Cell dynamics and gene expression control in tissue homeostasis and development

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.

Collective behavior and self-organization in neural rosette morphogenesis

Neural rosettes develop from the self-organization of differentiating human pluripotent stem cells. This process mimics the emergence of the embryonic central nervous system primordium, i.e., the neural tube, whose formation is under close investigation as errors during such process result in severe diseases like spina bifida and anencephaly. While neural tube formation is recognized as an example of self-organization, we still do not understand the fundamental mechanisms guiding the process. Here, we discuss the different theoretical frameworks that have been proposed to explain self-organization in morphogenesis. We show that an explanation based exclusively on stem cell differentiation cannot describe the emergence of spatial organization, and an explanation based on patterning models cannot explain how different groups of cells can collectively migrate and produce the mechanical transformations required to generate the neural tube. We conclude that neural rosette development is a relevant experimental 2D in-vitro model of morphogenesis because it is a multi-scale self-organization process that involves both cell differentiation and tissue development. Ultimately, to understand rosette formation, we first need to fully understand the complex interplay between growth, migration, cytoarchitecture organization, and cell type evolution.

Inferring gene regulatory networks using transcriptional profiles as dynamical attractors

Genetic regulatory networks (GRNs) regulate the flow of genetic information from the genome to expressed messenger RNAs (mRNAs) and thus are critical to controlling the phenotypic characteristics of cells. Numerous methods exist for profiling mRNA transcript levels and identifying protein-DNA binding interactions at the genome-wide scale. These enable researchers to determine the structure and output of transcriptional regulatory networks, but uncovering the complete structure and regulatory logic of GRNs remains a challenge. The field of GRN inference aims to meet this challenge using computational modeling to derive the structure and logic of GRNs from experimental data and to encode this knowledge in Boolean networks, Bayesian networks, ordinary differential equation (ODE) models, or other modeling frameworks. However, most existing models do not incorporate dynamic transcriptional data since it has historically been less widely available in comparison to "static" transcriptional data. We report the development of an evolutionary algorithm-based ODE modeling approach (named EA) that integrates kinetic transcription data and the theory of attractor matching to infer GRN architecture and regulatory logic. Our method outperformed six leading GRN inference methods, none of which incorporate kinetic transcriptional data, in predicting regulatory connections among TFs when applied to a small-scale engineered synthetic GRN in Saccharomyces cerevisiae. Moreover, we demonstrate the potential of our method to predict unknown transcriptional profiles that would be produced upon genetic perturbation of the GRN governing a two-state cellular phenotypic switch in Candida albicans. We established an iterative refinement strategy to facilitate candidate selection for experimentation; the experimental results in turn provide validation or improvement for the model. In this way, our GRN inference approach can expedite the development of a sophisticated mathematical model that can accurately describe the structure and dynamics of the in vivo GRN.

Drosophila DAxud1 Has a Repressive Transcription Activity on Hsp70 and Other Heat Shock Genes

Drosophila melanogaster DAxud1 is a transcription factor that belongs to the Cysteine Serine Rich Nuclear Protein (CSRNP) family, conserved in metazoans, with a transcriptional transactivation activity. According to previous studies, this protein promotes apoptosis and Wnt signaling-mediated neural crest differentiation in vertebrates. However, no analysis has been conducted to determine what other genes it might control, especially in connection with cell survival and apoptosis. To partly answer this question, this work analyzes the role of Drosophila DAxud1 using Targeted-DamID-seq (TaDa-seq), which allows whole genome screening to determine in which regions it is most frequently found. This analysis confirmed the presence of DAxud1 in groups of pro-apoptotic and Wnt pathway genes, as previously described; furthermore, stress resistance genes that coding heat shock protein (HSP) family genes were found as hsp70, hsp67, and hsp26. The enrichment of DAxud1 also identified a DNA-binding motif (AYATACATAYATA) that is frequently found in the promoters of these genes. Surprisingly, the following analyses demonstrated that DAxud1 exerts a repressive role on these genes, which are necessary for cell survival. This is coupled with the pro-apoptotic and cell cycle arrest roles of DAxud1, in which repression of hsp70 complements the maintenance of tissue homeostasis through cell survival modulation.

C1QBP Mediates Breast Cancer Cell Proliferation and Growth via Multiple Potential Signalling Pathways

Breast carcinoma is the most prevalent cancer in women globally, with complex genetic and molecular mechanisms that underlie its development and progression. Several challenges such as metastasis and drug resistance limit the prognosis of breast cancer, and hence a constant search for better treatment regimes, including novel molecular therapeutic targets is necessary. Complement component 1, q subcomponent binding protein (C1QBP), a promising molecular target, has been implicated in breast carcinogenesis. In this study, the role of C1QBP in breast cancer progression, in particular cancer cell growth, was determined in triple negative MDA-MB-231 breast cancer cells. Depletion of C1QBP decreased cell proliferation, whereas the opposite effect was observed when C1QBP was overexpressed in MDA-MB-231 cells. Furthermore, gene expression profiling and pathway analysis in C1QBP depleted cells revealed that C1QBP regulates several signaling pathways crucial for cell growth and survival. Taken together, these findings provide a deeper comprehension of the role of C1QBP in triple negative breast cancer, and could possibly pave the way for future advancement of C1QBP-targeted breast cancer therapy.

Mother cells control daughter cell proliferation in intestinal organoids to minimize proliferation fluctuations

During renewal of the intestine, cells are continuously generated by proliferation. Proliferation and differentiation must be tightly balanced, as any bias toward proliferation results in uncontrolled exponential growth. Yet, the inherently stochastic nature of cells raises the question how such fluctuations are limited. We used time-lapse microscopy to track all cells in crypts of growing mouse intestinal organoids for multiple generations, allowing full reconstruction of the underlying lineage dynamics in space and time. Proliferative behavior was highly symmetric between sister cells, with both sisters either jointly ceasing or continuing proliferation. Simulations revealed that such symmetric proliferative behavior minimizes cell number fluctuations, explaining our observation that proliferating cell number remained constant even as crypts increased in size considerably. Proliferative symmetry did not reflect positional symmetry but rather lineage control through the mother cell. Our results indicate a concrete mechanism to balance proliferation and differentiation with minimal fluctuations that may be broadly relevant for other tissues.

Zebrafish neuromesodermal progenitors undergo a critical state transition in vivo

The transition state model of cell differentiation proposes that a transient window of gene expression stochasticity precedes entry into a differentiated state. Here, we assess this theoretical model in zebrafish neuromesodermal progenitors (NMps) in vivo during late somitogenesis stages. We observed an increase in gene expression variability at the 24 somite stage (24ss) before their differentiation into spinal cord and paraxial mesoderm. Analysis of a published 18ss scRNA-seq dataset showed that the NMp population is noisier than its derivatives. By building in silico composite gene expression maps from image data, we assigned an 'NM index' to in silico NMps based on the expression of neural and mesodermal markers and demonstrated that cell population heterogeneity peaked at 24ss. Further examination revealed cells with gene expression profiles incongruent with their prospective fate. Taken together, our work supports the transition state model within an endogenous cell fate decision making event.

High-Throughput Microenvironment Microarray (MEMA) High-Resolution Imaging

The interaction between cells and their surrounding microenvironment has a crucial role in determining cell fate. In many pathological conditions, the microenvironment drives disease progression as well as therapeutic resistance. A number of challenges arise for researchers examining these cell-microenvironment interactions: (1) Tissue microenvironments are combinatorial and dynamic systems, and in pathological situations like cancer, microenvironments become infamously chaotic and highly heterogeneous. (2) Cells exhibit heterogeneous phenotypes, and even rare cell subpopulations can have a substantial role in tissue homeostasis and disease progression. This chapter discusses technical aspects relevant to dissecting cell-microenvironment interaction using the Microenvironment Microarray (MEMA) platform, which is a cell-based functional high-throughput screening of interactions between cells and combinatorial microenvironments at the single-cell level. MEMA provides insights into how cell phenotype and function is elicited by microenvironmental components. In this chapter, we describe automating a high-throughput and high-resolution imaging pipeline for single-cell-resolution analysis.

Struggle within: evolution and ecology of somatic cell populations

The extent to which normal (nonmalignant) cells of the body can evolve through mutation and selection during the lifetime of the organism has been a major unresolved issue in evolutionary and developmental studies. On the one hand, stable multicellular individuality seems to depend on genetic homogeneity and suppression of evolutionary conflicts at the cellular level. On the other hand, the example of clonal selection of lymphocytes indicates that certain forms of somatic mutation and selection are concordant with the organism-level fitness. Recent DNA sequencing and tissue physiology studies suggest that in addition to adaptive immune cells also neurons, epithelial cells, epidermal cells, hematopoietic stem cells and functional cells in solid bodily organs are subject to evolutionary forces during the lifetime of an organism. Here we refer to these recent studies and suggest that the expanding list of somatically evolving cells modifies idealized views of biological individuals as radically different from collectives.

Practical identifiability in the frame of nonlinear mixed effects models: the example of the in vitro erythropoiesis

Background

Nonlinear mixed effects models provide a way to mathematically describe experimental data involving a lot of inter-individual heterogeneity. In order to assess their practical identifiability and estimate confidence intervals for their parameters, most mixed effects modelling programs use the Fisher Information Matrix. However, in complex nonlinear models, this approach can mask practical unidentifiabilities.

Results

Herein we rather propose a multistart approach, and use it to simplify our model by reducing the number of its parameters, in order to make it identifiable. Our model describes several cell populations involved in the in vitro differentiation of chicken erythroid progenitors grown in the same environment. Inter-individual variability observed in cell population counts is explained by variations of the differentiation and proliferation rates between replicates of the experiment. Alternatively, we test a model with varying initial condition.

Conclusions

We conclude by relating experimental variability to precise and identifiable variations between the replicates of the experiment of some model parameters.

Dynamic spatiotemporal coordination of neural stem cell fate decisions occurs through local feedback in the adult vertebrate brain

Neural stem cell (NSC) populations persist in the adult vertebrate brain over a lifetime, and their homeostasis is controlled at the population level through unknown mechanisms. Here, we combine dynamic imaging of entire NSC populations in their in vivo niche over several weeks with pharmacological manipulations, mathematical modeling, and spatial statistics and demonstrate that NSCs use spatiotemporally resolved local feedback signals to coordinate their decision to divide in adult zebrafish brains. These involve Notch-mediated short-range inhibition from transient neural progenitors and a dispersion effect from the dividing NSCs themselves exerted with a delay of 9–12 days. Simulations from a stochastic NSC lattice model capturing these interactions demonstrate that these signals are linked by lineage progression and control the spatiotemporal distribution of output neurons. These results highlight how local and temporally delayed interactions occurring between brain germinal cells generate self-propagating dynamics that maintain NSC population homeostasis and coordinate specific spatiotemporal correlations.

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NSC activation events are spatiotemporally coordinated within adult NSC populations

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This involves inhibition by neural progenitors (relying on Notch) and by dividing NSCs

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A dynamic lattice model shows that these interactions are linked by lineage progression

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NSCs dynamics generate an intrinsic niche that maintains the NSC population long-term

Actin cables and comet tails organize mitochondrial networks in mitosis

Symmetric cell division requires the even partitioning of genetic information and cytoplasmic contents between daughter cells. While the mechanisms coordinating the segregation of the genome are well known, the processes which ensure organelle segregation between daughter cells remain less well-understood¹. Here, we identify multiple actin assemblies that play distinct but complementary roles in mitochondrial organization and inheritance in mitosis. First, we find a dense meshwork of subcortical actin cables assembled throughout the mitotic cytoplasm. This network scaffolds the endoplasmic reticulum and organizes three-dimensional mitochondrial positioning to ensure the equal segregation of mitochondrial mass at cytokinesis. Second, we identify a dynamic wave of actin filaments reversibly assembling on the surface of mitochondria through mitosis. Mitochondria sampled by this wave are enveloped within actin clouds that can spontaneously break symmetry to form elongated comet tails. Mitochondrial comet tails promote randomly directed bursts of movement that shuffle mitochondrial position within the mother cell to randomize inheritance of healthy and damaged mitochondria between daughter cells. Thus, parallel mechanisms mediated by the actin cytoskeleton ensure both equal and random inheritance of mitochondria in symmetrically dividing cells.

Phylodynamics for cell biologists

Multicellular organisms are composed of cells connected by ancestry and descent from progenitor cells. The dynamics of cell birth, death, and inheritance within an organism give rise to the fundamental processes of development, differentiation, and cancer. Technical advances in molecular biology now allow us to study cellular composition, ancestry, and evolution at the resolution of individual cells within an organism or tissue. Here, we take a phylogenetic and phylodynamic approach to single-cell biology. We explain how "tree thinking" is important to the interpretation of the growing body of cell-level data and how ecological null models can benefit statistical hypothesis testing. Experimental progress in cell biology should be accompanied by theoretical developments if we are to exploit fully the dynamical information in single-cell data.

Systematic Analysis of Functionally Related Gene Clusters in the Opportunistic Pathogen, Candida albicans

The proper balance of gene expression is essential for cellular health, organismal development, and maintaining homeostasis. In response to complex internal and external signals, the cell needs to modulate gene expression to maintain proteostasis and establish cellular identity within its niche. On a genome level, single-celled prokaryotic microbes display clustering of co-expressed genes that are regulated as a polycistronic RNA. This phenomenon is largely absent from eukaryotic microbes, although there is extensive clustering of co-expressed genes as functional pairs spread throughout the genome in Saccharomyces cerevisiae. While initial analysis demonstrated conservation of clustering in divergent fungal lineages, a comprehensive analysis has yet to be performed. Here we report on the prevalence, conservation, and significance of the functional clustering of co-regulated genes within the opportunistic human pathogen, Candida albicans. Our analysis reveals that there is extensive clustering within this organism-although the identity of the gene pairs is unique compared with those found in S. cerevisiae-indicating that this genomic arrangement evolved after these microbes diverged evolutionarily, rather than being the result of an ancestral arrangement. We report a clustered arrangement in gene families that participate in diverse molecular functions and are not the result of a divergent orientation with a shared promoter. This arrangement coordinates the transcription of the clustered genes to their neighboring genes, with the clusters congregating to genomic loci that are conducive to transcriptional regulation at a distance.

NEUROD1 Intrinsically Initiates Differentiation of Induced Pluripotent Stem Cells into Neural Progenitor Cells

Cell type specification is a delicate biological event in which every step is under tight regulation. From a molecular point of view, cell fate commitment begins with chromatin alteration, which kickstarts lineage-determining factors to initiate a series of genes required for cell specification. Several important neuronal differentiation factors have been identified from ectopic over-expression studies. However, there is scarce information on which DNA regions are modified during induced pluripotent stem cell (iPSC) to neuronal progenitor cell (NPC) differentiation, the cis regulatory factors that attach to these accessible regions, or the genes that are initially expressed. In this study, we identified the DNA accessible regions of iPSCs and NPCs via the Assay for Transposase-Accessible Chromatin sequencing (ATACseq). We identified which chromatin regions were modified after neuronal differentiation and found that the enhancer regions had more active histone modification changes than the promoters. Through motif enrichment analysis, we found that NEUROD1 controls iPSC differentiation to NPC by binding to the accessible regions of enhancers in cooperation with other factors such as the Hox proteins. Finally, by using Hi-C data, we categorized the genes that directly interacted with the enhancers under the control of NEUROD1 during iPSC to NPC differentiation.

RWRNET: A Gene Regulatory Network Inference Algorithm Using Random Walk With Restart

Inferring gene regulatory networks from expression data is essential in identifying complex regulatory relationships among genes and revealing the mechanism of certain diseases. Various computation methods have been developed for inferring gene regulatory networks. However, these methods focus on the local topology of the network rather than on the global topology. From network optimisation standpoint, emphasising the global topology of the network also reduces redundant regulatory relationships. In this study, we propose a novel network inference algorithm using Random Walk with Restart (RWRNET) that combines local and global topology relationships. The method first captures the local topology through three elements of random walk and then combines the local topology with the global topology by Random Walk with Restart. The Markov Blanket discovery algorithm is then used to deal with isolated genes. The proposed method is compared with several state-of-the-art methods on the basis of six benchmark datasets. Experimental results demonstrated the effectiveness of the proposed method.

Nanofat Cell Aggregates: A Nearly Constitutive Stromal Cell Inoculum for Regenerative Site-Specific Therapies

Supplemental Digital Content is available in the text.

Recent technology developed by Tulip Medical Products allows clinicians to mechanically disaggregate fat tissue into small fat particles known as nanofat. The present study aimed to evaluate the cell yield obtained from nanofat generation in comparison to traditional methods involving enzymatic dissociation (stromal vascular fraction).

XAB2 depletion induces intron retention in POLR2A to impair global transcription and promote cellular senescence

XAB2 is a multi-functional protein participating processes including transcription, splicing, DNA repair and mRNA export. Here, we report POLR2A, the largest catalytic subunit of RNA polymerase II, as a major target gene down-regulated after XAB2 depletion. XAB2 depletion led to severe splicing defects of POLR2A with significant intron retention. Such defects resulted in substantial loss of POLR2A at RNA and protein levels, which further impaired global transcription. Treatment of splicing inhibitor madrasin induced similar reduction of POLR2A. Screen using TMT-based quantitative proteomics identified several proteins involved in mRNA surveillance including Dom34 with elevated expression. Inhibition of translation or depletion of Dom34 rescued the expression of POLR2A by stabilizing its mRNA. Immuno-precipitation further confirmed that XAB2 associated with spliceosome components important to POLR2A expression. Domain mapping revealed that TPR motifs 2–4 and 11 of XAB2 were critical for POLR2A expression by interacting with SNW1. Finally, we showed POLR2A mediated cell senescence caused by XAB2 deficiency. Depletion of XAB2 or POLR2A induced cell senescence by up-regulation of p53 and p21, re-expression of POLR2A after XAB2 depletion alleviated cellular senescence. These data together support that XAB2 serves as a guardian of POLR2A expression to ensure global gene expression and antagonize cell senescence.

Redesigning the T-probe for mass spectrometry analysis of online lysis of non-adherent single cells

Single cell mass spectrometry (SCMS) allows for molecular analysis of individual cells while avoiding the inevitable drawbacks of using cell lysate prepared from populations of cells. Based on our previous design of the T-probe, a microscale sampling and ionization device for SCMS analysis, we further developed the device to perform online, and real time lysis of non-adherent live single cells for mass spectrometry (MS) analysis at ambient conditions. This redesigned T-probe includes three parts: a sampling probe with a small tip to withdraw a whole cell, a solvent-providing capillary to deliver lysis solution (i.e., acetonitrile), and a nano-ESI emitter in which rapid cell lysis and ionization occur followed by MS analysis. These three components are embedded between two polycarbonate slides and are jointed through a T-junction to form an integrated device. Colon cancer cells (HCT-116) under control and treatment (using anticancer drug irinotecan) conditions were analyzed. We detected a variety of intracellular species, and structural identification of selected ions was conducted using tandem MS (MS2). We further conducted statistical analysis (e.g., PLS-DA and t-test) to gain biological insights of cellular metabolism. Our results indicate that the influence of anticancer drugs on cellular metabolism of live non-adherent cells can be obtained using the SCMS experiments combined with statistical data analysis.

Information-theoretic analysis of multivariate single-cell signaling responses

Mathematical methods of information theory appear to provide a useful language to describe how stimuli are encoded in activities of signaling effectors. Exploring the information-theoretic perspective, however, remains conceptually, experimentally and computationally challenging. Specifically, existing computational tools enable efficient analysis of relatively simple systems, usually with one input and output only. Moreover, their robust and readily applicable implementations are missing. Here, we propose a novel algorithm, SLEMI—statistical learning based estimation of mutual information, to analyze signaling systems with high-dimensional outputs and a large number of input values. Our approach is efficient in terms of computational time as well as sample size needed for accurate estimation. Analysis of the NF-κB single—cell signaling responses to TNF-α reveals that NF-κB signaling dynamics improves discrimination of high concentrations of TNF-α with a relatively modest impact on discrimination of low concentrations. Provided R-package allows the approach to be used by computational biologists with only elementary knowledge of information theory.

In light of single-cell, live-imaging experiments understanding of how cells transmit information about identity and quantity of stimuli is incomplete. When exposed to the same stimulus individual cells exhibit substantial cell-to-cell heterogeneity. Besides, stimuli have been shown to regulate temporal profiles of signaling effectors. Therefore, it is, for instance, not entirely clear whether single-cell responses are binary or contain more information about the quantity of stimuli. The above questions resulted in a considerable interest to study cellular signaling within the framework of information theory. Unfortunately, the utilization of the information-theoretic perspective is handicapped in part by the lack of suitable methods that account for multivariate signaling data. Here, we propose a novel algorithm that breaks a considerable computational barrier by allowing the effective information-theoretic analysis of highly-dimensional single-cell measurements. Our approach is computationally efficient, robust and straightforward to use. Moreover, we provide a simple R-package implementation.

Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures

While single-cell gene expression experiments present new challenges for data processing, the cell-to-cell variability observed also reveals statistical relationships that can be used by information theory. Here, we use multivariate information theory to explore the statistical dependencies between triplets of genes in single-cell gene expression datasets. We develop PIDC, a fast, efficient algorithm that uses partial information decomposition (PID) to identify regulatory relationships between genes. We thoroughly evaluate the performance of our algorithm and demonstrate that the higher-order information captured by PIDC allows it to outperform pairwise mutual information-based algorithms when recovering true relationships present in simulated data. We also infer gene regulatory networks from three experimental single-cell datasets and illustrate how network context, choices made during analysis, and sources of variability affect network inference. PIDC tutorials and open-source software for estimating PID are available. PIDC should facilitate the identification of putative functional relationships and mechanistic hypotheses from single-cell transcriptomic data.

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PIDC infers gene regulatory networks from single-cell transcriptomic data

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Multivariate information measures and context in PIDC improve network inference

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Heterogeneity in single-cell data carries information about gene-gene interactions

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Fast, efficient, open-source software is made freely available

Quantitative single-cell live imaging links HES5 dynamics with cell-state and fate in murine neurogenesis

During embryogenesis cells make fate decisions within complex tissue environments. The levels and dynamics of transcription factor expression regulate these decisions. Here, we use single cell live imaging of an endogenous HES5 reporter and absolute protein quantification to gain a dynamic view of neurogenesis in the embryonic mammalian spinal cord. We report that dividing neural progenitors show both aperiodic and periodic HES5 protein fluctuations. Mathematical modelling suggests that in progenitor cells the HES5 oscillator operates close to its bifurcation boundary where stochastic conversions between dynamics are possible. HES5 expression becomes more frequently periodic as cells transition to differentiation which, coupled with an overall decline in HES5 expression, creates a transient period of oscillations with higher fold expression change. This increases the decoding capacity of HES5 oscillations and correlates with interneuron versus motor neuron cell fate. Thus, HES5 undergoes complex changes in gene expression dynamics as cells differentiate.

Id1 Stabilizes Epiblast Identity by Sensing Delays in Nodal Activation and Adjusting the Timing of Differentiation

Controlling responsiveness to prevailing signals is critical for robust transitions between cell states during development. For example, fibroblast growth factor (FGF) drives naive pluripotent cells into extraembryonic lineages before implantation but sustains pluripotency in primed cells of the post-implantation epiblast. Nanog supports pluripotency in naive cells, while Nodal supports pluripotency in primed cells, but the handover from Nanog to Nodal does not proceed seamlessly, opening up the risk of aberrant differentiation if FGF is activated before Nodal. Here, we report that Id1 acts as a sensor to detect delays in Nodal activation after the downregulation of Nanog. Id1 then suppresses FGF activity to delay differentiation. Accordingly, Id1 is not required for naive or primed pluripotency but rather stabilizes epiblast identity during the transition between these states. These findings help explain how development proceeds robustly in the face of imprecise signals and highlight the importance of mechanisms that stabilize cell identity during developmental transitions.

Single-Cell Protein Secretion Detection and Profiling

Secreted proteins play important roles in mediating various biological processes such as cell-cell communication, differentiation, migration, and homeostasis at the population or tissue level. Here, we review bioanalytical technologies and devices for detecting protein secretions from single cells. We begin by discussing conventional approaches followed by detailing the latest advances in microengineered systems for detecting single-cell protein secretions with an emphasis on multiplex measurement. These platforms include droplet microfluidics, micro-/nanowell-based assays, and microchamber-based assays, among which the advantages and limitations are compared. Microscale systems also enable the tracking of protein secretion dynamics in single cells, further empowering the study of the cell-cell communication network. Looking forward, we discuss the remaining challenges and future opportunities that will transform basic research of cellular secretion functions at the systems level and the clinical applications for immune monitoring and cancer treatment.

Regulation of Redox Homeostasis by Nonthermal Biocompatible Plasma Discharge in Stem Cell Differentiation

Recently, a growing body of evidence has shown the role of reactive species as secondary messengers in cell proliferation and differentiation, as opposed to the harmful metabolism byproducts that they were previously solely recognized as. Thus, the balance of intracellular reduction-oxidation (redox) homeostasis plays a vital role in the regulation of stem cell self-renewal and differentiation. Nonthermal biocompatible plasma (NBP) has emerged as a novel tool in biomedical applications. Recently, NBP has also emerged as a powerful tool in the tissue engineering field for the surface modification of biomaterial and the promotion of stem cell differentiation by the regulation of intracellular redox biology. NBP can generate various kinds of reactive oxygen species (ROS) and reactive nitrogen species (RNS), which may play the role of the second passenger in the cell signaling network and active antioxidant system in cells. Herein, we review the current knowledge on mechanisms by which NBP regulates cell proliferation and differentiation through redox modification. Considering the importance of redox homeostasis in the regulation of stem cell differentiation, understanding the underlying molecular mechanisms involved will provide important new insights into NBP-induced stem cell differentiation for tissue engineering.

DNA Damage Stress: Cui Prodest?

DNA is an entity shielded by mechanisms that maintain genomic stability and are essential for living cells; however, DNA is constantly subject to assaults from the environment throughout the cellular life span, making the genome susceptible to mutation and irreparable damage. Cells are prepared to mend such events through cell death as an extrema ratio to solve those threats from a multicellular perspective. However, in cells under various stress conditions, checkpoint mechanisms are activated to allow cells to have enough time to repair the damaged DNA. In yeast, entry into the cell cycle when damage is not completely repaired represents an adaptive mechanism to cope with stressful conditions. In multicellular organisms, entry into cell cycle with damaged DNA is strictly forbidden. However, in cancer development, individual cells undergo checkpoint adaptation, in which most cells die, but some survive acquiring advantageous mutations and selfishly evolve a conflictual behavior. In this review, we focus on how, in cancer development, cells rely on checkpoint adaptation to escape DNA stress and ultimately to cell death.

Time matters! Developmental shift in gene expression between the head and the trunk region of the cichlid fish Astatotilapia burtoni

BACKGROUND

Differential gene expression can be translated into differing phenotypic traits. Especially during embryogenesis, specific gene expression networks regulate the development of different body structures. Cichlid fishes, with their impressive phenotypic diversity and propensity to radiate, are an emerging model system in the genomics era. Here we set out to investigate gene expression throughout development in the well-studied cichlid fish Astatotilapia burtoni, native to Lake Tanganyika and its affluent rivers.

RESULTS

Combining RNA-sequencing from different developmental time points as well as integrating adult gene expression data, we constructed a new genome annotation for A. burtoni comprising 103,253 transcripts (stemming from 52,584 genomic loci) as well as a new reference transcriptome set. We compared our transcriptome to the available reference genome, redefining transcripts and adding new annotations. We show that about half of these transcripts have coding potential. We also characterize transcripts that are not present in the genome assembly. Next, using our newly constructed comprehensive reference transcriptome, we characterized differential gene expression through time and showed that gene expression is shifted between different body parts. We constructed a gene expression network that identified connected genes responsible for particular phenotypes and made use of it to focus on genes under potential positive selection in A. burtoni, which were implicated in fin development and vision.

CONCLUSIONS

We provide new genomic resources for the cichlid fish Astatotilapia burtoni, which will contribute to its further establishment as a model system. Tracing gene expression through time, we identified gene networks underlying particular functions, which will help to understand the genetic basis of phenotypic diversity in cichlids.

A common molecular logic determines embryonic stem cell self-renewal and reprogramming

During differentiation and reprogramming, new cell identities are generated by reconfiguration of gene regulatory networks. Here, we combined automated formal reasoning with experimentation to expose the logic of network activation during induction of naïve pluripotency. We find that a Boolean network architecture defined for maintenance of naïve state embryonic stem cells (ESC) also explains transcription factor behaviour and potency during resetting from primed pluripotency. Computationally identified gene activation trajectories were experimentally substantiated at single‐cell resolution by RT–qPCR. Contingency of factor availability explains the counterintuitive observation that Klf2, which is dispensable for ESC maintenance, is required during resetting. We tested 124 predictions formulated by the dynamic network, finding a predictive accuracy of 77.4%. Finally, we show that this network explains and predicts experimental observations of somatic cell reprogramming. We conclude that a common deterministic program of gene regulation is sufficient to govern maintenance and induction of naïve pluripotency. The tools exemplified here could be broadly applied to delineate dynamic networks underlying cell fate transitions.

An information-theoretic framework for deciphering pleiotropic and noisy biochemical signaling

Many components of signaling pathways are functionally pleiotropic, and signaling responses are marked with substantial cell-to-cell heterogeneity. Therefore, biochemical descriptions of signaling require quantitative support to explain how complex stimuli (inputs) are encoded in distinct activities of pathways effectors (outputs). A unique perspective of information theory cannot be fully utilized due to lack of modeling tools that account for the complexity of biochemical signaling, specifically for multiple inputs and outputs. Here, we develop a modeling framework of information theory that allows for efficient analysis of models with multiple inputs and outputs; accounts for temporal dynamics of signaling; enables analysis of how signals flow through shared network components; and is not restricted by limited variability of responses. The framework allows us to explain how identity and quantity of type I and type III interferon variants could be recognized by cells despite activating the same signaling effectors.

A mathematical model of stem cell regeneration with epigenetic state transitions

In this paper, we study a mathematical model of stem cell regeneration with epigenetic state transitions. In the model, the heterogeneity of stem cells is considered through the epigenetic state of each cell, and each epigenetic state defines a subpopulation of stem cells. The dynamics of the subpopulations are modeled by a set of ordinary differential equations in which epigenetic state transition in cell division is given by the transition probability. We present analysis for the existence and linear stability of the equilibrium state. As an example, we apply the model to study the dynamics of state transition in breast cancer stem cells.

Pathway analysis identifies altered mitochondrial metabolism, neurotransmission, structural pathways and complement cascade in retina/RPE/ choroid in chick model of form-deprivation myopia

Purpose

RNA sequencing analysis has demonstrated bidirectional changes in metabolism, structural and immune pathways during early induction of defocus induced myopia. Thus, the aim of this study was to investigate whether similar gene pathways are also related to the more excessive axial growth, ultrastructural and elemental microanalytic changes seen during the induction and recovery from form-deprivation myopia (FDM) in chicks and predicted by the RIDE model of myopia.

Methods

Archived genomic transcriptome data from the first three days of induction of monocularly occluded form deprived myopia (FDMI) in chicks was obtained from the GEO database (accession # GSE6543) while data from chicks monocularly occluded for 10 days and then given up to 24 h of normal visual recovery (FDMR) were collected. Gene set enrichment analysis (GSEA) software was used to determine enriched pathways during the induction (FDMI) and recovery (FDMR) from FD. Curated gene-sets were obtained from open access sources.

Results

Clusters of significant changes in mitochondrial energy metabolism, neurotransmission, ion channel transport, G protein coupled receptor signalling, complement cascades and neuron structure and growth were identified during the 10 days of induction of profound myopia and were found to correlate well with change in axial dimensions. Bile acid and bile salt metabolism pathways (cholesterol/lipid metabolism and sodium channel activation) were significantly upregulated during the first 24 h of recovery from 10 days of FDM.

Conclusions

The gene pathways altered during induction of FDM are similar to those reported in defocus induced myopia and are established indicators of oxidative stress, osmoregulatory and associated structural changes. These findings are also consistent with the choroidal thinning, axial elongation and hyperosmotic ion distribution patterns across the retina and choroid previously reported in FDM and predicted by RIDE.

Feedback from Lateral Organs Controls Shoot Apical Meristem Growth by Modulating Auxin Transport

Stem cells must balance self-renewal and differentiation; thus, their activities are precisely controlled. In plants, the control circuits that underlie division and differentiation within meristems have been well studied, but those that underlie feedback on meristems from lateral organs remain largely unknown. Here we show that long-distance auxin transport mediates this feedback in a non-cell-autonomous manner. A low-auxin zone is associated with the shoot apical meristem (SAM) organization center, and auxin levels negatively affect SAM size. Using computational model simulations, we show that auxin transport from lateral organs can inhibit auxin transport from the SAM through an auxin transport switch and thus maintain SAM auxin homeostasis and SAM size. Genetic and microsurgical analyses confirmed the model's predictions. In addition, the model explains temporary change in SAM size of yabby mutants. Our study suggests that the canalization-based auxin flux can be widely adapted as a feedback control mechanism in plants.

Stochastic system identification without an a priori chosen kinetic model-exploring feasible cell regulation with piecewise linear functions

Kinetic models are at the heart of system identification. A priori chosen rate functions may, however, be unfitting or too restrictive for complex or previously unanticipated regulation. We applied general purpose piecewise linear functions for stochastic system identification in one dimension using published flow cytometry data on E.coli and report on identification results for equilibrium state and dynamic time series. In metabolic labelling experiments during yeast osmotic stress response, we find mRNA production and degradation to be strongly co-regulated. In addition, mRNA degradation appears overall uncorrelated with mRNA level. Comparison of different system identification approaches using semi-empirical synthetic data revealed the superiority of single-cell tracking for parameter identification. Generally, we find that even within restrictive error bounds for deviation from experimental data, the number of viable regulation types may be large. Indeed, distinct regulation can lead to similar expression behaviour over time. Our results demonstrate that molecule production and degradation rates may often differ from classical constant, linear or Michaelis–Menten type kinetics.

Classical cell-regulation models are often imperfectly fitting or even inconsistent with experimental data suggesting inappropriate model assumptions. Martin Hoffmann from Fraunhofer ITEM Regensburg and Jörg Galle from IZBI Leipzig analysed different protein and gene expression data using general purpose piecewise linear functions for system identification. They assessed data corresponding to various experimental techniques for their potential to determine the parameters of their models. Single-cell recordings of expression values over time were most effective for parameter identification. Generally, different and often non-classical cell-regulation models were consistent with the experimental data, even for restrictive error bounds. The authors used virtual treatment experiments to demonstrate that precise knowledge of cell regulation is important for assessing therapy effects. Their findings clearly argue in favour of system identification being performed without an a priori chosen kinetic model.

MicroRNA-22 controls interferon alpha production and erythroid maturation in response to infectious stress in mice

MicroRNA-22 (miR-22) is a highly conserved microRNA that can regulate cell proliferation, oncogenesis, and cell maturation, especially during stress. In hematopoietic stem cells (HSCs), miR-22 has been reported to be involved in the regulation of key self-renewal factors, including Tet2. Recent work demonstrates that miR-22 also participates in regulation of the interferon (IFN) response, and expression profiling studies suggest that it is variably expressed at different stages in erythroid differentiation. We thus hypothesized that miR-22 regulates maturation of erythroid progenitors during stress hematopoiesis through its interaction with IFN. We compared the blood and bone marrow of wild-type (WT) and miR-22-deficient mice at baseline and upon infectious challenge with systemic lymphochoriomeningitis (LCMV) virus. miR-22-deficient mice maintained platelet counts better than WT mice during infection, but they showed significantly reduced red blood cells and hemoglobin. Analysis of bone marrow progenitors demonstrated better overall survival and improved HSC homeostasis in infected miR-22-null mice compared with WT, which was attributable to a blunted IFN response to LCMV challenge in the miR-22-null mice. We found that miR-22 was expressed exclusively in stage II erythroid precursors and downregulated upon infection in WT mice. Our results indicate that miR-22 promotes the IFN response to viral infection and that it functions at baseline as a brake to slow erythroid differentiation and maintain adequate erythroid potential. Impaired regulation of erythrogenesis in the absence of miR-22 can lead to anemia during infection.

Label-free analysis of the characteristics of a single cell trapped by acoustic tweezers

Single-cell analysis is essential to understand the physical and functional characteristics of cells. The basic knowledge of these characteristics is important to elucidate the unique features of various cells and causative factors of diseases and determine the most effective treatments for diseases. Recently, acoustic tweezers based on tightly focused ultrasound microbeam have attracted considerable attention owing to their capability to grab and separate a single cell from a heterogeneous cell sample and to measure its physical cell properties. However, the measurement cannot be performed while trapping the target cell, because the current method uses long ultrasound pulses for grabbing one cell and short pulses for interrogating the target cell. In this paper, we demonstrate that short ultrasound pulses can be used for generating acoustic trapping force comparable to that with long pulses by adjusting the pulse repetition frequency (PRF). This enables us to capture a single cell and measure its physical properties simultaneously. Furthermore, it is shown that short ultrasound pulses at a PRF of 167 kHz can trap and separate either one red blood cell or one prostate cancer cell and facilitate the simultaneous measurement of its integrated backscattering coefficient related to the cell size and mechanical properties.

Operating principles of tristable circuits regulating cellular differentiation

Many cell-fate decisions during embryonic development are governed by a motif comprised of two transcription factors (TFs) A and B that mutually inhibit each other and may self-activate. This motif, called as a self-activating toggle switch (SATS), can typically have three stable states (phenotypes)-two corresponding to differentiated cell fates, each of which has a much higher level of one TF than the other-[Formula: see text] or [Formula: see text]-and the third state corresponding to an 'undecided' stem-like state with similar levels of both A and B-[Formula: see text]. Furthermore, two or more SATSes can be coupled together in various topologies in different contexts, thereby affecting the coordination between multiple cellular decisions. However, two questions remain largely unanswered: (a) what governs the co-existence and relative stability of these three stable states? (b) What orchestrates the decision-making of coupled SATSes? Here, we first demonstrate that the co-existence and relative stability of the three stable states in an individual SATS can be governed by the relative strength of self-activation, external signals activating and/or inhibiting A and B, and mutual degradation between A and B. Simultaneously, we investigate the effects of these factors on the decision-making of two coupled SATSes. Our results offer novel understanding into the operating principles of individual and coupled tristable self-activating toggle switches (SATSes) regulating cellular differentiation and can yield insights into synthesizing three-way genetic circuits and understanding of cellular reprogramming.

Variation in gene expression within clones of the earthworm Dendrobaena octaedra

Gene expression is highly plastic, which can help organisms to both acclimate and adapt to changing environments. Possible variation in gene expression among individuals with the same genotype (among clones) is not widely considered, even though it could impact the results of studies that focus on gene expression phenotypes, for example studies using clonal lines. We examined the extent of within and between clone variation in gene expression in the earthworm Dendrobaena octaedra, which reproduces through apomictic parthenogenesis. Five microsatellite markers were developed and used to confirm that offspring are genetic clones of their parent. After that, expression of 12 genes was measured from five individuals each from six clonal lines after exposure to copper contaminated soil. Variation in gene expression was higher over all genotypes than within genotypes, as initially assumed. A subset of the genes was also examined in the offspring of exposed individuals in two of the clonal lines. In this case, variation in gene expression within genotypes was as high as that observed over all genotypes. One gene in particular (chymotrypsin inhibitor) also showed significant differences in the expression levels among genetically identical individuals. Gene expression can vary considerably, and the extent of variation may depend on the genotypes and genes studied. Ensuring a large sample, with many different genotypes, is critical in studies comparing gene expression phenotypes. Researchers should be especially cautious inferring gene expression phenotypes when using only a single clonal or inbred line, since the results might be specific to only certain genotypes.

Identifying niche-mediated regulatory factors of stem cell phenotypic state: a systems biology approach

Understanding how the cellular niche controls the stem cell phenotype is often hampered due to the complexity of variegated niche composition, its dynamics, and nonlinear stem cell–niche interactions. Here, we propose a systems biology view that considers stem cell–niche interactions as a many‐body problem amenable to simplification by the concept of mean field approximation. This enables approximation of the niche effect on stem cells as a constant field that induces sustained activation/inhibition of specific stem cell signaling pathways in all stem cells within heterogeneous populations exhibiting the same phenotype (niche determinants). This view offers a new basis for the development of single cell‐based computational approaches for identifying niche determinants, which has potential applications in regenerative medicine and tissue engineering.

Accelerated cell divisions drive the outgrowth of the regenerating spinal cord in axolotls

Axolotls are unique in their ability to regenerate the spinal cord. However, the mechanisms that underlie this phenomenon remain poorly understood. Previously, we showed that regenerating stem cells in the axolotl spinal cord revert to a molecular state resembling embryonic neuroepithelial cells and functionally acquire rapid proliferative divisions (Rodrigo Albors et al., 2015). Here, we refine the analysis of cell proliferation in space and time and identify a high-proliferation zone in the regenerating spinal cord that shifts posteriorly over time. By tracking sparsely-labeled cells, we also quantify cell influx into the regenerate. Taking a mathematical modeling approach, we integrate these quantitative datasets of cell proliferation, neural stem cell activation and cell influx, to predict regenerative tissue outgrowth. Our model shows that while cell influx and neural stem cell activation play a minor role, the acceleration of the cell cycle is the major driver of regenerative spinal cord outgrowth in axolotls.

DOI:http://dx.doi.org/10.7554/eLife.20357.001

Epigenetics of cell fate reprogramming and its implications for neurological disorders modelling

The reprogramming of human induced pluripotent stem cells (hiPSCs) proceeds in a stepwise manner with reprogramming factors binding and epigenetic composition changes during transition to maintain the epigenetic landscape, important for pluripotency. There arises a question as to whether the aberrant epigenetic state after reprogramming leads to epigenetic defects in induced stem cells causing unpredictable long term effects in differentiated cells. In this review, we present a comprehensive view of epigenetic alterations accompanying reprogramming, cell maintenance and differentiation as factors that influence applications of hiPSCs in stem cell based technologies. We conclude that sample heterogeneity masks DNA methylation signatures in subpopulations of cells and thus believe that beside a genetic evaluation, extensive epigenomic screening should become a standard procedure to ensure hiPSCs state before they are used for genome editing and differentiation into neurons of interest. In particular, we suggest that exploitation of the single-cell composition of the epigenome will provide important insights into heterogeneity within hiPSCs subpopulations to fast forward development of reliable hiPSC-based analytical platforms in neurological disorders modelling and before completed hiPSC technology will be implemented in clinical approaches.

Transition states and cell fate decisions in epigenetic landscapes

Waddington's epigenetic landscape is an abstract metaphor frequently used to represent the relationship between gene activity and cell fates during development. Over the past few years, it has become a useful framework for interpreting results from single-cell transcriptomics experiments. It has led to the proposal that, during fate transitions, cells experience smooth, continuous progressions of global transcriptional activity, which can be captured by (pseudo)temporal dynamics. Here, focusing strictly on the fate decision events, we suggest an alternative view: that fate transitions occur in a discontinuous, stochastic manner whereby signals modulate the probability of the transition events.

Modeling heterogeneity in the pluripotent state: A promising strategy for improving the efficiency and fidelity of stem cell differentiation

Pluripotency can be considered a functional characteristic of pluripotent stem cells (PSCs) populations and their niches, rather than a property of individual cells. In this view, individual cells within the population independently adopt a variety of different expression states, maintained by different signaling, transcriptional, and epigenetics regulatory networks. In this review, we propose that generation of integrative network models from single cell data will be essential for getting a better understanding of the regulation of self-renewal and differentiation. In particular, we suggest that the identification of network stability determinants in these integrative models will provide important insights into the mechanisms mediating the transduction of signals from the niche, and how these signals can trigger differentiation. In this regard, the differential use of these stability determinants in subpopulation-specific regulatory networks would mediate differentiation into different cell fates. We suggest that this approach could offer a promising avenue for the development of novel strategies for increasing the efficiency and fidelity of differentiation, which could have a strong impact on regenerative medicine.

Robustness of MEK-ERK Dynamics and Origins of Cell-to-Cell Variability in MAPK Signaling

Cellular signaling processes can exhibit pronounced cell-to-cell variability in genetically identical cells. This affects how individual cells respond differentially to the same environmental stimulus. However, the origins of cell-to-cell variability in cellular signaling systems remain poorly understood. Here, we measure the dynamics of phosphorylated MEK and ERK across cell populations and quantify the levels of population heterogeneity over time using high-throughput image cytometry. We use a statistical modeling framework to show that extrinsic noise, particularly that from upstream MEK, is the dominant factor causing cell-to-cell variability in ERK phosphorylation, rather than stochasticity in the phosphorylation/dephosphorylation of ERK. We furthermore show that without extrinsic noise in the core module, variable (including noisy) signals would be faithfully reproduced downstream, but the within-module extrinsic variability distorts these signals and leads to a drastic reduction in the mutual information between incoming signal and ERK activity.

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Active MEK and ERK levels differ profoundly among genetically identical cells

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A statistical framework is developed to identify the causes of this variability

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Analysis shows that extrinsic noise upstream MEK-ERK module causes cell variability

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Within-module extrinsic variability distorts signals

Stochasticity in the miR-9/Hes1 oscillatory network can account for clonal heterogeneity in the timing of differentiation

Recent studies suggest that cells make stochastic choices with respect to differentiation or division. However, the molecular mechanism underlying such stochasticity is unknown. We previously proposed that the timing of vertebrate neuronal differentiation is regulated by molecular oscillations of a transcriptional repressor, HES1, tuned by a post-transcriptional repressor, miR-9. Here, we computationally model the effects of intrinsic noise on the Hes1/miR-9 oscillator as a consequence of low molecular numbers of interacting species, determined experimentally. We report that increased stochasticity spreads the timing of differentiation in a population, such that initially equivalent cells differentiate over a period of time. Surprisingly, inherent stochasticity also increases the robustness of the progenitor state and lessens the impact of unequal, random distribution of molecules at cell division on the temporal spread of differentiation at the population level. This advantageous use of biological noise contrasts with the view that noise needs to be counteracted.

The bimodally expressed microRNA miR-142 gates exit from pluripotency

A stem cell's decision to self‐renew or differentiate is thought to critically depend on signaling cues provided by its environment. It is unclear whether stem cells have the intrinsic capacity to control their responsiveness to environmental signals that can be fluctuating and noisy. Using a novel single‐cell microRNA activity reporter, we show that miR‐142 is bimodally expressed in embryonic stem cells, creating two states indistinguishable by pluripotency markers. A combination of modeling and quantitative experimental data revealed that mESCs switch stochastically between the two miR‐142 states. We find that cells with high miR‐142 expression are irresponsive to differentiation signals while cells with low miR‐142 expression can respond to differentiation cues. We elucidate the molecular mechanism underpinning the bimodal regulation of miR‐142 as a double‐negative feedback loop between miR‐142 and KRAS/ERK signaling and derive a quantitative description of this bistable system. miR‐142 switches the activation status of key intracellular signaling pathways thereby locking cells in an undifferentiated state. This reveals a novel mechanism to maintain a stem cell reservoir buffered against fluctuating signaling environments.