Fast detection of slender bodies in high density microscopy data.

Computer-aided analysis of biological microscopy data has seen a massive improvement with the utilization of general-purpose deep learning techniques. Yet, in microscopy studies of multi-organism systems, the problem of collision and overlap remains challenging. This is particularly true for systems composed of slender bodies such as swimming nematodes, swimming spermatozoa, or the beating of eukaryotic or prokaryotic flagella. Here, we develop a end-to-end deep learning approach to extract precise shape trajectories of generally motile and overlapping slender bodies. Our method works in low resolution settings where feature keypoints are hard to define and detect. Detection is fast and we demonstrate the ability to track thousands of overlapping organisms simultaneously. While our approach is agnostic to area of application, we present it in the setting of and exemplify its usability on dense experiments of swimming Caenorhabditis elegans. The model training is achieved purely on synthetic data, utilizing a physics-based model for nematode motility, and we demonstrate the model's ability to generalize from simulations to experimental videos.

Protocol to assess metabolic activity of Pseudomonas aeruginosa by measuring heat flow using isothermal calorimetry.

Here, we present a protocol for assessing metabolic activity of bacterial populations by measuring heat flow using isothermal calorimetry. We outline the steps for preparing the different growth models of Pseudomonas aeruginosa and performing continuous metabolic activity measurements in the calScreener. We detail simple principal component analysis to differentiate between metabolic states of different populations and probabilistic logistic classification to assess resemblance to wild-type bacteria. This protocol for fine-scale metabolic measurement can aid in understanding microbial physiology. For complete details on the use and execution of this protocol, please refer to Lichtenberg et al. (2022).1.

Epigenetic switching with asymmetric bridging interactions.

Gene expression states are often stably sustained in cis despite massively disruptive events like DNA replication. This is achieved by on-going enzymatic activity that maintains parts of the DNA in either heterochromatic (packed) or euchromatic (free) states, each of which is stabilized by both positive feedback and bridging interactions between individual nucleosomes. In contrast to condensed matter, however, the dynamics is not only governed by equilibrium binding interactions but is also mediated by enzymes that recognize and act on specific amino acid tails of the nucleosomes. The mechanical result is that some nucleosomes can bind to one another and form tightly packed polymer configurations, whereas others remain unbound and form free, noncompact polymer configurations. Here, we study the consequences of such an asymmetric interaction pattern on the dynamics of epigenetic switching. We develop a 3D polymer model and show that traits associated with epigenetic switching, such as bistability and epigenetic memory, are permitted by such a model. We find, however, that the experimentally observed burst-like nature of some epigenetic switches is difficult to reproduce by this biologically motivated interaction. Instead, the behavior seen in experiments can be explained by introducing partial confinement, which particularly affects the euchromatic regions of the chromosome.

Emerging diversity in a population of evolving intransitive dice.

Exploiting the mathematical curiosity of intransitive dice, we present a simple theoretical model for coevolution that captures scales ranging from the genome of the individual to the system-wide emergence of species diversity. We study a set of evolving agents that interact competitively in a closed system, in which both the dynamics of mutations and competitive advantage emerge directly from interpreting a genome as the sides of a die. The model demonstrates sympatric speciation where new species evolve from existing ones while in contact with the entire ecosystem. Allowing free mutations both in the genomes and the mutation rates, we find, in contrast to hierarchical models of fitness, the emergence of a metastable state of finite mutation rate and diversity.

Cyclic-di-GMP signaling controls metabolic activity in Pseudomonas aeruginosa.

Bacteria in biofilms are embedded in extracellular matrix and display low metabolic activity, partly due to insufficient diffusive exchange of metabolic substrate. The extracellular matrix and low metabolic activity both contribute to the high antibiotic tolerance-the hallmark of biofilm bacteria. The second messenger molecule, c-di-GMP, regulates biofilm development in Pseudomonas aeruginosa, where high internal levels lead to biofilm formation and low levels are associated with planktonic bacteria. Using a microcalorimetric approach, we show that c-di-GMP signaling is a major determinant of the metabolic activity of P. aeruginosa, both in planktonic culture and in two biofilm models. The high c-di-GMP content of biofilm bacteria forces them to rapidly spend a large amount of energy on the production of exopolysaccharides, resulting in a subsequent low metabolic state. This suggests that the low metabolic state of bacteria in mature biofilms, to some extent, is a consequence of a c-di-GMP-regulated survival strategy.

Transcriptomic fingerprint of bacterial infection in lower extremity ulcers.

Clinicians and researchers utilize subjective, clinical classification systems to stratify lower extremity ulcer infections for treatment and research. The purpose of this study was to examine whether these clinical classifications are reflected in the ulcer's transcriptome. RNA sequencing (RNA-seq) was performed on biopsies from clinically infected lower extremity ulcers (n = 44). Resulting sequences were aligned to the host reference genome to create a transcriptome profile. Differential gene expression analysis and gene ontology (GO) enrichment analysis were performed between ulcer severities as well as between sample groups identified by k-means clustering. Lastly, a support vector classifier was trained to estimate clinical infection score or k-means cluster based on a subset of genes. Clinical infection severity did not explain the major sources of variability among the samples and samples with the same clinical classification demonstrated high inter-sample variability. High proportions of bacterial RNA were identified in some samples, which resulted in a strong effect on transcription and increased expression of genes associated with immune response and inflammation. K-means clustering identified two clusters of samples, one of which contained all of the samples with high levels of bacterial RNA. A support vector classifier identified a fingerprint of 20 genes, including immune-associated genes such as CXCL8, GADD45B, and HILPDA, which accurately identified samples with signs of infection via cross-validation. This study identified a unique, host-transcriptome signature in the presence of infecting bacteria, often incongruent with clinical infection-severity classifications. This suggests that stratification of infection status based on a transcriptomic fingerprint may be useful as an objective classification method to classify infection severity, as well as a tool for studying host-pathogen interactions.

Simultant: simultaneous curve fitting of functions and differential equations using analytical gradient calculations.

BACKGROUND: The initial step in comparing mathematical models to experimental data is to do a fit. This process can be complicated when either the mathematical models are not analytically solvable (e.g. because of nonlinear differential equations) or when the relation between data and models is complex (e.g. when some fitting parameters must be shared between many data sets). RESULTS: We introduce Simultant, a software package that allows complex fitting setups to be easily defined using a simple graphical user interface. Fitting functions can be defined directly as mathematical expressions or indirectly as the solution to specified ordinary differential equations. Analytical gradients of these functions, including the solution of differential equations, are automatically calculated to provide fast fitting even for functions with many parameters. The software enables easy definition of complex fitting setups in which parameters can be shared across both data sets and models to allow simultaneous fits to be performed. CONCLUSIONS: Simultant exploits differentiable programming and simplifies modern fitting approaches in a unified graphical interface.

Superspreading quantified from bursty epidemic trajectories.

The quantification of spreading heterogeneity in the COVID-19 epidemic is crucial as it affects the choice of efficient mitigating strategies irrespective of whether its origin is biological or social. We present a method to deduce temporal and individual variations in the basic reproduction number directly from epidemic trajectories at a community level. Using epidemic data from the 98 districts in Denmark we estimate an overdispersion factor k for COVID-19 to be about 0.11 (95% confidence interval 0.08-0.18), implying that 10 % of the infected cause between 70 % and 87 % of all infections.

Superspreading of airborne pathogens in a heterogeneous world.

Epidemics are regularly associated with reports of superspreading: single individuals infecting many others. How do we determine if such events are due to people inherently being biological superspreaders or simply due to random chance? We present an analytically solvable model for airborne diseases which reveal the spreading statistics of epidemics in socio-spatial heterogeneous spaces and provide a baseline to which data may be compared. In contrast to classical SIR models, we explicitly model social events where airborne pathogen transmission allows a single individual to infect many simultaneously, a key feature that generates distinctive output statistics. We find that diseases that have a short duration of high infectiousness can give extreme statistics such as 20% infecting more than 80%, depending on the socio-spatial heterogeneity. Quantifying this by a distribution over sizes of social gatherings, tracking data of social proximity for university students suggest that this can be a approximated by a power law. Finally, we study mitigation efforts applied to our model. We find that the effect of banning large gatherings works equally well for diseases with any duration of infectiousness, but depends strongly on socio-spatial heterogeneity.

Comparative Studies in the A30P and A53T α-Synuclein C. elegans Strains to Investigate the Molecular Origins of Parkinson's Disease.

The aggregation of α-synuclein is a hallmark of Parkinson's disease (PD) and a variety of related neurological disorders. A number of mutations in this protein, including A30P and A53T, are associated with familial forms of the disease. Patients carrying the A30P mutation typically exhibit a similar age of onset and symptoms as sporadic PD, while those carrying the A53T mutation generally have an earlier age of onset and an accelerated progression. We report two C. elegans models of PD (PDA30P and PDA53T), which express these mutational variants in the muscle cells, and probed their behavior relative to animals expressing the wild-type protein (PDWT). PDA30P worms showed a reduced speed of movement and an increased paralysis rate, control worms, but no change in the frequency of body bends. By contrast, in PDA53T worms both speed and frequency of body bends were significantly decreased, and paralysis rate was increased. α-Synuclein was also observed to be less well localized into aggregates in PDA30P worms compared to PDA53T and PDWT worms, and amyloid-like features were evident later in the life of the animals, despite comparable levels of expression of α-synuclein. Furthermore, squalamine, a natural product currently in clinical trials for treating symptomatic aspects of PD, was found to reduce significantly the aggregation of α-synuclein and its associated toxicity in PDA53T and PDWT worms, but had less marked effects in PDA30P. In addition, using an antibody that targets the N-terminal region of α-synuclein, we observed a suppression of toxicity in PDA30P, PDA53T and PDWT worms. These results illustrate the use of these two C. elegans models in fundamental and applied PD research.

Optimal Transport Flows for Distributed Production Networks.

Network flows often exhibit a hierarchical treelike structure that can be attributed to the minimization of dissipation. The common feature of such systems is a single source and multiple sinks (or vice versa). In contrast, here we study networks with only a single source and sink. These systems can arise from secondary purposes of the networks, such as blood sugar regulation through insulin production. Minimization of dissipation in these systems leads to vascular shunting, a single vessel connecting the inlet and outlet. We show instead how optimizing the transport time yields network topologies that match those observed in the insulin-producing pancreatic islets. These are patterns of periphery-to-center and center-to-periphery flows. The obtained flow networks are broadly independent of how the flow velocity depends on the flow flux, but continuous and discontinuous phase transitions appear at extreme flux dependencies. Lastly, we show how constraints on flows can lead to buckling of the branches of the network, a feature that is also observed in pancreatic islets.

Self-assembly, buckling and density-invariant growth of three-dimensional vascular networks.

The experimental actualization of organoids modelling organs from brains to pancreases has revealed that much of the diverse morphologies of organs are emergent properties of simple intercellular 'rules' and not the result of top-down orchestration. In contrast to other organs, the initial plexus of the vascular system is formed by aggregation of cells in the process known as vasculogenesis. Here we study this self-assembling process of blood vessels in three dimensions through a set of simple rules that align intercellular apical-basal and planar cell polarity. We demonstrate that a fully connected network of tubes emerges above a critical initial density of cells. Through planar cell polarity, our model demonstrates convergent extension, and this polarity furthermore allows for both morphology-maintaining growth and growth-induced buckling. We compare this buckling with the special vasculature of the islets of Langerhans in the pancreas and suggest that the mechanism behind the vascular density-maintaining growth of these islets could be the result of growth-induced buckling.

The noisy basis of morphogenesis: Mechanisms and mechanics of cell sheet folding inferred from developmental variability.

Variability is emerging as an integral part of development. It is therefore imperative to ask how to access the information contained in this variability. Yet most studies of development average their observations and, discarding the variability, seek to derive models, biological or physical, that explain these average observations. Here, we analyse this variability in a study of cell sheet folding in the green alga Volvox, whose spherical embryos turn themselves inside out in a process sharing invagination, expansion, involution, and peeling of a cell sheet with animal models of morphogenesis. We generalise our earlier, qualitative model of the initial stages of inversion by combining ideas from morphoelasticity and shell theory. Together with three-dimensional visualisations of inversion using light sheet microscopy, this yields a detailed, quantitative model of the entire inversion process. With this model, we show how the variability of inversion reveals that two separate, temporally uncoupled processes drive the initial invagination and subsequent expansion of the cell sheet. This implies a prototypical transition towards higher developmental complexity in the volvocine algae and provides proof of principle of analysing morphogenesis based on its variability.

Multistep Inhibition of α-Synuclein Aggregation and Toxicity in Vitro and in Vivo by Trodusquemine.

The aggregation of α-synuclein, an intrinsically disordered protein that is highly abundant in neurons, is closely associated with the onset and progression of Parkinson's disease. We have shown previously that the aminosterol squalamine can inhibit the lipid induced initiation process in the aggregation of α-synuclein, and we report here that the related compound trodusquemine is capable of inhibiting not only this process but also the fibril-dependent secondary pathways in the aggregation reaction. We further demonstrate that trodusquemine can effectively suppress the toxicity of α-synuclein oligomers in neuronal cells, and that its administration, even after the initial growth phase, leads to a dramatic reduction in the number of α-synuclein inclusions in a Caenorhabditis elegans model of Parkinson's disease, eliminates the related muscle paralysis, and increases lifespan. On the basis of these findings, we show that trodusquemine is able to inhibit multiple events in the aggregation process of α-synuclein and hence to provide important information about the link between such events and neurodegeneration, as it is initiated and progresses. Particularly in the light of the previously reported ability of trodusquemine to cross the blood-brain barrier and to promote tissue regeneration, the present results suggest that this compound has the potential to be an important therapeutic candidate for Parkinson's disease and related disorders.

Massively parallel C. elegans tracking provides multi-dimensional fingerprints for phenotypic discovery.

BACKGROUND: The nematode worm C. elegans is a model organism widely used for studies of genetics and of human disease. The health and fitness of the worms can be quantified in different ways, such as by measuring their bending frequency, speed or lifespan. Manual assays, however, are time consuming and limited in their scope providing a strong motivation for automation. NEW METHOD: We describe the development and application of an advanced machine vision system for characterising the behaviour of C. elegans, the Wide Field-of-View Nematode Tracking Platform (WF-NTP), which enables massively parallel data acquisition and automated multi-parameter behavioural profiling of thousands of worms simultaneously. RESULTS: We screened more than a million worms from several established models of neurodegenerative disorders and characterised the effects of potential therapeutic molecules for Alzheimer's and Parkinson's diseases. By using very large numbers of animals we show that the sensitivity and reproducibility of behavioural assays is very greatly increased. The results reveal the ability of this platform to detect even subtle phenotypes. COMPARISON WITH EXISTING METHODS: The WF-NTP method has substantially greater capacity compared to current automated platforms that typically either focus on characterising single worms at high resolution or tracking the properties of populations of less than 50 animals. CONCLUSIONS: The WF-NTP extends significantly the power of existing automated platforms by combining enhanced optical imaging techniques with an advanced software platform. We anticipate that this approach will further extend the scope and utility of C. elegans as a model organism.

Intra-chain organisation of hydrophobic residues controls inter-chain aggregation rates of amphiphilic polymers.

Aggregation of amphiphiles through the action of hydrophobic interactions is a common feature in soft condensed matter systems and is of particular importance in the context of biophysics as it underlies both the generation of functional biological machinery as well as the formation of pathological misassembled states of proteins. Here we explore the aggregation behaviour of amphiphilic polymers using lattice Monte Carlo calculations and show that the distribution of hydrophobic residues within the polymer sequence determines the facility with which dry/wet interfaces can be created and that such interfaces drive the aggregation process.

A natural product inhibits the initiation of α-synuclein aggregation and suppresses its toxicity.

The self-assembly of α-synuclein is closely associated with Parkinson's disease and related syndromes. We show that squalamine, a natural product with known anticancer and antiviral activity, dramatically affects α-synuclein aggregation in vitro and in vivo. We elucidate the mechanism of action of squalamine by investigating its interaction with lipid vesicles, which are known to stimulate nucleation, and find that this compound displaces α-synuclein from the surfaces of such vesicles, thereby blocking the first steps in its aggregation process. We also show that squalamine almost completely suppresses the toxicity of α-synuclein oligomers in human neuroblastoma cells by inhibiting their interactions with lipid membranes. We further examine the effects of squalamine in a Caenorhabditis elegans strain overexpressing α-synuclein, observing a dramatic reduction of α-synuclein aggregation and an almost complete elimination of muscle paralysis. These findings suggest that squalamine could be a means of therapeutic intervention in Parkinson's disease and related conditions.

Filter-feeding, near-field flows, and the morphologies of colonial choanoflagellates.

Efficient uptake of prey and nutrients from the environment is an important component in the fitness of all microorganisms, and its dependence on size may reveal clues to the origins of evolutionary transitions to multicellularity. Because potential benefits in uptake rates must be viewed in the context of other costs and benefits of size, such as varying predation rates and the increased metabolic costs associated with larger and more complex body plans, the uptake rate itself is not necessarily that which is optimized by evolution. Uptake rates can be strongly dependent on local organism geometry and its swimming speed, providing selective pressure for particular arrangements. Here we examine these issues for choanoflagellates, filter-feeding microorganisms that are the closest relatives of the animals. We explore the different morphological variations of the choanoflagellate Salpingoeca rosetta, which can exist as a swimming cell, as a sessile thecate cell, and as colonies of cells in various shapes. In the absence of other requirements and in a homogeneously nutritious environment, we find that the optimal strategy to maximize filter-feeding by the collar of microvilli is to swim fast, which favors swimming unicells. In large external flows, the sessile thecate cell becomes advantageous. Effects of prey diffusion are discussed and also found to be to the advantage of the swimming unicell.

Aerotaxis in the closest relatives of animals.

As the closest unicellular relatives of animals, choanoflagellates serve as useful model organisms for understanding the evolution of animal multicellularity. An important factor in animal evolution was the increasing ocean oxygen levels in the Precambrian, which are thought to have influenced the emergence of complex multicellular life. As a first step in addressing these conditions, we study here the response of the colony-forming choanoflagellate Salpingoeca rosetta to oxygen gradients. Using a microfluidic device that allows spatio-temporal variations in oxygen concentrations, we report the discovery that S. rosetta displays positive aerotaxis. Analysis of the spatial population distributions provides evidence for logarithmic sensing of oxygen, which enhances sensing in low oxygen neighborhoods. Analysis of search strategy models on the experimental colony trajectories finds that choanoflagellate aerotaxis is consistent with stochastic navigation, the statistics of which are captured using an effective continuous version based on classical run-and-tumble chemotaxis.

Fluctuations in the Kinetics of Linear Protein Self-Assembly.

Biological systems are characterized by compartmentalization from the subcellular to the tissue level, and thus reactions in small volumes are ubiquitous in living systems. Under such conditions, statistical number fluctuations, which are commonly negligible in bulk reactions, can become dominant and lead to stochastic behavior. We present here a stochastic model of protein filament formation in small volumes. We show that two principal regimes emerge for the system behavior, a small fluctuation regime close to bulk behavior and a large fluctuation regime characterized by single rare events. Our analysis shows that in both regimes the reaction lag-time scales inversely with the system volume, unlike in bulk. Finally, we use our stochastic model to connect data from small-volume microdroplet experiments of amyloid formation to bulk aggregation rates, and show that digital analysis of an ensemble of protein aggregation reactions taking place under microconfinement provides an accurate measure of the rate of primary nucleation of protein aggregates, a process that has been challenging to quantify from conventional bulk experiments.