Changes in Cell Morphology Are Coordinated with Cell Growth through the TORC1 Pathway

SummaryBackgroundGrowth rate is determined not only by extracellular cues such as nutrient availability but also by intracellular processes. Changes in cell morphology in budding yeast, mediated by polarization of the actin cytoskeleton, have been shown to reduce cell growth.ResultsHere we demonstrate that polarization of the actin cytoskeleton inhibits the highly conserved Target of Rapamycin Complex 1 (TORC1) pathway. This downregulation is suppressed by inactivation of the TORC1 pathway regulatory Iml1 complex, which also regulates TORC1 during nitrogen starvation. We further demonstrate that attenuation of growth is important for cell recovery after conditions of prolonged polarized growth.ConclusionsOur results indicate that extended periods of polarized growth inhibit protein synthesis, mass accumulation, and the increase in cell size at least in part through inhibiting the TORC1 pathway. We speculate that this mechanism serves to coordinate the ability of cells to increase in size with their biosynthetic capacity

Continuous and Long-Term Volume Measurements with a Commercial Coulter Counter

We demonstrate a method to enhance the time resolution of a commercial Coulter counter and enable continuous and long-term cell size measurements for growth rate analyses essential to understanding basic cellular processes, such as cell size regulation and cell cycle progression. Our simple modifications to a commercial Coulter counter create controllable cell culture conditions within the sample compartment and combine temperature control with necessary adaptations to achieve measurement stability over several hours. We also wrote custom software, detailed here, to analyze instrument data files collected by either this continuous method or standard, periodic sampling. We use the continuous method to measure the growth rate of yeast in G1 during a prolonged arrest and, in different samples, the dependency of growth rate on cell size and cell cycle position in arrested and proliferating cells. We also quantify with high time resolution the response of mouse lymphoblast cell culture to drug treatment. This method provides a technique for continuous measurement of cell size that is applicable to a large variety of cell types and greatly expands the set of analysis tools available for the Coulter counter.National Institutes of Health (U.S.) (EUREKA Exceptional, Unconventional Research Enabling Knowledge Acceleration (R01GM085457))National Institutes of Health (U.S.) (contract R21CA137695)National Cancer Institute (U.S.). Physical Sciences-Oncology Center (U54CA143874

High precision mass-based assay to examine growth regulation of the cell cycle

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biological Engineering, 2014.Cataloged from PDF version of thesis.Includes bibliographical references (pages 104-111).Studying biophysical properties of cells can provide insight into the metabolic mechanisms and regulation of cell cycle processes. Though size is considered to be a fundamental property of cell state, its measurement on a single-cell basis with high-resolution has been elusive primarily due to enormous experimental barriers. This thesis discusses the use of a cantilever based suspended microchannel resonator (SMR) to measure mass, and resistive pulse based Coulter counter to measure volume. First, we discuss the implementation of several engineering principles that have enabled the SMR to measure size with a high precision and temporal resolution. As a result, growth rates can now be estimated at a single-cell basis with unprecedented precision of ~170 fg.hr-¹. Second, we employ the SMR to investigate the coordination between the fundamental processes of cell growth and cell division cycle. Contrary to the reigning 60-yr old hypothesis of a deterministic size-control of the cell cycle, it is observed that cells display significant size variability at the Start checkpoint of the cell cycle. Furthermore, the measurements find only a weak size-control on the time spent in G1. Remarkably, it is observed that the cell's initial growth rate is a significantly better predictor of G1 duration than its initial size. Third, we develop a method to enable continuous, long-term volume measurement. Based on a commercial Coulter counter device, it provides a complementary technique for high-throughput measurement and continuous sampling of cell volume, as well volumetric growth rate on a population-scale.by Amneet Gulati.Ph. D

Yeast strains used in this study.

<p>Yeast strains used in this study.</p

Goodness of fit for exponential and linear growth patterns.

<p>Linear (y = ax+b) and exponential (y = Ae^bx) functions were fit to the bound and mode data of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0029866#pone-0029866-g005" target="_blank">Figure 5A</a>. Analysis was limited to data between 0 and 240 min, after which the population loses synchrony and is bimodal. The goodness of fit is determined by R², or the coefficient of determination, which measures scatter surrounding a fitted function. Higher R² values indicate a better fit and all populations from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0029866#pone-0029866-g005" target="_blank">Figure 5A</a> are better fit by the exponential function.</p

Timing of drug response by continuous volume data.

<p>After exposure to 0.5 µM staurosporine (STS), mouse lymphoblast leukemia cells (L1210) exhibit an average volume decrease, likely associated with the early stages of apoptosis. (<b>A</b>) In a standard protocol, volume measurements are recorded and a histogram mode is reported (stars) every 30 minutes. With these data, the time at which cells respond to environmental perturbations can be determined with ∼30 minute precision. (<b>B</b>) Continuous volume data for the same sample (colormap) provides a more complete description of the treatment's effect. For example, not only does a volume decrease occur following STS treatment, but there is a decrease in the population's variation. These data are then quantitatively analyzed to determine the time at which the measurement detects the culture response. Black lines designate the exclusion bounds (<b>B</b>, colormap black lines), or data removed from analysis. A linear regression is calculated for data before and after a breakpoint varied across the entire timecourse. For each of these points, the goodness of fit is measured by the sum of squared errors (SSE) and the minimum SSE indicates the time at which a change in volume growth rate is detected—the rate change point. L1210 cells treated with equal volume DMSO exhibit no significant rate change point, and a response in staurosporine-treated cells was detected at ∼9.7 min after drug exposure.</p

High resolution volume timecourse and growth rates of particles and cells.

<p>(<b>A</b>) 4.000±0.033 µm diameter beads (Duke Scientific) and (<b>B</b>) <i>cdc28-4</i> G1-arrested yeast volume measurements over a 2 hour timecourse. Color designates the relative fraction of particles in each 150 s measurement with the indicated volume (colorbar at right). Black lines designate the exclusion bounds used for growth rate calculations. (<b>C</b>) Growth rates for bead (blue, <b>A</b>) and yeast (red, <b>B</b>) data calculated by linear regression on a 60 min window shifted every 3 min. Bead data growth rates are 0.004±0.037 µm³/min (mean ± SD) and provide an estimate of measurement error. Additional error estimates were performed on 7.979±0.075 µm diameter beads (Duke Scientific) (growth rates: 0.013±0.169 µm³/min) and formaldehyde-fixed cells (growth rates: −0.022±0.118 µm³/min). Continuous sampling and single particle data increases statistical significance for growth rate data and makes possible the analysis of growth rates with different averaging windows for correlation with specific biological events.</p

Subpopulation tracking by volume histograms.

<p>Continuous volume measurements of an arrest in <i>cdc28-as1</i> yeast over a 3-hour timecourse show cells arrested at various points in the cell cycle. (<b>A</b>) Histogram features, such as bounds, on single-mode volume distributions separately analyze large and small cells. (<b>B</b>) At room temperature (∼22°C), cell volume steadily increases with a single population mode, and (<b>C</b>) the arrested cells' volume growth rates increase with cell size. Error bars on <i>cdc28-as1</i> data indicate the 95% confidence interval for the linear regression. The growth rate average (n = 6) and measured standard deviation (error bar) for formaldehyde-fixed cells with a 60 min window are shown for comparison to measurement error. (<b>D</b>) Subpopulations emerge at the optimal temperature for yeast (∼30°C) and a later timepoint (∼120 min), and the mode of each subpopulation is identified and tracked. These subpopulations likely represent slow-growing (SG), G1-arrested (G1), and metaphase-arrested (M) cells. (<b>E</b>) Histogram features are determined for each 150 s file, similar to those in <b>B</b>. The color of the solid circles indicates the fraction of cells in each subpopulation. Growth rates calculated with data in <b>B</b> and <b>E</b> are by linear regression on a 60 or 180 min window, as indicated. (<b>F</b>) Growth rates are separately calculated for a one hour window before and after the emergence of subpopulations. Fixed cell data is identical to that in <b>C</b>. As observed for room temperature measurements, growth rates are related to cell size and likely a result of the multi-phase arrest.</p