復現(xiàn)Cell附圖 |類器官的單細胞分析

大家好！我們又見面啦早龟！今兒帶領(lǐng)大家復現(xiàn)一個小圖。

這篇文章發(fā)表于2020年4月24日的Cell主刊，題為Inhibition of SARS-CoV-2 Infections in Engineered Human Tissues Using Clinical-Grade Soluble Human ACE2使套，其中作者利用類器官的單細胞分析為整個文章做到了錦上添花！

這篇文章發(fā)表前鞠柄，已經(jīng)有研究報道ACE2(angiotensin converting enzyme 2)是嚴重急性呼吸綜合征冠狀病毒（SARS-CoV）的關(guān)鍵受體侦高，并且ACE2可以保護肺臟免受傷害。ACE2現(xiàn)在也被確定為SARS-CoV-2感染的關(guān)鍵受體厌杜，并且有人提出抑制這種相互作用可以用于治療COVID-19患者的想法奉呛。但是，人類重組可溶性ACE2（hrsACE2）是否會阻止SARS-CoV-2的生長還尚不清楚夯尽。該團隊就這一問題研究發(fā)現(xiàn)hrsACE2抑制SARS-CoV-2感染呈現(xiàn)劑量依賴性瞧壮，SARS-CoV-2可以直接感染人血管類器官和腎臟類器官，并且可以被hrsACE2所抑制匙握。文章總結(jié)得到hrsACE2可以顯著阻斷SARS-CoV-2感染的早期階段咆槽。

作者使用單細胞轉(zhuǎn)錄組測序的原因非常清晰，就是腎臟類器官在ACE2的表達方面與正常細胞相同圈纺，在近端小管和足細胞II細胞亞群中分別存在表達ACE2的細胞罗晕，其中近端小管的標記基因為SLC3A1和SLC27A2济欢，足細胞的標記基因為PODXL，NPHS1和NPHS2小渊，說明利用類器官進行實驗的可靠性法褥。(說點別的，我第一次接觸這個概念時以為類器官指的是在器官體型上會非常相似酬屉，很是驚奇半等，后來得知類器官其實就是將病人的細胞進行培養(yǎng)，具有3D效果呐萨，并且能夠重現(xiàn)對應器官的部分功能)

下面就是本次要復現(xiàn)的圖Figure S2：

Figure S2. Single-Cell RNA-Seq Analysis of Kidney Organoids Reveals ACE2 Expression in Proximal Tubule Cells, Related to Figure 4

(A) UMAP plot displaying the results after unbiased clustering. Subpopulations of renal endothelial-like, mesenchymal, proliferating, podocyte and tubule cells were identified.
(B) Expression of ACE2 projected in the UMAP reduction.
(C) Expression of different cellular markers: SLC3A1, SLC27A2 (Proximal Tubule); PODXL, NPHS1, NPHS2 (Podocyte); CLDN4, MAL (Loop of Henle) and CD93 (Renal Endothelial-like cells).

Figure 4. SARS-CoV-2 Infections of Human Kidney Organoids??

(A)?Representative images of a kidney organoid at day 20 of differentiation visualized using light microscopy (top left inset; scale bar, 100 μm) and confocal microscopy. Confocal microscopy images show tubular-like structures labeled with Lotus tetraglobus lectin (LTL, in green) and podocyte-like cells showing positive staining for nephrin (in turquoise). Laminin (in red) was used as a basement membrane marker. DAPI labels nuclei. A magnified view of the boxed region shows a detail of tubular structures. Scale bars, 250 and 100 μm, respectively.
(B)?Recovery of viral RNA in the kidney organoids at day 6 dpi with SARS-CoV-2. Data are represented as mean ± SD.
(C)?Determination of progeny virus. Supernatants of SARS-CoV-2 infected kidney organoids were collected 6 dpi and then used to infect Vero E6 cells. After 48 h, Vero E6 cells were washed and viral RNA assessed by qRT-PCR. The data show that infected kidney organoids can produce progeny SARS-CoV-2 viruses, depending on the initial level of infection. Data are represented as mean ± SD.
(D)?Effect of hrsACE2 on SARS-CoV-2 infections kidney organoids. Organoids were infected with a mix of 106 infectious viral particles and hrsACE2 for 1 h. 3 dpi, levels of viral RNA were assessed by qRT-PCR. hrsACE2 significantly decreased the level of SARS-CoV-2 infections in the kidney organoids. Data are represented as mean ± SD (Student’s t test: ?p < 0.05).

測序數(shù)據(jù)分析介紹

1.工具:Chromium Single Cell 3′ Library
2.篩選：668 < UMIs per cell < 23101, 489 < Genes per cell < 5651 and % UMIs assigned to mitochondrial genes < 50.
3.降維及聚類：PCs=20,Resolution=0.44.細胞分型：KIT (Kidney Interactive Transcriptomics webpage )(http://humphreyslab.com/SingleCell/).

首先需要下載rawdata：GSE147863(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE147863) （建議使用VPN下載）

加載R包

library(Seurat)

使用Read10X_h5讀入數(shù)據(jù)

KidneyOrganoid<-Read10X_h5("KidneyOrganoid_FilteredGeneBCMatrices.h5")

建立seurat對象

KidneyOrganoid <- CreateSeuratObject(counts = KidneyOrganoid, project = "KidneyOrganoid_ACE2", min.cells = 3, min.features = 400)KidneyOrganoid[["percent.mt"]] <- PercentageFeatureSet(KidneyOrganoid, pattern = "^MT-") # 計算線粒體基因比例

QC

## QC Metrics PlotsVlnPlot(KidneyOrganoid, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3,pt.size = 0.3)

## Get QC Thresholds
quantile(KidneyOrganoid@meta.data$nCount_RNA,c(0.025,0.975))quantile(KidneyOrganoid@meta.data$nFeature_RNA,c(0.025,0.975))

QC plots

## QC Plots
plot(KidneyOrganoid@meta.data$nCount_RNA,KidneyOrganoid@meta.data$nFeature_RNA,pch=16,cex=0.7,bty="n")abline(h=c(488,5653),v=c(667,23108),lty=2,lwd=1,col="red")

按照QC參數(shù)進行過濾

## Filtering based on QC parametersKidneyOrganoid <- subset(KidneyOrganoid, subset = nFeature_RNA > 488 & nFeature_RNA < 5653 & nCount_RNA > 667 & nCount_RNA < 23108 & percent.mt < 50)

歸一化及標準化

## Log Normalization
KidneyOrganoid<-NormalizeData(KidneyOrganoid)

## Scale DataKidneyOrganoid <- ScaleData(KidneyOrganoid, features = rownames(KidneyOrganoid))

計算細胞周期

## Cell Cycle Effect
KidneyOrganoid<-CellCycleScoring(KidneyOrganoid,s.features = cc.genes$s.genes,g2m.features = cc.genes$g2m.genes)
KidneyOrganoid <- RunPCA(KidneyOrganoid, features = unlist(cc.genes))DimPlot(KidneyOrganoid, reduction = "pca",dims = c(1,2),group.by = "Phase")

去批次效應

發(fā)現(xiàn)細胞周期對細胞分群具有一定的影響杀饵，進行去批次：

## SCTransformKidneyOrganoid<-SCTransform(KidneyOrganoid,vars.to.regress = c("S.Score","G2M.Score","percent.mt","nFeature_RNA"))

重新PCA

## PCA
KidneyOrganoid <- RunPCA(KidneyOrganoid, features = VariableFeatures(object = KidneyOrganoid))
DimPlot(KidneyOrganoid, reduction = "pca",dims = c(1,2))

## Some Plots
VizDimLoadings(KidneyOrganoid, dims = 1:2, reduction = "pca")DimPlot(KidneyOrganoid, reduction = "pca",dims = c(1,2))

滾石圖

## Selecting PCA ComponentsElbowPlot(KidneyOrganoid,ndims = 30)

聚類可視化

## Clustering
KidneyOrganoid <- FindNeighbors(KidneyOrganoid, dims = 1:20)
KidneyOrganoid <- FindClusters(KidneyOrganoid, resolution = 0.4)

# Non Linear Dimensional Reduction
KidneyOrganoid <- RunUMAP(KidneyOrganoid, dims = 1:20)

# UMAP plot
colss<-c("#A6CEE3", "#1F78B4", "#08306B", "#B2DF8A", "#006D2C", "#8E0152",
         "#DE77AE", "#CAB2D6", "#6A3D9A", "#FB9A99", "#E31A1C", "#B15928",
         "#619CFF","#FF67A4","#00BCD8")
DimPlot(KidneyOrganoid, reduction = "umap",label = T,cols=colss)

確實是很像哦。谬擦。切距。。再看看基因的表達：

# Feature Plots on interesting genes
FeaturePlot(KidneyOrganoid,c("ACE2"),cols = c("lightgray","red"),order = T)FeaturePlot(KidneyOrganoid,c("SLC3A1","SLC27A2","PODXL","NPHS2","NPHS1","CLDN4","MAL","CD93"),cols = c("lightgray","red"),order = T)

# 尋找高變基因KidneyOrganoid.markers <- FindAllMarkers(KidneyOrganoid, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)

作者將源代碼放在https://github.com/jpromeror/SC_KidneyOrganoid_ACE2 惨远，大家可以試一試哈谜悟！

參考文獻

Monteil, Vanessa, et al. “Inhibition of SARS-CoV-2 infections in engineered human tissues using clinical-grade soluble human ACE2.” Cell (2020).