Cell Biology. Using cell-to-cell variability--a new era in molecular biology

Intelligent image-based in situ single-cell isolation

Quantifying heterogeneities within cell populations is important for many fields including cancer research and neurobiology; however, techniques to isolate individual cells are limited. Here, we describe a high-throughput, non-disruptive, and cost-effective isolation method that is capable of capturing individually targeted cells using widely available techniques. Using high-resolution microscopy, laser microcapture microscopy, image analysis, and machine learning, our technology enables scalable molecular genetic analysis of single cells, targetable by morphology or location within the sample.

The isolation of single cells while retaining context is important for quantifying cellular heterogeneity but technically challenging. Here, the authors develop a high-throughput, scalable workflow for microscopy-based single cell isolation using machine-learning, high-throughput microscopy and laser capture microdissection.

Nanoscale imaging and force probing of biomolecular systems using atomic force microscopy: from single molecules to living cells

Due to the lack of adequate tools for observation, native molecular behaviors at the nanoscale have been poorly understood. The advent of atomic force microscopy (AFM) provides an exciting instrument for investigating physiological processes on individual living cells with molecular resolution, which attracts the attention of worldwide researchers. In the past few decades, AFM has been widely utilized to investigate molecular activities on diverse biological interfaces, and the performances and functions of AFM have also been continuously improved, greatly improving our understanding of the behaviors of single molecules in action and demonstrating the important role of AFM in addressing biological issues with unprecedented spatiotemporal resolution. In this article, we review the related techniques and recent progress about applying AFM to characterize biomolecular systems in situ from single molecules to living cells. The challenges and future directions are also discussed.

Single-Cell Virology: On-Chip Investigation of Viral Infection Dynamics

We have developed a high-throughput, microfluidics-based platform to perform kinetic analysis of viral infections in individual cells. We have analyzed thousands of individual poliovirus infections while varying experimental parameters, including multiplicity of infection, cell cycle, viral genotype, and presence of a drug. We make several unexpected observations masked by population-based experiments: (1) viral and cellular factors contribute uniquely and independently to viral infection kinetics; (2) cellular factors cause wide variation in replication start times; and (3) infections frequently begin later and replication occurs faster than predicted by population measurements. We show that mutational load impairs interaction of the viral population with the host, delaying replication start times and explaining the attenuated phenotype of a mutator virus. We show that an antiviral drug can selectively extinguish the most-fit members of the viral population. Single-cell virology facilitates discovery and characterization of virulence determinants and elucidation of mechanisms of drug action eluded by population methods.

Digging deep into Golgi phenotypic diversity with unsupervised machine learning

Structural alterations of the Golgi apparatus may lead to phenotypes that human vision cannot easily discriminate. In this work, we present a high-content analysis framework including an unsupervised clustering step to automatically uncover Golgi phenotypic diversity. We use this deep phenotyping to quantitatively compare the effects of gene depletion.

The synthesis of glycans and the sorting of proteins are critical functions of the Golgi apparatus and depend on its highly complex and compartmentalized architecture. High-content image analysis coupled to RNA interference screening offers opportunities to explore this organelle organization and the gene network underlying it. To date, image-based Golgi screens have based on a single parameter or supervised analysis with predefined Golgi structural classes. Here, we report the use of multiparametric data extracted from a single marker and a computational unsupervised analysis framework to explore Golgi phenotypic diversity more extensively. In contrast with the three visually definable phenotypes, our framework reproducibly identified 10 Golgi phenotypes. They were used to quantify and stratify phenotypic similarities among genetic perturbations. The derived phenotypic network partially overlaps previously reported protein–protein interactions as well as suggesting novel functional interactions. Our workflow suggests the existence of multiple stable Golgi organizational states and provides a proof of concept for the classification of drugs and genes using fine-grained phenotypic information.

Variability and Order in Cytoskeletal Dynamics of Motile Amoeboid Cells

The chemotactic motion of eukaryotic cells such as leukocytes or metastatic cancer cells relies on membrane protrusions driven by the polymerization and depolymerization of actin. Here we show that the response of the actin system to a receptor stimulus is subject to a threshold value that varies strongly from cell to cell. Above the threshold, we observe pronounced cell-to-cell variability in the response amplitude. The polymerization time, however, is almost constant over the entire range of response amplitudes, while the depolymerization time increases with increasing amplitude. We show that cell-to-cell variability in the response amplitude correlates with the amount of Arp2/3, a protein that enhances actin polymerization. A time-delayed feedback model for the cortical actin concentration is consistent with all our observations and confirms the role of Arp2/3 in the observed cell-to-cell variability. Taken together, our observations highlight robust regulation of the actin response that enables a reliable timing of cell movement.

Quantitative profiling of innate immune activation by viral infection in single cells

Cells infected by viruses can exhibit diverse patterns of viral and cellular gene expression. The patterns arise in part from the stochastic or noisy reaction kinetics associated with the small number of genomes, enzymes, and other molecules that typically initiate virus replication and activate cellular anti-viral defenses. It is not known what features, if any, of the early viral or cellular gene expression correlate with later processes of viral replication or cell survival. Here we used two fluorescent reporters to visualize innate immune activation of human prostate cancer (PC3) cells against infection by vesicular stomatitis virus. The cells were engineered to express green-fluorescent protein under control of the promoter for IFIT2, an interferon-sensitive component of the anti-viral response, while red-fluorescent protein was expressed as a byproduct of virus infection. To isolate and quantitatively analyze single-cells, we used a unique microwell array device and open-source image processing software. Kinetic analysis of viral and cellular reporter profiles from hundreds of cells revealed novel relationships between gene expression and the outcome of infection. Specifically, the relative timing rather than the magnitude of the viral gene expression and innate immune activation correlated with the infection outcome. Earlier viral or anti-viral gene expression favored or hindered virus growth, respectively. Further, analysis of kinetic parameters estimated from these data suggests a trade-off between robust antiviral signaling and cell death, as indicated by a higher rate of detectable cell lysis in infected cells with a detectable immune response. In short, cells that activate an immune response lyse at a higher rate. More broadly, we demonstrate how the intrinsic heterogeneity of individual cell behaviors can be exploited to discover features of viral and host gene expression that correlate with single-cell outcomes, which will ultimately impact whether or not infections spread.

Digital Assays Part II: Digital Protein and Cell Assays

A digital assay is one in which the sample is partitioned into many containers such that each partition contains a discrete number of biological entities (0, 1, 2, 3, . . .). A powerful technique in the biologist’s toolkit, digital assays bring a new level of precision in quantifying nucleic acids, measuring proteins and their enzymatic activity, and probing single-cell genotype and phenotype. Where part I of this review focused on the fundamentals of partitioning and digital PCR, part II turns its attention to digital protein and cell assays. Digital enzyme assays measure the kinetics of single proteins with enzymatic activity. Digital enzyme-linked immunoassays (ELISAs) quantify antigenic proteins with 2 to 3 log lower detection limit than conventional ELISA, making them well suited for low-abundance biomarkers. Digital cell assays probe single-cell genotype and phenotype, including gene expression, intracellular and surface proteins, metabolic activity, cytotoxicity, and transcriptomes (scRNA-seq). These methods exploit partitioning to 1) isolate single cells or proteins, 2) detect their activity via enzymatic amplification, and 3) tag them individually by coencapsulating them with molecular barcodes. When scaled, digital assays reveal stochastic differences between proteins or cells within a population, a key to understanding biological heterogeneity. This review is intended to give a broad perspective to scientists interested in adopting digital assays into their workflows.

Resolving the Complex Genetic Basis of Phenotypic Variation and Variability of Cellular Growth

In all organisms, the majority of traits vary continuously between individuals. Explaining the genetic basis of quantitative trait variation requires comprehensively accounting for genetic and nongenetic factors as well as their interactions. The growth of microbial cells can be characterized by a lag duration, an exponential growth phase, and a stationary phase. Parameters that characterize these growth phases can vary among genotypes (phenotypic variation), environmental conditions (phenotypic plasticity), and among isogenic cells in a given environment (phenotypic variability). We used a high-throughput microscopy assay to map genetic loci determining variation in lag duration and exponential growth rate in growth rate-limiting and nonlimiting glucose concentrations, using segregants from a cross of two natural isolates of the budding yeast, Saccharomyces cerevisiae We find that some quantitative trait loci (QTL) are common between traits and environments whereas some are unique, exhibiting gene-by-environment interactions. Furthermore, whereas variation in the central tendency of growth rate or lag duration is explained by many additive loci, differences in phenotypic variability are primarily the result of genetic interactions. We used bulk segregant mapping to increase QTL resolution by performing whole-genome sequencing of complex mixtures of an advanced intercross mapping population grown in selective conditions using glucose-limited chemostats. We find that sequence variation in the high-affinity glucose transporter HXT7 contributes to variation in growth rate and lag duration. Allele replacements of the entire locus, as well as of a single polymorphic amino acid, reveal that the effect of variation in HXT7 depends on genetic, and allelic, background. Amplifications of HXT7 are frequently selected in experimental evolution in glucose-limited environments, but we find that HXT7 amplifications result in antagonistic pleiotropy that is absent in naturally occurring variants of HXT7 Our study highlights the complex nature of the genotype-to-phenotype map within and between environments.

Massive Parallel Analysis of Single Cells in an Integrated Microfluidic Platform

New tools that facilitate the study of cell-to-cell variability could help uncover novel cellular regulation mechanisms. We present an integrated microfluidic platform to analyze a large number of single cells in parallel. To isolate and analyze thousands of individual cells in multiplexed conditions, our platform incorporates arrays of microwells (7 pL each) in a multilayered microfluidic device. The device allows the simultaneous loading of cells into 16 separate chambers, each containing 4640 microwells, for a total of 74 240 wells per device. We characterized different parameters important for the operation of the microfluidic device including flow rate, solution exchange rate in a microchamber, shear stress, and time to fill up a single microwell with molecules of different molecular weight. In general, after ∼7.5 min of cell loading our device has an 80% microwell occupancy with 1-4 cells, of which 36% of wells contained a single cell. To test the functionality of our device, we carried out a cell viability assay with adherent and nonadherent cells. We also studied the production of neutrophil extracellular traps (NETs) from single neutrophils isolated from peripheral blood, observing the existence of temporal heterogeneity in NETs production, perhaps having implications in the type of the neutrophil response to an infection or inflammation. We foresee our platform will have a variety of applications in drug discovery and cellular biology by facilitating the characterization of phenotypic differences in a monoclonal cell population.

Single-cell pluripotency regulatory networks

Pluripotent stem cells (PSCs) are a popular model system for investigating development, tissue regeneration, and repair. Although much is known about the molecular mechanisms that regulate the balance between self-renewal and lineage commitment in PSCs, the spatiotemporal integration of responsive signaling pathways with core transcriptional regulatory networks are complex and only partially understood. Moreover, measurements made on populations of cells reveal only average properties of the underlying regulatory networks, obscuring their fine detail. Here, we discuss the reconstruction of regulatory networks in individual cells using novel single-cell transcriptomics and proteomics, in order to expand our understanding of the molecular basis of pluripotency, including the role of cell-cell variability within PSC populations, and ways in which networks may be controlled in order to reliably manipulate cell behavior.

Single-Cell Analysis of RNA Virus Infection Identifies Multiple Genetically Diverse Viral Genomes within Single Infectious Units

Genetic diversity enables a virus to colonize novel hosts, evade immunity, and evolve drug resistance. However, viral diversity is typically assessed at the population level. Given the existence of cell-to-cell variation, it is critical to understand viral genetic structure at the single-cell level. By combining single-cell isolation with ultra-deep sequencing, we characterized the genetic structure and diversity of a RNA virus shortly after single-cell bottlenecks. Full-length sequences from 881 viral plaques derived from 90 individual cells reveal that sequence variants pre-existing in different viral genomes can be co-transmitted within the same infectious unit to individual cells. Further, the rate of spontaneous virus mutation varies across individual cells, and early production of diversity depends on the viral yield of the very first infected cell. These results unravel genetic and structural features of a virus at the single-cell level, with implications for viral diversity and evolution.

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Genetic diversity is found within viral units infecting an individual cell

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Viral particles within the same infectious unit interact genetically and functionally

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The production of viral genetic diversity is marked by widespread cell-to-cell variation

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Single-cell virus yields determine the early production viral genetic diversity

Tissue homogeneity requires inhibition of unequal gene silencing during development

Multicellular organisms can generate and maintain homogenous populations of cells that make up individual tissues. However, cellular processes that can disrupt homogeneity and how organisms overcome such disruption are unknown. We found that ∼100-fold differences in expression from a repetitive DNA transgene can occur between intestinal cells in Caenorhabditis elegans These differences are caused by gene silencing in some cells and are actively suppressed by parental and zygotic factors such as the conserved exonuclease ERI-1. If unsuppressed, silencing can spread between some cells in embryos but can be repeat specific and independent of other homologous loci within each cell. Silencing can persist through DNA replication and nuclear divisions, disrupting uniform gene expression in developed animals. Analysis at single-cell resolution suggests that differences between cells arise during early cell divisions upon unequal segregation of an initiator of silencing. Our results suggest that organisms with high repetitive DNA content, which include humans, could use similar developmental mechanisms to achieve and maintain tissue homogeneity.

Inference for Stochastic Chemical Kinetics Using Moment Equations and System Size Expansion

Quantitative mechanistic models are valuable tools for disentangling biochemical pathways and for achieving a comprehensive understanding of biological systems. However, to be quantitative the parameters of these models have to be estimated from experimental data. In the presence of significant stochastic fluctuations this is a challenging task as stochastic simulations are usually too time-consuming and a macroscopic description using reaction rate equations (RREs) is no longer accurate. In this manuscript, we therefore consider moment-closure approximation (MA) and the system size expansion (SSE), which approximate the statistical moments of stochastic processes and tend to be more precise than macroscopic descriptions. We introduce gradient-based parameter optimization methods and uncertainty analysis methods for MA and SSE. Efficiency and reliability of the methods are assessed using simulation examples as well as by an application to data for Epo-induced JAK/STAT signaling. The application revealed that even if merely population-average data are available, MA and SSE improve parameter identifiability in comparison to RRE. Furthermore, the simulation examples revealed that the resulting estimates are more reliable for an intermediate volume regime. In this regime the estimation error is reduced and we propose methods to determine the regime boundaries. These results illustrate that inference using MA and SSE is feasible and possesses a high sensitivity.

In this manuscript, we introduce efficient methods for parameter estimation for stochastic processes. The stochasticity of chemical reactions can influence the average behavior of the considered system. For some biological systems, a microscopic, stochastic description is computationally intractable but a macroscopic, deterministic description too inaccurate. This inaccuracy manifests itself in an error in parameter estimates, which impede the predictive power of the proposed model. Until now, no rigorous analysis on the magnitude of the estimation error exists. We show by means of two simulation examples that using mesoscopic descriptions based on the system size expansions and moment-closure approximations can reduce this estimation error compared to inference using a macroscopic description. This reduction is most pronounced in an intermediate volume regime where the influence of stochasticity on the average behavior is moderately strong. For the JAK/STAT pathway where experimental data is available, we show that one parameter that was not structurally identifiable when using a macroscopic description becomes structurally identifiable when using a mesoscopic description for parameter estimation.

Cellular Interrogation: Exploiting Cell-to-Cell Variability to Discriminate Regulatory Mechanisms in Oscillatory Signalling

The molecular complexity within a cell may be seen as an evolutionary response to the external complexity of the cell’s environment. This suggests that the external environment may be harnessed to interrogate the cell’s internal molecular architecture. Cells, however, are not only nonlinear and non-stationary, but also exhibit heterogeneous responses within a clonal, isogenic population. In effect, each cell undertakes its own experiment. Here, we develop a method of cellular interrogation using programmable microfluidic devices which exploits the additional information present in cell-to-cell variation, without requiring model parameters to be fitted to data. We focussed on Ca²⁺ signalling in response to hormone stimulation, which exhibits oscillatory spiking in many cell types and chose eight models of Ca²⁺ signalling networks which exhibit similar behaviour in simulation. We developed a nonlinear frequency analysis for non-stationary responses, which could classify models into groups under parameter variation, but found that this question alone was unable to distinguish critical feedback loops. We further developed a nonlinear amplitude analysis and found that the combination of both questions ruled out six of the models as inconsistent with the experimentally-observed dynamics and heterogeneity. The two models that survived the double interrogation were mathematically different but schematically identical and yielded the same unexpected predictions that we confirmed experimentally. Further analysis showed that subtle mathematical details can markedly influence non-stationary responses under parameter variation, emphasising the difficulty of finding a “correct” model. By developing questions for the pathway being studied, and designing more versatile microfluidics, cellular interrogation holds promise as a systematic strategy that can complement direct intervention by genetics or pharmacology.

We have developed a cellular interrogation methodology that combines programmable microfluidics, fluorescence microscopy and mathematical analysis and have used it to discriminate between models of repetitive Ca²⁺ spiking in HeLa cells. Our approach exploits the natural variability in response of individual cells in a clonal population and the non-steady state behavior of the response in each cell, thereby providing more powerful discrimination. Interrogation consists of steps or pulses of histamine of fixed concentration and width but varying frequency. Eight mathematical models of repetitive Ca²⁺ spiking were chosen from the literature and methods of nonlinear frequency and nonlinear amplitude analysis were developed which ruled out all but two of the models, without having to fit the models to the data. Further analysis of the remaining models yielded predictions that were experimentally confirmed. Cellular interrogation offers a general approach to ruling out competing hypotheses about molecular mechanisms, which is complementary to traditional methods of genetics and biochemistry.

Dynamic NF-κB and E2F interactions control the priority and timing of inflammatory signalling and cell proliferation

Dynamic cellular systems reprogram gene expression to ensure appropriate cellular fate responses to specific extracellular cues. Here we demonstrate that the dynamics of Nuclear Factor kappa B (NF-κB) signalling and the cell cycle are prioritised differently depending on the timing of an inflammatory signal. Using iterative experimental and computational analyses, we show physical and functional interactions between NF-κB and the E2 Factor 1 (E2F-1) and E2 Factor 4 (E2F-4) cell cycle regulators. These interactions modulate the NF-κB response. In S-phase, the NF-κB response was delayed or repressed, while cell cycle progression was unimpeded. By contrast, activation of NF-κB at the G1/S boundary resulted in a longer cell cycle and more synchronous initial NF-κB responses between cells. These data identify new mechanisms by which the cellular response to stress is differentially controlled at different stages of the cell cycle.

DOI:http://dx.doi.org/10.7554/eLife.10473.001

Investigating how cells adapt to the constantly changing environment inside the body is vitally important for understanding how the body responds to an injury or infection. One of the ways in which human cells adapt is by dividing to produce new cells. This takes place in a repeating pattern of events, known as the cell cycle, through which a cell copies its DNA (in a stage known as S-phase) and then divides to make two daughter cells. Each stage of the cell cycle is tightly controlled; for example, a family of proteins called E2 factors control the entry of the cell into S phase.

“Inflammatory” signals produced by a wound or during an infection can activate a protein called Nuclear Factor-kappaB (NF-κB), which controls the activity of genes that allow cells to adapt to the situation. Research shows that the activity of NF-κB is also regulated by the cell cycle, but it has not been clear how this works. Here, Ankers et al. investigated whether the stage of the cell cycle might affect how NF-κB responds to inflammatory signals.

The experiments show that the NF-κB response was stronger in cells that were just about to enter S-phase than in cells that were already copying their DNA. An E2 factor called E2F-1 –which accumulates in the run up to S-phase – interacts with NF-κB and can alter the activity of certain genes. However, during S-phase, another E2 factor family member called E2F-4 binds to NF-κB and represses its activation. Next, Ankers et al. used a mathematical model to understand how these protein interactions can affect the response of cells to inflammatory signals.

These findings suggest that direct interactions between E2 factor proteins and NF-κB enable cells to decide whether to divide or react in different ways to inflammatory signals. The research tools developed in this study, combined with other new experimental techniques, will allow researchers to accurately predict how cells will respond to inflammatory signals at different points in the cell cycle.

DOI:http://dx.doi.org/10.7554/eLife.10473.002

Fluctuations and synchrony of RNA synthesis in nucleoli

Ribosomal RNA (rRNA) sequences are synthesized at exceptionally high rates and, together with ribosomal proteins (r-proteins), are utilized as building blocks for the assembly of pre-ribosomal particles. Although it is widely acknowledged that tight regulation and coordination of rRNA and r-protein production are fundamentally important for the maintenance of cellular homeostasis, still little is known about the real-time kinetics of the ribosome component synthesis in individual cells. In this communication we introduce a label-free MicroRaman spectrometric approach for monitoring rRNA synthesis in live cultured cells. Remarkably high and rapid fluctuations of rRNA production rates were revealed by this technique. Strikingly, the changes in the rRNA output were synchronous for ribosomal genes located in separate nucleoli of the same cell. Our findings call for the development of new concepts to elucidate the coordination of ribosomal components production. In this regard, numerical modeling further demonstrated that the production of rRNA and r-proteins can be coordinated, regardless of the fluctuations in rRNA synthesis. Overall, our quantitative data reveal a spectacular interplay of inherently stochastic rates of RNA synthesis and the coordination of gene expression.

MEMO: multi-experiment mixture model analysis of censored data

Motivation: The statistical analysis of single-cell data is a challenge in cell biological studies. Tailored statistical models and computational methods are required to resolve the subpopulation structure, i.e. to correctly identify and characterize subpopulations. These approaches also support the unraveling of sources of cell-to-cell variability. Finite mixture models have shown promise, but the available approaches are ill suited to the simultaneous consideration of data from multiple experimental conditions and to censored data. The prevalence and relevance of single-cell data and the lack of suitable computational analytics make automated methods, that are able to deal with the requirements posed by these data, necessary.

Results: We present MEMO, a flexible mixture modeling framework that enables the simultaneous, automated analysis of censored and uncensored data acquired under multiple experimental conditions. MEMO is based on maximum-likelihood inference and allows for testing competing hypotheses. MEMO can be applied to a variety of different single-cell data types. We demonstrate the advantages of MEMO by analyzing right and interval censored single-cell microscopy data. Our results show that an examination of censoring and the simultaneous consideration of different experimental conditions are necessary to reveal biologically meaningful subpopulation structures. MEMO allows for a stringent analysis of single-cell data and enables researchers to avoid misinterpretation of censored data. Therefore, MEMO is a valuable asset for all fields that infer the characteristics of populations by looking at single individuals such as cell biology and medicine.

Availability and Implementation: MEMO is implemented in MATLAB and freely available via github (https://github.com/MEMO-toolbox/MEMO).

Contacts: eva-maria.geissen@ist.uni-stuttgart.de or nicole.radde@ist.uni-stuttgart.de

Supplementary information:Supplementary data are available at Bioinformatics online.

Single-cell states versus single-cell atlases - two classes of heterogeneity that differ in meaning and method

Recent advances have created new opportunities to dissect cellular heterogeneity at the omics level. The enthusiasm for deep single-cell profiling has obscured a discussion of different types of heterogeneity and the most-appropriate techniques for studying each type. Here, I distinguish heterogeneity in regulation from heterogeneity in lineage. Snapshots of lineage heterogeneity provide a cell atlas that catalogs cellular diversity within complex tissues. Profiles of regulatory heterogeneity seek to interrogate one lineage deeply to capture an ensemble of single-cell states. Single-cell atlases require molecular signatures from many cells at a throughput afforded by mass cytometry-based, microfluidic-based, and microencapsulation-based methods. Single-cell states are more dependent on time, microenvironment, and low-abundance transcripts, emphasizing in situ methods that stress depth of profiling and quantitative accuracy.

Redefining Signaling Pathways with an Expanding Single-Cell Toolbox

Genetically identical cells respond heterogeneously to uniform environmental stimuli. Consequently, investigating the signaling networks that control these cell responses using 'average' bulk cell measurements can obscure underlying mechanisms and misses information emerging from cell-to-cell variability. Here we review recent technological advances including live-cell fluorescence imaging-based approaches and microfluidic devices that enable measurements of signaling networks, dynamics, and responses in single cells. We discuss how these single-cell tools have uncovered novel mechanistic insights for canonical signaling pathways that control cell proliferation (ERK), DNA-damage responses (p53), and innate immune and stress responses (NF-κB). Future improvements in throughput and multiplexing, analytical pipelines, and in vivo applicability will all significantly expand the biological information gained from single-cell measurements of signaling pathways.

Visualizing quantitative microscopy data: History and challenges

Data visualization is a fundamental aspect of science. In the context of microscopy-based studies, visualization typically involves presentation of the images themselves. However, data visualization is challenging when microscopy experiments entail imaging of millions of cells, and complex cellular phenotypes are quantified in a high-content manner. Most well-established visualization tools are inappropriate for displaying high-content data, which has driven the development of new visualization methodology. In this review, we discuss how data has been visualized in both classical and high-content microscopy studies; as well as the advantages, and disadvantages, of different visualization methods.

Tools for Single-Cell Kinetic Analysis of Virus-Host Interactions

Measures of cellular gene expression or behavior, when performed on individual cells, inevitably reveal a diversity of behaviors and outcomes that can correlate with normal or diseased states. For virus infections, the potential diversity of outcomes are pushed to an extreme, where measures of infection reflect features of the specific infecting virus particle, the individual host cell, as well as interactions between viral and cellular components. Single-cell measures, while revealing, still often rely on specialized fluid handling capabilities, employ end-point measures, and remain labor-intensive to perform. To address these limitations, we consider a new microwell-based device that uses simple pipette-based fluid handling to isolate individual cells. Our design allows different experimental conditions to be implemented in a single device, permitting easier and more standardized protocols. Further, we utilize a recently reported dual-color fluorescent reporter system that provides dynamic readouts of viral and cellular gene expression during single-cell infections by vesicular stomatitis virus. In addition, we develop and show how free, open-source software can enable streamlined data management and batch image analysis. Here we validate the integration of the device and software using the reporter system to demonstrate unique single-cell dynamic measures of cellular responses to viral infection.

A metric and workflow for quality control in the analysis of heterogeneity in phenotypic profiles and screens

Heterogeneity is well recognized as a common property of cellular systems that impacts biomedical research and the development of therapeutics and diagnostics. Several studies have shown that analysis of heterogeneity: gives insight into mechanisms of action of perturbagens; can be used to predict optimal combination therapies; and can be applied to tumors where heterogeneity is believed to be associated with adaptation and resistance. Cytometry methods including high content screening (HCS), high throughput microscopy, flow cytometry, mass spec imaging and digital pathology capture cell level data for populations of cells. However it is often assumed that the population response is normally distributed and therefore that the average adequately describes the results. A deeper understanding of the results of the measurements and more effective comparison of perturbagen effects requires analysis that takes into account the distribution of the measurements, i.e. the heterogeneity. However, the reproducibility of heterogeneous data collected on different days, and in different plates/slides has not previously been evaluated. Here we show that conventional assay quality metrics alone are not adequate for quality control of the heterogeneity in the data. To address this need, we demonstrate the use of the Kolmogorov-Smirnov statistic as a metric for monitoring the reproducibility of heterogeneity in an SAR screen, describe a workflow for quality control in heterogeneity analysis. One major challenge in high throughput biology is the evaluation and interpretation of heterogeneity in thousands of samples, such as compounds in a cell-based screen. In this study we also demonstrate that three heterogeneity indices previously reported, capture the shapes of the distributions and provide a means to filter and browse big data sets of cellular distributions in order to compare and identify distributions of interest. These metrics and methods are presented as a workflow for analysis of heterogeneity in large scale biology projects.

The female gametophyte: an emerging model for cell type-specific systems biology in plant development

Systems biology, a holistic approach describing a system emerging from the interactions of its molecular components, critically depends on accurate qualitative determination and quantitative measurements of these components. Development and improvement of large-scale profiling methods ("omics") now facilitates comprehensive measurements of many relevant molecules. For multicellular organisms, such as animals, fungi, algae, and plants, the complexity of the system is augmented by the presence of specialized cell types and organs, and a complex interplay within and between them. Cell type-specific analyses are therefore crucial for the understanding of developmental processes and environmental responses. This review first gives an overview of current methods used for large-scale profiling of specific cell types exemplified by recent advances in plant biology. The focus then lies on suitable model systems to study plant development and cell type specification. We introduce the female gametophyte of flowering plants as an ideal model to study fundamental developmental processes. Moreover, the female reproductive lineage is of importance for the emergence of evolutionary novelties such as an unequal parental contribution to the tissue nurturing the embryo or the clonal production of seeds by asexual reproduction (apomixis). Understanding these processes is not only interesting from a developmental or evolutionary perspective, but bears great potential for further crop improvement and the simplification of breeding efforts. We finally highlight novel methods, which are already available or which will likely soon facilitate large-scale profiling of the specific cell types of the female gametophyte in both model and non-model species. We conclude that it may take only few years until an evolutionary systems biology approach toward female gametogenesis may decipher some of its biologically most interesting and economically most valuable processes.

Stochastic sensitivity analysis and kernel inference via distributional data

Cellular processes are noisy due to the stochastic nature of biochemical reactions. As such, it is impossible to predict the exact quantity of a molecule or other attributes at the single-cell level. However, the distribution of a molecule over a population is often deterministic and is governed by the underlying regulatory networks relevant to the cellular functionality of interest. Recent studies have started to exploit this property to infer network states. To facilitate the analysis of distributional data in a general experimental setting, we introduce a computational framework to efficiently characterize the sensitivity of distributional output to changes in external stimuli. Further, we establish a probability-divergence-based kernel regression model to accurately infer signal level based on distribution measurements. Our methodology is applicable to any biological system subject to stochastic dynamics and can be used to elucidate how population-based information processing may contribute to organism-level functionality. It also lays the foundation for engineering synthetic biological systems that exploit population decoding to more robustly perform various biocomputation tasks, such as disease diagnostics and environmental-pollutant sensing.

Calcium Spikes in Epithelium: study on Drosophila early embryos

Calcium ion acts in nearly every aspect of cellular life. The versatility and specificity required for such a ubiquitous role is ensured by the spatio-temporal dynamics of calcium concentration variations. While calcium signal dynamics has been extensively studied in cell cultures and adult tissues, little is known about calcium activity during early tissue morphogenesis. We monitored intracellular calcium concentration in Drosophila gastrula and revealed single cell calcium spikes that were short-lived, rare and showed strong variability among embryos. We quantitatively described the spatio-temporal dynamics of these spikes and analyzed their potential origins and nature by introducing physical and chemical perturbations. Our data highlight the inter- and intra-tissue variability of calcium activity during tissue morphogenesis.

Nanoscale monitoring of drug actions on cell membrane using atomic force microscopy

Knowledge of the nanoscale changes that take place in individual cells in response to a drug is useful for understanding the drug action. However, due to the lack of adequate techniques, such knowledge was scarce until the advent of atomic force microscopy (AFM), which is a multifunctional tool for investigating cellular behavior with nanometer resolution under near-physiological conditions. In the past decade, researchers have applied AFM to monitor the morphological and mechanical dynamics of individual cells following drug stimulation, yielding considerable novel insight into how the drug molecules affect an individual cell at the nanoscale. In this article we summarize the representative applications of AFM in characterization of drug actions on cell membrane, including topographic imaging, elasticity measurements, molecular interaction quantification, native membrane protein imaging and manipulation, etc. The challenges that are hampering the further development of AFM for studies of cellular activities are aslo discussed.

To bet or not to bet: deciphering cell to cell variation in protozoan infections

Some of the most crucial phenotypic aspects of parasites, such as an antigen-coated surface, parasite sexual differentiation, virulence, and drug resistance, rely on adaptive plasticity and/or stochastic events. At a population level, cell to cell variability represents an avenue for rapid response to drastic changes in the environment. Single cell approaches can be used to unravel the different strategies used by parasites to survive in the context of regulated transcriptional control (apicomplexa) or in its absence (kinetoplastids).

CNS myelin sheath is stochastically built by homotypic fusion of myelin membranes within the bounds of an oligodendrocyte process

Myelin - the multilayer membrane that envelops axons - is a facilitator of rapid nerve conduction. Oligodendrocytes form CNS myelin; the prevailing hypothesis being that they do it by extending a process that circumnavigates the axon. It is pertinent to ask how myelin is built because oligodendrocyte plasma membrane and myelin are compositionally different. To this end, we examined oligodendrocyte cultures and embryonic avian optic nerves by electron microscopy, immuno-electron microscopy and three-dimensional electron tomography. The results support three novel concepts. Myelin membranes are synthesized as tubules and packaged into "myelinophore organelles" in the oligodendrocyte perikaryon. Myelin membranes are matured in and transported by myelinophore organelles within an oligodendrocyte process. The myelin sheath is generated by myelin membrane fusion inside an oligodendrocyte process. These findings abrogate the dogma of myelin resulting from a wrapping motion of an oligodendrocyte process and open up new avenues in the quest for understanding myelination in health and disease.

Identification of cell types from single-cell transcriptomes using a novel clustering method

MOTIVATION

The recent advance of single-cell technologies has brought new insights into complex biological phenomena. In particular, genome-wide single-cell measurements such as transcriptome sequencing enable the characterization of cellular composition as well as functional variation in homogenic cell populations. An important step in the single-cell transcriptome analysis is to group cells that belong to the same cell types based on gene expression patterns. The corresponding computational problem is to cluster a noisy high dimensional dataset with substantially fewer objects (cells) than the number of variables (genes).

RESULTS

In this article, we describe a novel algorithm named shared nearest neighbor (SNN)-Cliq that clusters single-cell transcriptomes. SNN-Cliq utilizes the concept of shared nearest neighbor that shows advantages in handling high-dimensional data. When evaluated on a variety of synthetic and real experimental datasets, SNN-Cliq outperformed the state-of-the-art methods tested. More importantly, the clustering results of SNN-Cliq reflect the cell types or origins with high accuracy.

AVAILABILITY AND IMPLEMENTATION

The algorithm is implemented in MATLAB and Python. The source code can be downloaded at http://bioinfo.uncc.edu/SNNCliq.

Single-cell and multivariate approaches in genetic perturbation screens

Large-scale genetic perturbation screens are a classical approach in biology and have been crucial for many discoveries. New technologies can now provide unbiased quantification of multiple molecular and phenotypic changes across tens of thousands of individual cells from large numbers of perturbed cell populations simultaneously. In this Review, we describe how these developments have enabled the discovery of new principles of intracellular and intercellular organization, novel interpretations of genetic perturbation effects and the inference of novel functional genetic interactions. These advances now allow more accurate and comprehensive analyses of gene function in cells using genetic perturbation screens.

On comparing heterogeneity across biomarkers

Microscopy reveals complex patterns of cellular heterogeneity that can be biologically informative. However, a limitation of microscopy is that only a small number of biomarkers can typically be monitored simultaneously. Thus, a natural question is whether additional biomarkers provide a deeper characterization of the distribution of cellular states in a population. How much information about a cell's phenotypic state in one biomarker is gained by knowing its state in another biomarker? Here, we describe a framework for comparing phenotypic states across biomarkers. Our approach overcomes the current limitation of microscopy by not requiring costaining biomarkers on the same cells; instead, we require staining of biomarkers (possibly separately) on a common collection of phenotypically diverse cell lines. We evaluate our approach on two image datasets: 33 oncogenically diverse lung cancer cell lines stained with 7 biomarkers, and 49 less diverse subclones of one lung cancer cell line stained with 12 biomarkers. We first validate our method by comparing it to the "gold standard" of costaining. We then apply our approach to all pairs of biomarkers and use it to identify biomarkers that yield similar patterns of heterogeneity. The results presented in this work suggest that many biomarkers provide redundant information about heterogeneity. Thus, our approach provides a practical guide for selecting independently informative biomarkers and, more generally, will yield insights into both the connectivity of biological networks and the complexity of the state space of biological systems.

Systems biology of cancer: entropy, disorder, and selection-driven evolution to independence, invasion and "swarm intelligence"

Our knowledge of the biology of solid cancer has greatly progressed during the last few years, and many excellent reviews dealing with the various aspects of this biology have appeared. In the present review, we attempt to bring together these subjects in a general systems biology narrative. It starts from the roles of what we term entropy of signaling and noise in the initial oncogenic events, to the first major transition of tumorigenesis: the independence of the tumor cell and the switch in its physiology, i.e., from subservience to the organism to its own independent Darwinian evolution. The development after independence involves a constant dynamic reprogramming of the cells and the emergence of a sort of collective intelligence leading to invasion and metastasis and seldom to the ultimate acquisition of immortality through inter-individual infection. At each step, the probability of success is minimal to infinitesimal, but the number of cells possibly involved and the time scale account for the relatively high occurrence of tumorigenesis and metastasis in multicellular organisms.

Local statistics allow quantification of cell-to-cell variability from high-throughput microscope images

MOTIVATION

Quantifying variability in protein expression is a major goal of systems biology and cell-to-cell variability in subcellular localization pattern has not been systematically quantified.

RESULTS

We define a local measure to quantify cell-to-cell variability in high-throughput microscope images and show that it allows comparable measures of variability for proteins with diverse subcellular localizations. We systematically estimate cell-to-cell variability in the yeast GFP collection and identify examples of proteins that show cell-to-cell variability in their subcellular localization.

CONCLUSIONS

Automated image analysis methods can be used to quantify cell-to-cell variability in microscope images.

The structure of dynamic GPCR signaling networks

G-protein-coupled receptors (GPCRs) stimulate signaling networks that control a variety of critical physiological processes. Static information on the map of interacting signaling molecules at the basis of many cellular processes exists, but little is known about the dynamic operation of these networks. Here we focus on two questions. First, Is the network architecture underlying GPCR-activated cellular processes unique in comparison with others such as transcriptional networks? We discuss how spatially localized GPCR signaling requires uniquely organized networks to execute polarized cell responses. Second, What approaches overcome challenges in deciphering spatiotemporally dynamic networks that govern cell behavior? We focus on recently developed microfluidic and optical approaches that allow GPCR signaling pathways to be triggered and perturbed with spatially and temporally variant input while simultaneously visualizing molecular and cellular responses. When integrated with mathematical modeling, these approaches can help identify design principles that govern cell responses to extracellular signals. We outline why optical approaches that allow the behavior of a selected cell to be orchestrated continually are particularly well suited for probing network organization in single cells.

Injection of Propidium Iodide into HeLa Cells Using a Silicon Nanoinjection Lance Array

Delivering foreign molecules into human cells is a wide and ongoing area of research. Gene therapy, or delivering nucleic acids into cells via nonviral or viral pathways, is an especially promising area for pharmaceutics. All gene therapy methods have their respective advantages and disadvantages, including limited delivery efficiency and low viability. We present an electromechanical method for delivering foreign molecules into human cells. Nanoinjection, or delivering molecules into cells using a solid lance, has proven to be highly efficient while maintaining high viability levels. This paper describes an array of solid silicon microlances that was tested to determine efficiency and viability when nanoinjecting tens of thousands of HeLa cells simultaneously. Propidium iodide (PI), a dye that fluoresces when bound to nucleic acids and does not fluoresce when unbound, was delivered into cells using the lance array. Results show that the lance array delivers PI into up to 78% of a nanoinjected HeLa cell culture, while maintaining 78–91% viability. With these results, we submit the nanoinjection method using a silicon lance array as another promising particle delivery method for mammalian culture cells.

How cells explore shape space: a quantitative statistical perspective of cellular morphogenesis

Through statistical analysis of datasets describing single cell shape following systematic gene depletion, we have found that the morphological landscapes explored by cells are composed of a small number of attractor states. We propose that the topology of these landscapes is in large part determined by cell-intrinsic factors, such as biophysical constraints on cytoskeletal organization, and reflects different stable signaling and/or transcriptional states. Cell-extrinsic factors act to determine how cells explore these landscapes, and the topology of the landscapes themselves. Informational stimuli primarily drive transitions between stable states by engaging signaling networks, while mechanical stimuli tune, or even radically alter, the topology of these landscapes. As environments fluctuate, the topology of morphological landscapes explored by cells dynamically adapts to these fluctuations. Finally we hypothesize how complex cellular and tissue morphologies can be generated from a limited number of simple cell shapes.

Single-cell imaging tools for brain energy metabolism: a review

Neurophotonics comes to light at a time in which advances in microscopy and improved calcium reporters are paving the way toward high-resolution functional mapping of the brain. This review relates to a parallel revolution in metabolism. We argue that metabolism needs to be approached both in vitro and in vivo, and that it does not just exist as a low-level platform but is also a relevant player in information processing. In recent years, genetically encoded fluorescent nanosensors have been introduced to measure glucose, glutamate, ATP, NADH, lactate, and pyruvate in mammalian cells. Reporting relative metabolite levels, absolute concentrations, and metabolic fluxes, these sensors are instrumental for the discovery of new molecular mechanisms. Sensors continue to be developed, which together with a continued improvement in protein expression strategies and new imaging technologies, herald an exciting era of high-resolution characterization of metabolism in the brain and other organs.

MicroRNAs as prognostic markers in ovarian cancer

Ovarian cancer (OC) is the most lethal gynecological malignancy among women. Over 70% of women with OC are diagnosed in advanced stages and most of these cases are incurable. Although most patients respond well to primary chemotherapy, tumors become resistant to treatment. Mechanisms of chemoresistance in cancer cells may be associated with mutational events and/or alterations of gene expression through epigenetic events. Although focusing on known genes has already yielded new information, previously unknown non-coding RNAs, such as microRNAs (miRNAs), also lead insight into the biology of chemoresistance. In this review we summarize the current evidence examining the role of miRNAs as biomarkers of response and survival to therapy in OC. Beside their clinical implications, we also discuss important differences between studies that may have limited their use as clinical biomarkers and suggest new approaches.

Evidence of disorder in biological molecules from single molecule pulling experiments

Heterogeneity in biological molecules, resulting in molecule-to-molecule variations in their dynamics and function, is an emerging theme. To elucidate the consequences of heterogeneous behavior at the single molecule level, we propose an exactly solvable model in which the unfolding rate due to mechanical force depends parametrically on an auxiliary variable representing an entropy barrier arising from fluctuations in internal dynamics. When the rate of fluctuations--a measure of dynamical disorder--is comparable to or smaller than the rate of force-induced unbinding, we show that there are two experimentally observable consequences: nonexponential survival probability at constant force, and a heavy-tailed rupture force distribution at constant loading rate. By fitting our analytical expressions to data from single molecule pulling experiments on proteins and DNA, we quantify the extent of disorder. We show that only by analyzing data over a wide range of forces and loading rates can the role of disorder due to internal dynamics be quantitatively assessed.

Microfluidic probe for single-cell analysis in adherent tissue culture

Single-cell analysis provides information critical to understanding key disease processes that are characterized by significant cellular heterogeneity. Few current methods allow single-cell measurement without removing cells from the context of interest, which not only destroys contextual information but also may perturb the process under study. Here we present a microfluidic probe that lyses single adherent cells from standard tissue culture and captures the contents to perform single-cell biochemical assays. We use this probe to measure kinase and housekeeping protein activities, separately or simultaneously, from single human hepatocellular carcinoma cells in adherent culture. This tool has the valuable ability to perform measurements that clarify connections between extracellular context, signals and responses, especially in cases where only a few cells exhibit a characteristic of interest.

Nanoscale imaging and mechanical analysis of Fc receptor-mediated macrophage phagocytosis against cancer cells

Fc receptor-mediated macrophage phagocytosis against cancer cells is an important mechanism in the immune therapy of cancers. Traditional research about macrophage phagocytosis was based on optical microscopy, which cannot reveal detailed information because of the 200-nm-resolution limit. Quantitatively investigating the macrophage phagocytosis at micro- and nanoscale levels is still scarce. The advent of atomic force microscopy (AFM) offers an excellent analytical instrument for quantitatively investigating the biological processes at single-cell and single-molecule levels under native conditions. In this work, we combined AFM and fluorescence microscopy to visualize and quantify the detailed changes in cell morphology and mechanical properties during the process of Fc receptor-mediated macrophage phagocytosis against cancer cells. Lymphoma cells were discernible by fluorescence staining. Then, the dynamic process of phagocytosis was observed by time-lapse optical microscopy. Next, AFM was applied to investigate the detailed cellular behaviors during macrophage phagocytosis under the guidance of fluorescence recognition. AFM imaging revealed the distinct features in cellular ultramicrostructures for the different steps of macrophage phagocytosis. AFM cell mechanical property measurements indicated that the binding of cancer cells to macrophages could make macrophages become stiffer. The experimental results provide novel insights in understanding the Fc-receptor-mediated macrophage phagocytosis.

Nanoscale distribution of CD20 on B-cell lymphoma tumour cells and its potential role in the clinical efficacy of rituximab

Rituximab is an exciting monoclonal antibody drug approved for treating B-cell lymphomas and its target is the CD20 antigen which is expressed on the surface of B cells. In recent years, the variable efficacies of rituximab among different lymphoma patients have become an important clinical issue and urgently need to be solved for further development of antibodies with enhanced efficacies. In this work, atomic force microscopy (AFM) was used to investigate the nanoscale distribution of CD20 on the surface of tumour B cells from lymphoma patients to examine its potential role in the clinical therapeutic effects of rituximab. By performing ROR1 fluorescence labelling (ROR1 is a specific tumour cell surface marker) on the bone marrow cells prepared from B-cell lymphoma patients, the tumour B cells were recognized, and then AFM tips carrying rituximabs via polyethylene glycol crosslinkers were moved to the tumour cells to probe the specific CD20-rituximab interactions. By applying AFM single-molecule force spectroscopy (SMFS) at the local areas (500×500 nm²) on the surface of tumour B cells, the nanoscale distributions of CD20 on the surface of tumour B cells were mapped, visually showing that CD20 distributed heterogeneously on the cell surface. Bone marrow cell samples from three clinical B-cell lymphoma cases were collected to analyze the binding affinity and nanoscale distribution of CD20 on tumour cells. The experimental results showed that CD20 distribution on tumour cells were to some extent related to the clinical therapeutic outcomes while the CD20-rituximab binding forces did not have distinct effects to the clinical outcomes. These results can provide novel insights in understanding the rituximab's clinical efficacies from the nanoscale distribution of CD20 on the tumour cells at single-cell and single-molecule levels.

Cross-talk between Rho and Rac GTPases drives deterministic exploration of cellular shape space and morphological heterogeneity

One goal of cell biology is to understand how cells adopt different shapes in response to varying environmental and cellular conditions. Achieving a comprehensive understanding of the relationship between cell shape and environment requires a systems-level understanding of the signalling networks that respond to external cues and regulate the cytoskeleton. Classical biochemical and genetic approaches have identified thousands of individual components that contribute to cell shape, but it remains difficult to predict how cell shape is generated by the activity of these components using bottom-up approaches because of the complex nature of their interactions in space and time. Here, we describe the regulation of cellular shape by signalling systems using a top-down approach. We first exploit the shape diversity generated by systematic RNAi screening and comprehensively define the shape space a migratory cell explores. We suggest a simple Boolean model involving the activation of Rac and Rho GTPases in two compartments to explain the basis for all cell shapes in the dataset. Critically, we also generate a probabilistic graphical model to show how cells explore this space in a deterministic, rather than a stochastic, fashion. We validate the predictions made by our model using live-cell imaging. Our work explains how cross-talk between Rho and Rac can generate different cell shapes, and thus morphological heterogeneity, in genetically identical populations.

Imaging mitochondrial flux in single cells with a FRET sensor for pyruvate

Mitochondrial flux is currently accessible at low resolution. Here we introduce a genetically-encoded FRET sensor for pyruvate, and methods for quantitative measurement of pyruvate transport, pyruvate production and mitochondrial pyruvate consumption in intact individual cells at high temporal resolution. In HEK293 cells, neurons and astrocytes, mitochondrial pyruvate uptake was saturated at physiological levels, showing that the metabolic rate is determined by intrinsic properties of the organelle and not by substrate availability. The potential of the sensor was further demonstrated in neurons, where mitochondrial flux was found to rise by 300% within seconds of a calcium transient triggered by a short theta burst, while glucose levels remained unaltered. In contrast, astrocytic mitochondria were insensitive to a similar calcium transient elicited by extracellular ATP. We expect the improved resolution provided by the pyruvate sensor will be of practical interest for basic and applied researchers interested in mitochondrial function.

Stochasticity and determinism in models of hematopoiesis

This chapter represents a novel view of modeling in hematopoiesis, synthesizing both deterministic and stochastic approaches. Whereas the stochastic models work in situations where chance dominates, for example when the number of cells is small, or under random mutations, the deterministic models are more important for large-scale, normal hematopoiesis. New types of models are on the horizon. These models attempt to account for distributed environments such as hematopoietic niches and their impact on dynamics. Mixed effects of such structures and chance events are largely unknown and constitute both a challenge and promise for modeling. Our discussion is presented under the separate headings of deterministic and stochastic modeling; however, the connections between both are frequently mentioned. Four case studies are included to elucidate important examples. We also include a primer of deterministic and stochastic dynamics for the reader's use.

Adaptive fluorescence microscopy by online feedback image analysis

Obtaining sufficient statistics in quantitative fluorescence microscopy is often hampered by the tedious and time-consuming task of manually locating comparable specimen and repeatedly launching the same acquisition protocol. Recent advances in combining fluorescence microscopy with online image analysis tackle this problem by fully integrating the task of identifying and locating the specimen of interest in an automated acquisition workflow. Here, we describe the general requirements and specific microscope control and image analysis software solutions for implementing such automated online feedback microscopy. We demonstrate the power of the method by two selected applications addressing high-throughput 3D imaging of sparsely parasite-infected tissue culture cells and automated fluorescence recovery after photobleaching experiments to quantify the turnover of vesicular coat proteins at ER exit sites.

Cell-to-cell expression variability followed by signal reinforcement progressively segregates early mouse lineages

It is now recognized that extensive expression heterogeneities among cells precede the emergence of lineages in the early mammalian embryo. To establish a map of pluripotent epiblast (EPI) versus primitive endoderm (PrE) lineage segregation within the inner cell mass (ICM) of the mouse blastocyst, we characterised the gene expression profiles of individual ICM cells. Clustering analysis of the transcriptomes of 66 cells demonstrated that initially they are non-distinguishable. Early in the segregation, lineage-specific marker expression exhibited no apparent correlation, and a hierarchical relationship was established only in the late blastocyst. Fgf4 exhibited a bimodal expression at the earliest stage analysed, and in its absence, the differentiation of PrE and EPI was halted, indicating that Fgf4 drives, and is required for, ICM lineage segregation. These data lead us to propose a model where stochastic cell-to-cell expression heterogeneity followed by signal reinforcement underlies ICM lineage segregation by antagonistically separating equivalent cells.