文章题目:Single-cell analysis reveals prognostic fibroblast subpopulations linked to molecular and immunological subtypes of lung cancer
期刊:Nature Communications
日期:2023年1月31日
DOI: 10.1038/s41467-023-35832-6
library(Seurat)
library(sctransform)
library(ggplot2)
library(WGCNA)
library(tidyverse)
library(ggpubr)
library(ggsci)
data_directory <- "H:\\文献复现\\6\\"
source(paste0(data_directory, "0_NewFunctions.R"))
load(paste0(data_directory, "IntegratedFibs_Zenodo.Rdata"))
load(paste0(data_directory, "CrossTissueAnalysis_Zenodo.Rdata"))
load(paste0(data_directory, "MxIHC_TMAdata_Zenodo.Rdata"))
分离自不同癌症类型并通过scRNA-seq分析的成纤维细胞的UMAP降维。检测这些成纤维细胞表型是否在不同癌症类型中是保守的,分析了PDAC49、HNSCC29和结肠直肠癌(CRC)。在每种情况下,成纤维细胞都是通过无监督聚类和壁细胞排除法鉴定
Sample_UMAP <-
Merged_MetaData %>%
filter(Group %in% c("Pancreas", "Oral", "Colon")) %>%
ggplot(aes(x = UMAP_1, y = UMAP_2, colour = Sample.type)) +
geom_point(size = 0.1) +
facet_wrap(~Group, scales = "free", nrow = 1) +
theme_pubr(base_size = 15) +
scale_color_npg(name = "Sample type") +
theme(legend.position = "right",legend.key.size = unit(10, "pt"))+
guides(colour = guide_legend(override.aes = list(size = 2)))
#突出显示机器学习分类器预测的与每个细胞相关的成纤维细胞亚群
Class_UMAP <-
Merged_MetaData %>%
filter(Group %in% c("Pancreas", "Oral", "Colon")) %>%
ggplot(aes(x = UMAP_1, y = UMAP_2, colour = predicted.id)) +
geom_point(size = 0.1) +
facet_wrap(~Group, scales = "free", nrow = 1) +
theme_pubr(base_size = 15) +
scale_colour_manual(values = Fibs_col.palette, name = "Predicted class") +
theme(legend.position = "right",legend.key.size = unit(10, "pt")) +
guides(colour = guide_legend(override.aes = list(size = 2)))
#小提琴图显示了按亚群分组的机器学习分类器模型预测的概率
Prob_VlnPlot <-
Merged_MetaData %>%
filter(Group %in% c("Pancreas", "Oral", "Colon")) %>%
ggplot(aes(x = predicted.id, y = prediction.score.max, fill = predicted.id)) +
geom_violin(scale = "width") +
geom_boxplot(width = 0.1, outlier.shape = NA, fill = "white") +
facet_wrap(~Group, scales = "free", nrow = 1) +
theme_pubr(base_size = 15) +
scale_fill_manual(values = Fibs_col.palette, name = "Predicted class") +
rotate_x_text(angle = 45) +
theme(legend.position = "right", axis.title.x = element_blank(),
legend.key.size = unit(10, "pt")) +
ylab("Classification Probability") +
ylim(c(0,1))
Fig_5ABC <- ggarrange(Sample_UMAP, Class_UMAP, Prob_VlnPlot, nrow = 3,
align = "v")
这表明,在分析的所有癌症类型中,外膜细胞和肌成纤维细胞群都是高度保守的,而分配给肺泡亚群的成纤维细胞的概率得分一直较低,表明与肺的表型差异程度更大。
来自组织微阵列(TMA)的mxIHC分析的代表性图像,所述组织微阵列由胰腺癌、口腔癌和结肠癌组织块构建。可视化显示成纤维细胞亚群的空间分布
s = "PANCREAS"
PDAC_CTR_C01 <-
All_TMA.data.df %>%
filter(TvN == "Normal" & Core == "C01") %>%
ggplot(aes(x = X.Center..Pxl., y = Y.Center..Pxl.,
colour = Cell.type2)) +
geom_point(size = 1.5) +
theme_void(base_size = 15) +
theme(legend.position = "none") +
guides(colour = guide_legend(override.aes = list(size = 4))) +
scale_colour_manual(values = Fibs_col.palette,
na.value = "grey80")
PDAC_Tumour_CO2 <-
All_TMA.data.df %>%
filter(TvN == "Tumour" & Core == "C02") %>%
ggplot(aes(x = X.Center..Pxl., y = Y.Center..Pxl.,
colour = Cell.type2)) +
geom_point(size = 1.5) +
theme_void(base_size = 15) +
theme(legend.position = "none") +
guides(colour = guide_legend(override.aes = list(size = 4))) +
scale_colour_manual(values = Fibs_col.palette,
na.value = "grey80")
HNSCC_CTR_E07 <-
All_TMA.data.df %>%
filter(TvN == "Normal" & Core == "E07") %>%
ggplot(aes(x = X.Center..Pxl., y = Y.Center..Pxl.,
colour = Cell.type2)) +
geom_point(size = 1.5) +
theme_void(base_size = 15) +
theme(legend.position = "none") +
guides(colour = guide_legend(override.aes = list(size = 4))) +
scale_colour_manual(values = Fibs_col.palette,
na.value = "grey80")
HNSCC_Tumour_E07 <-
All_TMA.data.df %>%
filter(TvN == "Tumour" & Core == "E07") %>%
ggplot(aes(x = X.Center..Pxl., y = Y.Center..Pxl.,
colour = Cell.type2)) +
geom_point(size = 1.5) +
theme_void(base_size = 15) +
theme(legend.position = "none") +
guides(colour = guide_legend(override.aes = list(size = 4))) +
scale_colour_manual(values = Fibs_col.palette,
na.value = "grey80")
COLON_CTR_B03 <-
All_TMA.data.df %>%
filter(TvN == "Normal" & Core == "B03") %>%
ggplot(aes(x = X.Center..Pxl., y = Y.Center..Pxl.,
colour = Cell.type2)) +
geom_point(size = 1.5) +
theme_void(base_size = 15) +
theme(legend.position = "none") +
guides(colour = guide_legend(override.aes = list(size = 4))) +
scale_colour_manual(values = Fibs_col.palette,
na.value = "grey80")
COLON_Tumour_B05 <-
All_TMA.data.df %>%
filter(TvN == "Tumour" & Core == "B05") %>%
ggplot(aes(x = X.Center..Pxl., y = Y.Center..Pxl.,
colour = Cell.type2)) +
geom_point(size = 1.5) +
theme_void(base_size = 15) +
theme(legend.position = "none") +
guides(colour = guide_legend(override.aes = list(size = 4))) +
scale_colour_manual(values = Fibs_col.palette,
na.value = "grey80")
Fig_5D <- ggarrange(PDAC_CTR_C01, PDAC_Tumour_CO2,
HNSCC_CTR_E07, HNSCC_Tumour_E07,
COLON_CTR_B03, COLON_Tumour_B05,
nrow = 6, ncol = 1)
通过将多重免疫组化面板应用于由来自PDAC、HNSCC和CRC的肿瘤和对照组织核心组成的组织微阵列来验证这些结果。与scRNA-seq结果一致,这表明在每种癌症类型中,外膜和肌成纤维细胞是主要的亚群
All_TMA.data.df.Fibroblasts <- All_TMA.data.df %>%
filter(Cell.type2 %in% c("Alveolar", "Adventitial", "Myo"))
table(All_TMA.data.df.Fibroblasts$Cell.type2)
All_TMA.data.df.Fibroblasts$Cell.type2 <- factor(
as.character(All_TMA.data.df.Fibroblasts$Cell.type2),
levels = c("Adventitial", "Alveolar", "Myo")
)
dt <- as.table(as.matrix(table(All_TMA.data.df.Fibroblasts$Core_ID,
All_TMA.data.df.Fibroblasts$Cell.type2)))
Sample.pct_long <- as.data.frame(dt/rowSums(dt)*100)
CoreData_long <- merge(MxIHC_TMA_metaData, Sample.pct_long,
by.x = "Core_ID", by.y = "Var1")
names(CoreData_long)[names(CoreData_long) == "Freq"] <- "Core.pct"
names(CoreData_long)[names(CoreData_long) == "Var2"] <- "Fibs_SubPop"
CoreData_long$Group <- factor(CoreData_long$Structure,
levels = unique(CoreData_long$Structure)[c(3,5,2,7,6,4,1)],
labels = c("Pancreas", "Oral", "Colon", "Lung", "Skin", "Breast", "Kidney"))
names(CoreData_long)
#箱形图显示肿瘤或对照组织中外膜成纤维细胞的相对丰度,通过TMA细胞核的mxIHC分析测定
Fig_5E <-
CoreData_long[] %>%
drop_na(Structure.filtered) %>%
filter(Fibs_SubPop == "Adventitial") %>%
filter(Structure %in% c("COLON", "PANCREAS", "HNSCC")) %>%
ggplot(aes(x = TvN, y = Core.pct)) +
theme_pubr(base_size = 15) +
facet_wrap(~Group) +
geom_boxplot(outlier.shape = NA, aes(fill = Fibs_SubPop)) +
geom_jitter(alpha = 0.5, width = 0.2) +
scale_fill_manual(values = Fibs_col.palette) +
rotate_x_text(angle = 45) +
scale_y_continuous(breaks = c(0,25,50, 75, 100),
limits = c(0,125)) +
stat_compare_means(comparisons = list(c("Normal", "Tumour")), size = 3,
label.y = 110, size = 2.5) +
ylab("% of all fibroblast per core\n(MxIHC)") +
theme(axis.title.x = element_blank(), legend.position = "none")
Fig_5E
#箱形图显示肿瘤或对照组织中肌成纤维细胞的相对丰度,通过TMA细胞核的mxIHC分析测定
Fig_5F <-
CoreData_long[] %>%
drop_na(Structure.filtered) %>%
filter(Fibs_SubPop == "Myo") %>%
filter(Structure %in% c("COLON", "PANCREAS", "HNSCC")) %>%
ggplot(aes(x = TvN, y = Core.pct)) +
theme_pubr(base_size = 15) +
facet_wrap(~Group) +
geom_boxplot(outlier.shape = NA, aes(fill = Fibs_SubPop)) +
geom_jitter(alpha = 0.5, width = 0.2) +
scale_fill_manual(values = Fibs_col.palette) +
rotate_x_text(angle = 45) +
scale_y_continuous(breaks = c(0,25,50, 75, 100),
limits = c(0,125)) +
stat_compare_means(comparisons = list(c("Normal", "Tumour")), size = 3,
#label = "p.signif", method = "wilcox",
label.y = 110, size = 2.5) +
ylab("% of all fibroblast per core\n(MxIHC)") +
theme(axis.title.x = element_blank(), legend.position = "none")
Fig_5F
Fig_5EF <- ggarrange(Fig_5E, Fig_5F, nrow = 2,
align = "v")
正如在非小细胞肺癌中发现的那样,与所有三种肿瘤类型的肿瘤组织相比,对照组中上皮成纤维细胞的丰度明显更高,而肿瘤组织中肌成纤维细胞的丰度更高。为了测试肺泡表型是否对肺纤维化具有特异性,对特发性肺纤维化(IPF)样本中产生的scRNA-seq数据进行了类似的分析,结果表明所有三个亚群都具有高概率得分,值得注意的是,该分析还显示,IPF中与肌成纤维细胞分类相关的概率低于癌症数据集,这表明癌症和纤维化中发现的肌成纤维细胞之间可能存在细微差异。
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