GSE243609
Chakkapong_Burudpakdee
2024-08-18
GSE243609-Medulloblastoma tumorigenesis. samples: cerebellum of Ptch1- Atoh1EGFP mice at P7, 6 weeks, and adulthood.
This file is at p7 (neural precursors).
Public on May 03, 2024
Using ATAC-seq analysis from GNPs to MBSHH we discovered a massive modification in chromatin accessibility during MBSHH formation. Next, using integrative bioinformatics, we identified genes of the nuclear factor I (NFI) family that function as oncogenes on the MBSHH epigenome. We demonstrate not only that these genes are essential in the early stages of murine MBSHHs tumorigenesis, but also that their genetic silencing inhibits tumor growth in murine and human MBSHHs in vitro and in vivo. Finally, we discovered that NFIA/B are post-translationally modified by an activating methylation in MBSHH, making it possibility to target this pathway. Further studies revealed that pharmacological inhibition of this methylated NFIA/B hindered tumor proliferation, demonstrating the requirement of NFIA/B for tumor progression. Collectively our data shed light on the mechanism underlying the epigenome during the formation of the most malignant pediatric brain tumor and emphasizes the importance of deciphering cancer-specific epigenome for identification of new therapeutic avenues.
Chromium Single-Cell Multiome ATAC + Gene Expression for mouse medulloblastoma model.
Japanese Foundation for Cancer Research
Shiraishi R, Cancila G, Kumegawa K, Maruyama R, Ayrault O, Kawauchi D
NextSeq 550 (Mus musculus)
All raw fastq files were downloaded from NCBI and aligned with STAR (mm10) using the 10x cellranger-arc pipeline for multiome data processing.
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## 7212## Normalizing layer: counts
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## Ldb2, Plekhg1, Plce1, Limch1, Col4a2, Gria1, Cpq, Apoe, Rfx4, Cdh11
## Negative: Grik2, Cacna2d1, Reln, Cadps2, Dcc, Erbb4, Hs3st5, Hs6st3, St18, Ndst3
## Fam155a, Trhde, Nrg1, Rasgef1b, Robo2, Kctd8, Unc5c, Plcb1, Xkr4, Stxbp5l
## Kcnd2, Zmat4, Fgf13, Ppp2r2b, Cntnap2, chr9:65514433-65515428, March1, Grm1, Rps6ka3, chr8:23477352-23478271
## PC_ 2
## Positive: Nrxn1, Grid2, Luzp2, Anks1b, Pcdh9, Lrrc4c, Lsamp, Cadm2, Csmd2, Lrp1b
## Zfpm2, Ncam2, Ptprz1, Csmd1, Cntn1, Unc5c, Sox6, Erbb4, Nrxn3, Malat1
## Pcdh7, Pde4b, Slc4a4, Nlgn1, Tmtc2, Ptprt, Npas3, Pde4d, Grid1, Macrod2
## Negative: Fli1, Cldn5, Adgrl4, Adgrf5, Mecom, Flt1, Cdh5, Selenop, Itm2a, Rasgrp3
## Cd93, Rbpms, Kdr, Abcb1a, Igfbp7, Egfl7, Tek, Ets1, Lama4, Epas1
## Slc40a1, Slc38a5, Col4a1, Eng, Itga1, Prkch, Erg, Tie1, Ctla2a, Slco1c1
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## Slc1a3, Gdf10, Gpc6, Paqr8, chr2:98666667-98667487, Fabp7, Lrig1, Col23a1, Npas3, Pax3
## Ttyh1, Cyp26b1, Plpp3, Grm3, Cacnb2, Spock3, chr6:94631019-94631945, Fat3, chr9:65514433-65515428, Pard3b
## Negative: Pde4d, Ccser1, Grin2b, Grm1, Arpp21, Cacna1e, Zmat4, Kcnd2, Nrg1, Erbb4
## Ndst3, Grik2, Xkr4, Hivep3, Camk2d, Plcb1, Tmtc2, Rps6ka3, Cadps2, Reln
## Cadm2, Unc5c, Nrxn1, Lrp1b, Anks1b, Stxbp5l, Prkn, Plxna2, Tenm2, Galntl6
## PC_ 4
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## Col6a1, Slc7a11, Apod, Tbx18, Adamts12, Vtn, Bmp6, Col4a6, Lama2, Col4a5
## Col26a1, Hlf, Cp, Nid2, Cxcl12, Trpm3, Itga8, Lama1, Lum, Abca8a
## Negative: Tnr, Gpr17, Ppp1r16b, Ptpre, Tmem132d, Sox6, Sox10, Dnm3, Bcan, Tmem163
## Sntg1, Sulf2, 9630013A20Rik, Dpp10, Slc9a9, Lhfpl3, Xylt1, Itpr2, 9530059O14Rik, Pllp
## Bcas1os2, Fli1, Gm16168, Lrrtm4, Tns3, Ncam2, Cspg5, Enpp6, Bcas1, Brinp3
## PC_ 5
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## Sidt1, Vtn, Tbx18, Colec12, Adamts12, Slc6a20a, Cfh, Nid1, Col4a6, Col6a1
## Lama1, Spock3, Lama2, Nid2, Cdh11, Dlc1, Grm3, Itih5, Col23a1, Cp
## Negative: Cfap54, Dnah12, Spag16, Dnah6, Cfap299, Rgs22, Spef2, Wdr49, Ak9, Lrriq1
## Dnah11, Hydin, Gm973, Cfap44, Ak7, Agbl4, Dnah5, Spag17, 3300002A11Rik, Dnah9
## Kif6, Cfap69, Ttc29, Spata17, Cfap65, Ccdc162, Ccdc146, Dnah3, Tmem232, Armc4## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session## 22:38:57 UMAP embedding parameters a = 0.9922 b = 1.112
## 22:38:57 Read 6134 rows and found 30 numeric columns
## 22:38:57 Using Annoy for neighbor search, n_neighbors = 30
## 22:38:57 Building Annoy index with metric = cosine, n_trees = 50
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## 22:38:57 Writing NN index file to temp file /tmp/Rtmpp9Xq0H/file58b611cbc54e
## 22:38:57 Searching Annoy index using 1 thread, search_k = 3000
## 22:38:58 Annoy recall = 100%
## 22:38:59 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 22:39:01 Initializing from normalized Laplacian + noise (using RSpectra)
## 22:39:01 Commencing optimization for 500 epochs, with 257792 positive edges
## 22:39:06 Optimization finished
## Computing nearest neighbor graph
## Computing SNN## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 6134
## Number of edges: 226324
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9459
## Number of communities: 7
## Elapsed time: 0 seconds##
## Astrocytes Dendritic cells Endothelial cells Epithelial cells
## 162 3 52 28
## Fibroblasts Granulocytes Macrophages Microglia
## 55 1 17 37
## Monocytes Neurons Oligodendrocytes T cells
## 2 5711 30 3##
## aNSCs Astrocytes Dendritic cells
## 56 156 2
## Endothelial cells Ependymal Fibroblasts
## 54 27 1
## Fibroblasts activated Fibroblasts senescent Granulocytes
## 52 2 1
## Macrophages Microglia Microglia activated
## 15 35 3
## Monocytes Neurons Neurons activated
## 3 1720 59
## NPCs Oligodendrocytes OPCs
## 3206 30 637
## qNSCs T cells
## 6 3## orig.ident nCount_RNA nFeature_RNA RNA_snn_res.0.1
## AAACAGCCAAAGGTAC-1 rna 14029 6931 0
## AAACAGCCAATGCCCG-1 rna 5632 2857 1
## AAACAGCCACCTCACC-1 rna 26092 10881 2
## AAACAGCCACGAACAG-1 rna 13607 5430 2
## AAACAGCCATAATCAC-1 rna 4378 2215 0
## AAACATGCAATTGCGC-1 rna 16135 7817 0
## seurat_clusters gen_celltype fine_celltype
## AAACAGCCAAAGGTAC-1 0 Neurons NPCs
## AAACAGCCAATGCCCG-1 1 Neurons Neurons
## AAACAGCCACCTCACC-1 2 Neurons NPCs
## AAACAGCCACGAACAG-1 2 Epithelial cells Neurons
## AAACAGCCATAATCAC-1 0 Neurons NPCs
## AAACATGCAATTGCGC-1 0 Neurons NPCs
##
## NPCs Neurons OPCs
## 3206 1720 637
## Fibroblasts activated Astrocytes Neurons activated
## 52 156 59
## Endothelial cells aNSCs Microglia
## 54 56 35
## Ependymal Oligodendrocytes T cells
## 27 30 3
## Macrophages Microglia activated qNSCs
## 15 3 6
## Monocytes Fibroblasts senescent Fibroblasts
## 3 2 1
## Granulocytes Dendritic cells
## 1 2## [1] "NPCs" "Neurons" "OPCs"
## [4] "Fibroblasts activated" "Astrocytes" "Neurons activated"
## [7] "Endothelial cells" "aNSCs" "Microglia"
## [10] "Ependymal" "Oligodendrocytes" "T cells"
## [13] "Macrophages" "Microglia activated" "qNSCs"
## [16] "Monocytes" "Fibroblasts senescent" "Fibroblasts"
## [19] "Granulocytes" "Dendritic cells"## Computing hash
## Extracting reads overlapping genomic regions## Warning in .merge_two_Seqinfo_objects(x, y): The 2 combined objects have no sequence levels in common. (Use
## suppressWarnings() to suppress this warning.)## Warning in .merge_two_Seqinfo_objects(x, y): The 2 combined objects have no sequence levels in common. (Use
## suppressWarnings() to suppress this warning.)
## Warning in .merge_two_Seqinfo_objects(x, y): The 2 combined objects have no sequence levels in common. (Use
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## Warning in .merge_two_Seqinfo_objects(x, y): The 2 combined objects have no sequence levels in common. (Use
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## Warning in .merge_two_Seqinfo_objects(x, y): The 2 combined objects have no sequence levels in common. (Use
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## Warning in .merge_two_Seqinfo_objects(x, y): The 2 combined objects have no sequence levels in common. (Use
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## Warning in .merge_two_Seqinfo_objects(x, y): The 2 combined objects have no sequence levels in common. (Use
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## Warning in .merge_two_Seqinfo_objects(x, y): The 2 combined objects have no sequence levels in common. (Use
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## Warning in .merge_two_Seqinfo_objects(x, y): The 2 combined objects have no sequence levels in common. (Use
## suppressWarnings() to suppress this warning.)
## Warning in .merge_two_Seqinfo_objects(x, y): The 2 combined objects have no sequence levels in common. (Use
## suppressWarnings() to suppress this warning.)
## Warning in .merge_two_Seqinfo_objects(x, y): The 2 combined objects have no sequence levels in common. (Use
## suppressWarnings() to suppress this warning.)
## Warning in .merge_two_Seqinfo_objects(x, y): The 2 combined objects have no sequence levels in common. (Use
## suppressWarnings() to suppress this warning.)
## Warning in .merge_two_Seqinfo_objects(x, y): The 2 combined objects have no sequence levels in common. (Use
## suppressWarnings() to suppress this warning.)
## Warning in .merge_two_Seqinfo_objects(x, y): The 2 combined objects have no sequence levels in common. (Use
## suppressWarnings() to suppress this warning.)
## Warning in .merge_two_Seqinfo_objects(x, y): The 2 combined objects have no sequence levels in common. (Use
## suppressWarnings() to suppress this warning.)
## Warning in .merge_two_Seqinfo_objects(x, y): The 2 combined objects have no sequence levels in common. (Use
## suppressWarnings() to suppress this warning.)
## Warning in .merge_two_Seqinfo_objects(x, y): The 2 combined objects have no sequence levels in common. (Use
## suppressWarnings() to suppress this warning.)
## Warning in .merge_two_Seqinfo_objects(x, y): The 2 combined objects have no sequence levels in common. (Use
## suppressWarnings() to suppress this warning.)
## Warning in .merge_two_Seqinfo_objects(x, y): The 2 combined objects have no sequence levels in common. (Use
## suppressWarnings() to suppress this warning.)
## Warning in .merge_two_Seqinfo_objects(x, y): The 2 combined objects have no sequence levels in common. (Use
## suppressWarnings() to suppress this warning.)
## Warning in .merge_two_Seqinfo_objects(x, y): The 2 combined objects have no sequence levels in common. (Use
## suppressWarnings() to suppress this warning.)## GRanges object with 1763965 ranges and 5 metadata columns:
## seqnames ranges strand | tx_id
## <Rle> <IRanges> <Rle> | <character>
## ENSMUSE00001236884 chr3 3508030-3508332 + | ENSMUST00000108393
## ENSMUSE00000676606 chr3 3634150-3634347 + | ENSMUST00000108394
## ENSMUSE00001345708 chr3 3638059-3638230 + | ENSMUST00000108393
## ENSMUSE00001345708 chr3 3638059-3638230 + | ENSMUST00000108394
## ENSMUSE00000149313 chr3 3641223-3641317 + | ENSMUST00000108393
## ... ... ... ... . ...
## ENSMUST00000082414 chrMT 10167-11544 + | ENSMUST00000082414
## ENSMUST00000082418 chrMT 11742-13565 + | ENSMUST00000082418
## ENSMUST00000082419 chrMT 13552-14070 - | ENSMUST00000082419
## ENSMUST00000082421 chrMT 14145-15288 + | ENSMUST00000082421
## ENSMUST00000084013 chrMT 9877-10173 + | ENSMUST00000084013
## gene_name gene_id gene_biotype type
## <character> <character> <character> <factor>
## ENSMUSE00001236884 Hnf4g ENSMUSG00000017688 protein_coding exon
## ENSMUSE00000676606 Hnf4g ENSMUSG00000017688 protein_coding exon
## ENSMUSE00001345708 Hnf4g ENSMUSG00000017688 protein_coding exon
## ENSMUSE00001345708 Hnf4g ENSMUSG00000017688 protein_coding exon
## ENSMUSE00000149313 Hnf4g ENSMUSG00000017688 protein_coding exon
## ... ... ... ... ...
## ENSMUST00000082414 mt-Nd4 ENSMUSG00000064363 protein_coding cds
## ENSMUST00000082418 mt-Nd5 ENSMUSG00000064367 protein_coding cds
## ENSMUST00000082419 mt-Nd6 ENSMUSG00000064368 protein_coding cds
## ENSMUST00000082421 mt-Cytb ENSMUSG00000064370 protein_coding cds
## ENSMUST00000084013 mt-Nd4l ENSMUSG00000065947 protein_coding cds
## -------
## seqinfo: 22 sequences (1 circular) from mm10 genome## ChromatinAssay data with 145308 features for 6361 cells
## Variable features: 0
## Genome: mm10
## Annotation present: TRUE
## Motifs present: FALSE
## Fragment files: 1## Extracting TSS positions
## Extracting fragments at TSSs
##
## Computing TSS enrichment score
##
## High Low
## 6007 27
## Performing TF-IDF normalization
## Running SVD
## Scaling cell embeddings
## 22:46:35 UMAP embedding parameters a = 0.9922 b = 1.112
## 22:46:35 Read 6007 rows and found 29 numeric columns
## 22:46:35 Using Annoy for neighbor search, n_neighbors = 30
## 22:46:35 Building Annoy index with metric = cosine, n_trees = 50
## 0% 10 20 30 40 50 60 70 80 90 100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 22:46:35 Writing NN index file to temp file /tmp/Rtmpp9Xq0H/file58b6f7a4f97
## 22:46:35 Searching Annoy index using 1 thread, search_k = 3000
## 22:46:37 Annoy recall = 100%
## 22:46:38 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 22:46:40 Initializing from normalized Laplacian + noise (using RSpectra)
## 22:46:40 Commencing optimization for 500 epochs, with 251932 positive edges
## 22:46:45 Optimization finished
## Computing nearest neighbor graph
## Computing SNN
## Extracting gene coordinates## Warning in SingleFeatureMatrix(fragment = fragments[[x]], features = features,
## : 13 features are on seqnames not present in the fragment file. These will be
## removed.## Extracting reads overlapping genomic regions
## Centering and scaling data matrix## Warning: 1456 features of the features specified were not present in both the reference query assays.
## Continuing with remaining 544 features.## Running CCA
## Merging objects
## Finding neighborhoods
## Finding anchors
## Found 16707 anchors
## Finding integration vectors
## Finding integration vector weights
## Predicting cell labels## List of 2
## $ legend.position: chr "none"
## $ title : chr "gene_expression"
## - attr(*, "class")= chr [1:2] "theme" "gg"
## - attr(*, "complete")= logi FALSE
## - attr(*, "validate")= logi TRUE## List of 2
## $ legend.position: chr "none"
## $ title : chr "chromatin_accessibility"
## - attr(*, "class")= chr [1:2] "theme" "gg"
## - attr(*, "complete")= logi FALSE
## - attr(*, "validate")= logi TRUE## tx_id gene_name gene_id
## ENSMUST00000162607 ENSMUST00000162607 Gli2 ENSMUSG00000048402
## gene_biotype type closest_region
## ENSMUST00000162607 protein_coding cds chr1-119053302-119053329
## query_region distance
## ENSMUST00000162607 chr1-118834061-119054405 0Here is a coverage plot for hedgehog activation marker GLI2.
Here is a tileplot of fragment enrichments at the GLI2 locus.
## Warning in asMethod(object): sparse->dense coercion: allocating vector of size
## 2.4 GiB
## 22:48:51 UMAP embedding parameters a = 0.9922 b = 1.112## 22:48:51 Read 6134 rows and found 15 numeric columns## 22:48:51 Using Annoy for neighbor search, n_neighbors = 30## 22:48:51 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 22:48:51 Writing NN index file to temp file /tmp/Rtmpp9Xq0H/file58b620babade
## 22:48:51 Searching Annoy index using 1 thread, search_k = 3000
## 22:48:53 Annoy recall = 100%
## 22:48:54 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 22:48:56 Initializing from normalized Laplacian + noise (using RSpectra)
## 22:48:56 Commencing optimization for 500 epochs, with 251848 positive edges
## 22:49:01 Optimization finished## Warning: Number of dimensions changing from 2 to 3Here are the 3-dimensional scRNAseq and scATACseq UMAPs.
## 22:49:02 UMAP embedding parameters a = 0.9922 b = 1.112## 22:49:02 Read 6007 rows and found 29 numeric columns## 22:49:02 Using Annoy for neighbor search, n_neighbors = 30## 22:49:02 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 22:49:02 Writing NN index file to temp file /tmp/Rtmpp9Xq0H/file58b644e9ec7f
## 22:49:02 Searching Annoy index using 1 thread, search_k = 3000
## 22:49:04 Annoy recall = 100%
## 22:49:05 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 22:49:06 Initializing from normalized Laplacian + noise (using RSpectra)
## 22:49:06 Commencing optimization for 500 epochs, with 251932 positive edges
## 22:49:12 Optimization finished## Warning: Number of dimensions changing from 2 to 3## [1] "NPCs" "Neurons" "Astrocytes"
## [4] "OPCs" "Fibroblasts activated" "Ependymal"
## [7] "Endothelial cells" "Microglia" "Oligodendrocytes"
## [10] "T cells"## umap_1 umap_2 umap_3
## TCAAGCTAGGCGAAAC-1 -0.6640886 1.7294044 0.30255266
## GAACCAAAGAGGATAT-1 -2.1087907 -1.6287875 12.38055757
## GCACATTAGTTGGCCA-1 -0.1347103 -0.9666887 -0.07426183
## CGTGACATCTTGCAAA-1 -0.9961666 -4.6471367 3.56368664
## TTTGTTGGTAACAGGG-1 -1.1919208 -0.5355835 -1.02610203
## GATAATCGTAACCTAG-1 0.4714787 -4.6105781 6.37907365R version 4.4.1 (2024-06-14) – “Race for Your Life” Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu
RStudio 2024.04.2+764 “Chocolate Cosmos” Release (e4392fc9ddc21961fd1d0efd47484b43f07a4177, 2024-06-05) for Ubuntu Jammy Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) rstudio/2024.04.2+764 Chrome/120.0.6099.291 Electron/28.3.1 Safari/537.36, Quarto 1.4.555