Machine learning sequence prioritization for cell type-specific enhancer design
Machine learning sequence prioritization for cell type-specific enhancer design
Abstract Recent discoveries of extreme cellular diversity in the brain warrant rapid development of technologies to access specific cell populations, enabling characterization of their roles in behavior and in disease states. Available approaches for engineering targeted technologies for new neuron subtypes are low-yield, involving intensive transgenic strain or virus screening. Here, we introduce SNAIL (Specific Nuclear-Anchored Independent Labeling), a new virus-based strategy for cell labeling and nuclear isolation from heterogeneous tissue. SNAIL works by leveraging machine learning and other computational approaches to identify DNA sequence features that confer cell type-specific gene activation and using them to make a probe that drives an affinity purification-compatible reporter gene. As a proof of concept, we designed and validated two novel SNAIL probes that target parvalbumin-expressing (PV) neurons. Furthermore, we show that nuclear isolation using SNAIL in wild type mice is sufficient to capture characteristic open chromatin features of PV neurons in the cortex, striatum, and external globus pallidus. Expansion of this technology has broad applications in cell type-specific observation, manipulation, and therapeutics across species and disease models.
Pfenning Andreas R、Kaplow Irene M、Zhang Xiaoyu、Wirthlin Morgan、Brown Ashley R、Fox Grant、Ramamurthy Easwaran、Kim Yeonju、Toong Noelle、Shin Naomi、Lawler Alyssa J
Computational Biology Department, School of Computer Science, Carnegie Mellon University||Biological Sciences Department, Mellon College of Science, Carnegie Mellon University||Neuroscience Institute, Carnegie Mellon UniversityComputational Biology Department, School of Computer Science, Carnegie Mellon University||Neuroscience Institute, Carnegie Mellon UniversityComputational Biology Department, School of Computer Science, Carnegie Mellon University||Neuroscience Institute, Carnegie Mellon UniversityComputational Biology Department, School of Computer Science, Carnegie Mellon University||Neuroscience Institute, Carnegie Mellon UniversityComputational Biology Department, School of Computer Science, Carnegie Mellon University||Neuroscience Institute, Carnegie Mellon UniversityComputational Biology Department, School of Computer Science, Carnegie Mellon University||Neuroscience Institute, Carnegie Mellon UniversityComputational Biology Department, School of Computer Science, Carnegie Mellon University||Neuroscience Institute, Carnegie Mellon UniversityComputational Biology Department, School of Computer Science, Carnegie Mellon University||Neuroscience Institute, Carnegie Mellon UniversityComputational Biology Department, School of Computer Science, Carnegie Mellon University||Neuroscience Institute, Carnegie Mellon UniversityComputational Biology Department, School of Computer Science, Carnegie Mellon University||Neuroscience Institute, Carnegie Mellon UniversityComputational Biology Department, School of Computer Science, Carnegie Mellon University||Biological Sciences Department, Mellon College of Science, Carnegie Mellon University||Neuroscience Institute, Carnegie Mellon University
生物科学研究方法、生物科学研究技术神经病学、精神病学细胞生物学
Pfenning Andreas R,Kaplow Irene M,Zhang Xiaoyu,Wirthlin Morgan,Brown Ashley R,Fox Grant,Ramamurthy Easwaran,Kim Yeonju,Toong Noelle,Shin Naomi,Lawler Alyssa J.Machine learning sequence prioritization for cell type-specific enhancer design[EB/OL].(2025-03-28)[2025-06-12].https://www.biorxiv.org/content/10.1101/2021.04.15.439984.点此复制
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