We are all individuals: causes and consequences of non-genetic heterogeneity in mammalian cells

Exploring the Surface: Sampling of Potential Skin Cancer Biomarkers Kynurenine and Tryptophan, Studied on 3D Melanocyte and Melanoma Models

Early detection of cancer via biomarkers is vital for improving patient survival rates. In the case of skin cancers, low-molecular-weight biomarkers can penetrate the skin barrier, enabling non-invasive sampling at an early stage. This study focuses on detecting tryptophan (Trp) and kynurenine (Kyn) on the surface of reconstructed 3D melanoma and melanocyte models. This is examined in connection with IDO-1 and IL-6 expression in response to IFN-γ or UVB stimulation, both crucial factors of the melanoma tumor microenvironment (TME). Using a polystyrene scaffold, full-thickness human skin equivalents containing fibroblasts, keratinocytes, and melanocytes or melanoma cells were developed. The samples were stimulated with IFN-γ or UVB, and Trp and Kyn secretion was measured using HPLC-PDA and HPLC-MS. The expression of IDO-1 and IL-6 was measured using RT-qPCR. Increased Trp catabolism to Kyn was observed in IFN-γ-stimulated melanoma and melanocyte models, along with higher IDO-1 expression. UVB exposure led to significant changes in Kyn levels but only in the melanoma model. This study demonstrates the potential of skin surface Trp and Kyn monitoring to capture TME metabolic changes. It also lays the groundwork for future in vivo studies, aiding in understanding and monitoring skin cancer progression.

CRIPTO-based micro-heterogeneity of mouse muscle satellite cells enables adaptive response to regenerative microenvironment

Skeletal muscle repair relies on heterogeneous populations of satellite cells (SCs). The mechanisms that regulate SC homeostasis and state transition during activation are currently unknown. Here, we investigated the emerging role of non-genetic micro-heterogeneity, i.e., intrinsic cell-to-cell variability of a population, in this process. We demonstrate that micro-heterogeneity of the membrane protein CRIPTO in mouse-activated SCs (ASCs) identifies metastable cell states that allow a rapid response of the population to environmental changes. Mechanistically, CRIPTO micro-heterogeneity is generated and maintained through a process of intracellular trafficking coupled with active shedding of CRIPTO from the plasma membrane. Irreversible perturbation of CRIPTO micro-heterogeneity affects the balance of proliferation, self-renewal, and myogenic commitment in ASCs, resulting in increased self-renewal in vivo. Our findings demonstrate that CRIPTO micro-heterogeneity regulates the adaptative response of ASCs to microenvironmental changes, providing insights into the role of intrinsic heterogeneity in preserving stem cell population diversity during tissue repair.

Modeling Cellular Signaling Variability Based on Single-Cell Data: The TGFβ-SMAD Signaling Pathway

Nongenetic heterogeneity is key to cellular decisions, as even genetically identical cells respond in very different ways to the same external stimulus, e.g., during cell differentiation or therapeutic treatment of disease. Strong heterogeneity is typically already observed at the level of signaling pathways that are the first sensors of external inputs and transmit information to the nucleus where decisions are made. Since heterogeneity arises from random fluctuations of cellular components, mathematical models are required to fully describe the phenomenon and to understand the dynamics of heterogeneous cell populations. Here, we review the experimental and theoretical literature on cellular signaling heterogeneity, with special focus on the TGFβ/SMAD signaling pathway.

Resolving Crosstalk Between Signaling Pathways Using Mathematical Modeling and Time-Resolved Single Cell Data

Crosstalk between signaling pathways can modulate the cellular response to stimuli and is therefore an important part of signal transduction. For a comprehensive understanding of cellular responses, identifying points of interaction between the underlying molecular networks is essential. Here, we present an approach that allows the systematic prediction of such interactions by perturbing one pathway and quantifying the concomitant alterations in the response of a second pathway. As the observed alterations contain information about the crosstalk, we use an ordinary differential equation-based model to extract this information by linking altered dynamics to individual processes. Consequently, we can predict the interaction points between two pathways. As an example, we employed our approach to investigate the crosstalk between the NF-κB and p53 signaling pathway. We monitored the response of p53 to genotoxic stress using time-resolved single cell data and perturbed NF-κB signaling by inhibiting the kinase IKK2. Employing a subpopulation-based modeling approach enabled us to identify multiple interaction points that are simultaneously affected by perturbation of NF-κB signaling. Hence, our approach can be used to analyze crosstalk between two signaling pathways in a systematic manner.

Origin of Cancer: Cell work is the Key to Understanding Cancer Initiation and Progression

The cell is the smallest unit of life. It is a structure that maintains order through self-organization, characterized by a high level of dynamism, which in turn is characterized by work. For this work to take place, a continuous high flow of energy is necessary. However, a focused view of the physical relationship between energy and work is inadequate for describing complex biological/medical mechanisms or systems. In this review, we try to make a connection between the fundamental laws of physics and the mechanisms and functions of biology, which are characterized by self-organization. Many different physical work processes (work) in human cells are called cell work and can be grouped into five forms: synthetic, mechanical, electrical, concentration, and heat generation cell work. In addition to the flow of energy, these cell functions are based on fundamental processes of self-organization that we summarize with the term Entirety of molecular interaction (EoMI). This illustrates that cell work is caused by numerous molecular reactions, flow equilibrium, and mechanisms. Their number and interactions are so complex that they elude our perception in their entirety. To be able to describe cell functions in a biological/medical context, the parameters influencing cell work should be summarized in overarching influencing variables. These are “biological” energy, information, matter, and cell mechanics (EMIM). This makes it possible to describe and characterize the cell work involved in cell systems (e.g., respiratory chain, signal transmission, cell structure, or inheritance processes) and to demonstrate changes. If cell work and the different influencing parameters (EMIM influencing variables) are taken as the central property of the cell, specific gene mutations cannot be regarded as the sole cause for the initiation and progression of cancer. This reductionistic monocausal view does not do justice to the dynamic and highly complex system of a cell. Therefore, we postulate that each of the EMIM influencing variables described above is capable of changing the cell work and thus the order of a cell in such a way that it can develop into a cancer cell.

Bioimaging approaches for quantification of individual cell behavior during cell fate decisions

Tracking individual cells has allowed a new understanding of cellular behavior in human health and disease by adding a dynamic component to the already complex heterogeneity of single cells. Technically, despite countless advances, numerous experimental variables can affect data collection and interpretation and need to be considered. In this review, we discuss the main technical aspects and biological findings in the analysis of the behavior of individual cells. We discuss the most relevant contributions provided by these approaches in clinically relevant human conditions like embryo development, stem cells biology, inflammation, cancer and microbiology, along with the cellular mechanisms and molecular pathways underlying these conditions. We also discuss the key technical aspects to be considered when planning and performing experiments involving the analysis of individual cells over long periods. Despite the challenges in automatic detection, features extraction and long-term tracking that need to be tackled, the potential impact of single-cell bioimaging is enormous in understanding the pathogenesis and development of new therapies in human pathophysiology.

Single-cell RNA profiling identifies diverse cellular responses to EWSR1/FLI1 downregulation in Ewing sarcoma cells

BACKGROUND

The EWSR1/FLI1 gene fusion is the most common rearrangement leading to cell transformation in Ewing sarcoma (ES). Previous studies have indicated that expression at the cellular level is heterogeneous, and that levels of expression may oscillate, conferring different cellular characteristics. In ES the role of EWSR1/FLI1 in regulating subpopulation dynamics is currently unknown.

METHODS

We used siRNA to transiently suppress EWSR1/FLI1 expression and followed population dynamics using both single cell expression profiling, CyTOF and functional assays to define characteristics of exponentially growing ES cells and of ES cells in which EWSR1/FLI1 had been downregulated. Novel transcriptional states with distinct features were assigned using random forest feature selection in combination with machine learning. Cells isolated from ES xenografts in immune-deficient mice were interrogated to determine whether characteristics of specific subpopulations of cells in vitro could be identified. Stem-like characteristics were assessed by primary and secondary spheroid formation in vitro, and invasion/motility was determined for each identified subpopulation. Autophagy was determined by expression profiling, cell sorting and immunohistochemical staining.

RESULTS

We defined a workflow to study EWSR1/FLI1 driven transcriptional states and phenotypes. We tracked EWSR1/FLI1 dependent proliferative activity over time to discover sources of intra-tumoral diversity. Single-cell RNA profiling was used to compare expression profiles in exponentially growing populations (si-Control) or in two dormant populations (D1, D2) in which EWSR1/FLI1 had been suppressed. Three distinct transcriptional states were uncovered contributing to ES intra-heterogeneity. Our predictive model identified ~1% cells in a dormant-like state and ~ 2-4% cells with stem-like and neural stem-like features in an exponentially proliferating ES cell line and in ES xenografts. Following EWSR1/FLI1 knockdown, cells re-entering the proliferative cycle exhibited greater stem-like properties, whereas for those cells remaining quiescent, FAM134B-dependent dormancy may provide a survival mechanism.

CONCLUSIONS

We show that time-dependent changes induced by suppression of oncogenic EWSR1/FLI1 expression induces dormancy, with different subpopulation dynamics. Cells re-entering the proliferative cycle show enhanced stem-like characteristics, whereas those remaining dormant for prolonged periods appear to survive through autophagy. Cells with these characteristics identified in exponentially growing cell populations and in tumor xenografts may confer drug resistance and could potentially contribute to metastasis.

Decoding the dynamics of multilayered stochastic antiviral IFN-I responses

Type I Interferon (IFN-I) responses were first recognized for their role in antiviral immunity, but it is now widely appreciated that IFN-Is have many immunomodulatory functions, influencing antitumor responses, autoimmune manifestations, and antimicrobial defenses. Given these pivotal roles, it may be surprising that multilayered stochastic events create highly heterogeneous, but tightly regulated, all-or-nothing cellular decisions. Recently, mathematical models have provided crucial insights into the stochastic nature of antiviral IFN-I responses, which we critically evaluate in this review. In this context, we emphasize the need for innovative single-cell technologies combined with mathematical models to further reveal, understand, and predict the complexity of the IFN-I system in physiological and pathological conditions that may be relevant to a plethora of diseases.

A top-down measure of gene-to-gene coordination for analyzing cell-to-cell variability

Recent technological advances, such as single-cell RNA sequencing (scRNA-seq), allow the measurement of gene expression profiles of individual cells. These expression profiles typically exhibit substantial variations even across seemingly homogeneous populations of cells. Two main different sources contribute to this measured variability: actual differences between the biological activity of the cells and technical measurement errors. Analysis of the biological variability may provide information about the underlying gene regulation of the cells, yet distinguishing it from the technical variability is a challenge. Here, we apply a recently developed computational method for measuring the global gene coordination level (GCL) to systematically study the cell-to-cell variability in numerical models of gene regulation. We simulate ‘biological variability’ by introducing heterogeneity in the underlying regulatory dynamic of different cells, while ‘technical variability’ is represented by stochastic measurement noise. We show that the GCL decreases for cohorts of cells with increased ‘biological variability’ only when it is originated from the interactions between the genes. Moreover, we find that the GCL can evaluate and compare—for cohorts with the same cell-to-cell variability—the ratio between the introduced biological and technical variability. Finally, we show that the GCL is robust against spurious correlations that originate from a small sample size or from the compositionality of the data. The presented methodology can be useful for future analysis of high-dimensional ecological and biochemical dynamics.

SCA-1 micro-heterogeneity in the fate decision of dystrophic fibro/adipogenic progenitors

The term micro-heterogeneity refers to non-genetic cell to cell variability observed in a bell-shaped distribution of the expression of a trait within a population. The contribution of micro-heterogeneity to physiology and pathology remains largely uncharacterised. To address such an issue, we investigated the impact of heterogeneity in skeletal muscle fibro/adipogenic progenitors (FAPs) isolated from an animal model of Duchenne muscular dystrophy (DMD), the mdx mouse. FAPs play an essential role in muscle homoeostasis. However, in pathological conditions or ageing, they are the source of intramuscular infiltrations of fibrotic or adipose tissue. By applying a multiplex flow cytometry assay, we characterised and purified from mdx muscles two FAP cell states expressing different levels of SCA-1. The two cell states are morphologically identical and repopulate each other after several growth cycles. However, they differ in their in vitro behaviour. Cells expressing higher levels of SCA-1 (SCA1-High-FAPs) differentiate more readily into adipocytes while, when exposed to a fibrogenic stimulation, increase the expression of Col1a1 and Timp1 mRNA. A transcriptomic analysis confirmed the adipogenic propensity of SCA1-High-FAPs. In addition, SCA1-High-FAPs proliferate more extensively ex vivo and display more proliferating cells in dystrophic muscles in comparison to SCA1-Low-FAPs. Adipogenesis of both FAP cell states is inhibited in vitro by leucocytes from young dystrophic mice, while leucocytes isolated from aged dystrophic mice are less effective in limiting the adipogenesis of SCA1-High-FAPs suggesting a differential regulatory effect of the microenvironment on micro-heterogeneity. Our data suggest that FAP micro-heterogeneity is modulated in pathological conditions and that this heterogeneity in turn may impact on the behaviour of interstitial mesenchymal cells in genetic diseases.

A systematic approach to decipher crosstalk in the p53 signaling pathway using single cell dynamics

The transcription factors NF-κB and p53 are key regulators in the genotoxic stress response and are critical for tumor development. Although there is ample evidence for interactions between both networks, a comprehensive understanding of the crosstalk is lacking. Here, we developed a systematic approach to identify potential interactions between the pathways. We perturbed NF-κB signaling by inhibiting IKK2, a critical regulator of NF-κB activity, and monitored the altered response of p53 to genotoxic stress using single cell time lapse microscopy. Fitting subpopulation-specific computational p53 models to this time-resolved single cell data allowed to reproduce in a quantitative manner signaling dynamics and cellular heterogeneity for the unperturbed and perturbed conditions. The approach enabled us to untangle the integrated effects of IKK/ NF-κB perturbation on p53 dynamics and thereby derive potential interactions between both networks. Intriguingly, we find that a simultaneous perturbation of multiple processes is necessary to explain the observed changes in the p53 response. Specifically, we show interference with the activation and degradation of p53 as well as the degradation of Mdm2. Our results highlight the importance of the crosstalk and its potential implications in p53-dependent cellular functions.

Cells can respond to external and internal inputs by transducing information to the nucleus where transcription factors initiate corresponding cellular responses. Cellular signaling is mediated by several pathways; molecular networks that can interact with each other, which alters signal processing and modulates cellular responses. As deregulated signaling can lead to the development of tumors it is important to understand not only how signaling pathways function but also the contribution of their interaction on the signaling dynamics. Here, we analyzed the interplay of the IKK/ NF-κB and p53 pathway, which are both critical for the cellular response to DNA damage and have been implicated in tumor development. To systematically identify interaction points between both pathways, we perturbed IKK/ NF-κB signaling and tracked the changes in the response of p53 to DNA damage. Using computational methods, we show that several reactions in the p53 pathway are simultaneously affected by NF-κB signaling and that this combined action is necessary to explain altered behaviour of the p53 pathway. Hence, our results provide new insights into the interplay between the NF-κB and p53 pathway and help to gain a more comprehensive understanding of the crosstalk.

Rapidly dynamic host cell heterogeneity in bacterial adhesion governs susceptibility to infection by Listeria monocytogenes

Interactions between host cells and individual pathogenic bacteria determine the clinical severity of disease during systemic infection in humans. Vascular endothelial cells, which line the lumen of blood vessels, represent a critical barrier for a bacterium in the bloodstream. These cells adopt a myriad of phenotypes that may modulate their susceptibility to infection; however, the precise determinants of their heterogeneity in susceptibility are not known. Here, we show that heterogeneity in susceptibility to Listeria monocytogenes infection among primary human vascular endothelial cells can be attributed entirely to robust, preexisting host cell heterogeneity in bacterial adhesion, and we find no evidence for significant heterogeneity in later steps of infection. High susceptibility to adhesion decays rapidly, within 30–60 min. Thus, rapidly fluctuating, nongenetic variability in bacterial adhesion diversifies susceptibility to infection, both among host cells and within individual cells over time.

Protein level variability determines phenotypic heterogeneity in proteotoxic stress response

Cell-to-cell variability in stress response is a bottleneck for the construction of accurate and predictive models which could guide clinical diagnosis and treatment of certain diseases, for example, cancer. Indeed, such phenotypic heterogeneity can lead to fractional killing and persistence of a subpopulation of cells which are resistant to a given treatment. The heat shock response network plays a major role in protecting the proteome against several types of injuries. Here, we combine high-throughput measurements and mathematical modeling to unveil the molecular origin of the phenotypic variability in the heat shock response network. Although the mean response coincides with known biochemical measurements, we found a surprisingly broad diversity in single-cell dynamics with a continuum of response amplitudes and temporal shapes for several stimulus strengths. We theoretically predict that the broad phenotypic heterogeneity is due to network ultrasensitivity together with variations in the expression level of chaperones controlled by the transcription factor heat shock factor 1. Furthermore, we experimentally confirm this prediction by mapping the response amplitude to chaperone and heat shock factor 1 expression levels.

Stem Cell Signaling Pathways in the Small Intestine

The ability of stem cells to divide and differentiate is necessary for tissue repair and homeostasis. Appropriate spatial and temporal mechanisms are needed. Local intercellular signaling increases expression of specific genes that mediate and maintain differentiation. Diffusible signaling molecules provide concentration-dependent induction of specific patterns of cell types or regions. Differentiation of adjacent cells, on the other hand, requires cell–cell contact and subsequent signaling. These two types of signals work together to allow stem cells to provide what organisms require. The ability to grow organoids has increased our understanding of the cellular and molecular features of small “niches” that modulate stem cell function in various organs, including the small intestine.

Quantification of the dynamics of population heterogeneities in CHO cultures with stably integrated fluorescent markers

Cell population heterogeneities and their changes in mammalian cell culture processes are still not well characterized. In this study, the formation and dynamics of cell population heterogeneities were investigated with flow cytometry and stably integrated fluorescent markers based on the lentiviral gene ontology (LeGO) vector system. To achieve this, antibody-producing CHO cells were transduced with different LeGO vectors to stably express single or multiple fluorescent proteins. This enables the tracking of the transduced populations and is discussed in two case studies from the field of bioprocess engineering: In case study I, cells were co-transduced to express red, green, and blue fluorescent proteins and the development of sub-populations and expression heterogeneities were investigated in high passage cultivations (total 130 days). The formation of a fast-growing and more productive population was observed with a simultaneous increase in cell density and product titer. In case study II, different preculture growth phases and their influence on the population dynamics were investigated in mixed batch cultures with flow cytometry (offline and automated). Four cell line derivatives, each expressing a different fluorescent protein, were generated and cultivated for different time intervals, corresponding to different growth phases. Mixed cultures were inoculated from them, and changes in the composition of the cell populations were observed during the first 48 h of cultivation with reduced process productivity. In summary, we showed how the dynamics of population heterogeneities can be characterized. This represents a novel approach to investigate the dynamics of cell population heterogeneities under near-physiological conditions with changing productivity in mammalian cell culture processes.

Cell-Based Assays on Microfluidics for Drug Screening

Microfluidics is an appealing platform for drug screening and discovery. Compared with the conventional drug screening methods based on Petri dishes and experimental animals, microfluidic devices have many advantages including miniaturized size, ease-to-use, high sensitivity, and high throughput. More importantly, bioassays on microfluidics can avoid ethical issues which can be a big obstacle hindering the performance of the experiments on animals or human being. Furthermore, three-dimensional (3D) microchips can recapitulate various biochemical and biophysical conditions in vivo and mimic the natural microenvironment of the tissues/organs, providing versatile in vitro models for biomedical applications. In this Perspective, we will focus on the cell-based microfluidic assays for drug screening. Meanwhile, we also propose potential solutions for the difficulties in this field and discuss the prospects of microfluidics-based technologies for drug screening.

HSV-1 single-cell analysis reveals the activation of anti-viral and developmental programs in distinct sub-populations

Viral infection is usually studied at the population level by averaging over millions of cells. However, infection at the single-cell level is highly heterogeneous, with most infected cells giving rise to no or few viral progeny while some cells produce thousands. Analysis of Herpes Simplex virus 1 (HSV-1) infection by population-averaged measurements has taught us a lot about the course of viral infection, but has also produced contradictory results, such as the concurrent activation and inhibition of type I interferon signaling during infection. Here, we combine live-cell imaging and single-cell RNA sequencing to characterize viral and host transcriptional heterogeneity during HSV-1 infection of primary human cells. We find extreme variability in the level of viral gene expression among individually infected cells and show that these cells cluster into transcriptionally distinct sub-populations. We find that anti-viral signaling is initiated in a rare group of abortively infected cells, while highly infected cells undergo cellular reprogramming to an embryonic-like transcriptional state. This reprogramming involves the recruitment of β-catenin to the host nucleus and viral replication compartments, and is required for late viral gene expression and progeny production. These findings uncover the transcriptional differences in cells with variable infection outcomes and shed new light on the manipulation of host pathways by HSV-1.

Self-organization and symmetry breaking in intestinal organoid development

Intestinal organoids are complex three-dimensional structures that mimic the cell-type composition and tissue organization of the intestine by recapitulating the self-organizing ability of cell populations derived from a single intestinal stem cell. Crucial in this process is a first symmetry-breaking event, in which only a fraction of identical cells in a symmetrical sphere differentiate into Paneth cells, which generate the stem-cell niche and lead to asymmetric structures such as the crypts and villi. Here we combine single-cell quantitative genomic and imaging approaches to characterize the development of intestinal organoids from single cells. We show that their development follows a regeneration process that is driven by transient activation of the transcriptional regulator YAP1. Cell-to-cell variability in YAP1, emerging in symmetrical spheres, initiates Notch and DLL1 activation, and drives the symmetry-breaking event and formation of the first Paneth cell. Our findings reveal how single cells exposed to a uniform growth-promoting environment have the intrinsic ability to generate emergent, self-organized behaviour that results in the formation of complex multicellular asymmetric structures.

Systems biology primer: the basic methods and approaches

Systems biology is an integrative discipline connecting the molecular components within a single biological scale and also among different scales (e.g. cells, tissues and organ systems) to physiological functions and organismal phenotypes through quantitative reasoning, computational models and high-throughput experimental technologies. Systems biology uses a wide range of quantitative experimental and computational methodologies to decode information flow from genes, proteins and other subcellular components of signaling, regulatory and functional pathways to control cell, tissue, organ and organismal level functions. The computational methods used in systems biology provide systems-level insights to understand interactions and dynamics at various scales, within cells, tissues, organs and organisms. In recent years, the systems biology framework has enabled research in quantitative and systems pharmacology and precision medicine for complex diseases. Here, we present a brief overview of current experimental and computational methods used in systems biology.

Controlling and exploiting cell-to-cell variation in metabolic engineering

Individual cells within a population can display diverse phenotypes due to differences in their local environment, genetic variation, and stochastic expression of genes. Understanding this cell-to-cell variation is important for metabolic engineering applications because variability can impact production. For instance, recent studies have shown that production can be highly heterogeneous among engineered cells, and strategies that manage this diversity improve yields of biosynthetic products. These results suggest the potential of controlling variation as a novel approach towards improving performance of engineered cells. In this review, we focus on identifying the origins of cell-to-cell variation in metabolic engineering applications and discuss recent developments on strategies that can be employed to diminish, accept, or even exploit cell-to-cell variation.

Gene expression distribution deconvolution in single-cell RNA sequencing

We developed deconvolution of single-cell expression distribution (DESCEND), a method to recover cross-cell distribution of the true gene expression level from observed counts in single-cell RNA sequencing, allowing adjustment of known confounding cell-level factors. With the recovered distribution, DESCEND provides reliable estimates of distribution-based measurements, such as the dispersion of true gene expression and the probability that true gene expression is positive. This is important, as with better estimates of these measurements, DESCEND clarifies and improves many downstream analyses including finding differentially expressed genes, identifying cell types, and selecting differentiation markers. Another contribution is that we verified using nine public datasets a simple “Poisson-alpha” noise model for the technical noise of unique molecular identifier-based single-cell RNA-sequencing data, clarifying the current intense debate on this issue.

Single-cell RNA sequencing (scRNA-seq) enables the quantification of each gene’s expression distribution across cells, thus allowing the assessment of the dispersion, nonzero fraction, and other aspects of its distribution beyond the mean. These statistical characterizations of the gene expression distribution are critical for understanding expression variation and for selecting marker genes for population heterogeneity. However, scRNA-seq data are noisy, with each cell typically sequenced at low coverage, thus making it difficult to infer properties of the gene expression distribution from raw counts. Based on a reexamination of nine public datasets, we propose a simple technical noise model for scRNA-seq data with unique molecular identifiers (UMI). We develop deconvolution of single-cell expression distribution (DESCEND), a method that deconvolves the true cross-cell gene expression distribution from observed scRNA-seq counts, leading to improved estimates of properties of the distribution such as dispersion and nonzero fraction. DESCEND can adjust for cell-level covariates such as cell size, cell cycle, and batch effects. DESCEND’s noise model and estimation accuracy are further evaluated through comparisons to RNA FISH data, through data splitting and simulations and through its effectiveness in removing known batch effects. We demonstrate how DESCEND can clarify and improve downstream analyses such as finding differentially expressed genes, identifying cell types, and selecting differentiation markers.

A Transcriptional Circuit Filters Oscillating Circadian Hormonal Inputs to Regulate Fat Cell Differentiation

Glucocorticoid and other adipogenic hormones are secreted in mammals in circadian oscillations. Loss of this circadian oscillation pattern correlates with obesity in humans, raising the intriguing question of how hormone secretion dynamics affect adipocyte differentiation. Using live, single-cell imaging of the key adipogenic transcription factors CEBPB and PPARG, endogenously tagged with fluorescent proteins, we show that pulsatile circadian hormone stimuli are rejected by the adipocyte differentiation control system. In striking contrast, equally strong persistent signals trigger maximal differentiation. We identify the mechanism of how hormone oscillations are filtered as a combination of slow and fast positive feedback centered on PPARG. Furthermore, we confirm in mice that flattening of daily glucocorticoid oscillations significantly increases the mass of subcutaneous and visceral fat pads. Together, our study provides a molecular mechanism for why stress, Cushing's disease, and other conditions for which glucocorticoid secretion loses its pulsatility may lead to obesity.

Automated cell cycle and cell size measurements for single-cell gene expression studies

OBJECTIVES

Recent rise of single-cell studies revealed the importance of understanding the role of cell-to-cell variability, especially at the transcriptomic level. One of the numerous sources of cell-to-cell variation in gene expression is the heterogeneity in cell proliferation state. In order to identify how cell cycle and cell size influences gene expression variability at the single-cell level, we provide an universal and automatic toxic-free label method, compatible with single-cell high-throughput RT-qPCR. The method consists of isolating cells after a double-stained, analyzing their morphological parameters and performing a transcriptomic analysis on the same identified cells.

RESULTS

This led to an unbiased gene expression analysis and could be also used for improving single-cell tracking and imaging when combined with cell isolation. As an application for this technique, we showed that cell-to-cell variability in chicken erythroid progenitors was negligibly influenced by cell size nor cell cycle.

p53 elevation in human cells halt SV40 infection by inhibiting T-ag expression

SV40 large T-antigen (T-ag) has been known for decades to inactivate the tumor suppressor p53 by sequestration and additional mechanisms. Our present study revealed that the struggle between p53 and T-ag begins very early in the infection cycle. We found that p53 is activated early after SV40 infection and defends the host against the infection. Using live cell imaging and single cell analyses we found that p53 dynamics are variable among individual cells, with only a subset of cells activating p53 immediately after SV40 infection. This cell-to-cell variabilty had clear consequences on the outcome of the infection. None of the cells with elevated p53 at the beginning of the infection proceeded to express T-ag, suggesting a p53-dependent decision between abortive and productive infection. In addition, we show that artificial elevation of p53 levels prior to the infection reduces infection efficiency, supporting a role for p53 in defending against SV40. We further found that the p53-mediated host defense mechanism against SV40 is not facilitated by apoptosis nor via interferon-stimulated genes. Instead p53 binds to the viral DNA at the T-ag promoter region, prevents its transcriptional activation by Sp1, and halts the progress of the infection. These findings shed new light on the long studied struggle between SV40 T-ag and p53, as developed during virus-host coevolution. Our studies indicate that the fate of SV40 infection is determined as soon as the viral DNA enters the nucleus, before the onset of viral gene expression.

Cell-specific responses to the cytokine TGFβ are determined by variability in protein levels

The cytokine TGFβ provides important information during embryonic development, adult tissue homeostasis, and regeneration. Alterations in the cellular response to TGFβ are involved in severe human diseases. To understand how cells encode the extracellular input and transmit its information to elicit appropriate responses, we acquired quantitative time-resolved measurements of pathway activation at the single-cell level. We established dynamic time warping to quantitatively compare signaling dynamics of thousands of individual cells and described heterogeneous single-cell responses by mathematical modeling. Our combined experimental and theoretical study revealed that the response to a given dose of TGFβ is determined cell specifically by the levels of defined signaling proteins. This heterogeneity in signaling protein expression leads to decomposition of cells into classes with qualitatively distinct signaling dynamics and phenotypic outcome. Negative feedback regulators promote heterogeneous signaling, as a SMAD7 knock-out specifically affected the signal duration in a subpopulation of cells. Taken together, we propose a quantitative framework that allows predicting and testing sources of cellular signaling heterogeneity.

Dynamic Proteomics of Herpes Simplex Virus Infection

The cellular response to viral infection is usually studied at the level of cell populations. Currently, it remains an open question whether and to what extent cell-to-cell variability impacts the course of infection. Here we address this by dynamic proteomics-imaging and tracking 400 yellow fluorescent protein (YFP)-tagged host proteins in individual cells infected by herpes simplex virus 1. By quantifying time-lapse fluorescence imaging, we analyze how cell-to-cell variability impacts gene expression from the viral genome. We identify two proteins, RFX7 and geminin, whose levels at the time of infection correlate with successful initiation of gene expression. These proteins are cell cycle markers, and we find that the position in the cell cycle at the time of infection (along with the cell motility and local cell density) can reasonably predict in which individual cells gene expression from the viral genome will commence. We find that the onset of cell division dramatically impacts the progress of infection, with 70% of dividing cells showing no additional gene expression after mitosis. Last, we identify four host proteins that are specifically modulated in infected cells, of which only one has been previously recognized. SUMO2 and RPAP3 levels are rapidly reduced, while SLTM and YTHDC1 are redistributed to form nuclear foci. These modulations are dependent on the expression of ICP0, as shown by infection with two mutant viruses that lack ICP0. Taken together, our results provide experimental validation for the long-held notion that the success of infection is dependent on the state of the host cell at the time of infection.IMPORTANCE High-throughput assays have revolutionized many fields in biology, both by allowing a more global understanding of biological processes and by deciphering rare events in subpopulations. Here we use such an assay, dynamic proteomics, to study viral infection at the single-cell level. We follow tens of thousands of individual cells infected by herpes simplex virus using fluorescence live imaging. Our results link the state of a cell at the time of virus infection with its probability to successfully initiate gene expression from the viral genome. Further, we identified three cellular proteins that were previously unknown to respond to viral infection. We conclude that dynamic proteomics provides a powerful tool to study single-cell differences during viral infection.

Building with intent: technologies and principles for engineering mammalian cell-based therapies to sense and respond

The engineering of cells as programmable devices has enabled therapeutic strategies that could not otherwise be achieved. Such strategies include recapitulating and enhancing native cellular functions and composing novel functions. These novel functions may be composed using both natural and engineered biological components, with the latter exemplified by the development of synthetic receptor and signal transduction systems. Recent advances in implementing these approaches include the treatment of cancer, where the most clinical progress has been made to date, and the treatment of diabetes. Principles for engineering cell-based therapies that are safe and effective are increasingly needed and beginning to emerge, and will be essential in the development of this new class of therapeutics.

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.

Modeling Cellular Noise Underlying Heterogeneous Cell Responses in the Epidermal Growth Factor Signaling Pathway

Cellular heterogeneity, which plays an essential role in biological phenomena, such as drug resistance and migration, is considered to arise from intrinsic (i.e., reaction kinetics) and extrinsic (i.e., protein variability) noise in the cell. However, the mechanistic effects of these types of noise to determine the heterogeneity of signal responses have not been elucidated. Here, we report that the output of epidermal growth factor (EGF) signaling activity is modulated by cellular noise, particularly by extrinsic noise of particular signaling components in the pathway. We developed a mathematical model of the EGF signaling pathway incorporating regulation between extracellular signal-regulated kinase (ERK) and nuclear pore complex (NPC), which is necessary for switch-like activation of the nuclear ERK response. As the threshold of switch-like behavior is more sensitive to perturbations than the graded response, the effect of biological noise is potentially critical for cell fate decision. Our simulation analysis indicated that extrinsic noise, but not intrinsic noise, contributes to cell-to-cell heterogeneity of nuclear ERK. In addition, we accurately estimated variations in abundance of the signal proteins between individual cells by direct comparison of experimental data with simulation results using Apparent Measurement Error (AME). AME was constant regardless of whether the protein levels varied in a correlated manner, while covariation among proteins influenced cell-to-cell heterogeneity of nuclear ERK, suppressing the variation. Simulations using the estimated protein abundances showed that each protein species has different effects on cell-to-cell variation in the nuclear ERK response. In particular, variability of EGF receptor, Ras, Raf, and MEK strongly influenced cellular heterogeneity, while others did not. Overall, our results indicated that cellular heterogeneity in response to EGF is strongly driven by extrinsic noise, and that such heterogeneity results from variability of particular protein species that function as sensitive nodes, which may contribute to the pathogenesis of human diseases.

Individual cell behaviors are controlled by a variety of noise, such as fluctuations in biochemical reactions, protein variability, molecular diffusion, transcriptional noise, cell-to-cell contact, temperature, and pH. Such cellular noise often interferes with signal responses from external stimuli, and such heterogeneity functions in induction of drug resistance, survival, and migration of cells. Thus, heterogeneous cellular responses have positive and negative roles. However, the regulatory mechanisms that produce cellular heterogeneity are unclear. By mathematical modeling and simulations, we investigated how heterogeneous signaling responses are evoked in the EGF signaling pathway and influence the switch-like activation of nuclear ERK. This study demonstrated that cellular heterogeneity of the EGF signaling response is evoked by cell-to-cell variation of particular signaling proteins, such as EGFR, Ras, Raf, and MEK, which act as sensitive nodes in the pathway. These results suggest that signaling responses in individual cells can be predicted from the levels of proteins of sensitive nodes. This study also suggested that proteins of sensitive nodes may serve as cell survival mechanisms.

Automated Analysis of Cell Cycle Phase-Specific DNA Damage Reveals Phase-Specific Differences in Cell Sensitivity to Etoposide

The comet assay is one of the most widely used approaches for detecting DNA damage; generally, it provides information on the cell population-averaged level of DNA damage. Here, we present an automatic technique for easy measurement of standard comet characteristics and an annotation of the cell cycle phase of each comet. The approach includes the modified neutral comet assay and a pipeline for CellProfiler software designed to analyze DNA damage-related characteristics and annotate the cell cycle phase of each comet. Using this technique we have performed cell cycle phase-specific analysis of DNA damage induced by the topoisomerase II poison etoposide and have shown that the sensitivity of cells to this drug dramatically differed according to their cell cycle phase. It became evident from our results that the proposed protocol provides important additional information that often remains hidden in a standard comet analysis of an asynchronous cell population. J. Cell. Biochem. 117: 2209-2214, 2016. © 2016 Wiley Periodicals, Inc.

Computational Analysis of AMPK-Mediated Neuroprotection Suggests Acute Excitotoxic Bioenergetics and Glucose Dynamics Are Regulated by a Minimal Set of Critical Reactions

Loss of ionic homeostasis during excitotoxic stress depletes ATP levels and activates the AMP-activated protein kinase (AMPK), re-establishing energy production by increased expression of glucose transporters on the plasma membrane. Here, we develop a computational model to test whether this AMPK-mediated glucose import can rapidly restore ATP levels following a transient excitotoxic insult. We demonstrate that a highly compact model, comprising a minimal set of critical reactions, can closely resemble the rapid dynamics and cell-to-cell heterogeneity of ATP levels and AMPK activity, as confirmed by single-cell fluorescence microscopy in rat primary cerebellar neurons exposed to glutamate excitotoxicity. The model further correctly predicted an excitotoxicity-induced elevation of intracellular glucose, and well resembled the delayed recovery and cell-to-cell heterogeneity of experimentally measured glucose dynamics. The model also predicted necrotic bioenergetic collapse and altered calcium dynamics following more severe excitotoxic insults. In conclusion, our data suggest that a minimal set of critical reactions may determine the acute bioenergetic response to transient excitotoxicity and that an AMPK-mediated increase in intracellular glucose may be sufficient to rapidly recover ATP levels following an excitotoxic insult.

Calcium response of KCl-excited populations of ventricular myocytes from the European sea bass (Dicentrarchus labrax): a promising approach to integrate cell-to-cell heterogeneity in studying the cellular basis of fish cardiac performance

Climate change challenges the capacity of fishes to thrive in their habitat. However, through phenotypic diversity, they demonstrate remarkable resilience to deteriorating conditions. In fish populations, inter-individual variation in a number of fitness-determining physiological traits, including cardiac performance, is classically observed. Information about the cellular bases of inter-individual variability in cardiac performance is scarce including the possible contribution of excitation-contraction (EC) coupling. This study aimed at providing insight into EC coupling-related Ca(2+) response and thermal plasticity in the European sea bass (Dicentrarchus labrax). A cell population approach was used to lay the methodological basis for identifying the cellular determinants of cardiac performance. Fish were acclimated at 12 and 22 °C and changes in intracellular calcium concentration ([Ca(2+)]i) following KCl stimulation were measured using Fura-2, at 12 or 22 °C-test. The increase in [Ca(2+)]i resulted primarily from extracellular Ca(2+) entry but sarcoplasmic reticulum stores were also shown to be involved. As previously reported in sea bass, a modest effect of adrenaline was observed. Moreover, although the response appeared relatively insensitive to an acute temperature change, a difference in Ca(2+) response was observed between 12- and 22 °C-acclimated fish. In particular, a greater increase in [Ca(2+)]i at a high level of adrenaline was observed in 22 °C-acclimated fish that may be related to an improved efficiency of adrenaline under these conditions. In conclusion, this method allows a rapid screening of cellular characteristics. It represents a promising tool to identify the cellular determinants of inter-individual variability in fishes' capacity for environmental adaptation.

Systems biology of IL-6, IL-12 family cytokines

Interleukin-6-type cytokines play important roles in the communication between cells of multicellular organisms. They are involved in the regulation of complex cellular processes such as proliferation and differentiation and act as key player during inflammation and immune response. A major challenge is to understand how these complex non-linear processes are connected and regulated. Systems biology approaches are used to tackle this challenge in an iterative process of quantitative experimental and mathematical analyses. Here we review quantitative experimental studies and systems biology approaches dealing with the function of Interleukin-6-type cytokines in physiological and pathophysiological conditions. These approaches cover the analyses of signal transduction on a cellular level up to pharmacokinetic and pharmacodynamic studies on a whole organism level.

Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells

It has long been the dream of biologists to map gene expression at the single-cell level. With such data one might track heterogeneous cell sub-populations, and infer regulatory relationships between genes and pathways. Recently, RNA sequencing has achieved single-cell resolution. What is limiting is an effective way to routinely isolate and process large numbers of individual cells for quantitative in-depth sequencing. We have developed a high-throughput droplet-microfluidic approach for barcoding the RNA from thousands of individual cells for subsequent analysis by next-generation sequencing. The method shows a surprisingly low noise profile and is readily adaptable to other sequencing-based assays. We analyzed mouse embryonic stem cells, revealing in detail the population structure and the heterogeneous onset of differentiation after leukemia inhibitory factor (LIF) withdrawal. The reproducibility of these high-throughput single-cell data allowed us to deconstruct cell populations and infer gene expression relationships. VIDEO ABSTRACT.

A millifluidic study of cell-to-cell heterogeneity in growth-rate and cell-division capability in populations of isogenic cells of Chlamydomonas reinhardtii

To address possible cell-to-cell heterogeneity in growth dynamics of isogenic cell populations of Chlamydomonas reinhardtii, we developed a millifluidic drop-based device that not only allows the analysis of populations grown from single cells over periods of a week, but is also able to sort and collect drops of interest, containing viable and healthy cells, which can be used for further experimentation. In this study, we used isogenic algal cells that were first synchronized in mixotrophic growth conditions. We show that these synchronized cells, when placed in droplets and kept in mixotrophic growth conditions, exhibit mostly homogeneous growth statistics, but with two distinct subpopulations: a major population with a short doubling-time (fast-growers) and a significant subpopulation of slowly dividing cells (slow-growers). These observations suggest that algal cells from an isogenic population may be present in either of two states, a state of restricted division and a state of active division. When isogenic cells were allowed to propagate for about 1000 generations on solid agar plates, they displayed an increased heterogeneity in their growth dynamics. Although we could still identify the original populations of slow- and fast-growers, drops inoculated with a single progenitor cell now displayed a wider diversity of doubling-times. Moreover, populations dividing with the same growth-rate often reached different cell numbers in stationary phase, suggesting that the progenitor cells differed in the number of cell divisions they could undertake. We discuss possible explanations for these cell-to-cell heterogeneities in growth dynamics, such as mutations, differential aging or stochastic variations in metabolites and macromolecules yielding molecular switches, in the light of single-cell heterogeneities that have been reported among isogenic populations of other eu- and prokaryotes.

Strength in numbers: quantitative single-molecule RNA detection assays

Gene expression is a fundamental process that underlies development, homeostasis, and behavior of organisms. The fact that it relies on nucleic acid intermediates, which can specifically interact with complementary probes, provides an excellent opportunity for studying the multiple steps—transcription, RNA processing, transport, translation, degradation, and so forth—through which gene function manifests. Over the past three decades, the toolbox of nucleic acid science has expanded tremendously, making high‐precision in situ detection of DNA and RNA possible. This has revealed that many—probably the vast majority of—transcripts are distributed within the cytoplasm or the nucleus in a nonrandom fashion. With the development of microscopy techniques we have learned not only about the qualitative localization of these molecules but also about their absolute numbers with great precision. Single‐molecule techniques for nucleic acid detection have been transforming our views of biology with elementary power: cells are not average members of their population but are highly distinct individuals with greatly and suddenly changing gene expression, and this behavior of theirs can be measured, modeled, and thus predicted and, finally, comprehended. WIREs Dev Biol 2015, 4:135–150. doi: 10.1002/wdev.170

For further resources related to this article, please visit the WIREs website.

Conflict of interest: The authors have declared no conflicts of interest for this article.

Origins of variation in conducted vasomotor responses

Regulation of blood flow in the microcirculation depends on synchronized vasomotor responses. The vascular conducted response is a synchronous dilatation or constriction, elicited by a local electrical event that spreads along the vessel wall. Despite the underlying electrical nature, however, the efficacy of conducted responses varies significantly between different initiating stimuli within the same vascular bed as well as between different vascular beds following the same stimulus. The differences have stimulated proposals of different mechanisms to account for the experimentally observed variation. Using a computational approach that allows for introduction of structural and electrophysiological heterogeneity, we systematically tested variations in both arteriolar electrophysiology and modes of stimuli. Within the same vessel, our simulations show that conduction efficacy is influenced by the type of cell being stimulated and, in case of depolarization, by the stimulation strength. Particularly, simultaneous stimulation of both endothelial and vascular smooth muscle cells augments conduction. Between vessels, the specific electrophysiology determines membrane resistance and conduction efficiency-notably depolarization or radial currents reduce electrical spread. Random cell-cell variation, ubiquitous in biological systems, only cause small or no reduction in conduction efficiency. Collectively, our simulations can explain why CVRs from hyperpolarizing stimuli tend to conduct longer than CVRs from depolarizing stimuli and why agonists like acetylcholine induce CVRs that tend to conduct longer than electrical injections. The findings demonstrate that although substantial heterogeneity is observed in conducted responses, it can be largely ascribed to the origin of electrical stimulus combined with the specific electrophysiological properties of the arteriole. We conclude by outlining a set of "principles of electrical conduction" in the microcirculation.

Single-cell imaging of bioenergetic responses to neuronal excitotoxicity and oxygen and glucose deprivation

Excitotoxicity is a condition occurring during cerebral ischemia, seizures, and chronic neurodegeneration. It is characterized by overactivation of glutamate receptors, leading to excessive Ca(2+)/Na(+) influx into neurons, energetic stress, and subsequent neuronal injury. We and others have previously investigated neuronal populations to study how bioenergetic parameters determine neuronal injury; however, such experiments are often confounded by population-based heterogeneity and the contribution of effects of non-neuronal cells. Hence, we here characterized bioenergetics during transient excitotoxicity in rat and mouse primary neurons at the single-cell level using fluorescent sensors for intracellular glucose, ATP, and activation of the energy sensor AMP-activated protein kinase (AMPK). We identified ATP depletion and recovery to energetic homeostasis, along with AMPK activation, as surprisingly rapid and plastic responses in two excitotoxic injury paradigms. We observed rapid recovery of neuronal ATP levels also in the absence of extracellular glucose, or when glycolytic ATP production was inhibited, but found mitochondria to be critical for fast and complete energetic recovery. Using an injury model of oxygen and glucose deprivation, we identified a similarly rapid bioenergetics response, yet with incomplete ATP recovery and decreased AMPK activity. Interestingly, excitotoxicity also induced an accumulation of intracellular glucose, providing an additional source of energy during and after excitotoxicity-induced energy depletion. We identified this to originate from extracellular, AMPK-dependent glucose uptake and from intracellular glucose mobilization. Surprisingly, cells recovering their elevated glucose levels faster to baseline survived longer, indicating that the plasticity of neurons to adapt to bioenergetic challenges is a key indicator of neuronal viability.

The metabolic response to excitotoxicity - lessons from single-cell imaging

Excitotoxicity is a pathological process implicated in neuronal death during ischaemia, traumatic brain injuries and neurodegenerative diseases. Excitotoxicity is caused by excess levels of glutamate and over-activation of NMDA or calcium-permeable AMPA receptors on neuronal membranes, leading to ionic influx, energetic stress and potential neuronal death. The metabolic response of neurons to excitotoxicity is complex and plays a key role in the ability of the neuron to adapt and recover from such an insult. Single-cell imaging is a powerful experimental technique that can be used to study the neuronal metabolic response to excitotoxicity in vitro and, increasingly, in vivo. Here, we review some of the knowledge of the neuronal metabolic response to excitotoxicity gained from in vitro single-cell imaging, including calcium and ATP dynamics and their effects on mitochondrial function, along with the contribution of glucose metabolism, oxidative stress and additional neuroprotective signalling mechanisms. Future work will combine knowledge gained from single-cell imaging with data from biochemical and computational techniques to garner holistic information about the metabolic response to excitotoxicity at the whole brain level and transfer this knowledge to a clinical setting.

Reliable encoding of stimulus intensities within random sequences of intracellular Ca2+ spikes

Ca(2+) is a ubiquitous intracellular messenger that regulates diverse cellular activities. Extracellular stimuli often evoke sequences of intracellular Ca(2+) spikes, and spike frequency may encode stimulus intensity. However, the timing of spikes within a cell is random because each interspike interval has a large stochastic component. In human embryonic kidney (HEK) 293 cells and rat primary hepatocytes, we found that the average interspike interval also varied between individual cells. To evaluate how individual cells reliably encoded stimuli when Ca(2+) spikes exhibited such unpredictability, we combined Ca(2+) imaging of single cells with mathematical analyses of the Ca(2+) spikes evoked by receptors that stimulate formation of inositol 1,4,5-trisphosphate (IP3). This analysis revealed that signal-to-noise ratios were improved by slow recovery from feedback inhibition of Ca(2+) spiking operating at the whole-cell level and that they were robust against perturbations of the signaling pathway. Despite variability in the frequency of Ca(2+) spikes between cells, steps in stimulus intensity caused the stochastic period of the interspike interval to change by the same factor in all cells. These fold changes reliably encoded changes in stimulus intensity, and they resulted in an exponential dependence of average interspike interval on stimulation strength. We conclude that Ca(2+) spikes enable reliable signaling in a cell population despite randomness and cell-to-cell variability, because global feedback reduces noise, and changes in stimulus intensity are represented by fold changes in the stochastic period of the interspike interval.

Single-cell RNA-seq reveals dynamic paracrine control of cellular variation

High-throughput single-cell transcriptomics offers an unbiased approach for understanding the extent, basis and function of gene expression variation between seemingly identical cells. Here we sequence single-cell RNA-seq libraries prepared from over 1,700 primary mouse bone-marrow-derived dendritic cells spanning several experimental conditions. We find substantial variation between identically stimulated dendritic cells, in both the fraction of cells detectably expressing a given messenger RNA and the transcript's level within expressing cells. Distinct gene modules are characterized by different temporal heterogeneity profiles. In particular, a 'core' module of antiviral genes is expressed very early by a few 'precocious' cells in response to uniform stimulation with a pathogenic component, but is later activated in all cells. By stimulating cells individually in sealed microfluidic chambers, analysing dendritic cells from knockout mice, and modulating secretion and extracellular signalling, we show that this response is coordinated by interferon-mediated paracrine signalling from these precocious cells. Notably, preventing cell-to-cell communication also substantially reduces variability between cells in the expression of an early-induced 'peaked' inflammatory module, suggesting that paracrine signalling additionally represses part of the inflammatory program. Our study highlights the importance of cell-to-cell communication in controlling cellular heterogeneity and reveals general strategies that multicellular populations can use to establish complex dynamic responses.

Homeostatic control of polo-like kinase-1 engenders non-genetic heterogeneity in G2 checkpoint fidelity and timing

The G2 checkpoint monitors DNA damage, preventing mitotic entry until the damage can be resolved. The mechanisms controlling checkpoint recovery are unclear. Here, we identify non-genetic heterogeneity in the fidelity and timing of damage-induced G2 checkpoint enforcement in individual cells from the same population. Single-cell fluorescence imaging reveals that individual damaged cells experience varying durations of G2 arrest, and recover with varying levels of remaining checkpoint signal or DNA damage. A gating mechanism dependent on polo-like kinase-1 (PLK1) activity underlies this heterogeneity. PLK1 activity continually accumulates from initial levels in G2-arrested cells, at a rate inversely correlated to checkpoint activation, until it reaches a threshold allowing mitotic entry regardless of remaining checkpoint signal or DNA damage. Thus, homeostatic control of PLK1 by the dynamic opposition between checkpoint signalling and pro-mitotic activities heterogeneously enforces the G2 checkpoint in each individual cell, with implications for cancer pathogenesis and therapy.

An image analysis method to quantify CFTR subcellular localization

Aberrant protein subcellular localization caused by mutation is a prominent feature of many human diseases. In Cystic Fibrosis (CF), a recessive lethal disorder that results from dysfunction of the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR), the most common mutation is a deletion of phenylalanine-508 (pF508del). Such mutation produces a misfolded protein that fails to reach the cell surface. To date, over 1900 mutations have been identified in CFTR gene, but only a minority has been analyzed at the protein level. To establish if a particular CFTR variant alters its subcellular distribution, it is necessary to quantitatively determine protein localization in the appropriate cellular context. To date, most quantitative studies on CFTR localization have been based on immunoprecipitation and western blot. In this work, we developed and validated a confocal microscopy-image analysis method to quantitatively examine CFTR at the apical membrane of epithelial cells. Polarized MDCK cells transiently transfected with EGFP-CFTR constructs and stained for an apical marker were used. EGFP-CFTR fluorescence intensity in a region defined by the apical marker was normalized to EGFP-CFTR whole cell fluorescence intensity, rendering "apical CFTR ratio". We obtained an apical CFTR ratio of 0.67 ± 0.05 for wtCFTR and 0.11 ± 0.02 for pF508del. In addition, this image analysis method was able to discriminate intermediate phenotypes: partial rescue of the pF508del by incubation at 27 °C rendered an apical CFTR ratio value of 0.23 ± 0.01. We concluded the method has a good sensitivity and accurately detects milder phenotypes. Improving axial resolution through deconvolution further increased the sensitivity of the system as rendered an apical CFTR ratio of 0.76 ± 0.03 for wild type and 0.05 ± 0.02 for pF508del. The presented procedure is faster and simpler when compared with other available methods and it is therefore suitable as a screening method to identify mutations that completely or mildly affect CFTR processing. Moreover, it could be extended to other studies on the biology underlying protein subcellular localization in health and disease.

The p53 response in single cells is linearly correlated to the number of DNA breaks without a distinct threshold

BACKGROUND

The tumor suppressor protein p53 is activated by cellular stress. DNA double strand breaks (DSBs) induce the activation of the kinase ATM, which stabilizes p53 and activates its transcriptional activity. Single cell analysis revealed that DSBs induced by gamma irradiation trigger p53 accumulation in a series of pulses that vary in number from cell to cell. Higher levels of irradiation increase the number of p53 pulses suggesting that they arise from periodic examination of the damage by ATM. If damage persists, additional pulses of p53 are triggered. The threshold of damage required for activating a p53 pulse is unclear. Previous studies that averaged the response across cell populations suggested that one or two DNA breaks are sufficient for activating ATM and p53. However, it is possible that by averaging over a population of cells important features of the dependency between DNA breaks and p53 dynamics are missed.

RESULTS

Using fluorescent reporters we developed a system for following in individual cells the number of DSBs, the kinetics of repair and the p53 response. We found a large variation in the initial number of DSBs and the rate of repair between individual cells. Cells with higher number of DSBs had higher probability of showing a p53 pulse. However, there was no distinct threshold number of breaks for inducing a p53 pulse. We present evidence that the decision to activate p53 given a specific number of breaks is not entirely stochastic, but instead is influenced by both cell-intrinsic factors and previous exposure to DNA damage. We also show that the natural variations in the initial amount of p53, rate of DSB repair and cell cycle phase do not affect the probability of activating p53 in response to DNA damage.

CONCLUSIONS

The use of fluorescent reporters to quantify DNA damage and p53 levels in live cells provided a quantitative analysis of the complex interrelationships between both processes. Our study shows that p53 activation differs even between cells that have a similar number of DNA breaks. Understanding the origin and consequences of such variability in normal and cancerous cells is crucial for developing efficient and selective therapeutic interventions.

Robustness of signal transduction pathways

Signal transduction pathways transduce information about the outside of the cell to the nucleus, regulating gene expression and cell fate. To reliably inform the cell about its surroundings, information transfer has to be robust against typical perturbation that a cell experiences. Robustness of several mammalian signaling pathways has been studied recently by quantitative experimentation and using mathematical modeling. Here, we review these studies, and describe the emerging concepts of robustness and the underlying mechanisms.