CODEX, a neural network approach to explore signaling dynamics landscapes

The mitogen-activated protein kinase network, wired to dynamically function at multiple scales

The mitogen-activated protein kinase (MAPK)/extracellular signal-regulated kinase (ERK) signaling network is a key transducer of signals from various receptors, including receptor tyrosine kinases (RTKs). It controls cell-cycle entry, survival, motility, differentiation, as well as other fates. After four decades of studying this pathway with biochemical methods, the use of fluorescent biosensors has revealed dynamic behaviors such as ERK pulsing, oscillations, and amplitude-modulated activity. Different RTKs equip the MAPK network with specific feedback mechanisms to encode these different ERK dynamics, which are then subsequently decoded into cytoskeletal events and transcriptional programs, actuating cellular fates. Recently, collective ERK wave behaviors have been observed in multiple systems to coordinate cytoskeletal dynamics with fate decisions within cell collectives. This emphasizes that a correct understanding of this pathway requires studying it at multiple scales.

Getting real about synthetic data ethics : Are AI ethics principles a good starting point for synthetic data ethics?

Synthetic data promises to be a viable alternative when data collection and data sharing may not be feasible or cost effective, but it raises distinct ethical issue that merit serious consideration.

Machine learning inference of continuous single-cell state transitions during myoblast differentiation and fusion

Cells modify their internal organization during continuous state transitions, supporting functions from cell division to differentiation. However, tools to measure dynamic physiological states of individual transitioning cells are lacking. We combined live-cell imaging and machine learning to monitor ERK1/2-inhibited primary murine skeletal muscle precursor cells, that transition rapidly and robustly from proliferating myoblasts to post-mitotic myocytes and then fuse, forming multinucleated myotubes. Our models, trained using motility or actin intensity features from single-cell tracking data, effectively tracked real-time continuous differentiation, revealing that differentiation occurs 7.5-14.5 h post induction, followed by fusion ~3 h later. Co-inhibition of ERK1/2 and p38 led to differentiation without fusion. Our model inferred co-inhibition leads to terminal differentiation, indicating that p38 is specifically required for transitioning from terminal differentiation to fusion. Our model also predicted that co-inhibition leads to changes in actin dynamics. Mass spectrometry supported these in silico predictions and suggested novel fusion and maturation regulators downstream of differentiation. Collectively, this approach can be adapted to various biological processes to uncover novel links between dynamic single-cell states and their functional outcomes.

A guide to ERK dynamics, part 1: mechanisms and models

Extracellular signal-regulated kinase (ERK) has long been studied as a key driver of both essential cellular processes and disease. A persistent question has been how this single pathway is able to direct multiple cell behaviors, including growth, proliferation, and death. Modern biosensor studies have revealed that the temporal pattern of ERK activity is highly variable and heterogeneous, and critically, that these dynamic differences modulate cell fate. This two-part review discusses the current understanding of dynamic activity in the ERK pathway, how it regulates cellular decisions, and how these cell fates lead to tissue regulation and pathology. In part 1, we cover the optogenetic and live-cell imaging technologies that first revealed the dynamic nature of ERK, as well as current challenges in biosensor data analysis. We also discuss advances in mathematical models for the mechanisms of ERK dynamics, including receptor-level regulation, negative feedback, cooperativity, and paracrine signaling. While hurdles still remain, it is clear that higher temporal and spatial resolution provide mechanistic insights into pathway circuitry. Exciting new algorithms and advanced computational tools enable quantitative measurements of single-cell ERK activation, which in turn inform better models of pathway behavior. However, the fact that current models still cannot fully recapitulate the diversity of ERK responses calls for a deeper understanding of network structure and signal transduction in general.

Morphodynamical cell state description via live-cell imaging trajectory embedding

Time-lapse imaging is a powerful approach to gain insight into the dynamic responses of cells, but the quantitative analysis of morphological changes over time remains challenging. Here, we exploit the concept of "trajectory embedding" to analyze cellular behavior using morphological feature trajectory histories-that is, multiple time points simultaneously, rather than the more common practice of examining morphological feature time courses in single timepoint (snapshot) morphological features. We apply this approach to analyze live-cell images of MCF10A mammary epithelial cells after treatment with a panel of microenvironmental perturbagens that strongly modulate cell motility, morphology, and cell cycle behavior. Our morphodynamical trajectory embedding analysis constructs a shared cell state landscape revealing ligand-specific regulation of cell state transitions and enables quantitative and descriptive models of single-cell trajectories. Additionally, we show that incorporation of trajectories into single-cell morphological analysis enables (i) systematic characterization of cell state trajectories, (ii) better separation of phenotypes, and (iii) more descriptive models of ligand-induced differences as compared to snapshot-based analysis. This morphodynamical trajectory embedding is broadly applicable to the quantitative analysis of cell responses via live-cell imaging across many biological and biomedical applications.

Quantifying the phenotypic information in mRNA abundance

Quantifying the dependency between mRNA abundance and downstream cellular phenotypes is a fundamental open problem in biology. Advances in multimodal single‐cell measurement technologies provide an opportunity to apply new computational frameworks to dissect the contribution of individual genes and gene combinations to a given phenotype. Using an information theory approach, we analyzed multimodal data of the expression of 83 genes in the Ca²⁺ signaling network and the dynamic Ca²⁺ response in the same cell. We found that the overall expression levels of these 83 genes explain approximately 60% of Ca²⁺ signal entropy. The average contribution of each single gene was 17%, revealing a large degree of redundancy between genes. Using different heuristics, we estimated the dependency between the size of a gene set and its information content, revealing that on average, a set of 53 genes contains 54% of the information about Ca²⁺ signaling. Our results provide the first direct quantification of information content about complex cellular phenotype that exists in mRNA abundance measurements.

Optogenetic actuator - ERK biosensor circuits identify MAPK network nodes that shape ERK dynamics

Combining single-cell measurements of ERK activity dynamics with perturbations provides insights into the MAPK network topology. We built circuits consisting of an optogenetic actuator to activate MAPK signaling and an ERK biosensor to measure single-cell ERK dynamics. This allowed us to conduct RNAi screens to investigate the role of 50 MAPK proteins in ERK dynamics. We found that the MAPK network is robust against most node perturbations. We observed that the ERK-RAF and the ERK-RSK2-SOS negative feedback operate simultaneously to regulate ERK dynamics. Bypassing the RSK2-mediated feedback, either by direct optogenetic activation of RAS, or by RSK2 perturbation, sensitized ERK dynamics to further perturbations. Similarly, targeting this feedback in a human ErbB2-dependent oncogenic signaling model increased the efficiency of a MEK inhibitor. The RSK2-mediated feedback is thus important for the ability of the MAPK network to produce consistent ERK outputs, and its perturbation can enhance the efficiency of MAPK inhibitors.

Modeling and Simulation of Cell Signaling Networks for Subsequent Analytics Processes Using Big Data and Machine Learning

This work explores how much the traditional approach to modeling and simulation of biological systems, specifically cell signaling networks, can be increased and improved by integrating big data, data mining, and machine learning techniques. Specifically, we first model, simulate, validate, and calibrate the behavior of the PI3K/AKT/mTOR cancer-related signaling pathway. Subsequently, once the behavior of the simulated signaling network matches the expected behavior, the capacity of the computational simulation is increased to grow data (data farming). First, we use big data techniques to extract, collect, filter, and store large volumes of data describing all the interactions among the simulated cell signaling system components over time. Afterward, we apply data mining and machine learning techniques-specifically, exploratory data analysis, feature selection techniques, and supervised neural network models-to the resulting biological dataset to obtain new inferences and knowledge about this biological system. The results showed how the traditional approach to the simulation of biological systems could be enhanced and improved by incorporating big data, data mining, and machine learning techniques, which significantly contributed to increasing the predictive power of the simulation.

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.

Imaging developmental cell cycles

The last decade has seen a major expansion in development of live biosensors, the tools needed to genetically encode them into model organisms, and the microscopic techniques used to visualize them. When combined, these offer us powerful tools with which to make fundamental discoveries about complex biological processes. In this review, we summarize the availability of biosensors to visualize an essential cellular process, the cell cycle, and the techniques for single-cell tracking and quantification of these reporters. We also highlight studies investigating the connection of cellular behavior to the cell cycle, particularly through live imaging, and anticipate exciting discoveries with the combination of these technologies in developmental contexts.

Shedding light on developmental ERK signaling with genetically encoded biosensors

The extracellular signal-regulated kinase (ERK) pathway governs cell proliferation, differentiation and migration, and therefore plays key roles in various developmental and regenerative processes. Recent advances in genetically encoded fluorescent biosensors have unveiled hitherto unrecognized ERK activation dynamics in space and time and their functional importance mainly in cultured cells. However, ERK dynamics during embryonic development have still only been visualized in limited numbers of model organisms, and we are far from a sufficient understanding of the roles played by developmental ERK dynamics. In this Review, we first provide an overview of the biosensors used for visualization of ERK activity in live cells. Second, we highlight the applications of the biosensors to developmental studies of model organisms and discuss the current understanding of how ERK dynamics are encoded and decoded for cell fate decision-making.

Data science in cell imaging

Cell imaging has entered the 'Big Data' era. New technologies in light microscopy and molecular biology have led to an explosion in high-content, dynamic and multidimensional imaging data. Similar to the 'omics' fields two decades ago, our current ability to process, visualize, integrate and mine this new generation of cell imaging data is becoming a critical bottleneck in advancing cell biology. Computation, traditionally used to quantitatively test specific hypotheses, must now also enable iterative hypothesis generation and testing by deciphering hidden biologically meaningful patterns in complex, dynamic or high-dimensional cell image data. Data science is uniquely positioned to aid in this process. In this Perspective, we survey the rapidly expanding new field of data science in cell imaging. Specifically, we highlight how data science tools are used within current image analysis pipelines, propose a computation-first approach to derive new hypotheses from cell image data, identify challenges and describe the next frontiers where we believe data science will make an impact. We also outline steps to ensure broad access to these powerful tools - democratizing infrastructure availability, developing sensitive, robust and usable tools, and promoting interdisciplinary training to both familiarize biologists with data science and expose data scientists to cell imaging.