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Deconvolution of heterogeneous wound tissue samples into relative macrophage phenotype composition via models based on gene expression.
Integrative Biology ( IF 2.5 ) Pub Date : 2017-03-16 , DOI: 10.1039/c7ib00018a
Nicole M Ferraro 1 , Will Dampier , Michael S Weingarten , Kara L Spiller
Affiliation  

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.

中文翻译:

通过基于基因表达的模型,将异质伤口组织样品反卷积为相对的巨噬细胞表型组成。

巨噬细胞是先天免疫系统的主要细胞,作用于与多种功能相对应的多种表型。巨噬细胞表型失调与许多疾病有关。尤其是,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,它阻止了慢性伤口(如糖尿病性溃疡)的愈合。为了设计有效的治疗方法,必须了解组织修复过程中巨噬细胞表型的相对存在。由于巨噬细胞本身以及组织样品的异质性,目前推断相对表型组成是具有挑战性的。我们在这里提出了一种方法,可以将来自异质组织样本的基因表达反卷积为两种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种表型的基因表达特征作为预测模型的输入,该模型推断样品成分的平均误差为0.16,其预测值适合于体外制备的已知成分,R2值为0.90。最后,我们使用该模型来描述使用临床分离的溃疡组织样本在人类糖尿病溃疡愈合中的巨噬细胞行为。该模型预测,与治愈的糖尿病性溃疡相比,未治愈的糖尿病性溃疡包含更高比例的M1巨噬细胞,这与许多研究表明糖尿病性溃疡的愈合受损的M1到M2转化异常有关。
更新日期:2019-11-01
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