精氨酸
核定位序列
分割
信号(编程语言)
计算机视觉
人工智能
细胞生物学
计算机科学
化学
计算生物学
模式识别(心理学)
生物
生物化学
氨基酸
核心
程序设计语言
作者
Eric R. Szelenyi,Jovana Navarrete,Alexandria D. Murry,Yizhe Zhang,Kasey S. Girven,Lauren M. Kuo,Marcella M. Cline,Mollie Bernstein,Mariia Burdyniuk,Bryce Bowler,Nastacia L. Goodwin,Barbara Juarez,Larry S. Zweifel,Sam A. Golden
标识
DOI:10.1073/pnas.2320250121
摘要
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.
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