title: Distinguishing between mechanisms of cell aggregation using pair-correlation functions.
abstract: Many cell types form clumps or aggregates when cultured in vitro through a variety of mechanisms including rapid cell proliferation, chemotaxis, or direct cell-to-cell contact. In this paper we develop an agent-based model to explore the formation of aggregates in cultures where cells are initially distributed uniformly, at random, on a two-dimensional substrate. Our model includes unbiased random cell motion, together with two mechanisms which can produce cell aggregates: (i) rapid cell proliferation and (ii) a biased cell motility mechanism where cells can sense other cells within a finite range, and will tend to move towards areas with higher numbers of cells. We then introduce a pair-correlation function which allows us to quantify aspects of the spatial patterns produced by our agent-based model. In particular, these pair-correlation functions are able to detect differences between domains populated uniformly at random (i.e. at the exclusion complete spatial randomness (ECSR) state) and those where the proliferation and biased motion rules have been employed - even when such differences are not obvious to the naked eye. The pair-correlation function can also detect the emergence of a characteristic inter-aggregate distance which occurs when the biased motion mechanism is dominant, and is not observed when cell proliferation is the main mechanism of aggregate formation. This suggests that applying the pair-correlation function to experimental images of cell aggregates may provide information about the mechanism associated with observed aggregates. As a proof of concept, we perform such analysis for images of cancer cell aggregates, which are known to be associated with rapid proliferation. The results of our analysis are consistent with the predictions of the proliferation-based simulations, which supports the potential usefulness of pair correlation functions for providing insight into the mechanisms of aggregate formation.
authors.affiliation: School of Mathematical Sciences, University of Adelaide, Adelaide, South Australia 5005, Australia.
authors.forename: D J G
authors.initials: DJ
authors.lastname: Agnew
authors.affiliation: School of Mathematical Sciences, University of Adelaide, Adelaide, South Australia 5005, Australia.
authors.forename: J E F
authors.initials: JE
authors.lastname: Green
authors.affiliation: School of Mathematical Sciences, University of Adelaide, Adelaide, South Australia 5005, Australia.
authors.forename: T M
authors.initials: TM
authors.lastname: Brown
authors.affiliation: School of Mathematical Sciences, Queensland University of Technology (QUT), Brisbane, Australia; Tissue Repair and Regeneration Program, Institute of Health and Biomedical Innovation, QUT, Brisbane, Australia.
authors.forename: M J
authors.initials: MJ
authors.lastname: Simpson
authors.affiliation: School of Mathematical Sciences, University of Adelaide, Adelaide, South Australia 5005, Australia. Electronic address: benjamin.binder@adelaide.edu.au.
authors.forename: B J
authors.initials: BJ
authors.lastname: Binder
mesh_terms: D002449:Cell Aggregation; D045744:Cell Line, Tumor; D006801:Humans; D066298:In Vitro Techniques; D008954:Models, Biological
keywords: Agent-based model; Cell proliferation; Cell–cell interaction; Spatial patterns