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SummaryAnimal behavior emerges from collective dynamics of interconnected neurons, making it vulnerable to connectome damage. Paradoxically, many organisms maintain significant behavioral output after large-scale neural injury. Molecular underpinnings of this extreme robustness remain largely unknown. Here, we develop a quantitative behavioral analysis pipeline to measure previously uncharacterized long-lasting latent memory states in planarian flatworms during whole-brain regeneration. By combining >20,000 animal trials with neural population dynamic modeling, we show that long-range volumetric peptidergic signals allow the planarian to rapidly reestablish latent states and restore coarse behavior after large structural perturbations to the nervous system, while small-molecule neuromodulators gradually refine the precision. The different time and length scales of neuropeptide and small-molecule transmission generate incoherent patterns of neural activity which competitively regulate behavior and memory. Controlling behavior through opposing communication mechanisms creates a more robust system than either alone and may serve as a generic approach to construct robust neural networks.

Our ability to dissect cell type diversity, development, and plasticity in the nervous system has been transformed by the recent surge of massive sequencing studies at the single-cell level. A large body of this work has focused primarily on organisms with nervous systems established early in development. Using planarian flatworms in which neurons are constantly respecified, replenished, and regenerated, we analyze several existing single-cell transcriptomic datasets and observe features in neuron identity, differentiation, maturation, and function that may provide the planarian nervous system with high levels of adaptability required to respond to various cues including injury. This analysis allows us to place many prior observations made by functional characterizations in a general framework and provide additional hypothesis and predictions to test in future investigations.

Imaging dense and diverse microbial communities has broad applications in basic microbiology and medicine, but remains a grand challenge due to the fact that many species adopt similar morphologies. While prior studies have relied on techniques involving spectral labeling, we have developed an expansion microscopy method (μExM) in which bacterial cells are physically expanded prior to imaging. We find that expansion patterns depend on the structural and mechanical properties of the cell wall, which vary across species and conditions. We use this phenomenon as a quantitative and sensitive phenotypic imaging contrast orthogonal to spectral separation to resolve bacterial cells of different species or in distinct physiological states. Focusing on host–microbe interactions that are difficult to quantify through fluorescence alone, we demonstrate the ability of μExM to distinguish species through an in vitro defined community of human gut commensals and in vivo imaging of a model gut microbiota, and to sensitively detect cell-envelope damage caused by antibiotics or previously unrecognized cell-to-cell phenotypic heterogeneity among pathogenic bacteria as they infect macrophages., This study describes a new expansion microscopy imaging technique that uses cell wall mechanics as a quantitative and phenotypic imaging contrast, orthogonal to spectral separation, in order to resolve bacterial cells of different species or in distinct physiological states.

The extensive heterogeneity of biological data poses challenges to analysis and interpretation. Construction of a large-scale mechanistic model of Escherichia coli enabled us to integrate and cross-evaluate a massive, heterogeneous dataset based on measurements reported by various groups over decades. We identified inconsistencies with functional consequences across the data, including that the total output of the ribosomes and RNA polymerases described by data are not sufficient for a cell to reproduce measured doubling times, that measured metabolic parameters are neither fully compatible with each other nor with overall growth, and that essential proteins are absent during the cell cycle—and the cell is robust to this absence. Finally, considering these data as a whole leads to successful predictions of new experimental outcomes, in this case protein half-lives.

Predicting the outcome of species invasion in ecosystems is a challenge of both theoretical and practical significance. Progress so far has been limited by the intractability of the far-from-equilibrium nature of the transitions that occur during invasion events. Here, we address this limitation by solving for the transition dynamics of a cross-feeding community along an analytically tractable manifold defined by the system carrying capacity. We find that species invasion induces discontinuous transitions in the community composition, resembling phase transitions in physical systems. These sharp transitions are emergent properties of species-resource interactions and relate directly to the extent of niche overlap between invasive and native species. The high susceptibility of community structure to small variations in species phenotype and resource conditions near the phase boundaries can explain empirically observed stochasticity and the emergence of tipping points in ecosystems. Moreover, we demonstrate that these phase transitions can be modulated by environmental variations to construct nonlinear organization of species both in space and time.

The extensive heterogeneity of biological data poses challenges to analysis and interpretation. Construction of a large-scale mechanistic model of