Single-cell approaches to cell competition: High-throughput imaging, machine learning and simulations

Abstract

Cell competition is a quality control mechanism in tissues that results in the elimination of less fit cells. Over the past decade, the phenomenon of cell competition has been identified in many physiological and pathological contexts, driven either by biochemical signaling or by mechanical forces within the tissue. In both cases, competition has generally been characterized based on the elimination of loser cells at the population level, but significantly less attention has been focused on determining how single-cell dynamics and interactions regulate population-wide changes. In this review, we describe quantitative strategies and outline the outstanding challenges in understanding the single cell rules governing tissue-scale competition dynamics. We propose quantitative metrics to characterize single cell behaviors in competition and use them to distinguish the types and outcomes of competition. We describe how such metrics can be measured experimentally using a novel combination of high-throughput imaging and machine learning algorithms. We outline the experimental challenges to quantify cell fate dynamics with high-statistical precision, and describe the utility of computational modeling in testing hypotheses not easily accessible in experiments. In particular, cell-based modeling approaches that combine mechanical interaction of cells with decision-making rules for cell fate choices provide a powerful framework to understand and reverse-engineer the diverse rules of cell competition.

Cell-scale biophysical determinants of cell competition in epithelia

“…When Δ HD was low or Λ was larger than 1, the change in competition outcome occurred because of a decrease in the local density of loser cells in mixed populations, which in turn led to decreased apoptosis. However, winners have an extra competitive edge because when free space becomes available due to cell death or cell area compressibility, they take advantage of the free space due to faster growth using a squeeze and take or a kill and take tactic [14]. At very long times, this effect alone may be sufficient for them to dominate in mixed populations.…”

“…One challenge in understanding cell competition from experimental data is that it takes place over several days, making the tracking of a cell’s environment and its eventual fate challenging. The emergence of automated long-term microscopy and advanced image analysis for segmentation and cell state recognition enables hypotheses to be formulated regarding the mechanisms of cell elimination [14]. For example, recent work has shown that loser cell death in an experimental model system for mechanical competition is strongly influenced by local cell density as expected, but that, in addition, division of winner cells appears favoured in neighbourhoods with many loser cells, something reminiscent of biochemical competition [15].…”

Single Cell Biology: Exploring Somatic Cell Behaviors, Competition and Selection in Chronic Disease

“…There is extensive evidence that environmental factors including stress, pollution and determinants of health all influence both acute and chronic inflammation, though in most instances the molecular mechanisms are not known or are incompletely understood. With the increasing accessibility of single cell biology ( Gradeci et al, 2020 ), in particular in immunophenotyping, there is accumulating evidence of the direct effects of even modest changes in acquired or environmental factors on integrated cellular responses.…”

PECAn, a pipeline for image processing and statistical analysis of complex mosaic 3D tissues

“…To demonstrate the utility of this software, we employed PECAn to analyse parameters of ribosome protein mutant ( Rp/+ ) cell competition in Drosophila imaginal wing discs – a phenomenon whereby cells carrying heterozygous mutations in ribosomal protein genes, known as Minutes , are eliminated from mosaic tissues when proximal to wildtype cells 7 (Supplementary Figure 1). Rp/+ cells are therefore said to act as ‘losers’ relative to wildtype ‘winners.’ In the field of cell competition, in particular, there is a pressing need for automated image and single cell analytic techniques 8 . The use of PECAn allowed us to: identify an unappreciated sexual dimorphism in Rp/+ cell competition, characterize rigorously Rp/+ cell death properties in competing and non-competing tissues, and identify by logistic regression analysis the tissue parameters that accurately model and predict competitive cell death.…”

“…Rp/+ cells are therefore said to act as 'losers' relative to wildtype 'winners.' In the field of cell competition, in particular, there is a pressing need for automated image and single cell analytic techniques 8 . The use of PECAn allowed us to: identify an unappreciated sexual dimorphism in Rp/+ cell competition, characterize rigorously Rp/+ cell death properties in competing and noncompeting tissues, and identify by logistic regression analysis the tissue parameters that accurately model and predict competitive cell death.…”