虽然免疫检查点抑制剂(ICI)被认为是肿瘤的突破治疗的突破口,但是只有少部分患者能从中获益。肿瘤干性可能是免疫检查点抑制剂耐药性的潜在罪魁祸首,但缺乏直接的临床数据。2022年4月,Genome Medicine杂志发表题目为Integrated analysis of single-cell and bulk RNA sequencing data reveals a pan-cancer stemness signature predicting immunotherapy response的研究论文,通过单细胞和常规转录组数据挖掘揭示肿瘤干性与免疫治疗耐药性之间的强大联系,并开发出一种有前景的生物标志物,为克服免疫检查点抑制剂耐药性提供启示。
肿瘤干性与免疫检查点抑制剂耐药相关。作者巧妙地利用单细胞和常规转录组公共数据,通过对免疫治疗耐药和肿瘤干性综合分析。作者将单细胞数据和转录组数据结合起来用,而不是只用单细胞数据或转录组数据。因为单细胞敏感,但是有时可靠性差,这是单细胞技术上的局限;转录组测序成熟,但是敏感性和精确性不如单细胞测序。
对两个单细胞数据集黑色素瘤 (GSE115978)和基底细胞癌 (GSE123813)进行数据分析,并用CytoTRACE对细胞群的进行干性评分。然后利用应答 (R)和非应答 (NR)数据对细胞群进行注释 (FigA、C),通过聚类图和箱线图 (FigB、D)的对比,验证了前人的研究结论:NR组表现出更高的肿瘤干性。
2024年3月,CytoTRACE2在预印本发表题为Mapping single-cell developmental potential in health and disease with interpretable deep learning的算法论文。除给出从0(分化)到1(全能)的发育潜能结果外,CytoTRACE2算法还可根据发育潜能将待分析细胞分为6类:①全能干细胞totipotent,②多能干细胞pluripotent,③谱系限制多能细胞lineage-restricted oligopotent,④多能细胞multipotent,⑤单能细胞unipotent和⑥分化细胞differentiated。
CytoTRACE (Cellular Trajectory Reconstruction Analysis using gene Counts and Expression)算法可在不需先验知识的情况下,利用基因计数特征(gene counts signature, GCS)在单细胞数据上评估细胞分化水平。与大多数现有的谱系轨迹分析方法不同,CytoTRACE能够独立于中间态(如RNA velocity)或数据中连续发育过程(如monocle)的存在与否,预测细胞分化的相对状态和方向。相比CytoTRACE版本,CytoTRACE2使用可解释性的AI算法来预测单细胞RNA测序数据的细胞分化潜能。
#############CytoTRACE2分析----------
# 安装和加载包
devtools::install_github("digitalcytometry/cytotrace2", subdir = "cytotrace2_r") #installing
# 检查函数是否存在
exists("CytoTRACE2") # 应返回 TRUE
# 加载包
library(CytoTRACE2)
library(Seurat)
library(ggplot2)
library(patchwork)
load("CD4.RData")
# 检查 counts 数据
sce@assays[["RNA"]]@layers[["counts"]]
# 运行分析
result_sce <- cytotrace2(sce,
is_seurat = TRUE,
slot_type = "counts",
species = "human",
seed = 1234)
result_sce
# 结果可视化
annotation <- data.frame(phenotype = result_sce@meta.data$celltype) %>% set_rownames(., colnames(result_sce))
plots <- plotData(cytotrace2_result = result_sce, annotation = annotation, is_seurat = TRUE)
p1 <- plots$CytoTRACE2_UMAP
p2 <- plots$CytoTRACE2_Potency_UMAP
p3 <- plots$CytoTRACE2_Relative_UMAP
p4 <- plots$CytoTRACE2_Boxplot_byPheno
(p1+p2+p3+p4) + plot_layout(ncol = 2)
# 调整风格
FeaturePlot(result_sce, "CytoTRACE2_Relative",pt.size = 1.5) +
scale_colour_gradientn(colours =
(c("#9E0142", "#F46D43", "#FEE08B", "#E6F598",
"#66C2A5", "#5E4FA2")),
na.value = "transparent",
limits = c(0, 1),
breaks = seq(0, 1, by = 0.2),
labels = c("0.0 (More diff.)",
"0.2", "0.4", "0.6", "0.8", "1.0 (Less diff.)"),
name = "Relative\norder \n",
guide = guide_colorbar(frame.colour = "black",
ticks.colour = "black")) +
ggtitle("CytoTRACE 2") +
xlab("UMAP1") + ylab("UMAP2") +
theme(legend.text = element_text(size = 10),
legend.title = element_text(size = 12),
axis.text = element_text(size = 12),
axis.title = element_text(size = 12),
plot.title = element_text(size = 12,
face = "bold", hjust = 0.5,
margin = margin(b = 20))) +
theme(aspect.ratio = 1)
# 细胞类型,箱线图
library(ggpubr)
p5 <- ggboxplot(result_sce@meta.data, x="celltype", y="CytoTRACE2_Score", width = 0.6,
color = "black", # 轮廓颜色
fill = "celltype", # 填充
palette = "npg",
xlab = F, # 不显示x轴的标签
bxp.errorbar = T, # 显示误差条
bxp.errorbar.width = 0.5, # 误差条大小
size = 1, # 箱型图边线的粗细
outlier.shape = NA, # 不显示outlier
legend = "right") # 图例放右边
###指定组比较
my_comparisons <- list(c("CD4 T Memory", "T helper"), c("CD4 T Memory", "CD4 T Naive"),c("CD4 T Memory", "Treg"))
p5+stat_compare_means(comparisons = my_comparisons,
method = "wilcox.test")左下图中细胞分化潜能打分热图,数值越大表示细胞分化潜能越高,越可能是祖细胞。
2021年2月,张泽民团队在Cell发表题为A pan-cancer single-cell transcriptional atlas of tumor infiltrating myeloid cells的研究论文。该文章中使用CytoTRACE辅助monocle来确定髓系(单核细胞,树突状,巨噬细胞,粒细胞)各种细分亚群发育顺序和起始点。在补充数据中,作者用monocle分析+CytoTRACE追踪免疫细胞的发育顺序。可以看到,CLEC9A阳性的cDC1和CD1C阳性的cDC2可发育成LAMP3阳性的cDC3。
在单细胞数据分析中,细胞干性和免疫细胞分化潜能绝对值得关注!
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