pd - l1等抑制性免疫检查点分子的表达在人类癌症中较为常见,可导致T细胞介导的免疫应答的抑制。在这里,我们应用ECCITE-seq技术来探索调控pd - l1表达的分子网络。ECCITE-seq技术将混合的CRISPR筛查与单细胞mRNA和表面蛋白测量相结合。我们还开发了一个计算框架,mixscape
,它通过识别和去除混杂的变异源,大大提高了单细胞扰动屏幕的信噪比。利用这些工具,我们识别和验证PD-L1的调控因子,并利用我们的多模态数据识别转录和转录后的调控模式。特别是,我们发现kelch样蛋白keap1和转录激活因子NRF2介导了IFN刺激后pd - l1的上调。我们的结果为免疫检查点的调节确定了一个新的机制,并为分析多模态单细胞perturbation screens提供了一个强大的分析框架 。
免疫检查点(IC)分子调节免疫反应中激活和抑制之间的关键平衡。在正常生理条件下,抑制IC分子对于维持自身耐受和防止自身免疫是必不可少的,但在人类癌症中,抑制IC分子的表达常常被错误调控,以逃避免疫监控。例如,抑制性IC PD-L1与T细胞上的pd -1受体相互作用,抑制T细胞活化,在许多癌症中过表达,并作为患者生存和免疫治疗反应的预后因素。因此,人们不仅对识别阻断这些相互作用的治疗途径感兴趣,而且对理解癌细胞上调PD-L1等ICs的分子网络也很感兴趣。
之前的研究已经初步建立了一套能够影响PD-L1 mRNA和表面蛋白水平的分子调控因子。大量研究发现,干扰素(IFN)的暴露可迅速诱导pd - l1在体外和肿瘤微环境中的表达[7-10]。因此,IFN反应的核心成分是pd - l1表达的上游调控因子,包括转录因子IRF1(直接与pd - l1启动子结合)、JAK-STATsignal转导通路和IFN受体本身。此外,还鉴定了其他IFN的阻断信号、pd - l1启动子染色质状态或对uv介导的应激的调节因子。
此外,最近人们特别关注pd - l1稳定性和降解的转录后调节因子的特性。例如,Cullin 3-SPOPE3-ligase复合物可以细胞周期依赖的方式直接泛素化PD-L1,导致其降解。此外,全基因组CRISPR筛选发现了两个之前未鉴定的调节因子cmtm6和CMTM4,它们通过防止溶酶体介导的降解来稳定PD-L1表面表达。在这些案例中,pd - l1调节剂的干扰被证明可以调节抗肿瘤T细胞的活性,这就突出了理解抑制IC分子调控的治疗兴趣。
我们最近引入了扩展的crispr兼容的CITE-seq (ECCITE-seq),它同时测量转录组、表面蛋白水平和在单细胞分辨率上的扰动,作为一种识别和表征分子调节剂[18]的新方法。ECCITE-seq建立在混合CRISPR屏幕的实验设计上,在单一实验中,多个扰动被复用在一起,但是有明显的优势。首先,单细胞测序读数(即 Perturb-seq, CROP-seq, CRISP-seq)能够测量详细的分子表型,而不是单个表型(单个蛋白的表达或细胞活力)。其次,通过同时耦合测量mRNA、表面蛋白和直接检测同一细胞内的导联,ECCITE-seq允许对每个扰动进行多模态表征。因此,我们认为ECCITE-seq将使我们能够同时测试和识别IC分子的新调控因子,特别是区分转录模式和转录后模式。此外,丰富和高维的读出容易促进网络和基于路径的分析,这可以超越鉴定单个基因和产生对其调控机制的洞察。
在这里,我们应用ECCITE-seq来同时扰乱和表征假定的调节抑制分子对IFN不刺激的反应。当分析我们的单细胞数据时,我们确定了混杂的异质性来源,包括那些接受了靶向引导RNA但没有表现出干扰效应的细胞,这些细胞在下游分析中引入了大量的噪声。我们开发并验证了计算方法来控制这些因素,并大幅增加了我们的统计能力来表征多模态扰动。
利用这些工具,我们确定了一组基因,其扰动会影响pd - l1转录水平、表面蛋白水平,或两者都影响,并确定了每个调控器所使用的潜在分子通路。特别是,我们发现在人类癌症中经常突变的kelch样蛋白keap1和转录激活因子NRF2可以改变pd - l1水平。我们将这些发现与CUL3的一个新的调节机制联系起来,并表明该基因通过稳定NRF2通路作为PD-L1mRNA的间接转录激活剂。综上所述,我们的发现确定了免疫检查点调节的一个重要途径,并提出了一个强大的和广泛适用的分析框架来分析ECCITE-seq数据。
在ECCITE-seq实验中,我们运行了10x Genomics 5’(Chromium Single Cell Immune Profiling Solution v1.0, #1000014, #1000020, #1000151)的8个lanes ,目标是每个lanes 捕获10000个细胞。在运行之前,细胞存活率被确定和细胞数量估计之前描述。为了跟踪每个生物学重复身份,样本按照细胞hashing protocol 进行。
- mRNA
- hashtags (Hashtag-derived oligos, HTOs)
- protein (Antibody-derived oligos, ADTs)
- gRNA (Guide-derived oligos, GDOs)
文库均由10x genomics and ECCITE-seq protocols方法构建。在NovaSeq运行时,所有库都在两个lanes上进行测序。利用Cellranger (V2.1.1)将来自mRNA文库的测序片段映射到hg19参考基因组。为了生成HTO、ADT和GDO库的计数矩阵,使用了CITE-seq-count package (https://github.com/Hoohm/CITE-seq-Count)。然后使用计数矩阵作为Seurat R包的输入来执行所有的下游分析。
下面我们跟着官网教程来看看是如何达到目的的。
首先,我们需要安装新的Seurat,下载示例数据:
remotes::install_github(“satijalab/seurat”, ref = “mixscape”)
# Load packages.
library(Seurat)
library(SeuratData)
library(ggplot2)
library(patchwork)
library(scales)
library(dplyr)
# Download dataset using SeuratData.
InstallData(ds = "thp1.eccite")
# Setup custom theme for plotting.
custom_theme <- theme(plot.title = element_text(size = 16, hjust = 0.5), legend.key.size = unit(0.7,
"cm"), legend.text = element_text(size = 14))
老规矩,我们来看一下数据格式。
# Load object.
eccite <- LoadData(ds = "thp1.eccite")
eccite
An object of class Seurat
18776 features across 20729 samples within 4 assays
Active assay: RNA (18649 features, 0 variable features)
3 other assays present: ADT, HTO, GDO
我们看到有4个assay: RNA , ADT, HTO, GDO,一个Seurat玩转4套数据,在才叫多模态啊。我们分别看看这四套数据的内容:
eccite@assays$RNA@data[1:4,1:4]
4 x 4 sparse Matrix of class "dgCMatrix"
l1_AAACCTGAGCCAGAAC l1_AAACCTGAGTGGACGT l1_AAACCTGCATGAGCGA l1_AAACCTGTCTTGTCAT
AL627309.1 . . . .
AP006222.2 . . . .
RP4-669L17.10 . . . .
RP11-206L10.3 . . . .
> eccite@assays$ADT@data[1:4,1:4]
4 x 4 sparse Matrix of class "dgCMatrix"
l1_AAACCTGAGCCAGAAC l1_AAACCTGAGTGGACGT l1_AAACCTGCATGAGCGA l1_AAACCTGTCTTGTCAT
CD86 118 243 99 110
PDL1 522 139 109 334
PDL2 94 104 56 48
CD366 67 59 80 47
> eccite@assays$HTO@data[1:4,1:4]
4 x 4 sparse Matrix of class "dgCMatrix"
l1_AAACCTGAGCCAGAAC l1_AAACCTGAGTGGACGT l1_AAACCTGCATGAGCGA l1_AAACCTGTCTTGTCAT
rep1-tx 82 18 55 14
rep1-ctrl 3 1 4 .
rep2-tx 13 11 6 6
rep2-ctrl 1 4 . .
> eccite@assays$GDO@data[1:4,1:4]
4 x 4 sparse Matrix of class "dgCMatrix"
l1_AAACCTGAGCCAGAAC l1_AAACCTGAGTGGACGT l1_AAACCTGCATGAGCGA l1_AAACCTGTCTTGTCAT
eGFPg1 1 1 1 1
CUL3g1 1 1 1 1
CUL3g2 1 1 1 1
CUL3g3 1 1 1 1
metadata的内容:
head(eccite@meta.data)
orig.ident nCount_RNA nFeature_RNA nCount_HTO nFeature_HTO nCount_GDO nFeature_GDO nCount_ADT nFeature_ADT percent.mito MULTI_ID MULTI_classification
l1_AAACCTGAGCCAGAAC Lane1 17207 3942 99 4 576 111 801 4 2.295577 rep1-tx rep1-tx
l1_AAACCTGAGTGGACGT Lane1 9506 2948 35 5 190 111 545 4 4.512939 rep1-tx rep1-tx
l1_AAACCTGCATGAGCGA Lane1 15256 4258 66 4 212 111 344 4 4.116413 rep1-tx rep1-tx
l1_AAACCTGTCTTGTCAT Lane1 5135 1780 22 3 243 111 539 4 5.491723 rep1-tx rep1-tx
l1_AAACGGGAGAACAACT Lane1 9673 2671 99 5 198 111 1053 4 3.359868 rep1-tx rep1-tx
l1_AAACGGGAGACAGAGA Lane1 14941 3918 97 5 124 111 487 4 3.379961 rep1-tx rep1-tx
MULTI_classification.global HTO_classification guide_ID guide_ID.global gene con NT crispr replicate S.Score G2M.Score Phase
l1_AAACCTGAGCCAGAAC rep1-tx rep1-tx STAT2g2 Singlet STAT2 tx STAT2g2 Perturbed rep1 -0.25271565 -0.77130934 G1
l1_AAACCTGAGTGGACGT rep1-tx rep1-tx CAV1g4 Singlet CAV1 tx CAV1g4 Perturbed rep1 -0.12380192 -0.33260303 G1
l1_AAACCTGCATGAGCGA rep1-tx rep1-tx STAT1g2 Singlet STAT1 tx STAT1g2 Perturbed rep1 -0.15463259 -0.69441836 G1
l1_AAACCTGTCTTGTCAT rep1-tx rep1-tx CD86g1 Singlet CD86 tx CD86g1 Perturbed rep1 -0.06126198 -0.03781951 G1
l1_AAACGGGAGAACAACT rep1-tx rep1-tx IRF7g2 Singlet IRF7 tx IRF7g2 Perturbed rep1 -0.13218850 -0.35315597 G1
l1_AAACGGGAGACAGAGA rep1-tx rep1-tx NTg1 Singlet NT tx NT NT rep1 -0.20998403 -0.58247261 G1
禁不住用我们的桑吉图看看分属关系:
library(ggforce)
eccite@meta.data %>%
gather_set_data(17:21) %>%
ggplot(aes(x, id = id, split = y, value = 1)) +
geom_parallel_sets(aes(fill = NT), show.legend = FALSE, alpha = 0.3) +
geom_parallel_sets_axes(axis.width = 0.1, color = "lightgrey", fill = "white") +
geom_parallel_sets_labels(angle = 0) +
theme_no_axes()
要理解这里的每一列是什么就要学习CRISPR的基本知识了。重要的一点是理解perturbation 的概念。
# Normalize protein.
eccite <- NormalizeData(object = eccite, assay = "ADT", normalization.method = "CLR", margin = 2)
为了获得全局的观点,我们先对RNA的数据执行Seurat的一般流程:基于rna的聚类是由混杂的变异源驱动的。
# Prepare RNA assay for dimensionality reduction: Normalize data, find variable features and
# scale data.
DefaultAssay(object = eccite) <- "RNA"
eccite <- NormalizeData(object = eccite) %>% FindVariableFeatures() %>% ScaleData()
# Run Principle Component Analysis (PCA) to reduce the dimensionality of the data.
eccite <- RunPCA(object = eccite)
# Run Uniform Manifold Approximation and Projection (UMAP) to visualize clustering in 2-D.
eccite <- RunUMAP(object = eccite, dims = 1:40)
# Generate plots to check if clustering is driven by biological replicate ID, cell cycle phase
# or target gene class.
p1 <- DimPlot(eccite, group.by = "replicate", label = F, pt.size = 0.2, reduction = "umap") + scale_color_brewer(palette = "Dark2") +
ggtitle("Biological Replicate") + xlab("UMAP 1") + ylab("UMAP 2") + custom_theme
p2 <- DimPlot(eccite, group.by = "Phase", label = F, pt.size = 0.2, reduction = "umap") + ggtitle("Cell Cycle Phase") +
ylab("UMAP 2") + xlab("UMAP 1") + custom_theme
p3 <- DimPlot(eccite, group.by = "crispr", pt.size = 0.2, reduction = "umap", split.by = "crispr",
ncol = 1, cols = c("grey39", "goldenrod3")) + ggtitle("Perturbation Status") + ylab("UMAP 2") +
xlab("UMAP 1") + custom_theme
# Visualize plots.
((p1/p2 + plot_layout(guides = "auto")) | p3)
接下来,计算局部扰动特征减轻混杂效应。
Calculate local perturbation signature.
eccite <- CalcPerturbSig(object = eccite, assay = "RNA", slot = "data", gd.class = "gene", nt.cell.class = "NT",
reduction = "pca", ndims = 40, num.neighbors = 20, split.by = "replicate", new.assay.name = "PRTB")
# Prepare PRTB assay for dimensionality reduction: Normalize data, find variable features and
# center data.
DefaultAssay(object = eccite) <- "PRTB"
# Use variable features from RNA assay.
VariableFeatures(object = eccite) <- VariableFeatures(object = eccite[["RNA"]])
eccite <- ScaleData(eccite, do.scale = F, do.center = T)
# Run PCA to reduce the dimensionality of the data.
eccite <- RunPCA(object = eccite, reduction.key = "prtbpca", reduction.name = "prtbpca")
# Run UMAP to visualize clustering in 2-D.
eccite <- RunUMAP(object = eccite, dims = 1:40, reduction = "prtbpca", reduction.key = "prtbumap",
reduction.name = "prtbumap")
# Generate plots to check if clustering is driven by biological replicate ID, cell cycle phase
# or target gene class.
q1 <- DimPlot(eccite, group.by = "replicate", reduction = "prtbumap", pt.size = 0.2) + scale_color_brewer(palette = "Dark2") +
ggtitle("Biological Replicate") + ylab("UMAP 2") + xlab("UMAP 1") + custom_theme
q2 <- DimPlot(eccite, group.by = "Phase", reduction = "prtbumap", pt.size = 0.2) + ggtitle("Cell Cycle Phase") +
ylab("UMAP 2") + xlab("UMAP 1") + custom_theme
q3 <- DimPlot(eccite, group.by = "crispr", reduction = "prtbumap", split.by = "crispr", ncol = 1,
pt.size = 0.2, cols = c("grey39", "goldenrod3")) + ggtitle("Perturbation Status") + ylab("UMAP 2") +
xlab("UMAP 1") + custom_theme
# Visualize plots.
(q1/q2 + plot_layout(guides = "auto") | q3)
Mixscape识别没有检测扰动的细胞。
#install.packages('mixtools')
# Run mixscape to classify cells based on their perturbation status.
eccite <- RunMixscape(object = eccite, assay = "PRTB", slot = "scale.data", labels = "gene", nt.class.name = "NT",
min.de.genes = 5, iter.num = 10, de.assay = "RNA", verbose = F)
# Show that only IFNG pathway KO cells have a reduction in PD-L1 protein expression.
Idents(object = eccite) <- "gene"
VlnPlot(object = eccite, features = "adt_PDL1", idents = c("NT", "JAK2", "STAT1", "IFNGR1", "IFNGR2",
"IRF1"), group.by = "gene", pt.size = 0.2, sort = T, split.by = "mixscape_class.global", cols = c("coral3",
"grey79", "grey39")) + ggtitle("PD-L1 protein") + theme(axis.text.x = element_text(angle = 0,
hjust = 0.5))
KO,NP,NT 是啥意思?
- If the cell received a NT gRNA, it retains its assignment as non-targeting (NT)
- If the cell received a targeting gRNA, and is classified in step 8 as NT, it is assigned a non-perturbed (NP) label
- If the cell received a targeting gRNA, and is classified in step 8 as KO, it receives a perturbed/knock-out (KO) label
用线性判别分析(LDA)可视化扰动响应。
# Remove non-perturbed cells and run LDA to reduce the dimensionality of the data.
Idents(eccite) <- "mixscape_class.global"
sub <- subset(eccite, idents = c("KO", "NT"))
sub <- MixscapeLDA(object = sub, assay = "RNA", pc.assay = "PRTB", labels = "gene", nt.label = "NT",
npcs = 10, logfc.threshold = 0.25, verbose = F)
# Use LDA results to run UMAP and visualize cells on 2-D.
sub <- RunUMAP(sub, dims = 1:11, reduction = "lda", reduction.key = "ldaumap", reduction.name = "ldaumap")
# Visualize UMAP clustering results.
Idents(sub) <- "mixscape_class"
sub$mixscape_class <- as.factor(sub$mixscape_class)
p <- DimPlot(sub, reduction = "ldaumap", label = T, repel = T, label.size = 5)
col = setNames(object = hue_pal()(12), nm = levels(sub$mixscape_class))
names(col) <- c(names(col)[1:7], "NT", names(col)[9:12])
col[8] <- "grey39"
p + scale_color_manual(values = col, drop = FALSE) + ylab("UMAP 2") + xlab("UMAP 1") + custom_theme
代码和降维图均来自mixscape官网,为什么没有自己画,因为画到一般发现自己的PC计算资源不够用了。对原理感兴趣看一看文章:Characterizing the molecular regulation of inhibitory immune checkpoints with multi-modal single-cell screens.
在分析的过程中注意Seurat数据的assay之间的切换,这是四套数据了。大部分函数是我们熟悉的Seurat的函数,几个关键的函数需要我们亲自查看help文档,如RunMixscape ``CalcPerturbSig
。当然,最为关键的还是我们对ECCITE-seq和CRISPR技术的原理和细节。
Mixscape 的出现再次验证了:单细胞只是概念,我们可以基于此开发相应的技术。
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