Girven, K. S. | bioRxiv Science

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↗

bioRxiv2020

Dopamine release and its control over early Pavlovian learning differs between the NAc core and medial NAc shell

Dopamine neurons respond to cues to reflect the value of associated outcomes. These cue-evoked dopamine responses can encode the relative rate of reward in rats with extensive Pavlovian training. Specifically, a cue that always follows the previous reward by a short delay (high reward rate) evokes a larger dopamine response in the nucleus accumbens (NAc) core relative to a distinct cue that always follows the prior reward by a long delay (low reward rate). However, it was unclear if these reward rate dopamine signals are evident during early Pavlovian training sessions and across NAc subregions. To address this, we performed fast-scan cyclic voltammetry recordings of dopamine levels to track the pattern of cue- and reward-evoked dopamine signals in the NAc core and medial NAc shell. We identified regional differences in the progression of cue-evoked dopamine signals across training. However, the dopamine response to cues did not reflect the reward rate in either the NAc core or the medial NAc shell during early training sessions. Pharmacological experiments found that dopamine-sensitive conditioned responding emerged in the NAc core before the medial NAc shell. Together, these findings illustrate regional differences in NAc dopamine release and its control over behavior during early Pavlovian learning.

neuroscience↗