Intracellular diffusion in the cytoplasm increases with cell size in fission yeast

Firstly, I thank my supervisor, Dr. Fred Chang, who welcomed me in the lab despite that I didn’t have any prior biophysics or microscopy experience, who believed in my eagerness to learn physical cell biology, and who allowed me to pursue my naïve ideas during the early years of my PhD. Thank you for always promoting my work to the larger scientific community and for showing me the wonders of Woods Hole year after year. Thank you for supporting me not only as a scientist but as a human being. You've supported my interests beyond the lab, particularly my DEI and activism endeavors. When I struggled personally, you prioritized my well-being, and I never felt that you judged me for my circumstances. Under your mentorship, I found not only intellectual growth but also the freedom to embrace my authentic self. Of my labmates, I thank Dr. Arthur Molines, Dr. Joël Lemière, and Paula Real Calderon who have been my peers for the entirety of my PhD. Thank you Arthur for providing me the technical foundations of microscopy and image analysis during my first years of grad school. Thank you Joël for continuing to help me develop my technical skills, for talking me off the edge of quitting grad school, and for assuming I could understand the math you tried to teach me. And last, but not least, thank you Paula for answering all my questions on yeast genetics and life in general, and for being the sole reason I came into lab some days because you always made lab fun. I thank my dissertation committee members Dr. Sophie Dumont, Dr. Wallace Marshall, and Dr. Abigail Buchwalter-Cool for years of mentorship and guidance. I thank current and past members of HSW6, the Department of Cell and Tissue Biology, MBL Physiology, ASCB-EMBO, the Biomedical Sciences Graduate Program, the v Graduate Division, Scientist 4 Diversity, my rotation labs, my qualifying exam committee, the Multicultural Resource Center, and the United Filipinx Association. Over the years, you fueled my joy for discovery, curated my sense of belonging, and empowered me to be a leader. Thank you all for being part of my PhD journey. I thank my mentors at the Sanford-Prebys Medical Discovery Research Institute, UCSD, Illumina, and Arima Genomics who helped steer my path towards graduate school. I thank my parents who financially supported me throughout my academic career, and I thank my extended family members for always being in awe of how “smart” I am. To those who made San Francisco my home, I thank my close friends Valerie Rosen, Paula Real Calderon, Charlotte Golden, and Donovan Trinidad. Thank you for your unconditional support, kindness, and love throughout these years. And finally, thank you to Roderick Mendoza for every laugh you’ve given me during those final tough years of grad school. vi Intracellular diffusion in the cytoplasm increases with cell size in fission yeast Catherine Louise Tan Abstract Diffusion in the cytoplasm can greatly impact cellular processes, yet regulation of macromolecular diffusion remains poorly understood. There is increasing evidence that cell size affects the density and macromolecular composition of the cytoplasm. Here, we studied whether cell size affects the diffusion of macromolecules in the cytoplasm in the fission yeast Schizosaccharomyces pombe cells by analyzing the diffusive motions of intracellular genetically-encoded 40nm nanoparticles (cytGEMs). Using cell size mutants, we found that cytGEMs diffusion coefficients decreased in smaller cells and increased in larger cells. To test if these changes in diffusion rates were due to DNA-to-Cytoplasm (DC) ratio, we used cytokinesis mutants to avoid decreasing DC ratio in large multinucleate cells and found that these cells have comparable cytGEMs diffusion as their normal-sized counterparts. In investigating the underlying causes of altered cytGEMs diffusion, we showed that larger cells have lower concentrations of ribosomal proteins. Finally, comparison of the proteomes of large and small cells defined size-specific changes in the proteome composition. These studies demonstrate that cell size is an important parameter in determining the biophysical properties of the cytoplasm. vii Table of Contents Chapter 1 – Introduction .................................................................................................... 1 Chapter 2 – Intracellular diffusion increases with cell size in fission yeast ............. 5 2.1 Nanoparticle diffusion in the cytoplasm increases with cell size ................................. 5 2.2 Large multinucleate cells maintain nanoparticle diffusion ............................................ 9 2.3 Ribosomal and total protein concentrations decrease in large cells ......................... 11 2.4 Proteome composition varies with cell size ................................................................... 13 Chapter 3 – Additional unpublished data ..................................................................... 19 3.1 Intracellular diffusion in the nucleus increases with cell size ..................................... 19 3.2 Inhibiting ribosomal biogenesis may be sufficient to alter intracellular diffusion in fission yeast ............................................................................................................................... 20 Chapter 4 – Discussion .................................................................................................... 23 Chapter 5 – Methods and Materials ............................................................................... 27 Yeast strains and media ........................................................................................................... 29 Preparation of cells for live cell microscopy ......................................................................... 30 Microscopy ................................................................................................................................. 31 Imaging and analysis of cytGEMs .......................................................................................... 32 Measurement of cell length and nuclei count ....................................................................... 32 Ribosomal concentration quantification ............................................................................... 33 Cellular protein concentration quantification ....................................................................... 34 viii LC-MS/MS sample preparation ................................................................................................ 34 LC-MS/MS data acquisition ...................................................................................................... 36 Spectral searches ...................................................................................................................... 37 Peptide quantitation .................................................................................................................. 37 Protein annotations ................................................................................................................... 38 Gene ontology enrichment analysis ....................................................................................... 38 Ortholog analysis ...................................................................................................................... 38 References ......................................................................................................................... 40 ix List of Figures Figure 2.1 Nanoparticle diffusion increases with cell size ........................................ 7 Figure 2.2 Supporting data for Figure 2.1 .................................................................... 8 Figure 2.3 Large multinucleate cells maintain nanoparticle diffusion rates .......... 10 Figure 2.4 Supporting data for Figure 2.3 .................................................................. 10 Figure 2.5 Large cells have decreased concentrations of ribosomes and overall protein content ............................................................................................................. 12 Figure 2.6 Proteome composition varies with cell size ........................................... 16 Figure 2.7 Comparison of S. pombe proteome size scaling with other studies .... 17 Figure 2.8 Cell size proteome remodeling is observed at smaller cell size differences .................................................................................................................... 18 Figure 3.1 Nanoparticle diffusion in the nucleus increases with cell size ............. 20 Figure 3.2 Inhibiting ribosomal biogenesis may be sufficient to alter intracellular diffusion ........................................................................................................................ 22 x List of Tables Table 4.1 Key resources used in this study .............................................................. 27 1 Chapter 1 – Introduction Cell size is an intrinsic physical property of all cells that can impact physiology from the cellular level to the organismal. Although cell size can vary over six orders of magnitude among diverse cell types, cell size varies within a much narrower range for a specific cell type due to homeostatic mechanisms (Ginzberg et al., 2015; Zatulovskiy and Skotheim, 2020). Cell size can impart different cellular functions and developmental potential (Hecht et al., 2016; Lengefeld et al., 2021). Aberrant cell size can also signify biological dysfunction and has been associated with aging, senescence, numerous cancers, and other human diseases (Lloyd, 2013). The mechanisms for how cell size impacts cellular physiology, however, remain poorly understood. Recent studies have begun to implicate effects of cell size on the global properties of the cytoplasm. The cytoplasm can be regarded as a heterogenous, dynamic, and crowded viscoelastic matrix that exerts osmotic forces, and impacts nearly all biochemical reactions through effects on viscosity, macromolecular crowding, phase separation, and likely many other biophysical phenomena (Zhou et al., 2008; Mitchison, 2019). For instance, variations in density, which can be the result of complex dynamics between biosynthesis, degradation, and osmotic water fluxes, can cause significant changes in cellular physiology or function as seen during the cell cycle, differentiation, and stress (Neurohr and Amon, 2020). Generally, the concentrations of cellular components are thought to be maintained at different cell sizes by scaling relationships. For instance, mRNA, protein, transcription, translation, and the volume of many organelles can scale with cell size (Elliott et al., 1979; Creanor and Mitchison, 1982; Elliott, 1983; Neumann and Nurse, 2007; Zhurinsky et al., 2 2010; Padovan-Merhar et al., 2015; Chadwick et al., 2020; Marshall, 2020; Basier and Nurse, 2023; Swaffer et al., 2023). However, such scaling relationships have limitations, and so, a breakdown in scaling mechanisms could explain cell size-dependent changes in cellular physiology. For example, it was observed that budding yeast cells arrested in G1 phase grew to very large sizes (up to 10 times larger in volume than normal), exhibited defects in protein synthesis, and progressively became less dense (Neurohr et al., 2019). Similar dilution effects have been seen in senescent metazoan cells, indicating that a large cell size may be causal for aspects of senescent physiology, and not merely a side effect (Demidenko and Blagosklonny, 2008; Neurohr et al., 2019; Lengefeld et al., 2021; Lanz et al., 2022). Similarly, in fission yeast, cells arrested in G2 phase grow very large and show a gradual slowdown in rates of cell growth and protein translation, further illustrating that a large cell size is detrimental for proliferation and normal cell function (Knapp et al., 2019a; Basier and Nurse, 2023). For size ranges where overall protein concentration remains relatively constant, recent studies have demonstrated that cell size can also change the composition of the proteome (Schmoller et al., 2015; Keifenheim et al., 2017; Lanz et al., 2022, 2023). One proposed explanation for certain cellular pathologies associated with overly large cells is that the DNA-to-Cytoplasm (DC) ratio in these cells has dropped below a critical threshold required to scale biosynthetic processes (Zhurinsky et al., 2010; Marguerat and Bähler, 2012; Neurohr et al., 2019; Balachandra et al., 2022; Cadart and Heald, 2022; Xie et al., 2022). As cells grows larger without a concomitant increase in DNA, there may be insufficient transcriptional or translational machinery to support biomass production for an exponentially-growing cell volume. This theory could explain 3 why diploid and polyploid cells can grow to larger sizes without exhibiting defects associated with large cell size (Neurohr et al., 2019; Mu et al., 2020; Lanz et al., 2022, 2023). The fission yeast S. pombe is a leading model organism in defining cell size control and scaling relationships (Nurse, 1985; Wood and Nurse, 2015). Many studies exploring scaling relationships have used mutants such as cdc25-22 and wee1-50, which alter cell size by affecting the length of G2 phase through regulation of CDK1 cell cycle dependent kinase (Nurse, 1975; Fantes and Nurse, 1978; Neumann and Nurse, 2007; Knapp et al., 2019a; Pickering et al., 2019; Sun et al., 2020). Recent studies show how the intracellular density of the cytoplasm fluctuates during the cell cycle and how properties of the cytoplasm are altered in starvation responses and during sporulation (Joyner et al., 2016; Munder et al., 2016; Heimlicher et al., 2019; Odermatt et al., 2021; Sakai et al., 2023). However, it remains unclear which cellular components are responsible for fluctuations in cytoplasmic density or how the composition of the cytoplasm changes when cell size is altered. Here, we studied how cell size affects diffusion within the cytoplasm to assess the effects of cell size on the biophysical properties of the cytoplasm. We measured diffusion within living cells by imaging and analyzing the diffusive motion of genetically-encoded multimeric cytoplasmic nanoparticles (cytGEMs), 40 nm-diameter fluorescent particles which inform on the diffusion of macromolecules that are approximately the size of ribosomes (Delarue et al., 2018; Lemière et al., 2022; Molines et al., 2022). We found that diffusion within the cytoplasm increases with increasing cell size in various cell size mutant strains. Using cytokinesis mutants to generate cells that not only became larger, 4 but also increased their DNA content with cell size thereby preventing a decrease in DC ratio, we find that diffusion does not change in these cells. To gain mechanistic insight into the cell size-dependent rheological effects, we discovered changes in the concentrations of ribosomes and total protein as well as in the composition of the proteome. These size-dependent changes in the physical properties of the cytoplasm provide novel perspectives on the effects of cell size on cellular physiology and function. 5 Chapter 2 – Intracellular diffusion increases with cell size in fission yeast 2.1 Nanoparticle diffusion in the cytoplasm increases with cell size To investigate the relationship between cell size and intracellular diffusion, we expressed and imaged 40nm cytGEMs nanoparticles in S. pombe wildtype and cell size mutant cells, and through analyses of their motion, determined the effective diffusion coefficient in each strain(Delarue et al., 2018; Lemière et al., 2022; Molines et al., 2022). We grew wee1-50, wildtype, and cdc25-22 cells at the permissive temperature 25°C and shifted to the non-permissive temperature 36°C for six hours before imaging (Fig. 2.1 A) (Nurse, 1975; Fantes and Nurse, 1978). At this temperature, cdc25-22 cells arrest in G2 phase and continue to grow in length, while wee1-50 cells exhibit cell cycles with shorter G2 phases and enter mitosis at an abnormally short length. As these rod-shaped cells maintain approximately similar cell widths, the length of the cell was used as an proxy of cellular volume (Mitchison, 1957; Facchetti et al., 2019; Knapp et al., 2019). In our conditions, wee1-50, wildtype, and cdc25-22 cells exhibited an average cell length of 5.61 ± 0.3 µm, 10.84 ± 1.39 µm, and 38.32 ± 1.36 µm, respectively (mean ± STD of replicate experiments). Measurement of the effective diffusion coefficient of cytGEMs in each cell population yielded average cytGEMs effective diffusion coefficients of 0.41 ± 0.04 µm2/s in wee1-50, 0.63 ± 0.07 µm2/s in wildtype, and 0.86 ± 0.04 µm2/s in cdc25-22 cells (Fig. 2.1 B-C; mean ± STD of replicate experiments). Thus, cytGEMs diffusion showed a striking positive correlation between cell size and nanoparticle diffusion in the cytoplasm at the population level. We then analyzed the relationship between cytGEMs diffusion and cell size in individual cells, which exhibited a similar trend of increasing diffusion with cell 6 size (Fig. 2.2 D). Positive correlations between cell length and cytGEMs diffusion (using a simple linear regression weighted by number of trajectories per cell) were also apparent when analyzing individual cells within wee1-50 and cdc25-22 strains (Fig. 2.1 D, Fig. 2.2 A-C), and no correlation was found in wildtype cells (Fig. 2.1 D; Fig. 2.2 B) (Garner et al., 2023). To address concerns of possible effects of temperature shifts on diffusion and cytoplasmic viscosity, we grew wee1-50, wildtype, and cdc25-22 cells at the permissive and semi-permissive temperatures of 25°C and 28°C, respectively, in steady state conditions (Sidell and Hazel, 1987; Persson et al., 2020; Bellotto et al., 2022). At these temperatures, the mutants generally exhibited detectable but more modest changes in cell size compared to those in the cells shifted to 36°C. At 25°C and 28°C, cytGEMs diffusion rates showed increasing positive trends with cell size across the three strains at the population level (Fig. 2.2 D-G). To generate large G2-arrested cells using another approach, we inhibited the analog-sensitive CDK1 allele cdc2-asM17 with the ATP analog drug 1-NM-PP1 at 30°C (Aoi et al., 2014). This treatment leads to G2 arrest and formation of large mononucleate cells, similar to the cdc25 mutants (Fig. 2.1 E). Untreated cdc2-asM17 cells had an average cell length of 11.32 ± 0.57 µm, while cdc2-asM17 cells treated with 1-NM-PP1 for 3 hours and 6 hours had average cell lengths of 18.93 ± 0.84 µm and 30.05 ± 3.18 µm, respectively (mean ± STD of replicate experiments). The average cytGEMs diffusion coefficient was 0.42 ± 0.04 µm2/s for control cells, 0.52 ± 0.02 µm2/s for cells treated with 3 hours of 1-NM-PP1, and 0.57 ± 0.02 for cells treated with 6 hours of 1-NM-PP1 (mean ± STD of replicate experiments) (Fig. 2.1 F). Control treatments did not alter cytGEMs 7 diffusion (Fig. 2.2 G). Overall, these results from a variety of strains and conditions showed a striking positive correlation of intracellular diffusion rates with cell size. Figure 2.1 Nanoparticle diffusion increases with cell size (A) Images of wee1-50, wildtype, and cdc25-22 cells with nuclear membrane marker Ish1-GFP (red) grown at the permissive temperature 25°C overnight and shifted to the non-permissive temperature 36°C for 6 hours before imaging. Scale bar is 5μm. (B) Cell length (mean ± STD of replicate experiments; NCELLS ≥ 106 per condition from at least 4 biological replicates) (1-way ANOVA, p < 0.0001) and (C) cytGEMs diffusion (mean ± STD of replicate experiments; NGEMS ≥ 3183 per condition from at least 4 biological replicates) for wee1-50, wildtype, and cdc25-22 cells grown with the temperature shift protocol described in (A) (1-way ANOVA, p < 0.0001). (D) Cell length and cytGEMs diffusion (mean ± SEM of cytGEMs trajectories per cell; NCELLS ≥ 106 per condition from at least 4 biological replicates) plotted for individual cells for wee1-50, wildtype, and cdc25-22 cells grown with the temperature shift protocol described in (A). (E) Cell length (mean ± STD of replicate experiments; NCELLS ≥ 113 per condition from 3 biological replicates) and (F) cytGEMs diffusion (mean ± STD of replicate experiments; NGEMS ≥ 5709 per condition from 3 biological replicates) for cdc2-asM17 cells treated with 0.25% DMSO or 10μM ATP analog 1-NM-PP1. (1-way ANOVA, * - p < 0.05, ** - p < 0.01, *** - p < 0.001, **** - p < 0.0001). 8 Figure 2.2 Supporting data for Figure 2.1 (A) Cell length and cytGEMs diffusion (mean ± SEM of cytGEMs trajectories per cell) plotted for individual cells for wee1-50, (B) wildtype, and (C) cdc25-22 cells grown with the temperature shift protocol described in Figure 1A. Weighted linear regression (orange solid line) with 95% confidence interval (orange dashed lines) shown. Best-fit slopes are 0.03, 0.003, and 0.005 for (A), (B), and (C), respectively. (D) Cell length and (E) cytGEMs diffusion (mean ± STD of replicate experiments; NGEMS ≥ 5135 per condition from at least 3 biological replicates) for wee1-50, wildtype, and cdc25-22 cells grown at the steady-state semi-permissive temperature 28°C. (F) Cell length and (G) cytGEMs diffusion (mean ± STD of replicate experiments; NGEMS ≥ 5981 per condition from at least 3 biological replicates) for wee1-50, wildtype, and cdc25-22 cells grown at the steady-state permissive temperature 25°C. (H) CytGEMs diffusion (mean ± STD of replicate experiments) in wildtype and cdc2-asM17 cells with varying conditions of 0.25% DMSO and 10μM 1-NM-PP1. (1-way ANOVA, * - p < 0.05, *** - p < 0.001, **** - p < 0.0001). 9 2.2 Large multinucleate cells maintain nanoparticle diffusion To determine whether the increase in cytGEMs diffusion in larger cell sizes was due to a decrease in the DNA-to-Cytoplasm (DC) ratio, we analyzed large multinucleated fission yeast cells in which the DC ratio does not decrease. We generated these multinucleate cells using well-established mutants sid2-as and cdc11-119 that are defective in the SIN regulatory pathway of cytokinesis (Nurse et al., 1976; Grallert et al., 2012). These conditional mutants continue to grow in length and undergo nuclear division cycles in the absence of septation. Sid2-as cells treated with the ATP analog 1-NM-PP1 formed progressively larger cells with multiple nuclei at three and six hours of treatment (Fig. 2.3 A-C). Control sid2-as cells had an average cell length of 10.36 ± 0.34 µm while sid2-as cells treated with 1-NM-PP1 for 3 hours and 6 hours had average cell lengths of 16.55 ± 0.91 µm and 25.95 ± 1.6 µm, respectively (mean ± STD of replicate experiments). Based on the average cell length and number of nuclei per condition, we estimated that the DC ratio of 1-NM-PP1-treated cells did not decrease compared to the control. Despite being larger in cell size, we found that cytGEMs diffusion coefficients in treated sid2-as cells (3 hours: 0.48 ± 0.6 µm2/s; 6 hours: 0.56 ± 0.01 µm2/s) were comparable with the control cells (0.52 ± 0.04 µm2/s) (mean ± STD of replicate experiments) (Fig. 2D). Next, we inhibited cytokinesis by using the temperature-sensitive mutant cdc11-119 (Nurse et al., 1976). Wildtype cells and cdc11-119 cells were grown at the permissive temperature 25°C overnight and shifted to the non-permissive temperature 36°C for 3 hours. We observed a comparable cytGEMs diffusion coefficient in the cdc11-119 cells compared to control populations (Fig. 2.4 B). Overall, these results suggest that a 10 decrease in DC ratio rather than an increase in cell size alone, underlies the increase in intracellular diffusion observed in large cells (Fig. 2.1). Figure 2.3 Large multinucleate cells maintain nanoparticle diffusion rates (A) Images of S. pombe sid2-as cells with nuclear membrane marker Ish1-mScarlet (red). Cells were grown at steady-state 30°C and treated with 0.25% DMSO or 10μM ATP analog 1-NM-PP1. Left to right: 6hr DMSO, 3hr 1-NM-PP1, and 6hr 1-NM-PP1. Scale bar is 5μm. (B) Cell length, (C) number of nuclei (mean ± STD of replicate experiments; NCELLS ≥ 140 per condition from 3 biological replicates), and (D) cytGEMs diffusion (mean ± STD of replicate experiments; NGEMS ≥ 5546 per condition from 4 biological replicates) for sid2-as cells described in (A). (1-way ANOVA, *** - p < 0.001, **** - p < 0.0001). Figure 2.4 Supporting data for Figure 2.3 Supplementary Figure 2. Large multinucleate cells maintain nanoparticle diffusion rates. (A) CytGEMs diffusion (mean ± STD of replicate experiments) in wildtype and sid2-as (Figure caption continued on the next page.) 11 Figure 2.4 Supporting data for Figure 2.3 (Figure caption continued from the previous page.) cells with varying conditions of 0.25% DMSO and 10μM 1-NM-PP1 (1-way ANOVA, p > 0.05). (B) CytGEMs diffusion (mean ± STD of replicate experiments; NGEMS ≥ 7630 per condition from 3 biological replicates) for wildtype and cdc11-119 cells grown at permissive temperature 25°C overnight and shifted to the non-permissive temperature 36°C for 3 hours before imaging (Komogorv-Smirnov test, p = 0.6). (C) CytGEMs diffusion (mean ± STD of replicate experiments; NGEMS ≥ 9782 per condition from 3 biological replicates) for wildtype and cdc11-119 cells grown at the steady-state permissive temperature 25°C (Komogorv-Smirnov test, p = 0.6). 2.3 Ribosomal and total protein concentrations decrease in large cells We hypothesized that the cell size-dependent changes in cytGEMs diffusion reflect changes in cytoplasmic composition or concentration. Previous studies suggest that such changes may correlate with a decrease in ribosome concentration or overall protein concentration (Delarue et al., 2018; Neurohr et al., 2019). To assess ribosomal concentration, we measured the fluorescence intensity of Rps2-GFP, a functional fusion of the essential small subunit ribosomal protein expressed at the native locus (Knapp et al., 2019; Lemière et al., 2022). Wee1-50, wildtype, and cdc25-22 cells expressing Rps2-GFP were grown at the permissive temperature 25°C overnight and shifted to the non-permissive temperature 36°C for 6 hours before imaging (Fig. 2.5 A). To facilitate equivalent processing, cells of the three strains were mixed, stained together, and imaged in the same field. We found a distinct inverse relationship of Rps2 intensity with cell size (Fig. 2.5 B). In binned data, the average intensity of Rps2-GFP was significantly lower in bigger cells (cell length ≥ 18 µm) compared to medium-sized cells (cell length between 9 and 18 µm), but no significant differences were detected between medium and smaller cells (cell length ≤ 9 µm) (Fig. 2.5 B-C). 12 To assess overall protein concentration, we measured the intensity of fluorescent dye fluorescein isothiocyanate (FITC) staining in fixed cells (Fig. 2.5 D). Wee1-50, wildtype, and cdc25-22 cells were shifted to 36°C for 6 hours, mixed, fixed, stained with FITC, and imaged. In binned data, compared to medium-sized cells, FITC intensity was about 6% lower in bigger cells (p=0.04) and 5% lower in smaller cells (not significant). These results were consistent with an overall decrease in dry mass density seen previously in cdc25-25 cells (Odermatt et al., 2021). Overall, our results showed that larger cells exhibited a decrease in ribosomal protein concentration and to a lesser extent, overall protein concentration, which begin to provide an explanation for the increase in cytGEMs mobility with increasing cell size. Figure 2.5 Large cells have decreased concentrations of ribosomes and overall protein content (A) Image of a mixture of wee1-50, wildtype, and cdc25-22 live cells with ribosomal protein marker Rps2-GFP. Scale bar is 5μm. (B) Rps2-GFP intensity and length per cell. (Figure caption continued on the next page.) 13 Figure 2.5 Large cells have decreased concentrations of ribosomes and overall protein content (Figure caption continued from the previous page.) (C) Rps2-GFP intensities (mean ± STD per cell length category; NCELLS ≥ 72 per condition from 3 biological replicates) measured in a mixture of wee1-50, wildtype, and cdc25-22 cells and categorized by cell length. (D) Image of a mixture of wee1-50, wildtype, and cdc25-22 fixed cells, treated with RNase A, and stained with FITC. (E) FITC intensity and length per cell. (F) FITC intensities (mean ± STD per length category; NCELLS ≥ 73 per condition from 3 biological replicates) measured in a mixture of wee1-50, wildtype, and cdc25-22 fixed cells and categorized by cell length. Intensity values for Rps2-GFP and FITC are normalized to the mean intensity of the 9 ≤ L <18 category (1-way ANOVA test, * - p < 0.05, **** - p < 0.0001). 2.4 Proteome composition varies with cell size To investigate how the composition of the cytoplasm changes with cell size, we characterized the proteomes of S. pombe wee1-50, wildtype, and cdc25-22 cells grown under various conditions. Mass spectrometry was analyzed using SILAC in pairwise comparisons. First, cdc25-22 and wee1-50 SILAC strains were labeled with the lysine and arginine isotopes at the permissive temperature 25°C overnight and shifted to the non-permissive temperature 36°C for six and half hours before peptide extraction, which produced similar size ranges to the cdc25-22 and wee1-50 cell populations analyzed in Figure 2.1. Proteomic analyses detected 3,353 proteins out of 5,117 identified S. pombe proteins (~65% coverage), and our two experimental repetitions yielded consistent results (R=0.83, Pearson) (Fig. 2.6 A). We categorized proteins by their subcellular location or macromolecular complex such as histones (magenta), ribosomes (orange), and ER (cyan) (Fig. 2.6 A). Finally, we grouped proteins by their subcellar location or macromolecular complex and averaged their collective ratios (Fig. 2.6 B). Because relative concentrations of each protein were calculated and normalized within each strain, 14 we note that these analyses cannot reveal alterations in real protein concentrations but only relative changes to other proteins. Overall, we observed differential scaling of proteins in comparing large and small cell proteomes. Proteins associated with the nucleus including the nucleolus, histones (magenta, out of range), and chromosome sub-scaled with cell size, i.e. they were underrepresented in the large cdc25-22 cells compared to small wee1-50 cells (Fig. 2.6 B, red quadrant). This sub-scaling behavior was expected, as chromosome-associated proteins such as histones are known to scale with DNA content, not cell size (Claude et al.; Amodeo et al., 2015). Notably, ribosomal proteins (Fig. 2.6 B, orange) also exhibited sub-scaling, which supported our observation that ribosome concentration is decreased in these large cells (Fig. 2.6 A-C). Overall, cytoplasmic proteins are also sub-scaling with cell size. In contrast, proteins associated with the endoplasmic reticulum (cyan), mitochondria, and vacuoles super-scaled with cell size, i.e. they were overrepresented in the large cdc25-22 cells compared to small wee1-50 cells (Fig. 2.6 B, blue quadrant). Next, we examined the proteome data for scaling of cellular processes and signaling pathways implicated in regulation of cytoplasmic properties in other studies. One candidate signaling pathway that regulates ribosome concentration and stress responses is the TORC1 pathway (Delarue et al., 2018). Proteins associated with TORC complexes and ribosome biogenesis sub-scaled with cell size (Fig. 2.7 A). To test whether TORC1 activity is decreased in large cells, we found that factors downstream of the Sfp1 transcription factor also sub-scaled with size (Tai et al., 2023). However, in contrast to other studies characterizing cell size proteome changes (Neurohr et al., 2019; Lanz et al., 2022, 2023), we detected no significant super-scaling effects on stress-associated 15 pathways such as the core environmental stress response (CESR) (Chen et al., 2003). As levels of the viscogen trehalose may be an additional mechanism to regulate cytoplasmic viscosity that is independent of ribosome concentration, we found that proteins involved in trehalose biosynthesis sub-scaled with size and those associated with trehalose breakdown super-scaled with size (Fig. 2.7 A) (Persson et al., 2020). To analyze the top hits for sub- and super-scaling proteins in our data set, we performed a gene ontology enrichment analysis (PANTHER overrepresentation test) (Fig. 2.7 B). Top super-scaling proteins were generally involved in metabolic pathways associated with membrane-bound organelles, whereas top sub-scaling proteins were associated with cell polarity regulation at cell tips, and mRNA regulation and gene expression in the nucleus. Of interest, among the sub-scaling cell polarity proteins was the DYRK protein kinase Pom1 as well its regulators Tea1 and Tea4, which all localize to cell tips and contribute to cell size sensing for cell size regulation (Martin and Berthelot-Grosjean, 2009; Moseley et al., 2009; Hachet et al., 2011; Wood and Nurse, 2015). Additional proteomic comparisons between cells with smaller size differences supported these results. First, comparison of cdc25-22 and wildtype cells, grown with the temperature shift protocol described in Fig. 2.6 , showed similar trends as our comparison between cdc25-22 and wee1-50 cells (Fig. 2.8 A-B). Second, we compared cdc25-22 and wee1-50 strains grown at steady-state at 28°C (similar to Fig. 2.2 D-E) to remove effects of temperature shift. Here, we observe the same general trends in the proteome, with the notable exception of ribosome proteins which scaled with cell size in these conditions (Fig. 2.8 C-D). 16 Overall, our data revealed that the proteome has characteristic composition changes with cell size and indicate that ribosomal proteins and certain nuclear proteins were less abundant in large cells relative to other sets of proteins, such as those associated with membrane organelles. Our data were consistent with the recent cell size findings in budding yeast and mammalian proteomes, demonstrating that these are likely to be general signatures of cell size (Fig. 2.7 C) (Lanz et al., 2022, 2023). In general, these results begin to demonstrate how remodeling of the composition of the cytoplasm lead to changes in diffusion in cells of varying sizes. Figure 2.6 Proteome composition varies with cell size Two replicates of SILAC proteomics experiments were performed on cdc25-22 and wee1-50 cells grown with temperature shift. Concentrations of each protein are determined per strain and normalized to the respective strain's proteome. To compare proteome differences between cdc25-22 and wee1-50, these relative concentration ratios were expressed as a ratio of cdc25-22/wee1-50. (A) Relative protein concentration ratios (cdc25-22/wee1-50) for each detected protein. Proteins highlighted according to select subcellular locations. Number of proteins per subcellular location category in parentheses. Upper right quadrant (blue) indicates proteins that have relative protein concentration ratios that are more than 1. These proteins are relatively more abundant in cdc25-22 compared to wee1-50. By contrast, the lower left quadrant (red) indicates proteins that have relative protein concentration ratios that are less than 1. These proteins are relatively less abundant in cdc25-22 compared to wee1-50. (B) Average relative concentration ratios of proteins grouped by subcellular localization. 17 Figure 2.7 Comparison of S. pombe proteome size scaling with other studies (A) Average relative concentration ratios of proteins grouped by biological processes for S. pombe (cdc25-22/wee1-50 grown with temperature shift). Biological processes were selected based on prior evidence that the biological process affects diffusion of nanoparticles or has been associated with cytoplasmic dilution. (B) Gene ontology analysis of sub- (red) and super-scaling (blue) proteins in S. pombe (cdc25-22/wee1-50 grown with temperature shift). (C) Correlations between size-scaling proteomics datasets of S. pombe (cdc25-22/wee1-50 grown with temperature shift) compared to (top) S. cerevisae (from Lanz et al. 2023, cell size mutants) and (bottom) human RPE-1 cells (from Lanz et al. 2022., size-sorted). 18 Figure 2.8 Cell size proteome remodeling is observed at smaller cell size differences Cell size proteome remodeling is observed at smaller cell size differences. (A) Average relative protein concentration ratios (cdc25-22/wildtype) of proteins grouped by subcellular localization. Cells are grown at the permissive temperature 25°C overnight and shifted to the non-permissive temperature 36°C for 6.5 hours (approximately two doublings) before sample collection. (B) Average relative concentration ratios (cdc25-22/wee1-50) proteins grouped by subcellular localization. Cells are grown at steady-state 28°C. 19 Chapter 3 – Additional unpublished data 3.1 Intracellular diffusion in the nucleus increases with cell size To investigate whether the phenomenon of diffusion increasing with cell size occurs not only in the cytoplasm, but also in the nucleus, we expressed and imaged 40nm nucGEMs nanoparticles in S. pombe wildtype and the cell size mutant cells wee1-50 and cdc25-22 (Szórádi et al.; Lemière et al., 2022). Similar to Fig. 2.1, we grew wee1-50, wildtype, and cdc25-22 cells at the permissive temperature 25°C and shifted to the non-permissive temperature 36°C for six hours before imaging (Nurse, 1975; Fantes and Nurse, 1978). Each cell population yielded average effective nucGEMs diffusion coefficients of 0.48 ± 0.05 µm2/s in wee1-50, 0.63 ± 0.04 µm2/s in wildtype, and 0.81 ± 0.03 µm2/s in cdc25-22 cells (Fig. 3.1 A; mean ± STD of replicate experiments). Thus, nucGEMs diffusion showed a striking positive correlation between cell size and nanoparticle diffusion in the nucleus at the population level. We then analyzed the relationship between nucGEMs diffusion and cell size in individual cells, which exhibited a similar trend of increasing diffusion with cell size (Fig. 3.1 B). Overall, our data show that diffusion increases with cell size both in the cytoplasm and the nucleoplasm. This suggests that, as cells grow large, both compartments may experience biophysical changes, such as a “dilution” of macromolecules, and can have profound effects on processes that occur in either compartment. 20 Figure 3.1 Nanoparticle diffusion in the nucleus increases with cell size (A) nucGEMs diffusion (mean ± STD of replicate experiments) for wee1-50, wildtype, and cdc25-22 cells grown with the temperature shift protocol described in Fig. 2.1 A (1-way ANOVA, p < 0.0001). (B) Cell length and nucGEMs diffusion (mean ± SEM of nucGEMs trajectories per cell; NCELLS ≥ 72 per condition from at least 4 biological replicates) plotted for individual cells for wee1-50, wildtype, and cdc25-22 cells grown with the temperature shift protocol described in (A). (1-way ANOVA, * - p < 0.05, ** - p < 0.01, *** - p < 0.001, **** - p < 0.0001). 3.2 Inhibiting ribosomal biogenesis may be sufficient to alter intracellular diffusion in fission yeast Ribosomes have been identified as a crowding agent in the cytoplasm in budding yeast and human cell lines as treating these cells with rapamycin, an TOR inhibitor, resulted in decreased ribosomal concentration and increased diffusion in the cytoplasm (Delarue et al., 2018). Additionally, deletion of the positive ribosome biogenesis transcriptional regulator Sfp1 also results in an cytGEMs diffusion increase in budding yeast (Delarue et al., 2018). Although we observe a concurrent decrease in ribosomal concentration and an increase in cytGEMs diffusion in very large fission yeast cells (Fig. 2.1 and 2.5), it was unclear whether decreasing ribosomal concentration, without greatly increasing cell size, would also result in increased intracellular diffusion. 21 We observed protein scaling behaviors in large fission yeast cells that were consistent with ribosome biogenesis inhibition, either through mutants that inhibited TOR function or had a Sfp1 deletion (Fig. 2.6 A). To test whether inhibition of ribosome biogenesis is sufficient to increase diffusion of GEMs, we first measured GEMs diffusion in cells that were treated with Rbin-1, a specific inhibitor of Midasin, an ATPase involved in the assembly of nucleolar precursors of the ribosome 60S subunit (Kawashima et al., 2016). After 2 hours of 10μM Rbin-1 treatment, 60S precursors accumulate in the nucleus (Fig. 3.2 A), and we observed that diffusion the cytoplasm increases (DMSO: 0.56 ± 0.07 µm2/s; Rbin-1: 0.75 ± 0.01 µm2/s) and diffusion the nucleus decreases (DMSO: 0.75 ± 0.01 µm2/s; Rbin-1: 0.58 ± 0.05 µm2/s) (Fig. 3.2 BC; mean ± STD of replicate experiments). Next, we treated cells with 1hour of 25μM Torin1, a TOR inhibitor that inhibits both TORC1 and TORC2 in fission yeast. Here, did not observe a significant change in diffusion in the cytoplasm (DMSO: 0.51 ± 0.07 µm2/s; Torin1: 0.53 ± 0.03 µm2/s), though we did see a modest increase in diffusion in the nucleus (DMSO: 0.52 ± 0.00 µm2/s; Torin1: 0.59 ± 0.03 µm2/s) (Fig. 3.2 DE; mean ± STD of replicate experiments). Finally, we measured nucGEMs diffusion in cells with an Sfp1 deletion and observed a significant increase in diffusion in the nucleus (wt: 0.45 ± 0.00 µm2/s; sfp1△: 0.54 ± 0.03 µm2/s) (Fig. 3.2 F; mean ± STD of replicate experiments). Overall, these data suggest that intracellular diffusion can be altered by inhibiting ribosome biogenesis with various methods thereby further implicating ribosomes as a crowding agent in fission yeast. 22 Figure 3.2 Inhibiting ribosomal biogenesis may be sufficient to alter intracellular diffusion (A) Images of S. pombe wildtype cells with various ribosomal proteins tagged with GFP treated with 0.5% DMSO or 10μM Rbin-1 for 2 hours at 30°C. (B) CytGEMs and (C) nucGEMs diffusion (mean ± STD of replicate experiments; NCELLS ≥ 55 and NGEMS ≥ 1118 per condition from 3 biological replicates) of wildtype cells treated with DMSO and Rbin-1 as described in (A). (D) CytGEMs and (E) nucGEMs diffusion (mean ± STD of replicate experiments; NCELLS ≥ 65 and NGEMS ≥ 1488 per condition from 3 biological replicates) of wildtype cells treated with 1% DMSO and 25μM Torin1 for 1 hour at 30°C. (F) NucGEMs diffusion (mean ± STD of replicate experiments; NCELLS ≥ 68 and NGEMS ≥ 1668 per condition from 3 biological replicates) of wildtype and sfp1△ cells. Unpaired t-test, * - p < 0.05, ** - p < 0.01). 23 Chapter 4 – Discussion Here, we demonstrate that intracellular diffusion coefficients of macromolecular-sized particles show a significant positive correlation with increasing cell size. Relative to wildtype cells, cytGEMs diffusion increased in large cdc25-22 mutant cells and decreased in the smaller wee1-50 mutant cells (Fig. 2.1). However, cytGEMs diffusion was not changed in large multinucleate cells, demonstrating that DNA-to-Cytoplasm (DC) ratio may be the critical parameter that underlies diffusion rates rather than cell size alone (Fig. 2.3). In investigating the mechanism underlying these changes in diffusion, we showed that large and small cells exhibited different proteome compositions, with large cells exhibiting decreased concentrations of ribosome and nuclear proteins relative to other elements of the proteome. These results are consistent with a model in which diffusion increases in larger cells due to a decrease in the concentration of ribosomes and changes to the concentrations of many other cytoplasmic components. In proliferating cells, a limiting factor may be the number of gene copies needed to maintain gene expression to support the exponential growth of the cytoplasm (Zhurinsky et al., 2010; Marguerat and Bähler, 2012; Neurohr et al., 2019; Balachandra et al., 2022; Cadart and Heald, 2022; Xie et al., 2022). Overall, our study supports the premise that the properties of the cytoplasm vary at different cell sizes. While previous studies focus on the apparent dilution of the cytoplasm and/or changes in the biochemical composition in large cells (Neurohr et al., 2019; Lanz et al., 2022), our findings show that cell size impacts diffusion and crowding in the cytoplasm. As diffusion and crowding have broad range effects on the inner workings of the cell, including the rates of most biochemical reactions, our findings 24 introduce a critical component in our understanding of the effects of cell size on cellular physiology. Our studies leverage certain advantages in the fission yeast model. The use of the cytGEMs nanoparticle is well-established as a quantitative tool in these cells (Lemière et al., 2022; Molines et al., 2022). In using well-studied cell cycle mutants, the molecular bases for the perturbations in cell size and genomic copy number are defined (Nurse, 1975; Nurse et al., 1976; Fantes and Nurse, 1978; Hagan et al., 2016). Rather than using polyploid or endoreplication lines that may have the specific effects on cellular physiology, we used well-characterized cytokinesis mutants to transiently produce large multinucleate cells (Neurohr et al., 2019; Lanz et al., 2022). Because of the abilities to determine cell size of individual cells, our study utilized single-cell analyses in addition to bulk population measurements. Although surface area-to-volume (SA/V) ratio can impose constraints on metabolism across cell sizes, SA/V ratios do not vary with cell size in our cells due to the characteristic rod-cell shape of fission yeast used in this study (Shi et al., 2021). The observed changes in cytGEMs diffusion are consistent with a previous study that highlighted the cell cycle-dependent fluctuations in intracellular density where cell density decreases during G2 phase and increases during cell division (Odermatt et al., 2021). One mechanism for density dilution in cdc25-22 arrested cells may be due to the prolonged time in G2 phase when the rate of volume growth slightly outpaces mass biosynthesis. By contrast, the density increase in wee1-50 cells may be due to the enrichment of dividing cells in the wee1-50 population. In addition, our studies contribute to a growing body of evidence that the cytoplasm not only becomes more dilute with increasing cell size, but also, that composition of the 25 cytoplasm remodels with cell size. Comparison of our results with recent data in human cells and budding yeast cells show that this remodeling of the proteome is largely conserved in these eukaryotic cells (Fig. 2.7) (Lanz et al., 2022, 2023). For example, in these three organisms, our studies agree in that sub-scaling proteins are enriched in nuclear proteins, while super-scaling proteins are enriched in ER and mitochondrial proteins as well as metabolic proteins (Fig. 2.6). Proteome remodeling in budding yeast is thought to be independent of metabolic state, but holds similarities to cells in the starvation state and during environmental stress response (Lanz et al., 2023). However, we did not detect these similarities for fission yeast (Fig. 2.7 A). Our proteome and fluorescence intensity analyses (Fig. 2.5, 2.7) together showed that the concentration of ribosomal proteins subscales in larger fission yeast cells. As ribosomes have been suggested to be the primary crowding agent of macromolecules in the cytoplasm, the decrease in not only ribosomal protein concentration but also the decrease in ribosome biogenesis proteins and TOR complex proteins altogether provide a possible mechanism for increased diffusion mediated through the TOR pathway (Delarue et al., 2018). In addition to ribosomal concentration, it is likely that other factors also contribute to cell size effects. We noted that ribosomal protein and protein content alone cannot readily account for all the diffusion data; for instance, we detected no significant decrease in ribosomal protein concentration associated with increased diffusion in wee1-50 cells (Fig. 2.1, 2.2, 2.5). Therefore, there are likely to be additional factors that contribute to diffusion changes, such as small viscogens like trehalose and glycerol (Fig. 2.7). Changes in macromolecular crowding and diffusion are predicted to have significant impact on the biochemistry and mechanobiology inside the cell. For instance, 26 these physical cytoplasmic properties not only affect rates of biochemical reactions, dynamics of molecular conformational changes, and protein expression, but they also impact organelle size and phase transitions that help to organize the cytoplasm (Rivas and Minton, 2016; Mitchison, 2019; Marshall, 2020). Our studies suggest that one reason why cell size is maintained in a homeostatic manner is to maintain the state of the cytoplasm. In abnormally-sized cells seen in senescence, aging, or disease states, altered cytoplasmic properties may contribute to slower growth rates, abnormal cellular function, and cell death (Neurohr and Amon, 2020; Xie et al., 2022). Future studies promise to reveal how cell size affects the intracellular environment responsible for cellular functions. 27 Chapter 5 – Methods and Materials Table 5.1 Key resources used in this study Reagent type (species) or resource Designation Source or reference Identifiers Additional Information Genetic reagent (Schizosaccharomyces pombe) Ish1-mScarlet Chang Lab collection FC 3318 h- ade6<<mCherry-psy1 ish1-GFP:kanMX ura4-D18 Genetic reagent (S. pombe) Ish1-mScarlet, cdc25 mutant This manuscript FC 3339 cdc25-22 ade6<<mCherry-psy1 ish1-GFP:kanMX Genetic reagent (S. pombe) Ish1-mScarlet, wee1 mutant This manuscript FC 3340 wee1-50 ish1-GFP:kanMX Genetic reagent (S. pombe) cytGEMs Chang Lab collection FC 287 h- pREP41X-PfV-Sapphire leu1-32 Genetic reagent (S. pombe) cytGEMs, cdc25 mutant This manuscript FC 3341 h+ cdc25-22, pREP41X-PfV-Sapphire leu1-32 Genetic reagent (S. pombe) cytGEMs, wee1 mutant This manuscript FC 3342 h- wee1-50, pREP41X-PfV-Sapphire leu1-32 hist? Genetic reagent (S. pombe) cytGEMs, cdc2 mutant This manuscript FC 3343 h90 cdc2-asM17, pREP41X-PfV-Sapphire, leu1-32, ura4-D18 Genetic reagent (S. pombe) cytGEMs, sid2 mutant, ish1-mScarlet This manuscript FC 3344 h+ sid2-as ish1:mScarlet-I:hphMX6 ade6-M210, pREP41X-PfV-Sapphire, leu1-32 ura4-D18 Genetic reagent (S. pombe) cytGEMs, cdc11 mutant This manuscript FC 3345 h- cdc11-119, pREP41X-PfV-Sapphire leu1-32 Genetic reagent (S. pombe) Rps2-GFP Chang Lab collection FC3209 h- rps2-GFP::kanR leu1-32 ura4-D18 ade6-210 Genetic reagent (S. pombe) Rps2-GFP, cdc25 mutant This manuscript FC 3346 cdc25-22 rps2-GFP::kanR leu1-32 ura4-D18 Genetic reagent (S. pombe) Rps2-GFP, wee1 mutant This manuscript FC 3347 wee1-50 rps2-GFP::kanR leu1-32 ade6-210 Genetic reagent (S. pombe) car2 mutant This manuscript FC 3348 car2∆::kanMX4 arg1-230 lys3-37 Genetic reagent (S. pombe) car2 mutant, cdc25 mutant This manuscript FC 3349 cdc25-22 car2∆::kanMX4 arg1-230 lys3-37 Genetic reagent (S. pombe) car2 mutant, wee1 mutant This manuscript FC 3350 wee1-50 car2∆::kanMX4 arg1-230 lys3-37 Chemical compound/drug YES 225 Media Sunrise Science Production #2011 Chemical compound/drug Edinburgh Minimum Media (EMM) MP Biomedicals #4110-32 Chemical compound/drug Agar Chemical compound/drug Histidine Sigma-Aldrich #H8000 Chemical compound/drug Uracil Sigma-Aldrich #U0750 28 Reagent type (species) or resource Designation Source or reference Identifiers Additional Information Chemical compound/drug Adenine Sigma-Aldrich #A9126 Chemical compound/drug Thiamine Sigma-Aldrich #T4625 Chemical compound/drug Dimethyl sulfoxide (DMSO) Fisher Scientific #67-68-5 Chemical compound/drug 1-NM-PP1 Fisher Scientific #50-203-0494 Chemical compound/drug Agarose Invitrogen #16500500 Chemical compound/drug Dulbecco's Phosphate Buffer Saline Thermo Scientific 14190144 Chemical compound/drug 4% formaldehyde (methanol-free) Thermo Scientific #28,906 Chemical compound/drug RNAse A Thermo Scientific #EN0531 Chemical compound/drug Fluorescin isothiocyanate isomer I Sigma #F7250 Chemical compound/drug Light arginine (L-ARGININE:HCL–Unlabeled) Cambridge Isotope Laboratories #ULM-8347-PK Chemical compound/drug Heavy arginine (L-ARGININE:HCL(13C6,99%)) Cambridge Isotope Laboratories #CNLM-2265-H-0.25 Chemical compound/drug Light lysine (L-LYSINE:2HCL–Unlabeled) Cambridge Isotope Laboratories #ULM-8766-PK Chemical compound/drug Heavy lysine (L-LYSINE:2HCL(4,4,5,5-D4,96-98%)) Cambridge Isotope Laboratories #DLM-2640-0.5 Chemical compound/drug Iodoacetamide Sigma #I1149-5G Chemical compound/drug TPCK-treated trypsin Worthington #LS003740 Chemical compound/drug Sep-Pak 50mg C18 column Waters ##054955 29 Reagent type (species) or resource Designation Source or reference Identifiers Additional Information Software, algorithm μManager v. 1.41 Edelstein et al., 2010; Edelstein et al., 2014 Software, algorithm Mathworks Mathworks 2018a Software, algorithm Python Drake Jr. and Van Rossum, 1995 3.8.8 Software, algorithm Prism GraphPad Version 9.4.1 Software, algorithm FIJI ImageJ Schindelin et al., 2012 Software, algorithm MaxQuant (v2.4.2) Other μ-Slide VI 0.4 channel slide Ibidi #80606 Yeast strains and media Schizosaccharomyces pombe strains were constructed and maintained using standard methods (Forsburg, 2003). The strains used in this study are listed in Table 4.1. For expression of 40nm cytGEMs, yeast cells were transformed with the plasmid pREP41X-PfV-mSapphire for expression of the protein fusion PfV encapsulin-mSapphire (Delarue et al., 2018; Lemière et al., 2022; Garner et al., 2023). These cells were grown in EMM3S (minus leucine) media with 0.1μg/mL thiamine for an intermediate level of expression from the nmt1* promoter to optimize the appropriate numbers of cytGEMs in each cell (Maundrell, 1993; Molines et al., 2022). In other experiments, cells were grown in rich YES (Fig. 2.5) or SILAC adjusted EMM media (Fig. 2.6). 30 Temperature shift and inhibitor treatments Fission yeast cells of different cell sizes and ploidy were generated using established conditional cell cycle mutants (see main text). For temperature-shift experiments (Figs. 2.1 A-D, 2.3) wildtype and temperature-sensitive mutant cells were inoculated from colonies freshly grown from the frozen stocks on EMM3S (minus leucine) agar plates grown at 25°C for 3 days and stored at room temp for less than 7 days. Cells were inoculated in liquid EMM3S medium and grown at 25°C with shaking for over 12 hours to exponential phase in the range of OD600 0.2 to 0.6. The flasks were then transferred to a 36°C shaking incubator for the indicated period (3-6 hours). The cells were then harvested and mounted in chambers for imaging on the lab bench and promptly returned to 36°C in the pre-warmed microscope system incubator. No significant differences in cytGEMs diffusion were found when mounting cells on the bench at room temperature (~5 minutes of preparation time) versus preparing cells inside the temperature-controlled cage installed on the microscope. For experiments at semi-permissive temperatures (Fig. S1), cells were maintained at a steady temperature (25-30°C) for ~18 hours and imaged at the indicated temperature in the incubator. For inhibition of cdc2-as and sid2-as alleles (Figs. 2.1, 2.2, 2.3, 2.4), cells were grown in liquid EMM3S at 30°C with shaking and treated with 10µM 1-NM-PP1 (100-fold dilution of a 4mM stock in DMSO) (#50-203-0494, #67-68-5, Fisher Scientific) for 3-6 hours. Cells were harvested and imaged as above. Preparation of cells for live cell microscopy Cells were placed just before imaging into μSlide VI 0.4 channel slides (#80606, Ibidi – 6 channels slide, channel height 0.4mm, length 17mm, and width 3.8mm, tissue culture 31 treated and sterilized). The μSlide was first pre-coated by incubation with 100μg/mL of lectin (#L1395, Sigma) for at least 15 min at room temperature and then removed from the chamber. For mounting cells, 1 mL of liquid yeast culture was centrifuged for 2 minutes in an microcentrifuge tube at 400 x G at room temperature. Most supernatant was removed, and the cell pellet was gently resuspended in the remaining ~100 µL media. 50 µL of this concentrated cell mixture was added the pre-coated chamber and allowed to adhere for 2 minutes and washed three times with pre-warmed media to remove non-adhered cells. Microscopy For imaging of cytGEMs (Fig. 2.1 and 2.3), live cells were imaged with a TIRF Diskovery system (Andor) with a Ti-Eclipse 2 inverted microscope stand (Nikon Instruments), 488nm laser illumination, a 60X TIRF oil objection (NA: 1:49, oil DIC N2) (#MRD01691, Nikon), and a sCMOS camera (Zyla, Andor). These components were controlled with μManager v. 1.41 (Edelstein et al., 2010; Edelstein et al., 2014). Temperature was maintained by a black panel cage incubation system (#748-3040, OkoLab). Cells were mounted in μSlide VI 0.4 channel slides (#80606, Ibidi – 6 channels slide, channel height 0.4mm, length 17mm, and width 3.8mm, tissue culture treated and sterilized). For imaging of nuclei and fluorescence intensity quantification (Fig. 2.3 and 2.5), cells were imaged on a Ti-Eclipse inverted microscope (Nikon Instruments) with a spinning-disk confocal system (Yokogawa CSU-10) that includes 488nm and 541nm laser illumination (with Borealis) and emission filters 525±25nm and 600±25nm respectively, 40X (NA: 0.6) and 60X (NA: 1.4) objectives, and an EM-CCD camera (Hamamatsu, 32 C9100-13). These components were controlled with μManager v. 1.41 (Edelstein et al., 2010, 2014). Temperature was maintained by a black panel cage incubation system (#748–3040, OkoLab). Imaging and analysis of cytGEMs Cells expressing cytGEMs nanoparticles were imaged in fields of 1K x 1.2K pixels or smaller using highly inclined laser beam illumination at 100Hz for 5 seconds. Cells generally exhibited 10-20 of cytGEMs particles/cell. CytGEMs were tracked with the ImageJ (Schindelin et al.) particle Tracker 2D-3D tracking algorithm from MosaicSuite (Sbalzarini and Koumoutsakos, 2005) with the following parameters: run("Particle Tracker 2D/3D", "radius = 3 cutoff= 0 per/abs = 0.03 link = 1 displacement = 6 dynamics = Brownian"). In Figures 1-2, GEMs were analyzed collectively in multiple cells in the whole field of view. For analyses of individual cells (Fig. 2.1 D), cells were individually cropped from field images, and cytGEMs were tracked with the same MosaiSuite parameters with the exception of per/abs = 0.03. The analyses of the cytGEMs tracks were as described in Delarue et al., 2018, with methods to compute mean square displacement (MSD) using MATLAB (MATLAB_R2018, MathWorks). The effective diffusion coefficient Deff was obtained by fitting the first 10 time points of the MSD curve (MSDtruncated) to the canonical 2D diffusion law for Brownian motion: MSDtruncated(τ)=4 ⋅ Deff ⋅ τ. Measurement of cell length and nuclei count As a proxy for cell size, cell length along the long axis of the rod-shaped cells was measured manually using ImageJ Line Selection tool on brightfield images of cells. 33 “Straight Line” or “Segmented Line” was used depending on cell morphology. For determination of the number of nuclei, strains with the nuclear envelope marker Ish1 tagged with a fluorescent protein were grown in EMM3S (minus leucine) media, and number of nuclei were counted manually. Septated-cells were excluded from analysis. Ribosomal concentration quantification Ribosomal concentration was measured in individual fission yeast cells using a ribosomal protein Rps2-GFP signal intensity, similarly as described (Knapp et al., 2019; Lemière et al., 2022). Cells expressing Rps2-GFP were grown in rich YES liquid media at 25°C overnight and shifted to 36°C for 6 hours before imaging. Cells were mounted on a 2% agarose (#16500500, Invitrogen) in YES 225 (#2011, Sunrise Science Production) pad and imaged with 488 nm laser illumination via spinning disk confocal microscopy. The Rps2-GFP signal was acquired in 500 nm z-step stacks, and a sum of stack of the middle 3 slices was used for intensity quantification. For each selected cell, the Rps2-GFP signal intensities were measured along the long cell axis (averaged over 4 µm in width) and normalized by cell length. The signal was corrected for background intensity and uneven illumination of the field. Rps2-GFP signals were defined as the average of the mean signal between 0.2-0.3 and the mean signal between 0.7–0.8 (peak signals in the cytoplasm, avoiding the nucleus) along the normalized cell length. Finally, all Rps2-GFP signals were normalized to the mean of the cell length (L) category 9 ≤ L < 18 µm. 34 Cellular protein concentration quantification Total protein was measured in individual fission yeast cells using FITC staining, similarly as described (Knapp et al., 2019b; Odermatt et al., 2021; Lemière et al., 2022). Cells were grown in YES liquid media at 25°C overnight and shifted to 36°C for 6 hours until fixation. 1 mL of exponential-phase (OD600 = 0.2-0.6) cell culture was fixed with 4% final concentration formaldehyde (methanol-free 37% solution, #28906, Thermo Scientific, Waltham) and incubated at 4°C overnight. Fixed cells were washed 3 times with phosphate buffered saline (PBS) (#14190, Thermo Scientific) and resuspended in 100 µL of PBS. 100 µL of fixed cells were treated with 0.1 mg/mL RNAse A (#EN0531, Thermo Scientific) and incubated in a rotator for 2 hours at 37°C. Next, cells were washed and re-suspended in PBS and stained with 50 ng/mL FITC (#F7250, Sigma) for 30 min, washed three times with PBS, and resuspended in PBS. Cells were mounted on a 2% agarose (#16500500, Invitrogen) in Dulbecco’s Phosphate Buffer Saline (Thermo Scientific, 14190144) pad and imaged with 488 nm laser illumination via spinning disk confocal microscopy. FITC signal was acquired and analyzed using similar methods as the Rps2-GFP experiments described above. LC-MS/MS sample preparation Proteomic experiments were performed using stable isotope labeling by amino acids in cell culture (SILAC) (Ong et al., 2002). SILAC-compatible fission yeast strains containing car2∆ were grown in SILAC adjusted media (Edinburgh Minimal Media (#4110712, MP Biomedicals) + 6 mM ammonium chloride + 0.04 mg/ml arginine and 0.03 mg/ml lysine) using either light or ‘‘heavy’’ versions of Lysine and Arginine (Swaffer et al., 2016). The 35 ‘‘light’’ (Agr0 Lys0) version of the media contained L-Arginine and L-Lysine built with normal 12C and 14N isotopes; the ‘‘heavy" (Arg6 Lys4) version had L-Arginine containing six 13C atoms and L-Lysine containing four deuterium atoms. For SILAC experiments, cells were grown for at least 8 generations at the indicated temperatures 25-36°C with shaking before collection, diluted in the morning and evening so they are always below OD600 = 0.3. The mean cell volume for proteomics samples was determined by Z2 Coulter Counter (Beckman Coulter), and the mean cell volumes of these samples matched those of the corresponding samples used in the cytGEMs experiments. 10 mL of fission yeast cultures were pelleted by centrifugation at 3,000 x G for 2 minutes at 4°C. The supernatant was removed, and cell pellets were snap frozen in liquid nitrogen and stored at -80°C. Frozen pellets were resuspended in 300 µL of yeast lysis buffer (50mM Tris, 150mM NaCl, 5mM EDTA, 0.2% Tergitol, pH 7.5 ; + a cOmplete ULTRA Tablet) with 700µL of glass beads. Lysis was performed at 4°C in a MPBio Fastprep24 (4 cycles with the following settings: 6.0 m/s, 40 seconds). Cell lysates were cleared by centrifugation at 12,000 x G for 5 minutes at 4°C. Protein concentration was quantified using a Pierce BCA Protein Assay Kit (Prod# 23255). Equal amounts of protein from each SILAC-labeled lysate were mixed. The mixed lysates were then denatured/reduced in 1% SDS and 10mM DTT (15 minutes at 65°C), alkylated with 5mM iodoacetamide (15 minutes at room temperature), and then precipitated with three volumes of a solution containing 50% acetone and 50% ethanol (on ice for 10 minutes). Proteins were re-solubilized in 2M urea, 50mM Tris-HCl, pH 8.0, and 150mM NaCl, and then digested with TPCK-treated trypsin (50:1) overnight at 37°C. Trifluoroacetic acid and formic acid were added to the digested peptides for a final concentration of 0.2% (pH ~3). Peptides were 36 desalted with a Sep-Pak 50 mg C18 column (Waters). The C18 column was conditioned with 500 µL of 80% acetonitrile and 0.1% acetic acid and then washed with 1000 µL of 0.1% trifluoroacetic acid. After samples were loaded, the column was washed with 2000 µL of 0.1% acetic acid followed by elution with 400 µL of 80% acetonitrile and 0.1% acetic acid. The elution was dried in a Concentrator at 45°C. LC-MS/MS data acquisition Desalted SILAC-labeled peptides were analyzed on a Fusion Lumos mass spectrometer (Thermo Fisher Scientific, San Jose, CA) equipped with a Thermo EASY-nLC 1200 LC system (Thermo Fisher Scientific, San Jose, CA). Peptides were separated by capillary reverse phase chromatography on a 25 cm column (75 µm inner diameter, packed with 1.6 µm C18 resin, AUR2-25075C18A, Ionopticks, Victoria Australia). Peptides were introduced into the Fusion Lumos mass spectrometer using a 125 minute stepped linear gradient at a flow rate of 300 nL/minute. The steps of the gradient are as follows: 3–27% buffer B (0.1% (v/v) formic acid in 80% acetonitrile) for 105 minutes, 27-40% buffer B for 15 minutes, 40-95% buffer B for 5 minutes, and finally maintained at 90% buffer B for 5 minutes. Column temperature was maintained at 50°C throughout the procedure. Xcalibur software (Thermo Fisher Scientific) was used for the data acquisition and the instrument was operated in data-dependent mode. Advanced peak detection was enabled. Survey scans were acquired in the Orbitrap mass analyzer (Profile mode) over the range of 375 to 1500 m/z with a mass resolution of 240,000 (at 200 m/z). For MS1, the Normalized AGC Target (%) was set at 250 and max injection time was set to “Auto”. Selected ions were fragmented by Higher-energy Collisional Dissociation (HCD) with 37 normalized collision energies set to 31, and the fragmentation mass spectra were acquired in the Ion trap mass analyzer with the scan rate set to “Turbo”. The isolation window was set to 0.7 m/z window. For MS2, the Normalized AGC Target (%) was set to “Standard” and max injection time was set to “Auto”. Repeated sequencing of peptides was kept to a minimum by dynamic exclusion of the sequenced peptides for 30 seconds. Maximum duty cycle length was set to 1 second. Spectral searches All raw files were searched using the Andromeda engine (Cox et al., 2011) embedded in MaxQuant (v1.6.7.0) (Cox and Mann, 2008). In brief, 2-label SILAC search was conducted using MaxQuant’s default Arg6/10 and Lys4/8. Variable modifications included oxidation (M) and protein N-terminal acetylation, and carbamidomthyl (C) was a fixed modification. The number of modifications per peptide was capped at 5. Digestion was set to tryptic (proline-blocked). Peptides were ‘‘Re-quantified’’, and maxquant’s match-between-runs feature was not enabled. Database search was conducted using the UniProt proteome - UP000002485. Minimum peptide length was 7 amino acids. FDR was determined using a reverse decoy proteome (Elias and Gygi, 2007). Peptide quantitation Our SILAC analysis utilized MaxQuant’s ‘‘proteinGroups.txt’’ output file. Contaminant and decoy peptide identifications were discarded. When applicable, the “Leading Razor Protein” designation was used to assign non-unique peptides to individual proteins. 38 Normalized SILAC ratios were used to determine changes in the relative concentrations of individual proteins. Protein annotations Protein annotations in Figure 4 were sourced from UniProt columns named “Gene Ontology IDs” “Subcellular localization [CC]” or PomBase “Complex Annotations” unless otherwise noted (Rutherford et al.; UniProt: the Universal Protein Knowledgebase in 2023 The UniProt Consortium, 2022). Protein localization was strictly parsed so that each annotated protein belongs to only one of the designated groups. Proteins with 2 or more annotations were ignored (except for the “Cytoplasm/Nucleus” category which required a nuclear and cytoplasmic annotation and for categories, e.g. Histone, Chromosome, Nucleolus, which also contained a “Nucleus” annotation). Gene ontology enrichment analysis Relative protein concentration ratios were averaged between the two repetitions of proteomics experiments. Under- and overrepresented proteins were defined as having a minimum of a 10% change in their mean relative protein concentration ratio. GO process characterization of protein lists was performed using Protein Analysis Through Evolutionary Relationships (PANTHER) overrepresentation analysis version PANTHER 18.0 (Thomas et al., 2022). Ortholog analysis Human ortholog pairs were retrieved using the DRSC Integrative Ortholog Prediction Tool (DIOPT) found at https://www.flyrnai.org/cgi-bin/DRSC_orthologs.pl. S. pombe proteins 39 were used as the “input” species and humans were set as the “output”. Only ortholog pairs with a DIOPT “weighted Score” of greater than 10 were considered for our analyses. Once S. pombe proteins were matched with a human ortholog protein, we imported protein slope values derived for human RPE-1 cell line from Lanz et al. 2021. 40

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