从单细胞数据中捕捉巨噬细胞的极化信号

生信菜鸟团

发布于 2025-03-06 21:40:07

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巨噬细胞极化（Macrophage Polarization）是指巨噬细胞在不同微环境信号刺激下，分化为不同功能表型的过程。通常，巨噬细胞可以分为两种主要的极化表型：

M1型巨噬细胞（经典激活型巨噬细胞）：由IFN-γ、LPS等信号诱导，具有促炎、抗微生物和抗肿瘤的功能。
M2型巨噬细胞（替代激活型巨噬细胞）：由IL-4、IL-13、IL-10等信号诱导，具有抗炎、组织修复和促肿瘤的功能。

目前的单细胞测序背景下，特别是针对不同类型的TME，研究者更倾向于将巨噬细胞分类为更精细的亚群，这有利于后续的轨迹分析。

但巨噬细胞极化是巨噬细胞共有的一个表型，我们如何对10多个macro亚型进行极化的评估呢？

从这样的散点图，我们就更能更直观地观察到macro亚群向M1或M2极化的趋势。

如何实现呢？首先需要一个做好细胞注释的seurat对象，然后：

定义基因模块

# M1 巨噬细胞基因模块
m1_genes <- c("CXCL9", "CCL19", "CXCL10", "IDO1", "EBI3", "TNFAIP6", "CCR7", "LAMP3", "CCL5", "CD40", "IRF8", "CD38", "CCL4", "APOL3", "CXCL11", "ADAMDEC1", "HCK", "BCL2A1", "RSAD2", "CYP27B1", "KYNU", "SIGLEC1", "SLAMF1", "SLC2A6", "IF144L", "SAMSN1", "LILRB2", "TLR2", "CCL8", "APOBEC3A", "AQP9", "C3AR1", "HLA-DQA1", "MNDA", "IL2RA", "SLC15A3", "CD80", "ADGRE1", "CHI3L1", "RASSF4", "IL4R", "AIM2", "PLA1A", "MMP9", "TLR8", "HESX1", "SOCS1", "LST1", "NOD2", "IL7R", "FLVCR2", "BIRC3", "PTGIR", "CHI3L2", "PLA2G7", "GPR183", "AIF1", "CD4", "HLA-DOB", "MSC", "ADGRE2", "CCR5", "CLEC2D", "INFRSF4", "CD86", "SPIB", "LAG3", "DHX58", "CLIC2", "ACHE", "CCL18", "LY86", "CCL14", "TNIP3", "Clorf54", "MMP25", "ACP5", "APOBEC3G")

# M2 巨噬细胞基因模块
m2_genes <- c("MMP9", "MS4A6A", "CCL18", "AIF1", "NCF2", "CD4", "ACP5", "CLEC4A", "CCL13", "LY86", "SLC15A3", "CLEC10A", "CCL23", "HLA-DQA1", "RNASE6", "ADAMDEC1", "HCK", "NPL", "TREM2", "IRF8", "SIGLEC1", "CCL4", "SAMSN1", "CLEC7A", "TLR2", "P2RY13", "CCL8", "CD86", "CD180", "CD68", "CD209", "C3AR1", "GPR183", "CCL14", "DPEP2", "FPR3", "CD37", "LST1", "CLIC2", "HRH1", "EGR2", "CHI3L1", "Clorf54", "CFP", "C5AR1", "MNDA", "PTGER2", "CD300A", "PIK3IP1", "IGSF6", "RENBP", "PLA2G7", "FCGR2B", "SLAMF8", "RASGRP3", "QPCT", "LILRB2", "ATP8B4", "PTGIR", "FRMD4A", "TLR8", "KYNU", "CHST15")

巨噬细胞的基因集来自：https://ars.els-cdn.com/content/image/1-s2.0-S0092867421000106-mmc5.xlsx

计算模块得分

macro_cells <- AddModuleScore(macro_cells, features = list(m1_genes), name = "M1_score")
macro_cells <- AddModuleScore(macro_cells, features = list(m2_genes), name = "M2_score")

可视化

# 定义颜色映射
library(paletteer)
paletteer_c("scico::berlin", 8)

celltype_colors <- c(
  "Macro-CCL3L1" = "#9EB0FFFF",
  "Macro-CXCL9" = "#4799C9FF",
  "Macro-FTH1" = "#225771FF",
  "Macro-IL1B" = "#00FF7FFF",
  "Macro-ISG15" = "#FAEFD1FF",
  "Macro-MT1X" = "#68240FFF",
  "Macro-RNASE1" = "#B36556FF",
  "Macro-SELENOP" = "#FFACACFF"
)

# 加载 ggplot2 包
library(ggplot2)

# 创建数据框
plot_data <- data.frame(
  M1_score = macro_cells$M1_score1,
  M2_score = macro_cells$M2_score1,
  celltype = macro_cells$celltype
)

# 使用 ggplot2 绘制散点图
p <- ggplot(plot_data, aes(x = M1_score, y = M2_score, color = celltype)) +
  geom_point(size = 1.5, alpha = 0.7) +  # 设置点的大小和透明度
  scale_color_manual(values = celltype_colors) +  # 使用自定义颜色映射
  labs(
    x = "M1 Score",  # x 轴标签
    y = "M2 Score",  # y 轴标签
    title = "M1 vs M2 Scores by Cell Type",  # 图表标题
    color = "Cell Type"  # 图例标题
  ) +
  theme_minimal() +  # 使用简洁的主题
  theme(
    plot.title = element_text(hjust = 0.5, size = 14, face = "bold"),  # 标题居中加粗
    legend.position = "right",  # 图例放在右侧
    legend.text = element_text(size = 10),  # 图例文字大小
    panel.grid.major = element_blank(),  # 删除主要网格线
    panel.grid.minor = element_blank(),  # 删除次要网格线
    panel.border = element_rect(color = "black", fill = NA, size = 1),  # 添加边框
    axis.line = element_line(color = "black", size = 0.5)  # 添加坐标轴线
  ) +
  guides(color = guide_legend(override.aes = list(size = 4))) +  # 调整图例中点的大小
  geom_hline(yintercept = 0, color = "black", linetype = "dashed", size = 0.5) +  # 添加 y=0 的水平线
  geom_vline(xintercept = 0, color = "black", linetype = "dashed", size = 0.5) +  # 添加 x=0 的垂直线
  coord_cartesian(xlim = c(min(plot_data$M1_score), max(plot_data$M1_score)),  # 设置 x 轴范围
                  ylim = c(min(plot_data$M2_score), max(plot_data$M2_score)))  # 设置 y 轴范围
p
ggsave(p,filename = "Outplot/Step3.1.macro_Polarization.pdf",
       width = 11, height = 8)

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原始发表：2025-03-05，如有侵权请联系 cloudcommunity@tencent.com 删除

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