A model for cell population size control using asymmetric division.

In multicellular organisms one can find examples where a growing tissue divides up until some final fixed cell number. Asymmetric division plays a prevalent feature in tissue differentiation in these organisms, where the daughters of each asymmetric division inherit unequal amounts of a fate determining molecule and as a result follow different developmental fates. In some tissues the accumulation or decrease of cell cycle regulators acts as an intrinsic timing mechanism governing proliferation. Here we present a minimal model based on asymmetric division and dilution of a cell-cycle regulator that can generate any final population size that might be needed. We show that within the model there are a variety of growth mechanisms from linear to non-linear that can lead to the same final cell count. Interestingly, when we include noise at division we find that there are special final cell population sizes that can be generated with high confidence that are flanked by population sizes that are less robust to division noise. When we include further perturbations in the division process we find that these special populations can remain relatively stable and in some cases even improve in their fidelity.

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

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 high-throughput 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.

High-throughput microfluidic single-cell RT-qPCR.

A long-sought milestone in microfluidics research has been the development of integrated technology for scalable analysis of transcription in single cells. Here we present a fully integrated microfluidic device capable of performing high-precision RT-qPCR measurements of gene expression from hundreds of single cells per run. Our device executes all steps of single-cell processing, including cell capture, cell lysis, reverse transcription, and quantitative PCR. In addition to higher throughput and reduced cost, we show that nanoliter volume processing reduced measurement noise, increased sensitivity, and provided single nucleotide specificity. We apply this technology to 3,300 single-cell measurements of (i) miRNA expression in K562 cells, (ii) coregulation of a miRNA and one of its target transcripts during differentiation in embryonic stem cells, and (iii) single nucleotide variant detection in primary lobular breast cancer cells. The core functionality established here provides the foundation from which a variety of on-chip single-cell transcription analyses will be developed.