Density fluctuations, homeostasis, and reproduction effects in bacteria

Single-cells grow by increasing their biomass and size. Here, we report that while mass and size accumulation rates of single Escherichia coli cells are exponential, their density and, thus, the levels of macromolecular crowding fluctuate during growth. As such, the average rates of mass and size accumulation of a single cell are generally not the same, but rather cells differentiate into increasing one rate with respect to the other. This differentiation yields a density homeostasis mechanism that we support mathematically. Further, we observe that density fluctuations can affect the reproduction rates of single cells, suggesting a link between the levels of macromolecular crowding with metabolism and overall population fitness. We detail our experimental approach and the “invisible” microfluidic arrays that enabled increased precision and throughput. Infections and natural communities start from a few cells, thus, emphasizing the significance of density-fluctuations when taking non-genetic variability into consideration.

Quantitative imaging, invisible microfluidics, and mathematical models demonstrate how the density of single E. coli cells fluctuates during the cell cycle, unmasking key homeostasis and population fitness effects.

Quantitative investigation of MDA-MB-231 breast cancer cell motility: dependence on epidermal growth factor concentration and its gradient

Enhanced cell motility is one of the primary features of cancer. Accumulated evidence demonstrates that Epidermal Growth Factor Receptor (EGFR) mediated pathways play an important role in breast cancer cell proliferation and migration. We have quantified the MDA-MB-231 breast cancer cell migration in response to the stimulation of EGFR pathways with their ligand EGF to determine how the cell motility of MDA-MB-231 cells depends on the ligand concentration and gradient. Analysis at the single cell level combined with mathematical modeling and the ability to vary the ligand concentration and gradients locally using microfluidic devices allowed us to separate the unique contributions of ligand concentration and ligand gradient to cell motility. We tracked the motility of 6600 cells individually using time lapse imaging under varying EGF stimulation conditions. Trajectory analysis of the tracked cells using non-linear multivariate regression models showed that: (i) cell migration of MDA-MB-231 breast cancer cells depends on the ligand gradient but not on the ligand concentration. This observation was valid for both the total (direction independent) and directed (along gradient direction) cell velocities. Although the dependence of the directed motility on ligand gradient is to be expected, the dependence of the total velocity solely on ligand gradient was an unexpected novel observation. (ii) Enhancement of the motilities of individual cells in a population upon exposure to the ligand was highly heterogeneous, and only a very small percentage of cells responded strongly to the external stimuli. Separating out the non-responding cells using quantitative analysis of individual cell motilities enabled us to establish that enhanced motility of the responding cells indeed increases monotonically with increasing EGF gradient. (iii) A large proportion of cells in a population were unresponsive to ligand stimulation, and their presence introduced considerable random intrinsic variability to the observations. This indicated that studying cell motilities at the individual cell level is necessary to better capture the biological reality and that population averaging methods should be avoided. Studying motilities at the individual cell level is particularly important to understand the biological processes that are possibly driven by the action of a small portion of cells in a population, such as metastasis. We discuss the implications of our results on the total and chemotactic movement of cancer cells in the tumor microenvironment.

Exploiting Bioprocessing Fluctuations to Elicit the Mechanistics of De Novo Lipogenesis in Yarrowia lipolytica

Despite substantial achievements in elucidating the metabolic pathways of lipogenesis, a mechanistic representation of lipid accumulation and degradation has not been fully attained to-date. Recent evidence suggests that lipid accumulation can occur through increases of either the cytosolic copy-number of lipid droplets (LDs), or the LDs size. However, the prevailing phenotype, or how such mechanisms pertain to lipid degradation remain poorly understood. To address this shortcoming, we employed the-recently discovered-innate bioprocessing fluctuations in Yarrowia lipolytica, and performed single-cell fluctuation analysis using optical microscopy and microfluidics that generate a quasi-time invariant microenvironment. We report that lipid accumulation at early stationary phase in rich medium is substantially more likely to occur through variations in the LDs copy-number, rather than the LDs size. Critically, these mechanistics are also preserved during lipid degradation, as well as upon exposure to a protein translation inhibitor. The latter condition additionally induced a lipid accumulation phase, accompanied by the downregulation of lipid catabolism. Our results enable an in-depth mechanistic understanding of lipid biogenesis, and expand longitudinal single-cell fluctuation analyses from gene regulation to metabolism.

Origins of Cell-to-Cell Bioprocessing Diversity and Implications of the Extracellular Environment Revealed at the Single-Cell Level

Bioprocess limitations imposed by microbial cell-to-cell phenotypic diversity remain poorly understood. To address this, we investigated the origins of such culture diversity during lipid production and assessed the impact of the fermentation microenvironment. We measured the single-cell lipid production dynamics in a time-invariant microfluidic environment and discovered that production is not monotonic, but rather sporadic with time. To characterize this, we introduce bioprocessing noise and identify its epigenetic origins. We linked such intracellular production fluctuations with cell-to-cell productivity diversity in culture. This unmasked the phenotypic diversity amplification by the culture microenvironment, a critical parameter in strain engineering as well as metabolic disease treatment.

Review of methods to probe single cell metabolism and bioenergetics

Single cell investigations have enabled unexpected discoveries, such as the existence of biological noise and phenotypic switching in infection, metabolism and treatment. Herein, we review methods that enable such single cell investigations specific to metabolism and bioenergetics. Firstly, we discuss how to isolate and immobilize individuals from a cell suspension, including both permanent and reversible approaches. We also highlight specific advances in microbiology for its implications in metabolic engineering. Methods for probing single cell physiology and metabolism are subsequently reviewed. The primary focus therein is on dynamic and high-content profiling strategies based on label-free and fluorescence microspectroscopy and microscopy. Non-dynamic approaches, such as mass spectrometry and nuclear magnetic resonance, are also briefly discussed.