科研工作者的信仰就是將真相大白于天下

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。

其實有關新冠患者樣本的單細胞文獻并不太多，bug也是比較明顯的，比如入組的臨床指征的判斷，高血壓等伴隨疾病的影響以及年齡本身導致的免疫差異，篩選條件的不同對最后的結果影響太大，得出相反結論也感覺正常，還有就是用藥，，，取樣時間。多個已知變量和未知變量的疊加對揭示真相產生重大挑戰，，，可能除了限制可控條件外，只能通過加大樣本量進行真相的闡釋。提到真相。。。忽然，想起了名偵探柯南里毛利小五郎的一句話：“偵探的信仰就是將真相大白于天下”，the same to great scientists 。

本期對查到的新冠患者樣本單細胞測序文獻進行匯總，尤其是第二篇！！！！，我想起了相聲界的一句話，叫“無人不宗馬”，第二篇文獻雖然還在預印本，但應該是新冠單細胞文獻的扛鼎之作，嘻嘻，不說廢話了！

1

Blood single cell immune profiling reveals the interferon-MAPK pathway mediated adaptive immune response for COVID-19

2020年3月15日，四川省人民醫院研究團隊在medRixv預印本上發表題為Blood single cell immune profiling reveals the interferon-MAPK pathway mediated adaptive immune response for COVID-19的研究內容，除了揭示了免疫細胞比例的變化外，還發現interferon-MAPK途徑和TCR/BCR在病毒抵御中的重要作用。

Abstract?:The coronavirus disease 2019 (COVID-19) outbreak is an ongoing global health emergence, but the pathogenesis remains unclear. We revealed blood cell immune response profiles using 5’ mRNA, TCR and BCR V(D)J transcriptome analysis with single-cell resolution. Data from 134,620 PBMCs and 83,387 TCR and 12,601 BCR clones was obtained, and 56 blood cell subtypes and 23 new cell marker genes were identified from 16 participants. The number of specific subtypes of immune cells changed significantly when compared patients with controls. Activation of the interferon-MAPK pathway is the major defense mechanism, but MAPK transcription signaling is inhibited in cured patients. TCR and BCR V(D)J recombination is highly diverse in generating different antibodies against SARS-CoV-2. Therefore, the interferon-MAPK pathway and TCR- and BCR-produced antibodies play important roles in the COVID-19 immune response. Immune deficiency or immune over-response may result in the condition of patients with COVID-19 becoming critical or severe.

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研究背景

（1）新冠疫情嚴重，致病機制不清楚；
（2）適應性免疫對抵抗病毒感染十分重要，TCR和BCR有利于揭示免疫組庫的多樣性；

研究方案

sample：PBMC(外周血單核細胞)，一共16位參與者，其中1例危重癥（critical case，patient 1，male），1例重癥（severe case，patient 2)，6例輕癥（moderate cases，patients 3-8），2例初愈患者（病毒檢測陰性）（cured patients， (patient 9-10），3例正常人（normal controls，NC 1-3），1例流感患者（patient 11），1例急性咽炎（patient 12），1例腦梗塞（patient 13）作為對照。

測序數據分析介紹：

工具：Chromium Single Cell 5′ Library and Gel Bead Kit（10X ?Genomics）；Chromium Single Cell V(D)J Reagent Kits

比對:?

轉錄組：GRCh38；

TCR/BCR data ：cellranger vdj with–reference = refdata-cellranger-vdj-GRCh38-alts-ensembl-2.0.0 to assemble TCR/BCR chains and determine clonotypes

降維聚類：Seurat v3標準流程；

篩選高變基因：p value < = 0.05 and more than 2 times differential expression range to screen the differential genes

偽時序分析：Monocle 2 (version 2.4.0)

結果分析

細胞分群

作者從16個樣本中共獲得134620個PBMC細胞，83387個TCR克隆，12601個BCR克隆，其中共有17個細胞類型和56種細胞亞型(Fig 1)，包括CD4+ T細胞（LTB，IL7R），CD8+ T細胞（LEF1，CD8A），CD4 cytotoxic T細胞（CST7，CCL4）等。這表明細胞在抵御病毒感染時進化為高分化的細胞亞型，并通過偽時序發現56種細胞亞型共出現3種主要狀態（Fig 1E），顯示了處于分化期的PBMC的發育軌跡。

Fig.1 Cellular composition of the PBMCs in COVID-19 patients and controls.

(A)?Schematic of single-cell immune transcriptome profiles of the PBMCs in COVID-19 patients and normal controls. The PBMCs were isolated for constructing single cell 5‘mRNA, TCR, and BCR libraries using chromium single cell V(D)J v1.1 reagent kits of 10x genomics chemistry.
(B)?Integration analysis results of COVID-19 patients and normal controls showing principle component (PC), t-SNE algorithm, and UMAP algorithm visualization. In total, 56 cell subtypes were identified.
(C) Seventeen different cell types. From these types, the 56 cell subtypes were derived.
(D) Cell subtypes in each main cell type (only cell types with two or more subtypes are displayed).
(E) Cell differentiation trajectory analysis, which indicates three states of cells.

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細胞比例變化

作者比較了危重、重癥和輕癥與健康對照中細胞比例的變化，其中COVID-19患者中CD1C+_B dendritic cells，CD8 cytotoxic T cells和plasmacytoid dendritic cells（pDC）增加，而B cells和CD4+ T細胞減少（Fig.2A）。然后作者細致的描述了56中細胞亞群中在不同病程下的變化情況，發現在危重癥中有許多的細胞亞群（40/56）都會減少，一些細胞亞群如CD8+ T cell和CD4+ T cell以及pDC均已經消失。在重癥中多數細胞亞群（45/56）都會增加，表明免疫細胞的過度激活。輕癥患者的細胞亞群變化在重癥與危重癥之間。

Fig. 2 The interferon-MAPK pathway is the key response in PBMCs for SARS-CoV-2 ?infection.

(A)?Comparisons of cell type behaviors of patients with COVID-19 and normal controls.
(B)?Cell subtypes and the number of pathways significantly differed between patients with COVID-19 and the normal controls.
(C-F)?The enrichment pathways of differential expressed (DE) genes in each cell subtype. (C) The critical patient (patient 1) vs normal controls. (D) The severe patient (patient 2) vs normal controls. (E) The moderate patients (patients 3-6) vs normal controls. (F) The cured patients (patient 9 and 10) vs normal controls.
(G-J)?Log2 fold changes of interferon pathway genes in critical, severe, moderate, and cured patients with COVID-19 vs normal controls, respectively. Each point represents a different cell subtype.
(K-N)?Log2 fold changes of MAPK pathway genes in critical, severe, moderate, and cured patients with COVID-19 vs normal controls, respectively. Each point represents a different cell subtype.

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不同細胞亞群中的基因表達變化

作者對每個亞群中COVID-19和對照組進行差異表達分析，并對DE genes進行local string network analysis（Fig.2B-F）。富集的信號通路主要涉及viral mRNA translation，interaction alpha/beta signaling，a mitogen-activated protein kinase(MAPK) pathway，immunology interaction between lymphoid and non-lymphoid cells，MHC class II protein complex，表明靶向病毒的抗原提呈已經激活。在重癥患者中，超過一半細胞亞群中18條信號通路顯著變化，最為顯著的是干擾素相關信號通路的過度激活，在其他的COVID-19病程中也發現了該通路不同程度的激活。為了判定18條信號通路是否僅在COVID-19患者中有顯著變化，作者將COVID-19病人與流感患者、急性咽炎患者和腦梗塞患者進行比較，經過篩選發現結果保持一致。在這邊通路中，MAPK信號通路是抵抗COVID-19的主要的血液免疫反應。

干擾素激活MAPK信號傳導途徑涉及一系列防御機制，以對抗病毒感染，隨后比較不同狀態下的COVID-19患者與對照組，進一步探索其中不同細胞的基因表達差異，研究發現interferon α-inducible protein 27（IFI27）在多種細胞類型中的表達水平增高，包括干擾素相關基因如IFITM1、IFITM3和IFITM6等也在不同種細胞亞群中增高。而在干擾素信號通路下游，作者也發現MAPK信號通路轉錄因子的表達增加，包括FOS、JUN、JUNB和DUSP1等，它們在治愈患者中則為低表達，該結果表明MAPK信號通路下游可以作為患者恢復的指征（Fig.2K-N）。

TCR和BCR的變化

為了探究TCR和BCR的抗體克隆擴增情況，研究團隊進行了TCR和BCR V（D）J單細胞轉錄組分析。在整合分析中，作者發現了83387個TCR細胞克隆和12601個BCR克隆，并且克隆多樣性在個體中變化巨大（Fig.3A）。在危重癥患者中，TCR克隆數最少，在輕癥中，TCR克隆數最多，因此TCR克隆擴增越少，病情越重。

在對top TCR antibody序列進行進化分析時，發現不同患者間的部分TCRa鏈、CDR3 IGLorK/IGH具有一定的相關性，可能對COVID-19具有特異性。在分析TCR編碼已知抗原抗體序列時，在重癥患者發現自身抗原反應的明顯激活，在危重癥中卻發現只有拮抗Ebola EBV（Epstein-Barr virus，EBV）抗原的兩個抗體的激活，表明危重癥情況下的免疫效應不完整，而重癥情況下自身免疫的過度放大（Fig3C-H）。

Fig.3 TCR and BCR V(D)J clone expression in patients with COVID-19.

(A)?The figure on the left shows the number of TCR clones detected by V(D)J in each patient, and the figure on the right shows the number of BCR clones detected by V(D)J in each patient.
(B)?The distribution of TCR clones in cell clusters of patients with COVID-19. The light blue dots indicate the distribution of all TRAV and TRBV clones, and the dark blue dots indicate the antibody sequence and quantity of the clone with the strongest TCR clone signal of the patient 8 (moderate condition).
(C)?The distribution of BCR clones in the cell clusters of each patient. Among them, light blue dots indicate the distribution of total IGHV, IGLV, and IGKV in this patient. The dark blue dots indicate the clones with the strongest signals in this patient. Patient 9 has the strongest B cell antibodies among all COVID-19 patients.
(D)?V-J heatmap of IGH + IGK + IGL in the B cells of patient 9.
(E)?The left and right graphs represent the isotype frequency of the heavy chain and the light chain detected in the B cells of patient 9, respectively.
(F)?The distribution of paired isotypes in the B cells of patient \9. Abscissa includes different chains, and the ordinate is frequency.
(G)?The usage of the V gene in the B cells of patient 9.
(H)?List of the known antigens and antibodies in patients with COVID-19.

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作者對以上觀點進行實驗驗證，并畫了模擬圖(Fig.4)。作者發現IFI27在新冠肺炎的患者中的表達水平均比正常對照高，表明IFI27可以作為病毒感染的候選marker（我親愛的authors，你們在這里一個基因用了3個名字，IRF27，IFI27，IIF27,其中IRF27，IIF27都不存在。。。。。），并且作者認為FOS可以成為COVID-19治愈的一個候選marker.在模擬圖中，作者說明病毒到達血液免疫細胞后，它可以激活干擾素信號途徑的多種細胞亞型，以產生效應因子（例如IFI27，IFITM1和IFITM3）來對抗病毒。通過MAPK，下游效應因子的表達被關鍵的轉錄因子FOS、JUN和JUNB被激活，從而導致血液系統中廣泛的抗病毒反應。

Fig. 4 The interferon-MAPK pathway in response to SARS-CoV-2 infection.

(A)?Real-time PCR validation of IFI27 and BST2 in the interferon pathway and FOS in the MAPK pathway. IFI27 and BST2 are up-regulated in patients with COVID-19. FOS is up-regulated in hospitalized patients but down-regulated in cured patients.
(B)?Immunofluorescence staining of IFI27 in PBMCs of patients with COVID-19 and normal controls.
(C)?Anti-SARS-CoV-2 response of the blood system.?After the virus reaches the blood immune cells, it can activate multiple cell subtypes of the interferon signal pathway to produce effectors, such as IFI27, IFITM1, and IFITM3, to fight the virus. Through downstream activation of MAPK, the expression of downstream effectors is activated by key transcription factors, FOS, JUN, and JUNB, which lead to a wide range of antiviral responses in the blood system.

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02

A single-cell atlas of the peripheral immune response to severe COVID-19

2020年4月17日，來自斯坦福大學的Catherine團隊于medRixv預印本上發表題為A single-cell atlas of the peripheral immune response to severe COVID-19的研究內容，揭示新冠患者PBMC中的表型重構，并提供了針對重癥COVID-19免疫反應的細胞圖譜。

Abstract:There is an urgent need to better understand the pathophysiology of Coronavirus disease 2019 (COVID-19), the global pandemic caused by SARS-CoV-2. Here, we apply single-cell RNA sequencing (scRNA-seq) to peripheral blood mononuclear cells (PBMCs) of 7 patients hospitalized with confirmed COVID-19 and 6 healthy controls. We identify substantial reconfiguration of peripheral immune cell phenotype in COVID-19, including a heterogeneous interferon-stimulated gene (ISG) signature, HLA class II downregulation, and a novel B cell-derived granulocyte population appearing in patients with acute respiratory failure requiring mechanical ventilation. Importantly, peripheral monocytes and lymphocytes do not express substantial amounts of pro-inflammatory cytokines, suggesting that circulating leukocytes do not significantly contribute to the potential COVID-19 cytokine storm. Collectively, we provide the most thorough cell atlas to date of the peripheral immune response to severe COVID-19.

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研究背景

（1）重癥COVID-19與白介素（IL）-6、IL-10和腫瘤壞死因子（TNF）-α4,5的水平升高有關。尚不清楚在重癥患者中較高的炎癥水平是否反映了對疾病的適當反應，或者這些反應是否反映了免疫反應失調（通常被稱為“細胞因子風暴”）；
（2）在嚴重的COVID-19中經常觀察到淋巴細胞減少，特別是CD4 +和CD8 + T細胞減少，而其余T細胞的功能性較低，并且呈現耗竭表型；

研究方案

sample：8 peripheral blood samples from 7 hospitalized patients and 6 healthy controls.

測序數據分析介紹：

工具：Seq-Well platform for scRNA-seq

比對: ?GRCh37 (hg19)

篩選：Cells that had fewer than 1,000 UMIs or greater than 15,000 UMIs, as well as cells that contained greater than 20% of reads from mitochondrial genes or rRNA genes (RNA18S5 or RNA28S5), were considered low quality and removed from further analysis. To remove putative multiplets (where more than one cell may have loaded into a given well on an array), cells that expressed more than 75 genes per 100 UMIs were also filtered out. Genes that were expressed in fewer than 10 cells were removed from the final count matrix.

降維聚類：Seurat

細胞注釋：手標+SingleR

基因通路及調控因子分析：Ingenuity Pathway Analysis (IPA; Qiagen)

結果分析

單細胞轉錄組譜分析捕獲SARS-CoV-2感染中外周免疫細胞組成的變化

作者一共捕獲了44,271個細胞，其中每個樣本平均3,194個細胞，并通過UMAP對共30個clusters進行表示(Fig. 1a)。計算亞群的高變基因后通過特異性marker對細胞類型進行標記，并通過SingleR對細胞分型進行確定(Fig. 1b,c)。

在觀察細胞比例變化時，發現COVID-19患者的一些先天免疫細胞亞群被耗竭，包括γδT細胞、漿細胞樣樹突細胞（pDC）、經典樹突細胞（DC）、CD16 +單核細胞和NK細胞（Fig. 1d）。并且作者注意到COVID-19患者的漿細胞比例的增加，尤其是在急性呼吸窘迫綜合征（acute respiratory distress syndrome，ARDS）患者中較為顯著，這表明重癥與強烈體液免疫之間的關系。作者在ARDS患者中捕獲到了“Activated Granulocytes”，特異性表達中性粒細胞顆粒蛋白的基因如ELANE、 LTF和MMP8。

Figure 1 | Expansion of plasmablasts and depletion of multiple innate immune cell subsets in the periphery of patients with COVID-19.

a, UMAP dimensionality reduction embedding of PBMCs from all profiled samples (n = 44,721 cells) colored by donor of origin. COVID-19 patient IDs (n = 7) begin with “C” and ?are colored in shades of orange (patients who were not ventilated at time of draw) or red ?(patients with ARDS who were ventilated at time of draw); healthy donors begin with “H” (n = 6) and are colored in blues.
b, UMAP embedding of the entire dataset colored by orthogonally generated clusters labeled by manual cell type annotation confirmed by ?SingleR14.
c, Dot plot showing average and percent expression of the 3 most defining genes of each cell type.
d, Proportions of each cell type in each sample colored by peripheral blood of healthy patients (Fig. 1d), the presence of plasmablasts in every COVID-19 sample analyzed suggests evidence of a SARS-CoV-2 humoral immune response. As plasmablasts express high levels of Ig-encoding mRNAs, we examined if we could detect conserved usage of V gene segments in the plasmablasts of COVID-19 patients. Peripheral ?plasmablasts from COVID-19 patients did not appear to converge on particular Ig V genes

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CD14 +單核細胞顯示MHC II類下調和IFN驅動的表型重構

在對單核細胞單獨進行UMAP降維可視化時發現CD14+ 有強烈的表型偏移和部分CD16+ 細胞的耗竭，在最年輕、病情最輕的患者C7中最不明顯（Fig.2a,b）。有趣的是研究者并沒有觀察到已經報道的外周單核細胞產生的部分促炎因子如TNF、IL6、 IL1B、CCL3、 CCL4、CXCL2（Fig.2c）。這表明循環單核細胞可能并不是引起細胞因子風暴的原因。

為了觀察單核細胞的表型重塑，作者在分析差異基因時發現相對于健康對照，至少6個COVID-19樣品中編碼HLA II類分子的8個基因的顯著下調（Fig.2d），并且這種下調在所有COVID-19患者中均顯著，但在通氣依賴患者中可能更明顯（Fig.2e）。由于這種下調可能抑制CD4 + T細胞反應的產生，因此可能削弱老年COVID-19患者的適應性免疫反應。非經典HLA I類基因HLA-E和HLA-F也被下調程度較小且樣品較少（Fig.2f），而經典HLA I類基因HLA-A，HLA-B和HLA-C并未持續上調或下調。

在至少一個COVID 19樣品中，CD14+ 單核細胞上調了35種I型干擾素（IFN）刺激的基因（ISG）（Fig.2d）。相應地，“IFN Signaling”是CD14+ 單核細胞中第二個高度上調的信號通路（Fig.2g）。對CD14+ 單核細胞中上游調節因子的分析表明，相對于其余COVID-19供體，供體C2、C3和C7中沒有預測的IFN和IFN調節因子（IRF）高表達（Fig.2h）。我們通過數據集中已知的人類I型IFN刺激基因對數據集中的CD14+ 單核細胞進行了評分，同樣得出了相同的結論。通氣/ARDS不能解釋ISG的差異性特征（Fig.2h，i），但是較高的ISG分數與年齡呈正相關，與發燒時間距離呈負相關（圖2j）。在兩次采樣的患者中（C1），在兩次采樣之間的間隔48小時內，ISG模塊評分顯著下降，在此期間，患者失去代償能力并變得依賴呼吸機（Fig.2i）。這些數據共同表明外周IFN應答、患者年齡和臨床嚴重性之間的相關性。

Figure 2 | Robust HLA class II downregulation and type I IFN-driven inflammatory ?signatures in monocytes are characteristics of SARS-CoV-2 infection.

a, UMAP embedding of all monocytes colored by sample of origin.
b, UMAP embedding of monocytes colored by CD14 and FCGR3A (CD16, to distinguish between CD14+ and CD16+ monocytes), HLA-DPB1 and HLA-DMA (illustrating HLA class II downregulation in COVID-19 patients), and S100A9 and IFI27 (demonstrating canonical inflammatory signatures in COVID-19 patients).
c, UMAP embedding of monocytes colored by genes encoding pro-inflammatory cytokines previously reported to be produced by circulating monocytes in severe COVID-19, namely TNF, IL6, IL1B, CCL3, CCL4, and CXCL2.
d, g, h, Heatmaps of (d) DE genes, (g) differentially regulated canonical pathways, and (h) differentially regulated predicted upstream regulators between CD14+ monocytes of each donor compared to CD14+ monocytes of all healthy controls. d is colored by average log(fold-change), while g and h are colored by z-score. All displayed genes, pathways, and regulators are statistically significant at the p<0.05 confidence level. The (d) 50 genes, (g) 25 pathways, or (h) 50 regulators with the highest absolute average log(fold-change) or z-score across all donors all labeled. Genes with a net positive average log(fold-change) or z-score are labeled in red; genes with a net negative average log(fold-change) or z-score are labeled in blue.
e, Box plot showing the mean ?HLA Class II module score of CD14+ monocytes from each sample, colored by healthy donors (blue), non-ventilated COVID-19 patients (orange), or ventilated COVID-19 patients (red). Shown are exact p values by Wilcoxon rank sum test.
f, Dot plot depicting percent expression and average expression of all detected HLA genes in CD14+monocytes by donor.
i, Box plot showing the IFNA module score of each cell, colored by ?healthy donors (blue), non-ventilated COVID-19 patients (orange), or ventilated COVID-19 patients (red).
j, Scatter plots depicting the correlation between the mean ISG module score of each sample and the patient age (top) and time-distance to first measured or reported fever (bottom).

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COVID-19中外周血NK細胞表型的異質性

接下來，我們分析了COVID-19患者樣品中T和NK淋巴細胞的轉錄組，因為與健康對照組相比，這些細胞似乎存在明顯的表型偏移（Fig.1a，b）。在CD4+ T， CD8+ T和NK cells (Fig. 3a, b)中作者發現，通過細胞介導的細胞毒性有助于抗病毒宿主防御的CD56dim NK細胞在呼吸機依賴的患者中耗竭，而CD56bright NK細胞，被認為是IFN-γ和TNF-α18的強大產生者，在所有COVID 19樣品中都顯著耗竭（Fig.3c）。

在NK與T細胞中也沒有觀察到促炎因子的大量表達 (Fig. 3e) ，與健康對照組相比，所有其他COVID-19患者在CD8+ T或NK細胞中均未表達更高水平的CCL3，CCL4，IFNG或TNF（Fig. 3e）。外周T和NK細胞不存在促炎細胞因子表達，這再次表明外周白細胞可能并不是導致COVID-19中的細胞因子風暴的原因。

為了進一步描述COVID-19中T和NK細胞的表型，作者通過每個COVID-19患者樣本相對于所有健康對照組的DE基因識別豐富的基因通路和上游調控因子。在COVID-19例患者中，NK細胞表現出明顯的異質性反應(Fig.3f)。最常見的下調基因包括FCGR3A、AHNAK、FGFBP2，這些基因與外周血NK細胞的成熟有關。最常見的上調基因包括ISGs和NK細胞活化基因，如PLEK和CD38。作者觀察到在CD4+和CD8+ T細胞中DE基因的類似異質性，其中最常見的上調基因是ISGs。

同時對預測的上游調節因子的分析表明，在NK細胞、CD4+ 和CD8+ T細胞中，有一半的COVID-19分析樣品中沒有明顯的干擾素驅動效應（Fig.3g）。盡管最廣泛的NK細胞表型轉移發生在具有強IFN標記的供體中（Fig.3f，g），不過C3患者中IFN標記極少（Fig.3f框內），作者仍從C3鑒定了一組表達上調的基因，這些基因在其他COVID-19患者中均未上調，包括有NK細胞毒性介質PRF1和GZMB，以及6種熱激蛋白（Fig.3h）。從患者C3的CD8+ T細胞中也鑒定到了一組相似的基因，主要由熱激蛋白組成。這些結果共同表明，外周血T和NK淋巴細胞的表型在COVID-19中異質重塑。

Figure 3 | Heterogeneous patterns of NK cell exhaustion and interferon response ?in COVID-19.

a, UMAP embedding of CD4+ T cells, CD8+ T cells, and NK cells colored by sample of origin.
b, UMAP embedding colored by lineage genes (CD3D, CD3G, CD4, CD8A, FCGR3A, and NCAM1) and selected functional/phenotypic markers (GZMB and MKI67).
c, Box plots depicting proportions of CD56dim NK cells, CD56bright NK cells, and proliferating lymphocytes among total T and NK cells by sample of origin. The cells used to calculate each proportion are highlighted in bold black in the adjacent UMAP embeddings.?, p<0.01; *, p<0.05; n.s., p>0.05 by Wilcoxon rank sum test.?

d, Dot plot showing the percent and average expression of three canonical markers of NK cell exhaustion: LAG3, PDCD1 (encoding PD-1), and HAVCR2 (encoding Tim-3).?

e, Dot plot showing the percent and average expression of four canonical NK cell cytokines (CCL3, CCL4, IFNG, and TNF) by NK cells.

?f, g, Heatmaps of (f) DE genes and (g) differentially ?regulated predicted upstream regulators between NK cells of each COVID-19 sample compared to NK cells of all healthy controls. As in Fig. 2, f is colored by average log(fold-change), while g is colored by z-score. All displayed genes and regulators are statistically significant at the p<0.05 confidence level. The 50 genes or regulators with the highest absolute average log(fold-change) or z-score across all donors all labeled. ?Genes with a net positive average log(fold-change) or z-score are labeled in red; genes ?with a net negative average log(fold-change) or z-score are labeled in blue. f, Genes that are selectively upregulated by the NK cells of donor C3 are boxed in black.?

h, Dot plot depicting the percent and average expression of C3 NK cell-specific genes boxed in (f) as well as selected ISGs upregulated by multiple COVID-19 patients.

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B細胞中新的粒細胞表型是出現ARDS的重癥COVID-19患者的重要特征

接下來，作者分析了漿母細胞，class switched B cells和活化的粒細胞的表型，UMAP圖表明其存在一定的相關性（Fig.1b）。活化的粒細胞似乎是從class-switched B cells線性投射的，表明兩種細胞類型之間存在連續的細胞表型。作者通過RNA velocity發現從class-switched B cells到活化粒細胞的線性過渡(Fig.4a)， 沿著這個分化軌跡的細胞失去了典型的漿細胞標記基因CD27，CD38和TNFRSF17（編碼BCMA）的表達，取而代之的是編碼中性粒細胞顆粒蛋白和其他粒細胞標記基因（包括ELANE，LTF和DEFA3）的表達（Fig.4b）。盡管此連續性開始時的細胞是通過Ig基因的表達來定義，但隨著時間的增長，粒細胞標志物（如CSF3R和MNDA）（編碼髓系核分化抗原）會被上調（Fig.4d）。

作者最后使用NicheNet（a technique that uses gene expression data to discover the putative ligand-target links mediating downstream transcriptional changes），表明如果在該數據集中，EGF和IL24的表達均有限，并且這些細胞因子驅動活化的粒細胞分化，那么它們可能是由外周血中不存在的細胞所產生。

Figure 4 | Activated granulocytes are characteristic of severe COVID-19 patients and differentiate from class-switched B cells

a, UMAP embedding of plasmablasts, class-switched B cells, and activated granulocytes, colored by annotated cell type and overlaid with RNA velocity stream.
b, UMAP embedding colored by canonical plasmablast markers (CD27, CD38, and TNFRSF17) and markers of activated granulocytes (ELANE, LTF, CEACAM8, DEFA3, MMP8, and LCN2).
c, UMAP embedding colored by inferred latent time.
d, Scatter plots ?showing expression of a selection of cluster-defining genes across inferred latent time.
e, UMAP embedding colored by orthogonally-generated clusters.
f, Dot plot depicting expression of CEBP family members in each?

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