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

• 成果类型: Article
• 署名作者: Szelenyi, Eric R.; Navarrete, Jovana S.; Murry, Alexandria D.; Zhang, Yizhe; Girven, Kasey S.; Kuo, Lauren; Cline, Marcella M.; Bernstein, Mollie X.; Burdyniuk, Mariia; Bowler, Bryce; Goodwin, Nastacia L.; Juarez, Barbara; Zweifel, Larry S.; Golden, Sam A.
• 署名单位: University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; University System of Maryland; University of Maryland Baltimore
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-11137
• DOI: 10.1073/pnas.2320250121
• 发表日期: 2024-08-06
• 关键词: fluorescent protein; messenger-rna; polyarginine; RESOLUTION; importin; purification; tomography; expression; transport; access

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.