Single-cell Variability Research Articles

Golgi Stabilization, Not Its Front-Rear Bias, Is Associated with EMT-Enhanced Fibrillar Migration

Epithelial-to-mesenchymal transition (EMT) and maturation of collagen fibrils in the tumor microenvironment play a significant role in cancer cell invasion and metastasis. Confinement along fiber-like tracks enhances cell migration. To what extent and in what manner EMT further promotes migration in a microenvironment already conducive to migration is poorly understood. Here, we show that TGFβ-mediated EMT significantly enhances migration on fiber-like micropatterned tracks of collagen, doubling migration speed and tripling persistence relative to untreated mammary epithelial cells. Thus, cell-intrinsic EMT and extrinsic fibrillar tracks have nonredundant effects on motility. To better understand EMT-enhanced fibrillar migration, we investigated the regulation of Golgi positioning, which is involved in front-rear polarization and persistent cell migration. Confinement along fiber-like tracks has been reported to favor posterior Golgi positioning, whereas anterior positioning is observed during 2-day wound healing. Although EMT also regulates cell polarity, little is known about its effect on Golgi positioning. Here, we show that EMT induces a 2:1 rearward bias in Golgi positioning; however, positional bias explains less than 2% of single-cell variability in migration speed and persistence. Meanwhile, EMT significantly stabilizes Golgi positioning. Cells that enhance migration in response to TGFβ maintain Golgi position for 2- to 4-fold longer than nonresponsive counterparts irrespective of whether the Golgi is ahead or behind the nucleus. In fact, 28% of TGFβ-responsive cells exhibit a fully committed Golgi phenotype with the organelle either in the anterior or posterior position for over 90% of the time. Furthermore, single-cell differences in Golgi stability capture up to 18% of variations in migration speed. These results suggest a hypothesis that the Golgi may be part of a core physical scaffold that affects how cell-generated forces are distributed during migration. A stable scaffold would be expected to more consistently and therefore more productively distribute forces over time, leading to efficient migration.

Single-cell variability drives phenotypic heterogeneity and intrinsic drug-resistance in cutaneous T-cell lymphoma

Aims: Phenotypic heterogeneity allows individual cells to behave different from the bulk. This non-genetic variability provides a critical route to therapeutic resistance and relapse of cancer. Herein, we characterize phenotypic dynamics that generate an intrinsic and pre-existent, drug-tolerant state and identify a resistance biomarker profile to detect these ‘persister’ cells in skin lesions of CTCL.

Single-cell variability in multicellular organisms

Noisy gene expression is of fundamental importance to single cells, and is therefore widely studied in single-celled organisms. Extending these studies to multicellular organisms is challenging since their cells are generally not isolated, but individuals in a tissue. Cell–cell coupling via signalling, active transport or pure diffusion, ensures that tissue-bound cells are neither fully independent of each other, nor an entirely homogeneous population. In this article, we show that increasing the strength of coupling between cells can either increase or decrease the single-cell variability (and, therefore, the heterogeneity of the tissue), depending on the statistical properties of the underlying genetic network. We confirm these predictions using spatial stochastic simulations of simple genetic networks, and experimental data from animal and plant tissues. The results suggest that cell–cell coupling may be one of several noise-control strategies employed by multicellular organisms, and highlight the need for a deeper understanding of multicellular behaviour.

Temperature-dependence of the single-cell variability in the kinetics of transcription activation in Escherichia coli

From in vivo single-cell, single-RNA measurements of the activation times and subsequent steady-state active transcription kinetics of a single-copy Lac-ara-1 promoter in Escherichia coli, we characterize the intake kinetics of the inducer (IPTG) from the media, following temperature shifts. For this, for temperature shifts of various degrees, we obtain the distributions of transcription activation times as well as the distributions of intervals between consecutive RNA productions following activation in individual cells. We then propose a novel methodology that makes use of deconvolution techniques to extract the mean and the variability of the distribution of intake times. We find that cells, following shifts to low temperatures, have higher intake times, although, counter-intuitively, the cell-to-cell variability of these times is lower. We validate the results using a new methodology for direct estimation of mean intake times from measurements of activation times at various inducer concentrations. The results confirm that E. coli’s inducer intake times from the environment are significantly higher following a shift to a sub-optimal temperature. Finally, we provide evidence that this is likely due to the emergence of additional rate-limiting steps in the intake process at low temperatures, explaining the reduced cell-to-cell variability in intake times.

Promoting Simultaneous Onset of Viral Gene Expression Among Cells Infected with Herpes Simplex Virus-1.

Synchronous viral infection facilitates the study of viral gene expression, viral host interactions, and viral replication processes. However, the protocols for achieving synchronous infections were hardly ever tested in proper temporal resolution at the single-cell level. We set up a fluorescence-based, time lapse microscopy assay to study sources of variability in the timing of gene expression during herpes simplex virus-1 (HSV-1) infection. We found that with the common protocol, the onset of gene expression within different cells can vary by more than 3 h. We showed that simultaneous viral genome entry to the nucleus can be achieved with a derivative of the previously characterized temperature sensitive mutant tsB7, however, this did not improve gene expression synchrony. We found that elevating the temperature in which the infection is done and increasing the multiplicity of infection (MOI) significantly promoted simultaneous onset of viral gene expression among infected cells. Further, elevated temperature result in a decrease in the coefficient of variation (a standardized measure of dispersion) of viral replication compartments (RCs) sizes among cells as well as a slight increment of viral late gene expression synchrony. We conclude that simultaneous viral gene expression can be improved by simple modifications to the infection process and may reduce the effect of single-cell variability on population-based assays.

Multimodal microfluidic platform for controlled culture and analysis of unicellular organisms.

Modern live-cell imaging approaches permit real-time visualization of biological processes, yet limitations exist for unicellular organism isolation, culturing, and long-term imaging that preclude fully understanding how cells sense and respond to environmental perturbations and the link between single-cell variability and whole-population dynamics. Here, we present a microfluidic platform that provides fine control over the local environment with the capacity to replace media components at any experimental time point, and provides both perfused and compartmentalized cultivation conditions depending on the valve configuration. The functionality and flexibility of the platform were validated using both bacteria and yeast having different sizes, motility, and growth media. The demonstrated ability to track the growth and dynamics of both motile and non-motile prokaryotic and eukaryotic organisms emphasizes the versatility of the devices, which should enable studies in bioenergy and environmental research.

Predictive modeling of microbial single cells: A review

ABSTRACTIn practice, food products tend to be contaminated with food-borne pathogens at a low inoculum level. However, the huge potential risk cannot be ignored because microbes may initiate high-speed growth suitable conditions during the food chain, such as transportation or storage. Thus, it is important to perform predictive modeling of microbial single cells. Several key aspects of microbial single-cell modeling are covered in this review. First, based on previous studies, the techniques of microbial single-cell data acquisition and growth data collection are presented in detail. In addition, the sources of microbial single-cell variability are also summarized. Due to model microbial growth, traditional deterministic mathematical models have been developed. However, most models fail to make accurate predictions at low cell numbers or at the single-cell level due to high cell-to-cell heterogeneity. Stochastic models have been a subject of great interest; and these models take into consideration the variability in microbial single-cell behavior.

Noncommutative Biology: Sequential Regulation of Complex Networks.

Single-cell variability in gene expression is important for generating distinct cell types, but it is unclear how cells use the same set of regulatory molecules to specifically control similarly regulated genes. While combinatorial binding of transcription factors at promoters has been proposed as a solution for cell-type specific gene expression, we found that such models resulted in substantial information bottlenecks. We sought to understand the consequences of adopting sequential logic wherein the time-ordering of factors informs the final outcome. We showed that with noncommutative control, it is possible to independently control targets that would otherwise be activated simultaneously using combinatorial logic. Consequently, sequential logic overcomes the information bottleneck inherent in complex networks. We derived scaling laws for two noncommutative models of regulation, motivated by phosphorylation/neural networks and chromosome folding, respectively, and showed that they scale super-exponentially in the number of regulators. We also showed that specificity in control is robust to the loss of a regulator. Lastly, we connected these theoretical results to real biological networks that demonstrate specificity in the context of promiscuity. These results show that achieving a desired outcome often necessitates roundabout steps.

GENE EXPRESSION. Single-cell variability guided by microRNAs.

Variability in miRNA activity, and therefore gene networks, may define cell state

Cell-to-Cell Transcript Variability: Seeing Signal in the Noise

How stochastic is gene expression in mammalian cells? Not very, according to Battich etal., who report that single-cell variability in cytoplasmic mRNAs is remarkably predictable given measurements of a cell's phenotypic state and microenvironment. The noise from transcriptional bursts is buffered by a hallmark of eukaryotes-the nucleus.

Assessment of the effect of a Salmonella enterica ser. Typhimurium culture supernatant on the single-cell lag time of foodborne pathogens

The objective of this study was the in vitro evaluation of the effect of a cell-free microbial supernatant, produced by a luxS-positive Salmonella enterica ser. Typhimurium strain, on the single-cell growth kinetic behavior of two strains of S. enterica (serotypes Enteritidis and Typhimurium) and a methicillin-resistant Staphylococcus aureus strain. The single-cell lag time (λ) of the pathogens was estimated in the absence and presence (20% v/v) of microbial supernatant based on optical density measurements. As demonstrated by the obtained results, the tested microbial supernatant had a strain-specific effect on the single-cell λ and its variability. Although the mean λ values were similar in the absence and presence of microbial supernatant in the case of Salmonella Enteritidis, a significant (P≤0.05) reduction and increase in the mean value of this parameter in the presence of microbial supernatant were observed for Salmonella Typhimurium and St. aureus, respectively. With regard to the effect of the tested microbial supernatant on the single-cell variability of λ, similar λ distributions were obtained in its absence and presence for S. Enteritidis, while considerable differences were noted for the other two tested organisms; the coefficient of variation of λ in the absence and presence of microbial supernatant was 41.6 and 69.8% for S. Typhimurium, respectively, with the corresponding values for St. aureus being 74.0 and 56.9%. As demonstrated by the results of bioassays, the tested microbial supernatant exhibited autoinducer-2 activity, indicating a potential association of such quorum sensing compounds with the observed effects. Although preliminary in nature, the collected data provide a good basis for future research on the role of quorum sensing in the single-cell growth behavior of foodborne pathogens.

Single-cell transcriptomics and functional target validation of brown adipocytes show their complex roles in metabolic homeostasis.

Brown adipocytes (BAs) are specialized for adaptive thermogenesis and, upon sympathetic stimulation, activate mitochondrial uncoupling protein (UCP)-1 and oxidize fatty acids to generate heat. The capacity for brown adipose tissue (BAT) to protect against obesity and metabolic disease is recognized, yet information about which signals activate BA, besides β3-adrenergic receptor stimulation, is limited. Using single-cell transcriptomics, we confirmed the presence of mRNAs encoding traditional BAT markers (i.e., UCP1, expressed in 100% of BAs Adrb3, expressed in <50% of BAs) in mouse and have shown single-cell variability (>1000-fold) in their expression at both the mRNA and protein levels. We further identified mRNAs encoding novel markers, orphan GPCRs, and many receptors that bind the classic neurotransmitters, neuropeptides, chemokines, cytokines, and hormones. The transcriptome variability between BAs suggests a much larger range of responsiveness of BAT than previously recognized and that not all BAs function identically. We examined the in vivo functional expression of 12 selected receptors by microinjecting agonists into live mouse BAT and analyzing the metabolic response. In this manner, we expanded the number of known receptors on BAs at least 25-fold, while showing that the expression of classic BA markers is more complex and variable than previously thought.

Single-cell measurement of red blood cell oxygen affinity

Oxygen is transported throughout the body by hemoglobin (Hb) in red blood cells (RBCs). Although the oxygen affinity of blood is well-understood and routinely assessed in patients by pulse oximetry, variability at the single-cell level has not been previously measured. In contrast, single-cell measurements of RBC volume and Hb concentration are taken millions of times per day by clinical hematology analyzers, and they are important factors in determining the health of the hematologic system. To better understand the variability and determinants of oxygen affinity on a cellular level, we have developed a system that quantifies the oxygen saturation, cell volume, and Hb concentration for individual RBCs in high throughput. We find that the variability in single-cell saturation peaks at an oxygen partial pressure of 2.9%, which corresponds to the maximum slope of the oxygen-Hb dissociation curve. In addition, single-cell oxygen affinity is positively correlated with Hb concentration but independent of osmolarity, which suggests variation in the Hb to 2,3-diphosphoglycerate (2-3 DPG) ratio on a cellular level. By quantifying the functional behavior of a cellular population, our system adds a dimension to blood cell analysis and other measurements of single-cell variability.

Deep sequencing reveals cell-type-specific patterns of single-cell transcriptome variation.

BackgroundDifferentiation of metazoan cells requires execution of different gene expression programs but recent single-cell transcriptome profiling has revealed considerable variation within cells of seeming identical phenotype. This brings into question the relationship between transcriptome states and cell phenotypes. Additionally, single-cell transcriptomics presents unique analysis challenges that need to be addressed to answer this question.ResultsWe present high quality deep read-depth single-cell RNA sequencing for 91 cells from five mouse tissues and 18 cells from two rat tissues, along with 30 control samples of bulk RNA diluted to single-cell levels. We find that transcriptomes differ globally across tissues with regard to the number of genes expressed, the average expression patterns, and within-cell-type variation patterns. We develop methods to filter genes for reliable quantification and to calibrate biological variation. All cell types include genes with high variability in expression, in a tissue-specific manner. We also find evidence that single-cell variability of neuronal genes in mice is correlated with that in rats consistent with the hypothesis that levels of variation may be conserved.ConclusionsSingle-cell RNA-sequencing data provide a unique view of transcriptome function; however, careful analysis is required in order to use single-cell RNA-sequencing measurements for this purpose. Technical variation must be considered in single-cell RNA-sequencing studies of expression variation. For a subset of genes, biological variability within each cell type appears to be regulated in order to perform dynamic functions, rather than solely molecular noise.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-015-0683-4) contains supplementary material, which is available to authorized users.

Chapter Thirteen - A Microfluidic Systems Biology Approach for Live Single-Cell Mitochondrial ROS Imaging

Most current studies of reactive oxygen species (ROS) production report globally averaged measurements within the cell; however, ROS can be produced in distinct subcellular locations and have local effects in their immediate vicinity. A microfluidic platform for high-throughput single-cell imaging allows mitochondrial ROS production to be monitored as varying in both space and time. Using this systems biology approach, single-cell variability can be viewed within a population. We discuss single-cell monitoring of contributors to mitochondrial redox state-mitochondrial hydrogen peroxide or superoxide-through the use of a small molecule probe or targeted fluorescent reporter protein. Jurkat T lymphoma cells were stimulated with antimycin A and imaged in an arrayed microfluidic device over time. Differences in single-cell responses were observed as a function of both inhibitor concentration and type of ROS measurement used.

Segregation of molecules at cell division reveals native protein localization

We introduce a non-intrusive method exploiting post-division single-cell variability to validate protein localization. The results show that Clp proteases, widely reported to form biologically relevant foci, are in fact uniformly distributed inside Escherichia coli cells, and that many commonly used fluorescent proteins (FPs) cause severe mislocalization when fused to homo-oligomers. Re-tagging five other reportedly foci-forming proteins with the most monomeric FP tested suggests the foci were caused by the FPs.

Trial-to-Trial Variability of Single Cells in Motor Cortices Is Dynamically Modified during Visuomotor Adaptation

Neurons in all brain areas exhibit variability in their spiking activity. Although part of this variability can be considered as noise that is detrimental to information processing, recent findings indicate that variability can also be beneficial. In particular, it was suggested that variability in the motor system allows for exploration of possible motor states and therefore can facilitate learning and adaptation to new environments. Here, we provide evidence to support this idea by analyzing the variability of neurons in the primary motor cortex (M1) and in the supplementary motor area (SMA-proper) of monkeys adapting to new rotational visuomotor tasks. We found that trial-to-trial variability increased during learning and exhibited four main characteristics: (1) modulation occurred preferentially during a delay period when the target of movement was already known, but before movement onset; (2) variability returned to its initial levels toward the end of learning; (3) the increase in variability was more apparent in cells with preferred movement directions close to those experienced during learning; and (4) the increase in variability emerged at early phases of learning in the SMA, whereas in M1 behavior reached plateau levels of performance. These results are highly consistent with previous findings that showed similar trends in variability across a population of neurons. Together, the results strengthen the idea that single-cell variability can be much more than mere noise and may be an integral part of the underlying mechanism of sensorimotor learning.

Estimating Risk from Small Inocula by Using Population Growth Parameters

Risk from an uncertain small inoculum depends on variability of single-cell lag times. However, quantifying single-cell variability is technically challenging. It is possible to estimate this variability using population growth parameters. We demonstrate this possibility using data from literature and show a Bayesian scheme for performing this task.

Single-cell variability in growing Saccharomyces cerevisiae cell populations measured with automated flow cytometry

Cell cultures normally are heterogeneous due to factors such as the cell cycle, inhomogeneous cell microenvironments, and genetic differences. However, distributions of cell properties usually are not taken into account in the characterization of a culture when only population averaged values are measured. In this study, the cell size, green fluorescence protein (Gfp) content, and viability after automated staining with propidium iodide (PI) are monitored at the single-cell level in Saccharomyces cerevisiae cultures growing in a batch bioreactor using an automated flow injection flow cytometer system. To demonstrate the wealth of information that can be obtained with this system, three cultures containing three different plasmids are compared. The first plasmid is a centromeric plasmid expressing under the control of a TEF2 promoter the S65T mutant form of Gfp. The other two plasmids are 2 μm plasmids and express the FM2 mutant of Gfp under the control of either the TEF1 or the TEF2 promoter. The automated sampling, cell preparation, and analysis permitted frequent quantification of the culture characteristics. The time course of the data representing not only population average values but also their variability, provides a detailed and reproducible “fingerprint” of the culture dynamics. The data demonstrate that small changes in the genetic make up of the recombinant system can result in large changes in the culture Gfp production and viability. Thus, the developed instrumentation is valuable for rapidly testing promoter strength, plasmid stability, cell viability, and culture variability.

Single-cell variability in growing Saccharomyces cerevisiae cell populations measured with automated flow cytometry

Cell cultures normally are heterogeneous due to factors such as the cell cycle, inhomogeneous cell microenvironments, and genetic differences. However, distributions of cell properties usually are not taken into account in the characterization of a culture when only population averaged values are measured. In this study, the cell size, green fluorescence protein (Gfp) content, and viability after automated staining with propidium iodide (PI) are monitored at the single-cell level in Saccharomyces cerevisiae cultures growing in a batch bioreactor using an automated flow injection flow cytometer system. To demonstrate the wealth of information that can be obtained with this system, three cultures containing three different plasmids are compared. The first plasmid is a centromeric plasmid expressing under the control of a TEF2 promoter the S65T mutant form of Gfp. The other two plasmids are 2 μm plasmids and express the FM2 mutant of Gfp under the control of either the TEF1 or the TEF2 promoter. The automated sampling, cell preparation, and analysis permitted frequent quantification of the culture characteristics. The time course of the data representing not only population average values but also their variability, provides a detailed and reproducible “fingerprint” of the culture dynamics. The data demonstrate that small changes in the genetic make up of the recombinant system can result in large changes in the culture Gfp production and viability. Thus, the developed instrumentation is valuable for rapidly testing promoter strength, plasmid stability, cell viability, and culture variability.