Genetic and Genomic Epidemiology Unit, Wellcome Trust Centre for Human Genetics, Oxford, UK.
BMC Genomics. 2010 Feb 8;11:96. doi: 10.1186/1471-2164-11-96.
Readily accessible samples such as peripheral blood or cell lines are increasingly being used in large cohorts to characterise gene expression differences between a patient group and healthy controls. However, cell and RNA isolation procedures and the variety of cell types that make up whole blood can affect gene expression measurements. We therefore systematically investigated global gene expression profiles in peripheral blood from six individuals collected during two visits by comparing five of the following cell and RNA isolation methods: whole blood (PAXgene), peripheral blood mononuclear cells (PBMCs), lymphoblastoid cell lines (LCLs), CD19 and CD20 specific B-cell subsets.
Gene expression measurements were clearly discriminated by isolation method although the reproducibility was high for all methods (range rho = 0.90-1.00). The PAXgene samples showed a decrease in the number of expressed genes (P < 110(-16)) with higher variability (P < 110(-16)) compared to the other methods. Differentially expressed probes between PAXgene and PBMCs were correlated with the number of monocytes, lymphocytes, neutrophils or erythrocytes. The correlations (rho = 0.83; rho = 0.79) of the expression levels of detected probes between LCLs and B-cell subsets were much lower compared to the two B-cell isolation methods (rho = 0.98). Gene ontology analysis of detected genes showed that genes involved in inflammatory responses are enriched in B-cells CD19 and CD20 whereas genes involved in alcohol metabolic process and the cell cycle were enriched in LCLs.
Gene expression profiles in blood-based samples are strongly dependent on the predominant constituent cell type(s) and RNA isolation method. It is crucial to understand the differences and variability of gene expression measurements between cell and RNA isolation procedures, and their relevance to disease processes, before application in large clinical studies.
越来越多的研究人员在大型队列研究中使用外周血或细胞系等易于获取的样本,来描述患者组与健康对照组之间的基因表达差异。然而,细胞和 RNA 分离程序以及构成全血的各种细胞类型都会影响基因表达测量。因此,我们通过比较以下五种细胞和 RNA 分离方法,系统地研究了来自六名个体的外周血中的全基因组基因表达谱:全血(PAXgene)、外周血单核细胞(PBMCs)、淋巴母细胞系(LCLs)、CD19 和 CD20 特异性 B 细胞亚群。
尽管所有方法的重现性都很高(范围 rho = 0.90-1.00),但分离方法明显区分了基因表达测量。与其他方法相比,PAXgene 样本的表达基因数量减少(P < 110(-16)),且变异性更高(P < 110(-16))。PAXgene 与 PBMCs 之间差异表达的探针与单核细胞、淋巴细胞、中性粒细胞或红细胞的数量相关。与两种 B 细胞分离方法相比,LCLs 和 B 细胞亚群之间检测探针的表达水平的相关性(rho = 0.83;rho = 0.79)要低得多。检测到的基因的基因本体分析表明,参与炎症反应的基因在 CD19 和 CD20 B 细胞中富集,而参与酒精代谢过程和细胞周期的基因在 LCLs 中富集。
基于血液样本的基因表达谱强烈依赖于主要的细胞类型和 RNA 分离方法。在将其应用于大型临床研究之前,了解细胞和 RNA 分离程序之间基因表达测量的差异和可变性及其与疾病过程的相关性至关重要。