PDF(Pubmed)
Abstract：
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 nondeleterious 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 machine learning-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.
摘要：
高通量体积荧光显微镜管道可以在单细胞的基础水平上在空间上整合全脑结构和功能。然而,用于区分单个细胞的常规荧光蛋白(FP)修饰具有有限的功效或对细胞健康有害。这里,我们介绍了一种合成的、无害的核定位信号(NLS)标签策略，称为“富含精氨酸的NLS”(ArgiNLS)，通过聚精氨酸机制将FP定位限制在细胞核中，从而优化了单细胞的遗传标记和下游图像分割。单个N端ArgiNLS标签在光谱分离的FP变体中一致地提供模块化核限制。ArgiNLS在体内的表现在主要皮质细胞类别中以及对局部和全身全脑AAV施用的反应中显示出功能保守性。至关重要的是，ArgiNLS提供的高信噪比增强了单个细胞的机器学习自动分割，这是由于快速的分类器训练和在2D大脑切片或3D体积全脑图像数据集内的标记细胞检测的富集。来自染色扩增和天然信号。这种遗传策略提供了一个简单而灵活的基础，以精确的图像分割遗传标记的单细胞的规模和配对的行为程序。
