類器官的單細胞分析

NGS系列文章包括NGS基礎、轉錄組分析?（Nature重磅綜述|關于RNA-seq你想知道的全在這）、ChIP-seq分析?（ChIP-seq基本分析流程）、單細胞測序分析?(重磅綜述：三萬字長文讀懂單細胞RNA測序分析的最佳實踐教程 （原理、代碼和評述）)、DNA甲基化分析、重測序分析、GEO數據挖掘（典型醫學設計實驗GEO數據分析 (step-by-step) - Limma差異分析、火山圖、功能富集）等內容。

大家好！我們又見面啦！今兒帶領大家復現一個小圖。

這篇文章發表于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，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！

這篇文章發表前，已經有研究報道ACE2(angiotensin converting enzyme 2)是嚴重急性呼吸綜合征冠狀病毒（SARS-CoV）的關鍵受體，并且ACE2可以保護肺臟免受傷害。ACE2現在也被確定為SARS-CoV-2感染的關鍵受體，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。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。該團隊就這一問題研究發現hrsACE2抑制SARS-CoV-2感染呈現劑量依賴性，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。文章總結得到hrsACE2可以顯著阻斷SARS-CoV-2感染的早期階段。

作者使用單細胞轉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，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，其中近端小管的標記基因為SLC3A1和SLC27A2，足細胞的標記基因為PODXL，NPHS1和NPHS2，說明利用類器官進行實驗的可靠性。(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，很是驚奇，后來得知類器官其實就是將病人的細胞進行培養，具有3D效果，并且能夠重現對應器官的部分功能)

下面就是本次要復現的圖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).

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測序數據分析介紹

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=0.4
4.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讀入數據

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 Plots VlnPlot(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參數進行過濾

## Filtering based on QC parameters KidneyOrganoid <- 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 Data KidneyOrganoid <- 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

發現細胞周期對細胞分群具有一定的影響，進行去批次：

## SCTransform KidneyOrganoid<-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 Components ElbowPlot(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。。。。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 ，大家可以試一試哈！

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).

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• 復現原文（一）：Single-cell RNA sequencing of human kidney（step by step）

• 復現原文（二）：Single-cell RNA sequencing of human kidney（step by step）

• 復現nature communication PCA原圖|代碼分析（一）

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