Origins of regulated cell-to-cell variability

Distinguishing between stochasticity and determinism: Examples from cell cycle duration variability

We describe a recent approach for distinguishing between stochastic and deterministic sources of variability, focusing on the mammalian cell cycle. Variability between cells is often attributed to stochastic noise, although it may be generated by deterministic components. Interestingly, lineage information can be used to distinguish between variability and determinism. Analysis of correlations within a lineage of the mammalian cell cycle duration revealed its deterministic nature. Here, we discuss the sources of such variability and the possibility that the underlying deterministic process is due to the circadian clock. Finally, we discuss the "kicked cell cycle" model and its implication on the study of the cell cycle in healthy and cancerous tissues.

Frequency modulation of ERK activation dynamics rewires cell fate

Transient versus sustained ERK MAP kinase (MAPK) activation dynamics induce proliferation versus differentiation in response to epidermal (EGF) or nerve (NGF) growth factors in PC‐12 cells. Duration of ERK activation has therefore been proposed to specify cell fate decisions. Using a biosensor to measure ERK activation dynamics in single living cells reveals that sustained EGF/NGF application leads to a heterogeneous mix of transient and sustained ERK activation dynamics in distinct cells of the population, different than the population average. EGF biases toward transient, while NGF biases toward sustained ERK activation responses. In contrast, pulsed growth factor application can repeatedly and homogeneously trigger ERK activity transients across the cell population. These datasets enable mathematical modeling to reveal salient features inherent to the MAPK network. Ultimately, this predicts pulsed growth factor stimulation regimes that can bypass the typical feedback activation to rewire the system toward cell differentiation irrespective of growth factor identity.

Regulation of Spatiotemporal Patterns by Biological Variability: General Principles and Applications to Dictyostelium discoideum

Spatiotemporal patterns often emerge from local interactions in a self-organizing fashion. In biology, the resulting patterns are also subject to the influence of the systematic differences between the system’s constituents (biological variability). This regulation of spatiotemporal patterns by biological variability is the topic of our review. We discuss several examples of correlations between cell properties and the self-organized spatiotemporal patterns, together with their relevance for biology. Our guiding, illustrative example will be spiral waves of cAMP in a colony of Dictyostelium discoideum cells. Analogous processes take place in diverse situations (such as cardiac tissue, where spiral waves occur in potentially fatal ventricular fibrillation) so a deeper understanding of this additional layer of self-organized pattern formation would be beneficial to a wide range of applications. One of the most striking differences between pattern-forming systems in physics or chemistry and those in biology is the potential importance of variability. In the former, system components are essentially identical with random fluctuations determining the details of the self-organization process and the resulting patterns. In biology, due to variability, the properties of potentially very few cells can have a driving influence on the resulting asymptotic collective state of the colony. Variability is one means of implementing a few-element control on the collective mode. Regulatory architectures, parameters of signaling cascades, and properties of structure formation processes can be "reverse-engineered" from observed spatiotemporal patterns, as different types of regulation and forms of interactions between the constituents can lead to markedly different correlations. The power of this biology-inspired view of pattern formation lies in building a bridge between two scales: the patterns as a collective state of a very large number of cells on the one hand, and the internal parameters of the single cells on the other.

Pattern formation is abundant in nature—from the rich ornaments of sea shells and the diversity of animal coat patterns to the myriad of fractal structures in biology and pattern-forming colonies of bacteria. Particularly fascinating are patterns changing with time, spatiotemporal patterns, like propagating waves and aggregation streams. Bacteria form large branched and nested aggregation-like patterns to immobilize themselves against water flow. The individual amoeba in Dictyostelium discoideum colonies initiates a transition to a collective multicellular state via a quorum-sensing form of communication—a cAMP signal propagating through the community in the form of spiral waves—and the subsequent chemotactic response of the cells leads to branch-like aggregation streams. The theoretical principle underlying most of these spatial and spatiotemporal patterns is self-organization, in which local interactions lead to patterns as large-scale collective”modes” of the system. Over more than half a century, these patterns have been classified and analyzed according to a”physics paradigm,” investigating such questions as how parameters regulate the transitions among patterns, which (types of) interactions lead to such large-scale patterns, and whether there are "critical parameter values" marking the sharp, spontaneous onset of patterns. A fundamental discovery has been that simple local interaction rules can lead to complex large-scale patterns. The specific pattern "layouts" (i.e., their spatial arrangement and their geometric constraints) have received less attention. However, there is a major difference between patterns in physics and chemistry on the one hand and patterns in biology on the other: in biology, patterns often have an important functional role for the biological system and can be considered to be under evolutionary selection. From this perspective, we can expect that individual biological elements exert some control on the emerging patterns. Here we explore spiral wave patterns as a prominent example to illustrate the regulation of spatiotemporal patterns by biological variability. We propose a new approach to studying spatiotemporal data in biology: analyzing the correlation between the spatial distribution of the constituents’ properties and the features of the spatiotemporal pattern. This general concept is illustrated by simulated patterns and experimental data of a model organism of biological pattern formation, the slime mold Dictyostelium discoideum. We introduce patterns starting from Turing (stripe and spot) patterns, together with target waves and spiral waves. The biological relevance of these patterns is illustrated by snapshots from real and theoretical biological systems. The principles of spiral wave formation are first explored in a stylized cellular automaton model and then reproduced in a model of Dictyostelium signaling. The shaping of spatiotemporal patterns by biological variability (i.e., by a spatial distribution of cell-to-cell differences) is demonstrated, focusing on two Dictyostelium models. Building up on this foundation, we then discuss in more detail how the nonlinearities in biological models translate the distribution of cell properties into pattern events, leaving characteristic geometric signatures.

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.

Intracellular Information Processing through Encoding and Decoding of Dynamic Signaling Features

Cell signaling dynamics and transcriptional regulatory activities are variable within specific cell types responding to an identical stimulus. In addition to studying the network interactions, there is much interest in utilizing single cell scale data to elucidate the non-random aspects of the variability involved in cellular decision making. Previous studies have considered the information transfer between the signaling and transcriptional domains based on an instantaneous relationship between the molecular activities. These studies predict a limited binary on/off encoding mechanism which underestimates the complexity of biological information processing, and hence the utility of single cell resolution data. Here we pursue a novel strategy that reformulates the information transfer problem as involving dynamic features of signaling rather than molecular abundances. We pursue a computational approach to test if and how the transcriptional regulatory activity patterns can be informative of the temporal history of signaling. Our analysis reveals (1) the dynamic features of signaling that significantly alter transcriptional regulatory patterns (encoding), and (2) the temporal history of signaling that can be inferred from single cell scale snapshots of transcriptional activity (decoding). Immediate early gene expression patterns were informative of signaling peak retention kinetics, whereas transcription factor activity patterns were informative of activation and deactivation kinetics of signaling. Moreover, the information processing aspects varied across the network, with each component encoding a selective subset of the dynamic signaling features. We developed novel sensitivity and information transfer maps to unravel the dynamic multiplexing of signaling features at each of these network components. Unsupervised clustering of the maps revealed two groups that aligned with network motifs distinguished by transcriptional feedforward vs feedback interactions. Our new computational methodology impacts the single cell scale experiments by identifying downstream snapshot measures required for inferring specific dynamical features of upstream signals involved in the regulation of cellular responses.

Single cell studies have shown that differential patterns in the dynamics of signaling proteins, transcription factor activity, gene expression, etc. produce distinct downstream outcomes. The opposite also holds true where particular cellular outcomes have been found to be associated with the dynamical pattern of one or more signaling molecules. Signaling pathways, therefore, serve as signal processing units to inform specific downstream regulation. However, the functional capabilities of the dynamic aspects of signaling are not well understood. To address this issue, we developed a new approach that evaluates information processing between dynamic features in signaling patterns and transcriptional regulatory activity. Our work demonstrates that the information transfer occur through decoding of temporal history of signals rather than only through instantaneous correlations. Moreover, our results identify regulatory network motifs as the critical components in the information processing and filtering of variability in signaling dynamics to produce distinct patterns of downstream transcriptional responses. Our methodology can be broadly applied to single cell scale data on experimentally accessible downstream measures to infer dynamic aspects of upstream signaling.

Dynamic and social behaviors of human pluripotent stem cells

Human pluripotent stem cells (hPSCs) can self-renew or differentiate to diverse cell types, thus providing a platform for basic and clinical applications. However, pluripotent stem cell populations are heterogeneous and functional properties at the single cell level are poorly documented leading to inefficiencies in differentiation and concerns regarding reproducibility and safety. Here, we use non-invasive time-lapse imaging to continuously examine hPSC maintenance and differentiation and to predict cell viability and fate. We document dynamic behaviors and social interactions that prospectively distinguish hPSC survival, self-renewal, and differentiation. Results highlight the molecular role of E-cadherin not only for cell-cell contact but also for clonal propagation of hPSCs. Results indicate that use of continuous time-lapse imaging can distinguish cellular heterogeneity with respect to pluripotency as well as a subset of karyotypic abnormalities whose dynamic properties were monitored.

Modeling Signaling Networks to Advance New Cancer Therapies

Cell signaling pathways control cells' responses to their environment through an intricate network of proteins and small molecules partitioned by intracellular structures, such as the cytoskeleton and nucleus. Our understanding of these pathways has been revised recently with the advent of more advanced experimental techniques; no longer are signaling pathways viewed as linear cascades of information flowing from membrane-bound receptors to the nucleus. Instead, such pathways must be understood in the context of networks, and studying such networks requires an integration of computational and experimental approaches. This understanding is becoming more important in designing novel therapies for diseases such as cancer. Using the MAPK (mitogen-activated protein kinase) and PI3K (class I phosphoinositide-3' kinase) pathways as case studies of cellular signaling, we give an overview of these pathways and their functions. We then describe, using a number of case studies, how computational modeling has aided in understanding these pathways' deregulation in cancer, and how such understanding can be used to optimally tailor current therapies or help design new therapies against cancer.

Surviving apoptosis: life-death signaling in single cells

Tissue development and homeostasis are regulated by opposing pro-survival and pro-death signals. An interesting feature of the Tumor Necrosis Factor (TNF) family of ligands is that they simultaneously activate opposing signals within a single cell via the same ligand-receptor complex. The magnitude of pro-death events such as caspase activation and pro-survival events such as Nuclear Factor (NF)-κB activation vary not only from one cell type to the next but also among individual cells of the same type due to intrinsic and extrinsic noise. The molecules involved in these pro-survival and/or pro-death pathways, and the different phenotypes that result from their activities, have been recently reviewed. Here we focus on the impact of cell-to-cell variability in the strength of these opposing signals on shaping cell fate decisions.

Distinct single-cell signaling characteristics are conferred by the MyD88 and TRIF pathways during TLR4 activation

Toll-like receptors (TLRs) recognize specific pathogen-associated molecular patterns and initiate innate immune responses through signaling pathways that depend on the adaptor proteins MyD88 (myeloid differentiation marker 88) or TRIF (TIR domain-containing adaptor protein-inducing interferon-β). TLR4, in particular, uses both adaptor proteins to activate the transcription factor nuclear factor κB (NF-κB); however, the specificity and redundancy of these two pathways remain to be elucidated. We developed a mathematical model to show how each pathway encodes distinct dynamical features of NF-κB activity and makes distinct contributions to the high variability observed in single-cell measurements. The assembly of a macromolecular signaling platform around MyD88 associated with receptors at the cell surface determined the timing of initial responses to generate a reliable, digital NF-κB signal. In contrast, ligand-induced receptor internalization into endosomes produced noisy, delayed, yet sustained NF-κB signals through TRIF. With iterative mathematical model development, we predicted the molecular mechanisms by which the MyD88- and TRIF-mediated pathways provide ligand concentration-dependent signaling dynamics that transmit information about the pathogen threat.

Complexity and Diversity of the Mammalian Sialome Revealed by Nidovirus Virolectins

Sialic acids (Sias), 9-carbon-backbone sugars, are among the most complex and versatile molecules of life. As terminal residues of glycans on proteins and lipids, Sias are key elements of glycotopes of both cellular and microbial lectins and thus act as important molecular tags in cell recognition and signaling events. Their functions in such interactions can be regulated by post-synthetic modifications, the most common of which is differential Sia-O-acetylation (O-Ac-Sias). The biology of O-Ac-Sias remains mostly unexplored, largely because of limitations associated with their specific in situ detection. Here, we show that dual-function hemagglutinin-esterase envelope proteins of nidoviruses distinguish between a variety of closely related O-Ac-Sias. By using soluble forms of hemagglutinin-esterases as lectins and sialate-O-acetylesterases, we demonstrate differential expression of distinct O-Ac-sialoglycan populations in an organ-, tissue- and cell-specific fashion. Our findings indicate that programmed Sia-O-acetylation/de-O-acetylation may be critical to key aspects of cell development, homeostasis, and/or function.

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Virolectins detect and distinguish between closely related O-Ac-Sias in situ

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O-Ac-sialoglycans occur in nature in a diversity not appreciated so far

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O-Ac-Sias are differentially expressed in a species-, tissue-, and cell-specific fashion

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There is extensive cell-to-cell variability in O-Ac-Sia expression in vivo and in vitro

Single-cell polyadenylation site mapping reveals 3' isoform choice variability

Cell-to-cell variability in gene expression is important for many processes in biology, including embryonic development and stem cell homeostasis. While heterogeneity of gene expression levels has been extensively studied, less attention has been paid to mRNA polyadenylation isoform choice. 3′ untranslated regions regulate mRNA fate, and their choice is tightly controlled during development, but how 3′ isoform usage varies within genetically and developmentally homogeneous cell populations has not been explored. Here, we perform genome-wide quantification of polyadenylation site usage in single mouse embryonic and neural stem cells using a novel single-cell transcriptomic method, BATSeq. By applying BATBayes, a statistical framework for analyzing single-cell isoform data, we find that while the developmental state of the cell globally determines isoform usage, single cells from the same state differ in the choice of isoforms. Notably this variation exceeds random selection with equal preference in all cells, a finding that was confirmed by RNA FISH data. Variability in 3′ isoform choice has potential implications on functional cell-to-cell heterogeneity as well as utility in resolving cell populations.

Bayesian inference of reaction kinetics from single-cell recordings across a heterogeneous cell population

Single-cell experimental techniques provide informative data to help uncover dynamical processes inside a cell. Making full use of such data requires dedicated computational methods to estimate biophysical process parameters and states in a model-based manner. In particular, the treatment of heterogeneity or cell-to-cell variability deserves special attention. The present article provides an introduction to one particular class of algorithms which employ marginalization in order to take heterogeneity into account. An overview of alternative approaches is provided for comparison. We treat two frequently encountered scenarios in single-cell experiments, namely, single-cell trajectory data and single-cell distribution data.

Fluorescence-Activated Cell Sorting-Based Analysis Reveals an Asymmetric Induction of Interferon-Stimulated Genes in Response to Seasonal Influenza A Virus

Influenza A virus (IAV) infection provokes an antiviral response involving the expression of type I and III interferons (IFN) and IFN-stimulated genes (ISGs) in infected cell cultures. However, the spatiotemporal dynamics of the IFN reaction are incompletely understood, as previous studies investigated mainly the population responses of virus-infected cultures, although substantial cell-to-cell variability has been documented. We devised a fluorescence-activated cell sorting-based assay to simultaneously quantify expression of viral antigens and ISGs, such as ISG15, MxA, and IFIT1, in IAV-infected cell cultures at the single-cell level. This approach revealed that seasonal IAV triggers an unexpected asymmetric response, as the major cell populations expressed either viral antigen or ISG, but rarely both. Further investigations identified a role of the viral NS1 protein in blocking ISG expression in infected cells, which surprisingly did not reduce paracrine IFN signaling to noninfected cells. Interestingly, viral ISG control was impaired in cultures infected with avian-origin IAV, including the H7N9 virus from eastern China. This phenotype was traced back to polymorphic NS1 amino acids known to be important for stable binding of the polyadenylation factor CPSF30 and concomitant suppression of host cell gene expression. Most significantly, mutation of two amino acids within the CPSF30 attachment site of NS1 from seasonal IAV diminished the strict control of ISG expression in infected cells and substantially attenuated virus replication. In conclusion, our approach revealed an asymmetric, NS1-dependent ISG induction in cultures infected with seasonal IAV, which appears to be essential for efficient virus propagation.

IMPORTANCE

Interferons are expressed by infected cells in response to IAV infection and play important roles in the antiviral immune response by inducing hundreds of interferon-stimulated genes (ISGs). Unlike many previous studies, we investigated the ISG response at the single-cell level, enabling novel insights into this virus-host interaction. Hence, cell cultures infected with seasonal IAV displayed an asymmetric ISG induction that was confined almost exclusively to noninfected cells. In comparison, ISG expression was observed in larger cell populations infected with avian-origin IAV, suggesting a more resolute antiviral response to these strains. Strict control of ISG expression by seasonal IAV was explained by the binding of the viral NS1 protein to the polyadenylation factor CPSF30, which reduces host cell gene expression. Mutational disruption of CPSF30 binding within NS1 concomitantly attenuated ISG control and replication of seasonal IAV, illustrating the importance of maintaining an asymmetric ISG response for efficient virus propagation.

A power-law dependence of bacterial invasion on mammalian host receptors

Pathogenic bacteria such as Listeria and Yersinia gain initial entry by binding to host target cells and stimulating their internalization. Bacterial uptake entails successive, increasingly strong associations between receptors on the surface of bacteria and hosts. Even with genetically identical cells grown in the same environment, there are vast differences in the number of bacteria entering any given cell. To gain insight into this variability, we examined uptake dynamics of Escherichia coli engineered to express the invasin surface receptor from Yersinia, which enables uptake via mammalian host β₁-integrins. Surprisingly, we found that the uptake probability of a single bacterium follows a simple power-law dependence on the concentration of integrins. Furthermore, the value of a power-law parameter depends on the particular host-bacterium pair but not on bacterial concentration. This power-law captures the complex, variable processes underlying bacterial invasion while also enabling differentiation of cell lines.

Uptake of bacteria by mammalian cells is highly variable within a population of host cells and between host cell types. A detailed but unwieldy mechanistic model describing individual host-pathogen receptor binding events is captured by a simple power-law dependence on the concentration of the host receptors. The power-law parameters capture characteristics of the host-bacterium pair interaction and can differentiate host cell lines. This study has important implications for understanding the accuracy and precision of therapeutics employing receptor-mediated transport of materials to mammalian hosts.

Negative feedback contributes to the stochastic expression of the interferon-β gene in virus-triggered type I interferon signaling pathways

Type I interferon (IFN) signaling pathways play an essential role in the defense against early viral infections; however, the diverse and intricate molecular mechanisms of virus-triggered type I IFN responses are still poorly understood. In this study, we analyzed and compared two classes of models i.e., deterministic ordinary differential equations (ODEs) and stochastic models to elucidate the dynamics and stochasticity of type I IFN signaling pathways. Bifurcation analysis based on an ODE model reveals that the system exhibits a bistable switch and a one-way switch at high or low levels when the strengths of the negative and positive feedbacks are tuned. Furthermore, we compared the stochastic simulation results under the Master and Langevin equations. Both of the stochastic equations generate the bistable switch phenomenon, and the distance between two stable states are smaller than normal under the simulation of the Langevin equation. The quantitative computations also show that a moderate ratio between positive and negative feedback strengths is required to ensure a reliable switch between the different IFN concentrations that regulate the immune response. Moreover, we propose a multi-state stochastic model based on the above deterministic model to describe the multi-cellular system coupled with the diffusion of IFNs. The perturbation and inhibition analysis showed that the positive feedback, as well as noises, has little effect on the stochastic expression of IFNs, but the negative feedback of ISG56 on the activation of IRF7 has a great influence on IFN stochastic expression. Together, these results reveal that positive feedback stabilizes IFN gene expression, and negative feedback may be the main contribution to the stochastic expression of the IFN gene in the virus-triggered type I IFN response. These findings will provide new insight into the molecular mechanisms of virus-triggered type I IFN signaling pathways.

Global variability in gene expression and alternative splicing is modulated by mitochondrial content

Noise in gene expression is a main determinant of phenotypic variability. Increasing experimental evidence suggests that genome-wide cellular constraints largely contribute to the heterogeneity observed in gene products. It is still unclear, however, which global factors affect gene expression noise and to what extent. Since eukaryotic gene expression is an energy demanding process, differences in the energy budget of each cell could determine gene expression differences. Here, we quantify the contribution of mitochondrial variability (a natural source of ATP variation) to global variability in gene expression. We find that changes in mitochondrial content can account for ∼50% of the variability observed in protein levels. This is the combined result of the effect of mitochondria dosage on transcription and translation apparatus content and activities. Moreover, we find that mitochondrial levels have a large impact on alternative splicing, thus modulating both the abundance and type of mRNAs. A simple mathematical model in which mitochondrial content simultaneously affects transcription rate and splicing site choice can explain the alternative splicing data. The results of this study show that mitochondrial content (and/or probably function) influences mRNA abundance, translation, and alternative splicing, which ultimately affects cellular phenotype.

Epigenetic predisposition to reprogramming fates in somatic cells

Reprogramming to pluripotency is a low-efficiency process at the population level. Despite notable advances to molecularly characterize key steps, several fundamental aspects remain poorly understood, including when the potential to reprogram is first established. Here, we apply live-cell imaging combined with a novel statistical approach to infer when somatic cells become fated to generate downstream pluripotent progeny. By tracing cell lineages from several divisions before factor induction through to pluripotent colony formation, we find that pre-induction sister cells acquire similar outcomes. Namely, if one daughter cell contributes to a lineage that generates induced pluripotent stem cells (iPSCs), its paired sibling will as well. This result suggests that the potential to reprogram is predetermined within a select subpopulation of cells and heritable, at least over the short term. We also find that expanding cells over several divisions prior to factor induction does not increase the per-lineage likelihood of successful reprogramming, nor is reprogramming fate correlated to neighboring cell identity or cell-specific reprogramming factor levels. By perturbing the epigenetic state of somatic populations with Ezh2 inhibitors prior to factor induction, we successfully modulate the fraction of iPSC-forming lineages. Our results therefore suggest that reprogramming potential may in part reflect preexisting epigenetic heterogeneity that can be tuned to alter the cellular response to factor induction.

Transcriptional and translational heterogeneity among neonatal mouse spermatogonia

Spermatogonial stem cells (SSCs) are a subset of undifferentiated spermatogonia responsible for ongoing spermatogenesis in mammalian testes. Spermatogonial stem cells arise from morphologically homogeneous prospermatogonia, but growing evidence suggests that only a subset of prospermatogonia develops into the foundational SSC pool. This predicts that subtypes of undifferentiated spermatogonia with discrete mRNA and protein signatures should be distinguishable in neonatal testes. We used single-cell quantitative RT-PCR to examine mRNA levels of 172 genes in individual spermatogonia from 6-day postnatal (P6) mouse testes. Cells enriched from P6 testes using the StaPut or THY1(+) magnetic cell sorting methods exhibited considerable heterogeneity in the abundance of specific germ cell and stem cell mRNAs, segregating into one somatic and three distinct spermatogonial clusters. However, P6 Id4-eGFP(+) transgenic spermatogonia, which are known to be enriched for SSCs, were more homogeneous in their mRNA levels, exhibiting uniform levels for the majority of genes examined (122 of 172). Interestingly, these cells displayed nonuniform (50 of 172) expression of a smaller cohort of these genes, suggesting there is substantial heterogeneity even within the Id4-eGFP(+) population. Further, although immunofluorescence staining largely demonstrated conformity between mRNA and protein levels, some proteins were observed in patterns that were disparate from those detected for the corresponding mRNAs in Id4-eGFP(+) spermatogonia (e.g., Kit, Sohlh2, Stra8), suggesting additional heterogeneity is introduced at the posttranscriptional level. Taken together, these data demonstrate the existence of multiple spermatogonial subtypes in P6 mouse testes and raise the intriguing possibility that these subpopulations may correlate with the development of functionally distinct spermatogenic cell types.

Microfluidic Proximity Ligation Assay for Profiling Signaling Networks with Single-Cell Resolution

The proximity ligation assay (PLA) is a technique that can be used to characterize proteins, protein-protein interactions, and protein modifications at the single-cell level. Image-based in situ detection of proteins using PLA is a quantitative method with a high degree of sensitivity and specificity. The miniaturization and parallelization of the PLA onto a microfluidic chip and concurrent use of an automated cell-culture system increase the throughput of this technology. Here, we describe the performance of PLA on a microfluidic chip. We provide protocols for on-chip cell culture, time-shifted cell stimulation and fixation, PLA implementation, and computational image analysis in order to achieve single-cell resolution. As a proof of concept, we studied the phosphorylation of Akt in response to stimulation with platelet-derived growth factor.

Simultaneous analysis of large-scale RNAi screens for pathogen entry

BACKGROUND

Large-scale RNAi screening has become an important technology for identifying genes involved in biological processes of interest. However, the quality of large-scale RNAi screening is often deteriorated by off-targets effects. In order to find statistically significant effector genes for pathogen entry, we systematically analyzed entry pathways in human host cells for eight pathogens using image-based kinome-wide siRNA screens with siRNAs from three vendors. We propose a Parallel Mixed Model (PMM) approach that simultaneously analyzes several non-identical screens performed with the same RNAi libraries.

RESULTS

We show that PMM gains statistical power for hit detection due to parallel screening. PMM allows incorporating siRNA weights that can be assigned according to available information on RNAi quality. Moreover, PMM is able to estimate a sharedness score that can be used to focus follow-up efforts on generic or specific gene regulators. By fitting a PMM model to our data, we found several novel hit genes for most of the pathogens studied.

CONCLUSIONS

Our results show parallel RNAi screening can improve the results of individual screens. This is currently particularly interesting when large-scale parallel datasets are becoming more and more publicly available. Our comprehensive siRNA dataset provides a public, freely available resource for further statistical and biological analyses in the high-content, high-throughput siRNA screening field.

Trg-deficient Salmonella colonize quiescent tumor regions by exclusively penetrating or proliferating

Chemotherapeutics fail to effectively treat tumors because they cannot reach quiescent regions far from blood vessels. Motile Salmonella are an attractive delivery system that could break this therapeutic barrier. However, little is known about the dissemination and tissue penetration of individual bacteria in tumors after intravenous administration. We hypothesized that eliminating the Trg receptor would improve accumulation in tumor quiescence. To test this hypothesis, we deleted the trg gene from nonpathogenic Salmonella. To quantify individual bacterial behavior, we measured tissue penetration in a tumor-on-a-chip device and measured colony localization in mouse tumors using immunofluorescence. In tumors in vitro and in mice, trg(-) Salmonella penetrated farther into tissue than control bacteria. This difference in localization was caused by the inability to sense sugars in well perfused tissue. Three distinct bacterial phenotypes were observed: proliferating, penetrating, and inactive. Large proliferating colonies, containing more than 40% of individual bacteria, only formed less than 60μm from blood vessels. Small colonies, in comparison, were present both near (inactive) and far (penetrating) from vessels. The farthest was 361.2μm from a vessel, demonstrating the ability to target avascular regions. In addition, colonization was most pronounced in poorly vascularized tumor regions. We show that deletion of trg amplifies Salmonella accumulation in quiescent tumor regions, and, for the first time, identify biological processes that control bacterial distribution in tumors. Understanding how Salmonella penetrate tissue, target quiescence and specifically replicate in tumors are essential steps toward creating a tightly controlled, tunable bacterial therapy.

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.

Unraveling liver complexity from molecular to organ level: challenges and perspectives

Biological responses are determined by information processing at multiple and highly interconnected scales. Within a tissue the individual cells respond to extracellular stimuli by regulating intracellular signaling pathways that in turn determine cell fate decisions and influence the behavior of neighboring cells. As a consequence the cellular responses critically impact tissue composition and architecture. Understanding the regulation of these mechanisms at different scales is key to unravel the emergent properties of biological systems. In this perspective, a multidisciplinary approach combining experimental data with mathematical modeling is introduced. We report the approach applied within the Virtual Liver Network to analyze processes that regulate liver functions from single cell responses to the organ level using a number of examples. By facilitating interdisciplinary collaborations, the Virtual Liver Network studies liver regeneration and inflammatory processes as well as liver metabolic functions at multiple scales, and thus provides a suitable example to identify challenges and point out potential future application of multi-scale systems biology.

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.

A nuclear circularity-based classifier for diagnostic distinction of desmoplastic from spindle cell melanoma in digitized histological images

Context:

Distinction of spindle cell melanoma (SM) and desmoplastic melanoma (DM) is clinically important due to differences in metastatic rate and prognosis; however, histological distinction is not always straightforward. During a routine review of cases, we noted differences in nuclear circularity between SM and DM.

Aim:

The primary aim in our study was to determine whether these differences in nuclear circularity, when assessed using a basic ImageJ-based threshold extraction, can serve as a diagnostic classifier to distinguish DM from SM.

Settings and Design:

Our retrospective analysis of an established patient cohort (SM n = 9, DM n = 9) was employed to determine discriminatory power.

Subjects and Methods:

Regions of interest (total n = 108; 6 images per case) were selected from scanned H and E-stained histological sections, and nuclear circularity was extracted and quantified by computational image analysis using open source tools (plugins for ImageJ).

Statistical Analysis:

Using analysis of variance, t-tests, and Fisher's exact tests, we compared extracted quantitative shape measures; statistical significance was defined as P < 0.05.

Results:

Classifying circularity values into four shape categories (spindled, elongated, oval, round) demonstrated significant differences in the spindled and round categories. Paradoxically, DM contained more spindled nuclei than SM (P = 0.011) and SM contained more round nuclei than DM (P = 0.026). Performance assessment using a combined shape-classification of the round and spindled fractions showed 88.9% accuracy and a Youden index of 0.77.

Conclusions:

Spindle cell melanoma and DM differ significantly in their nuclear morphology with respect to fractions of round and spindled nuclei. Our study demonstrates that quantifying nuclear circularity can be used as an adjunct diagnostic tool for distinction of DM and SM.

Nanoparticle vesicle encoding for imaging and tracking cell populations

For phenotypic behavior to be understood in the context of cell lineage and local environment, properties of individual cells must be measured relative to population-wide traits. However, the inability to accurately identify, track and measure thousands of single cells via high-throughput microscopy has impeded dynamic studies of cell populations. We demonstrate unique labeling of cells, driven by the heterogeneous random uptake of fluorescent nanoparticles of different emission colors. By sequentially exposing a cell population to different particles, we generated a large number of unique digital codes, which corresponded to the cell-specific number of nanoparticle-loaded vesicles and were visible within a given fluorescence channel. When three colors are used, the assay can self-generate over 17,000 individual codes identifiable using a typical fluorescence microscope. The color-codes provided immediate visualization of cell identity and allowed us to track human cells with a success rate of 78% across image frames separated by 8 h.

Agent-based modeling of autophagy reveals emergent regulatory behavior of spatio-temporal autophagy dynamics

Background

Autophagy is a vesicle-mediated pathway for lysosomal degradation, essential under basal and stressed conditions. Various cellular components, including specific proteins, protein aggregates, organelles and intracellular pathogens, are targets for autophagic degradation. Thereby, autophagy controls numerous vital physiological and pathophysiological functions, including cell signaling, differentiation, turnover of cellular components and pathogen defense. Moreover, autophagy enables the cell to recycle cellular components to metabolic substrates, thereby permitting prolonged survival under low nutrient conditions. Due to the multi-faceted roles for autophagy in maintaining cellular and organismal homeostasis and responding to diverse stresses, malfunction of autophagy contributes to both chronic and acute pathologies.

Results

We applied a systems biology approach to improve the understanding of this complex cellular process of autophagy. All autophagy pathway vesicle activities, i.e. creation, movement, fusion and degradation, are highly dynamic, temporally and spatially, and under various forms of regulation. We therefore developed an agent-based model (ABM) to represent individual components of the autophagy pathway, subcellular vesicle dynamics and metabolic feedback with the cellular environment, thereby providing a framework to investigate spatio-temporal aspects of autophagy regulation and dynamic behavior. The rules defining our ABM were derived from literature and from high-resolution images of autophagy markers under basal and activated conditions. Key model parameters were fit with an iterative method using a genetic algorithm and a predefined fitness function. From this approach, we found that accurate prediction of spatio-temporal behavior required increasing model complexity by implementing functional integration of autophagy with the cellular nutrient state. The resulting model is able to reproduce short-term autophagic flux measurements (up to 3 hours) under basal and activated autophagy conditions, and to measure the degree of cell-to-cell variability. Moreover, we experimentally confirmed two model predictions, namely (i) peri-nuclear concentration of autophagosomes and (ii) inhibitory lysosomal feedback on mTOR signaling.

Conclusion

Agent-based modeling represents a novel approach to investigate autophagy dynamics, function and dysfunction with high biological realism. Our model accurately recapitulates short-term behavior and cell-to-cell variability under basal and activated conditions of autophagy. Further, this approach also allows investigation of long-term behaviors emerging from biologically-relevant alterations to vesicle trafficking and metabolic state.

Electronic supplementary material

The online version of this article (doi:10.1186/s12964-014-0056-8) contains supplementary material, which is available to authorized users.

Applications of flow cytometry to characterize bacterial physiological responses

Although reports of flow cytometry (FCM) applied to bacterial analysis are increasing, studies of FCM related to human cells still vastly outnumber other reports. However, current advances in FCM combined with a new generation of cellular reporter probes have made this technique suitable for analyzing physiological responses in bacteria. We review how FCM has been applied to characterize distinct physiological conditions in bacteria including responses to antibiotics and other cytotoxic chemicals and physical factors, pathogen-host interactions, cell differentiation during biofilm formation, and the mechanisms governing development pathways such as sporulation. Since FCM is suitable for performing studies at the single-cell level, we describe how this powerful technique has yielded invaluable information about the heterogeneous distribution of differently and even specialized responding cells and how it may help to provide insights about how cell interaction takes place in complex structures, such as those that prevail in bacterial biofilms.

USNCTAM perspectives on mechanics in medicine

Over decades, the theoretical and applied mechanics community has developed sophisticated approaches for analysing the behaviour of complex engineering systems. Most of these approaches have targeted systems in the transportation, materials, defence and energy industries. Applying and further developing engineering approaches for understanding, predicting and modulating the response of complicated biomedical processes not only holds great promise in meeting societal needs, but also poses serious challenges. This report, prepared for the US National Committee on Theoretical and Applied Mechanics, aims to identify the most pressing challenges in biological sciences and medicine that can be tackled within the broad field of mechanics. This echoes and complements a number of national and international initiatives aiming at fostering interdisciplinary biomedical research. This report also comments on cultural/educational challenges. Specifically, this report focuses on three major thrusts in which we believe mechanics has and will continue to have a substantial impact. (i) Rationally engineering injectable nano/microdevices for imaging and therapy of disease. Within this context, we discuss nanoparticle carrier design, vascular transport and adhesion, endocytosis and tumour growth in response to therapy, as well as uncertainty quantification techniques to better connect models and experiments. (ii) Design of biomedical devices, including point-of-care diagnostic systems, model organ and multi-organ microdevices, and pulsatile ventricular assistant devices. (iii) Mechanics of cellular processes, including mechanosensing and mechanotransduction, improved characterization of cellular constitutive behaviour, and microfluidic systems for single-cell studies.

Protein distributions from a stochastic model of the lac operon of E. coli with DNA looping: analytical solution and comparison with experiments

Although noisy gene expression is widely accepted, its mechanisms are subjects of debate, stimulated largely by single-molecule experiments. This work is concerned with one such study, in which Choi et al., 2008, obtained real-time data and distributions of Lac permease in E. coli. They observed small and large protein bursts in strains with and without auxiliary operators. They also estimated the size and frequency of these bursts, but these were based on a stochastic model of a constitutive promoter. Here, we formulate and solve a stochastic model accounting for the existence of auxiliary operators and DNA loops. We find that DNA loop formation is so fast that small bursts are averaged out, making it impossible to extract their size and frequency from the data. In contrast, we can extract not only the size and frequency of the large bursts, but also the fraction of proteins derived from them. Finally, the proteins follow not the negative binomial distribution, but a mixture of two distributions, which reflect the existence of proteins derived from small and large bursts.

Analysis of the influence of cell heterogeneity on nanoparticle dose response

Understanding the effect of variability in the interaction of individual cells with nanoparticles on the overall response of the cell population to a nanoagent is a fundamental challenge in bionanotechnology. Here, we show that the technique of time-resolved, high-throughput microscopy can be used in this endeavor. Mass measurement with single-cell resolution provides statistically robust assessments of cell heterogeneity, while the addition of a temporal element allows assessment of separate processes leading to deconvolution of the effects of particle supply and biological response. We provide a specific demonstration of the approach, in vitro, through time-resolved measurement of fibroblast cell (HFF-1) death caused by exposure to cationic nanoparticles. The results show that heterogeneity in cell area is the major source of variability with area-dependent nanoparticle capture rates determining the time of cell death and hence the form of the exposure–response characteristic. Moreover, due to the particulate nature of the nanoparticle suspension, there is a reduction in the particle concentration over the course of the experiment, eventually causing saturation in the level of measured biological outcome. A generalized mathematical description of the system is proposed, based on a simple model of particle depletion from a finite supply reservoir. This captures the essential aspects of the nanoparticle–cell interaction dynamics and accurately predicts the population exposure–response curves from individual cell heterogeneity distributions.

Uncoupling of the dynamics of host-pathogen interaction uncovers new mechanisms of viral interferon antagonism at the single-cell level

Antiviral defence in mammals is mediated through type-I interferons (IFNs). Viruses antagonise this process through expression of IFN antagonist proteins (IAPs). Understanding and modelling of viral escape mechanisms and the dynamics of IAP action has the potential to facilitate the development of specific and safe drugs. Here, we describe the dynamics of interference by selected viral IAPs, NS1 from Influenza A virus and NS3/4A from Hepatitis C virus. We used Tet-inducible IAP gene expression to uncouple this process from virus-driven dynamics. Stochastic activation of the IFN-β gene required the use of single-cell live imaging to define the efficacy of the inhibitors during the virus-induced signalling processes. We found significant correlation between the onset of IAP expression and halted IFN-β expression in cells where IFN-β induction had already occurred. These data indicate that IAPs not only prevent antiviral signalling prior to IFN-β induction, but can also stop the antiviral response even after it has been activated. We found reduced NF-κB activation to be the underlying mechanism by which activated IFN expression can be blocked. This work demonstrates a new mechanism by which viruses can antagonise the IFN response.

Environmental sensing, information transfer, and cellular decision-making

The recognition that gene expression can be substantially stochastic poses the question of how cells respond to dynamic environments using biochemistry that itself fluctuates. The study of cellular decision-making aims to solve this puzzle by focusing on quantitative understanding of the variation seen across isogenic populations in response to extracellular change. This behaviour is complex, and a theoretical framework within which to embed experimental results is needed. Here we review current approaches, with an emphasis on information theory, sequential data processing, and optimality arguments. We conclude by highlighting some limitations of these techniques and the importance of connecting both theory and experiment to measures of fitness.

Global genetic response in a cancer cell: self-organized coherent expression dynamics

Understanding the basic mechanism of the spatio-temporal self-control of genome-wide gene expression engaged with the complex epigenetic molecular assembly is one of major challenges in current biological science. In this study, the genome-wide dynamical profile of gene expression was analyzed for MCF-7 breast cancer cells induced by two distinct ErbB receptor ligands: epidermal growth factor (EGF) and heregulin (HRG), which drive cell proliferation and differentiation, respectively. We focused our attention to elucidate how global genetic responses emerge and to decipher what is an underlying principle for dynamic self-control of genome-wide gene expression. The whole mRNA expression was classified into about a hundred groups according to the root mean square fluctuation (rmsf). These expression groups showed characteristic time-dependent correlations, indicating the existence of collective behaviors on the ensemble of genes with respect to mRNA expression and also to temporal changes in expression. All-or-none responses were observed for HRG and EGF (biphasic statistics) at around 10–20 min. The emergence of time-dependent collective behaviors of expression occurred through bifurcation of a coherent expression state (CES). In the ensemble of mRNA expression, the self-organized CESs reveals distinct characteristic expression domains for biphasic statistics, which exhibits notably the presence of criticality in the expression profile as a route for genomic transition. In time-dependent changes in the expression domains, the dynamics of CES reveals that the temporal development of the characteristic domains is characterized as autonomous bistable switch, which exhibits dynamic criticality (the temporal development of criticality) in the genome-wide coherent expression dynamics. It is expected that elucidation of the biophysical origin for such critical behavior sheds light on the underlying mechanism of the control of whole genome.

Revitalizing personalized medicine: respecting biomolecular complexities beyond gene expression

Despite recent advancements in "omic" technologies, personalized medicine has not realized its fullest potential due to isolated and incomplete application of gene expression tools. In many instances, pharmacogenomics is being interchangeably used for personalized medicine, when actually it is one of the many facets of personalized medicine. Herein, we highlight key issues that are hampering the advancement of personalized medicine and highlight emerging predictive tools that can serve as a decision support mechanism for physicians to personalize treatments.

Increasing the Content of High-Content Screening: An Overview

Target-based high-throughput screening (HTS) has recently been critiqued for its relatively poor yield compared to phenotypic screening approaches. One type of phenotypic screening, image-based high-content screening (HCS), has been seen as particularly promising.

In this article, we assess whether HCS is as high content as it can be. We analyze HCS publications and find that although the number of HCS experiments published each year continues to grow steadily, the information content lags behind. We find that a majority of high-content screens published so far (60−80%) made use of only one or two image-based features measured from each sample and disregarded the distribution of those features among each cell population. We discuss several potential explanations, focusing on the hypothesis that data analysis traditions are to blame. This includes practical problems related to managing large and multidimensional HCS data sets as well as the adoption of assay quality statistics from HTS to HCS. Both may have led to the simplification or systematic rejection of assays carrying complex and valuable phenotypic information.

We predict that advanced data analysis methods that enable full multiparametric data to be harvested for entire cell populations will enable HCS to finally reach its potential.

Stress induced telomere shortening: longer life with less mutations?

Background

Mutations accumulate as a result of DNA damage and imperfect DNA repair machinery. In higher eukaryotes the accumulation and spread of mutations is limited in two primary ways: through p53-mediated programmed cell death and cellular senescence mediated by telomeres. Telomeres shorten at every cell division and cell stops dividing once the shortest telomere reaches a critical length. It has been shown that the rate of telomere attrition is accelerated when cells are exposed to DNA damaging agents. However the implications of this mechanism are not fully understood.

Results

With the help of in silico model we investigate the effect of genotoxic stress on telomere attrition and apoptosis in a population of non-identical replicating cells. When comparing the populations of cells with constant vs. stress-induced rate of telomere shortening we find that stress induced telomere shortening (SITS) increases longevity while reducing mutation rate. Interestingly, however, the effect takes place only when genotoxic stresses (e.g. reactive oxygen species due to metabolic activity) are distributed non-equally among cells.

Conclusions

Our results for the first time show how non-equal distribution of metabolic load (and associated genotoxic stresses) combined with stress induced telomere shortening can delay aging and minimize mutations.

Populational equilibrium through exosome-mediated Wnt signaling in tumor progression of diffuse large B-cell lymphoma

Tumors are composed of phenotypically heterogeneous cell populations. The nongenomic mechanisms underlying transitions and interactions between cell populations are largely unknown. Here, we show that diffuse large B-cell lymphomas possess a self-organized infrastructure comprising side population (SP) and non-SP cells, where transitions between clonogenic states are modulated by exosome-mediated Wnt signaling. DNA methylation modulated SP-non-SP transitions and was correlated with the reciprocal expressions of Wnt signaling pathway agonist Wnt3a in SP cells and the antagonist secreted frizzled-related protein 4 in non-SP cells. Lymphoma SP cells exhibited autonomous clonogenicity and exported Wnt3a via exosomes to neighboring cells, thus modulating population equilibrium in the tumor.

Scalable inference of heterogeneous reaction kinetics from pooled single-cell recordings

Mathematical methods combined with measurements of single-cell dynamics provide a means to reconstruct intracellular processes that are only partly or indirectly accessible experimentally. To obtain reliable reconstructions, the pooling of measurements from several cells of a clonal population is mandatory. However, cell-to-cell variability originating from diverse sources poses computational challenges for such process reconstruction. We introduce a scalable Bayesian inference framework that properly accounts for population heterogeneity. The method allows inference of inaccessible molecular states and kinetic parameters; computation of Bayes factors for model selection; and dissection of intrinsic, extrinsic and technical noise. We show how additional single-cell readouts such as morphological features can be included in the analysis. We use the method to reconstruct the expression dynamics of a gene under an inducible promoter in yeast from time-lapse microscopy data.

Network of mutually repressive metastasis regulators can promote cell heterogeneity and metastatic transitions

The sources and consequences of nongenetic variability in metastatic progression are largely unknown. To address these questions, we characterized a transcriptional regulatory network for the metastasis suppressor Raf kinase inhibitory protein (RKIP). We previously showed that the transcription factor BACH1 is negatively regulated by RKIP and promotes breast cancer metastasis. Here we demonstrate that BACH1 acts in a double-negative (overall positive) feedback loop to inhibit RKIP transcription in breast cancer cells. BACH1 also negatively regulates its own transcription. Analysis of the BACH1 network reveals the existence of an inverse relationship between BACH1 and RKIP involving both monostable and bistable transitions that can potentially give rise to nongenetic variability. Single-cell analysis confirmed monostable and bistable-like behavior. Treatment with histone deacetylase inhibitors or depletion of the polycomb repressor enhancer of zeste homolog 2 altered relative RKIP and BACH1 levels in a manner consistent with a prometastatic state. Together, our results suggest that the mutually repressive relationship between metastatic regulators such as RKIP and BACH1 can play a key role in determining metastatic progression in cancer.

Nanotechnology: emerging tools for biology and medicine

Nanotechnology encompasses structures comparable in size with biomolecules and enables new ways to measure and detect biology, perturb cells, and treat patients. Here, Wong et al. review nanotechnology-based approaches for precisely measuring and perturbing living systems that may yield unexpected insights into systems biology as well as new therapeutic strategies for personalized medicine.

Historically, biomedical research has been based on two paradigms. First, measurements of biological behaviors have been based on bulk assays that average over large populations. Second, these behaviors have then been crudely perturbed by systemic administration of therapeutic treatments. Nanotechnology has the potential to transform these paradigms by enabling exquisite structures comparable in size with biomolecules as well as unprecedented chemical and physical functionality at small length scales. Here, we review nanotechnology-based approaches for precisely measuring and perturbing living systems. Remarkably, nanotechnology can be used to characterize single molecules or cells at extraordinarily high throughput and deliver therapeutic payloads to specific locations as well as exhibit dynamic biomimetic behavior. These advances enable multimodal interfaces that may yield unexpected insights into systems biology as well as new therapeutic strategies for personalized medicine.

Small-volume analysis of cell-cell signaling molecules in the brain

Modern science is characterized by integration and synergy between research fields. Accordingly, as technological advances allow new and more ambitious quests in scientific inquiry, numerous analytical and engineering techniques have become useful tools in biological research. The focus of this review is on cutting edge technologies that aid direct measurement of bioactive compounds in the nervous system to facilitate fundamental research, diagnostics, and drug discovery. We discuss challenges associated with measurement of cell-to-cell signaling molecules in the nervous system, and advocate for a decrease of sample volumes to the nanoliter volume regimen for improved analysis outcomes. We highlight effective approaches for the collection, separation, and detection of such small-volume samples, present strategies for targeted and discovery-oriented research, and describe the required technology advances that will empower future translational science.

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.

Opportunity for selection in human health

Natural selection defined by differential survival and reproduction of individuals in populations is influenced by genetic, developmental, and environmental factors operating at every age and stage in human life history: generation of gametes, conception, birth, maturation, reproduction, senescence, and death. Biological systems are built upon a hierarchical organization nesting subcellular organelles, cells, tissues, and organs within individuals, individuals within families, and families within populations, and the latter among other populations. Natural selection often acts simultaneously at more than one level of biological organization and on specific traits, which we define as multilevel selection. Under this model, the individual is a fundamental unit of biological organization and also of selection, imbedded in a larger evolutionary context, just as it is a unit of medical intervention imbedded in larger biological, cultural, and environmental contexts. Here, we view human health and life span as necessary consequences of natural selection, operating at all levels and phases of biological hierarchy in human life history as well as in sociological and environmental milieu. An understanding of the spectrum of opportunities for natural selection will help us develop novel approaches to improving healthy life span through specific and global interventions that simultaneously focus on multiple levels of biological organization. Indeed, many opportunities exist to apply multilevel selection models employed in evolutionary biology and biodemography to improving human health at all hierarchical levels. Multilevel selection perspective provides a rational theoretical foundation for a synthesis of medicine and evolution that could lead to discovering effective predictive, preventive, palliative, potentially curative, and individualized approaches in medicine and in global health programs.