Single cell transcriptional analysis reveals novel innate immune cell types
Single cell transcriptional analysis reveals novel innate immune cell types
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DOI：
10.7717/peerj.452
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发表时间：
2014-06-24
期刊：
影响因子：
2.7
通讯作者：
Kemp, Melissa L.
中科院分区：
文献类型：
--
作者：
Kippner, Linda E.;Kim, Jinhee;Kemp, Melissa L.
单细胞分析有可能为我们提供大量关于生物系统的新知识，但它也带来了正确解释生物信息的挑战。虽然新兴的技术已经使得在转录组水平上测量细胞间变异性成为可能，但是对于这种单细胞数据的最合适的数据分析方法还没有达成共识。用于在群体水平上分析转录数据的方法已被很好地建立，但由于它们依赖于群体平均值而不太适合于单细胞分析。为了解决这个问题，我们已经系统地测试了用于对从两种类型的原代免疫细胞（中性粒细胞和T淋巴细胞）产生的单细胞转录数据进行原代数据分析的方法组合。从健康个体获得细胞，并通过单细胞分选和纳米级定量真实的时间PCR（qRT-PCR）的组合获得单细胞转录物表达数据，用于细胞类型、细胞内信号传导和免疫功能的标志物。基因表达分析的重点是分层聚类，以确定群体内细胞亚群的存在。测试和评价了9种数据排除和标准化标准的组合。双峰基因表达表明存在的细胞亚群，这也揭示了数据聚类。我们观察到的证据表明，两个明确定义的细胞亚型在中性粒细胞群体和至少两个在T淋巴细胞群体。当通过不同的方法对数据进行标准化时，我们观察到不同的结果，并对细胞群的生物学特征进行了相应的解释。通过线性标准化对数据进行归一化，同时考虑到技术效应（如板效应），得到与生物学预期最接近的解释。单细胞转录谱提供了中性粒细胞和白细胞中细胞亚类的证据，这些细胞亚类可能独立于基于细胞表面标记的传统分类。主要数据分析方法的选择对数据的解释有重大影响。技术效应的调整对于防止单细胞转录数据的误解至关重要。
Single-cell analysis has the potential to provide us with a host of new knowledge about biological systems, but it comes with the challenge of correctly interpreting the biological information. While emerging techniques have made it possible to measure inter-cellular variability at the transcriptome level, no consensus yet exists on the most appropriate method of data analysis of such single cell data. Methods for analysis of transcriptional data at the population level are well established but are not well suited to single cell analysis due to their dependence on population averages. In order to address this question, we have systematically tested combinations of methods for primary data analysis on single cell transcription data generated from two types of primary immune cells, neutrophils and T lymphocytes. Cells were obtained from healthy individuals, and single cell transcript expression data was obtained by a combination of single cell sorting and nanoscale quantitative real time PCR (qRT-PCR) for markers of cell type, intracellular signaling, and immune functionality. Gene expression analysis was focused on hierarchical clustering to determine the existence of cellular subgroups within the populations. Nine combinations of criteria for data exclusion and normalization were tested and evaluated. Bimodality in gene expression indicated the presence of cellular subgroups which were also revealed by data clustering. We observed evidence for two clearly defined cellular subtypes in the neutrophil populations and at least two in the T lymphocyte populations. When normalizing the data by different methods, we observed varying outcomes with corresponding interpretations of the biological characteristics of the cell populations. Normalization of the data by linear standardization taking into account technical effects such as plate effects, resulted in interpretations that most closely matched biological expectations. Single cell transcription profiling provides evidence of cellular subclasses in neutrophils and leukocytes that may be independent of traditional classifications based on cell surface markers. The choice of primary data analysis method had a substantial effect on the interpretation of the data. Adjustment for technical effects is critical to prevent misinterpretation of single cell transcript data.