Deconvolution of heterogeneous wound tissue samples into relative macrophage phenotype composition via models based on gene expression.
Deconvolution of heterogeneous wound tissue samples into relative macrophage phenotype composition via models based on gene expression.
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DOI：
10.1039/c7ib00018a
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发表时间：
2017-04-18
期刊：
影响因子：
--
通讯作者：
Spiller KL
中科院分区：
文献类型：
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作者：
Ferraro NM;Dampier W;Weingarten MS;Spiller KL
巨噬细胞是先天性免疫系统的主要细胞，作用于对应于不同功能的一系列表型。巨噬细胞表型的失调与许多疾病有关。特别是，从促炎（M1）到抗炎（M2）行为的缺陷性转变被认为是持续炎症的潜在来源，可阻止慢性伤口（如糖尿病溃疡）的愈合。为了设计有效的治疗方法，有必要了解组织修复过程中巨噬细胞表型的相对存在。由于巨噬细胞本身和组织样本的异质性，推断相对表型组成目前具有挑战性。我们在这里提出了一种方法去卷积基因表达异质组织样本的两个主要的巨噬细胞表型（M1和M2）的组成。我们的最终方法使用体外培养的每个表型的基因表达特征作为预测模型的输入，该模型推断样品组成的平均误差为0.16，并且其预测适合体外制备的已知组合物，R2值为0.90。最后，我们应用这个模型来描述巨噬细胞的行为在人类糖尿病溃疡愈合临床分离的溃疡组织样本。该模型预测，与愈合的糖尿病溃疡相比，未愈合的糖尿病溃疡含有更高比例的M1巨噬细胞，这与许多研究一致，这些研究表明糖尿病溃疡愈合受损中存在功能障碍的M1至M2转变。这些结果表明，该模型在预测异质样品中的巨噬细胞行为方面具有实用性，具有作为伤口愈合诊断的潜在应用。
Macrophages, the primary cell of the innate immune system, act on a spectrum of phenotypes that correspond to diverse functions. Dysregulation of macrophage phenotype is associated with many diseases. In particular, defective transition from pro-inflammatory (M1) to anti-inflammatory (M2) behavior has been implicated as a potential source of sustained inflammation that prevents healing of chronic wounds such as diabetic ulcers. In order to design effective treatments, an understanding of the relative presence of macrophage phenotypes during tissue repair is necessary. Inferring the relative phenotype composition is currently challenging due to the heterogeneous nature of the macrophages themselves and also of tissue samples. We propose here a method to deconvolute gene expression from heterogeneous tissue samples into the composition of two primary macrophage phenotypes (M1 and M2). Our final method uses gene expression signatures for each phenotype cultivated in vitro as input to a predictive model that infers sample composition with an average error of 0.16, and whose predictions fit known compositions prepared in vitro with an R2 value of 0.90. Finally, we apply this model to describe macrophage behavior in human diabetic ulcer healing using clinically isolated ulcer tissue samples. The model predicted that non-healing diabetic ulcers contained higher proportions of M1 macrophages compared to healing diabetic ulcers, in agreement with numerous studies that have implicated a dysfunctional M1-to-M2 transition in the impaired healing of diabetic ulcers. These results show proof of concept that the model holds utility in making predictions regarding macrophage behavior in heterogeneous samples, with potential application as a wound healing diagnostic.