同样的单细胞研究，为什么发表的文章杂志等级差别那么大

新冠疫情期间，关于COVID-19病毒感染病人的单细胞研究很多，我看到《单细胞天地》解读了：COVID-19病人支气管免疫细胞单细胞测序分析，文章信息如下：

• 题目：Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19
• 投稿日期：2020年2月24日
• 接收日期：2020年4月23日
• 发表日期：2020年5月12日
• 杂志：Nature Medicine
• 文章在：https://www.nature.com/articles/s41591-020-0901-9

让我想起来了另外一个COVID-19病毒感染病人的单细胞研究，发表在Cell Discov. 2020 May ,标题是：Immune Cell Profiling of COVID-19 Patients in the Recovery Stage by Single-Cell Sequencing，差不多是同一时间发表的哦！

毕竟Cell Discov杂志和 Nature Medicine差别还是蛮大的，不知道是不是研究者特别想把研究写在祖国大地上。

文章实验设计

很清晰的实验设计，如下：

• 15个人
  • 5个early recovery stage (ERS)
  • 5个late recovery stage (LRS)
  • 5个heathy controls (HCs)
• 单细胞数量
  • 10个COVID-19 病人，共计 (70,858 PBMCs)
  • 5个正常人，共计 (57,238 cells)

第一次分群

使用 t-distributed stochastic neighbor embedding (t-SNE) 方法降维

• 全部15个人的 128,096 scRNA-seq profiles
  • 36,442 myeloid cells,
  • 64,247 NK and T cells,
  • 10,177 B cells
• 标记基因是：
  • CD14, CD1C, and FCGR3A for myeloid cells;
  • CD3E, CD4, CD8A, and NCAM1 for NK and T cells;
  • CD19 for B cells

第二次分群

使用 Uniform manifold approximation and projection (UMAP) 方法降维

• 36,442 myeloid cells 分成6群
  • Classical CD14++ monocytes (M1),
  • non-classical CD16++ (FCGR3A) CD14−/+ monocytes (M2),
  • intermediate CD14++ CD16+ monocytes (M3),
  • CD1C+ cDC2 (M4),
  • CLEC9A+ cDC1 (M5),
  • pDC (CLEC4C+CD123+) (M6)
• 64,247 NK and T cells 分成10群
  • naïve CD8+ T cells (T5), which expressed high levels of CCR7, LEF1, and TCF7, similar to naïve CD4+ T cells;
  • effector memory CD8+T cells (T6, CD8 Tm), which expressed high levels of GZMK;
  • cytotoxic CD8+ lymphocytes (CD8+ CTL) (T7), which expressed high levels of GZMB, GNLY, and PRF1. Proliferating T cells (T8, Tprol) were TYMS+MKI67+ cells.
  • naïve CD4+ T cells (T1), which expressed high levels ofCCR7, LEF1, and TCF7;
  • central memory CD4+ T cells (T2, CD4 Tcm), which expressed high levels of CCR7, but more AQP3 andCD69 compared to naïve CD4+ T cells;
  • effector memory CD4+ T cells (T3, CD4 Tem), which expressed high levels of CCR6, CXCR6, CCL5, and PRDM1;
  • regulatory T cells (T4, Treg), which expressed FOXP3.
  • C56−CD16+ NK cells (NK2), which expressed high levels of CD16 and low levels of CD56.
  • CD56+CD16− NK cells (NK1), which expressed high levels of CD56 and low levels of CD16;
  • NK cells highly expressed NCAM1, KLRF1, KLRC1, andKLRD1; then, we sub-divided the NK cells into
  • CD4+ T cells expressed CD3E and CD4; then, we sub-divided these cells into four clusters:
  • CD8+ T cells expressed CD8A and CD8B and were sub-divided into three clusters:
• 10,177 B cells 分成 4群
  • naïve B cells (B1) expressing CD19, CD20 (MS4A1), IGHD, IGHM, IL4R, and TCL1A;
  • memory B cells (B2) expressing CD27, CD38, andIGHG;
  • immature B cells (B3) only expressing CD19 and CD20 (MS4A1);
  • plasma cells (B4) expressing high levels ofXBP1 and MZB1

分析层面的细节，都展现在分群以及细胞亚群的定义上面了。

主要分析

文章的图表很清晰，都是显而易见的分析，读起来很友好反正：

• 3群细胞（myeloid, NK and T, and B cells），在3组人（five HCs, five ERS patients, and five LRS patients.）的比例
• myeloid的6个亚群，NK和T细胞的10亚群，以及4个B细胞亚群在3组人的比例情况
• Classical CD14++ monocytes (M1) 的差异分析，全套（火山图，热图，GO/KEGG数据库注释）
• CD4+ T cells 的差异分析，全套（火山图，热图，GO/KEGG数据库注释）
• Memory B cells and plasma cells (MPB) 的差异分析，全套（火山图，热图，GO/KEGG数据库注释）

也有一点点高级分析，包括sc-BCR, and sc-TCR 数据分析

• 主要是 (IgA+IgG+IgE) to (IgD+IgM) 比例情况

以及 Cell-to-cell communication