Macromolecular crowding has a profound impact on reaction rates and the physical properties of the cell interior, but the mechanisms that regulate crowding are poorly understood. We developed genetically encoded multimeric nanoparticles (GEMs) to dissect these mechanisms. GEMs are homomultimeric scaffolds fused to a fluorescent protein that self-assemble into bright, stable particles of defined size and shape. By combining tracking of GEMs with genetic and pharmacological approaches, we discovered that the mTORC1 pathway can modulate the effective diffusion coefficient of particles ≥20 nm in diameter more than 2-fold by tuning ribosome concentration, without any discernable effect on the motion of molecules ≤5 nm. This change in ribosome concentration affected phase separation both in vitro and in vivo. Together, these results establish a role for mTORC1 in controlling both the mesoscale biophysical properties of the cytoplasm and biomolecular condensation.

In order to address these issues, we developed genetically encoded multimeric (GEM) nanoparticles (henceforth GEMs), which are bright tracer particles of a defined shape and size. GEMs can serve as a standard microrheological tool across a broad range of organisms; in this study, we used GEMs in S. cerevisiae and human cell lines. By using GEMs from a different kingdom than the organism of study, we make it far less likely that the particles will be affected by specific interactions. With this technology in hand, we screened for mechanisms that regulate the biophysical properties of cells. We found that the mTORC1 kinase controls ribosome abundance through a combination of cell volume control, ribosome biogenesis, and autophagy. In situ cryo-electron tomography (cryo-ET) of the native cellular environment revealed that inhibition of mTORC1 nearly halves the cytosolic ribosome concentration in S. cerevisiae. As ribosomes account for ∼20% of the total cytosolic volume, modulation of their concentration has a dramatic effect on the biophysical properties of the cell. This modulation is significant: inhibition of mTORC1 can double the effective diffusion coefficient of particles that are ≥20 nm in diameter. Using the phenomenological Doolittle equation, which relates the diffusion of a tracer particle to the fraction of crowding, we were able to predict changes in the effective diffusion coefficient as a function of ribosome concentration in both budding yeast (S. cerevisiae) and human cells (HEK293). Finally, we found that changes in macromolecular crowding downstream of mTORC1 tune phase separation in both yeast and human cells, providing a direct link between in vivo crowding and phase separation.

One method to study macromolecular crowding and other cellular biophysical properties is to observe the motion of tracer particles as they move within the cell. This approach, known as passive microrheology, can be used to infer the viscosity, elasticity, structure, and dynamics of the surrounding material from the characteristic motion of these tracer particles (). Various groups have studied the motion of non-biological nanoparticles in cells (), but these techniques are labor-intensive and typically perturb the cell. For example, microinjection dilutes the cytoplasm, disrupts the cell membrane and cortex, and is not feasible in organisms with a cell wall, such as budding yeast. An alternative approach is to track the motion of endogenous structures, such as mRNA molecules tagged with specific loops that interact with loop-binding proteins tagged with fluorescent proteins (). However, if the motion of an endogenous molecule is affected by a perturbation, it is difficult to know if these changes in motion are due to impacts on the biophysical properties of the cell or caused by direct regulation of the tracer particle.

Phase separation is a key example of when the regulation of macromolecular crowding is crucial (). Proteins that have a stronger propensity to self-associate than to interact with the solvent can undergo a phase transition, where a large number of interacting proteins coalesce into a condensed liquid phase that is separate from the surrounding bulk liquid solvent (). These biological condensates are increasingly observed in diverse fields including cell division (), development (), cancer (), neurodegenerative disease (), T cell activation (), and even photosynthesis (). Macromolecular crowding tunes phase separation in vitro (). However, the physiological mechanisms that control crowding within the cell and the effects of crowding on phase separation in vivo remain obscure.

Molecular crowding is crucial for the efficient function of biological systems (). If Xenopus egg extracts are diluted by more than a few percent, fundamental biological processes such as mitosis and DNA replication fail (). High concentrations of crowding agents entropically favor molecular association events, thereby accelerating molecular reactions (). However, excessive crowding can also dramatically decrease molecular motion, just as the loss of a lane on a freeway can transform smooth traffic flow to instant gridlock (). The impact of crowding depends strongly on particle size: molecules with sizes equivalent to or larger than the dominant crowding agent will be more affected than small particles. These smaller particles can move more easily through the gaps between crowding particles. Thus, changes in molecular crowding can have profound effects on cell physiology and may affect some pathways disproportionately depending on the sizes of the molecules or complexes involved.

To avoid unknown effects that rapamycin may have in parallel to changes in ribosome concentration, we used the yeast deletion strains that we had previously determined to affect molecular crowding. For each mutant, we quantified ribosome concentration, the total concentration of SUMO-SIM, and the probability of finding a SUMO-SIMdroplet in a cell. Interestingly, we found very little correlation between phase separation and the concentration of SUMO-SIM. We also saw little correlation between SUMO-SIMconcentration and ribosome concentration ( Figures S7 G and S7H). In contrast, there was a strong correlation (r= 0.96) between droplet probability and ribosome concentration in this analysis ( Figure 7 D). Taken together, these data suggest that ribosomes act as macromolecular crowders that tune phase separation.

Next, we expressed an in-frame fusion of SUMOand SIM(SUMO-SIM) in yeast and HEK293 cells to study the effects of macromolecular crowding on phase separation in vivo. Inhibition of mTORC1 for 2 hr led to an 80% and 50% decrease in SUMO-SIMdroplet area in yeast and human HEK293 cells, respectively ( Figure 7 B). We were able to partially recover phase separation in rapamycin-treated cells by using an acute osmotic shock that reduced cell volume to an extent that restored ribosome concentrations to control levels ( Figure 7 C, orange cross-hatched bars). The degree of phase separation is not completely recovered by osmotic compression, perhaps because this process cannot reach steady state before cells adapt or because mTORC1 inhibition has effects in addition to crowding.

We assessed the effects of ribosomes on the phase separation of SUMOand SIM. Beginning in vitro, we added ribosomes purified from Escherichia coli over a biologically relevant concentration range determined from our cryo-ET experiments. We observed that the concentration of SUMOand SIMthat partitioned into the condensed liquid droplet phase (partition coefficient) increased as ribosome concentrations increased. Indeed, the partition coefficient was >50% higher at 23 μM ribosomes (the in vivo concentration in normal conditions) than at 13 μM (the in vivo concentrations after rapamycin treatment) ( Figure 7 A).

(C) Quantification of total area of phase-separated droplets in control cells (blue), cells treated with rapamycin (orange), and cells treated with rapamycin followed with a hyperosmotic shock with 1.5 M (yeast cells) or 0.1 M (human cells) sorbitol (orange bars with white cross hatches).

(B) An in-frame fusion of SUMO 10 -SIM 6 -GFP was expressed in budding yeast (S. cerevisiae W303) and HEK293 cells. Micrographs of control cells (DMSO) and cells treated with rapamycin for 2 hr.

(A) A homodecamer repeat of SUMO (SUMO 10 ) was mixed with a homohexamer repeat SUMO interaction motif peptide (SIM 6 ) to achieve equimolar concentrations of each monomer (60 μM). SUMO 10 + SIM 6 was kept at constant concentration and incubated with an increasing concentration of fully assembled 70S ribosomes (purified from E. coli). There was a >50% increase in the partition coefficient of SUMO 10 + SIM 6 when ribosome concentration was increased from 13 μM (equivalent to yeast treated with rapamycin) to 23 μM (the concentration of ribosomes in logarithmically growing yeast cells).

When multivalent proteins exceed a critical nucleation concentration, they can condense to form a phase separated liquid droplet. Phase separation is tuned by multiple physicochemical effects including the association and dissociation constants of interaction domains, the strength of the interaction of each molecule with the solvent, depletion attraction effects that can entropically favor condensation (), and linker solvation effects (). These two latter effects depend on macromolecular crowding. Because our results strongly linked ribosomes to cytoplasmic crowding, we hypothesized that ribosome concentration tunes phase separation. To test this idea, we took advantage of a synthetic system that forms liquid droplets both in vitro and in vivo. This system is comprised of ten repeats of the small ubiquitin-like modifier domain (SUMO) and six repeats of SUMO interaction motif (SIM). The condensation of SUMOand SIMhas been proven to be a reliable model for phase separation ().

We also experimentally determined the prefactor ζ for the endogenous GFA1 messenger ribonucleoprotein complex (mRNP) tagged with the PP7-GFP system. These particles are ∼100 nm in diameter. Our model accurately predicted their effective diffusion coefficient as a function of ribosome concentration ( Figure S7 C). Therefore, our results suggest that ribosome concentration is a crucial determinant of the mesoscale biophysical properties of the cytosol.

Once we had determined the parameters ϕ/ϕand ζ, we were able to predict the effective diffusion coefficient of GEMs as a function of ribosome concentration (see Equation 12 in the STAR Methods ). All parameters were experimentally determined with no data fitting. We compared our prediction to experimentally determined ribosome concentrations ( Figures 5 and S7 D–S7F). Our model was able to predict the effective diffusion coefficient of GEMs for all mutants and perturbations over a wide range of ribosome concentrations in both yeast and mammalian cells.

To determine the degree of crowding in the cytoplasm under normal conditions (ϕ), we manipulated crowding by rapidly changing cell volume through osmotic shock and measured the apparent diffusion coefficient (see Figures S7 A and S7B). We found that ϕ/ϕis smaller for HEK293 cells (0.35 ± 0.13) than for S. cerevisiae (0.48 ± 0.04), confirming our expectation that HEK293 cells are less crowded than yeast. Note that this ratio means that the cytoplasm of a cell is not close to a glass transition, where ϕ/ϕwould be ∼1. The parameter ζ is roughly equivalent in both species, perhaps suggesting that 40nm-GEMs have similar interactions with the human and yeast cytosol, a result most easily explained by GEMs having very little specific interaction with their local environment. This concordance further supports the use of GEMs as a microrheological standard across organisms.

(D–F) For various chemical and genetic conditions, we extracted total nucleic acid by neutral phenol (see STAR Methods ) and ran the extract on an agarose gel. The gel separates the DNA band (used as a proxy for the amount of cellular material extracted), mRNA, rRNA and tRNA. To assess the relative amount of rRNA, used as a proxy for ribosome abundance, we normalized the band of rRNA to mRNA and subsequently to DNA to get the quantity per cell. This value was determined for each condition and normalized to the control: this gives us the relative change in ribosome quantity in HEK293 drug and siRNA treatments (E) and yeast mutants (F).

(C) ϕ 0 / ϕ m and the ζ parameter for cells containing mRNP (inset) were calibrated in order to predict how this particle’s diffusion coefficient would be affected by a change in ribosome concentration caused by rapamycin treatment.

(B) We performed the same osmotic stress experiment on mammalian cells and initially measured different parameters (). Osmotic stress is known to strongly affect the actin cytoskeleton in mammalian cells, which was confirmed when we depolymerized actin with Latrunculin A concurrent with the osmotic stress (): theinteraction parameter of the GEMs with the environment increased. When the actin cytoskeleton was stabilized with the JLY cocktail (), we found that the two parameters of the model were closer to the yeast values:is very similar to yeast, suggesting that the interactions of the GEMs with the microenvironment is the same, whileis lower, suggesting that mammalian cells are less crowded.

(A) After performing hyper- and hypo-osmotic shocks to perturb yeast cell volume and then immediately assessing the diffusion coefficient for 40nm-GEMs, we fit the model ( Equation 10 ) for S. cerevisiae and found that it is in very good agreement with our data, suggesting that the Doolittle equation reasonably describes the dependence of diffusion coefficient on volume fraction of crowding agent (= 0.85), enabling the determination of parameters

Validation of the Doolittle Equation and Determination of Parameters Using Instantaneous Volume Change through Osmotic Stress, TSC Western Blot, and 18S rRNA Quantification, Related to Figures 4 6 , and 7

To further investigate how ribosome concentration controls the mesoscale viscosity of the cytosol, we developed a physical model based on the phenomenological Doolittle equation () (see Equation 5 in the STAR Methods ). Originally, the Doolittle equation was used to describe the viscosity of liquid polymer melts as a function of polymer density (i.e., polymer crowding). Later,derived the equation theoretically to describe the viscosity of hard-sphere colloids. Thus, the equation has been successfully used to fit the viscosity of a range of materials (). The Doolittle equation relates crowding to diffusion using an exponential function of the concentration of crowder (ϕ), maximum possible crowding (ϕ), and a prefactor ζ related to the strength of interaction of the tracer particle with its surrounding microenvironment ( Figure 6 A; Equation 5 in the STAR Methods ).

(B and C) A model based on the Doolittle equation to relate Dto the concentration of ribosomes,, parameterized empirically with no parameter fitting, accurately predicts the diffusion coefficient of 40 nm-GEMs in both (B) yeast and (C) HEK293 cells as a function of the concentration of ribosomes (measured by quantification of a total extracted nucleic acids, see Figures S7 E–S7G). Median coefficients of diffusion are normalized to wild-type conditions on the day the data were acquired. Prediction is shown as a dashed black line with gray confidence intervals based on the error associated with the estimation of ζ and φ/φ

(A) The phenomenological Doolittle equation describes the effective diffusion coefficient of particles as a function of excluded volume, the volume of the cytoplasm occupied by macromolecules.

Studies in Newtonian flow. III. The dependence of the viscosity of liquids on molecular weight and free space (in homologous series).

To further investigate the control of ribosome concentration by mTORC1, we used in situ cryo-ET to directly visualize ribosomes. Briefly, we thinned vitreous frozen yeast cells by focused ion beam (FIB) milling () and then performed in situ cryo-ET () to produce three-dimensional images of the native cellular environment at molecular resolution ( Figures 5 A, 5B, S3 S5 , and S6 Videos S3 and S4 ). Template matching enabled us identify ribosomes within the cellular volumes with high sensitivity ( Figure S4 ). Subsequent subtomogram averaging produced in situ structures of the ∼30 nm ribosomes and 40nm-GEMs at 11.5 Å and 26.3 Å resolutions, respectively ( Figures 5 C, S3 , and S4 ). In W303 yeast cells undergoing log phase growth, the concentration of ribosomes in the cytoplasm was ∼14,000 ribosomes/μm(23 μM), whereas this concentration decreased almost 2-fold to ∼8,000 ribosomes/μm(13 μM) when cells were treated with rapamycin for 2 hr ( Figure 5 D). This corresponds to a drop from ribosomes occupying ∼20% to ∼12% of the cytosolic volume.

Detected ribosomes are depicted in blue, GEMs in orange and the non-cytoplasmic volume that was excluded from the analysis in gray. The example tomogram from Figure 5 is not pictured here.

Detected ribosomes are depicted in blue, GEMs in orange and the non-cytoplasmic volume that was excluded from the analysis in gray. The example tomogram from Figure 5 and the 14tomogram are not pictured here.

(F) Enlarged view of the region indicated with a box in (D), comparing the ribosome structures from rapamycin-treated (upper panel) and control (lower panel) cells. The most significant density difference (red mesh, threshold level of 6 sigma) between both ribosome structures co-localizes with the P-site tRNA, which is resolved in the control but not the rapamycin-treated.

(E) FSC between subtomogram averages derived from two independent halves of the data (gold standard) for control (blue) and rapamycin- treated (orange) cells. Resolution was determined to 11.5 Å in both cases using the FSC = 0.143 resolution criterion.

(C) Distribution of cross-correlation coefficients for the 5000 highest-scoring peaks, which were extracted from the cross-correlation volume depicted in (B) while imposing a minimal Euclidean distance of 18.9 nm (9 voxels) between peaks. A Gaussian function (red) was fit to the distribution of coefficients corresponding to true positives. The integral of the Gaussian function corresponds to the number of ribosomes included in the cytosolic volume.

(B) Example cross correlation function (yellow) obtained from template matching against the de novo ribosome structure, superposed with the non-cytosolic cellular volume (gray) excluded from the analysis. Peaks in the cross-correlation function (yellow spots) indicate putative ribosome positions.

(A) 500 manually selected ribosome-containing subtomograms were iteratively aligned with a sphere as a starting structure (left). Within 12 iterations, the averaged density converged to a yeast 80S ribosome (right) that was subsequently used as a purely data-driven de novo template for correlation-based ribosome localization (template matching) in the tomograms.

(C) Subtomogram averages of the 40nm-GEM nanoparticles and ∼30 nm ribosomes from within the cellular volumes, shown in relative proportion.

(B) Rapamycin-treated cell. Left: slice through a representative cryo-electron tomogram of a FIB-milled yeast cell. The cell wall (CW), plasma membrane (PM), rough endoplasmic reticulum (rER), lipid droplets (LD), mitochondria (M), Golgi apparatus (G), vacuole (V), aggregates (Agg), and one example GEM nanoparticle are indicated. Right: 3D segmentation of the same tomogram showing ribosomes (cyan) and GEMs (orange). The non-cytosolic volume is gray.

Finally, we increased mTORC1 activity by siRNA-mediated knockdown of the mTORC1 inhibitor TSC1 (). This treatment led to a decrease in basal diffusion ( Figures 4 B, left, and S2 J). Thus, after screening over 40 mutants and drug treatments, we found that the conditions that most strongly affected the baseline of GEM diffusion and/or decreased the effect of rapamycin treatment fell into two general classes: ribosome biogenesis and autophagy. Together, these data suggest that mTORC1 controls macromolecular crowding by tuning ribosome concentration ( Figure 4 C).

Stimulation of autophagy using the SMER28 compound, thereby reducing ribosome concentration, led to an increase in the basal diffusion of 40nm-GEMs ( Figure 4 B, left) and strongly suppressed the effect of rapamycin ( Figure 4 B, right). In contrast, decreasing autophagy with Wortmannin, which is predicted to increase ribosome concentration, led to decreased basal diffusion ( Figure 4 B, left). This perturbation also led to a partial loss of the rapamycin effect ( Figure 4 B, right).

Inhibition of ribosome production using the small molecules BMH-21 or CX5461 reduced the rapamycin effect ( Figure 4 B, right). However, the basal diffusion coefficient only increased in CX5461 treatment ( Figure 4 B, left). We speculate that the failure of BMH-21 to impact GEM motion could be due to off-target effects of this drug, which could lead to compensatory effects in the basal biophysical properties of the cytoplasm. Nevertheless, these pharmacological perturbations suggest that control of rDNA transcription is part of the mechanism by which mTORC1 inhibition decreases the viscosity of mammalian cells.

Next, we sought to determine whether the mechanisms that we identified in S. cerevisiae would also hold true in mammalian cells. To this end, we employed HEK293 cells stably transduced or transfected with 40nm-GEMs and used pharmacological perturbations and small interfering RNA (siRNA) to test whether ribosome concentration was important in setting the biophysical properties of mammalian cells at the 40 nm length scale.

Ribosomes are usually quite stable, but starvation conditions can drive autophagy and ribophagy to accelerate ribosome degradation, especially when mTORC1 is inhibited (). This starvation response is thought to scavenge macromolecules and organelles to recycle cellular building blocks, but reduction in the concentration of ribosomes has also been proposed as a function for these pathways (). In accordance with this latter idea, mutations in the autophagy genes ATG1, ATG13, and ATG17 and the ribophagy gene RIM15 () all caused a significant abrogation of the rapamycin effect ( Figure 4 A, right).

We tested and rejected several hypotheses for the possible mechanism through which mTORC1 signaling might affect cytosolic biophysics ( Table S1 ). Eventually, we found that deletion of the SFP1 gene, which encodes a transcription factor involved in ribosomal RNA biogenesis (), increased the effective diffusion coefficient of 40nm-GEMs even more than rapamycin treatment ( Figure 4 A, left). Furthermore, the sfp1Δ strain led to a complete loss of the rapamycin effect ( Figure 4 A, right). The results implicated ribosome biogenesis as a key mechanism in the control of cellular rheology.

In our S. cerevisiae experiments, we could collect thousands of traces within a few seconds. Because every cell expressed GEMs, there was no time-delay associated with finding cells, and no laborious manipulations like microinjection. These advantages enabled us to use GEMs in a candidate-based genetic screen ( Table S1 ). In the absence of the FPR1 gene (encoding FKBP12), rapamycin cannot inhibit mTORC1 (). There was no detectable effect of rapamycin on the fpr1Δ strain ( Figure 4 A), indicating that rapamycin was affecting rheology by a canonical mechanism. SIT4 encodes a subunit of the PP2A phosphatase required for a major signaling branch downstream of mTORC1 (). Addition of rapamycin to sit4Δ cells had little to no effect on particle diffusion, suggesting that the changes in physical properties of the cytoplasm were downstream of this gene. Together, these results validated the use of 40nm-GEMs in genetic screens and constrained our genetic screen to the PP2A-dependent branch of mTORC1-signaling.

(A) Selected mutants from a candidate screen in S. cerevisiae. The change in the baseline effective diffusion coefficients of 40nm-GEMs (left, blue) is plotted for each mutant, along with the magnitude of the rapamycin effect normalized to the effect in wild-type cells (ε, right, orange; 0 = no rapamycin effect, 1 = same effect as wild-type).

Another plausible hypothesis is that mTORC1 might alter the dynamics or structure of the cytoskeleton. We treated yeast cells with Latrunculin A to depolymerize the actin cytoskeleton ( Figure S2 H) and found that, while the basal diffusion of 40nm-GEMs decreased, there was still a strong increase in Dupon rapamycin treatment. We also arrested actin dynamics in HEK293 cells using the JLY cocktail () ( Figure S2 F). Similar to yeast, perturbation of actin dynamics decreased basal GEM diffusion, but rapamycin still had a strong effect ( Figure S2 H). These results suggest that the actin cytoskeleton contributes substantially to the viscosity of both the mammalian and yeast cytoplasm, but that mTORC1 does not modulate mesoscale rheology through actin-dependent effects. We then used nocodazole to depolymerize microtubules. There was a slight decrease in viscosity in both yeast and mammalian cells, but there was not a strong influence on the relative effect of rapamycin ( Figures S2 G and S2H). Thus, actin and microtubules play an important role in defining the mesoscale properties of the cytosol, but do not appear to be the primary mechanistic explanation for the regulation of rheology by mTORC1.

Protein translation is regulated by mTORC1: when nutrients and growth factors are present, cells enter an anabolic state and protein translation is upregulated in an mTORC1-dependent manner. Inhibition of mTORC1 with rapamycin leads to rapid inhibition of translation. Therefore, we tested whether decreases in translation could explain the observed changes in the effective diffusion coefficients of 40nm-GEMs. To investigate this idea, we stalled translation by addition of 1 μM cycloheximide. The median half-life of yeast proteins is ∼40 min under these conditions (). The motion of 40nm-GEMs was neither affected during acute cycloheximide treatment, nor after 180 min of treatment ( Figure S2 F). These results suggest that neither translational inhibition nor protein degradation explain our observations.

Rapamycin treatment arrests cells in the G1 phase of the cell cycle. Therefore, we hypothesized that the increase in the effective diffusion coefficients of 40nm-GEMs might be due to cell-cycle regulation of rheology. To test this idea, we inhibited the cdc28-as1 allele of budding yeast cyclin-dependent kinase 1 (Cdk1) with 10 μM 1-NM-PP1 (). Cell division arrested in G1 and cell volume continued to increase, but no changes were observed in the motion of 40nm-GEMs ( Figures S2 F and S2G). Thus, cell-cycle regulation does not appear to explain the observed biophysical effects of mTORC1 inhibition.

Changes in Cell Cycle, Translation, and the Cytoskeleton Do Not Account for the Effects of mTORC1 on the Motion of 40nm-GEMs

To probe rheology at shorter length scales, we used fluorescence correlation spectroscopy to calculate the effective diffusion of a double-GFP molecule, which has a hydrodynamic radius of around 5 nm. The diffusion of this smaller protein was unaffected by the addition of rapamycin ( Figure 3 D; Table S1 ). Thus, mTORC1 inhibition increases the diffusion coefficients of particles at or above the typical size of multimeric protein complexes, but particles that are the typical size of monomeric proteins are unaffected ( Figure 3 E).

The change in effective diffusion of 40nm-GEMs was clear, but cellular rheology can vary considerably between particles of different sizes. Therefore, we studied other particles to check the generality and length scale dependence of the changes in microrheology downstream of mTORC1 signaling. First, we repeated our experiments with 20nm-GEMs and found that their diffusion also increased upon mTORC1 inhibition ( Figure 3 A). We also saw an increase in the diffusion coefficients of larger structures, including endogenous GFA1 mRNP tagged with the PP7-GFP system () and GFP-μNS particles ( Figures 3 B and 3C). These structures are ∼100 nm and ∼200 nm in diameter, respectively. Thus, mTORC1 modulates the effective diffusion coefficient of particles in the mesoscale, ranging from 20 nm to 200 nm in diameter.

(E) Effect of rapamycin on the effective diffusion coefficients of endogenous molecules and tracer particles of various sizes. Indicated, the −2 power-law scaling of diffusion coefficient as a function of diameter, which does not conform to Stokes-Einstein predictions. In all cases, control conditions are shown in blue and rapamycin in orange.

(D) Fluorescence correlation spectroscopy (FCS) autocorrelation function for a tandem GFP dimer (Stokes radius of ∼5 nm). There is no significant difference between DMSO and rapamycin (D DMSO = 13.3 ± 1.3 μm 2 /s and D rapamycin = 12.2 ± 2.8 μm 2 /s).

The mechanistic target of rapamycin complex (mTORC1) is the major amino acid sensor in eukaryotes (). Therefore, we hypothesized that mTORC1 signaling might cause the observed changes in cytoplasmic rheology. mTORC1 can be inhibited by the macrolide antibiotic rapamycin. Consistent with our hypothesis, 40-nm GEMs displayed increased mobility when mTORC1 was inhibited with rapamycin in both S. cerevisiae and HEK293 cells ( Figures 2 D and 2E; Videos S1 and S2 ). This increase in effective diffusion reached full effect after 2 and 3 hr of rapamycin treatment in yeast and HEK293 cells, respectively ( Figures S2 D and S2E). Changes in the distribution of diffusion coefficients were highly significant (p < 1 × 10; Kolmogorov-Smirnov test, Figure 2 F). Importantly, in situ cryo-electron tomography (cryo-ET) showed that 40nm-GEMs did not change size after rapamycin treatment ( Figure S3 ). These results suggest that mTORC1 controls the biophysical properties of the cytosol at the 40-nm length scale in both yeast and mammalian cells.

(D) Gallery of individual GEM particles from control (lower row) and rapamycin-treated (upper row) cells. Each image corresponds to a central tomogram slice through the GEM particle. The amount of cargo within the GEM lumen varies.

(C) FSC between the two resolution-limited subtomogram averages obtained for control and rapamycin-treated cells. High correlation (FSC > 0.5) within the trustworthy resolution range suggests that GEM structures under both conditions are identical.

(B) Fourier shell correlation (FSC) between subtomogram averages derived from two independent halves of the data (gold standard) for control (blue) and rapamycin-treated (orange) cells. Resolution was determined to 26.3 A° in both cases using the FSC = 0.143 resolution criterion.

(A) GEM subtomogram averages obtained for DMSO-treated control (left panels) and rapamycin treated (right panels) cells filtered to 26.3 A° resolution. In the central two panels, averages have been sliced open to show the interiors.

In initial experiments in yeast, we observed that cell culture conditions changed the apparent diffusion coefficients of 40nm-GEMs. When yeast cultures approached saturation, the effective diffusion of GEMs increased ( Figure S2 B). By specifically depleting nitrogen, glucose, and amino acids, the main components of synthetic complete growth medium, we found that both nitrogen and glucose starvation caused a slight decrease in the apparent diffusion of 40nm-GEMs, but this decrease was subtle compared to previous reports with larger particles () ( Figure S2 D). In contrast, we found that an increase in effective diffusion occurred in response to amino acid depletion ( Figure S2 C).

We expressed 40nm-GEMs in the budding yeast S. cerevisiae and an adenovirus transformed human embryonic kidney cell line (HEK293) ( Figures 2 A and 2B; Videos S1 and S2 ). 40nm-GEMs are bright enough to allow single particle tracking at 10-ms frame rates ( Figure 2 C; STAR Methods ). The duration of tracking was limited to the amount of time a particle remained in a single focal plane, as the required acquisition rate did not permit the collection of z stacks; the median track length was 35 frames, corresponding to 350 ms of imaging ( Figure S1 C). We compared thousands of individual traces to extract the effective coefficient of diffusion, D, at short timescales (100 ms). GEM motion differs between the two biological systems: 40nm-GEMs have a median effective diffusion coefficient of ∼0.3 μmin yeast and ∼0.5 μmin mammalian cells ( Figures 2 D and 2E). These estimates are in good agreement with expectations from the literature (), further supporting the use of GEMs as microrheological standards. Using time and ensemble-averaging, we inspected the mean-squared displacement (MSD) curves at longer timescales and found that 40nm-GEMs were subdiffusive (inset, Figures 2 D, 2E, and S2 A) with an anomalous exponent of ∼0.8 in yeast and ∼0.9 in HEK293 cells. This subdiffusive motion could be due to local caging within a crowded environment and/or interactions between the tracer particle and the environment (). However, the anomalous exponent did not change significantly in most of our perturbation experiments ( Figure S2 A), so we focused on the effective diffusion coefficient as our main metric to report on cytosolic rheology at the mesoscale.

(J) TSC1 was targeted using Silencer Select siRNA (Thermo Fisher). Knockdown was validated by western blot for Hamartin/TSC1 with a tubulin control using standard techniques (see STAR Methods ).

(H and I) Actin and microtubule perturbations alter the diffusion of 40nm-GEMs in yeast and mammalian cells but do not abolish the effect of rapamycin. Error bars represent mean ± SEM.

(F and G) Change in coefficient of diffusion (F) and normalized volume (G) over time for rapamycin treatment (orange), cyclohexamide treatment (green), and cell cycle inhibition of the conditional mutant cdc28-as with NMPP1 (blue).

(C) Effects of 2 hours of amino acid depletion, 30 minutes of carbon starvation and 30 minutes of nitrogen starvation on the diffusion coefficeint of 40nm-GEMs in BY4741 cells.

(A) The anomalous exponent, a measure of subdiffusive motion, is similar for rapamycin and DMSO treatment in both yeast and HEK293 cells (according a Student’s t test) but is higher in HEK293 compared to yeast, indicating that mammalian cells are less subdiffusive (p = 0.03).

Subdiffusive Motion of GEMs, the Time Course of Rapamycin Treatment, and the Effects of Culture Conditions, Cell-Cycle Inhibition, and Cytoskeletal Perturbation on Diffusion, Related to Figure 2

The 20nm-GEMs and 40nm-GEMs are in the size range of multi-subunit assemblies such as ribosomes, proteasomes, and chromatin remodeling complexes ( Figure 1 D), allowing us to investigate the mesoscale microrheological environment experienced by these complexes. Thus, these biologically orthogonal nanoparticles probe the biophysical properties of the cell at a length scale that was previously challenging to study.

Using negative stain electron microscopy, we measured a diameter of 15 nm for the A. aeolicus lumazine synthase GEM ( Figures 1 C and S1 A), in good agreement with crystallography data () ( Figure 1 C). However, it is likely that the T-Sapphire density was not visible in the negative stain images (see also Figure S1 B, where Pyrococcus furiosus encapuslin GEMs were measured at 37 nm by negative stain). Thus, to account for the expected extra diameter due to decoration with GFP molecules, we termed these particles 20nm-GEMs.

(C) The median track length for 40nm-GEMs in DMSO and rapamycin treatment is similar at 35.2 ± 2.1 and 34.8 ± 2.2 frames, respectively. Track lengths < 10 displacements were excluded from all analyses (gray shaded area).

(B) 40nm-GEMs, which are seen to be 41 nm in high-accuracy in situ cryo-ET, appear to be 37.23 ± 3.69nm by negative stain EM.

Using in situ cryo-ET to image the native cellular environment (), we determined that the Pyrococcus furiosus encapuslin GEM has a diameter of 41 nm, a little larger than the 35 nm diameter reported from crystallography data ( Figure 1 C). This larger diameter is likely due to the additional T-Sapphire molecules decorating the encapsulin particle. Thus, we termed these particles 40nm-GEMs.

In this study, we employed scaffolding domains based on the encapsulin protein from the hyperthermophilic archaeon Pyrococcus furiosus () and the lumazine synthase enzyme complex from the hyperthermophilic bacterium Aquifex aeolicus () ( Figures 1 A–1C). When expressed within cells, these GEMs self-assembled into bright, stable particles ( Figures 2 A and 2B ).

(D and E) Distribution of 40nm-GEM effective diffusion coefficients (D eff ) within (D) S. cerevisiae and (E) HEK293 cells; results from DMSO (carrier control) treatment and rapamycin treatment are displayed in blue and orange, respectively. Insets: time and ensemble-averaged mean-square displacements in log-log space with the anomalous exponent indicated.

(C) High-magnification example of tracking a 40nm-GEM particle (green) within a S. cerevisiae cell, imaged at 100 frames per second. Three other GEMs and the nucleus (magenta) are also seen within the image. Raw pixels are displayed.

(A and B) 40nm-GEMs expressed in (A) S. cerevisiae and (B) HEK293 cells. GEMs are visualized using the T-Sapphire fluorescent protein (green). The SiR-Hoeschst DNA stain is used to visualize the nucleus (Nuc, magenta). Yeast cell walls are visualized using calcofluor-white and HEK293 membrane with wheat germ agglutinin (cyan).

(D) Diameters of GEMs and other macromolecules at the meso length scale, shown in relation to small molecules, protein complexes, and cells.

(C) Left: cryo-ET subtomogram average of 40nm-GEMs within the cell. Right: negative stain EM image of a 20nm-GEM. Diameters are shown in red.

The crystal structure of a virus-like particle from the hyperthermophilic archaeon Pyrococcus furiosus provides insight into the evolution of viruses.

We developed GEMs to study the rheological properties of the eukaryotic cytoplasm. We began with natural homomultimeric scaffolds that self-assemble into icosahedral geometries and fused these scaffolds to fluorescent proteins (T-Sapphire).

Discussion

Parry et al., 2014 Parry B.R.

Surovtsev I.V.

Cabeen M.T.

O’Hern C.S.

Dufresne E.R.

Jacobs-Wagner C. The bacterial cytoplasm has glass-like properties and is fluidized by metabolic activity. Zoncu et al., 2011 Zoncu R.

Efeyan A.

Sabatini D.M. mTOR: from growth signal integration to cancer, diabetes and ageing. Recent work has reported dramatic changes in cytoplasmic rheology in response to changes in cellular energy state and metabolism. For example, depletion of ATP in E. coli leads to a glass transition that greatly reduces macromolecular mobility (), and glucose starvation in yeast leads to decreases in cytoplasmic pH that lead to a gel transition in the cytosol (Munder et al., 2016b). All of these responses increase the viscosity of the cytosol. In contrast, we show that inhibition of mTORC1 decreases cytosolic viscosity. Using GEM nanoparticles, we were able to determine the mechanism for this biophysical change. Ribosome concentration dominates the rheological properties of the cytoplasm at the mesoscale of tens to hundreds of nanometers. mTORC1 both drives ribosome biogenesis and decreases degradation through inhibition of autophagy (). Therefore, mTORC1 regulates the physical properties of the cytoplasm by tuning the concentration of ribosomes.

Munder et al., 2016 Munder M.C.

Midtvedt D.

Franzmann T.

Nüske E.

Otto O.

Herbig M.

Ulbricht E.

Müller P.

Taubenberger A.

Maharana S.

et al. A pH-driven transition of the cytoplasm from a fluid- to a solid-like state promotes entry into dormancy. Parry et al., 2014 Parry B.R.

Surovtsev I.V.

Cabeen M.T.

O’Hern C.S.

Dufresne E.R.

Jacobs-Wagner C. The bacterial cytoplasm has glass-like properties and is fluidized by metabolic activity. Length scale considerations in cytoplasmic viscosity have interesting implications for previous findings; for example, solidification of the yeast cytoplasm under glucose starvation was observed by tracking GFP-μNS particles, which are large condensates (). However, it would be surprising if the diffusion of all macromolecules is greatly decreased in carbon starvation. Our results show that the mobility of 40nm-GEMs is only decreased by 20% in this condition ( Figure S2 B). This result is in agreement with the particle size-dependency observed in the bacterial cytoplasm (). In some scenarios, larger macromolecules may become spatially confined while smaller macromolecules continue to diffuse unimpeded. This could affect processes dependent on large complexes, such as apoptosis, translation, or cell growth, while many basic cellular functions continue unaltered. In this way, general changes in cytoplasmic crowding could cause specific physiological consequences.

A major advantage of GEM nanoparticles is that they assemble into defined geometries and can therefore be used as rheological probes across multiple biological systems. We observe that GEMs have a higher diffusion coefficient in HEK293 cells than in S. cerevisiae, indicating that this human cell line is less crowded. Indeed, our osmotic compression experiments show a larger free water volume in HEK293 cells, consistent with this notion. In future studies, it will also be interesting to compare the physical properties of mammalian cells in different mechanical contexts, for example within tissues. Additionally, different cell types are likely to have distinct crowding, and disease mutations may lead to aberrant properties. GEMs will be a crucial tool to accelerate discovery in this area.

Beyond the diffusion coefficient, a second parameter that can be readily compared across conditions is the subdiffusive anomalous exponent, α. GEMs undergo subdiffusive motion in both cell types, but the origins of this subdiffusion remain unclear. A striking feature is that α is relatively invariant across conditions within one cell-type, but there is a species-dependent difference between yeast and human cells ( Figure S2 I). This difference in α points to a general difference within the disordered media of the cytoplasm in these two organisms. While the physical explanation for this difference is currently unknown, there are several possibilities. Notably, mammalian cells have intermediate filaments and far more extensive actin and microtubule networks. This more elaborate cytoskeleton drives more substantial active flows and rearrangements, all of which can affect cytosolic rheology. We are excited to investigate these possibilities in the future.

Warner, 1999 Warner J.R. The economics of ribosome biosynthesis in yeast. Duncan and Hershey, 1983 Duncan R.

Hershey J.W. Identification and quantitation of levels of protein synthesis initiation factors in crude HeLa cell lysates by two-dimensional polyacrylamide gel electrophoresis. Miermont et al., 2013 Miermont A.

Waharte F.

Hu S.

McClean M.N.

Bottani S.

Léon S.

Hersen P. Severe osmotic compression triggers a slowdown of intracellular signaling, which can be explained by molecular crowding. Cai et al., 2011 Cai L.-H.

Panyukov S.

Rubinstein M. Mobility of nonsticky nanoparticles in polymer liquids. −3. On the other hand, a simple Newtonian fluid or a dilute colloidal suspension predicts D ∼d−1. In our case, we find a scaling of D ∼d−2 indicating that the cytoplasm neither satisfies the model of a simple polymer nor a simple Newtonian fluid. Thus, the biophysical properties of the cytoplasm are likely to be driven by a mixture of the colloidal effects of ribosomes as well as polymer dynamics. Our model provides a starting point to begin to parse the relative contribution of these possible factors, for example from the cytoskeleton or polysomes. Ribosomes are one of the most abundant macromolecules in the cell (around 200,000 ribosomes per yeast cell [] and 3,000,000 per HeLa cell []), and we determined that ribosomes occupy 20% of the total volume of the yeast cytosol. Under normal conditions, the fraction of crowder in the cytoplasm is ∼50% of the maximum possible crowding (), thus ribosomes account for about half of this excluded volume. Indeed, when we use the phenomenological Doolittle equation to model the cytosol, we can predict the diffusion coefficient of 40nm-GEM tracer particles and endogenous mRNPs as a function of ribosome concentration. This predictive power suggests that ribosomes are crucial to set the biophysical properties of the cytosol. However, the cytoplasm is unlikely to be well described by a purely colloidal model. This point is indicated by the scaling of diffusion coefficients as a function of particle size ( Figure 3 E). Recent theories provide predictions for particle diffusion within polymer meshes (). In this framework, the diffusion coefficient (D) of tracer particles of a diameter comparable to the mesh size should scale with particle diameter (d) as D ∼d. On the other hand, a simple Newtonian fluid or a dilute colloidal suspension predicts D ∼d. In our case, we find a scaling of D ∼dindicating that the cytoplasm neither satisfies the model of a simple polymer nor a simple Newtonian fluid. Thus, the biophysical properties of the cytoplasm are likely to be driven by a mixture of the colloidal effects of ribosomes as well as polymer dynamics. Our model provides a starting point to begin to parse the relative contribution of these possible factors, for example from the cytoskeleton or polysomes.

Physiological regulation of the thousands to tens of thousands of different proteins found within cells is a complex task. This regulation is achieved through fine-grained mechanisms, including transcriptional and translational control of protein abundance as well as post-translational modifications such as protein phosphorylation and ubiquitylation. However, our studies suggest that macromolecular crowding could also lead to a broad regulation of cell state. Changes in macromolecular crowding may provide coarse-grained regulation of protein interactions, diffusion, and folding; the cell may become more solid-like in states of extreme stress or fluidize to tune reactions.