在发育中的小鼠大脑中,空间变量基因的时空动态被一个新的计算方案揭示出来
Yingzhou Hong1, Kai Song1, Zongbo Zhang1
1Center for Life Sciences, School of Life Sciences, Yunnan University, Kunming, 650500, China.
Cell death discovery
|July 27, 2023
概括
我们开发了SVGbit,这是一个新的计算工具,用于识别发育中的大脑中关键的空间变量基因 (SVG). 这种方法改善了从空间转录组学数据中的标记基因选择.
科学领域:
- 神经科学是一个神经科学.
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 了解大脑发育需要识别特定于大脑区域的空间变量基因 (SVGs).
- 空间转录学能够实现高通量SVG选择,但现有的方法忽视了基因排名和组合效应.
研究的目的:
- 介绍SVGbit,这是一个用于排名单个和组合SVG的新型计算管道.
- 为了增强用于大脑发育研究的空间转录组学数据的标记基因选择.
主要方法:
- 开发了SVGbit计算管道,根据其空间分布和组合效应对SVG进行排名.
- 将SVGbit应用于公开的人类和小鼠大脑数据集.
- 从正在发育的小鼠大脑中生成新的空间转录和免疫组织化学数据.
主要成果:
- SVGbit成功地确定了与发展中的小鼠大脑中已知的解剖区域相对应的关键SVG.
- 该管道促进了发现多个基因组合的发现,以区分大脑区域.
- 在发育过程中确定了特定的基因表达模式 (Sox2,Calb2,Nr4a2),这些基因表达模式在发育过程中划分了丘脑,新皮质和海马体内的子区域.
结论:
- SVGbit提供了一种可靠的方法,用于从空间转录组学数据中优先考虑单个和组合SVG.
- 这项研究揭示了SVG在发育中的大脑中的时空动态,有助于理解区域形成.
- 这种计算工具极大地促进了用于大脑发育研究的新型标记基因的识别.
相关概念视频
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03:21Genome Size and the Evolution of New Genes
While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.