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The desire to understand how the brain generates and patterns behavior has driven rapid methodological innovation in tools to quantify natural animal behavior. While advances in deep learning and computer vision have enabled markerless pose estimation in individual animals, extending these to multiple animals presents unique challenges for studies of social behaviors or animals in their natural environments. Here we present Social LEAP Estimates Animal Poses (SLEAP), a machine learning system for multi-animal pose tracking. This system enables versatile workflows for data labeling, model training and inference on previously unseen data. SLEAP features an accessible graphical user interface, a standardized data model, a reproducible configuration system, over 30 model architectures, two approaches to part grouping and two approaches to identity tracking. We applied SLEAP to seven datasets across flies, bees, mice and gerbils to systematically evaluate each approach and architecture, and we compare it with other existing approaches. SLEAP achieves greater accuracy and speeds of more than 800 frames per second, with latencies of less than 3.5 ms at full 1,024 × 1,024 image resolution. This makes SLEAP usable for real-time applications, which we demonstrate by controlling the behavior of one animal on the basis of the tracking and detection of social interactions with another animal.

The predatory deltaproteobacterium Myxococcus xanthus uses a helically-trafficked motor at bacterial focal-adhesion (bFA) sites to power gliding motility. Using total internal reflection fluorescence and force microscopies, we identify the von Willebrand A domain-containing outer-membrane (OM) lipoprotein CglB as an essential substratum-coupling adhesin of the gliding transducer (Glt) machinery at bFAs. Biochemical and genetic analyses reveal that CglB localizes to the cell surface independently of the Glt apparatus; once there, it is recruited by the OM module of the gliding machinery, a heteroligomeric complex containing the integral OM β barrels GltA, GltB, and GltH, as well as the OM protein GltC and OM lipoprotein GltK. This Glt OM platform mediates the cell-surface accessibility and retention of CglB by the Glt apparatus. Together, these data suggest that the gliding complex promotes regulated surface exposure of CglB at bFAs, thus explaining the manner by which contractile forces exerted by inner-membrane motors are transduced across the cell envelope to the substratum.

Significant advances in computational ethology have allowed the quantification of behavior in unprecedented detail. Tracking animals in social groups, however, remains challenging as most existing methods can either capture pose or robustly retain individual identity over time but not both. To capture finely resolved behaviors while maintaining individual identity, we built NAPS (NAPS is ArUco Plus SLEAP), a hybrid tracking framework that combines state-of-the-art, deep learning-based methods for pose estimation (SLEAP) with unique markers for identity persistence (ArUco). We show that this framework allows the exploration of the social dynamics of the common eastern bumblebee (Bombus impatiens). We provide a stand-alone Python package for implementing this framework along with detailed documentation to allow for easy utilization and expansion. We show that NAPS can scale to long timescale experiments at a high frame rate and that it enables the investigation of detailed behavioral variation within individuals in a group. Expanding the toolkit for capturing the constituent behaviors of social groups is essential for understanding the structure and dynamics of social networks. NAPS provides a key tool for capturing these behaviors and can provide critical data for understanding how individual variation influences collective dynamics.

A brief history of optical forces, the invention of optical tweezers, and their application to biological problems.

Significance While many bacteria can sense the presence of a surface, the mechanical properties of different surfaces vary tremendously and can be as rigid as bone or as soft as mucus. We show that the pathogen Pseudomonas aeruginosa distinguishes surfaces by stiffness and transcriptionally tunes its virulence to surface rigidity. This connection between pathogenicity and mechanical properties of the infection site presents an interesting potential for clinical applications. The mechanism behind stiffness sensing relies on the retraction of external appendages called type IV pili that deform the surface. While this mechanism has interesting parallels to stiffness sensing in mammalian cells, our results suggest that stiffness sensing in much smaller bacterial cells relies on temporal sensing instead of spatial sensing strategies.

Background Repetitive action, resistance to environmental change and fine motor disruptions are hallmarks of autism spectrum disorder (ASD) and other neurodevelopmental disorders, and vary considerably from individual to individual. In animal models, conventional behavioral phenotyping captures such fine-scale variations incompletely. Here we observed male and female C57BL/6J mice to methodically catalog adaptive movement over multiple days and examined two rodent models of developmental disorders against this dynamic baseline. We then investigated the behavioral consequences of a cerebellum-specific deletion in Tsc1 protein and a whole-brain knockout in Cntnap2 protein in mice. Both of these mutations are found in clinical conditions and have been associated with ASD. Methods We used advances in computer vision and deep learning, namely a generalized form of high-dimensional statistical analysis, to develop a framework for characterizing mouse movement on multiple timescales using a single popular behavioral assay, the open-field test. The pipeline takes virtual markers from pose estimation to find behavior clusters and generate wavelet signatures of behavior classes. We measured spatial and temporal habituation to a new environment across minutes and days, different types of self-grooming, locomotion and gait. Results Both Cntnap2 knockouts and L7-Tsc1 mutants showed forelimb lag during gait. L7-Tsc1 mutants and Cntnap2 knockouts showed complex defects in multi-day adaptation, lacking the tendency of wild-type mice to spend progressively more time in corners of the arena. In L7-Tsc1 mutant mice, failure to adapt took the form of maintained ambling, turning and locomotion, and an overall decrease in grooming. However, adaptation in these traits was similar between wild-type mice and Cntnap2 knockouts. L7-Tsc1 mutant and Cntnap2 knockout mouse models showed different patterns of behavioral state occupancy. Limitations Genetic risk factors for autism are numerous, and we tested only two. Our pipeline was only done under conditions of free behavior. Testing under task or social conditions would reveal more information about behavioral dynamics and variability. Conclusions Our automated pipeline for deep phenotyping successfully captures model-specific deviations in adaptation and movement as well as differences in the detailed structure of behavioral dynamics. The reported deficits indicate that deep phenotyping constitutes a robust set of ASD symptoms that may be considered for implementation in clinical settings as quantitative diagnosis criteria.

Microtubules are generated at centrosomes, chromosomes, and within spindles during cell division. Whereas microtubule nucleation at the centrosome is well characterized, much remains unknown about where, when, and how microtubules are nucleated at chromosomes. To address these questions, we reconstituted microtubule nucleation from purified chromosomes in meiotic Xenopus egg extract and found that chromosomes alone can form spindles. We visualized microtubule nucleation at chromosomes using total internal reflection fluorescence microscopy to find that this occurs through branching microtubule nucleation. The initial branches nucleate near and towards kinetochores, helping explain how kinetochores might be efficiently captured. By depleting molecular motors, we find that the organization of the resultant polar branched networks is consistent with a theoretical model where the effectors for branching nucleation are released by chromosomes, forming a concentration gradient around them that spatially biases branching nucleation. In the presence of motors, these branched networks are organized into multipolar spindles.

In mammals, subcellular protein localization of factors like planar cell polarity proteins is a key driver of the multicellular organization of tissues. Bacteria also form organized multicellular communities, but these patterns are largely thought to emerge from regulation of whole-cell processes like growth, motility, cell shape, and differentiation. Here we show that a unique intracellular patterning of appendages known as type IV pili (T4P) can drive multicellular development of complex bacterial communities. Specifically, dynamic T4P appendages localize in a line along the long axis of the cell in the bacterium Acinetobacter baylyi. This long-axis localization is regulated by a functionally divergent chemosensory Pil-Chp system, and an atypical T4P protein homologue (FimV) bridges Pil-Chp signaling and T4P positioning. We further demonstrate through modeling and empirical approaches that subcellular T4P localization controls how individual cells interact with one another, independently of T4P dynamics, with different patterns of localization giving rise to distinct multicellular architectures. Our results reveal how subcellular patterning of single cells regulates the development of multicellular bacterial communities.

SummarySocial isolation, particularly in early life, leads to deleterious physiological and behavioral outcomes. Few studies, if any, have been able to capture the behavioral and neurogenomic consequences of early life social isolation together in a single social animal system. Here, we leverage new high-throughput tools to comprehensively investigate the impact of isolation in the bumblebee (Bombus impatiens) from behavioral, molecular, and neuroanatomical perspectives. We reared newly emerged bumblebees either in complete isolation, small groups, or in their natal colony, and then analyzed their behaviors while alone or paired with another bee. We find that when alone, individuals of each rearing condition show distinct behavioral signatures. When paired with a conspecific, bees reared in small groups or in the natal colony express similar behavioral profiles. Isolated bees, however, showed increased social interactions. To identify the neurobiological correlates of these differences, we quantified brain gene expression and measured the volumes of key brain regions for a subset of individuals from each rearing condition. Overall, we find that isolation increases social interactions and disrupts gene expression and brain development. Limited social experience in small groups is sufficient to preserve typical patterns of brain development and social behavior.

The cerebellum regulates nonmotor behavior, but the routes by which it exerts its influence are not well characterized. Here we report a necessary role for the posterior cerebellum in guiding a reversal learning task, acting through a network of diencephalic and neocortical structures. After chemogenetic inhibition of Purkinje cells in lobule VI or crus I, mice could learn a water Y-maze task but were impaired in their ability to reverse their initial choice. To map targets of perturbation, we imaged c-Fos activation in cleared whole brains using light-sheet microscopy. Reversal learning activated diencephalic regions and associative neocortical regions. Distinctive subsets of structures were altered by perturbation of lobule VI (including thalamus and habenula) and crus I (including hypothalamus and prelimbic/orbital cortex), and both perturbations influenced anterior cingulate and infralimbic cortex. To identify functional networks, we used correlated variation in c-Fos activation within each test group. Lobule VI inactivation weakened within-thalamus correlations, while crus I inactivation divided neocortical activity into well-separated sensorimotor and associative subnetworks. In both groups, high-throughput automated analysis of complex whole-body movement revealed deficiencies in across-day adaptation to an open field environment. Neither perturbation affected gait, within-day open-field adaptation, or location preference. Taken together, these experiments reveal brainwide systems for cerebellar influence that can affect multiple flexible responses.Significance statementThe cerebellum, a part of all vertebrate brains, provides feedback to the rest of the brain on a split-second basis in response to unexpected events. The consequent changes can range from minute adjustments in movement to broad changes in behavior. In mice, we investigated cerebellar regions that help guide flexible behavior. Inactivation of these regions prevented mice from responding flexibly to a changing Y-shaped maze. We used light-sheet microscopy of c-fos protein expression to map brainwide effects of different stages of learning and cerebellar inactivation. Inactivation caused changes in thalamocortical activity that were similar in pattern, but opposite in sign, from normal learning. Lobule VI inactivation weakened thalamic functional networks, while crus I inactivation divided sensorimotor from associative neocortical networks. The same inactivation also impaired multiday adaptation to an open arena, as tracked using machine vision. Our work shows how the cerebellum’s influence over flexible response can be mediated by a widespread brain network.

Liquid-liquid phase separation and related phase transitions have emerged as generic mechanisms in living cells for the formation of membraneless compartments or biomolecular condensates. The surface between two immiscible phases has an interfacial tension, generating capillary forces that can perform work on the surrounding environment. Here we present the physical principles of capillarity, including examples of how capillary forces structure multiphase condensates and remodel biological substrates. As with other mechanisms of intracellular force generation, for example, molecular motors, capillary forces can influence biological processes. Identifying the biomolecular determinants of condensate capillarity represents an exciting frontier, bridging soft matter physics and cell biology.

The ability of eukaryotic cells to differentiate substrate stiffness is fundamental for many processes such as the development of stem cells into mature tissue. Here, we establish that bacteria feel their microenvironment in a similar manner. We show that Pseudomonas aeruginosa actively probes and measures substrate stiffness using type IV pili (TFP). The activity of the major virulence factor regulator Vfr is peaked with stiffness in a physiologically important range between 0.1 kPa (mucus) and 1000 kPa (cartilage). The local concentration of PilA at the base of dynamic TFP changes during extension and retraction in a surface dependent manner due to slow PilA diffusion in the cell membrane. Traction force measurements reveal that TFP retraction deforms even stiff substrates. Modeling of the measured substrate deformation and optical tweezers experiments suggest that TFP adhere at the tip only. Informed by these experimental results, we developed a model that describes substrate stiffness dependent dynamics of the polar PilA concentration which are quantitatively consistent with the transcriptional response to stiffness. Manipulating the ATPase activity of the TFP motors changes the TFP extension and retraction velocities and consequently the PilA concentration dynamics in a manner that is predictive of the experimental stiffness response. This work points to the use of a competition between PilA diffusion and TFP extension-retraction as a molecular shear rheometer. Our results highlight that stiffness sensing is a conserved property between the kingdoms of life.

Background: Repetitive action, resistance to environmental change, and fine motor disruptions are hallmarks of autism spectrum disorder (ASD) and other neurodevelopmental disorders, and vary considerably from individual to individual. In animal models, conventional behavioral phenotyping captures such fine-scale variations incompletely. Here, we aimed at investigating behavioral consequences of a cerebellum-specific deletion in Tsc1 protein and a whole-brain knockout in Cntnap2 protein in mice, both mutations are found in the clinical conditions and have been associated with ASD. We observed male and female C57BL/6J mice to methodically catalog adaptive movement over multiple days and examined two rodent models of developmental disorders against this dynamic baseline. Methods: Here, we use advances in computer vision and deep learning, a generalized form of high-dimensional statistical analysis, to develop a framework for characterizing mouse movement on multiple time scales using a single popular behavioral assay, the open field test. The pipeline takes virtual markers from pose estimation to find behavior clusters and generate wavelet signatures of behavior classes. We measured spatial and temporal habituation to a new environment across minutes and days, different types of self-grooming, locomotion and gait. Results: Both Cntnap2 knockout and L7-Tsc1 mutants showed forelimb lag during gait. L7-Tsc1 mutants showed complex defects in multi-day adaptation, lacking the tendency of wild-type mice to spend progressively more time in corners of the arena. In L7-Tsc1 mutant mice, failure-to-adapt took the form of maintained ambling, turning, and locomotion, and an overall decrease in grooming. Adaptation in Cntnap2 knockout mice more broadly resembled that of wild-type. L7-Tsc1 mutant and Cntnap2 knockout mouse models showed different patterns of behavioral state occupancy. Limitations: Genetic risk factors for autism are numerous, and here we tested only two. Our pipeline was only applied to conditions of free behavior. Testing under task or social conditions would reveal more information about behavioral dynamics and variability. Conclusions: Our automated pipeline for deep phenotyping successfully captures model-specific deviations in adaptation and movement as well as differences in the detailed structure of behavioral dynamics. The reported deficits indicate that deep phenotyping constitutes a robust set of ASD symptoms that may be considered for implementation in clinical settings as a quantitative diagnosis criteria.

In the limit of zero Reynolds number (Re), swimmers propel themselves exploiting a series of non-reciprocal body motions. For an artificial swimmer, a proper selection of the power source is required to drive its motion, in cooperation with its geometric and mechanical properties. Although various external fields (magnetic, acoustic, optical, etc.) have been introduced, electric fields are rarely utilized to actuate such swimmers experimentally in unbounded space. Here we use uniform and static electric fields to demonstrate locomotion of a bi-flagellated sphere at low Re via Quincke rotation. These Quincke swimmers exhibit three different forms of motion, including a self-oscillatory state due to elasto-electro-hydrodynamic interactions. Each form of motion follows a distinct trajectory in space. Our experiments and numerical results demonstrate a new method to generate, and potentially control, the locomotion of artificial flagellated swimmers., Comment: 19 pages, 13 figures

The activity of an animal’s brain contains information about that animal’s actions and movements. We investigated the neural representation of locomotion in the nematode C. elegans by recording population calcium activity during unrestrained movement. We report that a neural population more accurately decodes locomotion than any single neuron. Relevant signals are distributed across neurons with diverse tunings to locomotion. Two distinct subpopulations are informative for decoding velocity and body curvature, and different neurons’ activities contribute features relevant for different instances of behavioral motifs. We labeled neurons AVAL and AVAR and found their activity was highly correlated with one another. They exhibited expected transients during backward locomotion, although they were not always the most informative neurons for decoding velocity. Finally, we compared population neural activity during movement and immobilization. Immobilization alters the correlation structure of neural activity and its dynamics. Some neurons previously correlated with AVA become anti-correlated and vice versa.The activity of an animal’s brain contains information about that animal’s actions and movements. We investigated the neural representation of locomotion in the nematode C. elegans by recording brain-wide neural dynamics in freely moving animals. We report that a population of neurons more accurately decodes the animal’s locomotion than any single neuron. Neural signals are distributed across neurons in the population with a diversity of tuning to locomotion. Two distinct subpopulations are most informative for decoding velocity and body curvature, and different neurons’ activities contribute features relevant for different instances of behavioral motifs within these subpopulations. We additionally labeled the AVA neurons within our population recordings. AVAL and AVAR exhibit activity that is highly correlated with one another, and they exhibit the expected responses to locomotion, although we find that AVA is not always the most informative neuron for decoding velocity. Finally, we compared brain-wide neural activity during movement and immobilization and observe that immobilization alters the correlation structure of neural activity and its dynamics. Some neurons that were previously correlated with AVA become anti-correlated and vice versa during immobilization. We conclude that neural population codes are important for understanding neural dynamics of behavior in moving animals.

Bacteria use extracellular appendages called type IV pili (T4P) for diverse behaviors including DNA uptake, surface sensing, virulence, protein secretion, and twitching motility. Dynamic extension and retraction of T4P is essential for their function, and T4P extension is thought to occur through the action of a single, highly conserved motor, PilB. Here, we develop Acinetobacter baylyi as a model to study T4P by employing a recently developed pilus labeling method. By contrast to previous studies of other bacterial species, we find that T4P synthesis in A. baylyi is dependent not only on PilB but also on an additional, phylogenetically distinct motor, TfpB. Furthermore, we identify a protein (CpiA) that inhibits T4P extension by specifically binding and inhibiting PilB but not TfpB. These results expand our understanding of T4P regulation and highlight how inhibitors might be exploited to disrupt T4P synthesis., Type IV pili (T4P) are retractile appendages used by bacteria for DNA uptake and other purposes. T4P extension is thought to occur through the action of a single motor protein, PilB. Here, Ellison et al. show that T4P synthesis in Acinetobacter baylyi depends not only on PilB but also on an additional, distinct motor, TfpB.

Aging affects almost all aspects of an organism – its morphology, its physiology, its behavior. Isolating which biological mechanisms are regulating these changes, however, has proven difficult, potentially due to our inability to characterize the full repertoire of an animal’s behavior across the lifespan. Using data from fruit flies (D. melanogaster) we measure the full repertoire of behaviors as a function of age. We observe a sexually dimorphic pattern of changes in the behavioral repertoire during aging. Although the stereotypy of the behaviors and the complexity of the repertoire overall remains relatively unchanged, we find evidence that the observed alterations in behavior can be explained by changing the fly’s overall energy budget, suggesting potential connections between metabolism, aging, and behavior.

Multifocal microscopes (MFMs) are becoming increasingly popular in fluorescence microscopy due to their high speed three-dimensional (3D) imaging capabilities. Conventional MFMs use a fixed fabricated grating as the multifocal grating but these are limited to a restricted wavelength range and a fixed object-plane separation. Spatial light modulators (SLMs) represent an alternative to fabricated gratings due to their real-time programmability, providing complete control over emission wavelength range and object plane separations. However, algorithms commonly used to obtain multifocal grating patterns which provide uniform intensity across the subimages are not directly applicable to SLM-based MFMs due to inherent pixel-to-pixel crosstalk effects present in the SLM chip. We recently developed an in-situ iterative algorithm which generates grating patterns that provide near-uniform illumination of the subimages in SLM-based MFMs. This algorithm is universal across wavelengths, object-plane separations, and SLM manufacturers. As part of our efforts to develop an SLM-based MFM that can respond rapidly to changing experimental parameters, we implement a gradient descent-based optimization method. We evaluate its performance in comparison with a grid search based routine. Experimental results obtained on a custom-made SLM-based MFM indicate that the grid-search optimized grating patterns provide superior subimage intensity uniformity versus the gradient-descent method. These experiments also provide an insight into the energy landscape involved in these optimizations. This study increases the utility of SLM-based MFMs in high-speed imaging.

Autism is noted for both its genotypic and phenotypic diversity. Repetitive action, resistance to environmental change, and motor disruptions vary from individual to individual. In animal models, conventional behavioral phenotyping captures such fine-scale variations incompletely. Here we use advances in computer vision and deep learning to develop a framework for characterizing mouse behavior on multiple time scales using a single popular behavioral assay, the open field test. We observed male and female C57BL/6J mice to develop a dynamic baseline of adaptive behavior over multiple days. We then examined two rodent models of autism, a cerebellum-specific model, L7-Tsc1, and a whole-brain knockout model, Cntnap2. Both Cntnap2 knockout and L7-Tsc1 mutants showed forelimb lag during gait. L7-Tsc1 mutants showed complex defects in multi-day adaptation, lacking the tendency of wild-type mice to spend progressively more time in corners of the arena. In L7-Tsc1 mutant mice, failure-to-adapt took the form of maintained ambling, turning, and locomotion, and an overall decrease in grooming. Adaptation in Cntnap2 knockout mice more broadly resembled that of wild-type. L7-Tsc1 mutant and Cntnap2 knockout mouse models showed different patterns of behavioral state occupancy. Our automated pipeline for deep phenotyping successfully captures model-specific deviations in adaptation and movement as well as differences in the detailed structure of behavioral dynamics.

We investigated the neural representation of locomotion in the nematode C. elegans by recording population calcium activity during movement. We report that population activity more accurately decodes locomotion than any single neuron. Relevant signals are distributed across neurons with diverse tunings to locomotion. Two largely distinct subpopulations are informative for decoding velocity and curvature, and different neurons’ activities contribute features relevant for different aspects of a behavior or different instances of a behavioral motif. To validate our measurements, we labeled neurons AVAL and AVAR and found that their activity exhibited expected transients during backward locomotion. Finally, we compared population activity during movement and immobilization. Immobilization alters the correlation structure of neural activity and its dynamics. Some neurons positively correlated with AVA during movement become negatively correlated during immobilization and vice versa. This work provides needed experimental measurements that inform and constrain ongoing efforts to understand population dynamics underlying locomotion in C. elegans.

Bacteria use extracellular appendages called type IV pili (T4P) for diverse behaviors including DNA uptake, surface sensing, virulence, protein secretion, and twitching motility1. Dynamic extension and retraction of T4P is essential for their function, yet little is known about the mechanisms controlling these dynamics or the extent to which their regulation is conserved across bacterial species. Here, we develop Acinetobacter baylyi as a new model to study T4P by employing a recently developed pilus labeling method2,3. Our findings overturn the current dogma that T4P extension occurs through the action of a single, highly conserved motor, PilB, by showing that T4P synthesis in A. baylyi is dependent on an additional, phylogenetically distinct motor, TfpB. Furthermore, we uncover an inhibitor of T4P extension that specifically binds to and inhibits PilB but not TfpB. These results expand our understanding of T4P regulation and highlight how inhibitors might be exploited to inhibit T4P synthesis.

The desire to understand how the brain generates and patterns behavior has driven rapid methodological innovation to quantify and model natural animal behavior. This has led to important advances in deep learning-based markerless pose estimation that have been enabled in part by the success of deep learning for computer vision applications. Here we present SLEAP (Social LEAP Estimates Animal Poses), a framework for multi-animal pose tracking via deep learning. This system is capable of simultaneously tracking any number of animals during social interactions and across a variety of experimental conditions. SLEAP implements several complementary approaches for dealing with the problems inherent in moving from single-to multi-animal pose tracking, including configurable neural network architectures, inference techniques, and tracking algorithms, enabling easy specialization and tuning for particular experimental conditions or performance requirements. We report results on multiple datasets of socially interacting animals (flies, bees, and mice) and describe how dataset-specific properties can be leveraged to determine the best configuration of SLEAP models. Using a high accuracy model (sleap.ai.

The predatory deltaproteobacterium Myxococcus xanthus uses a helically-trafficked motor at bacterial focal adhesion (bFA) sites to power gliding motility. Using TIRF and force microscopy, we herein identify the integrin αI-domain-like outer-membrane (OM) lipoprotein CglB as an essential substratum-coupling protein of the gliding motility complex. Similar to most known OM lipoproteins, CglB is anchored on the periplasmic side of the OM and thus a mechanism must exist to secrete it to the cell surface in order for it to interact with the underlying substratum. We reveal this process to be mediated by a predicted OM β-barrel structure of the gliding complex. This OM platform was found to regulate the conformational activation and secretion of CglB across the OM. These data suggest that the gliding complex promotes surface exposure of CglB at bFAs, thus explaining the manner by which forces exerted by inner-membrane motors are transduced across the cell envelope to the substratum; they also uncover a novel protein secretion mechanism, highlighting the ubiquitous connection between secretion and bacterial motility.