Machine learning sequence prioritization for cell type-specific enhancer design.

Machine learning sequence prioritization for cell type-specific enhancer design.
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DOI:
10.7554/elife.69571
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发表时间:
2022-05-16
期刊:
影响因子:
7.7
通讯作者:
Pfenning, Andreas R.
Pfenning, Andreas R.
中科院分区:
生物学1区
文献类型:
--
作者:
Lawler, Alyssa J.;Ramamurthy, Easwaran;Brown, Ashley R.;Shin, Naomi;Kim, Yeonju;Toong, Noelle;Kaplow, Irene M.;Wirthlin, Morgan;Zhang, Xiaoyu;Phan, BaDoi N.;Fox, Grant A.;Wade, Kirsten;He, Jing;Ozturk, Bilge Esin;Byrne, Leah C.;Stauffer, William R.;Fish, Kenneth N.;Pfenning, Andreas R.

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Recent discoveries of extreme cellular diversity in the brain warrant rapid development of technologies to access specific cell populations within heterogeneous tissue. Available approaches for engineering-targeted technologies for new neuron subtypes are low yield, involving intensive transgenic strain or virus screening. Here, we present Specific Nuclear-Anchored Independent Labeling (SNAIL), an improved 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 then 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. 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. The SNAIL framework also has high utility for multispecies cell probe engineering; expression from a mouse PV+ SNAIL enhancer sequence was enriched in PV+ neurons of the macaque cortex. Expansion of this technology has broad applications in cell type-specific observation, manipulation, and therapeutics across species and disease models.