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小组织样本的多核糖体谱分析。

Polysome-profiling in small tissue samples.

机构信息

Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institutet, Stockholm, Sweden.

International Research Center, A.C.Camargo Cancer Center, São Paulo, Brazil.

出版信息

Nucleic Acids Res. 2018 Jan 9;46(1):e3. doi: 10.1093/nar/gkx940.

Abstract

Polysome-profiling is commonly used to study translatomes and applies laborious extraction of efficiently translated mRNA (associated with >3 ribosomes) from a large volume across many fractions. This property makes polysome-profiling inconvenient for larger experimental designs or samples with low RNA amounts. To address this, we optimized a non-linear sucrose gradient which reproducibly enriches for efficiently translated mRNA in only one or two fractions, thereby reducing sample handling 5-10-fold. The technique generates polysome-associated RNA with a quality reflecting the starting material and, when coupled with smart-seq2 single-cell RNA sequencing, translatomes in small tissues from biobanks can be obtained. Translatomes acquired using optimized non-linear gradients resemble those obtained with the standard approach employing linear gradients. Polysome-profiling using optimized non-linear gradients in serum starved HCT-116 cells with or without p53 showed that p53 status associates with changes in mRNA abundance and translational efficiency leading to changes in protein levels. Moreover, p53 status also induced translational buffering whereby changes in mRNA levels are buffered at the level of mRNA translation. Thus, here we present a polysome-profiling technique applicable to large study designs, primary cells and frozen tissue samples such as those collected in biobanks.

摘要

多核糖体谱分析通常用于研究翻译组,并需要从大量样本中费力地提取与 >3 个核糖体结合的高效翻译 mRNA。这种特性使得多核糖体谱分析在较大的实验设计或 RNA 含量较低的样本中不太方便。为了解决这个问题,我们优化了一种非线性蔗糖梯度,该梯度可在仅一两个级分中重复富集高效翻译的 mRNA,从而将样本处理量减少 5-10 倍。该技术生成的多核糖体相关 RNA 质量反映了起始材料,当与 smart-seq2 单细胞 RNA 测序结合使用时,可从生物库中的小组织中获得翻译组。使用优化的非线性梯度获得的翻译组与使用线性梯度的标准方法获得的翻译组相似。在有或没有 p53 的血清饥饿 HCT-116 细胞中使用优化的非线性梯度进行多核糖体谱分析表明,p53 状态与 mRNA 丰度和翻译效率的变化相关,从而导致蛋白质水平的变化。此外,p53 状态还诱导了翻译缓冲作用,从而在 mRNA 翻译水平上缓冲了 mRNA 水平的变化。因此,我们在这里介绍了一种适用于大型研究设计、原代细胞和冷冻组织样本(如生物库中收集的样本)的多核糖体谱分析技术。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/46ad/5758873/032636ee4334/gkx940fig1.jpg

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