Cellular Decision-making Processes Research Articles

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.

Exploring the interplay of intrinsic fluctuation and complexity in intracellular calcium dynamics

The concentration of intracellular calcium ion (Ca2＋) exhibits complex oscillations, including bursting and chaos, as observed experimentally. These dynamics are influenced by inherent fluctuations within cells, which serve as crucial determinants in cellular decision-making processes and fate determination. In this study, we systematically explore the interplay between intrinsic fluctuation and the complexity of various dynamic states of intracellular cytosolic Ca2＋. To investigate this interplay, we employ complexity measures such as permutation entropy and statistical complexity. Using a stochastic chemical Langevin equation, we simulate the dynamics of cytosolic Ca2＋. Our findings reveal that permutation entropy and statistical complexity effectively characterize the diverse, dynamic states of cytosolic Ca2＋ and illustrate their interactions with intrinsic fluctuation. Permutation entropy analysis elucidates that the chaotic state is more sensitive to intrinsic fluctuation than the other periodic states. Furthermore, we identify distinct states of cytosolic Ca2＋ occupying specific locations within the theoretical bounds of the complexity–entropy causality plane. These locations indicate varying complexity and information content as intrinsic fluctuation varies. When adjusting the permutation order, the statistical complexity measure for the different periodic and chaotic states exhibits peaks in an intermediate range of intrinsic fluctuation values. Additionally, we identify scale-free or fractal patterns in this intermediate range, which are further corroborated by multifractal detrended fluctuation analysis. These high-complexity states likely correspond to optimal Ca2＋ dynamics with biological significance, revealing rich and complex dynamics shaped by the interplay of intrinsic fluctuation and complexity. Our investigation enhances our understanding of the intricate regulatory mechanisms governing intracellular Ca2＋ dynamics and how intrinsic fluctuation modulates the complexity of the various dynamics that play crucial roles in biological cells.

Complex interplay of MAPK signaling in cellular stress responses and apoptosis regulation

The manuscript focusing on the nuanced roles of MAPK signaling pathways, particularly JNK and p38, in cellular stress responses and decision-making processes involved in apoptosis and survival strategies, is essential for understanding their implications in disease mechanisms and therapeutic potentials.

The LKB1–TSSK1B axis controls YAP phosphorylation to regulate the Hippo–YAP pathway

The Hippo pathway’s main effector, Yes-associated protein (YAP), plays a crucial role in tumorigenesis as a transcriptional coactivator. YAP’s phosphorylation by core upstream components of the Hippo pathway, such as mammalian Ste20 kinase 1/2 (MST1/2), mitogen-activated protein kinase kinase kinase kinases (MAP4Ks), and their substrate, large tumor suppressor 1/2 (LATS1/2), influences YAP’s subcellular localization, stability, and transcriptional activity. However, recent research suggests the existence of alternative pathways that phosphorylate YAP, independent of these core upstream Hippo pathway components, raising questions about additional means to inactivate YAP. In this study, we present evidence demonstrating that TSSK1B, a calcium/calmodulin-dependent protein kinase (CAMK) superfamily member, is a negative regulator of YAP, suppressing cellular proliferation and oncogenic transformation. Mechanistically, TSSK1B inhibits YAP through two distinct pathways. Firstly, the LKB1–TSSK1B axis directly phosphorylates YAP at Ser94, inhibiting the YAP–TEAD complex’s formation and suppressing its target genes’ expression. Secondly, the TSSK1B–LATS1/2 axis inhibits YAP via phosphorylation at Ser127. Our findings reveal the involvement of TSSK1B-mediated molecular mechanisms in the Hippo–YAP pathway, emphasizing the importance of multilevel regulation in critical cellular decision-making processes.

Single-Cell Measurements and Modeling and Computation of Decision-Making Errors in a Molecular Signaling System with Two Output Molecules.

A cell constantly receives signals and takes different fates accordingly. Given the uncertainty rendered by signal transduction noise, a cell may incorrectly perceive these signals. It may mistakenly behave as if there is a signal, although there is none, or may miss the presence of a signal that actually exists. In this paper, we consider a signaling system with two outputs, and introduce and develop methods to model and compute key cell decision-making parameters based on the two outputs and in response to the input signal. In the considered system, the tumor necrosis factor (TNF) regulates the two transcription factors, the nuclear factor κB (NFκB) and the activating transcription factor-2 (ATF-2). These two system outputs are involved in important physiological functions such as cell death and survival, viral replication, and pathological conditions, such as autoimmune diseases and different types of cancer. Using the introduced methods, we compute and show what the decision thresholds are, based on the single-cell measured concentration levels of NFκB and ATF-2. We also define and compute the decision error probabilities, i.e., false alarm and miss probabilities, based on the concentration levels of the two outputs. By considering the joint response of the two outputs of the signaling system, one can learn more about complex cellular decision-making processes, the corresponding decision error rates, and their possible involvement in the development of some pathological conditions.

A hybrid physics-based and data-driven framework for cellular biological systems: Application to the morphogenesis of organoids

How cells orchestrate their cellular functions remains a crucial question to unravel how they organize in different patterns. We present a framework based on artificialintelligence to advance the understanding of how cell functions are coordinated spatially and temporally in biological systems. It consists of a hybrid physics-based model that integrates both mechanical interactions and cell functions with a data-driven model that regulates the cellular decision-making process through a deep learning algorithm trained on image data metrics. To illustrate our approach, we used data from 3D cultures of murine pancreatic ductal adenocarcinoma cells (PDAC) grown in Matrigel as tumor organoids. Our approach allowedus to find the underlying principles through which cells activate different cell processes to self-organize in different patterns according to the specific microenvironmental conditions. The framework proposed here expands the tools for simulating biological systems at the cellular level, providing a novel perspective to unravel morphogenetic patterns.

Grainyhead-like (Grhl) Target Genes in Development and Cancer.

Grainyhead-like (GRHL) factors are essential, highly conserved transcription factors (TFs) that regulate processes common to both natural cellular behaviours during embryogenesis, and de-regulation of growth and survival pathways in cancer. Serving to drive the transcription, and therefore activation of multiple co-ordinating pathways, the three GRHL family members (GRHL1-3) are a critical conduit for modulating the molecular landscape that guides cellular decision-making processes during proliferation, epithelial-mesenchymal transition (EMT) and migration. Animal models and in vitro approaches harbouring GRHL loss or gain-of-function are key research tools to understanding gene function, which gives confidence that resultant phenotypes and cellular behaviours may be translatable to humans. Critically, identifying and characterising the target genes to which these factors bind is also essential, as they allow us to discover and understand novel genetic pathways that could ultimately be used as targets for disease diagnosis, drug discovery and therapeutic strategies. GRHL1-3 and their transcriptional targets have been shown to drive comparable cellular processes in Drosophila, C. elegans, zebrafish and mice, and have recently also been implicated in the aetiology and/or progression of a number of human congenital disorders and cancers of epithelial origin. In this review, we will summarise the state of knowledge pertaining to the role of the GRHL family target genes in both development and cancer, primarily through understanding the genetic pathways transcriptionally regulated by these factors across disparate disease contexts.

Cellular feedback dynamics and multilevel regulation driven by the hippo pathway.

The Hippo pathway is a dynamic cellular signalling nexus that regulates differentiation and controls cell proliferation and death. If the Hippo pathway is not precisely regulated, the functionality of the upstream kinase module is impaired, which increases nuclear localisation and activity of the central effectors, the transcriptional co-regulators YAP and TAZ. Pathological YAP and TAZ hyperactivity consequently cause cancer, fibrosis and developmental defects. The Hippo pathway controls an array of fundamental cellular processes, including adhesion, migration, mitosis, polarity and secretion of a range of biologically active components. Recent studies highlight that spatio-temporal regulation of Hippo pathway components are central to precisely controlling its context-dependent dynamic activity. Several levels of feedback are integrated into the Hippo pathway, which is further synergized with interactors outside of the pathway that directly regulate specific Hippo pathway components. Likewise, Hippo core kinases also ‘moonlight’ by phosphorylating multiple substrates beyond the Hippo pathway and thereby integrates further flexibility and robustness in the cellular decision-making process. This topic is still in its infancy but promises to reveal new fundamental insights into the cellular regulation of this therapeutically important pathway. We here highlight recent advances emphasising feedback dynamics and multilevel regulation of the Hippo pathway with a focus on mitosis and cell migration, as well as discuss potential productive future research avenues that might reveal novel insights into the overall dynamics of the pathway.

Utilizing Split Fluorescent Proteins to Visualize Binary Cell Fate Decisions

Binary cell fate decisions serve at a cornerstone of cellular decision-making processes during embryonic development. Understanding and studying these decisions require an intimate knowledge of the spatial and temporal expression dynamics of critical genes. Split fluorescent proteins (sFP) can serve as a novel tool to study these binary cell fate decisions, with unique applications such as the potential to amplify weak genetic signals. Ultimately, sFPs can be utilized to revolutionize the study of protein-protein interactions during embryonic development and beyond.

Behavioural analysis of single-cell aneural ciliate, Stentor roeseli, using machine learning approaches.

There is still a significant gap between our understanding of neural circuits and the behaviours they compute-i.e. the computations performed by these neural networks (Carandini 2012 Nat. Neurosci.15, 507-509. (doi:10.1038/nn.3043)). Cellular decision-making processes, learning, behaviour and memory formation-all that have been only associated with animals with neural systems-have also been observed in many unicellular aneural organisms, namely Physarum, Paramecium and Stentor (Tang & Marshall2018 Curr. Biol.28, R1180-R1184. (doi:10.1016/j.cub.2018.09.015)). As these are fully functioning organisms, yet being unicellular, there is a much better chance to elucidate the detailed mechanisms underlying these learning processes in these organisms without the complications of highly interconnected neural circuits. An intriguing learning behaviour observed in Stentor roeseli (Jennings 1902 Am. J. Physiol. Legacy Content8, 23-60. (doi:10.1152/ajplegacy.1902.8.1.23)) when stimulated with carmine has left scientists puzzled for more than a century. So far, none of the existing learning paradigm can fully encapsulate this particular series of five characteristic avoidance reactions. Although we were able to observe all responses described in the literature and in a previous study (Dexter et al. 2019), they do not conform to any particular learning model. We then investigated whether models inferred from machine learning approaches, including decision tree, random forest and feed-forward artificial neural networks could infer and predict the behaviour of S. roeseli. Our results showed that an artificial neural network with multiple 'computational' neurons is inefficient at modelling the single-celled ciliate's avoidance reactions. This has highlighted the complexity of behaviours in aneural organisms. Additionally, this report will also discuss the significance of elucidating molecular details underlying learning and decision-making processes in these unicellular organisms, which could offer valuable insights that are applicable to higher animals.

Hippo signalling during development.

The Hippo signalling pathway and its transcriptional co-activator targets Yorkie/YAP/TAZ first came to attention because of their role in tissue growth control. Over the past 15 years, it has become clear that, like other developmental pathways (e.g. the Wnt, Hedgehog and TGFβ pathways), Hippo signalling is a ‘jack of all trades’ that is reiteratively used to mediate a range of cellular decision-making processes from proliferation, death and morphogenesis to cell fate determination. Here, and in the accompanying poster, we briefly outline the core pathway and its regulation, and describe the breadth of its roles in animal development.

Excitable dynamics of Ras triggers spontaneous symmetry breaking of PIP3 signaling in motile cells.

ABSTRACTSpontaneous cell movement is underpinned by an asymmetric distribution of signaling molecules including small G proteins and phosphoinositides on the cell membrane. However, the molecular network necessary for spontaneous symmetry breaking has not been fully elucidated. Here, we report that, in Dictyostelium discoideum, the spatiotemporal dynamics of GTP bound Ras (Ras-GTP) breaks the symmetry due its intrinsic excitability even in the absence of extracellular spatial cues and downstream signaling activities. A stochastic excitation of local and transient Ras activation induced phosphatidylinositol (3,4,5)-trisphosphate (PIP3) accumulation via direct interaction with Phosphoinositide 3-kinase (PI3K), causing tightly coupled traveling waves that propagated along the membrane. Comprehensive phase analysis of the waves of Ras-GTP and PIP3 metabolism-related molecules revealed the network structure of the excitable system including positive-feedback regulation of Ras-GTP by the downstream PIP3. A mathematical model reconstituted a series of the observed symmetry-breaking phenomena, illustrating the essential involvement of Ras excitability in the cellular decision-making process.

How can a binary switch within the pars tuberalis control seasonal timing of reproduction?

Life in seasonally changing environments is challenging. Biological systems have to not only respond directly to the environment, but also schedule life history events in anticipation of seasonal changes. The cellular and molecular basis of how these events are scheduled is unknown. Cellular decision-making processes in response to signals above certain thresholds regularly occur i.e. cellular fate determination, apoptosis and firing of action potentials. Binary switches, the result of cellular decision-making processes, are defined as a change in phenotype between two stable states. A recent study presents evidence of a binary switch operating in the pars tuberalis (PT) of the pituitary, seemingly timing seasonal reproduction in sheep. Though, how a binary switch would allow for anticipation of seasonal environmental changes, not just direct responsiveness, is unclear. The purpose of this review is to assess the evidence for a binary switching mechanism timing seasonal reproduction and to hypothesize how a binary switch would allow biological processes to be timed over weeks to years. I draw parallels with mechanisms used in development, cell fate determination and seasonal timing in plants. I propose that the adult PT is a plastic tissue, showing a seasonal cycle of cellular differentiation, and that the underlying processes are likely to be epigenetic. Therefore, considering the mechanisms behind adult cellular plasticity offers a framework to hypothesize how a long-term timer functions within the PT.

Abstract LB-330: Investigating how extracellular signals contribute to single cancer cell heterogeneity using near-infrared quantum dots

Abstract Cancer cells heterogeneity presents a major roadblock in clinical diagnostics and therapy. Investigations of tumor cell heterogeneity using single-cell analytical techniques have revealed not only the presence of multiple clonal subpopulations but also phenotypic variability among genetically identical cells. This heterogeneity plays a critical role in therapeutics, as population extrema, rather than cells near the ensemble mean, can dominate pathogenesis as well as drug resistance. To characterize single cell heterogeneity, multiple techniques quantifying single-cell gene expression have been developed. However, there's a lack of experimental techniques to measure how cellular decision-making processes underlying population variability derive from extracellular biochemical input signals, such as peptide growth factors, which cannot be measured at the single-cell level. Here, to digitally count growth factors in single cells, we develop a novel method combining fluorescent quantum dots and calibrated three-dimensional deconvolution microscopy (QDC-3DM). Using quantum dots with near-infrared emission to overcome intrinsic cellular autofluorescence, we were able to detect and accurately count individual quantum dots conjugated to epidermal growth factor (EGF) using their fluorescent intensities. Analyzing triple-negative breast cancer cells (MDA-MB-231) with QDC-3DM, we observed that single-cell heterogeneity in growth factor stimulation led to heterogeneity in receptor activation. When treating cells with increasing concentration of phamarcological inhibitor blocking receptor activation, we observed a proportional increase in receptor activation heterogeneity. Together, our results indicate that external stimulation contributes to signaling variation and drug response variation at the single-cell level. We anticipate that QDC-3DM can be applied to any peptidic ligands to study how extracellular signaling stimulation contributes to phenotypic variability to provide new insight into cancer cell heterogeneity that plays a critical role in therapeutic resistance. Citation Format: Phuong Le, Brian C. Baculis, Hee Jung Chung, Kristpher Kilian, Andrew M. Smith. Investigating how extracellular signals contribute to single cancer cell heterogeneity using near-infrared quantum dots [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr LB-330.

ScEpath: energy landscape-based inference of transition probabilities and cellular trajectories from single-cell transcriptomic data.

MotivationSingle-cell RNA-sequencing (scRNA-seq) offers unprecedented resolution for studying cellular decision-making processes. Robust inference of cell state transition paths and probabilities is an important yet challenging step in the analysis of these data.ResultsHere we present scEpath, an algorithm that calculates energy landscapes and probabilistic directed graphs in order to reconstruct developmental trajectories. We quantify the energy landscape using ‘single-cell energy’ and distance-based measures, and find that the combination of these enables robust inference of the transition probabilities and lineage relationships between cell states. We also identify marker genes and gene expression patterns associated with cell state transitions. Our approach produces pseudotemporal orderings that are—in combination—more robust and accurate than current methods, and offers higher resolution dynamics of the cell state transitions, leading to new insight into key transition events during differentiation and development. Moreover, scEpath is robust to variation in the size of the input gene set, and is broadly unsupervised, requiring few parameters to be set by the user. Applications of scEpath led to the identification of a cell-cell communication network implicated in early human embryo development, and novel transcription factors important for myoblast differentiation. scEpath allows us to identify common and specific temporal dynamics and transcriptional factor programs along branched lineages, as well as the transition probabilities that control cell fates.Availability and implementationA MATLAB package of scEpath is available at https://github.com/sqjin/scEpath.Supplementary information Supplementary data are available at Bioinformatics online.

Engineering Approaches to Study Cellular Decision Making.

In their native environment, cells are immersed in a complex milieu of biochemical and biophysical cues. These cues may include growth factors, the extracellular matrix, cell-cell contacts, stiffness, and topography, and they are responsible for regulating cellular behaviors such as adhesion, proliferation, migration, apoptosis, and differentiation. The decision-making process used to convert these extracellular inputs into actions is highly complex and sensitive to changes both in the type of individual cue (e.g., growth factor dose/level, timing) and in how these individual cues are combined (e.g., homotypic/heterotypic combinations). In this review, we highlight recent advances in the development of engineering-based approaches to study the cellular decision-making process. Specifically, we discuss the use of biomaterial platforms that enable controlled and tailored delivery of individual and combined cues, as well as the application of computational modeling to analyses of the complex cellular decision-making networks.

PAF and EZH2 Induce Wnt/β-Catenin Signaling Hyperactivation

Fine control of Wnt signaling is essential for various cellular and developmental decision-making processes. However, deregulation of Wnt signaling leads to pathological consequences, one of which is cancer. Here, we identify a function of PAF, a component of translesion DNA synthesis, in modulating Wnt signaling. PAF is specifically overexpressed in colon cancer cells and intestinal stem cells and is required for colon cancer cell proliferation. In Xenopus laevis, ventrovegetal expression of PAF hyperactivates Wnt signaling, developing a secondary axis with β-catenin target gene upregulation. Upon Wnt signaling activation, PAF dissociates from PCNA and binds directly to β-catenin. Then, PAF recruits EZH2 to the β-catenin transcriptional complex and specifically enhances Wnt target gene transactivation, independently of EZH2's methyltransferase activity. In mice, conditional expression of PAF induces intestinal neoplasia via Wnt signaling hyperactivation. Our studies reveal an unexpected role of PAF in regulating Wnt signaling and propose a regulatory mechanism of Wnt signaling during tumorigenesis.

Erratum: Gene Set Enrichment Analysis Identifies LIF as a Negative Regulator of Human Th2 Cell Differentiation

In this study we show that IL-4 is crucial during reinforcement window of human Th2 differentiation for optimal Th2 development. We have also shown here that during this stage, IL-4 helps in cellular decision-making process of differentiation versus proliferation. We have combined computational and experimental methods to analyze Th2 transcription network to name novel players of the process of Th2 differentiation. Here we report that LIF through STAT3 negatively regulates Th2 differentiation. This approach can be generalized to analyze “omics” data to identify key regulatory modules.

Gene Set Enrichment Analysis Identifies LIF as a Negative Regulator of Human Th2 Cell Differentiation

In this study we show that IL-4 is crucial during reinforcement window of human Th2 differentiation for optimal Th2 development. We have also shown here that during this stage, IL-4 helps in cellular decision-making process of differentiation versus proliferation. We have combined computational and experimental methods to analyze Th2 transcription network to name novel players of the process of Th2 differentiation. Here we report that LIF through STAT3 negatively regulates Th2 differentiation. This approach can be generalized to analyze “omics” data to identify key regulatory modules.

Temporal competition between differentiation programs determines cell fate choice

Multipotent differentiation, where cells adopt one of several possible fates, occurs in diverse systems ranging from bacteria to mammals. This decision-making process is driven by multiple differentiation programs that operate simultaneously in the cell. How these programs interact to govern cell fate choice is poorly understood. To investigate this issue, we simultaneously measured activities of the competing sporulation and competence programs in single Bacillus subtilis cells. This approach revealed that these competing differentiation programs progress independently without cross-regulation before the decision point. Cells seem to arrive at a fate choice through differences in the relative timing between the two programs. To test this proposed dynamic mechanism, we altered the relative timing by engineering artificial cross-regulation between the sporulation and competence circuits. Results suggest a simple model that does not require a checkpoint or intricate cross-regulation before cellular decision-making. Rather, cell fate choice appears to be the outcome of a 'molecular race' between differentiation programs that compete in time, providing a simple dynamic mechanism for decision-making.