1. Biophysics and Structural Biology
  2. Cell Biology
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A pH-driven transition of the cytoplasm from a fluid- to a solid-like state promotes entry into dormancy

  1. Matthias Christoph Munder
  2. Daniel Midtvedt
  3. Titus Franzmann
  4. Elisabeth Nüske
  5. Oliver Otto
  6. Maik Herbig
  7. Elke Ulbricht
  8. Paul Müller
  9. Anna Taubenberger
  10. Shovamayee Maharana
  11. Liliana Malinovska
  12. Doris Richter
  13. Jochen Guck
  14. Vasily Zaburdaev
  15. Simon Alberti Is a corresponding author
  1. Max Planck Institute of Molecular Cell Biology and Genetics, Germany
  2. Max Planck Institute for the Physics of Complex Systems, Germany
  3. Technische Universität Dresden, Germany
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Cite as: eLife 2016;5:e09347 doi: 10.7554/eLife.09347

Abstract

Cells can enter into a dormant state when faced with unfavorable conditions. However, how cells enter into and recover from this state is still poorly understood. Here, we study dormancy in different eukaryotic organisms and find it to be associated with a significant decrease in the mobility of organelles and foreign tracer particles. We show that this reduced mobility is caused by an influx of protons and a marked acidification of the cytoplasm, which leads to widespread macromolecular assembly of proteins and triggers a transition of the cytoplasm to a solid-like state with increased mechanical stability. We further demonstrate that this transition is required for cellular survival under conditions of starvation. Our findings have broad implications for understanding alternative physiological states, such as quiescence and dormancy, and create a new view of the cytoplasm as an adaptable fluid that can reversibly transition into a protective solid-like state.

https://doi.org/10.7554/eLife.09347.001

eLife digest

Most organisms live in unpredictable environments, which can often lead to nutrient shortages and other conditions that limit their ability to grow. To survive in these harsh conditions, many organisms adopt a dormant state in which their metabolism slows down to conserve vital energy. When the environmental conditions improve, the organisms can return to their normal state and continue to grow.

The interior of cells is known as the cytoplasm. It is very crowded and contains many molecules and compartments called organelles that carry out a variety of vital processes. The cytoplasm has long been considered to be fluid-like in nature, but recent evidence suggests that in bacterial cells it can solidify to resemble a soft glass-type material under certain conditions. When cells become dormant they stop dividing and reorganise their cytoplasm in several ways; for example, the water content drops and many essential proteins form storage compartments. However, it was not clear how cells regulate the structure of the cytoplasm to enter into or exit from dormancy.

Now, Munder et al. analyse the changes that occur in the cytoplasm when baker’s yeast cells enter a dormant state. The experiments show that when yeast cells are deprived of energy – as happens during dormancy – the cytoplasm becomes more acidic than normal. This limits the ability of molecules and organelles to move around the cytoplasm. Similar results were also seen in other types of fungi and an amoeba. Munder et al. found that this increase in acidity during dormancy causes many proteins to interact with each other and form large clumps or filament structures that result in the cytoplasm becoming stiffer.

A separate study by Joyner et al. found that when yeast cells are starved of sugar, two large molecules are less able to move around the cell interior. Together, the findings of the studies suggest that the interior of cells can undergo a transition from a fluid-like to a more solid-like state to protect the cells from damage when energy is in short supply. The next challenge is to understand the molecular mechanisms that cause the physical properties of the cytoplasm to change under different conditions.

https://doi.org/10.7554/eLife.09347.002

Introduction

The cytoplasm of living cells is highly dynamic and yet exquisitely organized. Maintenance of this state requires a constant input of energy and a metabolism that is far from thermodynamic equilibrium. However, organisms typically live in unpredictable environments and frequently experience conditions that are not optimal for growth and reproduction. Under such conditions, cells must protect themselves by entering into a non-dividing state, generally referred to as dormancy (Lennon and Jones, 2011).

Dormancy is defined as a state of reversible cell cycle arrest with reduced metabolic activity and changes in cellular organization (Lennon and Jones, 2011). It often involves execution of a developmental program, which culminates in the formation of specialized cell types such as spores, seeds, or cysts. These cell types can endure long periods of nutrient starvation, low temperatures, and even desiccation. Dormancy is also accompanied by extensive changes in cellular architecture, some of which are drastic. For instance, dormant cells have a very low water content, their cytoplasm is densely packed, and they show strongly diminished intracellular dynamics (Ablett et al., 1999; Cowan et al., 2003; Dijksterhuis et al., 2007; Parry et al., 2014). However, how cells enter into and recover from such a state is still unresolved.

The current paradigm of cellular biochemistry is based on studies in dilute solutions, often performed with only a handful of proteins. Findings made in such dilute regimes have been extrapolated to the cellular interior. In recent years awareness has been increasing that the cellular environment is very different from such dilute regimes. One reason for this is that the cytoplasm is densely packed with macromolecules. The overall concentration of macromolecules in the cytoplasm is estimated to be around 200–350 mg/ml (Ellis, 2001; Zimmerman and Trach, 1991), which amounts to a volume fraction of up to 40%. This dense packing of macromolecules is referred to as macromolecular crowding. How macromolecules remain soluble at such high concentrations inside a cell is unknown, but it presumably involves a fine balance of attractive and repulsive interactions between the different cytoplasmic components.

The highly crowded conditions inside a cell generate an environment with specific physical properties. These properties have traditionally been explored by following the diffusive behavior of tracer particles or organelles. Such particle-tracking approaches have led to the realization that intracellular diffusion is anomalous in cells (Dix and Verkman, 2008; Hall and Hoshino, 2010; Luby-Phelps, 2000; Tolić-Nørrelykke et al., 2004). Based on these findings, proposals have been made about the physical nature of the cytoplasm, which has either been described as a hydrogel (Fels et al., 2009) or, more recently, as a liquid at the transition to a glass-like state (Parry et al., 2014). Because many metabolic reactions and signaling processes take place in the cytoplasm, changes in its physicochemical properties should have far-reaching effects on cellular function and survival.

Recent findings indicate that the organization of the cytoplasm can change considerably, in particular under stress conditions such as starvation. In energy-depleted yeast cells, many proteins and RNAs assemble into microscopically visible structures (Laporte et al., 2008; Narayanaswamy et al., 2009; Noree et al., 2010; O'Connell et al., 2012; Sagot et al., 2006). These structures may constitute storage depots for proteins and RNAs (Daignan-Fornier and Sagot, 2011; Laporte et al., 2008; Sagot et al., 2006). Indeed, several metabolic enzymes assemble into filamentous structures in response to starvation, and the formation of these filaments leads to enzymatic inactivation (Petrovska et al., 2014). Importantly, the enzymes contained in these filaments can be reused, when cells escape from dormancy. This suggests that cells may regulate the structure of the cytoplasm to enter into and exit from a metabolically inactive state.

In this study, we demonstrate that, in acidic environments, entry into dormancy is triggered by an influx of protons that promotes a transition of the cytoplasm from a fluid- to a solid-like state through widespread assembly of proteins into higher-order structures. We show that this transition arrests the movement of organelles and foreign tracer particles. We provide further evidence that this state of reduced intracellular mobility is required for survival of energy depletion stress. Thus, we propose that organisms have global control mechanisms in place to fine-tune the material properties of the cytoplasm, allowing them to enter into a protective solid-like state, when challenged by extreme environmental conditions.

Results

Reduced dynamics of the cytoplasm upon depletion of energy

To investigate how eukaryotic cells enter into a dormant state, we focused on budding yeast, a single-celled organism, which can enter into dormancy upon depletion of energy (De Virgilio, 2012; Gray et al., 2004; Neiman, 2011; Valcourt et al., 2012). Because a strong reduction in intracellular dynamics is a hallmark of dormant cells (Cowan et al., 2003; Dijksterhuis et al., 2007; Mastro et al., 1984; Parry et al., 2014), we first compared the mobility of different cellular organelles in dividing and dormant yeast cells (Huh et al., 2003). Induction of a dormant state was achieved by treating yeast cells with 2-deoxyglucose (2-DG, an inhibitor of glycolysis) and antimycin A (an inhibitor of mitochondrial respiration), a treatment that decreases cellular ATP levels by more than 95% (Serrano, 1977). Using single particle tracking (SPT) and mean squared displacement (MSD) analysis we found a striking reduction in intracellular movements upon energy depletion (Figure 1A and B).

Figure 1 with 3 supplements see all
Reduced mobility of organelles and foreign tracer particles under energy depletion conditions.

(A) Representative trajectories of a range of different organelle markers tracked under control conditions (●, cells in log-phase, upper panel) and upon energy depletion (▲, lower panel). Organelle markers are GFP fusion proteins expressed under control of their endogenous promoters (Huh et al., 2003). (B) Corresponding time- and ensemble-averaged MSD plots for both control (●) and energy depletion (▲) conditions. (C) Representative trajectories (upper panel) and corresponding MSD plots (lower panel) of the foreign tracer particle GFP-µNS (see Figure 1—figure supplement 1–3 for details).

https://doi.org/10.7554/eLife.09347.003

Tracking of endogenous particles provides only limited information on the material properties of the cytoplasm, because they are often membrane-associated and/or move by active transport. Therefore, foreign tracer particles are better suited as probes of the subcellular environment. However, direct injection methods of foreign particles, as they have been developed for mammalian cells, are not feasible for yeast cells because of their small size. We therefore adopted a technique that relies on a genetically encoded viral capsid protein (µNS), which has been used successfully in bacteria (Parry et al., 2014). We could show that GFP-µNS self-assembles into distinct particles in the yeast cytoplasm (Figure 1—figure supplement 1, Video 1). These particles have bead-like properties with a size-dependent MSD and generalized diffusion coefficient K (Figure 1—figure supplement 2 and 3), indicating that they are a valuable tool to study the properties of the yeast cytoplasm. Using GFP-µNS particles and SPT, we found that energy depletion caused a similar reduction in the mobility of these foreign particles (Figure 1C). Thus, we conclude that upon energy depletion, the cytoplasm of budding yeast transitions into a state with strongly reduced dynamics.

Video 1
Time-lapse microscopy of GFP-µNS particles moving in the S. cerevisiae cytoplasm.

To illustrate how particles explore the yeast cytoplasm over time, the fluorescence channel and the reference bright field channel were merged.

https://doi.org/10.7554/eLife.09347.007

A drop in cytosolic pH leads to reduced particle mobility in energy-depleted cells

In higher eukaryotes, ATP-driven processes exert fluctuating forces on the cytoplasm, which lead to random movements of particles and thus cytoplasmic mixing (Brangwynne et al., 2008, 2009; Guo et al., 2014). These effects are predominantly driven by motor proteins, which are linked to the cytoskeleton. However, in contrast to mammalian cells, yeast cells have a cell wall, and thus only a rudimentary cytoskeleton, which is primarily based on actin. Importantly, the actin cytoskeleton of yeast disassembles upon starvation (Sagot et al., 2006), suggesting that this event may be responsible for the reduced particle mobility by removing tracks for motor-based mixing. To test this, we depolymerized the actin cytoskeleton by adding the drug latrunculin A (LatA) to dividing yeast cells. Indeed, GFP-µNS particle mobility was reduced, but the effect was much less pronounced than under conditions of energy depletion (Figure 2A). Next, we treated yeast cells with the drug nocodazole to inhibit microtubule-based motor movements. Again, we only observed marginal effects on particle mobility (Figure 2B). This indicates that a lack of active motor-driven movements can only partially explain the reduced particle mobility.

Figure 2 with 1 supplement see all
Energy depletion causes a drop in cytosolic pH, which may explain reduced particle mobility.

(A) MSD of GFP-µNS particles in untreated cells (control), cells treated with 100 μM latrunculin A (LatA) and energy-depleted cells. (B) MSD of GFP-µNS particles in untreated cells (control), cells treated with 15 μg/ml nocodazol and energy-depleted cells (C) The cytosolic pH of yeast cells was measured in response to energy depletion in growth media with two different pHs. (D) MSD of GFP-μNS particles tracked under the conditions shown in C. Cells were energy-depleted in growth medium without glucose containing 20 mM 2-deoxyglucose and 10 µM antimycin A. In panel A and B particles were tracked over a longer time and with lower time resolution (5 s) than in panel D. All MSD plots represent time- and-ensemble averaged MSDs and particles of all sizes were considered.

https://doi.org/10.7554/eLife.09347.008

Yeast typically live in acidic environments. The standard laboratory growth media therefore have a pH of around 5.5 (see materials and methods for details). However, the cytosolic pH is kept in the neutral range by proton-translocating ATPases such as Pma1, which use a large amount of energy to continuously pump protons out of the cell, thus preventing cytosolic acidification (Orij et al., 2011). In agreement with this, previous studies have reported that energy depletion leads to a drop in cytosolic pH (pHc) (Dechant et al., 2010; Orij et al., 2012). Indeed, using a ratiometric, pH-sensitive variant of GFP (Mahon, 2011) (Figure 2—figure supplement 1), we observed a significant pHc decrease from around 7.3 to around 5.8 in yeast cells that were energy-depleted in normal growth medium of pH 5.5 (Figure 2C). If this drop in pHc was responsible for the reduced particle mobility, it should be possible to prevent particle immobilization by keeping the pHc in the neutral range. Indeed, when yeast cells were energy-depleted in growth medium of neutral pH, cytosolic acidification could be prevented (Figure 2C) and the reduction in particle mobility was much less pronounced (Figure 2D). Thus, we conclude that strong energy depletion leads to a rapid drop in cytosolic pH, which in turn causes reduced particle mobility.

Reduced particle mobility can be induced by lowering cytosolic pH in the presence of glucose

We next tested whether direct manipulation of the cytosolic pH in the presence of an energy source is sufficient to induce reduced particle mobility. The protonophore DNP rapidly carries protons across the cell membrane and effectively equilibrates the intracellular with the extracellular pH (Dechant et al., 2010; Petrovska et al., 2014). This allowed us to manipulate the intracellular pH by keeping cells in DNP-containing buffers of different pH (Figure 3A, left panel). Cells exposed to DNP-containing buffers generally showed a reduced particle mobility, when compared to cells growing in medium (see Figure 1C), most likely because of direct effects of DNP on metabolism. However, the particle mobility was much more strongly reduced at pHc 6 and 5.5 than at pH 7.0 (Figure 3A, right panel). To exclude possible secondary effects of DNP, we also used a mild membrane-permeable acid (sorbic acid) to alter pHc (Orij et al., 2009) and found that it had a similar effect on cytosolic pH (Figure 3B, left panel) and on particle mobility (Figure 3B, right panel). These experiments were performed in the presence of glucose as an energy source, suggesting that the pHc change acts downstream of ATP depletion.

Acidification of the cytosol reduces particle mobility.

(A) The cytosolic pH of yeast cells exposed to phosphate buffers of different pH containing 2 mM 2,4-dinitrophenol (DNP) and 2% glucose was measured over time in a microfluidic flow chamber (left). The MSD of GFP-µNS particles tracked under the same conditions is shown on the right. (B) The cytosolic pH of yeast cells exposed to synthetic complete medium (pH ~5.5) containing increasing concentrations of sorbic acid was measured in a microfluidic flow chamber (left). The MSD of GFP-µNS particles tracked under the same conditions is shown on the right. (C) MSD of GFP-µNS particles in S. pombe cells. Particles were tracked in untreated cells (control), energy-depleted cells and cells treated with phosphate buffers of different pH containing 2 mM DNP. (D) MSD of GFP-µNS particles in D. discoideum cells. Particles were tracked in untreated cells (control), energy-depleted cells and cells treated with Lo-Flo buffer of different pH containing 0.2 mM DNP. Cells were energy-depleted in growth media or buffer, respectively, without glucose containing 2-deoxyglucose and antimycin A. All MSD plots represent time-and-ensemble averaged MSDs and particles of all sizes were considered.

https://doi.org/10.7554/eLife.09347.010

Our experimental setup allows for rapid changes of the intracellular proton concentration by almost two orders of magnitude (from 7.4 to 5.5). To test whether such pronounced pH fluctuations affect cell viability, we exposed yeast cells to repeated pHc changes in a microfluidic chamber. Remarkably, pH changes of this magnitude did not affect the viability of yeast (Video 2). Moreover, when yeast cells were acidified with DNP, reduced particle mobility manifested within minutes, and it was readily reversed on a similar time scale (Video 3). Thus, pH-induced changes are readily reversible and well tolerated by yeast.

Video 2
Brightfield time-lapse microscopy of S. cerevisiae cells growing in a microfluidic flow chamber.

Cells were exposed to buffers of different pH containing 2 mM DNP as indicated.

https://doi.org/10.7554/eLife.09347.011
Video 3
Fluorescence time-lapse microscopy of GFP-µNS expressing S. cerevisiae cells growing in a microfluidic flow chamber.

Cells were repeatedly exposed to buffers of different pH containing 2 mM DNP as indicated.

https://doi.org/10.7554/eLife.09347.012

Next, we tested whether other eukaryotic organisms undergo similar changes. We focused on another fungus, fission yeast, and a protist, the social amoeba Dictyostelium discoideum. As S. cerevisiae, both organisms can enter into a dormant state (Jímenez et al., 1988; Sajiki et al., 2009), form spores upon starvation (Egel et al., 1994; Xu et al., 2004) and undergo cytosolic pH fluctuations in response to energy depletion (Gross et al., 1983; Karagiannis and Young, 2001). Consistent with this, both organisms showed reduced GFP-µNS particle mobility in an energy- and pH-dependent manner (Figure 3C and D). Thus, we conclude that the pH-induced reduction in particle mobility is not limited to budding yeast, but also extends to other, distantly related organisms.

Analysis of single particle trajectories indicates a transition of the cytoplasm from a fluid- to a solid-like state

Our findings so far show that the mobility of particles is reduced upon energy depletion and acidification of the cytoplasm. In Figure 4A, we show the MSDs and their subdiffusive scaling for different experimental conditions. Particles of all sizes were included in the analysis. We see a dramatic decrease in particle mobility for energy depleted and acidified cells. To gain more insight into the rheological properties of the cytoplasm, which might explain this behavior, we performed a comprehensive analysis of the particle trajectories. From a rheological point of view, the cytoplasm can be considered as an active viscoelastic material (Guo et al., 2014; Mizuno et al., 2007). The motion of an inert tracer particle in the cytoplasm results from the balance between stochastic driving forces and opposing material forces. Due to a possible non-thermal origin of stochastic forces in living cells, in general, a combination of passive and active microrheology experiments is required to quantify the material properties of the cytoplasm (Guo et al., 2014; Mizuno et al., 2007). However, active microrheology experiments in yeast are challenging, because of its small size and stiff outer cell wall. Nonetheless, a detailed analysis of particle trajectories in the passive microrheology approach provides first pointers towards changes in the physical properties of the cytoplasm. One example is the power spectral density (PSD) of particle displacements, which is related to the power spectrum of stochastic forces and the material properties (Guo et al., 2014). The PSD can be calculated as a Fourier transform of the MSD and can, for our data in all conditions, be well approximated by a power law dependence P(ω)ωγ (see Figure 4—figure supplement 1). We find that the exponent γ is smaller for acidified and energy-depleted cells. For a viscoelastic material driven by thermal fluctuations, a decrease in γ would correspond to a transition from a fluid to a more solid-like state (Squires and Mason, 2010). Although we demonstrate that active cytoskeleton-dependent forces do not strongly affect mobility of particles, and we expect no active motion to occur in energy-depleted cells, the thermal nature of driving forces remains an assumption and therefore limits our interpretation of the PSD data. To obtain further insight into particle diffusion, we turn to the analysis of the displacement data on the level of individual trajectories and suggest a statistical model for the observed particle motion.

Figure 4 with 6 supplements see all
Characterization of the acidified and energy-depleted cytoplasm from a particle perspective.

(A) The time- and ensemble-averaged MSD are shown as a function of lag-time, together with the fitted power-law scalings as dashed lines. We obtained the following power-law exponents α  0.73 (DNP treated cells with external pH 6.0), α  0.84 (DNP treated cells with external pH 7.4), α  0.64 (energy depleted cells) and α  0.88 (log phase cells). (B) The master curve for the probability density function of particle displacements as a function of rescaled displacement (full lines) for log-phase cells (left side) and energy-depleted cells (right side). Symbols and colors indicate the probability density function extracted from the data at different lag-times after rescaling the displacements. This plot is symmetric for each condition. Plots were split at the dotted line to allow comparison of both datasets. The dashed line corresponds to a Gaussian distribution. (C) Correlation of subsequent displacements (defined as c=δx'δx|δx|, where δx and δx'are the displacement vectors at two consecutive time intervals) were calculated from trajectories recorded in control cells and energy-depleted cells for two different lag-times (time used to calculate the displacement) of 200 ms and 2 s. Correlations are plotted as a function of the initial displacement length |δx|. By assuming a linear dependence c=b|δx|, the slope b quantifies the negative correlations of subsequent particle displacements and its magnitude increases from b  0.15 to b  0.34 upon energy depletion. The cytosolic pH was adjusted by treating cells with phosphate buffers of pH 5.5, pH 6.0 and pH 7.4, respectively, containing 2 mM DNP and 2% glucose. The cells were energy-depleted in SD-complete medium without glucose containing 20 mM 2-deoxyglucose and 10 µM antimycin A.

https://doi.org/10.7554/eLife.09347.013

It has been suggested that the trajectories of tracer particles in cells (Jeon et al., 2011; Tejedor et al., 2010) and hydrogels (Stempfle et al., 2014) can be well described by the model of fractional Brownian motion (fBm). In contrast to ordinary Brownian motion, the displacements in fBm are correlated in time. Positively correlated displacements lead to superdiffusion, whereas negative correlations lead to subdiffusion. As for ordinary Brownian motion, the distribution of displacements of individual particles performing subdiffusive fBm is Gaussian, but the width of the distribution increases with the lag time sub-linearly σi2(t)=2dKα,itα (Hofling and Franosch, 2013). Here, α<1 is the subdiffusion exponent, d is the dimension of space, and Kα,i is the generalized diffusion constant. The subscripts i indicate the diffusivities of individual particle trajectories. We show that our displacement data are consistent with the model of fBm: For sufficiently long individual trajectories, the cumulative distribution functions (CDF) of displacements are well described by CDFs of the corresponding Gaussian distributions (Figure 4—figure supplement 2). Also consistent with the model of fBm, the combined (ensemble) distributions of all particle displacements measured for different lag times collapse onto each other after rescaling the displacements as x/tα/2 (Figure 4B). The value of α is read out from the scaling of the MSD for the corresponding experimental condition (see Figure 4A). Remarkably, the shape of this distribution is not Gaussian. However, if we additionally rescale the displacements of each trajectory by its corresponding generalized diffusivity Kα,i, the combined distributions collapse onto a Gaussian distribution with unit variance (Figure 4—figure supplement 3). This collapse shows that the non-Gaussian shape of the combined distribution is a result of the variation in individual particle diffusivities (see materials and methods for details). The variability of generalized diffusivities could be due to differences in particle sizes, but it could also reflect variations in the properties of the particles’ microenvironments. Indeed, we find that even for particles of similar sizes the diffusivities vary strongly for all size groups (see Figure 1—figure supplement 3), suggesting that the heterogeneity of the particle microenvironment has a strong impact on particle mobility.

To further test how the microenvironment of the particles changes upon energy depletion and acidification, we analyzed the correlations in the displacements of particles. Negative displacement correlations are the origin of subdiffusive fBm. Indeed, we found that subsequent particle displacements were negatively correlated (see Figure 4—figure supplement 4 and material and methods for the definition of the correlation function). Interestingly, such negative correlations are a hallmark of particle motion in an elastic environment; particles surrounded by elastic structures tend to be pushed back to their original position. The further the particle is initially displaced, the stronger are the forces pushing it back in the subsequent time interval, which results in stronger negative correlations. Indeed, we find on average that the restoring motion is opposite to the initial step and is linearly proportional to the initial displacement length (Figure 4C). In general, the slope of this linear dependence changes from 0 for viscous fluid to -1/2 for an elastic material (Weeks and Weitz, 2002). In our data, the magnitude of the slope b increases from b0.15 to b0.34 upon energy depletion and acidification (Figure 4C and Figure 4—figure supplement 5). This result is consistent with the idea that energy depletion and acidification increase the stiffness of the particles’ microenvironments and that the cytoplasm transitions from a fluid-like to a more solid-like state under these conditions.

Energy-depleted and acidified cells experience a cell volume reduction and display increased mechanical stability

We next tested whether the transition of the cytoplasm to a more solid-like state, as proposed by our particle analysis, also manifests in global changes in the mechanical properties of cells. To experimentally address this question, we mechanically phenotyped budding yeast cells. This required enzymatic removal of the rigid cell wall, which provides mechanical stability to yeast cells, by a process known as spheroplasting. Spheroplasted budding yeast cells were investigated using atomic force microscopy (AFM, [Radmacher, 2007]), the standard in cell mechanical characterization, and real-time deformability cytometry (RT-DC, [Otto et al., 2015]), a novel microfluidic technique with 100000 times higher throughput. The AFM-based indentation experiments, performed with 10 µm-sized spherical probes to test whole cell mechanics, revealed that acidified cells were about 2.5 times as stiff as control cells (Figure 5A and Figure 5—figure supplement 1). Importantly, the apparent elastic modulus we measured for spheroplasted cells was three orders of magnitude lower than for yeast cells surrounded by a cell wall (Figure 5—figure supplement 1), thus clearly showing that the cell wall had been completely removed. However, cells are usually viscoelastic, and the apparent elastic modulus, as extracted using the Hertz model, reflects their combined elastic and viscous response. To test whether the observed 2.5-fold increase in the apparent elastic modulus of spheroplasts at low pH could also be caused by a strong increase in the viscous resistance to deformation, we extracted the viscosity of the cells from the AFM indentation-retraction curves similar to a recently published method (Rebelo et al., 2013; for details see Methods section). We found that the viscosity even decreased from pH 7.4 to pH 6.0 (Figure 5—figure supplement 2). Together, the analysis of apparent elastic modulus and viscosity unambiguously demonstrates that the cell body transitions from a compliant, more viscous material to a stiffer, more elastic material at low pH.

Figure 5 with 9 supplements see all
Mechanical characterization of acidified and energy-depleted cells.

(A) The apparent elastic modulus of S. cerevisiae spheroplasts (without rigid cell walls) at pH 7.4 (E = 636 ± 16 Pa (mean ± SEM); N = 249) and pH 6.0 (E = 1459 ± 59 Pa; N = 257) was measured by AFM-based indentation. The cytosolic pH of spheroplasts was adjusted with phosphate buffers of pH 6.0 and pH 7.4, respectively, containing 2 mM DNP, 1% glucose and 1 M sorbitol. (B) The same cells as in (A) characterized with real-time deformability cytometry (RT-DC). Each measured cell results in a dot in this deformation-cell diameter plot. Also shown are 90% (solid) and 50% (dashed) density lines, and the histograms of size and deformation including Gaussian fits. (C) The cell wall of rod-shaped S. pombe cells was removed under control, energy depletion, and pH-adjusted conditions. The cytosolic pH of cells was adjusted during spheroplasting with phosphate buffers of pH 5.5 and pH 7.4, respectively, containing 2 mM DNP, 2% glucose, 1 M sorbitol and cell wall-digesting enzymes. Cells were energy-depleted in growth medium without glucose containing 20 mM 2-deoxyglucose and 10 µM antimycin A for 2 hr prior to spheroplasting. Energy depletion was continued during spheroplasting by including 20 mM 2-deoxyglucose and 10 µM antimycin A in the spheroplasting buffer. (D) The roundness of more than 160 cells per condition at the start of the experiment and after 3 hr of incubation in the presence of cell wall digesting enzymes (end) was quantified. ∗∗p<0.01; ∗∗∗p<0.001.

https://doi.org/10.7554/eLife.09347.020

The increase in stiffness was independently confirmed by RT-DC measurements, which showed that the mechanical deformability of yeast cells was significantly reduced upon cytosolic acidification (Figure 5B). In the RT-DC assay, we also noticed that acidification was associated with differences in cell size, with acidified yeast being smaller (equivalent diameter: 3.503 +/- 0.012 microns; mean +/- SEM; N = 2938) than control cells (equivalent diameter: 3.724 +/- 0.014 microns; mean +/- SEM; N = 2354) (Figure 5B). Overall, these whole cell measurements show that acidification changes the mechanical properties of the cells in line with a transition of the cytoplasm to a more elastic, solid-like state, which is accompanied by a reduction in cell volume.

Our findings so far can be explained in two ways: First, cytosolic acidification could lead to a regulatory cell volume decrease, including water loss and increased macromolecular crowding (Mourão et al., 2014). Second, acidification could trigger the formation of macromolecular assemblies, which provide increased mechanical stability to the cytoplasm. In this case, the cell volume reduction could be a result of the exclusion of water from these assemblies (Cameron et al., 2006; Cameron and Fullerton, 2014; Fullerton et al., 2006; Thirumalai et al., 2012).

To investigate which scenario might apply, we performed a series of experiments with budding and fission yeast. First, we determined the volume of budding yeast cells using an image-based approach (see materials and methods for details). We found that the cell volume was reduced in a pH-dependent manner. After 30 min at pH 5.5, the cell volume was reduced by ~7% (Figure 5—figure supplement 3). Cell volume changes can also be induced by altering the osmotic strength of the growth medium with sorbitol (Miermont et al., 2013). Thus, we exposed yeast to different sorbitol concentrations to determine the concentration at which the cell volume was similar to that of acidified yeast. We found that at a sorbitol concentration of 0.8 M the cell volume decrease was of the same magnitude (Figure 5—figure supplement 3). As a next step, we compared the particle mobility of osmotically compressed and acidified yeast showing a similar decrease in cell volume. We found that under both conditions particle motion was strongly reduced (Figure 5—figure supplement 4). However, in osmotically compressed cells particles of all sizes still performed small movements. These movements could also be detected when yeast cells were exposed to a sorbitol concentration of 1 M, which triggers an even more pronounced cell volume reduction of ~30%. In contrast, particle motion was abolished in acidified yeast (Figure 5—figure supplement 4). This suggests that a regulatory cell volume decrease cannot fully explain the reduced particle dynamics of acidified yeast. To further investigate this, we compared the diffusivity of a mCherry-GFP fusion protein (54 kDa) in energy-depleted, acidified and sorbitol-treated cells. The diffusion of mCherry-GFP was not affected in acidified or energy-depleted yeast (Figure 5—figure supplement 5), but strongly decreased in cells subjected to high levels of osmotic compression. This indicates the presence of a fluid phase that allows unimpaired diffusion of small macromolecules such as mCherry-GFP in cells exposed to low pH or energy depletion conditions. Thus, cytosolic acidification and osmotic compression seem to induce qualitatively different states of the cytoplasm.

We next analyzed the mechanical stability of fission yeast cells. Fission yeast has an elongated shape, which is supported by the cell wall. However, when the cell wall is removed, fission yeast cells rapidly round up into a spherical shape (Kelly and Nurse, 2011; Sipiczki et al., 1985). This process requires the cytoplasm to be in a fluid-like state, and it is most likely driven by the osmotic pressure of the cytoplasm and by the passive tendency of the cell to minimize its surface to volume ratio. Remarkably, when we spheroplasted energy-depleted yeast cells, they did not relax into spheres, but maintained their initial rod-like shape (Figure 5C and D, Video 4). This effect was not due to incomplete removal of the cell wall (Figure 5—figure supplement 6, 7 and 8, Video 6). Importantly, for wall-free cells, maintenance of the rod-like shape was pH-dependent and could also be induced by reducing the cytosolic pH with DNP (Figure 5C and D, Video 5).

Video 4
Brightfield time-lapse microscopy of S. pombe cells during cell wall removal.

Cells were imaged in EMM5 medium containing glucose (control, left panel) or in EMM5 medium without glucose containing 20 mM 2-desoxyglucose (2DG) and 10 µM antimycin A (right panel). Cell wall removing enzyme mix was added immediately before the recording was started. Medium contained 1 M sorbitol to osmotically stabilize the cells.

https://doi.org/10.7554/eLife.09347.030
Video 5
Brightfield time-lapse microscopy of S. pombe cells during cell wall removal.

Cells were imaged in phosphate buffer of pH 5.5 (right panel) and pH 7.4 (left) containing 2 mM DNP and 2% glucose. Cell wall removing enzyme mix was added after 30 min of imaging. Buffers contained 1 M sorbitol to osmotically stabilize the cells.

https://doi.org/10.7554/eLife.09347.031
Video 6
Brightfield time-lapse microscopy of energy-depleted S. pombe cells during enzymatic cell wall removal.

Cells were energy-depleted in EMM5 medium (pH 6.0) without glucose supplemented with 20 mM 2-deoxyglucose and 10 μM antimycin A for 2 hr before imaging. Imaging was then done in cell wall removal buffer (phosphate buffer pH 6.0 containing 20 mM 2-deoxyglucose and 10 μM antimycin A as well as cell wall removal enzymes). Rod-like cells are slipping out of what seems to be a sheath of cell wall material without changing their shape.

https://doi.org/10.7554/eLife.09347.032

To investigate whether rod-shaped spheroplasts would eventually round up into spheres, we observed them for extended times by time-lapse microscopy. However, the spheroplasts maintained their elongated shape for several hours and did not show signs of rounding up (Video 4 and 5). Given this remarkable cellular phenotype, we tested whether the cells are still alive and get softer when energy is provided and the internal pH rebounds to neutral values. Indeed, when acidified yeast cells were re-exposed to medium, the cells quickly became spherical and started to enter the cell cycle (Figure 5—figure supplement 9, Video 7). Importantly, this rounding up process occurred in the presence of 1 M sorbitol, which was used to osmotically stabilize the cells. Under these conditions, yeast cells experience a substantial reduction in cell volume (Figure 5—figure supplement 3), suggesting that an increase in molecular crowding alone does not generate enough mechanical stability to keep the cells in a rod-like shape. Rather, these findings support our idea that cellular stiffening may involve the formation of rigid cytoplasmic structures, which dissolve when energy-depleted yeast re-adjust their cytosolic pH to neutral values. Thus, we conclude that the cytoplasm of energy-depleted cells undergoes a pH-dependent transition from a fluid- to a solid-like state, which may be accompanied by the formation of structures that significantly increase the mechanical stability of cells.

Video 7
Brightfield time-lapse microscopy of a S. pombe spheroplast.

The spheroplast was generated prior to imaging in phosphate buffer of pH 6.0 containing 1 M sorbitol, 2 mM DNP and cell wall digesting enzyme mix and kept in this cell wall removal buffer for 4.5 hr. Directly before the start of the recording, the spheroplast was washed with EMM5 medium containing 1% glucose and 1 M sorbitol. Buffer and medium contained 1 M sorbitol to osmotically stabilize the spheroplast.

https://doi.org/10.7554/eLife.09347.033

Widespread macromolecular assembly may explain reduced particle mobility and changes in the mechanical properties of cells

Which cytoplasmic structures could be underlying this remarkable change in cellular rigidity? A large number of in vitro studies have shown that the solubility of proteins drops precipitously, when the pH of the solution approaches their isoelectric points (Tanford and De, 1961). Under these conditions, proteins interact with each other to form higher-order assemblies, which macroscopically manifest as structures with solid-like properties (Boye et al., 1996; Matsudomi et al., 1991; Parker et al., 2005; Renard and Lefebvre, 1992). Thus, we reasoned that the densely packed cytoplasm of yeast cells undergoes a similar transition on a global scale.

To investigate this possibility, we first analyzed the distribution of the isoelectric points of all proteins in the yeast proteome. In agreement with previous work (Weiller et al., 2004), we found that the isoelectric points of yeast proteins are largely excluded from the neutral pH range and cluster into two peaks, one in the acidic and one in the basic range (Figure 6A). Importantly, the acidic peak overlaps with the pH that cells experience under starvation conditions. This suggests that many proteins have a reduced net charge in energy-depleted cells (Chan et al., 2006) and thus become less soluble. This is in agreement with previous results, where it was shown that starvation triggers the assembly of many proteins into higher-order structures (Narayanaswamy et al., 2009; Noree et al., 2010; Petrovska et al., 2014). Importantly, protein complexes remain intact in energy-depleted cells, as shown by the fact that the different proteins in a hetero-complex colocalize in the same structures (Figure 6—figure supplement 1). This suggests that the proteins assemble into structures in a native-like state, ensuring that this step is readily reversible. To investigate whether protein assembly and reduced particle mobility are temporally linked, we exposed yeast cells to pH manipulations in a microfluidic chamber and followed assembly and particle movement by fluorescence microscopy. Indeed, we found that these two events coincided (Video 8), suggesting a causal relationship.

Figure 6 with 2 supplements see all
Acidification of the cytosol causes widespread assembly of cytoplasmic proteins.

(A) The isoelectric points and the molecular weight of all yeast proteins were computed from their primary amino acid sequence and plotted as a virtual 2D gel. The green line indicates optimal growth pH, the red line indicates pH reported for dormant yeast cells. (B) We systematically tested the response of 68 cytoplasmic proteins to a drop in cytosolic pH. Shown are representative images of proteins that responded with assembly formation to low pH. The same proteins also form assemblies in yeast spores. (C) The percentage of cells showing protein assemblies at high versus low pH was quantified. The cytosolic pH was adjusted by treating cells with phosphate buffers of pH 5.5 and pH 7.4, respectively, containing 2 mM DNP and 2% glucose.

https://doi.org/10.7554/eLife.09347.034
Video 8
Fluorescence time-lapse microscopy of Gcn3-GFP (left side) and GFP-µNS (right side) expressing yeast cells growing in a microfluidic flow chamber.

Cells were exposed to a phosphate buffer of pH 5.5 containing 2 mM DNP and 2% glucose as indicated.

https://doi.org/10.7554/eLife.09347.037

Given these observations, we reasoned that many proteins might be able to form structures in a pH-dependent manner. Widespread formation of macromolecular protein assemblies could lead to considerable changes in the cellular architecture, and could trigger the formation of a percolated filamentous-colloidal network that would obstruct the movement of particles and provide mechanical stability to the cell. To investigate this possibility, we tested a set of 70 proteins that had previously been shown to assemble into higher-order structures upon starvation (Narayanaswamy et al., 2009; Noree et al., 2010). We found that the majority of these proteins formed structures upon acidification, whereas such structures were less abundant or absent at neutral pH (Figure 6B and C) or in 1 M sorbitol (Figure 6—figure supplement 2). Similar structures were observed in dormant yeast spores (Figure 6B), which reportedly have a pH in the acidic range (Aon and Cortassa, 1997; Barton et al., 1980). Thus, we conclude that many proteins assemble into higher-order structures in energy-depleted and acidified cells and that this causes extensive changes in the organization and material properties of the cytoplasm.

Cytoplasmic acidification promotes survival of energy depletion stress

A hallmark of dormant cells is that they can survive extended periods of energy depletion. We therefore wondered whether pH-induced formation of a solid-like cytoplasm promotes cellular survival. Indeed, when energy-depleted budding yeast cells were kept in the presence of neutral medium to prevent cytoplasmic acidification, they rapidly lost viability (Figure 7A). Moreover, when we fixed the pHc in the acidic or neutral range in energy-depleted cells, only acidified S. cerevisiae (Figure 7B) and S. pombe (Figure 7C) cells survived. Thus, we conclude that the change in the physical properties of the cytoplasm is protective and that yeast cells use a simple physicochemical signal—the pH of the cytosol—to signal a depletion of energy and to regulate entry into a dormant state.

Acidification of the cytosol promotes survival under energy depletion conditions.

(A) Growth assay of S. cerevisiae cells that were energy-depleted with 20 mM 2-DG and 10 µM antimycin A in growth medium adjusted to pH 7.0 and 6.0, respectively. (B) Similar to A, but cells were incubated in buffers of pH 7.0 or pH 6.0, respectively, containing 2 mM DNP. (C) Similar to A, but the experiment was performed with fission yeast. (D) A flow chart showing the hypothetical sequence of events promoting entry into a dormant state.

https://doi.org/10.7554/eLife.09347.038

Discussion

Many biochemical reactions inside a cell take place in the cytoplasm. Thus, changes in the physical or chemical properties of the cytoplasm will have far-reaching consequences for cellular metabolism and survival. Here, we demonstrate that adaptive changes in the cytosolic pH alter the material properties of the cytoplasm and arrest the diffusion of cellular organelles and foreign tracer particles (see schematic in Figure 7D). We further show that pH-controlled macromolecular assembly drives a transition of the cytoplasm to a solid-like state, which provides protection and increased mechanical stability.

Many previous studies have demonstrated a role for energy in controlling intracellular dynamics. In eukaryotic cells, ATP-driven motor proteins carry organelles and other cargo along cytoskeletal tracks to specific subcellular locations, thus regulating the distribution of cytoplasmic components (Hirokawa et al., 2009; Roberts et al., 2013). Recent findings also indicate that the movements of motor proteins generate random fluctuating forces, which drive diffusive-like non-thermal motion (Brangwynne et al., 2008, 2009; Guo et al., 2014). These non-thermal force fluctuations facilitate the mixing of the cytoplasm and thus are important for cellular function. As a consequence, energy depletion in mammalian cells causes a cessation of motor movements and thus a strong impairment of intracellular motion, with a significant impact on the distribution of macromolecules in the cytoplasm.

The diffusion of a particle in the cytoplasm is the result of two opposing forces: fluctuating forces of thermal or non-thermal origin, which transfer energy onto the particle, thus propelling its motion, and opposing material forces that restrict the movement of a particle and result from interactions of the particle with cytoplasmic components. Studies so far have largely focused on the driving forces of particle motion, and most prominently those that are dependent on ATP (Brangwynne et al., 2008, 2009; Guo et al., 2014; Parry et al., 2014). However, we now show that changes in the material properties of the cytoplasm can also significantly affect particle motion. These changes occur in an energy-dependent manner, indicating that energy not only regulates motor-driven diffusive processes, but also has an impact on the organization of the cytoplasm. In the organisms we investigate, these changes are largely independent of the cytoskeleton, but reflect the collective effects of many cytosolic proteins that assemble into higher-order structures.

The chemistry and physics of proteins has been under investigation for many decades. For example, it is a well-known fact that changes in the salt concentration can have a strong impact on protein solubility. One parameter with particularly strong effects on protein solubility is the pH. When the pH of a solution approaches a protein’s isoelectric point, the solubility of the protein drops to a minimum (Tanford and De, 1961). The reason is that under these conditions proteins attain a low net charge and thus are subject to weaker repulsive interactions. As a consequence, attractive interactions dominate, which—provided that the protein is present at a high enough concentration—can trigger the assembly of proteins into higher-order structures.

The pH-driven assembly of proteins can result in the formation of large structures with solid-like properties (Boye et al., 1996; Matsudomi et al., 1991; Parker et al., 2005; Renard and Lefebvre, 1992). Thus, on the macroscopic level, protein assembly can exhibit signs of a phase transition. Although this tendency of proteins to form higher-order structures is well known, this knowledge has not yet been applied to the understanding of living organisms. One reason may be the widespread but misleading assumption that the physicochemical properties of the cytoplasm are invariant. However, in recent years, an increasing number of reports have shown that key physical and chemical parameters of the cytoplasm can fluctuate, especially under conditions of stress. In the case of cytosolic proton concentrations, these fluctuations can span almost two orders of magnitude (our study and Imai and Ohno, 1995; Orij et al., 2009; Valli et al., 2005). Given that the cytoplasm is highly crowded with proteins, it is not surprising that pH changes of this scale have strong effects on the physical properties and the dynamics of the cytoplasm.

Previous studies have analyzed the distribution of the isoelectric points in the proteome (Chan et al., 2006; VanBogelen et al., 1999; Weiller et al., 2004). These studies found that only few proteins have isoelectric points in the neutral pH range and that most proteins only become electrically neutral when the pH shifts to the acidic or basic range. Importantly, proteins become more insoluble when their net charge decreases. Thus, to maintain maximum proteome solubility, cells have to keep the cytosolic pH in the neutral range. However, when the pH of the cytosol becomes more acidic, as during starvation, a large fraction of the proteome will become less soluble. Consistent with this, we could previously show that cytosolic acidification triggers the assembly of a group of metabolic enzymes into higher-order structures and that assembly inactivates their enzymatic activities (Petrovska et al., 2014). Here, we provide evidence that many more proteins assemble into microscopically visible structures upon acidification (Figure 6B and C), and we propose that assembly of these and probably many other proteins promotes a transition of the cytoplasm to a more solid-like state. Importantly, pH-dependent assembly of proteins does not seem to go along with protein denaturation, as the proteins in these assemblies retain their native structure (Petrovska et al., 2014) and oligomeric states (Figure 6—figure supplement 2). This ensures that assembly formation can readily be reversed and that the cytoplasm can rapidly recover from pH-induced alterations, thus allowing swift reentry into the cell cycle. Most importantly, protein assembly and the solid-like state of the cytoplasm protect cells from the adverse effects of energy depletion stress (Figure 7). We do not yet know why these processes are protective, but we favor a combination of different explanations, such as energy conservation (Bernstein et al., 2006), regulation of metabolism (Petrovska et al., 2014), and potentially protection of macromolecules from damage.

One of the hallmarks of dormant cells is a loss of water. Although we do not determine the water content of energy-depleted and acidified cells, we find that acidification goes along with a significant reduction of the cellular volume, which is consistent with a loss of water. Previous studies have shown that protein assembly leads to the exclusion of water (Cameron et al., 2006; Cameron and Fullerton, 2014; Fullerton et al., 2006; Thirumalai et al., 2012). The released water becomes osmotically available and can be lost to the surrounding environment, inducing a compaction of the cytoplasm and a change in cell volume. Thus, we propose that the observed cellular shrinkage is to a large degree caused by the formation of cytoplasmic structures and a subsequent release of water.

Additional evidence for this scenario comes from a study that characterized the cytoplasm as a material with distinctive gel-like properties (Fels et al., 2009). The authors of this study found that the cytoplasm of mammalian cells behaves like a hydrogel, which can swell and shrink depending on its water content. Importantly, changes in the cytosolic pH could modulate swelling and shrinking (Fels et al., 2009). This suggests that the cytoplasm with its many macromolecular components is inherently pH sensitive, a property, which may have been exploited repeatedly during evolution as a strategy for adaptation or survival. In fact, the germination of spores goes along with a drastic increase in water content (Cowan et al., 2003; Dijksterhuis et al., 2007) and spores consistently have a pH in the acidic range (Aon and Cortassa, 1997; Barton et al., 1980; Busa and Crowe, 1983; Setlow and Setlow, 1980; van Beilen and Brul, 2013). However, what is still unclear is how water is released from forming spores and re-enters into spores upon germination. Given our findings, we propose that the dehydration/rehydration cycle of spores is at least partially driven by changes in the cytosolic pH. A regulatory cell volume decrease with increased macromolecular crowding may also contribute to the water loss in dormant cells (Mourão et al., 2014). Dissection of this important problem will require the use of sophisticated biophysical, biochemical, and genetic approaches.

We show that the cytoplasm of energy-depleted cells transitions from a fluid- to a solid-like state. This transition was evident for the cytoplasm (as determined by particle tracking) and on the level of the entire cell (as determined by cellular deformability assays). This is, to our knowledge, the first viscous and elastic characterization of S. cerevisiae spheroplasts by AFM-indentation. The elastic modulus (on the order of 1 kPa) is several orders of magnitude lower than what has been reported for intact yeast cells surrounded by a rigid cell wall (about 500 kPa; [Pillet et al., 2014]). A similar difference in stiffness between spheroplasts and intact cells with a rigid cell wall has previously been found in E. coli (Sullivan et al., 2007). The increased mechanical stiffness at low pH was independently confirmed by a new microfluidic technique (RT-DC). The transition from a compliant, more viscous cytoplasm to a stiff, elastic cytoplasm in energy-depleted yeast cells is in agreement with a model in which many proteins assemble into a dense network, thus restricting the diffusion of large particles. This network could have the overall physical properties of a glass, as recently proposed for bacteria (Parry et al., 2014). Future studies will have to determine the molecular mechanisms and physical causes promoting the formation of a solid-like cytoplasm.

In contrast to mammalian cells, yeast cells are much smaller in size. This may explain why yeast rely more strongly on thermal diffusion for macromolecular dispersal. However, this also means that yeast cells have to alter the material properties of the cytoplasm to restrict diffusion during dormancy. We believe this is achieved by promoting a pH-controlled transition to a solid-like state, which significantly changes the fluidity of the cytoplasm. Acidification of the cellular interior of yeast seems to occur through an influx of protons from the outside, suggesting that this transition is dependent on an acidic environment, which may be generated through the normal metabolic activity of yeast. Thus, we predict that single-celled organisms make extensive use of the pH responsiveness of the cytoplasm in order to protect themselves and regulate their metabolism. However, even multicellular organisms such as marine brine shrimp can enter into a dormant state in a pH-dependent manner (Busa and Crowe, 1983). Importantly, in this organism dormancy is induced through protons that are released from intracellular stores (Covi et al., 2005), indicating that dependence on outside pH could be a peculiarity of yeast. Moreover, cytosolic pH changes have also been observed when organisms such as yeast and Dictyostelium are challenged with other types of stresses, such as heat stress or osmotic stress (Pintsch et al., 2001; Weitzel et al., 1985; 1987). Thus, global control over the material properties of the cytoplasm through simple physicochemical signals such as the pH could be a frequently used means to regulate cellular function in fluctuating environments.

Materials and methods

Strains and culture conditions

S. cerevisiae was grown at 25°C or 30°C in yeast extract peptone dextrose (YPD), synthetic complete (S-complete) or synthetic dropout (SD) medium. Standard pH of SD media is around pH 5.5. S. pombe was grown in either YE5 or EMM5 (standard pH is 6.0) medium at 30°C. D. discoideum was grown in AX medium (ForMedium, standard pH 6.0-6.5) at 23°C under light. A list of all S. cerevisiae strains used in this study can be found in Supplementary file 1. S. pombe wild type strain L972 was used for spheroplasting and spotting experiments. The same strain was transformed with plasmid pDUAL2HFG-µNS-sfGFP for particle tracking experiments. D. discoideum wild type strain AX2-214 (DictyBase) transformed with plasmid pDM353-µNS-GFP (Veltman et al., 2009) was used for particle tracking experiments.

Plasmids and cloning

A list of all plasmids used in this study can be found in Supplementary file 2. All cloning was done using the Gateway cloning system (Invitrogen) as described previously (Alberti et al., 2007).

pH adjustment of cells

The intracellular pH of S. cerevisiae and S. pombe cells was adjusted by incubation in phosphate buffers of different pH in the presence of 2 mM 2,4-dinitrophenol (DNP) as described previously (Dechant et al., 2010; Petrovska et al., 2014). DNP was added to the buffers from a 0.2 M (100x) stock solution in methanol. Alternatively, cytosolic acidification was achieved by incubation in SD medium containing 1, 2, or 6 mM sorbic acid. D. discoideum cells were pH adjusted by treatment with either 4 mM sorbic acid or 0.2 mM DNP in LoFlo medium (pH 5.5). For generation of pH calibration curves, cells were treated with 75 µM monensin, 10 µM nigericin, 10 mM 2-deoxyglucose and 10 mM NaN3 in buffers of pH 5.0, 5.5, 6.0. 6.5, 7.0, 7.5, and 8.0 containing 50 mM MES, 50 mM HEPES, 50 mM KCl, 50 mM NaCl, 0.2 M ammonium acetate as described previously (Brett et al., 2005).

Energy depletion of cells

S. cerevisiae and S. pombe cells were energy-depleted by incubation in SD medium or EMM medium, respectively, without glucose containing 20 mM 2-deoxyglucose (2-DG, inhibition of glycolysis) and 10 µM antimycin A (inhibition of mitochondrial ATP production). This treatment causes a more than 95% reduction in cellular ATP (Serrano, 1977). D. discoideum cells were energy-depleted with 40 mM 2-DG and 200 µM azide in Soerensen-phosphate buffer (pH 6.0).

Drug treatments

To test the influence of the actin cytoskeleton on particle mobility, cells were treated with 100 µM latrunculin A (LatA) in SD medium for 30 min prior to imaging. To test the role of the microtubule cytoskeleton, cells were treated with 15 µg/ml nocodazole in SD medium for 1 hr prior to imaging.

Spheroplasting (cell wall removal)

S. cerevisiae spheroplasts were generated by incubating cells in PBS containing 1 M sorbitol (Sigma), 1% glucose and 0.25 mg/mL Zymolyase 100T (USBiological) at 25°C for at least one hour under shaking. S. pombe spheroplasts were generated, with minor adaptations, as described previously (Kelly and Nurse, 2011). Shortly, cells were incubated in buffers of different pH (depending on experiment) in the presence of 1.2 M sorbitol (Sigma), 0.5 mg/mL Zymolyase 100T (USBiological) and 2.5 mg/mL lysing enzymes from Trichoderma harzianum (Sigma).

S. pombe spheroplasting assay

Wild type S. pombe L972 cells were grown in liquid EMM medium containing 0.5% glucose at 30°C overnight shaking at 200 rpm, diluted and re-grown to mid-log phase the next day. Cells were harvested by centrifugation, washed twice with medium or buffer containing 1.2 M sorbitol and applied to a 4-chamber glass-bottom dish (Greiner BIO-ONE) coated with concanavalin-A. For energy depletion experiments, cells were energy-depleted as described above prior to loading to the dish. Unbound cells were washed off with EMM medium or phosphate buffers containing 1.2 M sorbitol. Bound cells were covered with 400 µl of phosphate buffer of different pH containing 1.2 M sorbitol and either 2 mM DNP (pH experiment) or 20 mM 2-DG and 10 µM antimycin A (energy depletion experiment). Cells were imaged for five frames before addition of 40 µl cell wall digesting enzymes (final concentrations: 0.5 mg/ml Zymolyase 100T, 2.5 mg/ml lysing enzymes from Trichoderma harzianum). Spheroplasting and rounding up of cells was followed by time-lapse bright-field microscopy with a DeltaVision (Applied Precision) microscope (Olympus IX70 stand, Olympus UPlanSApo 20x objective, CoolSnap HQ2 camera).

Yeast growth assays

S. cerevisiae wild type strain W303, or S. pombe wild type strain L972 were grown overnight, diluted to OD600 ~ 0.1 the next morning and regrown to OD600 ~0.5. Cells were harvested and resuspended in either phosphate buffers of pH 6.0 or pH 7.0, respectively, containing 2 mM DNP (S. cerevisiae and S. pombe) or in S-medium without glucose containing 20 mM 2-DG and 10 µM antimycin A (S. cerevisiae). Cells were then incubated under shaking at 25°C. Samples were taken after 2, 24 and 48 hr, cells were washed once with H2O and subsequently spotted on YPD as five-fold serial dilutions.

Ratiometric pH measurements

For cytosolic pH measurements a codon-optimized version of the ratiometric fluorescent protein pHluorin2 (Mahon, 2011) was integrated into the W303 ADE+ genome at the trp locus. The protein was expressed under control of a GPD promoter. A pH calibration curve was obtained as described previously (Brett et al., 2005), except that we used a microscopy-based fluorescence readout. Briefly, cells were incubated in buffers of different pH-containing proton carriers (75 μM monensin, 10 μM nigericin) and inhibitors (10 mM 2-deoxyglucose) to rapidly deplete cells of energy and allow for complete equilibration of the intracellular and extracellular pH. Cells were immobilized in 4-chamber dishes (Greiner BIO-ONE) with concanavalin A and imaged using DAPI/FITC (Excitation: DAPI; Emission: FITC) and FITC/FITC (Excitation and emission: FITC) filter sets on a DeltaVision (Applied Precision) microscope (Olympus IX70 stand, Osram Mercury short arc HBO light source, 100x Olympus UPlanSApo objective, CoolSnap HQ2 camera). Six different Z-stacks each with 6 planes (Z-resolution 0.5 µm) were recorded for each pH condition. Imaging settings were: 10% excitation intensity, 0.1 s exposure time, 512x512 pixels, 2x2 binning. After background subtraction, the mean DAPI/FITC to FITC/FITC ratio was calculated from the intensity readouts of both channels and plotted against pH to obtain a calibration curve. Subsequent pH measurements were calculated from a fourth degree polynomial fit to the calibration curve. Time series of pH measurements were obtained using identical imaging settings and a CellASIC (Millipore) microfluidics flow setup combined with CellASIC ONIX Y04C microfluidic plates.

Particle tracking experiments

For particle tracking experiments, samples were prepared in 4-chamber glass-bottom dishes (Greiner BIO-ONE). Dishes were coated with concanavalin A coating solution for at least 30 min. Subsequently the coating solution was removed and the glass surface washed with H2O twice before adding 1 ml of a log phase yeast culture (OD600 = 0.5). Cells were allowed to settle onto the glass surface for 10 min. The supernatant was then removed and cells sticking to the surface were washed with appropriate medium or buffer twice. This normally results in a single layer of yeast cells that stick tightly to the glass surface. For control experiments cells were then incubated in 500 µl of S-complete medium for 30 min before imaging. When treated with DNP or sorbic acid cells were incubated in 500 µl of appropriate buffer or medium for 30 min before imaging. For energy depletion experiments, cells were incubated in energy depletion medium for 2 hr before imaging.

Imaging was done on different microscope setups depending on requirements for image acquisition rate and camera chip size. Data with a time resolution of 5 s were recorded on a Deltavision (Applied Precision) microscope (Olympus IX70 stand, Osram Mercury short arc HBO light source, Olympus UPlanSApo 100x oil objective, CoolSnap HQ2 camera, resulting pixel size (x, y) = 65 nm). Z stacks with 10 focal planes were collected at each time point. Imaging settings were: 10% excitation intensity, 0.08 s exposure time, 1024x1024 pixels, total imaging time 10 min. All data with a time resolution of 1 s were recorded on an Andor spinning disk confocal microscope (Olympus IX81 stand, Andor iXon+ EMCCD camera, resulting pixel size (x, y) = 81 nm). Z-stacks with 16 focal planes were collected at each time point. Imaging settings were: 40% laser intensity, minimum possible exposure time (~16 ms), 512x512 pixels, total imaging time 20 s. Data with 10 millisecond time resolution was recorded on an Andor Spinning disk setup (Olympus IX71 stand, Olympus UPlanSApo 60x silicon oil objective, resulting pixel size (x, y) = 108 nm). Imaging was done in a single focal plane of 764x1190 pixels, which allowed us to track a reasonable number of particles at this frame rate in a single experiment. Imaging settings were: 15% laser intensity, 10 milliseconds exposure time, total imaging time of 10 s.

If recorded, Z stacks were sum-projected using the Fiji image-processing package (Schindelin et al., 2012). All particle tracking was done with the MosaicSuite particle tracker (Sbalzarini and Koumoutsakos, 2005) a Fiji plugin freely available from http://mosaic.mpi-cbg.de. The following settings were used for tracking data with 5 s time resolution: particle radius: 7, cutoff: 0, percent: variable, link range: 3, displacement: 20. Data with 1 s time resolution was tracked with: particle radius: 8, cutoff: 0, percent: variable, link range: 1, displacement: 20. Data recorded with 10 ms time resolution was tracked with: particle radius: 8, cutoff: 0, percent: variable, link range: 1, displacement: 5. The MosaicSuite particle tracker also measures particle intensities (m0) during tracking. The mean intensity was computed from m0 for each trajectory and used as a proxy for particle size. Particle trajectories were binned into three roughly equally populated size bins (small, medium, large) to illustrate the dependence of the MSD on particle intensity. Computations and plotting were either done in R, making use of the plyr, reshape and ggplot2 (Wickham, 2009) packages or in MATLAB.

Microscopy of protein assemblies

We imaged a list of 73 strains (see Supplementary file 1) from the yeast GFP collection (Huh et al., 2003) under conditions of high and low intracellular pH. Samples were prepared in 4-chamber glass bottom dishes as described for the single particle tracking experiments. Cells were incubated in phosphate buffers of pH 5.5 or pH 7.4, respectively, containing 2 mM DNP and 2% glucose for exactly 30 min before imaging. Imaging was done on a DeltaVision (Applied Precision) microscope (Olympus IX70 stand, Osram Mercury short arc HBO light source, Olympus UPlanSApo 100x oil objective, CoolSnap HQ2 camera). Z stacks with 14 focal planes were collected at 6 points for each sample. Imaging settings were: 50% excitation intensity, 0.1 s exposure time, 512x512 pixels, 2x2 binning. Imaging of protein assemblies in yeast spores was done with similar settings.

Cell volume measurements

GFP-expressing yeast cells were used to determine the volume of cells using an imaging-based approach. Samples were prepared in 4-chamber glass bottom dishes as described for the single particle tracking experiments. In control experiments cells were subsequently incubated in 500 µl of S-complete medium for 30 min before imaging. For pH adjustment cells were incubated in 500 µl of phosphate buffers of pH 5.5, 6.5 or 7.4, respectively, containing 2 mM DNP and 2% glucose for exactly 30 min before imaging. For volume adjustment cells were incubated in S-complete medium containing 0.6, 0.8, 1 or 2 M sorbitol for exactly 30 min before imaging. Cells were imaged on an Andor spinning disk confocal microscope (Olympus IX81 stand, Olympus UPlanSApo 100x oil objective, Andor iXon+ EMCCD camera, resulting pixel size (x, y) = 81 nm). Z-stacks were obtained with z=210 nm resolution. Z stacks were projected to obtain 2D maximum intensity projections, which were then processed further for image segmentation and object detection. For image segmentation, objects smaller than 10 pixels were considered to be noise and removed, and a structural filter of ellipsoid shape was applied to detect the foreground of the cells. The background was identified by computing the distance transform matrix of the foreground. Using the watershed transform matrix, the background markers were turned into regional minima, and the foreground image was segmented to obtain individual cells. The mask for the individual cells was then used to select the corresponding Z stack, and the pixels above the stack threshold were considered as resulting from the GFP signal of the cell. Finally, the empty vacuolar regions were filled, and the resultant image was counted for total number of pixels above threshold to compute the total cellular volume. Image processing and analysis was done in MATLAB. To obtain an accurate measurement of the cell volume, budding and overlapping cells were not quantified.

FRAP measurements

The mobility of a mCherry-GFP fusion protein was measured using fluorescence recovery after photobleaching (FRAP). Prior to imaging, yeast cells were either pH adjusted in phosphate buffers of pH 5.5, 6.0 or 7.4, respectively, containing 2 mM DNP and 2% glucose, or treated with SD-medium containing 0.8, 1.0, 1.5 or 2.0 M sorbitol, respectively, or energy-depleted in SD-medium without glucose containing 20 mM 2-DG and 10 µM antimycin A. Cells were then immobilized on a cover slip with concanavalin A coating solution and imaged on an Andor spinning disc microscope (Nicon eclipse Ti stand, Nikon Plan Apo TIRF 100x oil objective, Andor iXon+ camera, resulting pixel size 70 nm) equipped with a FRAPPA unit (Andor). A single pixel region of interest was bleached with a 405 nm laser pulse (1 repeat, 40% intensity, dwell time 60 ms). Recovery from photobleaching was then recorded in a single focal plane with a time resolution of 5.4 ms (EM gain 200, laser intensity of 5%). Image analysis was carried out in FIJI.

Atomic force microscopy

AFM-based indentation experiments were performed using a Nanowizard I (JPK Instruments, Berlin) in combination with the CellHesion module. Tip-less silicone cantilevers (Arrow-TL1, Nanoworld, Switzerland) were equipped with a polystyrene bead of 10 µm diameter (microParticles GmbH, Germany) and calibrated prior to measurements using the thermal noise method. Cell-Tak (Corning, USA) was used for immobilization of spheroplasts (Gautier et al., 2015). To determine the stiffness of single spheroplasts (S. cerevisiae), the cantilever was positioned above individual cells and lowered with a speed of 10 µm/s. Force-distance curves were recorded (maximum force 2 nN) and analyzed using the JPK data processing software (JPK instruments): Force-distance data were corrected for the tip-sample separation (Radmacher, 2007) and fitted with the Hertz model for a spherical indenter (Sneddon, 1965). An effective probe radius was used according to the Hertz model for two spheres. A Poisson ratio of 0.5 was assumed. Experiments were carried out in phosphate buffer (containing 1 M sorbitol and 1% glucose) adjusted to pH 6.0 or pH 7.4 at room temperature both with and without DNP (see Figure 5 and Figure 5—figure supplements). Reporting an apparent elastic (Young’s) modulus acknowledges the fact that several assumptions of the Hertz model (isotropic, homogeneous, semi-infinite space) are not met; but this still serves well for quantitative comparison of cells in different pH conditions. The Hertz model also assumes an elastic material, but cells are viscoelastic and an observed increase in apparent elastic modulus could also be caused by an increase in viscous resistance to deformation. To directly determine the viscosity η of spheroplasted cells from the recorded force-distance data, we adapted a method proposed by (Rebelo et al., 2013). Briefly, this method extracts the viscosity η by comparing the dissipated energy during the indentation process, Wdiss, to the viscous work, Wv, which is modeled taking into account the indenter shape and indenter velocity. Wdiss corresponds to the area between the approach and retract force-distance curves, Wdiss=0δmax(F(app)Fret)dδ, where δ is the tip-sample-separation, or indentation, and the superscripts app and ret indicate the forces recorded during approach and retraction, respectively. The viscous work is the integral of the viscous force Fv, which is described by Fv=2πη(Rδ)dδ/dt for a spherical indenter of radius R. Force-distance curves were smoothened using a median filter and a multi-exponential fit to compute the time-derivative of the indentation. Finally, the viscosity was calculated as η=Wdiss2π0δmax(Rδ)(δ'(app)δ'(ret))dδ, where δ'=dδdt. All calculations were implemented in Python.

Real time deformability cytometry

Real-time deformability cytometry (RT-DC) has recently been introduced as a method for high-throughput cell mechanical characterization (Otto et al., 2015). Briefly, the experimental setup consists of an inverted microscope (Zeiss, Axiovert200M), a high-speed video camera (MC1362, Mikrotron) and a syringe pump (NemeSys, Cetoni), which are assembled around a microfluidic chip. The chip is made of polydimethylsiloxane using soft lithography and its geometry is defined by two reservoirs connected by a 300 μm long constriction with a 10 μm x 10 μm squared cross-section. When a cell suspension is driven through the narrow channel, cells experience a significant hydrodynamic stress and the resulting deformation is captured and analyzed in real-time using the high-speed camera. Deformation, D, is quantified by the circularity c: D=1c=2πAl, where A is the projected surface area and l the perimeter of the cell inside the channel. The more a cell deviates from an ideal circular shape the larger is D. For simplicity, the size measure reported is the diameter of an equivalent circle with area A. A typical experiment requires a cell concentration of 106 cells/ml and a minimal absolute sample volume of 100 μl. Here, S. cerevisiae spheroplasts were resuspended in PBS-methylcellulose medium adjusted to different pH, containing 1 M sorbitol and 1% glucose. Cells were drawn into a 1 ml syringe and connected to polymer tubing, which had been cleaned using 70% ethanol and 200 nm sterile-filtered (Sigma Aldrich) deionized water. After connecting tubing to the syringe pump and microfluidic chip a flow was stabilized for 1 min. Here, measurements were carried out at a constant flow rate of 0.012 μl/s. For reference, data are also acquired inside the reservoirs to ensure no deformed cell shape in the absence of mechanical stress (data not shown).

Mean squared displacement analysis

The ensemble-averaged mean squared displacement (MSD) was calculated as

MSD(t)=1Ni=1N(xi(t)xi(0))2+(yi(t)yi(0))2

where N is the number of particles, and xi(t) and yi(t) are the coordinates of particle i at time t.

The time-and-ensemble averaged MSD, MSDτ, was computed as

MSDτ(t)=1Ni=1N1mtj=0mt1(xi(t+τj)xi(τj))2+(yi(t+τj)yi(τj))2

where t is the frameshift, N is the number of particles and m is the length of the particle trajectory. The maximum frameshift was limited to 1/3 of the full trajectory length. The subdiffusion exponent α was estimated by fitting the time-and-ensemble averaged MSD to a power law between 0.2-2 s. Before fitting, the MSD was noise corrected assuming the positional noise of 11nm estimated from the power spectra. In this way, we obtained α  0.73 (DNP treated cells with external pH 6), α  0.84 (DNP treated cells with external pH 7.4), α  0.64 (energy depleted cells) and α  0.88 (log phase cells).

Analysis of the power spectrum of displacements

The power spectrum of displacements for a one-dimension signal x(t) is given by x(ω)2, where the angular brackets denote an average over the ensemble, and x(ω)=0x(t)eiωtdt. For the two-dimensional tracking data, the total power spectrum is given by the sum of the power spectra of each component as x(ω)2+y(ω)2. The integral is approximated using the built-in MATLAB function fft (Fast Fourier Transform). The tracking accuracy is estimated by considering the plateau of the power spectrum. The experimental power spectrum is given by x^(ω)2+y^(ω)2=x(ω)2+y(ω)2+x02+y02, where x02+y02 is the power spectrum of the positional noise. This positional noise will manifest itself as a plateau in the power spectrum at high frequencies. We find that the plateau can be reproduced by generating Gaussian noise with standard deviation of σ11 nm. The true power spectrum is obtained by subtracting the power spectrum of this estimated positional noise from the experimentally obtained power spectrum. Thereafter, the low-frequency regime (from 0.1 Hz to 5 Hz) was fitted to a power law in a least square sense. The low frequency regime was used for the fitting as this procedure minimizes potential additional errors from the tracking procedure.

To check whether the deviation of the slope of the PSD from the expected slope from Brownian motion is an artifact of the finite length of the trajectories or a result of correlations in the data, signifying a viscoelastic material state, the displacements of the individual trajectories were randomly reshuffled. This was done by randomly permuting the order of the displacements using the MATLAB function randperm. This way, subsequent displacements in the reshuffled trajectories are uncorrelated and the corresponding PSD should therefore scale as for Brownian motion. We find that this is indeed the case under all conditions (Figure 4—figure supplement 1).

Fractional Brownian motion as a model for particle diffusion

We find that the time-averaged and ensemble-averaged MSD agrees well under all conditions, implying that the process is stationary (does not age) on the experimental time-scale (Figure 4—figure supplement 6). A statistical process that is consistent with our experimental observations (stationarity, subdiffusive scaling of the MSD, and an anomalous power-law scaling of the positional power spectrum (Weiss, 2013) is fractional Brownian motion (fBm). For fBm, the probability density function (PDF) of displacements is Gaussian, but the displacements are correlated, x(t)x(s)=Kα[tα+sα|ts|α], where Kα measures the strength of the fBm and may depend on particle size and the local microenvironment. As a consequence of the Gaussian property, the PDF is completely determined by its second moment, proportional to the MSD of the particle. The correlations cause the MSD to increase as a power law, MSDτ(t)=2dKαtα where d is the dimension of space, and 0<α<1 is the subdiffusion exponent. From this expression we see that Kα can be referred to as a generalized diffusion constant. In the following discussion, we consider the motion along the two coordinate axes as two independent realizations of the same random process. The motion is consequently analyzed as a one-dimensional process, d=1.

CDF of particle displacements

For one-dimensional fBm the PDF of displacements of each individual particle is given by Pi(x,t)=1(4πKα,itα)1/2exp(x24Kα,itα). The lengths of the individual trajectories are too short (~1000 time points) to provide a reliable estimate of the PDF on a single-particle level. A statistical measure that can be estimated also for small datasets (Weiss, 2013) is the cumulative distribution function (CDF). The CDF Fi(x,t)=xPi(x',t)dx' of a single particle trajectory is a measure for the probability that the particle makes a displacement not larger than x (note that x has both negative and positive values). To build a CDF Fi(x,t) at a given moment of time (t=2 s) for an individual trajectory we just count the number of displacements which are smaller than x and divide this number by the total number of displacements in a given trajectory. The CDFs are fitted to a Gaussian CDF with zero mean and variance given by the variance of the displacements in the array (see Figure 4—figure supplement 2). In addition, for each individual trajectory we can calculate the so-called non-Gaussian parameter γi=x4i3x2i21, which vanishes for displacements x with the Gaussian distribution. Indeed we see that for all experimental conditions this parameter is close to zero, see inset in Figure 4—supplement figure 2.

Scaling property and the master curve

By rescaling the displacements by the lag time as x˜=x/tα/2, the PDF of displacements at different times can be collapsed to a single master curve Gi(x˜)=1(4πKα,i)1/2exp(x˜24Kα,i). For an ensemble of N particles performing fBm in a heterogeneous environment, the total master curve is given by G(x˜)=(1N)i=1NGi(x˜). To obtain the master curve G(x˜) for the fBm process underlying the particle motion, we need to estimate the generalized diffusion constants Kα,i of fBm for each trajectory. The strength of the fBm is obtained as Kα,i=MSDτ,i(t)2tα, for a lag time t=2 s. As we see on Figure 4B the master curve perfectly fits the ensemble data for the PDF of particle displacements. Moreover, if we rescale displacements of each trajectory by the corresponding generalized diffusion constant, they should all collapse on a single Gaussian distribution with a unit variance, and this is what we show in Figure 4—figure supplement 3). For reference we also provide the unscaled PDFs of displacements for log phase and energy depleted cells for two lag times t=0.2 and 2.0 s, see Figure 4—figure supplement 3, right panel.

Displacement correlation

The directional correlation function of the displacements was calculated as C(nτ)=Δx(t)|Δx(t)|Δx(t+nτ)|Δx(t+nτ)|, where τ is the lag-time, n is an integer, Δx(t) is the change in particle position between time t and t+τ and |Δx(t)| denotes the length of the vector Δx(t). Averaging is performed over the ensemble of particles and time t.

To quantify the strength of correlations we considered two displacements δxand δx' during two consecutive time intervals of length τ (Weeks and Weitz, 2002). We then calculate the projection of the second displacement onto the direction of the preceding one, c=δx'δx|δx|. If this value is negative, it indicates that the second displacement tends to move oppositely to the first. For small displacements, we expect this quantity to be a linear function of the initial particle displacement,  c=-b|(δx)| . For a viscous material the slope b vanishes, whereas for an elastic material the slope is b=1/2 and is independent of the lag time τ for which the displacements are calculated. To find out how c depends on the magnitude of the initial displacement, we first extract all pairs of subsequent displacements at a certain lag time. Thereafter the projection is calculated for each pair of displacements. In order to consider the relation between the correlation c and the initial displacement length |δx'|, these lengths were binned in 38 equidistant bins. The correlation was averaged in each bin to obtain the correlation c as a function of displacement length |δx'|, see Figure 4C and Figure 4—figure supplement 5). The linear scaling of c with |δx'| and its independence on the lag time also rule out the localization error as a possible (dominating) source of negative correlations in the displacement data.

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Decision letter

  1. Robert H Singer
    Reviewing Editor; Albert Einstein College of Medicine, United States

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

[Editors’ note: this article was originally rejected after discussions between the reviewers, but the authors were invited to resubmit after an appeal against the decision.]

Thank you for choosing to send your work entitled "A sol-gel transition of the cytoplasm driven by adaptive intracellular pH changes promotes entry into dormancy" for consideration at eLife. Your full submission has been evaluated by Vivek Malhotra (Senior editor) and two peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the decision was reached after discussions between the reviewers. Based on our discussions and the individual reviews below, we regret to inform you that your work will not be considered further for publication in eLife.

While both reviewers found the work of interest and importance, they were unconvinced that the data supported the conclusions and felt there were numerous flaws in the experiments and analysis. One reviewer summarizes: "To me, there are two major problems. First, the authors seemed to propose a 'gel' transition originating from an increase in (presumably cross-linked?) protein filaments in the cytoplasm under starved/acidified conditions, but I find the evidence unconvincing. Second, the AFM and cell shape retention results (that the authors interpret as an increase of stiffness of the cytoplasm) could be due to the presence of residual cell wall." In view of these considerations, we do not feel that the manuscript warrants further consideration unless these concerns can be resolved.

Reviewer #1:

This study shows that carbon starvation and acidification of the cytoplasm change the physical properties of the cytoplasm of yeast and Dictyostelium cells. Cytoplasmic acidification appears sufficient to reduce the motion of large tracer particles. It also increases survival rate under energy-depleted conditions (i.e., starvation), showcasing the biological significance of the findings. The authors propose that particle confinement under starvation is caused by the cytoplasm transitioning from a solution to a gel-like state. The topic is important and has large implications to our understanding of cellular dynamics and dormancy in eukaryotes. While the results are potentially exciting, there are major concerns with some interpretations and conclusions.

First, the proposed terms 'cytoplasmic freezing' are misleading. If what they observe was analogous to the cytoplasm freezing, all cellular components, including proteins, would reduce their motion as the dynamics of particles of all sizes are affected by temperature. This is not the case here as large particles are affected but not proteins (GFP). The size dependence is inconsistent with a cytoplasmic freezing, and this characterization should be avoided.

The evidence for a gel transition is not convincing. A gel suggests a meshwork. The authors argue that many proteins assemble into higher order structures under starvation, citing Noree et al. (2010), Narayanaswamy et al. (2009) and their own previous work. The Noree et al. (2010) paper talks about a wide genetic screen identifying metabolic proteins that form 4 distinct filaments, with some being induced by energy depletion, but others being unaffected or even reduced upon energy depletion. The Narayanaswamy et al. (2009) paper and Figure 6 here show mostly puncta forming, but puncta (compact aggregates) do not form gels. I agree that a few filaments form under energy-depleted/acidified conditions. But it is unclear that there are more of them under these conditions than under the normal situation, especially given the loss of cytoskeletal filaments under energy-depletion/acidification? To form a gel and trap probes, the cell would need a pervasive meshwork (cross-linked filaments), not just proteins that form higher-order structures.

Figure 4A: The method used to show that elasticity of the cytoplasm is increased under acidification is based on a model that explicitly assumes elasticity. The argument is circular.

Furthermore, in Figure 4A, the shift in power spectral density (PSD) is not sufficient to conclude elasticity, since there are other materials (e.g., dense colloidal suspensions, laponite) that would be expected to shift the PSD but are not elastic.

Additionally, a theoretical relationship between power spectrum and single-particle displacements is valid for long-time observations. To avoid distortions of the power spectrum (especially, in the low and high frequency parts) due to the finite length of the trajectories, one needs to test if the experimental time frame was long enough.

Paragraph two, subheading “Energy-depleted and acidified cells display increased mechanical stability”: It is stated that an increase in crowding implies an increase in elasticity. This is not necessarily true. What is the evidence for this statement?

In sum, there is insufficient evidence to propose a gel-like state, which implies a physical meshwork. In my opinion, it would be safer to propose a transition into a 'glassy' system (as in Parry et al., 2014). Broadly speaking, the glassy term can applicable to a wide range of out-of-equilibrium soft materials, including gels, foams, interacting or concentrated colloidal suspensions, etc. (Cipelletti and Ramos, Phys Condensed Matter, 2005).

There are also questions about the cell stiffness measurements. If S. pombe cells do indeed maintain their rod shape at low pH even after their cell wall has been removed, it would be an absolutely amazing result. This increases the burden of proof. An alternative and trivial explanation for the result is that the cells still have residual cell wall that contributes to the rod shape maintenance and stiffness of the cell. Perhaps enzymatic digestion of the cell wall is incomplete at low pH, which would not be surprising. To me, the maintenance of the rod shape actually argues that wall-free cells were not generated, casting doubt on the claim of increased stiffness. Complete removal of cell wall must be ascertained. Perhaps (round) spheroplasts could first be generated at neutral pH, and then re-shaped (e.g., to rod-like) by an external force (e.g., their pressure-based microfluidic device might do the trick). The authors could then test whether the new (non-round) shape is maintained after a switch to low pH even when the external force is relieved.

Similarly, the authors need to rule out that the increased stiffness observed in energy-depleted/acidified budding yeast cells is not due to the presence of residual cell wall. Stiffness measurements could be performed on spheroplasts created under normal (log phase, neutral pH conditions when the cell wall is known to be completely digested) and then switched to energy-depleted or acidic conditions for stiffness measurements.

Figure 5—figure supplement 2: The statement about the reduction in cell volume is not convincing at this stage. A reduction in cell volume doesn't mean an increase in crowding, unless the reduction in cytoplasmic volume occurred very rapidly before there can be a change in cytoplasmic composition. Under their experimental conditions (30 min), the composition of the cytoplasm can change. Their microfluidic device allows them to measure the cytoplasmic volume immediately following acidification/energy depletion.

Also, what was the osmolality of the external media and buffers used? Can the authors exclude the possibility of an osmotic shock causing the difference in cytoplasmic volume? Also, could they clarify what 'relative cell volume' mean? How many cells were measured?

Figure 5—figure supplement 4: I am not sure what these FRAP curves are showing. Are they for a single cell or for many cells averaged? More importantly, there is no obvious difference in recovery time between the three conditions, contrary to what is stated. Furthermore, the FRAP data should be analyzed, and diffusion coefficients for GFP should be provided. And why was 2M of sorbitol used when the authors suggest that the difference in volume for DNP+pH5.5 cells is equivalent to the sorbitol 0.8 M condition? FRAP measurements under 0.8M sorbitol would be a fairer comparison.

What is the evidence for fractional Brownian motion? There are other types of motion where displacement distributions collapse after re-scaling. It is not clear what fractional Brownian motion brings to the story.

Is the starvation-induced acidification observed regardless of the pH of the external medium? Or is it only when the external medium is acidic? If it is the latter, it should be clearly stated.

Similarly, fission yeast and Dictyostelium are said to undergo cytosolic pH fluctuations in response to energy depletion. Is this true in any medium regardless of its pH or only in acidic media?

How do the MSD curves in Figure 3A and B compare to Figure 1C? If the authors' proposal is correct (reduction in particle dynamics associated with starvation is due to cytosolic acidification), shouldn't we expect the pH 7 condition in Figure 3 to look like the control (untreated/log-phase culture) in Figure 1C?

Reviewer #2:

The article is well written and compelling for the most part. The numerous control experiments and the redundant mechanical measurements (microrheology and AFM indentation) support the sol to gel transition hypothesis. There are a few major flaws in the interpretation and presentation of the data that need addressing if the reader is to believe the authors' explanation of pH dependent fluctuations within the cytoplasm. My concerns are below.

1) The use of the term "cytoplamsic freezing" is really questionable. It is common in cell mechanics and active matter to find analogies to inanimate or inactive matter. The "soft glassy rheology" of the cytoskeleton is such an example. This is a good thing to do if there are truly fundamental characteristics shared between the two analogs. In the SGR case, great care was taken to show the analogy. Other examples in cell mechanics have been published to show in detail the analogy between cellular dynamics and some inanimate physical systems. In the submitted manuscript such a level of care is not taken, and the term "freezing" is used without any thought about what are the essential phenomena of freezing, and whether this gelation is anything like true freezing. If the authors believe that what they observe is truly a sol-gel transition associated with assembly of macromolecules in the cytoplasm, they should name the phenomenon accordingly. Their work will be taken more seriously by a broader audience if they let go of this name.

2) A lot of the particle tracking and MSD calculation is performed assuming thermal fluctuations are the driving force. This assumption allows connecting MSDs to material properties. A decent argument is made for this by showing that the depolymerization of actin and microtubules has little effect on their results. I do not know whether this argument is legitimate. Is it really true that no other non-equilibrium or biochemical driving forces can occur in the cytoplasm, other than those driven by the action of ATP on filament bound motors? The work by McIntosh and Schmidt from a few years ago sidestepped this question by comparing active microrheology (manually driven beads with E or B fields) to passive microrheology, and carefully measuring a driving spectrum. The recent paper by Guo and Weitz did something similar. I think this sort of thing would have to be done again here, and the outcome would have to show that the spectrum is indeed thermal, then a thermal analysis could be performed to link MSD to material properties.

Alternatively, the authors could just keep the same analysis and experiments that they have, and tone down their interpretation. I think that most of their story can be told without claiming that they can infer material properties from MSDs. They can just say that the MSDs say things are more "liquid-like" or "solid-like", and then focus on the general phenomena like the dramatic reduction in motion. The supplementary movies show this beautifully. Several years ago many high-profile papers were published that flippantly linked particle MSDs to material properties, which ultimately damaged the reputations of the authors. I think it is imperative that care is taken to prevent the same thing from occurring, for the sake of the authors and the broader research community.

To strengthen their story and focus on a transition in material properties from fluid-like to solid-like, more attention could be paid to the AFM indentation data. They use a Hertz model to fit their data. If they showed the F-d curve on a log-log scale and showed that the indent really scaled like F~d(3/2) (a fit on a lin-lin scale is not sufficient to convince the reader of the right power law), then this would be a first step that they really have a gel. They would also have to show that this could not be attributed to membrane tension, since a balloon filled with water will seem like a solid when indented from the outside. Again, I don't suggest new experiments, but perhaps extracting more compelling information about their current data.

3) The correlation analysis is seriously confusing. At first I thought I was looking at correlation functions, and concluded that the results made no sense. After re-reading the description of their analysis several times, I understood the steps taken by the authors to come up with Figure 4C and the associated supplementary figure. I think the authors need to include a few examples of the displacement autocorrelation functions (as a function of time), and show the process to come up with this correlation map as a function of displacement length.

[Editors’ note: what now follows is the decision letter after the authors submitted for further consideration.]

Thank you for resubmitting your work entitled "A pH-driven transition of the cytoplasm from a fluid- to a solid-like state promotes entry into dormancy" for further consideration at eLife. Your revised article has been favorably evaluated by Vivek Malhotra (Senior editor), a Reviewing editor, and two reviewers. Both reviewers agree that the manuscript has been improved but there are some minor issues that need to be addressed before acceptance.

Both reviewers felt that some modifications to the text were warranted as outlined below. One felt that there were some technical concerns and improvements in clarity needed. The other suggests a further consideration be added to the Discussion. We feel that these should be accommodated in the final version.

Reviewer #1:

The revised manuscript is vastly improved, and the additional results on the cell wall-free cells are simply amazing. However, I do have some technical concerns with some of the new analyses. These issues do not affect the major conclusions of the paper, but I believe that the authors should address them before publication.

Figure 4A. Why is α measured in the middle of the MSD? The center has fewer statistics, measurements involving the MSD should be made at the beginning where the statistics are the greatest.

Figure 4B and Figure 4—figure supplement 3 and Figure 4—figure supplement 2. I still think that the fractional Brownian motion (fBm) model doesn't bring much to the story (and disrupts the flow), and the evidence is not very strong. Also, a quantitative metric should be used to distinguish Gaussian from non-Gaussian (see equation 1 in Weeks et al., Science, 2000). This can be applied to individual trajectories. The particle size (since it is a mixture) should be considered in the rationale for scaling displacement distributions. What would they see if the distributions were scaled by the relative particle size instead?

It is confusing that Figure 4—figure supplement 5 shows a break in correlation linearity (consistent with caging and cage escape) whereas Figure 4C doesn't show the break (more consistent with purely elastic material). What are the experimental differences and what does it mean to their fBm model?

Figure 4—figure supplement 4. The shape of this correlation plot (a single negative correlation for the first displacement) is precisely what is expected for localization error. They could remove the plot altogether (going back to the support for the fBm model being quite weak). But if they want to show this, the authors should validate that this does not reflect localization error (imperfect localization of a static object will always give this correlation plot). The time scale of the negative correlation should be robust over experiments of different frame rates. If experiments of all frame rates produce anti-correlated displacements over one frame, the result is an artifact.

Reviewer #2:

The authors have addressed all of my previous concerns and I recommend publication.

https://doi.org/10.7554/eLife.09347.048

Author response

[Editors’ note: the author responses to the first round of peer review follow.]

We were pleased to hear that Reviewer 1 found our manuscript important: “This study shows that carbon starvation and acidification of the cytoplasm change the physical properties of the cytoplasm of yeast and Dictyostelium cells. […] The topic is important and has large implications to our understanding of cellular dynamics and dormancy in eukaryotes.”

Reviewer 2 also found our study compelling and well executed: “[…]the article is well written and compelling for the most part. The numerous control experiments and the redundant mechanical measurements (microrheology and AFM indentation) support the sol to gel transition hypothesis.”

Given the overall positive nature of the reviews, we were surprised to hear that our paper was rejected.

The two main reasons for rejecting the paper were the following: “[…]there are two major problems. First, the authors seemed to propose a 'gel' transition originating from an increase in (presumably cross-linked?) protein filaments in the cytoplasm under starved/acidified conditions, but I find the evidence unconvincing. Second, the AFM and cell shape retention results (that the authors interpret as an increase of stiffness of the cytoplasm) could be due to the presence of residual cell wall. In view of these considerations, we do not feel that the manuscript warrants further consideration unless these concerns can be resolved.”

As explained below, we can easily resolve these two major concerns.

First, we can provide additional data supporting a gel-like state. We can immediately include additional fluorescence images where assemblies form network-like structures instead of puncta or filaments. We can also add data showing that a large fraction of the cytoplasm becomes insoluble under starvation conditions. However, we agree with the reviewers that the use of the term “gel” is problematic, because it implies a cross-linked network of proteins. We will therefore use more appropriate language to refer to the state of the cytoplasm as “glassy” (as suggested by Reviewer 1) or as “solid-like” (as suggested by Reviewer 2). We think that this resolves the first concern.

The second concern was that there still is a residual cell wall. Reviewer 1 pointed out: “If S. pombe cells do indeed maintain their rod shape at low pH even after their cell wall has been removed, it would be an absolutely amazing result.” Convincingly showing that the cell wall is removed is therefore very important, and we should have been more focused on this issue in the paper. Although our previous submission included some supporting data, we now realize that their significance was not emphasized enough. Importantly, other data, which made us confident about complete cell wall removal, was available at the time of submission, but we did not include them in the manuscript. Altogether, we have the following data that supports a complete removal of the cell wall:

1) The submitted manuscript included a movie showing that spheroplasted acidified yeast did not have a cell wall (Video 6). In this movie, a spheroplasted rod-shaped cell can be seen, which was re-exposed to media. After media addition, the cell rapidly rounds up (Author response image 1A), which indicates that the cell wall had been removed previously.

2) We measured the activity of the enzymes used for spheroplasting and found that they were even more active under low pH than under neutral pH conditions. This is in agreement with information provided by the supplier of the enzymes.

3) We performed calcofluor white staining of spheroplasted cells under starvation and acidification conditions. These data show that the cell wall was completely removed during our spheroplasting procedure (Author response image 1B).

4) Further evidence for the absence of the cell wall comes from our AFM measurements. While earlier reports had quantified the wild-type yeast stiffness (Young’s modulus) with the cell wall present to be on the order of 1-2 MPa (see Alsteens et al., Nanotechnology, 2008) and with cell wall mutant strains in the range of 0.1–1 MPa (see Dague et al., Yeast, 2010), our results were on the order of 1 kPa, which is 100-1000 times lower. This directly shows that the cell wall had been removed and that we were measuring spheroplasts.

5) Finally, we include a movie (Video 8) showing acidified yeast cells, which were exposed to spheroplasting enzymes at time point zero. Forty minutes later, rod-shaped cells can be seen slipping out of their cell-wall sheath (indicated by the yellow arrows in Author response image 1C). This directly demonstrates that under the conditions we used, the cell wall was removed.

Taken together, our evidence for cell wall removal is very clear and, in our opinion, leaves no room for alternative interpretations.

Author response image 1
Summary of experiments showing that the cell wall was completely removed in our spheroplasting procedure.

(A) A rod-shaped spheroplasted fission yeast cell rounds up when media is added. (B) Acidified control and spheroplasted cells were treated with calcofluor white to stain the cell wall. Note the difference in signal intensity. The graphs on the right show the average calcofluor intensity of ten cells measured through a line scan. The location of the line scan is indicated for two representative cells with a red dashed line. (C) Stills of a time-lapse experiment where acidified rodshaped cells can be seen loosing their cell wall (yellow arrows). Also see corresponding Movie R1

https://doi.org/10.7554/eLife.09347.042

We value the additional comments of the reviewers and appreciate their suggestions, which we think will help us clarify the terminology used in the paper. We agree for example with Reviewer 2 that more care should be taken in the interpretation of the MSD data, in particular with regards to making claims about material properties. We also agree that the term “cytoplasmic freezing” was poorly chosen, and we will use more appropriate language in a revised manuscript. Other minor issues have already been addressed by us experimentally, and can be added to a revised paper.

Like the reviewers, we believe that understanding the physical-chemical mechanisms of dormancy is a very important topic. We hope that, given the overall positive nature of the reviews and our ability to address all major concerns raised in the review process, you reconsider the decision and allow us to resubmit a revised manuscript.

Reviewer #1:

While the results are potentially exciting, there are major concerns with some interpretations and conclusions.

We thank the reviewer for asking us to clarify our interpretations and conclusions. In the revised manuscript all these concerns have been addressed through new experiments or textual revisions. We think that these changes have substantially improved the paper, and we are grateful to the reviewer for encouraging us to do this.

First, the proposed terms 'cytoplasmic freezing' are misleading. If what they observe was analogous to the cytoplasm freezing, all cellular components, including proteins, would reduce their motion as the dynamics of particles of all sizes are affected by temperature. This is not the case here as large particles are affected but not proteins (GFP). The size dependence is inconsistent with a cytoplasmic freezing, and this characterization should be avoided.

This is a valid point. We used the phrase “cytoplasmic freezing” as an analogy to describe our observations of slower particle movements, but we now have to admit that, from a physical perspective, this term is very misleading for the reasons mentioned by the reviewer. We have therefore changed all appearances of the phrase “cytoplasmic freezing” to the physically more appropriate term “reduced particle mobility”.

The evidence for a gel transition is not convincing. A gel suggests a meshwork. The authors argue that many proteins assemble into higher order structures under starvation, citing Noree et al. (2010), Narayanaswamy et al. (2009) and their own previous work. The Noree et al. (2010) paper talks about a wide genetic screen identifying metabolic proteins that form 4 distinct filaments, with some being induced by energy depletion, but others being unaffected or even reduced upon energy depletion. The Narayanaswamy et al. (2009) paper and Figure 6 here show mostly puncta forming, but puncta (compact aggregates) do not form gels. I agree that a few filaments form under energy-depleted/acidified conditions. But it is unclear that there are more of them under these conditions than under the normal situation, especially given the loss of cytoskeletal filaments under energy-depletion/acidification? To form a gel and trap probes, the cell would need a pervasive meshwork (cross-linked filaments), not just proteins that form higher-order structures.

We agree with the reviewer that more direct evidence is required to prove the existence of a gel state, and we have therefore removed all references to a gel. Instead, we now use the less-contentious term solid-like, as also proposed by Reviewer 2. In the Discussion section, we also consider the possibility that the energy-depleted cytoplasm is in a glassy state, as suggested by this reviewer in one of the following comments. Although it would be desirable to make a more detailed statement about the material properties of the cytoplasm, a proper analysis would require substantial experimental effort, and therefore we feel is beyond the scope of the present paper.

Figure 4A: The method used to show that elasticity of the cytoplasm is increased under acidification is based on a model that explicitly assumes elasticity. The argument is circular. Furthermore, in Figure 4A, the shift in power spectral density (PSD) is not sufficient to conclude elasticity, since there are other materials (e.g., dense colloidal suspensions, laponite) that would be expected to shift the PSD but are not elastic.

We agree with the referee that the PSD data alone it not sufficient to claim the changes in elasticity. However, the overall evidence accumulated in the manuscript strongly supports the transition of the cytoplasm to a more solid-like state. We resolve this issue by carefully rephrasing the flow of the text, so that the statements strictly correspond to the data presented to that point. For Figure 4, following the suggestion of Reviewer 2, we focus on the discussion of the displacement data. The PSD plot (previously Figure 4A) is now a supplemental figure (Figure 4—figure supplement 1), and we no longer claim to know the exact relation between the PSD and the rheology of the cytoplasm. Instead we mention that in a viscoelastic material driven by thermal fluctuations the similar change in the PSD behavior would correspond to a transition to more solid-like state. We explicitly state that “the thermal nature of driving forces remains an assumption and therefore limits our interpretation of the PSD data”.

Additionally, a theoretical relationship between power spectrum and single-particle displacements is valid for long-time observations. To avoid distortions of the power spectrum (especially, in the low and high frequency parts) due to the finite length of the trajectories, one needs to test if the experimental time frame was long enough.

As a control, we randomly reshuffled the pieces of the trajectories to eliminate any temporal correlations. This should correspond to a pure random walk in two dimensions. We show that this leads to Brownian scaling, as expected (Figure 4—figure supplement 1). This indicates that in our data the changing slope in the PSD as a function of frequency is indeed the result of existing correlations in the particle displacements. Additionally, in this response we provide a figure for the PSD obtained for longer trajectories in the control conditions (see Author response image 2). The new data consistently follows the same scaling at low frequencies.

Author response image 2
Comparison of the power spectral density of displacements for trajectories of different length (control conditions).

The black line corresponds to the data set presented in the paper and the blue line is the new data for 227 trajectories that are 10 min long and acquired with 5 s resolution. As we see from this plot the slope of the PSD for large times (small frequencies) is properly captured by shorter trajectories.

https://doi.org/10.7554/eLife.09347.043

Paragraph two, subheading “Energy-depleted and acidified cells display increased mechanical stability”: It is stated that an increase in crowding implies an increase in elasticity. This is not necessarily true. What is the evidence for this statement?

We agree that this statement is not necessarily correct and have removed it.

In sum, there is insufficient evidence to propose a gel-like state, which implies a physical meshwork. In my opinion, it would be safer to propose a transition into a 'glassy' system (as in Parry et al., 2014). Broadly speaking, the glassy term can applicable to a wide range of out-of-equilibrium soft materials, including gels, foams, interacting or concentrated colloidal suspensions, etc. (Cipelletti and Ramos, Phys Condensed Matter, 2005).

We agree with the reviewer and appreciate the comment, which we think helped us clarify the terminology in the revised paper. To prevent misunderstanding, we now use the term solid-like to refer to the state of the cytoplasm under energy depletion conditions. We also discuss the possibility of a glass-like state in the Discussion section, and we refer to both papers mentioned by the reviewer.

There are also questions about the cell stiffness measurements. If S. pombe cells do indeed maintain their rod shape at low pH even after their cell wall has been removed, it would be an absolutely amazing result. This increases the burden of proof. An alternative and trivial explanation for the result is that the cells still have residual cell wall that contributes to the rod shape maintenance and stiffness of the cell. Perhaps enzymatic digestion of the cell wall is incomplete at low pH, which would not be surprising. To me, the maintenance of the rod shape actually argues that wall-free cells were not generated, casting doubt on the claim of increased stiffness. Complete removal of cell wall must be ascertained. Perhaps (round) spheroplasts could first be generated at neutral pH, and then re-shaped (e.g., to rod-like) by an external force (e.g., their pressure-based microfluidic device might do the trick). The authors could then test whether the new (non-round) shape is maintained after a switch to low pH even when the external force is relieved. Similarly, the authors need to rule out that the increased stiffness observed in energy-depleted/acidified budding yeast cells is not due to the presence of residual cell wall. Stiffness measurements could be performed on spheroplasts created under normal (log phase, neutral pH conditions when the cell wall is known to be completely digested) and then switched to energy-depleted or acidic conditions for stiffness measurements.

We agree with the reviewer that it would be an “absolutely amazing” result, if the cells can keep their shape after cell wall removal. Convincingly showing that the cell wall is removed is therefore paramount, and we should have been more focused on this issue in the paper. Although our previous submission included some supporting data, we now realize that their significance was not emphasized enough. Importantly, other data, which made us confident about complete cell wall removal, was available at the time of submission, but we did not include them in the manuscript. Therefore, in the revised version of the manuscript we now add the following data supporting the conclusion that the cell wall was completely removed in our experiments:

1) The manuscript includes a movie showing that spheroplasted acidified yeast did not have a cell wall (Video 7). In this movie, a spheroplasted rod-shaped cell can be seen, which was re-exposed to media (Figure 5—figure supplement 8). After media addition, the cell rapidly rounds up, which indicates that the cell wall had been removed previously. The cell also starts to divide again, showing that the cells were still alive during the treatment.

2) We measured the activity of the enzymes used for spheroplasting and found that they were similarly active under low and neutral pH conditions. This is in agreement with information provided by the supplier of the enzymes. These data are now included in the manuscript in Figure 5—figure supplement 6.

3) We performed calcofluor white staining of spheroplasted cells under starvation and acidification conditions (see Figure 5—figure supplement 5). These data show that the cell wall was completely removed during our spheroplasting procedure.

4) We performed additional AFM-based indentation measurements on S. cerevisiae where the cell wall had not been removed. These measurements resulted in an apparent elastic (Young’s) modulus of 1.3 ± 0.2 MPa (mean ± SEM; N = 69), while the spheroplasted cells had an apparent elastic modulus of around 1 kPa – three orders of magnitude lower (see Author response image 3 below). A similar difference in stiffness between spheroplasts and intact cells with a rigid cell wall has previously been found in E. coli (Sullivan et al., Ultramicroscopy, 2007). Our values for walled yeast cells are consistent with those reported in the literature for wild-type yeast stiffness (Young’s modulus on the order of 1-2 MPa; see Alsteens et al., Nanotechnology, 2008). These values can drop with cell wall mutant strains by up to a factor of 10 (Young’s modulus in the range of 0.1–1 MPa; see Dague et al., Yeast, 2010), but not by a factor of 1,000. This clearly shows that the cells we analyzed had their cell wall removed. This additional evidence has now been included in Figure 5—figure supplement 1.

5) Finally, we now include a movie showing acidified yeast cells loosing their cell wall (Video 4). The cells in this movie were exposed to spheroplasting enzymes at time point zero. Forty minutes later, rod-shaped cells can be seen slipping out of their cell-wall sheath (indicated by the yellow arrows in Figure 5—figure supplement 8). This directly demonstrates that under the conditions we used, the cell wall was removed.

Author response image 3
AFM based indentation measurements of the apparent elastic modulus of cells with and without cell wall.

In both cases, the cells were indented up to a maximum force of 2 nN and the resulting force-distance curves were analyzed with the Hertz model as described in the material and Methods section. Note the logarithmic scale of the y-axis.

https://doi.org/10.7554/eLife.09347.044

Taken together, our evidence for cell wall removal is very clear. Therefore, in our opinion, all results obtained with yeast spheroplasting experiments can only be explained by assuming a transition of the cytoplasm from a fluid-like to a more solid-like state.

Figure 5—figure supplement 2: The statement about the reduction in cell volume is not convincing at this stage. A reduction in cell volume doesn't mean an increase in crowding, unless the reduction in cytoplasmic volume occurred very rapidly before there can be a change in cytoplasmic composition. Under their experimental conditions (30 min), the composition of the cytoplasm can change. Their microfluidic device allows them to measure the cytoplasmic volume immediately following acidification/energy depletion.

The reviewer is correct to point out that a change in cell volume does not necessarily lead to an increase in molecular crowding. We also agree that the time at which the cell volume measurement is performed is very important. For this reason, we did further experiments with our microfluidic device (RT-DC) to test the time dependence of the cell volume reduction. We found a linear and rather slow reduction in cell volume over a time span of 30 min in response to low pH conditions, as shown below in Author response image 4. We agree with the reviewer that this slow change in cell volume would allow for changes in the composition of the cytoplasm and does therefore not necessarily imply increased molecular crowding. We have rephrased the paragraph and now make clear that a volume decrease can, but does not have to, lead to an increase in crowding.

Author response image 4
The cell size of S. cerevisiae cells in response to low pH adjustment was measured over time with an RT-DC setup.

Cells were loaded into the microfluidic chip of the RT-DC device immediately after exposure to phosphate buffer of pH 6 containing 2 mM DNP and 2% glucose. The median diameter of more than 530 cells was measured for each time-point. We observed a small but significant decrease in cell size over a time span of 30 min. A two sided t-test shows that the slope differs significantly from zero.

https://doi.org/10.7554/eLife.09347.045

Also, what was the osmolality of the external media and buffers used? Can the authors exclude the possibility of an osmotic shock causing the difference in cytoplasmic volume?

We measured the osmolality of the external media and buffers to exclude the possibility that the observed cell volume changes are caused by an osmotic shock. The results of these measurements are now included as a supplementary table (Table S3). We did not observe differences in the osmolality that would explain the observed differences in cytoplasmic volume.

Also, could they clarify what 'relative cell volume' mean? How many cells were measured?

The term “relative cell volume” was used to indicate that the volume of control cells (cells in SD medium containing all nutrients) was normalized to 100%. The volume of cells exposed to low pH or sorbitol is then expressed relative to the volume of these control cells. For each condition the volume of more than 160 cells was measured. This is now mentioned in the figure legend.

Figure 5—figure supplement 4: I am not sure what these FRAP curves are showing. Are they for a single cell or for many cells averaged? More importantly, there is no obvious difference in recovery time between the three conditions, contrary to what is stated. Furthermore, the FRAP data should be analyzed, and diffusion coefficients for GFP should be provided. And why was 2M of sorbitol used when the authors suggest that the difference in volume for DNP+pH5.5 cells is equivalent to the sorbitol 0.8 M condition? FRAP measurements under 0.8M sorbitol would be a fairer comparison.

We agree and have improved and repeated these experiments with a mCherry-GFP fusion protein, and added a description in the Materials and methods section and in the figure legend. We also included measurements for different sorbitol concentrations (0.8 M, 1 M, 1.5 M and 2 M sorbitol) as suggested by the reviewer. The results of these new experiments are now included as Figure 5—figure supplement 5 and show – as correctly stated by the reviewer – that there is no obvious difference in recovery time between control and low pH adjusted cells. Only in cells subjected to strong osmotic compression (1.5 M sorbitol) could we detect a significant difference in the recovery time. The fact that mCherry-GFP diffusion is not impaired under low pH conditions indicates the presence of a fluid phase that allows unimpaired diffusion of mCherry-GFP. This argues against osmotic compression as the mechanism and is in agreement with a previous paper on the glassy nature of the bacterial cytoplasm (Parry et al., 2014). This is now stated more clearly in the text, and further considered in the Discussion section.

What is the evidence for fractional Brownian motion? There are other types of motion where displacement distributions collapse after re-scaling. It is not clear what fractional Brownian motion brings to the story.

Several complementing pieces of evidence point to fractional Brownian motion (fBm) as a relevant model. Rescaling of distributions for different lag times is a necessary step to demonstrate that the displacements can be characterized by a self-similar distribution. The corresponding time scaling is sub-diffusive, which indeed can be accommodated by several common statistical models of sub-diffusion. As we now show in the revised version of the manuscript, the probability distribution function (PDF) of displacements obtained for individual trajectories is Gaussian (see Figure 4—figure supplement 2). The PDF of displacements of all trajectories can be collapsed onto a single Gaussian master curve via an additional rescaling of displacements by the corresponding generalized diffusivities of individual trajectories (see Figure 4—figure supplement 3). Finally, from our data we see that displacements are negatively correlated. These key features (sub-diffusive scaling, Gaussian profile of PDF, no ageing, and negative correlations) narrow down the choice of known (common) models to the model of fBm.

Analysis of the experimental data through the prism of fBm brings several important consequences. We can use the model to interpret the difference between the experimental conditions in a very low-dimensional parameter space: fBm is fully characterized by its scaling exponent and the generalized diffusion constant. Deviations of the PDF of the ensemble of trajectories from a Gaussian distribution give access to the variability of the generalized diffusivities and therefore to the heterogeneity of the particles’ environment. Furthermore, the strength of negative correlations makes a link to the changing elastic properties of the cytoplasm.

Is the starvation-induced acidification observed regardless of the pH of the external medium? Or is it only when the external medium is acidic? If it is the latter, it should be clearly stated.

Cytosolic acidification is only observed when the external medium is acidic, which is now clearly stated in the text and in the figure legends.

Similarly, fission yeast and Dictyostelium are said to undergo cytosolic pH fluctuations in response to energy depletion. Is this true in any medium regardless of its pH or only in acidic media?

We believe that Dictyostelium and fission yeast – like budding yeast – only undergo cytosolic pH fluctuations when the outside medium or buffer is acidic. For these two organisms, we also used DNP to adjust the cytosolic pH, and we found that the particle movement is not impaired in neutral media (see Figure 3D). We therefore assume that energy depletion causes a passive adjustment of the cytosolic pH to the pH of the external medium, as also observed by us in budding yeast. This is in agreement with previous observations that Dictyostelium and S. pombe undergo cytosolic pH fluctuations of a similar magnitude (Gross et al., 1983; Karagiannis and Young, 2001). Furthermore, these organisms preferably live in acidic environments and presumably spend a lot of energy to maintain their cytosolic pH via ATP-dependent proton pumps. Unfortunately, our molecular understanding of these organisms is very limited, and further work is required to learn more about the link between energy depletion and cytosolic pH. However, we would like to point out that acidification through outside protons may not be the only mechanism of acidifying the cytosol. In fact, a previous paper, which reported intracellular pH fluctuations in dormant marine brine shrimp, implicated intracellular stores as proton source (Covi et al., 2005). We now clarify the role of the outside pH in the text, and we added additional explanations and citations to the Discussion section to discuss the broader implications.

How do the MSD curves in Figure 3A and B compare to Figure 1C? If the authors' proposal is correct (reduction in particle dynamics associated with starvation is due to cytosolic acidification), shouldn't we expect the pH 7 condition in Figure 3 to look like the control (untreated/log-phase culture) in Figure 1C?

The experiments in Figure 1C were performed with cells growing in medium, whereas the control MSDs in Figure 3A and 3B were performed with cells treated with DNP at neutral pH (Figure 3A) or with 1 mM sorbic acid (Figure 3B). We consistently saw a difference between the first condition and the latter two conditions, and we can only speculate about the causes. First, both treatments slightly change the cytosolic pH and may also change the concentrations of other ions, which could already have an effect on particle mobility. Second, DNP equilibrates proton gradients over all membranes in the cell. It may therefore perturb the proton gradient over the inner mitochondrial membrane (this is what DNP is normally used for), thus inhibiting mitochondrial ATP production. Thus, cells treated with DNP or low amounts of sorbic acid could already experience some form of energy depletion, which could lead to the partial reduction in MSD that we observe. We clearly state this in the Results section.

Reviewer #2:

1) The use of the term "cytoplamsic freezing" is really questionable. It is common in cell mechanics and active matter to find analogies to inanimate or inactive matter. The "soft glassy rheology" of the cytoskeleton is such an example. This is a good thing to do if there are truly fundamental characteristics shared between the two analogs. In the SGR case, great care was taken to show the analogy. Other examples in cell mechanics have been published to show in detail the analogy between cellular dynamics and some inanimate physical systems. In the submitted manuscript such a level of care is not taken, and the term "freezing" is used without any thought about what are the essential phenomena of freezing, and whether this gelation is anything like true freezing. If the authors believe that what they observe is truly a sol-gel transition associated with assembly of macromolecules in the cytoplasm, they should name the phenomenon accordingly. Their work will be taken more seriously by a broader audience if they let go of this name.

We completely agree and have corrected this. We had used the phrase “cytoplasmic freezing” as a mere analogy to describe our observations of slower particle movements, but did not mean to imply an actual freezing in the real physical meaning of the term. We have therefore changed all appearances of the misleading phrase “cytoplasmic freezing” to the more appropriate term “reduced particle mobility”.

2) A lot of the particle tracking and MSD calculation is performed assuming thermal fluctuations are the driving force. This assumption allows connecting MSDs to material properties. A decent argument is made for this by showing that the depolymerization of actin and microtubules has little effect on their results. I do not know whether this argument is legitimate. Is it really true that no other non-equilibrium or biochemical driving forces can occur in the cytoplasm, other than those driven by the action of ATP on filament bound motors? The work by McIntosh and Schmidt from a few years ago sidestepped this question by comparing active microrheology (manually driven beads with E or B fields) to passive microrheology, and carefully measuring a driving spectrum. The recent paper by Guo and Weitz did something similar. I think this sort of thing would have to be done again here, and the outcome would have to show that the spectrum is indeed thermal, then a thermal analysis could be performed to link MSD to material properties.

We fully agree with the referee that in such an inherently out-of-equilibrium and active medium as the cytoplasm, a reliable measurement of material properties can be achieved only by a combination of active and passive microrheology, which we now also state in the text. At the moment, however, active microrheology in yeast cells is extremely challenging because of their small size and stiff cell wall and cannot be achieved in a reasonable time frame. Therefore, we followed the alternative suggestion of the referee below.

Alternatively, the authors could just keep the same analysis and experiments that they have, and tone down their interpretation. I think that most of their story can be told without claiming that they can infer material properties from MSDs. They can just say that the MSDs say things are more "liquid-like" or "solid-like", and then focus on the general phenomena like the dramatic reduction in motion. The supplementary movies show this beautifully. Several years ago many high-profile papers were published that flippantly linked particle MSDs to material properties, which ultimately damaged the reputations of the authors. I think it is imperative that care is taken to prevent the same thing from occurring, for the sake of the authors and the broader research community.

We thank the reviewer for proposing an alternative way of addressing this issue. We completely rephrased the part of the text when discussing the connection of the particle displacements and material properties. We stress that both active and passive microrheology measurements would be necessary to recover the material properties of the cytoplasm. The assumption of thermal fluctuations is indeed at best approximate for cells under normal conditions. However, it becomes more reasonable for acidified and energy depleted cells, where we expect most of the chemical reactions to shut down and proteins to form assemblies. Still, we now use the example of the near-thermal fluctuations only as a way to illustrate the relative changes in apparent properties of the cytoplasm, hinting at the build-up of elastic response for acidified and energy depleted cells. We hope that this is now adequately worded in the text.

To strengthen their story and focus on a transition in material properties from fluid-like to solid-like, more attention could be paid to the AFM indentation data. They use a Hertz model to fit their data. If they showed the F-d curve on a log-log scale and showed that the indent really scaled like F~d(3/2) (a fit on a lin-lin scale is not sufficient to convince the reader of the right power law), then this would be a first step that they really have a gel. They would also have to show that this could not be attributed to membrane tension, since a balloon filled with water will seem like a solid when indented from the outside. Again, I don't suggest new experiments, but perhaps extracting more compelling information about their current data.

We appreciate this suggestion to glean more information from the existing AFM data in order to support the fluid-to-solid transition in the yeast cytoplasm under acidification. AFM-based indentation experiments on biological cells have most commonly been used to extract an apparent elastic modulus using the Hertz model – and our experiments were set up to do just that. The 2.5-fold increase in apparent elastic modulus (extracted from the fit to the Hertz model) from E = 636 ± 16 Pa at pH 7.4 to E = 1459 ± 59 Pa at pH 6.0 is certainly consistent with an increased solidification. However, as the reviewer rightly points out, this does not prove a transition from fluid- to solid-like as the Hertz model assumes an elastic solid. The fact that we measure an increased apparent elastic modulus could also be due to an increase in viscosity, and a large viscous resistance to deformation, which we wrongly assign to an increased elastic resistance to deformation.

In order to address this ambiguity, we have now further analyzed the existing force-distance curves during the indentation-retraction cycle and extracted the viscosity of the cells following the approach published by the group of Manfred Radmacher (Rebelo et al., 2013; for details see Methods section). We find that the viscosity decreases from η≈ 90 Pa s to η≈ 70 Pa s (see Author response image 5). There is not only no 2.5-fold increase in viscosity, which could otherwise account for the increase in apparent elastic modulus previously reported, the viscosity is actually decreasing. This also means that the actual elastic modulus increases by more than a factor of 2.5 from pH 7.4 to pH 6.0.

Author response image 5
Viscosity of S. cerevisiae spheroplasts at pH 7.4 and pH 6.0 measured by AFM nano-indentation experiments and analyzed according to Rebelo et al.

The viscosity reduces from η= 90 ± 16 Pa s (mean ± SEM; N = 31; median is 81 Pa s) at pH 7.4 to η = 70 ± 14 Pa s (N = 23; median is 59 Pa s) at pH 6.0.

https://doi.org/10.7554/eLife.09347.046

The fluid-to-solid transition can also be expressed in terms of a phase angle. Since the viscosity is proportional to the loss modulus (as a crude estimate E” = ηω, with ω≈ 2π / 2 sec in our case), we can estimate the phase angle to change from about 25° to less than 10°, indicating a solidification. We are hesitant to report these phase angle values in the manuscript, as they contain very crude assumptions. However, the reduction in viscosity, together with the increase in elasticity necessitates a reduction in phase angle and clearly demonstrates that the cells transition from a compliant, more viscous material to a stiffer, more elastic material at lower pH. We have added this new additional analysis to the main manuscript, which now further strengthens our main message.

We also considered the suggestion made by the reviewer of plotting the F-d data in a log-log plot to test for 3/2 slope, which holds for a conical indenter. However, since we used a spherical indenter, which does not have a simple power-law dependence, this analysis did not yield any useful information.

We agree that some of the elastic resistance to deformation is likely contributed by membrane tension, as the reviewer stipulates. However, it is unlikely that all of the increase in stiffness comes from an increase in membrane tension. In fact, other evidence presented in the manuscript suggests a strong contribution from intracellular elastic elements, as evident by the strong increase in elasticity, which we extract from the SPT experiments inside the cytoplasm, and the lack of rounding-up of yeast cells at pH 6.0, when the cell wall is removed (see Figure 5C and 5D, Video 4). In fact, higher membrane tension at low pH should lead to faster rounding up, and not a lack of rounding up. Furthermore, we observed that the cell volume decreases under low pH conditions, which should, if anything, lead to more available membrane and lower membrane tension in acidified yeast. The emerging picture, consistent with all available evidence, is thus more of a balloon filled with a fluid at pH 7.4 and an elastic bulk material at pH 6.0 with membrane tension changes playing a minor role.

3) The correlation analysis is seriously confusing. At first I thought I was looking at correlation functions, and concluded that the results made no sense. After re-reading the description of their analysis several times, I understood the steps taken by the authors to come up with Figure 4C and the associated supplementary figure. I think the authors need to include a few examples of the displacement autocorrelation functions (as a function of time), and show the process to come up with this correlation map as a function of displacement length.

We thank the reviewer for this comment. We reworked the corresponding part of the text discussing negative correlations. First the standard displacement correlation function as a function of the lag time is calculated, and it shows negative correlations (Figure 4—figure supplement 4). Next, we look at the strength of correlations between two consecutive steps. For that we calculate the average projection of the second step on the direction of the initial step and plot it as a function of the initial displacement length (Figure 4C and Figure 4—figure supplement 5). This allows us to test if a large initial fluctuation in a particle’s position is followed by a proportionally larger displacement in the opposing direction at the next instant of time. The (negative) slope of the linear dependence connecting the strength of negative correlation to the length of the initial step varies from 0 (viscous material) to -1/2 (elastic material). For our data we can see that the negative slopes larger in magnitude correspond to energy-depleted or acidified cells. We also make sure that this result does not change, if we vary the lag time for which the displacements are calculated.

[Editors’ note: the author responses to the re-review follow.]

Reviewer #1:

Figure 4A. Why is α measured in the middle of the MSD? The center has fewer statistics, measurements involving the MSD should be made at the beginning where the statistics are the greatest.

Reviewer 1 correctly points out that the statistics are greatest at the beginning of the MSD. However, we are interested in the longer time asymptotic behavior of the system, or a time span during which the displacements of the particles have the same scaling properties. Therefore measuring the slope of the MSD in the middle region (0.2 to 2 s) is a compromise between the long times and still sufficient statistics. Moreover, expanding the fitting region (0.05 to 5 s) does not significantly change the resulting exponents: cells at pH 6.0: 0.77 (previously 0.73), cells at pH 7.4: 0.81 (previously 0.84), energy-depleted cells: 0.63 (previously 0.64), log phase cells: 0.87 (previously 0.88).

Figure 4B and Figure 4—figure supplement 3 and Figure 4—figure supplement 2. I still think that the fractional Brownian motion (fBm) model doesn't bring much to the story (and disrupts the flow), and the evidence is not very strong.

We agree that this very theoretical part affects the overall flow of the manuscript, but we think that it is important to have a coherent view of the experimental data, a consistent comparison, and a characterization of the cytoplasmic changes with a small number of parameters. Moreover, the additional tests suggested by Reviewer 1 further support the consistency of the fBm model with our tracking data (see below). We therefore think the model should be discussed in the manuscript.

Also, a quantitative metric should be used to distinguish Gaussian from non-Gaussian (see equation 1 in Weeks et al., Science, 2000). This can be applied to individual trajectories.

The suggested formula defines the non-Gaussian parameter as (kurtosis/3-1), where kurtosis is the ratio of the fourth and the square of the second moments of distribution. For a Gaussian distribution the value of this parameter is zero. We followed the advice of the reviewer and calculated this parameter for each individual trajectory for each experimental condition. The corresponding scatter plot (now included in the inset in Figure 4—figure supplement 2) indeed shows that they are all close to zero. The corresponding text is added to the Materials and methods section. Note that the non-Gaussian parameter being equal to zero is a necessary but not sufficient condition for a Gaussian distribution. Therefore the CDFs shown in the same figure provide further evidence for the Gaussian behavior of individual trajectories.

The particle size (since it is a mixture) should be considered in the rationale for scaling displacement distributions. What would they see if the distributions were scaled by the relative particle size instead?

This point was already addressed in the manuscript. We rescale the displacements of particles by their generalized diffusivities, which contain effects of both the particle size and the heterogeneous environment. However, as shown in Figure 1—figure supplement 3, even for particles of similar sizes, the diffusivities can vary by two orders of magnitude. Therefore, just rescaling by the particle size does not collapse the data.

It is confusing that Figure 4—figure supplement 5 shows a break in correlation linearity (consistent with caging and cage escape) whereas Figure 4C doesn't show the break (more consistent with purely elastic material). What are the experimental differences and what does it mean to their fBm model?

The experimental conditions in in Figure 4C and Figure 4—figure supplement 5 are indeed different: (WT and energy-depleted) and (pH 7.4 and 6 induced by DNP), respectively. However, the apparent deviation from a linear behavior is in the regime of large displacements with very limited statistics. We would therefore like to refrain from over-interpreting this part of the data.

Figure 4—figure supplement 4. The shape of this correlation plot (a single negative correlation for the first displacement) is precisely what is expected for localization error. They could remove the plot altogether (going back to the support for the fBm model being quite weak). But if they want to show this, the authors should validate that this does not reflect localization error (imperfect localization of a static object will always give this correlation plot). The time scale of the negative correlation should be robust over experiments of different frame rates. If experiments of all frame rates produce anti-correlated displacements over one frame, the result is an artifact.

As discussed in (Weber et al., 2012) one of the ways to test for localization error is to plot the normalized velocity autocorrelation functions for increasing time steps (which are used to determine velocity from the displacement data). For the localization error, the negative correlations will approach zero with increasing lag time. We provide such a plot below and show that the negative correlations do not disappear even for very long lag times. In fact the existing plots in the manuscript already contain similar information: we see that a negative correlation is not just constant but increases as a function of the previous step length, and it remains unchanged for the different lag time used to define the displacements (see Figure 4C for example). We put a corresponding sentence in the Materials and methods subsection discussing displacement correlations.

Author response image 6
Normalized velocity autocorrelation function plotted for different lag times τ from 30 ms to 500 ms, which are used to define the velocity from the displacement data (the lag time change is color-coded from blue to red in steps of 10 ms).

Non-vanishing negative correlations indicate that they are not caused by a localization error. Left and right panels show log phase and energy depleted cells data respectively.

https://doi.org/10.7554/eLife.09347.047
https://doi.org/10.7554/eLife.09347.049

Article and author information

Author details

  1. Matthias Christoph Munder

    Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
    Contribution
    MCM, Performed the pH measurements and analyzed the data, Imaged the formation of assemblies in yeast cells and spores, Devised an image-based assay to measure cell size, Performed the particle tracking experiments, Performed and analyzed the experiments with spheroplasted fission yeast, Performed and analyzed the particle tracking experiments with Dictyostelium, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon 0000-0003-3594-4725
  2. Daniel Midtvedt

    Max Planck Institute for the Physics of Complex Systems, Dresden, Germany
    Contribution
    DM, Analyzed the particle tracking data, Developed a model to describe particle motion, Conception and design, Analysis and interpretation of data
    Competing interests
    The authors declare that no competing interests exist.
  3. Titus Franzmann

    Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
    Contribution
    TF, Performed and analyzed the particle tracking experiments with fission yeast, Performed and analyzed the experiments with spheroplasted fission yeast, Conception and design, Acquisition of data, Analysis and interpretation of data
    Competing interests
    The authors declare that no competing interests exist.
  4. Elisabeth Nüske

    Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
    Contribution
    EN̈ske, Imaged the formation of assemblies in yeast cells and spores, Acquisition of data, Analysis and interpretation of data
    Competing interests
    The authors declare that no competing interests exist.
  5. Oliver Otto

    Biotechnology Center, Technische Universität Dresden, Dresden, Germany
    Contribution
    OO, Performed the RT-DC measurements of spheroplasted yeast, Analyzed RT-DC data, Acquisition of data, Analysis and interpretation of data
    Competing interests
    The authors declare that no competing interests exist.
  6. Maik Herbig

    Biotechnology Center, Technische Universität Dresden, Dresden, Germany
    Contribution
    MH, Analyzed RT-DC data, Acquisition of data, Analysis and interpretation of data
    Competing interests
    The authors declare that no competing interests exist.
  7. Elke Ulbricht

    Biotechnology Center, Technische Universität Dresden, Dresden, Germany
    Contribution
    EU, Conducted the AFM measurements of spheroplasted yeast, Analyzed AFM data, Acquisition of data, Analysis and interpretation of data
    Competing interests
    The authors declare that no competing interests exist.
  8. Paul Müller

    Biotechnology Center, Technische Universität Dresden, Dresden, Germany
    Contribution
    PM, Analyzed AFM data, Acquisition of data, Analysis and interpretation of data
    Competing interests
    The authors declare that no competing interests exist.
  9. Anna Taubenberger

    Biotechnology Center, Technische Universität Dresden, Dresden, Germany
    Contribution
    AT, Analyzed AFM data, Acquisition of data, Analysis and interpretation of data
    Competing interests
    The authors declare that no competing interests exist.
  10. Shovamayee Maharana

    Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
    Contribution
    SM, Devised an image-based assay to measure cell size, Acquisition of data, Analysis and interpretation of data
    Competing interests
    The authors declare that no competing interests exist.
  11. Liliana Malinovska

    Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
    Contribution
    LM, Performed and analyzed the particle tracking experiments with Dictyostelium, Acquisition of data, Analysis and interpretation of data
    Competing interests
    The authors declare that no competing interests exist.
  12. Doris Richter

    Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
    Contribution
    DR, Generated strains and constructs, Acquisition of data, Analysis and interpretation of data
    Competing interests
    The authors declare that no competing interests exist.
  13. Jochen Guck

    Biotechnology Center, Technische Universität Dresden, Dresden, Germany
    Contribution
    JG, Analyzed AFM data, Analyzed RT-DC data, Conception and design, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon 0000-0002-1453-6119
  14. Vasily Zaburdaev

    Max Planck Institute for the Physics of Complex Systems, Dresden, Germany
    Contribution
    VZ, Analyzed the particle tracking data, Developed a model to describe particle motion, Conception and design, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
  15. Simon Alberti

    Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
    Contribution
    SA, Conception and design, Analysis and interpretation of data, Drafting or revising the article
    For correspondence
    alberti@mpi-cbg.de
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon 0000-0003-4017-6505

Funding

Max-Planck-Gesellschaft (Core Funding)

  • Daniel Midtvedt
  • Titus Franzmann
  • Doris Richter
  • Vasily Zaburdaev
  • Simon Alberti

Dresden International Graduate School for Biomedicine and Bioengineering (Graduate Student Fellowship)

  • Matthias Christoph Munder

Dresden International Graduate School for Biomedicine and Bioengineering (Sprinboard-to-Postdoc-Fellowship)

  • Matthias Christoph Munder

Alexander von Humboldt-Stiftung (Alexander von Humboldt Professorship)

  • Oliver Otto
  • Maik Herbig
  • Elke Ulbricht
  • Paul Müller
  • Anna Taubenberger

Alexander von Humboldt-Stiftung (Postdoc Fellowship)

  • Shovamayee Maharana

Deutsche Forschungsgemeinschaft (Reserach Grant, AL 1061/5-1)

  • Elisabeth Nüske
  • Simon Alberti

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank several members of the MPI-CBG and Christoph Weber from the MPI-PKS for critical reading of the manuscript. We are grateful to Eli Barkai and Daniela Frömberg for helpful discussions. We thank Cammie Lesser for the GFP-μNS plasmid. The light microscopy facility of the MPI-CBG is acknowledged for expert technical assistance. MM was supported by a DIGS-BB doctoral and a springboard-to-postdoc fellowship. SM was supported by a postdoctoral fellowship by the Alexander von Humboldt Foundation. JG was supported by an Alexander von Humboldt Professorship by the Alexander von Humboldt Foundation. We acknowledge founding by the Max Planck Society and the German Research Foundation (DFG, AL 1061/5-1).

Reviewing Editor

  1. Robert H Singer, Reviewing Editor, Albert Einstein College of Medicine, United States

Publication history

  1. Received: June 11, 2015
  2. Accepted: February 13, 2016
  3. Version of Record published: March 22, 2016 (version 1)

Copyright

© 2016, Munder et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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