Ferraro Nicole M, Dampier Will, Weingarten Michael S, Spiller Kara L
School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, USA.
Integr Biol (Camb). 2017 Apr 18;9(4):328-338. doi: 10.1039/c7ib00018a.
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 R 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.
巨噬细胞是先天免疫系统的主要细胞，其表现出一系列与不同功能相对应的表型。巨噬细胞表型失调与许多疾病相关。特别是，从促炎（M1）到抗炎（M2）行为的缺陷转变被认为是持续性炎症的潜在来源，这种炎症会阻碍糖尿病溃疡等慢性伤口的愈合。为了设计有效的治疗方法，了解组织修复过程中巨噬细胞表型的相对存在情况是必要的。由于巨噬细胞本身以及组织样本的异质性，推断相对表型组成目前具有挑战性。我们在此提出一种方法，将来自异质组织样本的基因表达解卷积为两种主要巨噬细胞表型（M1和M2）的组成。我们最终的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，该模型推断样本组成的平均误差为0.16，其预测结果与体外制备的已知组成的拟合度R值为0.90。最后，我们应用该模型，使用临床分离的溃疡组织样本来描述人类糖尿病溃疡愈合过程中的巨噬细胞行为。该模型预测，与愈合的糖尿病溃疡相比，不愈合的糖尿病溃疡中M1巨噬细胞的比例更高，这与许多研究结果一致，这些研究表明M1到M2的功能失调转变与糖尿病溃疡愈合受损有关。这些结果证明了该模型在对异质样本中的巨噬细胞行为进行预测方面具有实用性，具有作为伤口愈合诊断方法的潜在应用价值。
