Answering open questions in biology using spatial genomics and structured methods.

Genomics methods have uncovered patterns in a range of biological systems, but obscure important aspects of cell behavior: the shapes, relative locations, movement, and interactions of cells in space. Spatial technologies that collect genomic or epigenomic data while preserving spatial information have begun to overcome these limitations. These new data promise a deeper understanding of the factors that affect cellular behavior, and in particular the ability to directly test existing theories about cell state and variation in the context of morphology, location, motility, and signaling that could not be tested before. Rapid advancements in resolution, ease-of-use, and scale of spatial genomics technologies to address these questions also require an updated toolkit of statistical methods with which to interrogate these data. We present a framework to respond to this new avenue of research: four open biological questions that can now be answered using spatial genomics data paired with methods for analysis. We outline spatial data modalities for each open question that may yield specific insights, discuss how conflicting theories may be tested by comparing the data to conceptual models of biological behavior, and highlight statistical and machine learning-based tools that may prove particularly helpful to recover biological understanding.

Towards 'end-to-end' analysis and understanding of biological timecourse data.

Petabytes of increasingly complex and multidimensional live cell and tissue imaging data are generated every year. These videos hold large promise for understanding biology at a deep and fundamental level, as they capture single-cell and multicellular events occurring over time and space. However, the current modalities for analysis and mining of these data are scattered and user-specific, preventing more unified analyses from being performed over different datasets and obscuring possible scientific insights. Here, we propose a unified pipeline for storage, segmentation, analysis, and statistical parametrization of live cell imaging datasets.

A self-exciting point process to study multicellular spatial signaling patterns.

Multicellular organisms rely on spatial signaling among cells to drive their organization, development, and response to stimuli. Several models have been proposed to capture the behavior of spatial signaling in multicellular systems, but existing approaches fail to capture both the autonomous behavior of single cells and the interactions of a cell with its neighbors simultaneously. We propose a spatiotemporal model of dynamic cell signaling based on Hawkes processes-self-exciting point processes-that model the signaling processes within a cell and spatial couplings between cells. With this cellular point process (CPP), we capture both the single-cell pathway activation rate and the magnitude and duration of signaling between cells relative to their spatial location. Furthermore, our model captures tissues composed of heterogeneous cell types with different bursting rates and signaling behaviors across multiple signaling proteins. We apply our model to epithelial cell systems that exhibit a range of autonomous and spatial signaling behaviors basally and under pharmacological exposure. Our model identifies known drug-induced signaling deficits, characterizes signaling changes across a wound front, and generalizes to multichannel observations.

Roadmap on biology in time varying environments.

Biological organisms experience constantly changing environments, from sudden changes in physiology brought about by feeding, to the regular rising and setting of the Sun, to ecological changes over evolutionary timescales. Living organisms have evolved to thrive in this changing world but the general principles by which organisms shape and are shaped by time varying environments remain elusive. Our understanding is particularly poor in the intermediate regime with no separation of timescales, where the environment changes on the same timescale as the physiological or evolutionary response. Experiments to systematically characterize the response to dynamic environments are challenging since such environments are inherently high dimensional. This roadmap deals with the unique role played by time varying environments in biological phenomena across scales, from physiology to evolution, seeking to emphasize the commonalities and the challenges faced in this emerging area of research.

Engineering combinatorial and dynamic decoders using synthetic immediate-early genes.

Many cell- and tissue-level functions are coordinated by intracellular signaling pathways that trigger the expression of context-specific target genes. Yet the input-output relationships that link pathways to the genes they activate are incompletely understood. Mapping the pathway-decoding logic of natural target genes could also provide a basis for engineering novel signal-decoding circuits. Here we report the construction of synthetic immediate-early genes (SynIEGs), target genes of Erk signaling that implement complex, user-defined regulation and can be monitored by using live-cell biosensors to track their transcription and translation. We demonstrate the power of this approach by confirming Erk duration-sensing by FOS, elucidating how the BTG2 gene is differentially regulated by external stimuli, and designing a synthetic immediate-early gene that selectively responds to the combination of growth factor and DNA damage stimuli. SynIEGs pave the way toward engineering molecular circuits that decode signaling dynamics and combinations across a broad range of cellular contexts.

Regulation of spatiotemporal limits of developmental gene expression via enhancer grammar.

The regulatory specificity of a gene is determined by the structure of its enhancers, which contain multiple transcription factor binding sites. A unique combination of transcription factor binding sites in an enhancer determines the boundary of target gene expression, and their disruption often leads to developmental defects. Despite extensive characterization of binding motifs in an enhancer, it is still unclear how each binding site contributes to overall transcriptional activity. Using live imaging, quantitative analysis, and mathematical modeling, we measured the contribution of individual binding sites in transcriptional regulation. We show that binding site arrangement within the Rho-GTPase component t48 enhancer mediates the expression boundary by mainly regulating the timing of transcriptional activation along the dorsoventral axis of Drosophila embryos. By tuning the binding affinity of the Dorsal (Dl) and Zelda (Zld) sites, we show that single site modulations are sufficient to induce significant changes in transcription. Yet, no one site seems to have a dominant role; rather, multiple sites synergistically drive increases in transcriptional activity. Interestingly, Dl and Zld demonstrate distinct roles in transcriptional regulation. Dl site modulations change spatial boundaries of t48, mostly by affecting the timing of activation and bursting frequency rather than transcriptional amplitude or bursting duration. However, modulating the binding site for the pioneer factor Zld affects both the timing of activation and amplitude, suggesting that Zld may potentiate higher Dl recruitment to target DNAs. We propose that such fine-tuning of dynamic gene control via enhancer structure may play an important role in ensuring normal development.

A Live-Cell Screen for Altered Erk Dynamics Reveals Principles of Proliferative Control.

Complex, time-varying responses have been observed widely in cell signaling, but how specific dynamics are generated or regulated is largely unknown. One major obstacle has been that high-throughput screens are typically incompatible with the live-cell assays used to monitor dynamics. Here, we address this challenge by screening a library of 429 kinase inhibitors and monitoring extracellular-regulated kinase (Erk) activity over 5 h in more than 80,000 single primary mouse keratinocytes. Our screen reveals both known and uncharacterized modulators of Erk dynamics, including inhibitors of non-epidermal growth factor receptor (EGFR) receptor tyrosine kinases (RTKs) that increase Erk pulse frequency and overall activity. Using drug treatment and direct optogenetic control, we demonstrate that drug-induced changes to Erk dynamics alter the conditions under which cells proliferate. Our work opens the door to high-throughput screens using live-cell biosensors and reveals that cell proliferation integrates information from Erk dynamics as well as additional permissive cues.

Deconstructing and repurposing the light-regulated interplay between Arabidopsis phytochromes and interacting factors.

Phytochrome photoreceptors mediate adaptive responses of plants to red and far-red light. These responses generally entail light-regulated association between phytochromes and other proteins, among them the phytochrome-interacting factors (PIF). The interaction with Arabidopsis thaliana phytochrome B (AtPhyB) localizes to the bipartite APB motif of the A. thaliana PIFs (AtPIF). To address a dearth of quantitative interaction data, we construct and analyze numerous AtPIF3/6 variants. Red-light-activated binding is predominantly mediated by the APB N-terminus, whereas the C-terminus modulates binding and underlies the differential affinity of AtPIF3 and AtPIF6. We identify AtPIF variants of reduced size, monomeric or homodimeric state, and with AtPhyB affinities between 10 and 700 nM. Optogenetically deployed in mammalian cells, the AtPIF variants drive light-regulated gene expression and membrane recruitment, in certain cases reducing basal activity and enhancing regulatory response. Moreover, our results provide hitherto unavailable quantitative insight into the AtPhyB:AtPIF interaction underpinning vital light-dependent responses in plants.

Erratum: Publisher Correction: Deconstructing and repurposing the light-regulated interplay between Arabidopsis phytochromes and interacting factors.

[This corrects the article DOI: 10.1038/s42003-019-0687-9.].

A random graph model of density thresholds in swarming cells.

Swarming behaviour is a type of bacterial motility that has been found to be dependent on reaching a local density threshold of cells. With this in mind, the process through which cell-to-cell interactions develop and how an assembly of cells reaches collective motility becomes increasingly important to understand. Additionally, populations of cells and organisms have been modelled through graphs to draw insightful conclusions about population dynamics on a spatial level. In the present study, we make use of analogous random graph structures to model the formation of large chain subgraphs, representing interactions between multiple cells, as a random graph Markov process. Using numerical simulations and analytical results on how quickly paths of certain lengths are reached in a random graph process, metrics for intercellular interaction dynamics at the swarm layer that may be experimentally evaluated are proposed.