MAC-Seq:将低成本,高通量RNA-Seq与2D和3D细胞模型中的基于图像的表型查相结合
Xiang Mark Li1,2, David Yoannidis3, Susanne Ramm4,5
1Victorian Centre for Functional Genomics, Peter MacCallum Cancer Centre, Melbourne, Australia. kaylene.simpson@petermac.org.
Methods in molecular biology (Clifton, N.J.)
|June 24, 2023
概括
细胞的多重分析 (MAC-seq) 提供了一种低成本,高通量RNA测序方法,用于分析细胞干扰. 这种方法可以进行详细的时间分析,并在规模上进行表型-基因型相关性.
科学领域:
- 基因组学就是基因组学.
- 分子生物学分子生物学
- 细胞生物学 细胞生物学
背景情况:
- 转录基因分析对了解癌症至关重要,但标准方法的产量低,成本高.
- 复杂的体外细胞模型需要资源高效,高通量技术进行详细分析.
- 现有的方法在有限的样本可用性和大规模的表型-基因型相关性方面扎.
研究的目的:
- 介绍和详细介绍细胞的多重分析 (MAC-seq) 工作流.
- 为了证明MAC-seq在2D和3D细胞培养中384井格式的适用性.
- 为了突出MAC-seq与高通量成像和查的兼容性.
主要方法:
- 开发和优化了一个低成本,超高通量RNA测序工作流程 (MAC-seq) 在板式.
- 在2D和3D细胞培养中应用MAC-seq,采用384井格式.
- 测序样本的深度为每井100万次阅读.
主要成果:
- 在实验条件下平均每小时识别出超过一万个表达的基因.
- 证明了MAC-seq用于测量细胞扰动的可行性.
- 显示了与用于表型化高含量细胞成像的兼容性.
结论:
- MAC-seq为转录组分析提供了一个资源高效,高通量解决方案.
- 该协议促进了详细的时间过程分析和尺度上的表型-基因型相关性.
- 在细胞生物学和药物发现领域,MAC-seq具有与高通量查应用集成的巨大潜力.
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