Systematic mapping of organism-scale gene-regulatory networks in aging using population asynchrony.

In aging, physiologic networks decline in function at rates that differ between individuals, producing a wide distribution of lifespan. Though 70% of human lifespan variance remains unexplained by heritable factors, little is known about the intrinsic sources of physiologic heterogeneity in aging. To understand how complex physiologic networks generate lifespan variation, new methods are needed. Here, we present Asynch-seq, an approach that uses gene-expression heterogeneity within isogenic populations to study the processes generating lifespan variation. By collecting thousands of single-individual transcriptomes, we capture the Caenorhabditis elegans "pan-transcriptome"-a highly resolved atlas of non-genetic variation. We use our atlas to guide a large-scale perturbation screen that identifies the decoupling of total mRNA content between germline and soma as the largest source of physiologic heterogeneity in aging, driven by pleiotropic genes whose knockdown dramatically reduces lifespan variance. Our work demonstrates how systematic mapping of physiologic heterogeneity can be applied to reduce inter-individual disparities in aging.

Controlling human organoid symmetry breaking reveals signaling gradients drive segmentation clock waves.

Axial development of mammals involves coordinated morphogenetic events, including axial elongation, somitogenesis, and neural tube formation. To gain insight into the signals controlling the dynamics of human axial morphogenesis, we generated axially elongating organoids by inducing anteroposterior symmetry breaking of spatially coupled epithelial cysts derived from human pluripotent stem cells. Each organoid was composed of a neural tube flanked by presomitic mesoderm sequentially segmented into somites. Periodic activation of the somite differentiation gene MESP2 coincided in space and time with anteriorly traveling segmentation clock waves in the presomitic mesoderm of the organoids, recapitulating critical aspects of somitogenesis. Timed perturbations demonstrated that FGF and WNT signaling play distinct roles in axial elongation and somitogenesis, and that FGF signaling gradients drive segmentation clock waves. By generating and perturbing organoids that robustly recapitulate the architecture of multiple axial tissues in human embryos, this work offers a means to dissect mechanisms underlying human embryogenesis.

Cancer-on-a-chip model shows that the adenomatous polyposis coli mutation impairs T cell engagement and killing of cancer spheroids.

Evaluating the ability of cytotoxic T lymphocytes (CTLs) to eliminate tumor cells is crucial, for instance, to predict the efficiency of cell therapy in personalized medicine. However, the destruction of a tumor by CTLs involves CTL migration in the extra-tumoral environment, accumulation on the tumor, antigen recognition, and cooperation in killing the cancer cells. Therefore, identifying the limiting steps in this complex process requires spatio-temporal measurements of different cellular events over long periods. Here, we use a cancer-on-a-chip platform to evaluate the impact of adenomatous polyposis coli (APC) mutation on CTL migration and cytotoxicity against 3D tumor spheroids. The APC mutated CTLs are found to have a reduced ability to destroy tumor spheroids compared with control cells, even though APC mutants migrate in the extra-tumoral space and accumulate on the spheroids as efficiently as control cells. Once in contact with the tumor however, mutated CTLs display reduced engagement with the cancer cells, as measured by a metric that distinguishes different modes of CTL migration. Realigning the CTL trajectories around localized killing cascades reveals that all CTLs transition to high engagement in the 2 h preceding the cascades, which confirms that the low engagement is the cause of reduced cytotoxicity. Beyond the study of APC mutations, this platform offers a robust way to compare cytotoxic cell efficiency of even closely related cell types, by relying on a multiscale cytometry approach to disentangle complex interactions and to identify the steps that limit the tumor destruction.

Mechanics limits ecological diversity and promotes heterogeneity in confined bacterial communities.

Multispecies bacterial populations often inhabit confined and densely packed environments where spatial competition determines the ecological diversity of the community. However, the role of mechanical interactions in shaping the ecology is still poorly understood. Here, we study a model system consisting of two populations of nonmotile Escherichia coli bacteria competing within open, monolayer microchannels. The competitive dynamics is observed to be biphasic: After seeding, either one strain rapidly fixates or both strains orient into spatially stratified, stable communities. We find that mechanical interactions with other cells and local spatial constraints influence the resulting community ecology in unexpected ways, severely limiting the overall diversity of the communities while simultaneously allowing for the establishment of stable, heterogeneous populations of bacteria displaying disparate growth rates. Surprisingly, the populations have a high probability of coexisting even when one strain has a significant growth advantage. A more coccus morphology is shown to provide a selective advantage, but agent-based simulations indicate this is due to hydrodynamic and adhesion effects within the microchannel and not from breaking of the nematic ordering. Our observations are qualitatively reproduced by a simple Pólya urn model, which suggests the generality of our findings for confined population dynamics and highlights the importance of early colonization conditions on the resulting diversity and ecology of bacterial communities. These results provide fundamental insights into the determinants of community diversity in dense confined ecosystems where spatial exclusion is central to competition as in organized biofilms or intestinal crypts.

Developmental mechanisms understood quantitatively.

Across developmental systems, quantitative and imaging-based approaches have provided unprecedented resolution of dynamic changes in gene regulation and cell fate specification, along with complex changes in tissue morphology. This has set the stage for a wealth of comprehensive theoretical models, parameterised by experimental data, able to reproduce key aspects of biological behaviour and jointly enabling a higher level of abstraction, going from the identification of the molecular components to understanding complex functional relationships between these components. Despite these successes, gaining a cross-scale understanding of developmental systems will require further collaboration between disciplines, from developmental biology to bioengineering, systems biology and biophysics. We highlight the exciting multi-disciplinary research discussed at The Company of Biologists workshop 'Fostering quantitative modelling and experimentation in Developmental Biology'.

The global biomass of wild mammals.

Wild mammals are icons of conservation efforts, yet there is no rigorous estimate available for their overall global biomass. Biomass as a metric allows us to compare species with very different body sizes, and can serve as an indicator of wild mammal presence, trends, and impacts, on a global scale. Here, we compiled estimates of the total abundance (i.e., the number of individuals) of several hundred mammal species from the available data, and used these to build a model that infers the total biomass of terrestrial mammal species for which the global abundance is unknown. We present a detailed assessment, arriving at a total wet biomass of ≈20 million tonnes (Mt) for all terrestrial wild mammals (95% CI 13-38 Mt), i.e., ≈3 kg per person on earth. The primary contributors to the biomass of wild land mammals are large herbivores such as the white-tailed deer, wild boar, and African elephant. We find that even-hoofed mammals (artiodactyls, such as deer and boars) represent about half of the combined mass of terrestrial wild mammals. In addition, we estimated the total biomass of wild marine mammals at ≈40 Mt (95% CI 20-80 Mt), with baleen whales comprising more than half of this mass. In order to put wild mammal biomass into perspective, we additionally estimate the biomass of the remaining members of the class Mammalia. The total mammal biomass is overwhelmingly dominated by livestock (≈630 Mt) and humans (≈390 Mt). This work is a provisional census of wild mammal biomass on Earth and can serve as a benchmark for human impacts.

The spread of interferon-γ in melanomas is highly spatially confined, driving nongenetic variability in tumor cells.

Interferon-γ (IFNγ) is a critical antitumor cytokine that has varied effects on different cell types. The global effect of IFNγ in the tumor depends on which cells it acts upon and the spatial extent of its spread. Reported measurements of IFNγ spread vary dramatically in different contexts, ranging from nearest-neighbor signaling to perfusion throughout the entire tumor. Here, we apply theoretical considerations to experiments both in vitro and in vivo to study the spread of IFNγ in melanomas. We observe spatially confined niches of IFNγ signaling in 3-D mouse melanoma cultures and human tumors that generate cellular heterogeneity in gene expression and alter the susceptibility of affected cells to T cell killing. Widespread IFNγ signaling only occurs when niches overlap due to high local densities of IFNγ-producing T cells. We measured length scales of ~30 to 40 µm for IFNγ spread in B16 mouse melanoma cultures and human primary cutaneous melanoma. Our results are consistent with IFNγ spread being governed by a simple diffusion-consumption model and offer insight into how the spatial organization of T cells contributes to intratumor heterogeneity in inflammatory signaling, gene expression, and immune-mediated clearance. Solid tumors are often viewed as collections of diverse cellular "neighborhoods": Our work provides a general explanation for such nongenetic cellular variability due to confinement in the spread of immune mediators.

Resolving morphogenesis into quantifiable cell behaviours.

Morphogenesis is extremely diverse, but its systematic quantification to determine the physical mechanisms that produce different phenotypes is possible by quantifying the underlying cell behaviours. These are limited and definable: they consist of cell proliferation, orientation of cell division, cell rearrangement, directional matrix production, cell addition/subtraction and cell size/shape change. Although minor variations in these categories are possible, in sum they capture all possible morphogenetic behaviours. This article summarises these processes, discusses their measurement, and highlights some salient examples.

Catch and Release of Cytokines Mediated by Tumor Phosphatidylserine Converts Transient Exposure into Long-Lived Inflammation.

Immune cells constantly survey the host for pathogens or tumors and secrete cytokines to alert surrounding cells of these threats. In vivo, activated immune cells secrete cytokines for several hours, yet an acute immune reaction occurs over days. Given these divergent timescales, we addressed how cytokine-responsive cells translate brief cytokine exposure into phenotypic changes that persist over long timescales. We studied melanoma cell responses to transient exposure to the cytokine interferon γ (IFNγ) by combining a systems-scale analysis of gene expression dynamics with computational modeling and experiments. We discovered that IFNγ is captured by phosphatidylserine (PS) on the surface of viable cells both in vitro and in vivo then slowly released to drive long-term transcription of cytokine-response genes. This mechanism introduces an additional function for PS in dynamically regulating inflammation across diverse cancer and primary cell types and has potential to usher in new immunotherapies targeting PS and inflammatory pathways.

Systematic Investigation of Transcription Factor Activity in the Context of Chromatin Using Massively Parallel Binding and Expression Assays.

Precise gene expression patterns are established by transcription factor (TFs) binding to regulatory sequences. While these events occur in the context of chromatin, our understanding of how TF-nucleosome interplay affects gene expression is highly limited. Here, we present an assay for high-resolution measurements of both DNA occupancy and gene expression on large-scale libraries of systematically designed regulatory sequences. Our assay reveals occupancy patterns at the single-cell level. It provides an accurate quantification of the fraction of the population bound by a nucleosome and captures distinct, even adjacent, TF binding events. By applying this assay to over 1,500 promoter variants in yeast, we reveal pronounced differences in the dependency of TF activity on chromatin and classify TFs by their differential capacity to alter chromatin and promote expression. We further demonstrate how different regulatory sequences give rise to nucleosome-mediated TF collaborations that quantitatively account for the resulting expression.

Molecules interact. But how strong and how much?

Interactomics aims to characterize all interactions formed between molecules that comprise our body. Although it emerged from quantitative biophysics, it has devolved into a predominantly qualitative field of science over the past decades. Due to technical limitations at its onset, almost all tools in interactomics are qualitative, which persists in defining the discipline. Here, we argue that interactomics needs to return to a quantitative direction because the technical achievements of the last decade have overcome the original limitations that forced its current path. In contrast to qualitative interactomics which is constrained to charting lists of observed interactions, quantitative interactomics can also uncover answers to key questions such as the strength of interactions or how many of certain complexes can form in cells, thus providing researchers with more immediate proxies for understanding and predicting biological processes.

Guidelines for mechanistic modeling and analysis in cardiovascular research.

Computational, or in silico, models are an effective, noninvasive tool for investigating cardiovascular function. These models can be used in the analysis of experimental and clinical data to identify possible mechanisms of (ab)normal cardiovascular physiology. Recent advances in computing power and data management have led to innovative and complex modeling frameworks that simulate cardiovascular function across multiple scales. While commonly used in multiple disciplines, there is a lack of concise guidelines for the implementation of computer models in cardiovascular research. In line with recent calls for more reproducible research, it is imperative that scientists adhere to credible practices when developing and applying computational models to their research. The goal of this manuscript is to provide a consensus document that identifies best practices for in silico computational modeling in cardiovascular research. These guidelines provide the necessary methods for mechanistic model development, model analysis, and formal model calibration using fundamentals from statistics. We outline rigorous practices for computational, mechanistic modeling in cardiovascular research and discuss its synergistic value to experimental and clinical data.

Positive Feedback Keeps Duration of Mitosis Temporally Insulated from Upstream Cell-Cycle Events.

Cell division is characterized by a sequence of events by which a cell gives rise to two daughter cells. Quantitative measurements of cell-cycle dynamics in single cells showed that despite variability in G1-, S-, and G2 phases, duration of mitosis is short and remarkably constant. Surprisingly, there is no correlation between cell-cycle length and mitotic duration, suggesting that mitosis is temporally insulated from variability in earlier cell-cycle phases. By combining live cell imaging and computational modeling, we showed that positive feedback is the molecular mechanism underlying the temporal insulation of mitosis. Perturbing positive feedback gave rise to a sluggish, variable entry and progression through mitosis and uncoupled duration of mitosis from variability in cell cycle length. We show that positive feedback is important to keep mitosis short, constant, and temporally insulated and anticipate it might be a commonly used regulatory strategy to create modularity in other biological systems.

Trajectory Analysis in Single-Particle Tracking: From Mean Squared Displacement to Machine Learning Approaches.

Single-particle tracking is a powerful technique to investigate the motion of molecules or particles. Here, we review the methods for analyzing the reconstructed trajectories, a fundamental step for deciphering the underlying mechanisms driving the motion. First, we review the traditional analysis based on the mean squared displacement (MSD), highlighting the sometimes-neglected factors potentially affecting the accuracy of the results. We then report methods that exploit the distribution of parameters other than displacements, e.g., angles, velocities, and times and probabilities of reaching a target, discussing how they are more sensitive in characterizing heterogeneities and transient behaviors masked in the MSD analysis. Hidden Markov Models are also used for this purpose, and these allow for the identification of different states, their populations and the switching kinetics. Finally, we discuss a rapidly expanding field-trajectory analysis based on machine learning. Various approaches, from random forest to deep learning, are used to classify trajectory motions, which can be identified by motion models or by model-free sets of trajectory features, either previously defined or automatically identified by the algorithms. We also review free software available for some of the analysis methods. We emphasize that approaches based on a combination of the different methods, including classical statistics and machine learning, may be the way to obtain the most informative and accurate results.

Integration of quantitative methods and mathematical approaches for the modeling of cancer cell proliferation dynamics.

Physiological processes rely on the control of cell proliferation, and the dysregulation of these processes underlies various pathological conditions, including cancer. Mathematical modeling can provide new insights into the complex regulation of cell proliferation dynamics. In this review, we first examine quantitative experimental approaches for measuring cell proliferation dynamics in vitro and compare the various types of data that can be obtained in these settings. We then explore the toolbox of common mathematical modeling frameworks that can describe cell behavior, dynamics, and interactions of proliferation. We discuss how these wet-laboratory studies may be integrated with different mathematical modeling approaches to aid the interpretation of the results and to enable the prediction of cell behaviors, specifically in the context of cancer.

EPySeg: a coding-free solution for automated segmentation of epithelia using deep learning.

Epithelia are dynamic tissues that self-remodel during their development. During morphogenesis, the tissue-scale organization of epithelia is obtained through a sum of individual contributions of the cells constituting the tissue. Therefore, understanding any morphogenetic event first requires a thorough segmentation of its constituent cells. This task, however, usually involves extensive manual correction, even with semi-automated tools. Here, we present EPySeg, an open-source, coding-free software that uses deep learning to segment membrane-stained epithelial tissues automatically and very efficiently. EPySeg, which comes with a straightforward graphical user interface, can be used as a Python package on a local computer, or on the cloud via Google Colab for users not equipped with deep-learning compatible hardware. By substantially reducing human input in image segmentation, EPySeg accelerates and improves the characterization of epithelial tissues for all developmental biologists.

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.

Metabolomics meets lipidomics: Assessing the small molecule component of metabolism.

Metabolomics, including lipidomics, is emerging as a quantitative biology approach for the assessment of energy flow through metabolism and information flow through metabolic signaling; thus, providing novel insights into metabolism and its regulation, in health, healthy ageing and disease. In this forward-looking review we provide an overview on the origins of metabolomics, on its role in this postgenomic era of biochemistry and its application to investigate metabolite role and (bio)activity, from model systems to human population studies. We present the challenges inherent to this analytical science, and approaches and modes of analysis that are used to resolve, characterize and measure the infinite chemical diversity contained in the metabolome (including lipidome) of complex biological matrices. In the current outbreak of metabolic diseases such as cardiometabolic disorders, cancer and neurodegenerative diseases, metabolomics appears to be ideally situated for the investigation of disease pathophysiology from a metabolite perspective.

The global mass and average rate of rubisco.

Photosynthetic carbon assimilation enables energy storage in the living world and produces most of the biomass in the biosphere. Rubisco (d-ribulose 1,5-bisphosphate carboxylase/oxygenase) is responsible for the vast majority of global carbon fixation and has been claimed to be the most abundant protein on Earth. Here we provide an updated and rigorous estimate for the total mass of Rubisco on Earth, concluding it is ≈0.7 Gt, more than an order of magnitude higher than previously thought. We find that >90% of Rubisco enzymes are found in the ≈2 × 1014 m2 of leaves of terrestrial plants, and that Rubisco accounts for ≈3% of the total mass of leaves, which we estimate at ≈30 Gt dry weight. We use our estimate for the total mass of Rubisco to derive the effective time-averaged catalytic rate of Rubisco of ≈0.03 s-1 on land and ≈0.6 s-1 in the ocean. Compared with the maximal catalytic rate observed in vitro at 25 °C, the effective rate in the wild is ≈100-fold slower on land and sevenfold slower in the ocean. The lower ambient temperature, and Rubisco not working at night, can explain most of the difference from laboratory conditions in the ocean but not on land, where quantification of many more factors on a global scale is needed. Our analysis helps sharpen the dramatic difference between laboratory and wild environments and between the terrestrial and marine environments.

Assessment of assumptions underlying models of prokaryotic pangenome evolution.

BACKGROUND: The genomes of bacteria and archaea evolve by extensive loss and gain of genes which, for any group of related prokaryotic genomes, result in the formation of a pangenome with the universal, asymmetrical U-shaped distribution of gene commonality. However, the evolutionary factors that define the specific shape of this distribution are not thoroughly understood. RESULTS: We investigate the fit of simple models of genome evolution to the empirically observed gene commonality distributions and genome intersections for 33 groups of closely related bacterial genomes. A model with an infinite external gene pool available for gene acquisition and constant genome size (IGP-CGS model), and two gene turnover rates, one for slow- and the other one for fast-evolving genes, allows two approaches to estimate the parameters for gene content dynamics. One is by fitting the model prediction to the distribution of the number of genes shared by precisely k genomes (gene commonality distribution) and another by analyzing the distribution of the number of genes common for k genome sets (k-cores). Both approaches produce a comparable overall quality of fit, although the former significantly overestimates the number of the universally conserved genes, while the latter overestimates the number of singletons. We further explore the effect of dropping each of the assumptions of the IGP-CGS model on the fit to the gene commonality distributions and show that models with either a finite gene pool or unequal rates of gene loss and gain (greater gene loss rate) eliminate the overestimate of the number of singletons or the core genome size. CONCLUSIONS: We examine the assumptions that are usually adopted for modeling the evolution of the U-shaped gene commonality distributions in prokaryote genomes, namely, those of infinitely many genes and constant genome size. The combined analysis of genome intersections and gene commonality suggests that at least one of these assumptions is invalid. The violation of both these assumptions reflects the limited ability of prokaryotes to gain new genes. This limitation seems to stem, at least partly, from the horizontal gene transfer barrier, i.e., the cost of accommodation of foreign genes by prokaryotes. Further development of models taking into account the complexity of microbial evolution is necessary for an improved understanding of the evolution of prokaryotes.