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通过基因表达谱进行生物标志物发现的组织隔室分析。

Tissue compartment analysis for biomarker discovery by gene expression profiling.

机构信息

UPMC Univ Paris 06, UMRS 872, Laboratoire de Génomique, Physiologie et Physiopathologie Rénales, Paris, France.

出版信息

PLoS One. 2009 Nov 10;4(11):e7779. doi: 10.1371/journal.pone.0007779.

Abstract

BACKGROUND

Although high throughput technologies for gene profiling are reliable tools, sample/tissue heterogeneity limits their outcomes when applied to identify molecular markers. Indeed, inter-sample differences in cell composition contribute to scatter the data, preventing detection of small but relevant changes in gene expression level. To date, attempts to circumvent this difficulty were based on isolation of the different cell structures constituting biological samples. As an alternate approach, we developed a tissue compartment analysis (TCA) method to assess the cell composition of tissue samples, and applied it to standardize data and to identify biomarkers.

METHODOLOGY/PRINCIPAL FINDINGS: TCA is based on the comparison of mRNA expression levels of specific markers of the different constitutive structures in pure isolated structures, on the one hand, and in the whole sample on the other. TCA method was here developed with human kidney samples, as an example of highly heterogeneous organ. It was validated by comparison of the data with those obtained by histo-morphometry. TCA demonstrated the extreme variety of composition of kidney samples, with abundance of specific structures varying from 5 to 95% of the whole sample. TCA permitted to accurately standardize gene expression level amongst >100 kidney biopsies, and to identify otherwise imperceptible molecular disease markers.

CONCLUSIONS/SIGNIFICANCE: Because TCA does not require specific preparation of sample, it can be applied to all existing tissue or cDNA libraries or to published data sets, inasmuch specific operational compartments markers are available. In human, where the small size of tissue samples collected in clinical practice accounts for high structural diversity, TCA is well suited for the identification of molecular markers of diseases, and the follow up of identified markers in single patients for diagnosis/prognosis and evaluation of therapy efficiency. In laboratory animals, TCA will interestingly be applied to central nervous system where tissue heterogeneity is a limiting factor.

摘要

背景

尽管高通量基因分析技术是可靠的工具,但当应用于识别分子标记物时,样本/组织异质性限制了它们的结果。事实上,细胞组成的样本间差异导致数据分散,从而无法检测到基因表达水平的微小但相关变化。迄今为止,为了克服这一困难,人们尝试了从构成生物样本的不同细胞结构中进行分离。作为一种替代方法,我们开发了一种组织区室分析(TCA)方法来评估组织样本的细胞组成,并将其应用于数据标准化和生物标志物的识别。

方法/主要发现:TCA 基于对纯分离结构中不同组成结构的特定标志物的 mRNA 表达水平的比较,一方面是在整个样本上的比较。TCA 方法是用人肾样本作为高度异质器官的一个例子开发的。通过与组织形态计量学获得的数据进行比较,验证了该方法的有效性。TCA 证明了肾脏样本组成的极端多样性,特定结构的丰度从整个样本的 5%到 95%不等。TCA 允许准确地对>100 个肾活检样本中的基因表达水平进行标准化,并识别出其他难以察觉的分子疾病标志物。

结论/意义:因为 TCA 不需要对样本进行特殊准备,所以它可以应用于所有现有的组织或 cDNA 文库,或者应用于已发表的数据集,只要有特定的操作区室标志物可用。在人类中,由于临床实践中收集的组织样本体积较小,导致结构多样性较高,TCA 非常适合识别疾病的分子标志物,以及在单个患者中对识别出的标志物进行随访,用于诊断/预后和评估治疗效果。在实验动物中,TCA 将非常适用于中枢神经系统,因为组织异质性是一个限制因素。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6178/2771357/926065de4769/pone.0007779.g001.jpg

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