Biology subjects

Stednitz, S. J.

Publications and source records attributed to Stednitz, S. J..

5 recordsLinked to original sources

bioRxiv2025

Plug-and-Play automated behavioral tracking of zebrafish larvae with DeepLabCut and SLEAP: pre-trained networks and datasets of annotated poses

Zebrafish are an important model system in behavioral neuroscience due to their rapid development and suite of distinct, innate behaviors. Quantifying many of these larval behaviors requires detailed tracking of eye and tail kinematics, which in turn demands imaging at high spatial and temporal resolution, ideally using semi or fully automated tracking methods for throughput efficiency. However, creating and validating accurate tracking models is time-consuming and labor intensive, with many research groups duplicating efforts on similar images. With the goal of developing a useful community resource, we trained pose estimation models using a diverse array of video parameters and a 15-keypoint pose model. We deliver an annotated dataset of free-swimming and head-embedded behavioral videos of larval zebrafish, along with four pose estimation networks from DeepLabCut and SLEAP (two variants of each). We also evaluated model performance across varying imaging conditions to guide users in optimizing their imaging setups. This resource will allow other researchers to skip the tedious and laborious training steps for setting up behavioral analyses, guide model selection for specific research needs, and provide ground truth data for benchmarking new tracking methods. SIGNIFICANCE STATEMENTLarval zebrafish are an emerging model in systems neuroscience, offering unique advantages for linking brain activity to behavior. However, detailed behavioral tracking, essential for such studies, requires time- and labor-intensive annotation and model training. To eliminate this bottleneck, here we provide a high-quality, annotated dataset of zebrafish behaviors for both free-swimming and head-embedded preparations alongside four pre-trained pose estimation models using DeepLabCut and SLEAP. We benchmark models performance across diverse imaging conditions to guide optimal setup choices. This community resource will allow researchers to bypass the most time-consuming stages of data annotation and training, enabling immediate behavioral analysis. By removing this key hurdle, this work will accelerate project initiation, support reproducibility, and provide a foundation for future tracking method development.

animal behavior and cognition↗

bioRxiv2024

Probabilistic modeling reveals coordinated social interaction states and their multisensory bases

Social behavior across animal species ranges from simple pairwise interactions to thousands of individuals coordinating goal-directed movements. Regardless of the scale, these interactions are governed by the interplay between multimodal sensory information and the internal state of each animal. Here, we investigate how animals use multiple sensory modalities to guide social behavior in the highly social zebrafish (Danio rerio) and uncover the complex features of pairwise interactions early in development. To identify distinct behaviors and understand how they vary over time, we developed a new hidden Markov model with constrained linear-model emissions to automatically classify states of coordinated interaction, using the movements of one animal to predict those of another. We discovered that social behaviors alternate between two interaction states within a single experimental session, distinguished by unique movements and timescales. Long-range interactions, akin to shoaling, rely on vision, while mechanosensation underlies rapid synchronized movements and parallel swimming, precursors of schooling. Altogether, we observe spontaneous interactions in pairs of fish, develop novel hidden Markov modeling to reveal two fundamental interaction modes, and identify the sensory systems involved in each. Our modeling approach to pairwise social interactions has broad applicability to a wide variety of naturalistic behaviors and species and solves the challenge of detecting transient couplings between quasi-periodic time series. HIGHLIGHTSO_LIZebrafish exhibit distinct correlated interaction states with unique timescales. C_LIO_LIDelayed interactions are visual while synchronization requires mechanosensation. C_LIO_LIA new class of hidden Markov model segments social interactions into discrete states. C_LIO_LIStates alternate within a session, revealing real-time dynamics of social behavior. C_LI

animal behavior and cognition↗

bioRxiv2024

Evidence for auditory stimulus-specific adaptation but not deviance detection in larval zebrafish brains

Animals receive a constant stream of sensory input, and detecting changes in this sensory landscape is critical to their survival. One signature of change detection in humans is the auditory mismatch negativity (MMN), a neural response to unexpected stimuli that deviate from a predictable sequence. This process requires the auditory system to adapt to specific repeated stimuli while remaining sensitive to novel input (stimulus-specific adaptation). MMN was originally described in humans, and equivalent responses have been found in other mammals and birds, but it is not known to what extent this deviance detection circuitry is evolutionarily conserved. Here we present the first evidence for stimulus-specific adaptation in the brain of a teleost fish, using whole-brain calcium imaging of larval zebrafish at single-neuron resolution with selective plane illumination microscopy. We found frequency-specific responses across the brain with variable response amplitudes for frequencies of the same volume, and created a loudness curve to model this effect. We presented an auditory oddball stimulus in an otherwise predictable train of pure tone stimuli, and did not find a population of neurons with specific responses to deviant tones that were not otherwise explained by stimulus-specific adaptation. Further, we observed no deviance responses to an unexpected omission of a sound in a repetitive sequence of white noise bursts. These findings extend the known scope of auditory adaptation and deviance responses across the evolutionary tree, and lay groundwork for future studies to describe the circuitry underlying auditory adaptation at the level of individual neurons.

neuroscience↗

bioRxiv2020

The microbiota promotes social behavior by neuro-immune modulation of neurite complexity

Host-associated microbiotas guide the trajectory of developmental programs, and microbial dysbiosis is linked to neurodevelopmental conditions such as autism spectrum disorder. Recent work suggests that microbiotas modulate behavioral phenotypes associated with these disorders. We discovered that the zebrafish microbiota is required for normal social behavior, and reveal a molecular pathway linking the microbiota, microglial remodeling of neural circuits, and social behavior in this experimentally tractable model vertebrate. Examining neuronal correlates of behavior, we found that the microbiota restrains neurite complexity and targeting of forebrain neurons required for normal social behavior and is necessary for localization of forebrain microglia, brain resident phagocytes that remodel neuronal arbors. The microbiota also influences microglial molecular functions, including promoting expression of the remodeling factor c1q, which we find is required for normal forebrain neurite complexity. Several distinct bacterial taxa are individually sufficient for normal microglial and neuronal phenotypes, suggesting that host neuroimmune development is sensitive to a feature common among many bacteria. Our results demonstrate that the microbiota influences zebrafish social behavior by stimulating microglial remodeling of forebrain circuits during early neurodevelopment, and suggest pathways for new interventions in multiple neurodevelopmental disorders.

neuroscience↗

bioRxiv2019

Zebrafish mbnl mutants model molecular and physical phenotypes of human MBNL loss of function disorders

The muscleblind RNA binding proteins (MBNL1, MBNL2, and MBNL3) are highly conserved across vertebrates and are important regulators of RNA alternative splicing. Loss of MBNL protein function through sequestration by CUG or CCUG RNA repeats is largely responsible for the phenotypes of the human genetic disorder myotonic dystrophy (DM). We generated the first stable zebrafish (Danio rerio) models of DM-associated MBNL loss of function through mutation of the three zebrafish mbnl genes. In contrast to mouse models, zebrafish double and triple homozygous mbnl mutants were viable to adulthood. Zebrafish mbnl mutants displayed disease-relevant physical phenotypes including decreased body size and impaired movement. They also exhibited widespread alternative splicing changes, including the misregulation of many DM-relevant exons. Physical and molecular phenotypes were more severe in compound mbnl mutants than in single mbnl mutants, suggesting partially redundant functions of Mbnl proteins. The high fecundity and larval optical transparency of this complete series of zebrafish mbnl mutants will make them useful for studying DM-related phenotypes and how individual Mbnl proteins contribute to them, and for testing potential therapeutics.

molecular biology↗