Cell volume distributions reveal cell growth rates and division times

Bayesian cell therapy process optimization

Optimizing complex bioprocesses poses a significant challenge in several fields, particularly in cell therapy manufacturing. The development of customized, closed, and automated processes is crucial for their industrial translation and for addressing large patient populations at a sustainable price. Limited understanding of the underlying biological mechanisms, coupled with highly resource-intensive experimentation, are two contributing factors that make the development of these next-generation processes challenging. Bayesian optimization (BO) is an iterative experimental design methodology that addresses these challenges, but has not been extensively tested in situations that require parallel experimentation with significant experimental variability. In this study, we present an evaluation of noisy, parallel BO for increasing noise levels and parallel batch sizes on two in silico bioprocesses, and compare it to the industry state-of-the-art. As an in vitro showcase, we apply the method to the optimization of a monocyte purification unit operation. The in silico results show that BO significantly outperforms the state-of-the-art, requiring approximately 50% fewer experiments on average. This study highlights the potential of noisy, parallel BO as valuable tool for cell therapy process development and optimization.

Microfluidic single-cell measurements of oxidative stress as a function of cell cycle position

Single-cell measurements routinely demonstrate high levels of variation between cells, but fewer studies provide insight into the analytical and biological sources of this variation. This is particularly true of chemical cytometry, in which individual cells are lysed and their contents separated, compared to more established single-cell measurements of the genome and transcriptome. To characterize population-level variation and its sources, we analyzed oxidative stress levels in 1278 individual Dictyostelium discoideum cells as a function of exogenous stress level and cell cycle position. Cells were exposed to varying levels of oxidative stress via singlet oxygen generation using the photosensitizer Rose Bengal. Single-cell data reproduced the dose-response observed in ensemble measurements by CE-LIF, superimposed with high levels of heterogeneity. Through experiments and data analysis, we explored possible biological sources of this heterogeneity. No trend was observed between population variation and oxidative stress level, but cell cycle position was a major contributor to heterogeneity in oxidative stress. Cells synchronized to the same stage of cell division were less heterogeneous than unsynchronized cells (RSD of 37-51% vs 93%), and mitotic cells had higher levels of reactive oxygen species than interphase cells. While past research has proposed changes in cell size during the cell cycle as a source of biological noise, the measurements presented here use an internal standard to normalize for effects of cell volume, suggesting a more complex contribution of cell cycle to heterogeneity of oxidative stress.

Selection of Culture Conditions and Cell Morphology for Biocompatible Extraction of β-Carotene from Dunaliella salina

Biocompatible extraction emerges recently as a means to reduce costs of biotechnology processing of microalgae. In this frame, this study aimed at determining how specific culture conditions and the associated cell morphology impact the biocompatibility and the extraction yield of β-carotene from the green microalga Dunaliella salina using n-decane. The results highlight the relationship between the cell disruption yield and cell volume, the circularity and the relative abundance of naturally permeabilized cells. The disruption rate increased with both the cell volume and circularity. This was particularly obvious for volume and circularity exceeding 1500 µm³ and 0.7, respectively. The extraction of β-carotene was the most biocompatible with small (600 µm³) and circular cells (0.7) stressed in photobioreactor (30% of carotenoids recovery with 15% cell disruption). The naturally permeabilized cells were disrupted first; the remaining cells seems to follow a gradual permeabilization process: reversibility (up to 20 s) then irreversibility and cell disruption. This opens new carotenoid production schemes based on growing robust β-carotene enriched cells to ensure biocompatible extraction.

Lytic Release of Cellular ATP: Physiological Relevance and Therapeutic Applications

The lytic release of ATP due to cell and tissue injury constitutes an important source of extracellular nucleotides and may have physiological and pathophysiological roles by triggering purinergic signalling pathways. In the lungs, extracellular ATP can have protective effects by stimulating surfactant and mucus secretion. However, excessive extracellular ATP levels, such as observed in ventilator-induced lung injury, act as a danger-associated signal that activates NLRP3 inflammasome contributing to lung damage. Here, we discuss examples of lytic release that we have identified in our studies using real-time luciferin-luciferase luminescence imaging of extracellular ATP. In alveolar A549 cells, hypotonic shock-induced ATP release shows rapid lytic and slow-rising non-lytic components. Lytic release originates from the lysis of single fragile cells that could be seen as distinct spikes of ATP-dependent luminescence, but under physiological conditions, its contribution is minimal <1% of total release. By contrast, ATP release from red blood cells results primarily from hemolysis, a physiological mechanism contributing to the regulation of local blood flow in response to tissue hypoxia, mechanical stimulation and temperature changes. Lytic release of cellular ATP may have therapeutic applications, as exemplified by the use of ultrasound and microbubble-stimulated release for enhancing cancer immunotherapy in vivo.

Potassium and Sodium Salt Stress Characterization in the Yeasts Saccharomyces cerevisiae, Kluyveromyces marxianus, and Rhodotorula toruloides

Biotechnology requires efficient microbial cell factories. The budding yeast Saccharomyces cerevisiae is a vital cell factory, but more diverse cell factories are essential for the sustainable use of natural resources. Here, we benchmarked nonconventional yeasts Kluyveromyces marxianus and Rhodotorula toruloides against S. cerevisiae strains CEN.PK and W303 for their responses to potassium and sodium salt stress. We found an inverse relationship between the maximum growth rate and the median cell volume that was responsive to salt stress. The supplementation of K⁺ to CEN.PK cultures reduced Na⁺ toxicity and increased the specific growth rate 4-fold. The higher K⁺ and Na⁺ concentrations impaired ethanol and acetate metabolism in CEN.PK and acetate metabolism in W303. In R. toruloides cultures, these salt supplementations induced a trade-off between glucose utilization and cellular aggregate formation. Their combined use increased the beta-carotene yield by 60% compared with that of the reference. Neural network-based image analysis of exponential-phase cultures showed that the vacuole-to-cell volume ratio increased with increased cell volume for W303 and K. marxianus but not for CEN.PK and R. toruloides in response to salt stress. Our results provide insights into common salt stress responses in yeasts and will help design efficient bioprocesses. IMPORTANCE Characterization of microbial cell factories under industrially relevant conditions is crucial for designing efficient bioprocesses. Salt stress, typical in industrial bioprocesses, impinges upon cell volume and affects productivity. This study presents an open-source neural network-based analysis method to evaluate volumetric changes using yeast optical microscopy images. It allows quantification of cell and vacuole volumes relevant to cellular physiology. On applying salt stress in yeasts, we found that the combined use of K⁺ and Na⁺ improves the cellular fitness of Saccharomyces cerevisiae strain CEN.PK and increases the beta-carotene productivity in Rhodotorula toruloides, a commercially important antioxidant and a valuable additive in foods.

Organ-wide and ploidy-dependent regulation both contribute to cell-size determination: evidence from a computational model of tomato fruit

The development of a new organ is the result of coordinated events of cell division and expansion, in strong interaction with each other. This study presents a dynamic model of tomato fruit development that includes cell division, endoreduplication, and expansion processes. The model is used to investigate the potential interactions among these developmental processes within the context of the neo-cellular theory. In particular, different control schemes (either cell-autonomous or organ-controlled) are tested and compared to experimental data from two contrasting genotypes. The model shows that a pure cell-autonomous control fails to reproduce the observed cell-size distribution, and that an organ-wide control is required in order to get realistic cell-size variations. The model also supports the role of endoreduplication as an important determinant of the final cell size and suggests that a direct effect of endoreduplication on cell expansion is needed in order to obtain a significant correlation between size and ploidy, as observed in real data.

Measuring Single-Cell Phenotypic Growth Heterogeneity Using a Microfluidic Cell Volume Sensor

An imaging-integrated microfluidic cell volume sensor was used to evaluate the volumetric growth rate of single cells from a Saccharomyces cerevisiae population exhibiting two phenotypic expression states of the PDR5 gene. This gene grants multidrug resistance by transcribing a membrane transporter capable of pumping out cytotoxic compounds from the cell. Utilizing fluorescent markers, single cells were isolated and trapped, then their growth rates were measured in two on-chip environments: rich media and media dosed with the antibiotic cycloheximide. Approximating growth rates to first-order, we assessed the fitness of individual cells and found that those with low PDR5 expression had higher fitness in rich media whereas cells with high PDR5 expression had higher fitness in the presence of the drug. Moreover, the drug dramatically reduced the fitness of cells with low PDR5 expression but had comparatively minimal impact on the fitness of cells with high PDR5 expression. Our experiments show the utility of this imaging-integrated microfluidic cell volume sensor for high-resolution, single-cell analysis, as well as its potential application for studies that characterize and compare the fitness and morphology of individual cells from heterogeneous populations under different growth conditions.

Analysis of Noise Mechanisms in Cell-Size Control

At the single-cell level, noise arises from multiple sources, such as inherent stochasticity of biomolecular processes, random partitioning of resources at division, and fluctuations in cellular growth rates. How these diverse noise mechanisms combine to drive variations in cell size within an isoclonal population is not well understood. Here, we investigate the contributions of different noise sources in well-known paradigms of cell-size control, such as adder (division occurs after adding a fixed size from birth), sizer (division occurs after reaching a size threshold), and timer (division occurs after a fixed time from birth). Analysis reveals that variation in cell size is most sensitive to errors in partitioning of volume among daughter cells, and not surprisingly, this process is well regulated among microbes. Moreover, depending on the dominant noise mechanism, different size-control strategies (or a combination of them) provide efficient buffering of size variations. We further explore mixer models of size control, where a timer phase precedes/follows an adder, as has been proposed in Caulobacter crescentus. Although mixing a timer and an adder can sometimes attenuate size variations, it invariably leads to higher-order moments growing unboundedly over time. This results in a power-law distribution for the cell size, with an exponent that depends inversely on the noise in the timer phase. Consistent with theory, we find evidence of power-law statistics in the tail of C. crescentus cell-size distribution, although there is a discrepancy between the observed power-law exponent and that predicted from the noise parameters. The discrepancy, however, is removed after data reveal that the size added by individual newborns in the adder phase itself exhibits power-law statistics. Taken together, this study provides key insights into the role of noise mechanisms in size homeostasis, and suggests an inextricable link between timer-based models of size control and heavy-tailed cell-size distributions.

Cell-size distribution in epithelial tissue formation and homeostasis

How cell growth and proliferation are orchestrated in living tissues to achieve a given biological function is a central problem in biology. During development, tissue regeneration and homeostasis, cell proliferation must be coordinated by spatial cues in order for cells to attain the correct size and shape. Biological tissues also feature a notable homogeneity of cell size, which, in specific cases, represents a physiological need. Here, we study the temporal evolution of the cell-size distribution by applying the theory of kinetic fragmentation to tissue development and homeostasis. Our theory predicts self-similar probability density function (PDF) of cell size and explains how division times and redistribution ensure cell size homogeneity across the tissue. Theoretical predictions and numerical simulations of confluent non-homeostatic tissue cultures show that cell size distribution is self-similar. Our experimental data confirm predictions and reveal that, as assumed in the theory, cell division times scale like a power-law of the cell size. We find that in homeostatic conditions there is a stationary distribution with lognormal tails, consistently with our experimental data. Our theoretical predictions and numerical simulations show that the shape of the PDF depends on how the space inherited by apoptotic cells is redistributed and that apoptotic cell rates might also depend on size.

Three-dimensional simulations of the cell growth and cytokinesis using the immersed boundary method

In this paper, we present a three-dimensional immersed boundary method to simulate the eukaryotic cell growth and cytokinesis. The proposed model and numerical method are a non-trivial three-dimensional extension of the previous work (Li et al., 2012). Unstructured triangular meshes are employed to discretize the cell membrane. The nodes of the surface mesh constitute a set of Lagrangian control points used to track the motion of the cell. A surface remeshing algorithm is applied to prevent mesh distortion during evolution. We also use a volume-conserving algorithm to maintain the mass of cells in cytokinesis. The ability of the proposed method to simulate cell growth and division processes is numerically demonstrated.

Scaling laws governing stochastic growth and division of single bacterial cells

Uncovering the quantitative laws that govern the growth and division of single cells remains a major challenge. Using a unique combination of technologies that yields unprecedented statistical precision, we find that the sizes of individual Caulobacter crescentus cells increase exponentially in time. We also establish that they divide upon reaching a critical multiple (≈ 1.8) of their initial sizes, rather than an absolute size. We show that when the temperature is varied, the growth and division timescales scale proportionally with each other over the physiological temperature range. Strikingly, the cell-size and division-time distributions can both be rescaled by their mean values such that the condition-specific distributions collapse to universal curves. We account for these observations with a minimal stochastic model that is based on an autocatalytic cycle. It predicts the scalings, as well as specific functional forms for the universal curves. Our experimental and theoretical analysis reveals a simple physical principle governing these complex biological processes: a single temperature-dependent scale of cellular time governs the stochastic dynamics of growth and division in balanced growth conditions.

Heterogeneous structure of stem cells dynamics: statistical models and quantitative predictions

Understanding stem cell (SC) population dynamics is essential for developing models that can be used in basic science and medicine, to aid in predicting cells fate. These models can be used as tools e.g. in studying patho-physiological events at the cellular and tissue level, predicting (mal)functions along the developmental course, and personalized regenerative medicine. Using time-lapsed imaging and statistical tools, we show that the dynamics of SC populations involve a heterogeneous structure consisting of multiple sub-population behaviors. Using non-Gaussian statistical approaches, we identify the co-existence of fast and slow dividing subpopulations, and quiescent cells, in stem cells from three species. The mathematical analysis also shows that, instead of developing independently, SCs exhibit a time-dependent fractal behavior as they interact with each other through molecular and tactile signals. These findings suggest that more sophisticated models of SC dynamics should view SC populations as a collective and avoid the simplifying homogeneity assumption by accounting for the presence of more than one dividing sub-population, and their multi-fractal characteristics.

Testing Equality of Cell Populations Based on Shape and Geodesic Distance

Image cytometry has emerged as a valuable in vitro screening tool and advances in automated microscopy have made it possible to readily analyze large cellular populations of image data. The purpose of this paper is to illustrate the viability of using cell shape to test equality of cell populations based on image data. Shape space theory is reviewed, from which differences between shapes can be quantified in terms of geodesic distance. Several multivariate nonparametric statistical hypothesis tests are adapted to test equality of cell populations. It is illustrated that geodesic distance can be a better feature than cell spread area and roundness in distinguishing between cell populations. Tests based on geodesic distance are able to detect natural perturbations of cells, whereas Kolmogorov-Smirnov tests based on area and roundness are not.

Age-dependent stochastic models for understanding population fluctuations in continuously cultured cells

For symmetrically dividing cells, large variations in the cell cycle time are typical, even among clonal cells. The consequence of this variation is important in stem cell differentiation, tissue and organ size control, and cancer development, where cell division rates ultimately determine the cell population. We explore the connection between cell cycle time variation and population-level fluctuations using simple stochastic models. We find that standard population models with constant division and death rates fail to predict the level of population fluctuation. Instead, variations in the cell division time contribute to population fluctuations. An age-dependent birth and death model allows us to compute the mean squared fluctuation or the population dispersion as a function of time. This dispersion grows exponentially with time, but scales with the population. We also find a relationship between the dispersion and the cell cycle time distribution for synchronized cell populations. The model can easily be generalized to study populations involving cell differentiation and competitive growth situations.

Subcellular redistribution and mitotic inheritance of transition metals in proliferating mouse fibroblast cells

Synchrotron X-ray fluorescence microscopy of non-synchronized NIH 3T3 fibroblasts revealed an intriguing redistribution dynamics that defines the inheritance of trace metals during mitosis. At metaphase, the highest density areas of Zn and Cu are localized in two distinct regions adjacent to the metaphase plate. As the sister chromatids are pulled towards the spindle poles during anaphase, Zn and Cu gradually move to the center and partition into the daughter cells to yield a pair of twin pools during cytokinesis. Colocalization analyses demonstrated high spatial correlations between Zn, Cu, and S throughout all mitotic stages, while Fe showed consistently different topographies characterized by high-density spots distributed across the entire cell. Whereas the total amount of Cu remained similar compared to interphase cells, mitotic Zn levels increased almost 3-fold, suggesting a prominent physiological role that lies beyond the requirement of Zn as a cofactor in metalloproteins or messenger in signaling pathways.

Microfluidic magnetophoretic separations of immunomagnetically labeled rare mammalian cells

Immunomagnetic isolation and magnetophoresis in microfluidics have emerged as viable techniques for the separation, fractionation, and enrichment of rare cells. Here we present the development and characterization of a microfluidic system that incorporates an angled permanent magnet for the lateral magnetophoresis of superparamagnetic beads and labeled cell-bead complexes. A numerical model, based on the relevant transport processes, is developed as a design tool for the demonstration and prediction of magnetophoretic displacement. We employ a dimensionless magnetophoresis parameter to efficiently investigate the design space, gain insight into the physics of the system, and compare results across the vast spectrum of magnetophoretic microfluidic systems. The numerical model and theoretical analysis are experimentally validated by the lateral magnetophoretic deflection of superparamagnetic beads and magnetically labeled breast adenocarcinoma MCF-7 cells in a microfluidic device that incorporates a permanent magnet angled relative to the flow. Through the dimensionless magnetophoresis parameter, the transition between regimes of magnetophoretic action, from hydrodynamically dominated (magnetic deflection) to magnetically dominated (magnetic capture), is experimentally identified. This powerful tool and theoretical framework enables efficient device and experiment design of biologically relevant systems, taking into account their inherent variability and labeling distributions. This analysis identifies the necessary beads, magnet configuration (orientation), magnet type (permanent, ferromagnetic, electromagnet), flow rate, channel geometry, and buffer to achieve the desired level of magnetophoretic deflection or capture.

Evaluation of segmentation algorithms on cell populations using CDF curves

Cell segmentation is a critical step in the analysis pipeline for most imaging cytometry experiments and evaluating the performance of segmentation algorithms is important for aiding the selection of segmentation algorithms. Four popular algorithms are evaluated based on their cell segmentation performance. Because segmentation involves the classification of pixels belonging to regions within the cell or belonging to background, these algorithms are evaluated based on their total misclassification error. Misclassification error is particularly relevant in the analysis of quantitative descriptors of cell morphology involving pixel counts, such as projected area, aspect ratio and diameter. Since the cumulative distribution function captures completely the stochastic properties of a population of misclassification errors it is used to compare segmentation performance.

Bardet-Biedl syndrome highlights the major role of the primary cilium in efficient water reabsorption

Studies of the primary cilium, now known to be present in all cells, have undergone a revolution, in part, because mutation of many of its proteins causes a large number of diseases, including cystic kidney disease. Bardet-Biedl syndrome (BBS) is an inherited ciliopathy characterized, among other dysfunctions, by renal defects for which the precise role of the cilia in kidney function remains unclear. We studied a cohort of patients with BBS where we found that these patients had a urinary concentration defect even when kidney function was near normal and in the absence of major cyst formation. Subsequent in vitro analysis showed that renal cells in which a BBS gene was knocked down were unciliated, but did not exhibit cell cycle defects. As the vasopressin receptor 2 is located in the primary cilium, we studied BBS-derived unciliated renal epithelial cells and found that they were unable to respond to luminal arginine vasopressin treatment and activate their luminal aquaporin 2. The ability to reabsorb water was restored by treating these unciliated renal epithelial cells with forskolin, a receptor-independent adenylate cyclase activator, showing that the intracellular machinery for water absorption was present but not activated. These findings suggest that the luminal receptor located on the primary cilium may be important for efficient transepithelial water absorption.

Getting more from cell size distributions: establishing more accurate biovolumes by estimating viable cell populations

Current approaches for cell size distribution modeling are attempting to describe the behavior of the entire distribution with respect to time. Although some advances have been made in this area, the modeling process requires a large number of culture-specific parameters and an a priori assumption of the distribution nature (Poisson, Gaussian, etc.). In this work, we propose a deconvolution of the distribution into size ranges and an iterative regression process with respect to a single culture variable, such as viability. Following this approach, two example applications are outlined using data collected with a Coulter Counter Multisizer. In the first, traditional biovolume measurements are corrected to account for the noneven distribution of nonviable cells. These corrections amount to an average increase of 7-65% in the calculated biovolume from 24 to 72 h postinfection and are expected to aid in the development of a new basis for nutrient consumption postinfection. In the second example, viability is predicted from the cell size distribution using both linear and exponential regressions. Differences between predicted and measured viabilities were found to be normally distributed with means of 0.4% and 0% as well as standard deviations of 7.6% and 8.1% for linear and exponential regression, respectively. Although only viability relationships were tested, our approach yielded significant results for both applications, allowing the possibility for further development.

Model-based extension of high-throughput to high-content data

Background

High-quality quantitative data is a major limitation in systems biology. The experimental data used in systems biology can be assigned to one of the following categories: assays yielding average data of a cell population, high-content single cell measurements and high-throughput techniques generating single cell data for large cell populations. For modeling purposes, a combination of data from different categories is highly desirable in order to increase the number of observable species and processes and thereby maximize the identifiability of parameters.

Results

In this article we present a method that combines the power of high-content single cell measurements with the efficiency of high-throughput techniques. A calibration on the basis of identical cell populations measured by both approaches connects the two techniques. We develop a mathematical model to relate quantities exclusively observable by high-content single cell techniques to those measurable with high-content as well as high-throughput methods. The latter are defined as free variables, while the variables measurable with only one technique are described in dependence of those. It is the combination of data calibration and model into a single method that makes it possible to determine quantities only accessible by single cell assays but using high-throughput techniques. As an example, we apply our approach to the nucleocytoplasmic transport of STAT5B in eukaryotic cells.

Conclusions

The presented procedure can be generally applied to systems that allow for dividing observables into sets of free quantities, which are easily measurable, and variables dependent on those. Hence, it extends the information content of high-throughput methods by incorporating data from high-content measurements.