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用于基于RNA测序的表达分析的玉米籽粒各部分的激光捕获显微切割

Laser-Capture Microdissection of Maize Kernel Compartments for RNA-Seq-Based Expression Analysis.

作者信息

Zhang Shanshan, Thakare Dhiraj, Yadegari Ramin

机构信息

School of Plant Sciences, University of Arizona, Tucson, Arizona, 85721-0036, USA.

出版信息

Methods Mol Biol. 2018;1676:153-163. doi: 10.1007/978-1-4939-7315-6_9.

Abstract

Laser-capture microdissection (LCM) enables isolation of single cells or groups of cells for a variety of downstream applications including transcriptome profiling. Recently, this methodology has found a more widespread use particularly with the advent of next-generation sequencing techniques that enable deep profiling of the limited amounts of RNA obtained from fixed or frozen sections. When used with fixed tissues, a major experimental challenge is to balance the tissue integrity needed for microscopic visualization of the cell types of interest with that of the RNA quality necessary for deep profiling. Complex biological structures such as seeds or kernels pose an especially difficult case in this context as in many instances the key internal structures such as the embryo and the endosperm are relatively inaccessible. Here, we present an optimized LCM protocol for maize kernel that has been developed specifically to enable profiling of the early stages of endosperm development using RNA-Seq.

摘要

激光捕获显微切割(LCM)能够分离单个细胞或细胞群,以用于包括转录组分析在内的各种下游应用。最近,随着下一代测序技术的出现,这种方法得到了更广泛的应用,下一代测序技术能够对从固定或冷冻切片中获得的有限量RNA进行深度分析。当与固定组织一起使用时,一个主要的实验挑战是在感兴趣的细胞类型的显微镜可视化所需的组织完整性与深度分析所需的RNA质量之间取得平衡。在这种情况下,种子或籽粒等复杂的生物结构是一个特别困难的例子,因为在许多情况下,关键的内部结构,如胚和胚乳,相对难以获取。在这里,我们提出了一种针对玉米籽粒的优化LCM方案,该方案是专门为使用RNA测序对胚乳发育的早期阶段进行分析而开发的。

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