CellMarker | 人骨骼肌组织细胞Marker大全！~（强烈建议火速收藏！）

2024-05-07 02:36
文章标签 大全 收藏 marker 强烈建议 火速 cellmarker 骨骼肌 组织细胞

本文主要是介绍CellMarker | 人骨骼肌组织细胞Marker大全！~（强烈建议火速收藏！），希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

1写在前面

分享一下最近看到的2篇paper关于骨骼肌组织的细胞Marker，绝对的Atlas级好东西。👍

希望做单细胞的小伙伴觉得有用哦。😏

2常用marker（一）

general_mrkrs <- c(
  'MYH7', 'TNNT1', 'TNNT3', 'MYH1', 'MYH2', "CKM", "MB", # Myofibers
           'PAX7', 'DLK1', # MuSCs
           'PDGFRA', 'DCN', 'ANGPTL7', 'OSR2', 'NGFR', 'SLC22A3','ITGA6', # Fibroblasts
           'FMOD', 'TNMD' ,  'MKX', # Tenocytes
 'MPZ', 'MBP',  # Schwann cells
'CDH2', 'L1CAM', # SCG
'MSLN', 'ITLN1', # mesothelium
"ADIPOQ", "PLIN1", # adipocytes
'PTPRC', 'CD3D', 'IL7R', # T cells
'NKG7', 'PRF1', #NK cells
'CD79A', "TCL1A",  # B cells
'MZB1', 'JCHAIN', # B plasma 
"CD14", "FCGR3A",'S100A8', 'S100A12', # Mono
"CD163", "C1QA", # Macrop
"XCR1", "CLEC9A", # cDC1 "CADM1",
"CD1C", "CLEC10A", "CCR7", # cDC2
'LILRA4', 'IL3RA', "IRF7",  # pDC 
'FCGR3B', 'CSF3R', 'SORL1',  # Neutrophils
'EPX', 'PRG2', # Eosinophils 'CLC'
'TPSB2', 'MS4A2', # Mast cells
'PECAM1', 'HEY1','CLU', # art EC
'CA4', 'LPL', # capEC
'ACKR1', 'SELE', # venEC
'LYVE1', 'TFF3',  # lymphEC
'RGS5','ABCC9', # pericytes           
'MYH11', 'ACTA2', # SMC
'HBA1', #RBC
                  )

出自下面paper:👇

Human skeletal muscle aging atlas. Veronika R. Kedlian, Yaning Wang, Tianliang Liu, Xiaoping Chen, Liam Bolt, Catherine Tudor, Zhuojian Shen, Eirini S. Fasouli, Elena Prigmore, Vitalii Kleshchevnikov, Jan Patrick Pett, Tong Li, John E G Lawrence, Shani Perera, Martin Prete, Ni Huang, Qin Guo, Xinrui Zeng, Lu Yang, Krzysztof Polański, Nana-Jane Chipampe, Monika Dabrowska, Xiaobo Li, Omer Ali Bayraktar, Minal Patel, Natsuhiko Kumasaka, Krishnaa T. Mahbubani, Andy Peng Xiang, Kerstin B. Meyer, Kourosh Saeb-Parsy, Sarah A Teichmann & Hongbo Zhang 2024 Apr.

3常用marker（二）

Mural Cell Markers

#SMOOTH MUSCLE CELLS
FeaturePlot(df.harmony, features = "MYH11", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "ACTA2", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "TAGLN", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
#PERICYTES
FeaturePlot(df.harmony, features = "RGS5", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "CSPG4", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "PDGFRB", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)

Glial Cells Markers

FeaturePlot(df.harmony, features = "PROX1", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "MPZ", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "NCAM1", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "CDH19", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "SOX10", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "PLP1", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)

Adipocites Markers

FeaturePlot(df.harmony, features = "PLIN1", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "ADIPOQ", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "MMRN1", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "CCL21", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)

Tenocytes Markers

FeaturePlot(df.harmony, features = "FMOD", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "TNMD", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "COL22A1", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "SCX", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "DLG2", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "FBN1", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)

Endothelial Markers

#FeaturePlot(df.harmony, features = "PCDHA6", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)

#ARTERIAL 
FeaturePlot(df.harmony, features = "FBLN5", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "DLL4", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "SEMA3G", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)

#CAPILLARIES
FeaturePlot(df.harmony, features = "RGCC", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)

#VENOUS
FeaturePlot(df.harmony, features = "EPHB4", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)

Myonuclei Markers

FeaturePlot(df.harmony, features = "TTN", min.cutoff = "q9", order = T, cols = c("lightblue", "navy"), raster = FALSE)

#IMMATURE MYOCYTE
FeaturePlot(df.harmony, features = "MYMX", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "MYOG", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)

#REG MYONUCLEI
FeaturePlot(df.harmony, features = "FLNC", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "MYH3", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "MYH8", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "XIRP1", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)

NMJ Myonuclei Markers (Neuromuscular junction)

#NMJ
FeaturePlot(df.harmony, features = "CHRNE", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "CHRNA1", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "PRKAR1A", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "COL25A1", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "UTRN", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "COLQ", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "ABLIM2", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "VAV3", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "UFSP1", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)

MTJ Myonuclei Markers (Myotendinous junction)

#MTJ
FeaturePlot(df.harmony, features = "COL22A1", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "PIEZO2", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "COL24A1", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "COL6A1", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "FSTL1", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "COL6A3", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "TIGD4", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)

FeaturePlot(mini_df.harmony.harmony, features = "EYS", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)

出自下面paper:👇

Lai, Y., Ramírez-Pardo, I., Isern, J. et al. Multimodal cell atlas of the ageing human skeletal muscle. Nature (2024).

alt
最后祝大家早日不卷!~

点个在看吧各位~ ✐.ɴɪᴄᴇ ᴅᴀʏ 〰

📍 往期精彩

📍 🤩 LASSO | 不来看看怎么美化你的LASSO结果吗！？
📍 🤣 chatPDF | 别再自己读文献了！让chatGPT来帮你读吧！~
📍 🤩 WGCNA | 值得你深入学习的生信分析方法！~
📍 🤩 ComplexHeatmap | 颜狗写的高颜值热图代码！
📍 🤥 ComplexHeatmap | 你的热图注释还挤在一起看不清吗！？
📍 🤨 Google | 谷歌翻译崩了我们怎么办！？（附完美解决方案）
📍 🤩 scRNA-seq | 吐血整理的单细胞入门教程
📍 🤣 NetworkD3 | 让我们一起画个动态的桑基图吧~
📍 🤩 RColorBrewer | 再多的配色也能轻松搞定！~
📍 🧐 rms | 批量完成你的线性回归
📍 🤩 CMplot | 完美复刻Nature上的曼哈顿图
📍 🤠 Network | 高颜值动态网络可视化工具
📍 🤗 boxjitter | 完美复刻Nature上的高颜值统计图
📍 🤫 linkET | 完美解决ggcor安装失败方案（附教程）
📍 ......

alt
alt
alt
alt

本文由 mdnice 多平台发布

这篇关于CellMarker | 人骨骼肌组织细胞Marker大全！~（强烈建议火速收藏！）的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！


http://www.chinasem.cn/article/966086。

相关文章

秒变高手：玩转CentOS 7软件更换的方法大全

秒变高手：玩转CentOS 7软件更换的方法大全

在 CentOS 7 中更换软件源可以通过以下步骤完成。更换源可以加快软件包的下载速度，特别是当默认源速度较慢时。以下是详细步骤： 前言 为了帮助您解决在使用CentOS 7安装不了软件速度慢的问题，我们推出了这份由浪浪云赞助的教程——“CentOS7如何更换软件源加快下载速度”。 浪浪云，以他们卓越的弹性计算、云存储和网络服务受到广泛好评，他们的支持和帮助使得我们可以将最前沿的技术知识分
阅读更多...
收藏：解决 pip install 出现 error: subprocess-exited-with-error 错误的方法

收藏：解决 pip install 出现 error: subprocess-exited-with-error 错误的方法

在使用 pip 安装 Python 包时，有时候会遇到 error: subprocess-exited-with-error 错误。这种错误通常是由于 setuptools 版本问题引起的。本文将介绍如何解决这一问题 当你使用 pip install 安装某个 Python 包时，如果 setuptools 版本过高或过低，可能会导致安装过程出错，并出现类似以下错误信息：error: subpr
阅读更多...
设计模式大全和详解，含Python代码例子

设计模式大全和详解，含Python代码例子

若有不理解，可以问一下这几个免费的AI网站 https://ai-to.cn/chathttp://m6z.cn/6arKdNhttp://m6z.cn/6b1quhhttp://m6z.cn/6wVAQGhttp://m6z.cn/63vlPw 下面是设计模式的简要介绍和 Python 代码示例，涵盖主要的创建型、结构型和行为型模式。 一、创建型模式 1. 单例模式 (Singleton
阅读更多...
Java开发实例大全提高篇——Applet的应用

Java开发实例大全提高篇——Applet的应用

开发十年，就只剩下这套架构体系了！ >>>    第21章  Applet的应用 21.1  Applet在html中的使用 实例549  在html中显示Applet HtmlShowApplet.java     public void paint(Graphics g){         g.drawString("html文件已经运行", 50, 50);// 绘制文本
阅读更多...
Java开发实例大全提高篇——Java安全

Java开发实例大全提高篇——Java安全

开发十年，就只剩下这套架构体系了！ >>>    第6篇  Java安全与Applet应用篇 第20章  Java安全 20.1  Java对称加密 实例531  使用BASE64加密     public static String encryptBASE64(byte[] data) {         //加密数据         return (new BASE64Encoder()
阅读更多...
120张网络安全等保拓扑大全

120张网络安全等保拓扑大全

安全意识培训不是一个ppt通吃，不同的场景应该用不同的培训方式和内容http://mp.weixin.qq.com/s?__biz=MzkwNjY1Mzc0Nw==&mid=2247484385&idx=1&sn=92f5e7f3ee36bdb513379b833651711d&chksm=c0e47abdf793f3ab7f4621b64d29c04acc03b45c0fc1eb613c37f3
阅读更多...
Linux 中常用的 Vim 命令大全

Linux 中常用的 Vim 命令大全

Vim 是 Linux 系统中最常用的文本编辑器之一，因其强大的功能和轻量级的性能广泛应用于开发者和系统管理员的日常工作中。无论是代码编辑、系统配置还是日志分析，掌握 Vim 的基础和高级命令都能极大提升工作效率。本文将汇总 Vim 的常用命令及其功能，帮助你在 Linux 环境中高效操作 Vim。 一、Vim 基础介绍 Vim 是一个基于 vi 的高级文本编辑器，提供了更多强大的功能。它
阅读更多...
大厂面试：小米嵌入式面试题大全及参考答案（130+道 12万长文）

大厂面试：小米嵌入式面试题大全及参考答案（130+道 12万长文）

Flink 架构介绍 Flink 是一个分布式流处理和批处理框架，具有高吞吐、低延迟、高可靠等特点。其架构主要由以下几个部分组成： 客户端（Client）：负责将作业提交到集群，并与作业管理器进行交互，获取作业的状态信息。客户端可以是命令行工具、IDE 插件或者自定义的应用程序。作业管理器（JobManager）：负责接收客户端提交的作业，协调资源分配，调度任务执行，并监控作业的执
阅读更多...
AI产品经理：ai产品经理从零基础到精通，非常详细收藏我这一篇就够了

AI产品经理：ai产品经理从零基础到精通，非常详细收藏我这一篇就够了

在互联网的浪潮中，AI人工智能领域无疑是最引人注目的风口。AI产品经理，作为这一领域的新兴岗位，以其高薪、低压力、无年龄限制等优势，吸引了众多互联网从业者的目光。随着GPT等AIGC工具的兴起，AI产品经理的市场需求日益增长。 AI产品经理需不需要懂算法？🤔‍‍‍ AI产品经理不必像算法工程师那样精通算法，但必须能够与算法工程师有效沟通，了解如何管理AI项目，协调项目资源。 成功转行AI产
阅读更多...
IDEA快捷键大全——工作笔记

IDEA快捷键大全——工作笔记

IDEA快捷键大全，如果有些快捷键无效可能会是其他应用程序占用。 编辑 Ctrl+Space 基本代码补全，输入字母按后列出匹配的词组 Ctrl+Shift+Space 智能代码补全，列出与预期类型一致的方法或变量 Ctrl+Alt+Space 补全类名 Ctrl+Shift+Enter 补全语句 Ctrl+P 显示方法参数 Ctrl+Q 显示注释文档 Shift+F1 显示外部文档 Ctrl+
阅读更多...

Copyright China编程 23002807@qq.com

沪ICP备2023033072号-2