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自己如何做网站关键词排名,百度公司官网招聘,大亚湾做网站公司,建设项目环保备案登记网站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&#xff0c;绝对的Atlas级好东西。&#x1f44d; 希望做单细胞的小伙伴觉得有用哦。&#x1f60f; 2常用marker&#xff08;一&#xff09; general_mrkrs <- c( MYH7, TNNT1, TNNT3, MYH1, MYH2, "C…

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).

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最后祝大家早日不卷!~

点个在看吧各位~ ✐.ɴɪᴄᴇ ᴅᴀʏ 〰

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