Department of Biology, New Mexico State University, Las Cruces, NM, USA.
BMC Bioinformatics. 2018 Nov 20;19(Suppl 14):412. doi: 10.1186/s12859-018-2382-0.
The potential for astrocyte participation in central nervous system recovery is highlighted by in vitro experiments demonstrating their capacity to transdifferentiate into neurons. Understanding astrocyte plasticity could be advanced by comparing astrocytes with stem cells. RNA sequencing (RNA-seq) is ideal for comparing differences across cell types. However, this novel multi-stage process has the potential to introduce unwanted technical variation at several points in the experimental workflow. Quantitative understanding of the contribution of experimental parameters to technical variation would facilitate the design of robust RNA-Seq experiments.
RNA-Seq was used to achieve biological and technical objectives. The biological aspect compared gene expression between normal human fetal-derived astrocytes and human neural stem cells cultured in identical conditions. When differential expression threshold criteria of |log fold change| > 2 were applied to the data, no significant differences were observed. The technical component quantified variation arising from particular steps in the research pathway, and compared the ability of different normalization methods to reduce unwanted variance. To facilitate this objective, a liberal false discovery rate of 10% and a |log fold change| > 0.5 were implemented for the differential expression threshold. Data were normalized with RPKM, TMM, and UQS methods using JMP Genomics. The contributions of key replicable experimental parameters (cell lot; library preparation; flow cell) to variance in the data were evaluated using principal variance component analysis. Our analysis showed that, although the variance for every parameter is strongly influenced by the normalization method, the largest contributor to technical variance was library preparation. The ability to detect differentially expressed genes was also affected by normalization; differences were only detected in non-normalized and TMM-normalized data.
The similarity in gene expression between astrocytes and neural stem cells supports the potential for astrocytic transdifferentiation into neurons, and emphasizes the need to evaluate the therapeutic potential of astrocytes for central nervous system damage. The choice of normalization method influences the contributions to experimental variance as well as the outcomes of differential expression analysis. However irrespective of normalization method, our findings illustrate that library preparation contributed the largest component of technical variance.
体外实验表明星形胶质细胞有向神经元转分化的能力，这突显了星形胶质细胞参与中枢神经系统修复的潜力。通过比较星形胶质细胞和干细胞来了解星形胶质细胞的可塑性，可能会取得进展。RNA 测序（RNA-seq）非常适合比较细胞类型之间的差异。然而，这种新的多阶段过程有可能在实验工作流程的几个点引入不必要的技术变异。定量了解实验参数对技术变异的贡献将有助于设计稳健的 RNA-seq 实验。
RNA-seq 用于实现生物学和技术目标。生物学方面是将在相同条件下培养的正常人类胎儿来源的星形胶质细胞和人神经干细胞的基因表达进行比较。当将数据的差异表达阈值标准应用于 |log 倍变化|>2 时，未观察到显著差异。技术部分量化了研究途径中特定步骤产生的变异，并比较了不同归一化方法减少不必要方差的能力。为了实现这一目标，对差异表达阈值实施了宽松的 10%错误发现率和 |log 倍变化|>0.5。使用 JMP Genomics 用 RPKM、TMM 和 UQS 方法对数据进行归一化。使用主方差分量分析评估关键可重复实验参数（细胞批次；文库制备；流动池）对数据方差的贡献。我们的分析表明，尽管每个参数的方差都受到归一化方法的强烈影响，但对技术方差的最大贡献者是文库制备。基因表达差异检测能力也受到归一化的影响；仅在未归一化和 TMM 归一化数据中检测到差异。
星形胶质细胞和神经干细胞之间基因表达的相似性支持星形胶质细胞向神经元转分化的潜力，并强调需要评估星形胶质细胞对中枢神经系统损伤的治疗潜力。归一化方法的选择不仅会影响实验方差的贡献，还会影响差异表达分析的结果。然而，无论使用何种归一化方法，我们的发现都表明文库制备是技术方差的最大组成部分。
