Szelenyi, E. R. | Search bioRxiv

bioRxiv2024

Distinct dynamics and intrinsic properties in ventral tegmental area populations mediate reward association and motivation

Ventral tegmental area (VTA) dopamine neurons regulate reward-related associative learning and reward-driven motivated behaviors, but how these processes are coordinated by distinct VTA neuronal subpopulations remains unresolved. Here we examine the neural correlates of reward-related prediction-error, action, cue, and outcome encoding as well as effort exertion and reward anticipation during reward-seeking behaviors. We compare the contribution of two primarily dopaminergic and largely non-overlapping VTA subpopulations, all VTA dopamine neurons, and VTA GABAergic neurons of the mouse midbrain to these processes. The dopamine subpopulation that projects to the nucleus accumbens (NAc) core preferentially encodes prediction-error and reward-predictive cues. In contrast, the dopamine subpopulation that projects to the NAc shell preferentially encodes goal-directed actions and reflects relative reward anticipation. VTA GABA neuron activity strongly contrasts VTA dopamine population activity and preferentially encodes reward outcome and retrieval. Electrophysiology, targeted optogenetics, and whole-brain input mapping reveal heterogeneity among VTA dopamine subpopulations. Our results demonstrate that VTA subpopulations carry distinct reward-related learning and motivation signals and reveal a striking pattern of functional heterogeneity among projection-defined VTA dopamine neuron populations.

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↗