Functional phases define the response of the soil microbiome to environmental change.

A major challenge in microbiome research is understanding how natural communities respond to environmental change. The ecological, spatial, and chemical complexity of soils makes understanding the metabolic response of these communities to perturbations particularly challenging. Here we measure the dynamics of respiratory nitrate utilization in >1,500 soil microcosms from 20 soil samples subjected to pH perturbations. Despite the complexity of the soil microbiome a minimal mathematical model with two parameters, the quantity of active biomass and the availability of a limiting nutrient, quantifies observed nitrate utilization dynamics across soils and pH perturbations. Across environmental perturbations, the model reveals the existence of three functional phases each with distinct qualitative dynamics of nitrate utilization over time: a phase where acidic perturbations induce cell death that limits metabolic activity, a nutrient-limiting phase where nitrate uptake is performed by dominant taxa that utilize nutrients released from the soil matrix, and a resurgent growth phase in basic conditions, where nutrients are in excess and rare taxa rapidly outgrow dominant populations. The underlying mechanism of each phase is predicted by our interpretable model and tested via amendment experiments, nutrient measurements, and sequencing. Finally, our data suggest that how soils transition between functional phases depends on the long-term history of environmental variation in the wild. Therefore, quantitative measurements and a minimal mathematical formalism reveal the existence of qualitative phases that capture the mechanisms and dynamics of a community responding to environmental change.

Bayesian inference of relative fitness on high-throughput pooled competition assays.

The tracking of lineage frequencies via DNA barcode sequencing enables the quantification of microbial fitness. However, experimental noise coming from biotic and abiotic sources complicates the computation of a reliable inference. We present a Bayesian pipeline to infer relative microbial fitness from high-throughput lineage tracking assays. Our model accounts for multiple sources of noise and propagates uncertainties throughout all parameters in a systematic way. Furthermore, using modern variational inference methods based on automatic differentiation, we are able to scale the inference to a large number of unique barcodes. We extend this core model to analyze multi-environment assays, replicate experiments, and barcodes linked to genotypes. On simulations, our method recovers known parameters within posterior credible intervals. This work provides a generalizable Bayesian framework to analyze lineage tracking experiments. The accompanying open-source software library enables the adoption of principled statistical methods in experimental evolution.

Pluripotency of a founding field: rebranding developmental biology.

The field of developmental biology has declined in prominence in recent decades, with off-shoots from the field becoming more fashionable and highly funded. This has created inequity in discovery and opportunity, partly due to the perception that the field is antiquated or not cutting edge. A 'think tank' of scientists from multiple developmental biology-related disciplines came together to define specific challenges in the field that may have inhibited innovation, and to provide tangible solutions to some of the issues facing developmental biology. The community suggestions include a call to the community to help 'rebrand' the field, alongside proposals for additional funding apparatuses, frameworks for interdisciplinary innovative collaborations, pedagogical access, improved science communication, increased diversity and inclusion, and equity of resources to provide maximal impact to the community.

Multiscale biophysical analysis of nucleolus disassembly during mitosis.

During cell division, precise and regulated distribution of cellular material between daughter cells is a critical step and is governed by complex biochemical and biophysical mechanisms. To achieve this, membraneless organelles and condensates often require complete disassembly during mitosis. The biophysical principles governing the disassembly of condensates remain poorly understood. Here, we used a physical biology approach to study how physical and material properties of the nucleolus, a prominent nuclear membraneless organelle in eukaryotic cells, change during mitosis and across different scales. We found that nucleolus disassembly proceeds continuously through two distinct phases with a slow and reversible preparatory phase followed by a rapid irreversible phase that was concurrent with the nuclear envelope breakdown. We measured microscopic properties of nucleolar material including effective diffusion rates and binding affinities as well as key macroscopic properties of surface tension and bending rigidity. By incorporating these measurements into the framework of critical phenomena, we found evidence that near mitosis surface tension displays a power-law behavior as a function of biochemically modulated interaction strength. This two-step disassembly mechanism maintains structural and functional stability of nucleolus while enabling its rapid and efficient disassembly in response to cell cycle cues.

Global patterns in gene content of soil microbiomes emerge from microbial interactions.

Microbial metabolism sustains life on Earth. Sequencing surveys of communities in hosts, oceans, and soils have revealed ubiquitous patterns linking the microbes present, the genes they possess, and local environmental conditions. One prominent explanation for these patterns is environmental filtering: local conditions select strains with particular traits. However, filtering assumes ecological interactions do not influence patterns, despite the fact that interactions can and do play an important role in structuring communities. Here, we demonstrate the insufficiency of the environmental filtering hypothesis for explaining global patterns in topsoil microbiomes. Using denitrification as a model system, we find that the abundances of two characteristic genotypes trade-off with pH; nar gene abundances increase while nap abundances decrease with declining pH. Contradicting the filtering hypothesis, we show that strains possessing the Nar genotype are enriched in low pH conditions but fail to grow alone. Instead, the dominance of Nar genotypes at low pH arises from an ecological interaction with Nap genotypes that alleviates nitrite toxicity. Our study provides a roadmap for dissecting how global associations between environmental variables and gene abundances arise from environmentally modulated community interactions.

Bayesian inference of relative fitness on high-throughput pooled competition assays.

The tracking of lineage frequencies via DNA barcode sequencing enables the quantification of microbial fitness. However, experimental noise coming from biotic and abiotic sources complicates the computation of a reliable inference. We present a Bayesian pipeline to infer relative microbial fitness from high-throughput lineage tracking assays. Our model accounts for multiple sources of noise and propagates uncertainties throughout all parameters in a systematic way. Furthermore, using modern variational inference methods based on automatic differentiation, we are able to scale the inference to a large number of unique barcodes. We extend this core model to analyze multi-environment assays, replicate experiments, and barcodes linked to genotypes. On simulations, our method recovers known parameters within posterior credible intervals. This work provides a generalizable Bayesian framework to analyze lineage tracking experiments. The accompanying open-source software library enables the adoption of principled statistical methods in experimental evolution.

The Physical Biology of Nucleolus Disassembly.

During cell division, precise and regulated distribution of cellular material between daughter cells is a critical step and is governed by complex biochemical and biophysical mechanisms. To achieve this, membraneless organelles and condensates often require complete disassembly during mitosis. The biophysical principles governing the disassembly of condensates remain poorly understood. Here, we used a physical biology approach to study how physical and material properties of the nucleolus, a prominent nuclear membraneless organelle in eukaryotic cells, change during mitosis and across different scales. We found that nucleolus disassembly proceeds continuously through two distinct phases with a slow and reversible preparatory phase followed by a rapid irreversible phase that was concurrent with the nuclear envelope breakdown. We measured microscopic properties of nucleolar material including effective diffusion rates and binding affinities as well as key macroscopic properties of surface tension and bending rigidity. By incorporating these measurements into the framework of critical phenomena, we found evidence that near mitosis surface tension displays a power-law behavior as a function of biochemically modulated interaction strength. This two-step disassembly mechanism, which maintains structural and functional stability of nucleolus while allowing for its rapid and efficient disassembly in response to cell cycle cues, may be a universal design principle for the disassembly of other biomolecular condensates.

Inferring single-cell transcriptomic dynamics with structured latent gene expression dynamics.

Gene expression dynamics provide directional information for trajectory inference from single-cell RNA sequencing data. Traditional approaches compute RNA velocity using strict modeling assumptions about transcription and splicing of RNA. This can fail in scenarios where multiple lineages have distinct gene dynamics or where rates of transcription and splicing are time dependent. We present "LatentVelo," an approach to compute a low-dimensional representation of gene dynamics with deep learning. LatentVelo embeds cells into a latent space with a variational autoencoder and models differentiation dynamics on this "dynamics-based" latent space with neural ordinary differential equations. LatentVelo infers a latent regulatory state that controls the dynamics of an individual cell to model multiple lineages. LatentVelo can predict latent trajectories, describing the inferred developmental path for individual cells rather than just local RNA velocity vectors. The dynamics-based embedding batch corrects cell states and velocities, outperforming comparable autoencoder batch correction methods that do not consider gene expression dynamics.

A dynamical systems treatment of transcriptomic trajectories in hematopoiesis.

Inspired by Waddington's illustration of an epigenetic landscape, cell-fate transitions have been envisioned as bifurcating dynamical systems, wherein exogenous signaling dynamics couple to the enormously complex signaling and transcriptional machinery of a cell to elicit qualitative transitions in its collective state. Single-cell RNA sequencing (scRNA-seq), which measures the distributions of possible transcriptional states in large populations of differentiating cells, provides an alternate view, in which development is marked by the variations of a myriad of genes. Here, we present a mathematical formalism for rigorously evaluating, from a dynamical systems perspective, whether scRNA-seq trajectories display statistical signatures consistent with bifurcations and, as a case study, pinpoint regions of multistability along the neutrophil branch of hematopoeitic differentiation. Additionally, we leverage the geometric features of linear instability to identify the low-dimensional phase plane in gene expression space within which the multistability unfolds, highlighting novel genetic players that are crucial for neutrophil differentiation. Broadly, we show that a dynamical systems treatment of scRNA-seq data provides mechanistic insights into the high-dimensional processes of cellular differentiation, taking a step toward systematic construction of mathematical models for transcriptomic dynamics.

EMBEDR: Distinguishing signal from noise in single-cell omics data.

Single-cell "omics"-based measurements are often high dimensional so that dimensionality reduction (DR) algorithms are necessary for data visualization and analysis. The lack of methods for separating signal from noise in DR outputs has limited their utility in generating data-driven discoveries in single-cell data. In this work we present EMBEDR, which assesses the output of any DR algorithm to distinguish evidence of structure from algorithm-induced noise in DR outputs. We apply EMBEDR to DR-generated representations of single-cell omics data of several modalities to show where they visually show real-not spurious-structure. EMBEDR generates a "p" value for each sample, allowing for direct comparisons of DR algorithms and facilitating optimization of algorithm hyperparameters. We show that the scale of a sample's neighborhood can thus be determined and used to generate a novel "cell-wise optimal" embedding. EMBEDR is available as a Python package for immediate use.

Genomic structure predicts metabolite dynamics in microbial communities.

The metabolic activities of microbial communities play a defining role in the evolution and persistence of life on Earth, driving redox reactions that give rise to global biogeochemical cycles. Community metabolism emerges from a hierarchy of processes, including gene expression, ecological interactions, and environmental factors. In wild communities, gene content is correlated with environmental context, but predicting metabolite dynamics from genomes remains elusive. Here, we show, for the process of denitrification, that metabolite dynamics of a community are predictable from the genes each member of the community possesses. A simple linear regression reveals a sparse and generalizable mapping from gene content to metabolite dynamics for genomically diverse bacteria. A consumer-resource model correctly predicts community metabolite dynamics from single-strain phenotypes. Our results demonstrate that the conserved impacts of metabolic genes can predict community metabolite dynamics, enabling the prediction of metabolite dynamics from metagenomes, designing denitrifying communities, and discovering how genome evolution impacts metabolism.

Emergence of a geometric pattern of cell fates from tissue-scale mechanics in the Drosophila eye.

Pattern formation of biological structures involves the arrangement of different types of cells in an ordered spatial configuration. In this study, we investigate the mechanism of patterning the Drosophila eye epithelium into a precise triangular grid of photoreceptor clusters called ommatidia. Previous studies had led to a long-standing biochemical model whereby a reaction-diffusion process is templated by recently formed ommatidia to propagate a molecular prepattern across the eye. Here, we find that the templating mechanism is instead, mechanochemical in origin; newly born columns of differentiating ommatidia serve as a template to spatially pattern flows that move epithelial cells into position to form each new column of ommatidia. Cell flow is generated by a source and sink, corresponding to narrow zones of cell dilation and contraction respectively, that straddle the growing wavefront of ommatidia. The newly formed lattice grid of ommatidia cells are immobile, deflecting, and focusing the flow of other cells. Thus, the self-organization of a regular pattern of cell fates in an epithelium is mechanically driven.

Global constraints within the developmental program of the Drosophila wing.

Organismal development is a complex process, involving a vast number of molecular constituents interacting on multiple spatio-temporal scales in the formation of intricate body structures. Despite this complexity, development is remarkably reproducible and displays tolerance to both genetic and environmental perturbations. This robustness implies the existence of hidden simplicities in developmental programs. Here, using the Drosophila wing as a model system, we develop a new quantitative strategy that enables a robust description of biologically salient phenotypic variation. Analyzing natural phenotypic variation across a highly outbred population and variation generated by weak perturbations in genetic and environmental conditions, we observe a highly constrained set of wing phenotypes. Remarkably, the phenotypic variants can be described by a single integrated mode that corresponds to a non-intuitive combination of structural variations across the wing. This work demonstrates the presence of constraints that funnel environmental inputs and genetic variation into phenotypes stretched along a single axis in morphological space. Our results provide quantitative insights into the nature of robustness in complex forms while yet accommodating the potential for evolutionary variations. Methodologically, we introduce a general strategy for finding such invariances in other developmental contexts.

Evolution of Generalists by Phenotypic Plasticity.

Adapting organisms face a tension between specializing their phenotypes for certain ecological tasks and developing generalist strategies that permit persistence in multiple environmental conditions. Understanding when and how generalists or specialists evolve is an important question in evolutionary dynamics. Here, we study the evolution of bacterial range expansions by selecting Escherichia coli for faster migration through porous media containing one of four different sugars supporting growth and chemotaxis. We find that selection in any one sugar drives the evolution of faster migration in all sugars. Measurements of growth and motility of all evolved lineages in all nutrient conditions reveal that the ubiquitous evolution of fast migration arises via phenotypic plasticity. Phenotypic plasticity permits evolved strains to exploit distinct strategies to achieve fast migration in each environment, irrespective of the environment in which they were evolved. Therefore, selection in a homogeneous environment drives phenotypic plasticity that improves performance in other environments.

The Wg and Dpp morphogens regulate gene expression by modulating the frequency of transcriptional bursts.

Morphogen signaling contributes to the patterned spatiotemporal expression of genes during development. One mode of regulation of signaling-responsive genes is at the level of transcription. Single-cell quantitative studies of transcription have revealed that transcription occurs intermittently, in bursts. Although the effects of many gene regulatory mechanisms on transcriptional bursting have been studied, it remains unclear how morphogen gradients affect this dynamic property of downstream genes. Here we have adapted single molecule fluorescence in situ hybridization (smFISH) for use in the Drosophila wing imaginal disc in order to measure nascent and mature mRNA of genes downstream of the Wg and Dpp morphogen gradients. We compared our experimental results with predictions from stochastic models of transcription, which indicated that the transcription levels of these genes appear to share a common method of control via burst frequency modulation. Our data help further elucidate the link between developmental gene regulatory mechanisms and transcriptional bursting.

Ordered patterning of the sensory system is susceptible to stochastic features of gene expression.

Sensory neuron numbers and positions are precisely organized to accurately map environmental signals in the brain. This precision emerges from biochemical processes within and between cells that are inherently stochastic. We investigated impact of stochastic gene expression on pattern formation, focusing on senseless (sens), a key determinant of sensory fate in Drosophila. Perturbing microRNA regulation or genomic location of sens produced distinct noise signatures. Noise was greatly enhanced when both sens alleles were present in homologous loci such that each allele was regulated in trans by the other allele. This led to disordered patterning. In contrast, loss of microRNA repression of sens increased protein abundance but not sensory pattern disorder. This suggests that gene expression stochasticity is a critical feature that must be constrained during development to allow rapid yet accurate cell fate resolution.

The role of intracellular interactions in the collective polarization of tissues and its interplay with cellular geometry.

Planar cell polarity (PCP), the long-range in-plane polarization of epithelial tissues, provides directional information that guides a multitude of developmental processes at cellular and tissue levels. While it is manifest that cells utilize both intracellular and intercellular interactions, the coupling between the two modules, essential to the coordination of collective polarization, remains an active area of investigation. We propose a generalized reaction-diffusion model to study the role of intracellular interactions in the emergence of long-range polarization, and show that the nonlocality of cytoplasmic interactions, i.e. coupling of membrane proteins localized on different cell-cell junctions, is of vital importance to the faithful detection of weak directional signals, and becomes increasingly more crucial to the stability of polarization against the deleterious effects of large geometric irregularities. We demonstrate that nonlocal interactions are necessary for geometric information to become accessible to the PCP components. The prediction of the model regarding polarization in elongated tissues, is shown to be in agreement with experimental observations, where the polarity emerges perpendicular to the axis of elongation. Core PCP is adopted as a model pathway, in term of which we interpret the model parameters. To this end, we introduce three distinct classes of mutations, (I) in membrane proteins, (II) in cytoplasmic proteins, and (III) local enhancement of geometric disorder. Comparing the in silico and in vivo phenotypes, we show that our model successfully recapitulates the salient phenotypic features of these mutations. Exploring the parameter space helps us shed light on the role of cytoplasmic proteins in cell-cell communications, and make falsifiable predictions regarding the cooperation of cytoplasmic and membrane proteins in the establishment of long-range polarization.

A Jump-Distance-Based Parameter Inference Scheme for Particulate Trajectories.

This study presents an improved quantitative tool for the analysis of particulate trajectories. Particulate trajectory data appears in several different biological contexts, from the trajectory of chemotaxing bacteria to the nuclear mobility inferred from the trajectory of MS2 spots. Presently, the majority of analyses performed on particulate trajectory data have been limited to mean-squared displacement (MSD) analysis. Although simple, MSD analysis has several pitfalls, including difficulty in selecting between competing methods of motion, handling systems with multiple distinct subpopulations, and parameter extraction from limited time-series data. Here, we provide an alternative to MSD analysis using the jump distance distribution (JDD), which addresses the aforementioned issues. In particular, the method outperforms MSD analysis in the data-poor limit, thereby giving access to a larger range of temporal dynamics. In this work, we construct and validate a derivation of the JDD for different transportation modes and dimensions and implement a parameter estimation and model selection scheme. This scheme is validated, and direct improvements over MSD analysis are shown. Through an analysis of bacterial chemotaxis data, we highlight the JDD's ability to extract parameters at a variety of timescales, as well as extract underlying biological features of interest.

QuBiT: a quantitative tool for analyzing epithelial tubes reveals unexpected patterns of organization in the Drosophila trachea.

Biological tubes are essential for animal survival, and their functions are dependent on tube shape. Analyzing the contributions of cell shape and organization to the morphogenesis of small tubes has been hampered by the limitations of existing programs in quantifying cell geometry on highly curved tubular surfaces and calculating tube-specific parameters. We therefore developed QuBiT (Quantitative Tool for Biological Tubes) and used it to analyze morphogenesis of the embryonic Drosophila trachea (airway). In the main tube, we find previously unknown anterior-to-posterior (A-P) gradients of cell apical orientation and aspect ratio, and periodicity in the organization of apical cell surfaces. Inferred cell intercalation during development dampens an A-P gradient of the number of cells per cross-section of the tube, but does not change the patterns of cell connectivity. Computationally 'unrolling' the apical surface of wild-type trachea and the hindgut reveals previously unrecognized spatial patterns of the apical marker Uninflatable and a non-redundant role for the Na+/K+ ATPase in apical marker organization. These unexpected findings demonstrate the importance of a computational tool for analyzing small diameter biological tubes.