A Faithful In Vivo Model Of Human Macrophages In Metastatic Melanoma
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https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE173625
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Despite recent therapeutic progress, advanced melanoma remains lethal for many patients. The composition of the immune tumor microenvironment (TME) has decisive impacts on disease outcome and therapy response. High dimensional analyses of patient samples provide insight on the composition and heterogeneity of the immune TME. Macrophages are known for their cancer-supportive role, but the underlying mechanisms are incompletely understood, and experimental in vivo systems are needed to test the functional properties of these cells. We characterize a humanized mouse model, reconstituted with a human immune system and a human melanoma, in which: (1) human macrophages support metastatic spread of the tumor; and (2) tumor-infiltrating macrophages have a specific transcriptional signature that faithfully represents the transcriptome of macrophages from patient melanoma samples and is associated with shorter survival. This model complements patient sample analyses, enabling the elucidation of fundamental principles in melanoma biology, and the development and evaluation of candidate therapies. Immunodeficient "MISTRG" mice were repopulated with human CD34+ hematopoietic stem and progenitor cells. A human melanoma cell line (Me275) was implanted in the flank of the mice. Human CD45+ cells were isolated from tissues of 5 mice (repopulated with CD34+ cells from 2 donors) and pooled for scRNAseq analysis.
尽管当前黑色素瘤治疗已取得一定进展,但晚期黑色素瘤仍对多数患者构成致命威胁。免疫肿瘤微环境(immune tumor microenvironment, TME)的组成对疾病转归与治疗应答具有决定性影响。对患者样本开展的高维度分析,可揭示免疫TME的组成与异质性。巨噬细胞的促癌功能已被证实,但其介导该作用的分子机制尚未完全阐明,因此亟需构建体内实验系统以验证这类细胞的功能特性。本研究对一种人源化小鼠模型进行了系统表征:该模型重构了人类免疫系统与人类黑色素瘤,具备以下两大核心特征:(1)人类巨噬细胞可促进肿瘤的转移扩散;(2)肿瘤浸润巨噬细胞具有特异性转录特征,该特征可精准反映患者黑色素瘤样本中巨噬细胞的转录组,且与患者更短的生存期显著相关。该模型可作为患者样本分析的有力补充,助力阐明黑色素瘤生物学的基础原理,并推动候选治疗方案的开发与评估。本研究将人类CD34+造血干祖细胞移植至免疫缺陷型"MISTRG"小鼠体内,随后在小鼠胁部皮下植入人类黑色素瘤细胞系(Me275)。研究人员从5只小鼠(其CD34+细胞来自2名供体)的组织中分离出人类CD45+细胞并混合,进行单细胞RNA测序(single-cell RNA sequencing, scRNAseq)分析。
创建时间:
2022-05-01



