Integrated Platform for Monitoring Single-cell MAPK Kinetics in Computer-controlled Temporal Stimulations

Brain organoid-on-a-chip: A next-generation human brain avatar for recapitulating human brain physiology and pathology

Neurodegenerative diseases and neurodevelopmental disorders have become increasingly prevalent; however, the development of new pharmaceuticals to treat these diseases has lagged. Animal models have been extensively utilized to identify underlying mechanisms and to validate drug efficacies, but they possess inherent limitations including genetic heterogeneity with humans. To overcome these limitations, human cell-based in vitro brain models including brain-on-a-chip and brain organoids have been developed. Each technique has distinct advantages and disadvantages in terms of the mimicry of structure and microenvironment, but each technique could not fully mimic the structure and functional aspects of the brain tissue. Recently, a brain organoid-on-a-chip (BOoC) platform has emerged, which merges brain-on-a-chip and brain organoids. BOoC can potentially reflect the detailed structure of the brain tissue, vascular structure, and circulation of fluid. Hence, we summarize recent advances in BOoC as a human brain avatar and discuss future perspectives. BOoC platform can pave the way for mechanistic studies and the development of pharmaceuticals to treat brain diseases in future.

Signalling dynamics, cell decisions, and homeostatic control in health and disease

Cell signalling engenders cells with the capability to receive and process information from the intracellular and extracellular environments, trigger and execute biological responses, and communicate with each other. Ultimately, cell signalling is responsible for maintaining homeostasis at the cellular, tissue and systemic level. For this reason, cell signalling is a topic of intense research efforts aimed to elucidate how cells coordinate transitions between states in developing and adult organisms in physiological and pathological conditions. Here, we review current knowledge of how cell signalling operates at multiple spatial and temporal scales, focusing on how single-cell analytical techniques reveal mechanisms underpinning cell-to-cell variability, signalling plasticity, and collective cellular responses.

Signalling dynamics in embryonic development

In multicellular organisms, cellular behaviour is tightly regulated to allow proper embryonic development and maintenance of adult tissue. A critical component in this control is the communication between cells via signalling pathways, as errors in intercellular communication can induce developmental defects or diseases such as cancer. It has become clear over the last years that signalling is not static but varies in activity over time. Feedback mechanisms present in every signalling pathway lead to diverse dynamic phenotypes, such as transient activation, signal ramping or oscillations, occurring in a cell type- and stage-dependent manner. In cells, such dynamics can exert various functions that allow organisms to develop in a robust and reproducible way. Here, we focus on Erk, Wnt and Notch signalling pathways, which are dynamic in several tissue types and organisms, including the periodic segmentation of vertebrate embryos, and are often dysregulated in cancer. We will discuss how biochemical processes influence their dynamics and how these impact on cellular behaviour within multicellular systems.

A predictive model of gene expression reveals the role of network motifs in the mating response of yeast

Cells use signaling pathways to receive and process information about their environment. These nonlinear systems rely on feedback and feedforward regulation to respond appropriately to changing environmental conditions. Mathematical models describing signaling pathways often lack predictive power because they are not trained on data that encompass the diverse time scales on which these regulatory mechanisms operate. We addressed this limitation by measuring transcriptional changes induced by the mating response in Saccharomyces cerevisiae exposed to different dynamic patterns of pheromone. We found that pheromone-induced transcription persisted after pheromone removal and showed long-term adaptation upon sustained pheromone exposure. We developed a model of the regulatory network that captured both characteristics of the mating response. We fit this model to experimental data with an evolutionary algorithm and used the parameterized model to predict scenarios for which it was not trained, including different temporal stimulus profiles and genetic perturbations to pathway components. Our model allowed us to establish the role of four architectural elements of the network in regulating gene expression. These network motifs are incoherent feedforward, positive feedback, negative feedback, and repressor binding. Experimental and computational perturbations to these network motifs established a specific role for each in coordinating the mating response to persistent and dynamic stimulation.

Expanding the Toolkit of Fluorescent Biosensors for Studying Mitogen Activated Protein Kinases in Plants

Mitogen-activated protein kinases (MAPKs) are key regulators of numerous biological processes in plants. To better understand the mechanisms by which these kinases function, high resolution measurement of MAPK activation kinetics in different biological contexts would be beneficial. One method to measure MAPK activation in plants is via fluorescence-based genetically-encoded biosensors, which can provide real-time readouts of the temporal and spatial dynamics of kinase activation in living tissue. Although fluorescent biosensors have been widely used to study MAPK dynamics in animal cells, there is currently only one MAPK biosensor that has been described for use in plants. To facilitate creation of additional plant-specific MAPK fluorescent biosensors, we report the development of two new tools: an in vitro assay for efficiently characterizing MAPK docking domains and a translocation-based kinase biosensor for use in plants. The implementation of these two methods has allowed us to expand the available pool of plant MAPK biosensors, while also providing a means to generate more specific and selective MAPK biosensors in the future. Biosensors developed using these methods have the potential to enhance our understanding of the roles MAPKs play in diverse plant signaling networks affecting growth, development, and stress response.

Stimulation strength controls the rate of initiation but not the molecular organisation of TCR-induced signalling

Millions of naïve T cells with different TCRs may interact with a peptide-MHC ligand, but very few will activate. Remarkably, this fine control is orchestrated using a limited set of intracellular machinery. It remains unclear whether changes in stimulation strength alter the programme of signalling events leading to T cell activation. Using mass cytometry to simultaneously measure multiple signalling pathways during activation of murine CD8+ T cells, we found a programme of distal signalling events that is shared, regardless of the strength of TCR stimulation. Moreover, the relationship between transcription of early response genes Nr4a1 and Irf8 and activation of the ribosomal protein S6 is also conserved across stimuli. Instead, we found that stimulation strength dictates the rate with which cells initiate signalling through this network. These data suggest that TCR-induced signalling results in a coordinated activation program, modulated in rate but not organization by stimulation strength.

A System for Analog Control of Cell Culture Dynamics to Reveal Capabilities of Signaling Networks

Cellular microenvironments are dynamic. When exposed to extracellular cues, such as changing concentrations of inflammatory cytokines, cells activate signaling networks that mediate fate decisions. Exploring responses broadly to time-varying microenvironments is essential to understand the information transmission capabilities of signaling networks and how dynamic milieus influence cell fate decisions. Here, we present a gravity-driven cell culture and demonstrate that the system accurately produces user-defined concentration profiles for one or more dynamic stimuli. As proof of principle, we monitor nuclear factor-κB activation in single cells exposed to dynamic cytokine stimulation and reveal context-dependent sensitivity and uncharacterized single-cell response classes distinct from persistent stimulation. Using computational modeling, we find that cell-to-cell variability in feedback rates within the signaling network contributes to different response classes. Models are validated using inhibitors to predictably modulate response classes in live cells exposed to dynamic stimuli. These hidden capabilities, uncovered through dynamic stimulation, provide opportunities to discover and manipulate signaling mechanisms.