Cellular Decisions Research Articles

Optogenetic control of cAMP oscillations reveals frequency-selective transcription factor dynamics in Dictyostelium.

Oscillatory dynamics and their modulation are crucial for cellular decision-making; however, analysing these dynamics remains challenging. Here, we present a tool that combines the light-activated adenylate cyclase mPAC with the cAMP biosensor Pink Flamindo, enabling precise manipulation and real-time monitoring of cAMP oscillation frequencies in Dictyostelium. High-frequency modulation of cAMP oscillations induced cell aggregation and multicellular formation, even at low cell densities, such as a few dozen cells. At the population level, chemotactic aggregation is driven by modulated frequency signals. Additionally, modulation of cAMP frequency significantly reduced the amplitude of the shuttling behaviour of the transcription factor GtaC, demonstrating low-pass filter characteristics capable of converting subtle oscillation changes, such as from 6 min to 4 min, into gene expression. These findings enhance our understanding of frequency-selective cellular decoding and its role in cellular signalling and development.

Analyzing the role of ferroptosis in ribosome-related bone marrow failure disorders: From pathophysiology to potential pharmacological exploitation.

Within the last decade, the scientific community has witnessed the importance of ferroptosis as a novel cascade of molecular events leading to cellular decisions of death distinct from apoptosis and other known forms of cell death. Notably, such non- apoptotic and iron-dependent regulated cell death has been found to be intricately linked to several physiological processes as well as to the pathogenesis of various diseases. To this end, recent data support the notion that a potential molecular connection between ferroptosis and inherited bone marrow failure (IBMF) in individuals with ribosomopathies may exist. In this review, we suggest that in ribosome-related IBMFs the identified mutations in ribosomal proteins lead to changes in the ribosome composition of the hematopoietic progenitors, changes that seem to affect ribosomal function, thus enhancing the expression of some mRNAs subgroups while reducing the expression of others. These events lead to an imbalance inside the cell as some molecular pathways are promoted while others are inhibited. This disturbance is accompanied by ROS production and lipid peroxidation, while an additional finding in most of them is iron accumulation. Once lipid peroxidation and iron accumulation are the two main characteristics of ferroptosis, it is possible that this mechanism plays a key role in the manifestation of IBMF in this type of disease. If this molecular mechanism is further confirmed, new pharmacological targets such as ferroptosis inhibitors that are already exploited for the treatment of other diseases, could be utilized to improve the treatment of ribosomopathies.

Transcriptional regulators ensuring specific gene expression and decision-making at high TGFβ doses.

TGFβ-signaling regulates cancer progression by controlling cell division, migration, and death. These outcomes are mediated by gene expression changes, but the mechanisms of decision-making toward specific fates remain unclear. Here, we combine SMAD transcription factor imaging, genome-wide RNA sequencing, and morphological assays to quantitatively link signaling, gene expression, and fate decisions in mammary epithelial cells. Fitting genome-wide kinetic models to our time-resolved data, we find that most of the TGFβ target genes can be explained as direct targets of SMAD transcription factors, whereas the remainder show signs of complex regulation, involving delayed regulation and strong amplification at high TGFβ doses. Knockdown experiments followed by global RNA sequencing revealed transcription factors interacting with SMADs in feedforward loops to control delayed and dose-discriminating target genes, thereby reinforcing the specific epithelial-to-mesenchymal transition at high TGFβ doses. We identified early repressors, preventing premature activation, and a late activator, boosting gene expression responses for a sufficiently strong TGFβ stimulus. Taken together, we present a global view of TGFβ-dependent gene regulation and describe specificity mechanisms reinforcing cellular decision-making.

Logic-based modeling of biological networks with Netflux.

Molecular signaling networks drive a diverse range of cellular decisions, including whether to proliferate, how and when to die, and many processes in between. Such networks often connect hundreds of proteins, genes, and processes. Understanding these complex networks is aided by computational modeling, but these tools require extensive programming knowledge. In this article, we describe a user-friendly, programming-free network simulation tool called Netflux (https://github.com/saucermanlab/Netflux). Over the last decade, Netflux has been used to construct numerous predictive network models that have deepened our understanding of how complex biological networks make cell decisions. Here, we provide a Netflux tutorial that covers how to construct a network model and then simulate network responses to perturbations. Upon completion of this tutorial, you will be able to construct your own model in Netflux and simulate how perturbations to proteins and genes propagate through signaling and gene-regulatory networks.

Membrane potential mediates the cellular response to mechanical pressure.

Mechanical forces have been shown to influence cellular decisions to grow, die, or differentiate, through largely mysterious mechanisms. Separately, changes in resting membrane potential have been observed in development, differentiation, regeneration, and cancer. We now demonstrate that membrane potential is the central mediator of cellular response to mechanical pressure. We show that mechanical forces acting on the cell change cellular biomass density, which in turn alters membrane potential. Membrane potential then regulates cell number density in epithelia by controlling cell growth, proliferation, and cell elimination. Mechanistically, we show that changes in membrane potential control signaling through the Hippo and MAPK pathways, and potentially other signaling pathways that originate at the cell membrane. While many molecular interactions are known to affect Hippo signaling, the upstream signal that activates the canonical Hippo pathway at the membrane has previously been elusive. Our results establish membrane potential as a central regulator of growth and tissue homeostasis.

A mechanistic integration of hypoxia signaling with energy, redox, and hormonal cues.

Oxygen deficiency (hypoxia) occurs naturally in many developing plant tissues but can become a major threat during acute flooding stress. Consequently, plants as aerobic organisms must rapidly acclimate to hypoxia and the associated energy crisis to ensure cellular and ultimately organismal survival. In plants, oxygen sensing is tightly linked with oxygen-controlled protein stability of group VII ETHYLENE-RESPONSE FACTORs (ERFVII), which, when stabilized under hypoxia, act as key transcriptional regulators of hypoxia-responsive genes (HRGs). Multiple signaling pathways feed into hypoxia signaling to fine-tune cellular decision-making under stress. First, ATP shortage upon hypoxia directly affects the energy status and adjusts anaerobic metabolism. Secondly, altered redox homeostasis leads to reactive oxygen and nitrogen species (ROS and RNS) accumulation, evoking signaling and oxidative stress acclimation. Finally, the phytohormone ethylene promotes hypoxia signaling to improve acute stress acclimation, while hypoxia signaling in turn can alter ethylene, auxin, abscisic acid, salicylic acid, and jasmonate signaling to guide development and stress responses. In this Update, we summarize the current knowledge on how energy, redox, and hormone signaling pathways are induced under hypoxia and subsequently integrated at the molecular level to ensure stress-tailored cellular responses. We show that some HRGs are responsive to changes in redox, energy, and ethylene independently of the oxygen status, and we propose an updated HRG list that is more representative for hypoxia marker gene expression. We discuss the synergistic effects of hypoxia, energy, redox, and hormone signaling and their phenotypic consequences in the context of both environmental and developmental hypoxia.

Mapping lineage-traced cells across time points with moslin

Simultaneous profiling of single-cell gene expression and lineage history holds enormous potential for studying cellular decision-making. Recent computational approaches combine both modalities into cellular trajectories; however, they cannot make use of all available lineage information in destructive time-series experiments. Here, we present moslin, a Gromov-Wasserstein-based model to couple cellular profiles across time points based on lineage and gene expression information. We validate our approach in simulations and demonstrate on Caenorhabditis elegans embryonic development how moslin predicts fate probabilities and putative decision driver genes. Finally, we use moslin to delineate lineage relationships among transiently activated fibroblast states during zebrafish heart regeneration.

Optimal performance objectives in the highly conserved bone morphogenetic protein signaling pathway

Throughout development, complex networks of cell signaling pathways drive cellular decision-making across different tissues and contexts. The transforming growth factor β (TGF-β) pathways, including the BMP/Smad pathway, play crucial roles in determining cellular responses. However, as the Smad pathway is used reiteratively throughout the life cycle of all animals, its systems-level behavior varies from one context to another, despite the pathway connectivity remaining nearly constant. For instance, some cellular systems require a rapid response, while others require high noise filtering. In this paper, we examine how the BMP-Smad pathway balances trade-offs among three such systems-level behaviors, or “Performance Objectives (POs)”: response speed, noise amplification, and the sensitivity of pathway output to receptor input. Using a Smad pathway model fit to human cell data, we show that varying non-conserved parameters (NCPs) such as protein concentrations, the Smad pathway can be tuned to emphasize any of the three POs and that the concentration of nuclear phosphatase has the greatest effect on tuning the POs. However, due to competition among the POs, the pathway cannot simultaneously optimize all three, but at best must balance trade-offs among the POs. We applied the multi-objective optimization concept of the Pareto Front, a widely used concept in economics to identify optimal trade-offs among various requirements. We show that the BMP pathway efficiently balances competing POs across species and is largely Pareto optimal. Our findings reveal that varying the concentration of NCPs allows the Smad signaling pathway to generate a diverse range of POs. This insight identifies how signaling pathways can be optimally tuned for each context.

FateNet: an integration of dynamical systems and deep learning for cell fate prediction.

Understanding cellular decision-making, particularly its timing and impact on the biological system such as tissue health and function, is a fundamental challenge in biology and medicine. Existing methods for inferring fate decisions and cellular state dynamics from single-cell RNA sequencing data lack precision regarding decision points and broader tissue implications. Addressing this gap, we present FateNet, a computational approach integrating dynamical systems theory and deep learning to probe the cell decision-making process using scRNA-seq data. By leveraging information about normal forms and scaling behavior near bifurcations common to many dynamical systems, FateNet predicts cell decision occurrence with higher accuracy than conventional methods and offers qualitative insights into the new state of the biological system. Also, through in-silico perturbation experiments, FateNet identifies key genes and pathways governing the differentiation process in hematopoiesis. Validated using different scRNA-seq data, FateNet emerges as a user-friendly and valuable tool for predicting critical points in biological processes, providing insights into complex trajectories. github.com/ThomasMBury/fatenet.

Entropy and adaptation: exploring cellular decision-making and cancer dynamics.

Entropy and adaptation: exploring cellular decision-making and cancer dynamics.

DiPRO1 distinctly reprograms muscle and mesenchymal cancer cells

We have recently identified the uncharacterized ZNF555 protein as a component of a productive complex involved in the morbid function of the 4qA locus in facioscapulohumeral dystrophy. Subsequently named DiPRO1 (Death, Differentiation, and PROliferation related PROtein 1), our study provides substantial evidence of its role in the differentiation and proliferation of human myoblasts. DiPRO1 operates through the regulatory binding regions of SIX1, a master regulator of myogenesis. Its relevance extends to mesenchymal tumors, such as rhabdomyosarcoma (RMS) and Ewing sarcoma, where DiPRO1 acts as a repressor via the epigenetic regulators TIF1B and UHRF1, maintaining methylation of cis-regulatory elements and gene promoters. Loss of DiPRO1 mimics the host defense response to virus, awakening retrotransposable repeats and the ZNF/KZFP gene family. This enables the eradication of cancer cells, reprogramming the cellular decision balance towards inflammation and/or apoptosis by controlling TNF-α via NF-kappaB signaling. Finally, our results highlight the vulnerability of mesenchymal cancer tumors to si/shDiPRO1-based nanomedicines, positioning DiPRO1 as a potential therapeutic target.

Building better habitats: Spatiotemporal signaling cues in 3D biointerfaces for tailored cellular functionality.

A promising research direction in the field of biological engineering is the design and functional programming of three-dimensional (3D) biointerfaces designed to support living cell functionality and growth in vitro, offering a route to precisely regulate cellular behaviors and phenotypes for addressing therapeutic challenges. While traditional two-dimensional (2D) biointerfaces have provided valuable insights, incorporating specific signaling cues into a 3D biointeractive microenvironment at the right locations and time is now recognized as crucial for accurately programming cellular decision-making and communication processes. This approach aims to engineer cell-centric microenvironments with the potential to recapitulate complex biological functions into a finite set of growing cellular organizations. Additionally, they provide insights into the hierarchical logic governing the relationship between molecular components and higher-order multicellular functionality. The functional live cell-based microenvironment engineered through such innovative biointerfaces has the potential to be used as an in vitro model system for expanding our understanding of cellular behaviors or as a therapeutic habitat where cellular functions can be reprogrammed.

An intermediate Rb–E2F activity state safeguards proliferation commitment

Tissue repair, immune defence and cancer progression rely on a vital cellular decision between quiescence and proliferation1,2. Mammalian cells proliferate by triggering a positive feedback mechanism3,4. The transcription factor E2F activates cyclin-dependent kinase 2 (CDK2), which in turn phosphorylates and inactivates the E2F inhibitor protein retinoblastoma (Rb). This action further increases E2F activity to express genes needed for proliferation. Given that positive feedback can inadvertently amplify small signals, understanding how cells keep this positive feedback in check remains a puzzle. Here we measured E2F and CDK2 signal changes in single cells and found that the positive feedback mechanism engages only late in G1 phase. Cells spend variable and often extended times in a reversible state of intermediate E2F activity before committing to proliferate. This intermediate E2F activity is proportional to the amount of phosphorylation of a conserved T373 residue in Rb that is mediated by CDK2 or CDK4/CDK6. Such T373-phosphorylated Rb remains bound on chromatin but dissociates from it once Rb is hyperphosphorylated at many sites, which fully activates E2F. The preferential initial phosphorylation of T373 can be explained by its relatively slower rate of dephosphorylation. Together, our study identifies a primed state of intermediate E2F activation whereby cells sense external and internal signals and decide whether to reverse and exit to quiescence or trigger the positive feedback mechanism that initiates cell proliferation.

CellRank 2: unified fate mapping in multiview single-cell data

Single-cell RNA sequencing allows us to model cellular state dynamics and fate decisions using expression similarity or RNA velocity to reconstruct state-change trajectories; however, trajectory inference does not incorporate valuable time point information or utilize additional modalities, whereas methods that address these different data views cannot be combined or do not scale. Here we present CellRank 2, a versatile and scalable framework to study cellular fate using multiview single-cell data of up to millions of cells in a unified fashion. CellRank 2 consistently recovers terminal states and fate probabilities across data modalities in human hematopoiesis and endodermal development. Our framework also allows combining transitions within and across experimental time points, a feature we use to recover genes promoting medullary thymic epithelial cell formation during pharyngeal endoderm development. Moreover, we enable estimating cell-specific transcription and degradation rates from metabolic-labeling data, which we apply to an intestinal organoid system to delineate differentiation trajectories and pinpoint regulatory strategies.

ATP dependence of decision-making capacity in a fine-grained model of gene regulatory networks

Cellular decision-making is fundamental to life, from developmental biology to environmental responses and antimicrobial resistance. Many regulatory processes that drive cellular decisions rely on gene expression, which requires energy in the form of ATP. As even genetically identical cells can have dramatically different ATP levels, bioenergetic status can be an important source of variability in cellular decision-making. Existing studies have investigated this energy dependence but often use coarse-grained modelling approaches (which are not always readily connected to the underlying molecular processes of gene regulation). Here, we use a fine-grained mathematical model of gene expression in a two-gene decision-making regulatory network to explore cellular decision-making capacity as energy availability varies. We simulate both a deterministic model, to explore the emergence of different cell fate attractors as ATP levels vary, and a stochastic case to explore how ATP influences the noisy dynamics of stochastic cell decision-making. Higher energy levels typically support increased decision-making capacity (higher numbers of, and more separated, cell states that can be selected), and the fine-grained modelling reveals some differences in behaviour from previous coarse-grained modelling approaches.

Feedback between stochastic gene networks and population dynamics enables cellular decision-making.

Phenotypic selection occurs when genetically identical cells are subject to different reproductive abilities due to cellular noise. Such noise arises from fluctuations in reactions synthesizing proteins and plays a crucial role in how cells make decisions and respond to stress or drugs. We propose a general stochastic agent-based model for growing populations capturing the feedback between gene expression and cell division dynamics. We devise a finite state projection approach to analyze gene expression and division distributions and infer selection from single-cell data in mother machines and lineage trees. We use the theory to quantify selection in multi-stable gene expression networks and elucidate that the trade-off between phenotypic switching and selection enables robust decision-making essential for synthetic circuits and developmental lineage decisions. Using live-cell data, we demonstrate that combining theory and inference provides quantitative insights into bet-hedging-like response to DNA damage and adaptation during antibiotic exposure in Escherichia coli.

Living in a noisy world-origins of gene expression noise and its impact on cellular decision-making.

The expression level of a gene can vary between genetically identical cells under the same environmental condition-a phenomenon referred to as gene expression noise. Several studies have now elucidated a central role of transcription factors in the generation of expression noise. Transcription factors, as the key components of gene regulatory networks, drive many important cellular decisions in response to cellular and environmental signals. Therefore, a very relevant question is how expression noise impacts gene regulation and influences cellular decision-making. In this Review, we summarize the current understanding of the molecular origins of expression noise, highlighting the role of transcription factors in this process, and discuss the ways in which noise can influence cellular decision-making. As advances in single-cell technologies open new avenues for studying expression noise as well as gene regulatory circuits, a better understanding of the influence of noise on cellular decisions will have important implications for many biological processes.

A network-based modeling framework reveals the core signal transduction network underlying high carbon dioxide-induced stomatal closure in guard cells.

Stomata are pores on plant aerial surfaces, each bordered by a pair of guard cells. They control gas exchange vital for plant survival. Understanding how guard cells respond to environmental signals such as atmospheric carbon dioxide (CO2) levels is not only insightful to fundamental biology but also relevant to real-world issues of crop productivity under global climate change. In the past decade, multiple important signaling elements for stomatal closure induced by elevated CO2 have been identified. Yet, there is no comprehensive understanding of high CO2-induced stomatal closure. In this work, we assemble a cellular signaling network underlying high CO2-induced stomatal closure by integrating evidence from a comprehensive literature analysis. We further construct a Boolean dynamic model of the network, which allows in silico simulation of the stomatal closure response to high CO2 in wild-type Arabidopsis thaliana plants and in cases of pharmacological or genetic manipulation of network nodes. Our model has a 91% accuracy in capturing known experimental observations. We perform network-based logical analysis and reveal a feedback core of the network, which dictates cellular decisions in closure response to high CO2. Based on these analyses, we predict and experimentally confirm that applying nitric oxide (NO) induces stomatal closure in ambient CO2 and causes hypersensitivity to elevated CO2. Moreover, we predict a negative regulatory relationship between NO and the protein phosphatase ABI2 and find experimentally that NO inhibits ABI2 phosphatase activity. The experimental validation of these model predictions demonstrates the effectiveness of network-based modeling and highlights the decision-making role of the feedback core of the network in signal transduction. We further explore the model's potential in predicting targets of signaling elements not yet connected to the CO2 network. Our combination of network science, in silico model simulation, and experimental assays demonstrates an effective interdisciplinary approach to understanding system-level biology.

Reconstructing axial progenitor field dynamics in mouse stem cell-derived embryoids

Embryogenesis requires substantial coordination to translate genetic programs to the collective behavior of differentiating cells, but understanding how cellular decisions control tissue morphology remains conceptually and technically challenging. Here, we combine continuous Cas9-based molecular recording with a mouse embryonic stem cell-based model of the embryonic trunk to build single-cell phylogenies that describe the behavior of transient, multipotent neuro-mesodermal progenitors (NMPs) as they commit into neural and somitic cell types. We find that NMPs show subtle transcriptional signatures related to their recent differentiation and contribute to downstream lineages through a surprisingly broad distribution of individual fate outcomes. Although decision-making can be heavily influenced by environmental cues to induce morphological phenotypes, axial progenitors intrinsically mature over developmental time to favor the neural lineage. Using these data, we present an experimental and analytical framework for exploring the non-homeostatic dynamics of transient progenitor populations as they shape complex tissues during critical developmental windows.

Governing principles of transcriptional logic out of equilibrium

To survive, adapt, and develop, cells respond to external and internal stimuli by tightly regulating transcription. Transcriptional regulation involves the combinatorial binding of a repertoire of transcription factors to DNA, which often results in switch-like binary outputs akin to Boolean logic gates. Recent experimental studies have demonstrated that in eukaryotes, transcription factor binding to DNA often involves energy expenditure, thereby driving the system out of equilibrium. The governing principles of transcriptional logic operations out of equilibrium remain unexplored. Here, we employ a simple two-input, single-locus model of transcription that can accommodate both equilibrium and nonequilibrium mechanisms. Using this model, we find that nonequilibrium regimes can give rise to all the logic operations accessible in equilibrium. Strikingly, energy expenditure alters the regulatory function of the two transcription factors in a mutually exclusive manner. This allows for the emergence of new logic operations that are inaccessible in equilibrium. Overall, our results show that energy expenditure can expand the range of cellular decision-making without the need for more complex promoter architectures.