Cell size control and gene expression homeostasis in single-cells

Heat shock transcription factor-mediated thermal tolerance and cell size plasticity in marine diatoms

Diatoms are a crucial component of marine ecosystems, recognized for their broad environmental adaptability and wide temperature tolerance. However, the molecular mechanisms underlying their adaptability to diverse temperatures are unknown. In this study, we discover that heat shock transcription factors (HSFs) are potentially important for thermal tolerance in diatoms. Our study focuses on PtHSF2, annotated as HSF2 in Phaeodactylum tricornutum's genome, which is ubiquitous in diatoms. Overexpression of PtHSF2 markedly enhances thermal tolerance and increases cell size; causes significant differential expression of several genes, including cell division cycle protein 45-like (PtCdc45-like), ATM (ataxia telangiectasia mutated), ATR (ataxia telangiectasia and Rad3-related), light-harvesting complex protein 2 (Lhcx2), and fatty acid desaturase. Cleavage Under Targets and Tagmentation (CUT&Tag) and CUT&Tag-qPCR analyses demonstrate that PtHSF2 directly targets and upregulates PtCdc45-like and Lhcx2 while downregulating ATP-binding cassette transporter. Functional validation of PtCdc45-like shows that its overexpression results in larger cell size, enhances antioxidant capacity, and improves cell survival at elevated temperatures. Collectively, our findings elucidate the molecular mechanism by which PtHSF2 mediates high-temperature tolerance in diatoms and validate the functions of its target gene PtCdc45-like. These results highlight the importance of HSFs in diatom temperature adaptation and provide insights into temperature acclimation in microalgae.

Bacterial cell size modulation along the growth curve across nutrient conditions

Under stable growth conditions, bacteria maintain cell size homeostasis through coordinated elongation and division. However, fluctuations in nutrient availability result in dynamic regulation of the target cell size. Using microscopy imaging and mathematical modelling, we examine how bacterial cell volume changes over the growth curve in response to nutrient conditions. We find that two rod-shaped bacteria, Escherichia coli and Salmonella enterica, exhibit similar cell volume distributions in stationary phase cultures irrespective of growth media. Cell resuspension in rich media results in a transient peak with a five-fold increase in cell volume ≈ 2h after resuspension. This maximum cell volume, which depends on nutrient composition, subsequently decreases to the stationary phase cell size. Continuous nutrient supply sustains the maximum volume. In poor nutrient conditions, cell volume shows minimal changes over the growth curve, but a markedly decreased cell width compared to other conditions. The observed cell volume dynamics translate into non-monotonic dynamics in the ratio between biomass (optical density) and cell number (colony-forming units), highlighting their non-linear relationship. Our findings support a heuristic model comparing modulation of cell division relative to growth across nutrient conditions and providing novel insight into the mechanisms of cell size control under dynamic environmental conditions.

A Generalized Adder mechanism for Cell Size Homeostasis: Implications for Stochastic Dynamics of Clonal Proliferation

Measurements of cell size dynamics have revealed phenomenological principles by which individual cells control their size across diverse organisms. One of the emerging paradigms of cell size homeostasis is the adder, where the cell cycle duration is established such that the cell size increase from birth to division is independent of the newborn cell size. We provide a mechanistic formulation of the adder considering that cell size follows any arbitrary non-exponential growth law. Our results show that the main requirement to obtain an adder regardless of the growth law (the time derivative of cell size) is that cell cycle regulators are produced at a rate proportional to the growth law and cell division is triggered when these molecules reach a prescribed threshold level. Among the implications of this generalized adder, we investigate fluctuations in the proliferation of single-cell derived colonies. Considering exponential cell size growth, random fluctuations in clonal size show a transient increase and then eventually decay to zero over time (i.e., clonal populations become asymptotically more similar). In contrast, several forms of non-exponential cell size dynamics (with adder-based cell size control) yield qualitatively different results: clonal size fluctuations monotonically increase over time reaching a non-zero value. These results characterize the interplay between cell size homeostasis at the single-cell level and clonal proliferation at the population level, explaining the broad fluctuations in clonal sizes seen in barcoded human cell lines.

Transcriptomic balance and optimal growth are determined by cell size

Cell size and growth are intimately related across the evolutionary scale, but whether cell size is important to attain maximal growth or fitness is still an open question. We show that growth rate is a non-monotonic function of cell volume, with maximal values around the critical size of wild-type yeast cells. The transcriptome of yeast and mouse cells undergoes a relative inversion in response to cell size, which we associate theoretically and experimentally with the necessary genome-wide diversity in RNA polymerase II affinity for promoters. Although highly expressed genes impose strong negative effects on fitness when the DNA/mass ratio is reduced, transcriptomic alterations mimicking the relative inversion by cell size strongly restrain cell growth. In all, our data indicate that cells set the critical size to obtain a properly balanced transcriptome and, as a result, maximize growth and fitness during proliferation.

Stochastic Gene Expression in Proliferating Cells: Differing Noise Intensity in Single-Cell and Population Perspectives

Random fluctuations (noise) in gene expression can be studied from two complementary perspectives: following expression in a single cell over time or comparing expression between cells in a proliferating population at a given time. Here, we systematically investigated scenarios where both perspectives lead to different levels of noise in a given gene product. We first consider a stable protein, whose concentration is diluted by cellular growth, and the protein inhibits growth at high concentrations, establishing a positive feedback loop. For a stochastic model with molecular bursting of gene products, we analytically predict and contrast the steady-state distributions of protein concentration in both frameworks. Although positive feedback amplifies the noise in expression, this amplification is much higher in the population framework compared to following a single cell over time. We also study other processes that lead to different noise levels even in the absence of such dilution-based feedback. When considering randomness in the partitioning of molecules between daughters during mitosis, we find that in the single-cell perspective, the noise in protein concentration is independent of noise in the cell cycle duration. In contrast, partitioning noise is amplified in the population perspective by increasing randomness in cell-cycle time. Overall, our results show that the commonly used single-cell framework that does not account for proliferating cells can, in some cases, underestimate the noise in gene product levels. These results have important implications for studying the inter-cellular variation of different stress-related expression programs across cell types that are known to inhibit cellular growth.

m6A RNA modification regulates innate lymphoid cell responses in a lineage-specific manner

Innate lymphoid cells (ILCs) can quickly switch from a quiescent state to an active state and rapidly produce effector molecules that provide critical early immune protection. How the post-transcriptional machinery processes different stimuli and initiates robust gene expression in ILCs is poorly understood. Here, we show that deletion of the N6-methyladenosine (m6A) writer protein METTL3 has little impact on ILC homeostasis or cytokine-induced ILC1 or ILC3 responses but significantly diminishes ILC2 proliferation, migration and effector cytokine production and results in impaired antihelminth immunity. m6A RNA modification supports an increase in cell size and transcriptional activity in activated ILC2s but not in ILC1s or ILC3s. Among other transcripts, the gene encoding the transcription factor GATA3 is highly m6A methylated in ILC2s. Targeted m6A demethylation destabilizes nascent Gata3 mRNA and abolishes the upregulation of GATA3 and ILC2 activation. Our study suggests a lineage-specific requirement of m6A for ILC2 responses.

Concentration fluctuations in growing and dividing cells: Insights into the emergence of concentration homeostasis

Intracellular reaction rates depend on concentrations and hence their levels are often regulated. However classical models of stochastic gene expression lack a cell size description and cannot be used to predict noise in concentrations. Here, we construct a model of gene product dynamics that includes a description of cell growth, cell division, size-dependent gene expression, gene dosage compensation, and size control mechanisms that can vary with the cell cycle phase. We obtain expressions for the approximate distributions and power spectra of concentration fluctuations which lead to insight into the emergence of concentration homeostasis. We find that (i) the conditions necessary to suppress cell division-induced concentration oscillations are difficult to achieve; (ii) mRNA concentration and number distributions can have different number of modes; (iii) two-layer size control strategies such as sizer-timer or adder-timer are ideal because they maintain constant mean concentrations whilst minimising concentration noise; (iv) accurate concentration homeostasis requires a fine tuning of dosage compensation, replication timing, and size-dependent gene expression; (v) deviations from perfect concentration homeostasis show up as deviations of the concentration distribution from a gamma distribution. Some of these predictions are confirmed using data for E. coli, fission yeast, and budding yeast.

Cell Size as a Primary Determinant in Targeted Nanoparticle Uptake

Nanoparticle (NP) internalization by cells is complex, highly heterogeneous, and fundamentally important for nanomedicine. We report powerful probabilistic statistics from single-cell data on quantitative NP uptake of PEG-coated transferrin receptor-targeted gold NPs for cancer-derived and fibroblast cells according to their cell size, receptor expression, and receptor density. The smaller cancer cells had a greater receptor density and more efficient uptake of targeted NPs. However, simply due to fibroblasts being larger with more receptors, they exhibited greater NP uptake. While highly heterogeneous, targeted NP uptake strongly correlated with receptor expression. When uptake was normalized to cell size, no correlation existed. Consequently, skewed population distributions in cell sizes explain the distribution in NP uptake. Furthermore, exposure to the transferrin receptor-targeted NPs alters the fibroblast size and receptor expression, suggesting that the receptor-targeted NPs may interfere with the metabolic flux and nutrient exchange, which could assist in explaining the altered regulation of cells exposed to nanoparticles.

RBFOX2-regulated TEAD1 alternative splicing plays a pivotal role in Hippo-YAP signaling

Alternative pre-mRNA splicing is key to proteome diversity; however, the biological roles of alternative splicing (AS) in signaling pathways remain elusive. Here, we focus on TEA domain transcription factor 1 (TEAD1), a YAP binding factor in the Hippo signaling pathway. Public database analyses showed that expression of YAP-TEAD target genes negatively correlated with the expression of a TEAD1 isoform lacking exon 6 (TEAD1ΔE6) but did not correlate with overall TEAD1 expression. We confirmed that the transcriptional activity and oncogenic properties of the full-length TEAD1 isoform were greater than those of TEAD1ΔE6, with the difference in transcription related to YAP interaction. Furthermore, we showed that RNA-binding Fox-1 homolog 2 (RBFOX2) promoted the inclusion of TEAD1 exon 6 via binding to the conserved GCAUG element in the downstream intron. These results suggest a regulatory mechanism of RBFOX2-mediated TEAD1 AS and provide insight into AS-specific modulation of signaling pathways.

Realization of Lodging Tolerance in the Aromatic Grass, Cymbopogon khasianus Through Ploidy Intervention

Artificial polyploidy that brings about increase in cell size confers changes in histo-morphology leading to altered phenotype, causing changes in physiological attributes and enhanced concentration of secondary metabolites. The altered phenotype is generally a manifestation of tissue hardiness reflected as robust plant type. Based on a case study undertaken on an industrially important grass, Cymbopogon khasianus (2n = 60) valued for its citral rich essential oil, here we report that the artificial polyploidy not only brings about enhancement in concentration of essential oil but also facilitates lodging tolerance. The latter is contributed by ploidy mediated changes that occur to the cells and tissues in various plant organs by way of increased wall thickening, tissue enhancement and epidermal depositions that enable robust features. An exhaustive illustrated account covering various micro-/macro-morphological, skeletal and histochemical features constituting growth and development vis-a-vis ploidy mediated changes is presented highlighting the novelties realized on account of induced polyploidy.

Density fluctuations, homeostasis, and reproduction effects in bacteria

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

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

Characterizing non-exponential growth and bimodal cell size distributions in fission yeast: An analytical approach

Unlike many single-celled organisms, the growth of fission yeast cells within a cell cycle is not exponential. It is rather characterized by three distinct phases (elongation, septation, and reshaping), each with a different growth rate. Experiments also showed that the distribution of cell size in a lineage can be bimodal, unlike the unimodal distributions measured for the bacterium Escherichia coli. Here we construct a detailed stochastic model of cell size dynamics in fission yeast. The theory leads to analytic expressions for the cell size and the birth size distributions, and explains the origin of bimodality seen in experiments. In particular, our theory shows that the left peak in the bimodal distribution is associated with cells in the elongation phase, while the right peak is due to cells in the septation and reshaping phases. We show that the size control strategy, the variability in the added size during a cell cycle, and the fraction of time spent in each of the three cell growth phases have a strong bearing on the shape of the cell size distribution. Furthermore, we infer all the parameters of our model by matching the theoretical cell size and birth size distributions to those from experimental single-cell time-course data for seven different growth conditions. Our method provides a much more accurate means of determining the size control strategy (timer, adder or sizer) than the standard method based on the slope of the best linear fit between the birth and division sizes. We also show that the variability in added size and the strength of size control in fission yeast depend weakly on the temperature but strongly on the culture medium. More importantly, we find that stronger size homeostasis and larger added size variability are required for fission yeast to adapt to unfavorable environmental conditions.

Advances in microscopy enable us to follow single cells over long timescales from which we can understand how their size varies with time and the nature of innate strategies developed to control cell size. These data show that in many cell types, growth is exponential and the distribution of cell size has one peak, namely there is a single characteristic cell size. However data for fission yeast show remarkable differences: growth is non-exponential and the distribution of cell sizes has two peaks, corresponding to different growth phases. Here we construct a detailed stochastic mathematical model of this organism; by solving the model analytically, we show that it is able to predict the two peaked distributions of cell size seen in data and provide an explanation for each peak in terms of various growth phases of the single-celled organism. Furthermore, by fitting the model to the data, we infer values for the rates of all microscopic processes in our model. This method is shown to provide a much more reliable inference than current methods and shed light on how the strategy used by fission yeast cells to control their size varies with external conditions.

Continuous rate modeling of bacterial stochastic size dynamics

Bacterial division is an inherently stochastic process with effects on fluctuations of protein concentration and phenotype variability. Current modeling tools for the stochastic short-term cell-size dynamics are scarce and mainly phenomenological. Here we present a general theoretical approach based on the Chapman-Kolmogorov equation incorporating continuous growth and division events as jump processes. This approach allows us to include different division strategies, noisy growth, and noisy cell splitting. Considering bacteria synchronized from their last division, we predict oscillations in both the central moments of the size distribution and its autocorrelation function. These oscillations, barely discussed in past studies, can arise as a consequence of the discrete time displacement invariance of the system with a period of one doubling time, and they do not disappear when including stochasticity on either division times or size heterogeneity on the starting population but only after inclusion of noise in either growth rate or septum position. This result illustrates the usefulness of having a solid mathematical description that explicitly incorporates the inherent stochasticity in various biological processes, both to understand the process in detail and to evaluate the effect of various sources of variability when creating simplified descriptions.

Noise suppression in stochastic genetic circuits using PID controllers

Inside individual cells, protein population counts are subject to molecular noise due to low copy numbers and the inherent probabilistic nature of biochemical processes. We investigate the effectiveness of proportional, integral and derivative (PID) based feedback controllers to suppress protein count fluctuations originating from two noise sources: bursty expression of the protein, and external disturbance in protein synthesis. Designs of biochemical reactions that function as PID controllers are discussed, with particular focus on individual controllers separately, and the corresponding closed-loop system is analyzed for stochastic controller realizations. Our results show that proportional controllers are effective in buffering protein copy number fluctuations from both noise sources, but this noise suppression comes at the cost of reduced static sensitivity of the output to the input signal. In contrast, integral feedback has no effect on the protein noise level from stochastic expression, but significantly minimizes the impact of external disturbances, particularly when the disturbance comes at low frequencies. Counter-intuitively, integral feedback is found to amplify external disturbances at intermediate frequencies. Next, we discuss the design of a coupled feedforward-feedback biochemical circuit that approximately functions as a derivate controller. Analysis using both analytical methods and Monte Carlo simulations reveals that this derivative controller effectively buffers output fluctuations from bursty stochastic expression, while maintaining the static input-output sensitivity of the open-loop system. In summary, this study provides a systematic stochastic analysis of biochemical controllers, and paves the way for their synthetic design and implementation to minimize deleterious fluctuations in gene product levels.

In the noisy cellular environment, biochemical species such as genes, RNAs and proteins that often occur at low molecular counts, are subject to considerable stochastic fluctuations in copy numbers over time. How cellular biochemical processes function reliably in the face of such randomness is an intriguing fundamental problem. Increasing evidence suggests that random fluctuations (noise) in protein copy numbers play important functional roles, such as driving genetically identical cells to different cell fates. Moreover, many disease states have been attributed to elevated noise levels in specific proteins. Here we systematically investigate design of biochemical systems that function as proportional, integral and derivative-based feedback controllers to suppress protein count fluctuations arising from bursty expression of the protein and external disturbance in protein synthesis. Our results show that different controllers are effective in buffering different noise components, and identify ranges of feedback gain for minimizing deleterious fluctuations in protein levels.

Single-nucleus RNA-seq2 reveals functional crosstalk between liver zonation and ploidy

Single-cell RNA-seq reveals the role of pathogenic cell populations in development and progression of chronic diseases. In order to expand our knowledge on cellular heterogeneity, we have developed a single-nucleus RNA-seq2 method tailored for the comprehensive analysis of the nuclear transcriptome from frozen tissues, allowing the dissection of all cell types present in the liver, regardless of cell size or cellular fragility. We use this approach to characterize the transcriptional profile of individual hepatocytes with different levels of ploidy, and have discovered that ploidy states are associated with different metabolic potential, and gene expression in tetraploid mononucleated hepatocytes is conditioned by their position within the hepatic lobule. Our work reveals a remarkable crosstalk between gene dosage and spatial distribution of hepatocytes.

Coordination of gene expression noise with cell size: analytical results for agent-based models of growing cell populations

The chemical master equation and the Gillespie algorithm are widely used to model the reaction kinetics inside living cells. It is thereby assumed that cell growth and division can be modelled through effective dilution reactions and extrinsic noise sources. We here re-examine these paradigms through developing an analytical agent-based framework of growing and dividing cells accompanied by an exact simulation algorithm, which allows us to quantify the dynamics of virtually any intracellular reaction network affected by stochastic cell size control and division noise. We find that the solution of the chemical master equation-including static extrinsic noise-exactly agrees with the agent-based formulation when the network under study exhibits stochastic concentration homeostasis, a novel condition that generalizes concentration homeostasis in deterministic systems to higher order moments and distributions. We illustrate stochastic concentration homeostasis for a range of common gene expression networks. When this condition is not met, we demonstrate by extending the linear noise approximation to agent-based models that the dependence of gene expression noise on cell size can qualitatively deviate from the chemical master equation. Surprisingly, the total noise of the agent-based approach can still be well approximated by extrinsic noise models.

Cell size distribution of lineage data: analytic results and parameter inference

Recent advances in single-cell technologies have enabled time-resolved measurements of the cell size over several cell cycles. These data encode information on how cells correct size aberrations so that they do not grow abnormally large or small. Here, we formulate a piecewise deterministic Markov model describing the evolution of the cell size over many generations, for all three cell size homeostasis strategies (timer, sizer, and adder). The model is solved to obtain an analytical expression for the non-Gaussian cell size distribution in a cell lineage; the theory is used to understand how the shape of the distribution is influenced by the parameters controlling the dynamics of the cell cycle and by the choice of cell tracking protocol. The theoretical cell size distribution is found to provide an excellent match to the experimental cell size distribution of E. coli lineage data collected under various growth conditions.

Tos4 mediates gene expression homeostasis through interaction with HDAC complexes independently of H3K56 acetylation

Saccharomyces cerevisiae exhibits gene expression homeostasis, which is defined as the buffering of transcription levels against changes in DNA copy number during the S phase of the cell cycle. It has been suggested that S. cerevisiae employs an active mechanism to maintain gene expression homeostasis through Rtt109-Asf1-dependent acetylation of histone H3 on lysine 56 (H3K56). Here, we show that gene expression homeostasis can be achieved independently of H3K56 acetylation by Tos4 (Target of Swi6-4). Using Nanostring technology, we establish that Tos4-dependent gene expression homeostasis depends on its forkhead-associated (FHA) domain, which is a phosphopeptide recognition domain required to bind histone deacetylases (HDACs). We demonstrate that the mechanism of Tos4-dependent gene expression homeostasis requires its interaction with the Rpd3L HDAC complex. However, this is independent of Rpd3's well-established roles in both histone deacetylation and controlling the DNA replication timing program, as established by deep sequencing of Fluorescence-Activated Cell Sorted (FACS) S and G2 phase populations. Overall, our data reveals that Tos4 mediates gene expression homeostasis through its FHA domain-dependent interaction with the Rpd3L complex, which is independent of H3K56ac.

Prokaryotic single-cell RNA sequencing by in situ combinatorial indexing

Despite longstanding appreciation of gene expression heterogeneity in isogenic bacterial populations, affordable and scalable technologies for studying single bacterial cells have been limited. Although single-cell RNA sequencing (scRNA-seq) has revolutionized studies of transcriptional heterogeneity in diverse eukaryotic systems1-13, the application of scRNA-seq to prokaryotes has been hindered by their extremely low mRNA abundance14-16, lack of mRNA polyadenylation and thick cell walls17. Here, we present prokaryotic expression profiling by tagging RNA in situ and sequencing (PETRI-seq)-a low-cost, high-throughput prokaryotic scRNA-seq pipeline that overcomes these technical obstacles. PETRI-seq uses in situ combinatorial indexing11,12,18 to barcode transcripts from tens of thousands of cells in a single experiment. PETRI-seq captures single-cell transcriptomes of Gram-negative and Gram-positive bacteria with high purity and low bias, with median capture rates of more than 200 mRNAs per cell for exponentially growing Escherichia coli. These characteristics enable robust discrimination of cell states corresponding to different phases of growth. When applied to wild-type Staphylococcus aureus, PETRI-seq revealed a rare subpopulation of cells undergoing prophage induction. We anticipate that PETRI-seq will have broad utility in defining single-cell states and their dynamics in complex microbial communities.

Modeling homeostasis mechanisms that set the target cell size

How organisms maintain cell size homeostasis is a long-standing problem that remains unresolved, especially in multicellular organisms. Recent experiments in diverse animal cell types demonstrate that within a cell population, cellular proliferation is low for small and large cells, but high at intermediate sizes. Here we use mathematical models to explore size-control strategies that drive such a non-monotonic profile resulting in the proliferation capacity being maximized at a target cell size. Our analysis reveals that most models of size control yield proliferation capacities that vary monotonically with cell size, and non-monotonicity requires two key mechanisms: (1) the growth rate decreases with increasing size for excessively large cells; and (2) cell division occurs as per the Adder model (division is triggered upon adding a fixed size from birth), or a Sizer-Adder combination. Consistent with theory, Jurkat T cell growth rates increase with size for small cells, but decrease with size for large cells. In summary, our models show that regulation of both growth and cell-division timing is necessary for size control in animal cells, and this joint mechanism leads to a target cell size where cellular proliferation capacity is maximized.

The Application of Single-Cell RNA Sequencing in Vaccinology

Single-cell RNA sequencing allows highly detailed profiling of cellular immune responses from limited-volume samples, advancing prospects of a new era of systems immunology. The power of single-cell RNA sequencing offers various opportunities to decipher the immune response to infectious diseases and vaccines. Here, we describe the potential uses of single-cell RNA sequencing methods in prophylactic vaccine development, concentrating on infectious diseases including COVID-19. Using examples from several diseases, we review how single-cell RNA sequencing has been used to evaluate the immunological response to different vaccine platforms and regimens. By highlighting published and unpublished single-cell RNA sequencing studies relevant to vaccinology, we discuss some general considerations how the field could be enriched with the widespread adoption of this technology.

Correlation between protein concentration and bacterial cell size can reveal mechanisms of gene expression

Classically, gene expression is modeled as a chemical process with reaction rates dependent on the concentration of the reactants (typically, DNA loci, plasmids, RNA, enzymes, etc). Other variables like cell size are in general ignored. Size dynamics can become an important variable due to the low number of many of these reactants, imperfectly symmetric cell partitioning and molecule segregation. In this work we measure the correlation between size and protein concentration by observing the gene expression of the RpOD gene from a low-copy plasmid in Escherichia coli during balanced growth in different media. A positive correlation was found, and we used it to examine possible models of cell size dynamics and plasmid replication. We implemented a previously developed model describing the full gene expression process including transcription, translation, loci replication, cell division and molecule segregation. By comparing with the observed correlation, we determine that the transcription rate must be proportional to the size times the number of plasmids. We discuss how fluctuations in plasmid segregation, due to the low copy number, can impose limits in this correlation.

Cellular Heterogeneity in Pressure and Growth Emerges from Tissue Topology and Geometry

Cell-to-cell heterogeneity prevails in many systems, as exemplified by cell growth, although the origin and function of such heterogeneity are often unclear. In plants, growth is physically controlled by cell wall mechanics and cell hydrostatic pressure, alias turgor pressure. Whereas cell wall heterogeneity has received extensive attention, the spatial variation of turgor pressure is often overlooked. Here, combining atomic force microscopy and a physical model of pressurized cells, we show that turgor pressure is heterogeneous in the Arabidopsis shoot apical meristem, a population of stem cells that generates all plant aerial organs. In contrast with cell wall mechanical properties that appear to vary stochastically between neighboring cells, turgor pressure anticorrelates with cell size and cell neighbor number (local topology), in agreement with the prediction by our model of tissue expansion, which couples cell wall mechanics and tissue hydraulics. Additionally, our model predicts two types of correlations between pressure and cellular growth rate, where high pressure may lead to faster- or slower-than-average growth, depending on cell wall extensibility, yield threshold, osmotic pressure, and hydraulic conductivity. The meristem exhibits one of these two regimes, depending on conditions, suggesting that, in this tissue, water conductivity may contribute to growth control. Our results unravel cell pressure as a source of patterned heterogeneity and illustrate links between local topology, cell mechanical state, and cell growth, with potential roles in tissue homeostasis.

Analytical distributions for detailed models of stochastic gene expression in eukaryotic cells

The stochasticity of gene expression presents significant challenges to the modeling of genetic networks. A two-state model describing promoter switching, transcription, and messenger RNA (mRNA) decay is the standard model of stochastic mRNA dynamics in eukaryotic cells. Here, we extend this model to include mRNA maturation, cell division, gene replication, dosage compensation, and growth-dependent transcription. We derive expressions for the time-dependent distributions of nascent mRNA and mature mRNA numbers, provided two assumptions hold: 1) nascent mRNA dynamics are much faster than those of mature mRNA; and 2) gene-inactivation events occur far more frequently than gene-activation events. We confirm that thousands of eukaryotic genes satisfy these assumptions by using data from yeast, mouse, and human cells. We use the expressions to perform a sensitivity analysis of the coefficient of variation of mRNA fluctuations averaged over the cell cycle, for a large number of genes in mouse embryonic stem cells, identifying degradation and gene-activation rates as the most sensitive parameters. Furthermore, it is shown that, despite the model's complexity, the time-dependent distributions predicted by our model are generally well approximated by the negative binomial distribution. Finally, we extend our model to include translation, protein decay, and auto-regulatory feedback, and derive expressions for the approximate time-dependent protein-number distributions, assuming slow protein decay. Our expressions enable us to study how complex biological processes contribute to the fluctuations of gene products in eukaryotic cells, as well as allowing a detailed quantitative comparison with experimental data via maximum-likelihood methods.

Efficient computation of stochastic cell-size transient dynamics

BACKGROUND

How small, fast-growing bacteria ensure tight cell-size distributions remains elusive. High-throughput measurement techniques have propelled efforts to build modeling tools that help to shed light on the relationships between cell size, growth and cycle progression. Most proposed models describe cell division as a discrete map between size at birth and size at division with stochastic fluctuations assumed. However, such models underestimate the role of cell size transient dynamics by excluding them.

RESULTS

We propose an efficient approach for estimation of cell size transient dynamics. Our technique approximates the transient size distribution and statistical moment dynamics of exponential growing cells following an adder strategy with arbitrary precision.

CONCLUSIONS

We approximate, up to arbitrary precision, the distribution of division times and size across time for the adder strategy in rod-shaped bacteria cells. Our approach is able to compute statistical moments like mean size and its variance from such distributions efficiently, showing close match with numerical simulations. Additionally, we observed that these distributions have periodic properties. Our approach further might shed light on the mechanisms behind gene product homeostasis.

Stiffness of MDCK II Cells Depends on Confluency and Cell Size

Mechanical phenotyping of adherent cells has become a serious tool in cell biology to understand how cells respond to their environment and eventually to identify disease patterns such as the malignancy of cancer cells. In the steady state, homeostasis is of pivotal importance, and cells strive to maintain their internal stresses even in challenging environments and in response to external chemical and mechanical stimuli. However, a major problem exists in determining mechanical properties because many techniques, such as atomic force microscopy, that assess these properties of adherent cells locally can only address a limited number of cells and provide elastic moduli that vary substantially from cell to cell. The origin of this spread in stiffness values is largely unknown and might limit the significance of measurements. Possible reasons for the disparity are variations in cell shape and size, as well as biological reasons such as the cell cycle or polarization state of the cell. Here, we show that stiffness of adherent epithelial cells rises with increasing projected apical cell area in a nonlinear fashion. This size stiffening not only occurs as a consequence of varying cell-seeding densities, it can also be observed within a small area of a particular cell culture. Experiments with single adherent cells attached to defined areas via microcontact printing show that size stiffening is limited to cells of a confluent monolayer. This leads to the conclusion that cells possibly regulate their size distribution through cortical stress, which is enhanced in larger cells and reduced in smaller cells.

Cell Biology: The Health Hazards of Super-Sizing

Cells typically occupy a narrow range of sizes according to their type. A new study reveals that cells grown to gigantic proportions fail to synthesize sufficient macromolecules, resulting in cytoplasm dilution and a loss of fitness reminiscent of old cells.

Advanced proteomics approaches to unravel protein homeostasis

Quantitative proteomics methods are instrumental in measuring the interplay between protein synthesis and protein degradation in cells and tissues in different conditions and substantially contribute to the understanding of control mechanisms for protein homeostasis. Proteomics and chemoproteomics approaches enable the characterization of small molecule modifiers of protein degradation for therapeutic applications. Here, we review recent developments and applications of mass spectrometry-based (chemo-)proteomics methods for the study of cellular homeostasis.

Emergence of robust patterns from local rules during plant development

The formation of spatial and temporal patterns is an essential component of organismal development. Patterns can be observed on every level from subcellular to organismal and may emerge from local rules that correspond to the interactions between molecules, cells, or tissues. The emergence of robust patterns may seem in contradiction with the prominent heterogeneity at subcellular and cellular scales, however it has become increasingly clear that heterogeneity can be instrumental for pattern formation. Here we review recent examples in plant development, involving genetic regulation, cell arrangement, growth and signal gradient. We discuss how patterns emerge from local rules, whether heterogeneity is stochastic or can be patterned, and whether stochastic noise is amplified or requires filtering for robust patterns to be achieved. We also stress the importance of modelling in investigating such questions.

Intrinsic and extrinsic noise of gene expression in lineage trees

Cell-to-cell heterogeneity is driven by stochasticity in intracellular reactions and the population dynamics. While these sources are usually studied separately, we develop an agent-based framework that accounts for both factors while tracking every single cell of a growing population. Apart from the common intrinsic variability, the framework also predicts extrinsic noise without the need to introduce fluctuating rate constants. Instead, extrinsic fluctuations are explained by cell cycle fluctuations and differences in cell age. We provide explicit formulas to quantify mean molecule numbers, intrinsic and extrinsic noise statistics in two-colour experiments. We find that these statistics differ significantly depending on the experimental setup used to observe the cells. We illustrate this fact using (i) averages over an isolated cell lineage tracked over many generations as observed in the mother machine, (ii) population snapshots with known cell ages as recorded in time-lapse microscopy, and (iii) snapshots with unknown cell ages as measured from static images or flow cytometry. Applying the method to models of stochastic gene expression and feedback regulation elucidates that isolated lineages, as compared to snapshot data, can significantly overestimate the mean number of molecules, overestimate extrinsic noise but underestimate intrinsic noise and have qualitatively different sensitivities to cell cycle fluctuations.

Controlling organism size by regulating constituent cell numbers

How living cells employ counting mechanisms to regulate their numbers or density is a long-standing problem in developmental biology that ties directly with organism or tissue size. Diverse cells types have been shown to regulate their numbers via secretion of factors in the extracellular space. These factors act as a proxy for the number of cells and function to reduce cellular proliferation rates creating a negative feedback. It is desirable that the production rate of such factors be kept as low as possible to minimize energy costs and detection by predators. Here we formulate a stochastic model of cell proliferation with feedback control via a secreted extracellular factor. Our results show that while low levels of feedback minimizes random fluctuations in cell numbers around a given set point, high levels of feedback amplify Poisson fluctuations in secreted-factor copy numbers. This trade-off results in an optimal feedback strength, and sets a fundamental limit to noise suppression in cell numbers with short-lived factors providing more efficient noise buffering. We further expand the model to consider external disturbances in key physiological parameters, such as, proliferation and factor synthesis rates. Intriguingly, while negative feedback effectively mitigates disturbances in the proliferation rate, it amplifies disturbances in the synthesis rate. In summary, these results provide unique insights into the functioning of feedback-based counting mechanisms, and apply to organisms ranging from unicellular prokaryotes and eukaryotes to human cells.

Perturbations of Transcription and Gene Expression-Associated Processes Alter Distribution of Cell Size Values in Saccharomyces cerevisiae

The question of what determines whether cells are big or small has been the focus of many studies because it is thought that such determinants underpin the coupling of cell growth with cell division. In contrast, what determines the overall pattern of how cell size is distributed within a population of wild type or mutant cells has received little attention. Knowing how cell size varies around a characteristic pattern could shed light on the processes that generate such a pattern and provide a criterion to identify its genetic basis. Here, we show that cell size values of wild type Saccharomyces cerevisiae cells fit a gamma distribution, in haploid and diploid cells, and under different growth conditions. To identify genes that influence this pattern, we analyzed the cell size distributions of all single-gene deletion strains in Saccharomyces cerevisiae. We found that yeast strains which deviate the most from the gamma distribution are enriched for those lacking gene products functioning in gene expression, especially those in transcription or transcription-linked processes. We also show that cell size is increased in mutants carrying altered activity substitutions in Rpo21p/Rpb1, the largest subunit of RNA polymerase II (Pol II). Lastly, the size distribution of cells carrying extreme altered activity Pol II substitutions deviated from the expected gamma distribution. Our results are consistent with the idea that genetic defects in widely acting transcription factors or Pol II itself compromise both cell size homeostasis and how the size of individual cells is distributed in a population.