Dissecting genealogy and cell cycle as sources of cell-to-cell variability in MAPK signaling using high-throughput lineage tracking

Abstract

Cells, even those having identical genotype, exhibit variability in their response to external stimuli. This variability arises from differences in the abundance, localization, and state of cellular components. Such nongenetic differences are likely heritable between successive generations and can also be influenced by processes such as cell cycle, age, or interplay between different pathways. To address the contribution of nongenetic heritability and cell cycle in cell-to-cell variability we developed a highthroughput and fully automated microfluidic platform that allows for concurrent measurement of gene expression, cell-cycle periods, age, and lineage information under a large number of temporally changing medium conditions and using multiple strains. We apply this technology to examine the role of nongenetic inheritance in cell heterogeneity of yeast pheromone signaling. Our data demonstrate that the capacity to respond to pheromone is passed across generations and that the strength of the response correlations between related cells is affected by perturbations in the signaling pathway. We observe that a ste50Δ mutant strain exhibits highly heterogeneous response to pheromone originating from a unique asymmetry between mother and daughter response. On the other hand, fus3Δ cells were found to exhibit an unusually high correlation between mother and daughter cells that arose from a combination of extended cell-cycle periods of fus3Δ mothers, and decreased cell-cycle modulation of the pheromone pathway. Our results contribute to the understanding of the origins of cell heterogeneity and demonstrate the importance of automated platforms that generate single-cell data on several parameters.single cell | microscopy | image analysis C ells must detect biochemical cues and respond appropriately to changing environmental conditions. However, under identical chemical stimuli not all cells respond the same. Heterogeneity in "cellular decision making" exists across all levels of life, from bacteria (1, 2) to simple eukaryotes (3-5) to mammalian cells (6, 7), and may be important in cellular adaptation to quickly changing microenvironments (8), in differentiation programs during development (6), and in sensitivity to drugs (9, 10). In addition to the intrinsic stochastic nature of gene expression (1, 3), each cell has an inherent capacity to respond, which is determined by the state and abundance of cellular components. This extrinsic variability may be passed across multiple generations, producing a nongenetic but heritable source of variation. Additional sources that contribute to extrinsic variability include asynchrony and interplay between different signaling pathways (11, 12), cell-cycle effects (3, 13), asymmetry in cellular divisions (14), and cellular aging (13).Investigating these effects requires tracking the genealogy of multiple generations of cells, followed by stimulation and quantitative single-cell measurement of response, including growth kinetics and gene expression. Although improved image analysis, ...

Single-cell dynamics and variability of MAPK activity in a yeast differentiation pathway

“…In addition to these cell-cycle effects, we also found a strong response similarity between successive generations of cells, a potential underpinning to the observed heritability of mating gene expression across cell lineages (48). In particular, we found responses of mother and newborn daughter cell pairs to be highly similar when the cell-cycle phases of both cells were pre-Start at the time of pheromone treatment (Fig.…”

Cellular Noise Suppression by the Regulator of G Protein Signaling Sst2

“…Less appreciated are the effects of cell-to-cell differences arising from stochasticity in biochemical reactions, differences in the expression or activity of internal signaling components, as well as heterogeneity in cell states (i.e. cell cycle, cell age and metabolic state) (Becskei et al, 2005; Colman-Lerner et al, 2005; Elowitz et al, 2002; Fraser et al, 2004; Maheshri and O'Shea, 2007; McAdams and Arkin, 1999; Ricicova et al, 2013; Volfson et al, 2006). Such differences greatly increase the number of possible outcomes during signaling.…”

A living vector field reveals constraints on galactose network induction in yeast

“…When we subtracted the below from the above images, constructive interference from the diffraction patterns around the cells roughly outlined the cell boundaries (Ricicova et al, 2013). We then performed a series of image processing steps on this image.…”

“…This involves determining how segmented objects in one frame correspond to the segmented objects in the next. The tracking algorithm of Ricicova et al (2013) matches cells across frames using the Hungarian algorithm (Kuhn, 1955) to minimize the distances between the centroids of segmented objects in one frame and the predicted locations of objects from the previous frame based on their previous location and velocity. Unlike Ricicova et al, our interval between frames was only 2 min, and so there was limited movement between frames.…”