Biology subjects

Goodwin, N. L.

Publications and source records attributed to Goodwin, N. L..

4 recordsLinked to original sources

bioRxiv2024

Oxytocin receptor controls distinct components of pair bonding and development in prairie voles

Oxytocin receptor (Oxtr) signaling influences complex social behaviors in diverse species, including social monogamy in prairie voles. How Oxtr regulates specific components of social attachment behaviors and the neural mechanisms mediating them remains unknown. Here, we examine prairie voles lacking Oxtr and demonstrate that pair bonding comprises distinct behavioral modules: the preference for a bonded partner, and the rejection of novel potential mates. Our longitudinal study of social attachment shows that Oxtr sex-specifically influences early interactions between novel partners facilitating the formation of partner preference. Additionally, Oxtr suppresses promiscuity towards novel potential mates following pair bonding, contributing to rejection. Oxtr function regulates coordinated patterns of gene expression in regions implicated in attachment behaviors and regulates the expression of oxytocin in the paraventricular nucleus of the hypothalamus, a principal source of oxytocin. Thus, Oxtr controls genetically separable components of pair bonding behaviors and coordinates development of the neural substrates of attachment.

neuroscience↗

bioRxiv2023

An arginine-rich nuclear localization signal (ArgiNLS) strategy for streamlined image segmentation of single-cells

High-throughput volumetric fluorescent microscopy pipelines can spatially integrate whole-brain structure and function at the foundational level of single-cells. However, conventional fluorescent protein (FP) modifications used to discriminate single-cells possess limited efficacy or are detrimental to cellular health. Here, we introduce a synthetic and non-deleterious nuclear localization signal (NLS) tag strategy, called Arginine-rich NLS (ArgiNLS), that optimizes genetic labeling and downstream image segmentation of single-cells by restricting FP localization near-exclusively in the nucleus through a poly-arginine mechanism. A single N-terminal ArgiNLS tag provides modular nuclear restriction consistently across spectrally separate FP variants. ArgiNLS performance in vivo displays functional conservation across major cortical cell classes, and in response to both local and systemic brain wide AAV administration. Crucially, the high signal-to-noise ratio afforded by ArgiNLS enhances ML-automated segmentation of single-cells due to rapid classifier training and enrichment of labeled cell detection within 2D brain sections or 3D volumetric whole-brain image datasets, derived from both staining-amplified and native signal. This genetic strategy provides a simple and flexible basis for precise image segmentation of genetically labeled single-cells at scale and paired with behavioral procedures. Significance StatementQuantifying labeled cells in fluorescent microscopy is a fundamental aspect of modern biology. Critically, the use of short nuclear localization sequences (NLS) is a key genetic modification for discriminating single-cells labeled with fluorescent proteins (FPs). However, mainstay NLS approaches typically localize proteins to the nucleus with limited efficacy, while alternative non-NLS tag strategies can enhance efficacy at the cost of cellular health. Thus, quantitative cell counting using FP labels remains suboptimal or not compatible with health and behavior. Here, we present a novel genetic tagging strategy - named ArgiNLS - that flexibly and safely achieves FP nuclear restriction across the brain to facilitate machine learning-based segmentation of single-cells at scale, delivering a timely update to the behavioral neuroscientists toolkit.

neuroscience↗

bioRxiv2022

Individual differences in volitional social motivation in male and female mice following social stress.

BackgroundA key challenge in developing new treatments for neuropsychiatric illness is the disconnect between preclinical models and the complexity of human social behavior. We aimed to integrate voluntary social self-administration into a preclinical rodent stress model, as a platform for the identification of basic brain and behavior mechanisms underlying stress-induced individual differences in social motivation. Here, we introduce an operant social stress (OSS) procedure with male and female mice, where lever presses are reinforced by freely moving social interaction with a familiar social partner across social stress exposure. MethodsOSS is composed of three phases: (i) social self-administration training, (ii) social stress concurrent with daily reinforced social self-administration testing, and (iii) post-stress operant social reward testing under both non-reinforced and reinforced conditions. We resolve social stress-induced changes to social motivation behaviors using hierarchical clustering and aggregated z-scores, capturing the spectrum of individual differences that we describe with a social index score. ResultsOSS captures a range of stress-related dynamic social motivation behaviors inclusive of sex as a biological variable. Both male and female mice lever press for access to a social partner, independent of social partner coat color or familiarity. Social stress attenuates social self-administration in males and promotes social reward seeking behavior in females. Hierarchical clustering does not adequately describe the relative distributions of social motivation following stress, which we find is better described as a non-binary behavioral distribution that we define by introducing the social index score. This index is stable across individual mice. ConclusionWe demonstrate that OSS can be used to detect stable individual differences in stress-induced changes to social motivation in male and female mice. These differences may reflect unique neurobiological, cellular and circuit mechanisms not captured by preclinical models that omit voluntary social behaviors. The inclusion of volitional social procedures may enhance the understanding of behavioral adaptations promoting stress resiliency and their mechanisms under more naturalistic conditions.

neuroscience↗

bioRxiv2020

Simple Behavioral Analysis (SimBA): an open source toolkit for computer classification of complex social behaviors in experimental animals

Aberrant social behavior is a core feature of many neuropsychiatric disorders, yet the study of complex social behavior in freely moving rodents is relatively infrequently incorporated into preclinical models. This likely contributes to limited translational impact. A major bottleneck for the adoption of socially complex, ethology-rich, preclinical procedures are the technical limitations for consistently annotating detailed behavioral repertoires of rodent social behavior. Manual annotation is subjective, prone to observer drift, and extremely time-intensive. Commercial approaches are expensive and inferior to manual annotation. Open-source alternatives often require significant investments in specialized hardware and significant computational and programming knowledge. By combining recent computational advances in convolutional neural networks and pose-estimation with further machine learning analysis, complex rodent social behavior is primed for inclusion under the umbrella of computational neuroethology. Here we present an open-source package with graphical interface and workflow (Simple Behavioral Analysis, SimBA) that uses pose-estimation to create supervised machine learning predictive classifiers of rodent social behavior, with millisecond resolution and accuracies that can out-perform human observers. SimBA does not require specialized video acquisition hardware nor extensive computational background. Standard descriptive statistical analysis, along with graphical region of interest annotation, are provided in addition to predictive classifier generation. To increase ease-of-use for behavioural neuroscientists, we designed SimBA with accessible menus for pre-processing videos, annotating behavioural training datasets, selecting advanced machine learning options, robust classifier validation functions and flexible visualizations tools. This allows for predictive classifier transparency, explainability and tunability prior to, and during, experimental use. We demonstrate that this approach is flexible and robust in both mice and rats by classifying social behaviors that are commonly central to the study of brain function and social motivation. Finally, we provide a library of poseestimation weights and behavioral predictive classifiers for resident-intruder behaviors in mice and rats. All code and data, together with detailed tutorials and documentation, are available on the SimBA GitHub repository. Graphical abstractSimBA graphical interface (GUI) for creating supervised machine learning classifiers of rodent social behavior. (a) Pre-process videos. SimBA supports common video pre-processing functions (e.g., cropping, clipping, sampling, format conversion, etc.) that can be performed either on single videos, or as a batch. (b) Managing poseestimation data and creating classification projects. Pose-estimation tracking projects in DeepLabCut and DeepPoseKit can be either imported or created and managed within the SimBA graphical user interface, and the tracking results are imported into SimBA classification projects. SimBA also supports userdrawn region-of-interests (ROIs) for descriptive statistics of animal movements, or as features in machine learning classification projects. (c) Create classifiers, perform classifications, and analyze classification data. SimBA has graphical tools for correcting poseestimation tracking inaccuracies when multiple subjects are within a single frame, annotating behavioral events from videos, and optimizing machine learning hyperparameters and discrimination thresholds. A number of validation checkpoints and logs are included for increased classifier explainability and tunability prior to, and during, experimental use. Both detailed and summary data are provided at the end of classifier analysis. SimBA accepts behavioral annotations generated elsewhere (such as through JWatcher) that can be imported into SimBA classification projects. (d) Visualize classification results. SimBA has several options for visualizing machine learning classifications, animal movements and ROI data, and analyzing the durations and frequencies of classified behaviors. See the SimBA GitHub repository for a comprehensive documentation and user tutorials. O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

animal behavior and cognition↗