The article presents a novel stochastic mathematical model of mitosis in heterogeneous (multiple-phenotype), age-dependent cell populations. The developed computational techniques involve flexible use of differentiation tree diagrams. The applicability of the model is discussed in the context of the Haeckelian (biogenetic) paradigm. In particular, the article puts forward the conjecture of generality of Haeckel's recapitulation law. The conjecture is briefly collated against relevant scientific evidence and elaborated for the specific case of evolving/mutable cell phenotypes as considered by the model. The feasibility, basic regimes and the convenience of the model are tested on examples and experimental data, and the corresponding open source simulation software is described and demonstrated.

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Convolutional Neural Networks for Classifying Chromatin Morphology in Live-Cell Imaging

Ulicna

Soelistyo

et al. 2022

Methods in Molecular Biology

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Chromatin is highly structured, and changes in its organisation are essential in many cellular processes, including cell division. Recently, advances in machine learning have enabled researchers to automatically classify chromatin morphology in fluorescence microscopy images.In this protocol, we develop user-friendly tools to perform this task. We provide an open-source annotation tool, and a cloud-based computational framework to train and utilise a convolutional neural network to automatically classify chromatin morphology. Using cloud compute enables users without significant resources or computational experience to use a machine learning approach to analyse their own microscopy data.

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Local cellular neighborhood controls proliferation in cell competition

Cited by 69 publications

References 36 publications

Nessys: a novel method for accurate nuclear segmentation in 3D

Nessys: a novel method for accurate nuclear segmentation in 3D

Modelling mitosis with multiple phenotypes: relation to Haeckel’s recapitulation law

Convolutional Neural Networks for Classifying Chromatin Morphology in Live-Cell Imaging

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