Distinguishing between mechanisms of cell aggregation using pair-correlation functions

Agnew, Dylan; Green, J. E. F.; Brown, Thomas M.; Simpson, Matthew J.; Binder, Benjamin J.

doi:10.1016/j.jtbi.2014.02.033

Cited by 57 publications

(88 citation statements)

References 26 publications

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“…However, computational techniques are available to quantify whether cell clustering is significant in an image sequence. 60 4. The relative contribution of each of the above factors, and other metrics of interest in a particular assay such as cell proliferation and apoptosis, to collective motion.…”

Section: Future Directionsmentioning

confidence: 99%

Quantifying trophoblast migration: In vitro approaches to address in vivo situations

James

Tun

Clark

2016

Cell Adhesion & Migration

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When trophoblasts migrate and invade in vivo, they do so by interacting with a range of other cell types, extracellular matrix proteins, chemotactic factors and physical forces such as fluid shear stress. These factors combine to influence overall trophoblast migration and invasion into the decidua, which in turn determines the success of spiral artery remodeling, and pregnancy itself. Our understanding of these important but complex processes is limited by the simplified conditions in which we often study cell migration in vitro, and many discrepancies are observed between different in vitro models in the literature. On top of these experimental considerations, the migration of individual trophoblasts can vary widely. While time-lapse microscopy provides a wealth of information on trophoblast migration, manual tracking of individual cell migration is a time consuming task that ultimately restricts the numbers of cells quantified, and thus the ability to extract meaningful information from the data. However, the development of automated imaging algorithms is likely to aid our ability to accurately interpret trophoblast migration in vitro, and better allow us to relate these observations to in vivo scenarios. This commentary discusses the advantages and disadvantages of techniques commonly used to quantify trophoblast migration and invasion, both from a cell biology and a mathematical perspective, and examines how such techniques could be improved to help us relate trophoblast migration more accurately to in vivo function in the future.

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Section: Future Directionsmentioning

confidence: 99%

Quantifying trophoblast migration: In vitro approaches to address in vivo situations

James

Tun

Clark

2016

Cell Adhesion & Migration

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Section: Introductionmentioning

confidence: 99%

“…For instance, many cell types are known to form clusters or aggregates as a result of attractive interactions [17,18]. Examples include breast cancer cells [19] and hepatocyte-stellate aggregates [17]. Others, such as retinal neurons [12,20], arrange themselves into patterns that minimize their proximity to neighbouring cells.…”

Section: Introductionmentioning

confidence: 99%

Spatial moment dynamics for collective cell movement incorporating a neighbour-dependent directional bias

Binny

Plank

James

2015

J. R. Soc. Interface.

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The ability of cells to undergo collective movement plays a fundamental role in tissue repair, development and cancer. Interactions occurring at the level of individual cells may lead to the development of spatial structure which will affect the dynamics of migrating cells at a population level. Models that try to predict population-level behaviour often take a mean-field approach, which assumes that individuals interact with one another in proportion to their average density and ignores the presence of any small-scale spatial structure. In this work, we develop a lattice-free individual-based model (IBM) that uses random walk theory to model the stochastic interactions occurring at the scale of individual migrating cells. We incorporate a mechanism for local directional bias such that an individual's direction of movement is dependent on the degree of cell crowding in its neighbourhood. As an alternative to the mean-field approach, we also employ spatial moment theory to develop a population-level model which accounts for spatial structure and predicts how these individual-level interactions propagate to the scale of the whole population. The IBM is used to derive an equation for dynamics of the second spatial moment (the average density of pairs of cells) which incorporates the neighbour-dependent directional bias, and we solve this numerically for a spatially homogeneous case.

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“…Following the work of Agnew et al [19] (and others), we re-define the distance between two points in equation (2.1) as…”

Section: Periodic Pair-correlation Functionmentioning

confidence: 99%

Identifying the necrotic zone boundary in tumour spheroids with pair-correlation functions

Dini

Binder

Fischer

et al. 2016

J. R. Soc. Interface.

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Automatic identification of the necrotic zone boundary is important in the assessment of treatments on in vitro tumour spheroids. This has been difficult especially when the difference in cell density between the necrotic and viable zones of a tumour spheroid is small. To help overcome this problem, we developed novel one-dimensional pair-correlation functions (PCFs) to provide quantitative estimates of the radial distance of the necrotic zone boundary from the centre of a tumour spheroid. We validate our approach on synthetic tumour spheroids in which the position of the necrotic zone boundary is known a priori. It is then applied to nine real tumour spheroids imaged with light sheet-based fluorescence microscopy. PCF estimates of the necrotic zone boundary are compared with those of a human expert and an existing standard computational method.

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Distinguishing between mechanisms of cell aggregation using pair-correlation functions

Cited by 57 publications

References 26 publications

Quantifying trophoblast migration: In vitro approaches to address in vivo situations

Quantifying trophoblast migration: In vitro approaches to address in vivo situations

Spatial moment dynamics for collective cell movement incorporating a neighbour-dependent directional bias

Identifying the necrotic zone boundary in tumour spheroids with pair-correlation functions

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