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Abstract Internal signals from the body and external signals from the environment are processed by brain-wide circuits to guide behavior. However, the complete brain-wide circuit activity underlying interoception—the perception of bodily signals—and its interactions with sensorimotor circuits remain unclear due to technical barriers to accessing whole-brain activity at the cellular level during organ physiology perturbations. We developed an all-optical system for whole-brain neuronal imaging in behaving larval zebrafish during optical uncaging of gut-targeted nutrients and visuo-motor stimulation. Widespread neural activity throughout the brain encoded nutrient delivery, unfolding on multiple timescales across many specific peripheral and central regions. Evoked activity depended on delivery location and occurred with amino acids and D-glucose, but not L-glucose. Many gut-sensitive neurons also responded to swimming and visual stimuli, with brainstem areas primarily integrating gut and motor signals and midbrain regions integrating gut and visual signals. This platform links body-brain communication studies to brain-wide neural computation in awake, behaving vertebrates. 

Abstract The brain has long been conceptualized as a network of neurons connected by synapses. However, attempts to describe the connectome using established network science models have yielded conflicting outcomes, leaving the architecture of neural networks unresolved. Here, by performing a comparative analysis of eight experimentally mapped connectomes, we find that their degree distributions cannot be captured by the well-established random or scale-free models. Instead, the node degrees and strengths are well approximated by lognormal distributions, although these lack a mechanistic explanation in the context of the brain. By acknowledging the physical network nature of the brain, we show that neuron size is governed by a multiplicative process, which allows us to analytically derive the lognormal nature of the neuron length distribution. Our framework not only predicts the degree and strength distributions across each of the eight connectomes, but also yields a series of novel and empirically falsifiable relationships between different neuron characteristics. The resulting multiplicative network represents a novel architecture for network science, whose distinctive quantitative features bridge critical gaps between neural structure and function, with implications for brain dynamics, robustness, and synchronization. 

Abstract Object tracking in microscopy videos is crucial for understanding biological processes. While existing methods often require fine-tuning tracking algorithms to fit the image dataset, here we explored an alternative paradigm: augmenting the image time-lapse dataset to fit the tracking algorithm. To test this approach, we evaluated whether generative video frame interpolation can augment the temporal resolution of time-lapse microscopy and facilitate object tracking in multiple biological contexts. We systematically compared the capacity of Latent Diffusion Model for Video Frame Interpolation (LDMVFI), Real-time Intermediate Flow Estimation (RIFE), Compression-Driven Frame Interpolation (CDFI), and Frame Interpolation for Large Motion (FILM) to generate synthetic microscopy images derived from interpolating real images. Our testing image time series ranged from fluorescently labeled nuclei to bacteria, yeast, cancer cells, and organoids. We showed that the off-the-shelf frame interpolation algorithms produced bio-realistic image interpolation even without dataset-specific retraining, as judged by high structural image similarity and the capacity to produce segmentations that closely resemble results from real images. Using a simple tracking algorithm based on mask overlap, we confirmed that frame interpolation significantly improved tracking across several datasets without requiring extensive parameter tuning and capturing complex trajectories that were difficult to resolve in the original image time series. Taken together, our findings highlight the potential of generative frame interpolation to improve tracking in time-lapse microscopy across diverse scenarios, suggesting that a generalist tracking algorithm for microscopy could be developed by combining deep learning segmentation models with generative frame interpolation. 

ABSTRACT The physical organization of DNA within the nucleus is fundamental to a wide range of biological processes. The experimental investigation of the structure of genomic DNA remains challenging due to its large size and hierarchical arrangement. These challenges present considerable opportunities for combined experimental and modeling approaches. Physics‐based computational models, in particular, have emerged as essential tools for probing chromatin structure and dynamics across a wide range of length scales. Such models must necessarily be capable of bridging scales, and each scale presents its own subtleties and intricacies. This review discusses recent methodological advances in genomic structural modeling, emphasizing the need for multiscale integration to capture the hierarchical organization and molecular mechanisms that underlie chromatin structure and function. We present an analysis of state‐of‐the‐art methods, as well as a perspective on challenges and future opportunities across length scales ranging from bare DNA to nucleosomes and chromatin fibers, up to TAD and chromosome‐scale models. We emphasize models that connect genome organization to gene expression, models that leverage emerging machine learning capabilities, and models that develop multiscale approaches. We examine gaps in experimental data that computational models are poised to address and propose directions for future research that bridge theory and experiment in DNA structural biology. 

Abstract Microbial communities experience environmental fluctuations across timescales from rapid changes in moisture, temperature, or light levels to long-term seasonal or climactic variations. Understanding how microbial populations respond to these changes is critical for predicting the impact of perturbations, interventions, and climate change on communities. Since communities typically harbor tens to hundreds of distinct taxa, the response of microbial abundances to perturbations is potentially complex. However, while taxonomic diversity is high, in many communities taxa can be grouped into functional guilds of strains with similar metabolic traits. These guilds effectively reduce the complexity of the system by providing a physiologically motivated coarse-graining. Here, using a combination of simulations, theory, and experiments, we show that the response of guilds to nutrient fluctuations depends on the timescale of those fluctuations. Rapid changes in nutrient levels drive cohesive, positively correlated abundance dynamics within guilds. For slower timescales of environmental variation, members within a guild begin to compete due to similar resource preferences, driving negative correlations in abundances between members of the same guild. Our results provide a route to understanding the relationship between functional guilds and community response to changing environments, as well as an experimental approach to discovering functional guilds via designed nutrient perturbations to communities. 

SUMMARY Transcription factors (TFs) regulate gene expression despite constraints from chromatin structure and the cell cycle. Here we examine the concentration-dependent regulation ofhunchbackby the Bicoid morphogen through a combination of quantitative imaging, mathematical modeling and epigenomics inDrosophilaembryos. By live imaging of MS2 reporters, we find that, following mitosis, the timing of transcriptional activation driven by thehunchbackP2 (hbP2) enhancer directly reflects Bicoid concentration. We build a stochastic model that can explainin vivoonset time distributions by accounting for both the competition between Bicoid and nucleosomes athbP2 and a negative influence of DNA replication on transcriptional elongation. Experimental modulation of nucleosome stability alters onset time distributions and the posterior boundary ofhunchbackexpression. We conclude that TF-nucleosome competition is the molecular mechanism whereby the Bicoid morphogen gradient specifies the posterior boundary ofhunchbackexpression. 

Abstract The physics of complex systems stands to greatly benefit from the qualitative changes in data availability and advances in data-driven computational methods. Many of these systems can be represented by interacting degrees of freedom on inhomogeneous graphs. However, the lack of translational invariance presents a fundamental challenge to theoretical tools, such as the renormalization group, which were so successful in characterizing the universal physical behaviour in critical phenomena. Here we show that compression theory allows the extraction of relevant degrees of freedom in arbitrary geometries, and the development of efficient numerical tools to build an effective theory from data. We demonstrate our method by applying it to a strongly correlated system on an Ammann-Beenker quasicrystal, where it discovers an exotic critical point with broken conformal symmetry. We also apply it to an antiferromagnetic system on non-bipartite random graphs, where any periodicity is absent. 

Abstract Quantifying the inheritance of regulatory networks among proteins during asymmetric cell division remains a challenge due to the complexity of these systems and the lack of robust mathematical definitions for inheritance. We propose a novel statistical framework called ODEinherit to measure how much a mother cell’s regulatory network explains its daughter’s trajectories, addressing this gap. Using time-lapse microscopy, we tracked the expression dynamics of six proteins across 85 dividingS. cerevisiaecells, observed over eight hours at 12-minute intervals. Our framework employs a two-step approach. First, we estimate an ordinary differential equation (ODE) system for each cell to characterize protein interactions, introducing novel adjustments for non-oscillatory time series and leveraging multi-cell data. Second, we assess inheritance by clustering cells based on cycling markers and quantifying how well a mother’s regulatory network predicts her daughter’s. Preliminary findings suggest stage-dependent differences in inheritance rates, paving the way for applications in cellular stress response and cell-fate prediction studies across generations. 

ABSTRACT Plants recognize a variety of environmental molecules, thereby triggering appropriate responses to biotic or abiotic stresses. Substances containing microbes-associated molecular patterns (MAMPs) and damage-associated molecular patterns (DAMPs) are representative inducers of pathogen resistance and damage repair, thus treatment of healthy plants with such substances can pre-activate plant immunity and cell repair functions. In this study, the effects of DAMP/MAMP oligosaccharides mixture (Oligo-Mix) derived from plant cell wall (cello-oligosaccharide and xylo-oligosaccharide), and fungal cell wall (chitin-oligosaccharide) were examined in cucumber. Treatment of cucumber with Oligo-Mix promoted root germination and plant growth, along with increased chlorophyll contents in the leaves. Oligo-Mix treatment also induced typical defense responses such as MAP kinase activation and callose deposition in leaves. Pretreatment of Oligo-Mix enhanced disease resistance of cucumber leaves against pathogenic fungiPodosphaera xanthii(powdery mildew) andColletotrichum orbiculare(anthracnose). Oligo-Mix treatment increased the induction of hypersensitive cell death around the infection site of pathogens, which inhibited further infection and the conidial formation of pathogens on the cucumber leaves. RNA-seq analysis revealed that Oligo-Mix treatment upregulated genes associated with plant structural reinforcement, responses to abiotic stresses and plant defense. These results suggested that Oligo-Mix has beneficial effects on growth and disease resistance in cucumber, making it a promising biostimulant for agricultural application. 

Abstract The tumor microenvironment (TME) is an immensely complex ecosystem^1,2. This complexity underlies difficulties in elucidating principles of spatial organization and using molecular profiling of the TME for clinical use³. Through statistical analysis of 96 spatial transcriptomic (ST-seq) datasets spanning twelve diverse tumor types, we found a conserved distribution of multicellular, transcriptionally covarying units termed ‘Spatial Groups’ (SGs). SGs were either dependent on a hierarchical local spatial context – enriched for cell-extrinsic processes such as immune regulation and signal transduction – or independent from local spatial context – enriched for cell-intrinsic processes such as protein and RNA metabolism, DNA repair, and cell cycle regulation. We used SGs to define a measure of gene spatial heterogeneity – ‘spatial lability’ – and categorized all 96 tumors by their TME spatial lability profiles. The resulting classification captured spatial variation in cell-extrinsic versus cell-intrinsic biology and motivated class-specific strategies for therapeutic intervention. Using this classification to characterize pre-treatment biopsy samples of 16 non-small cell lung cancer (NSCLC) patients outside our database distinguished responders and non-responders to immune checkpoint blockade while programmed death-ligand 1 (PD-L1) status and spatially unaware bulk transcriptional markers did not. Our findings show conserved principles of TME spatial biology that are both biologically and clinically significant.