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Articles, Cellular/Molecular

An RNA-Sequencing Transcriptome and Splicing Database of Glia, Neurons, and Vascular Cells of the Cerebral Cortex

Ye Zhang, Kenian Chen, Steven A. Sloan, Mariko L. Bennett, Anja R. Scholze, Sean O'Keeffe, Hemali P. Phatnani, Paolo Guarnieri, Christine Caneda, Nadine Ruderisch, Shuyun Deng, Shane A. Liddelow, Chaolin Zhang, Richard Daneman, Tom Maniatis, Ben A. Barres and Jia Qian Wu
Journal of Neuroscience 3 September 2014, 34 (36) 11929-11947; DOI: https://doi.org/10.1523/JNEUROSCI.1860-14.2014
Ye Zhang
1Department of Neurobiology, Stanford University School of Medicine, Stanford, California 94305-5125,
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Kenian Chen
2The Vivian L. Smith Department of Neurosurgery, University of Texas Medical School at Houston, Houston, Texas 77057,
3Center for Stem Cell and Regenerative Medicine, University of Texas Brown Institute of Molecular Medicine, Houston, Texas 77057,
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Steven A. Sloan
1Department of Neurobiology, Stanford University School of Medicine, Stanford, California 94305-5125,
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Mariko L. Bennett
1Department of Neurobiology, Stanford University School of Medicine, Stanford, California 94305-5125,
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Anja R. Scholze
1Department of Neurobiology, Stanford University School of Medicine, Stanford, California 94305-5125,
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Sean O'Keeffe
4Department of Biochemistry and Molecular Biophysics, Columbia University Medical Center, New York, New York 10032,
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Hemali P. Phatnani
4Department of Biochemistry and Molecular Biophysics, Columbia University Medical Center, New York, New York 10032,
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Paolo Guarnieri
7Department of Systems Biology,
9Herbert Irving Comprehensive Cancer Center, Columbia University, New York, New York 10032
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  • ORCID record for Paolo Guarnieri
Christine Caneda
1Department of Neurobiology, Stanford University School of Medicine, Stanford, California 94305-5125,
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Nadine Ruderisch
5Department of Anatomy, University of California, San Francisco, San Francisco, California 94143-0452,
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Shuyun Deng
2The Vivian L. Smith Department of Neurosurgery, University of Texas Medical School at Houston, Houston, Texas 77057,
3Center for Stem Cell and Regenerative Medicine, University of Texas Brown Institute of Molecular Medicine, Houston, Texas 77057,
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Shane A. Liddelow
1Department of Neurobiology, Stanford University School of Medicine, Stanford, California 94305-5125,
6Department of Pharmacology and Therapeutics, University of Melbourne, Parkville, Victoria, Australia 3010, and
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Chaolin Zhang
4Department of Biochemistry and Molecular Biophysics, Columbia University Medical Center, New York, New York 10032,
7Department of Systems Biology,
8Center for Motor Neuron Biology and Disease, and
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Richard Daneman
5Department of Anatomy, University of California, San Francisco, San Francisco, California 94143-0452,
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Tom Maniatis
4Department of Biochemistry and Molecular Biophysics, Columbia University Medical Center, New York, New York 10032,
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Ben A. Barres
1Department of Neurobiology, Stanford University School of Medicine, Stanford, California 94305-5125,
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Jia Qian Wu
2The Vivian L. Smith Department of Neurosurgery, University of Texas Medical School at Houston, Houston, Texas 77057,
3Center for Stem Cell and Regenerative Medicine, University of Texas Brown Institute of Molecular Medicine, Houston, Texas 77057,
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  • Figure 1.
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    Figure 1.

    Purification of neurons, glia, and vascular cells. A, We purified eight cell types—neurons, astrocytes, OPCs, NFOs, MOs, microglia, endothelial cells, and pericytes—from mouse cerebral cortex with a combination of immunopanning and FACS procedures (for details, see Materials and Methods). B, RNA was extracted from purified cells and analyzed by microarray and RNA-Seq. C, Spearman's rank correlation of RNA-Seq biologically independent replicates. Each replicate consists of pooled cortices from 3–12 animals. D, Expression of classic cell-specific markers in purified glia, neurons, and vascular cells samples determined by RNA-Seq. Two biological replicates of each cell type are shown. Specific expression of known cell-specific markers demonstrates the purity of the glial, neuronal, and vascular samples.

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    Figure 2.

    Comparison of RNA-Seq and microarray analyses showed that RNA-Seq identified more differentially expressed genes across cell types than microarray. A, Spearman's rank correlation between gene expression data obtained by the RNA-Seq and microarray methods across cell types. Gene expression profiles of the same cell type obtained by the RNA-Seq and microarray methods showed a high degree of correlation. B, Numbers of differentially expressed genes identified by the RNA-Seq and microarray methods. A snapshot of the data is summarized in the Venn diagram. Using a fourfold difference as the cutoff, RNA-Seq analysis identified 3129 genes as differentially expressed by astrocytes, whereas microarray identified only 1367 genes. The majority of genes identified by microarray as differentially expressed are similarly classified by RNA-Seq (1279). RNA-Seq identified an additional 1850 genes as differentially expressed that were not identified by microarray as differentially expressed. C–E, RNA-Seq versus microarray comparisons for neurons, oligodendrocytes, and microglia. F, The relationship between fold enrichment and expression level in astrocytes. There is a sharp cutoff line on the left (FPKM = 0.1 or −3.3 on the log2 scale) because we set any FPKM value <0.1 to 0.1 to avoid ratio inflation in fold enrichment calculations. G, The relationship between fold enrichment and expression level in astrocytes.

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    Figure 3.

    RNA-Seq analysis revealed cell type-specific gene expression profiles. A, Dendrogram and unsupervised hierarchical clustering heat map (using Euclidean distance) of purified cortical glia, neurons, and vascular cells. The vertical distances on each branch of the dendrogram represent the degree of similarity between gene expression profiles of various samples. Biological replicates showed the highest degree of correlation represented by short vertical distances. Cells in the oligodendrocyte lineage cluster closely together, and the order of the three oligodendrocyte-lineage cell types corresponds to their maturation stages (OPC–NFO–MO). Although astrocytes and oligodendrocytes are both glial cells, their gene expression profiles are as different between each other as they are different from neurons. Consistent with their embryonic origin, mesodermal-derived endothelial cells and microglia cluster farther away from ectoderm-derived neurons, astrocytes, and oligodendrocytes. B, The top 40 enriched genes per cell type are shown in a heat map. Only highly expressed genes with FPKM >20 are included in this analysis. Fold enrichment is calculated as FPKM of one cell type divided by the average FPKM of all other cell types. The majority of these genes showed specific expression by only one cell type, with the exception that some are expressed during more than one maturation stage in the oligodendrocyte lineage.

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    Figure 4.

    Cell-specific markers and transcription factors. The top 40 genes and top 10 transcription factors ranked by fold enrichment of each cell type are listed. The most highly expressed genes are highlighted. Green, FPKM >150; blue, FPKM >50 for transcription factors. Fold enrichment of astrocytes, neurons, microglia, and endothelial cells are calculated as FPKM of one cell type divided by the average FPKM of all other cell types. Fold enrichment of OPC, NFO, and MO are calculated as FPKM of one cell type divided by the average FPKM of all non-oligodendrocyte-lineage cells, to highlight top genes specifically expressed by a particular maturation stage during oligodendrocyte development. Only highly expressed genes with FPKM >20 are included in the ranking to highlight genes that are most likely to have significant cell type-specific functions.

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    Figure 5.

    Alternative splicing analysis of neurons, glia and vascular cells. A, Six types of alternative splicing events are detected by RNA-Seq. Boxes and black lines represent exons and introns, respectively. Blue lines and red lines represent alternative splicing events detected in the dataset. The 5′ end is to the left, and the 3′ end is to the right. Cassette, The inclusion or exclusion of an exon; Tandom Cassette, the inclusion or exclusion of two or more tandom exons; Mutually Exclusive, the inclusion of one exon in one transcript and inclusion of a different exon in another transcript; Intron Retention, the inclusion or exclusion of a segment previously annotated to be an intron; Alternative 5′ SS, the alternative usage of a splicing site on the 5′ end of an exon; Alternative 3′ SS, the alternative usage of a splicing site on the 3′ end of an exon. B, Frequencies of the six types of alternative splicing events detected in the entire dataset and in individual cell types. In all cell types, cassette exon events, i.e., the inclusion or exclusion of an exon, are the most frequently detected alternative splicing events. C, The numbers of genes that are alternatively spliced in each cell type and the union of these samples. The dotted line represents the total number of genes that are known to contain a potential splicing event in the mouse genome. The number of these genes that are expressed in a given cell type are represented by gray bars. The black bars indicate the numbers of genes that are alternatively spliced in a given cell type based on criteria outlined in Materials and Methods. D, The numbers of statistically significant cell type-specific alternative splicing events in each cell type. Neurons have the highest number of specific splicing events, whereas oligodendrocyte-lineage cells have the least amount of specific splicing events. E, Pkm2 is an example of a gene spliced uniquely in astrocytes and neurons. The traces represent raw data of the number of reads mapped to the Pkm2 gene from astrocytes and neurons. The height of the blue bars represents number of reads. The bottom schematic is the transcript model of Pkm2 gene from the UCSC Genome Browser. Boxes represent exons, and black lines represent introns. The exon shown in blue is predominantly included in neurons, whereas the exon shown in yellow is included only in astrocytes. This is an example of a mutually exclusive event. F, Validation of PKM1/2 splicing differences by PCR. We designed primers targeting exons unique to PKM1, unique to PKM2, and exons common to PKM1 and PKM2. PCR products were detected from neuron, astrocyte, and whole-brain samples in patterns predicted by the RNA-Seq data.

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    Figure 6.

    Validation of RNA-Seq results by qRT-PCR and in situ hybridization. A, qRT-PCR validation of cell type-enriched genes identified by RNA-Seq. We performed qRT-PCR with Fluidigm BioMark microfluidic technology. Expression of several genes identified by RNA-Seq as enriched in each cell type was examined by qRT-PCR. The housekeeping gene Gapdh was included for comparison. Twelve replicates of each purified cell type and three replicates of whole-brain samples were analyzed. Warmer colors represent lower Ct values (higher abundance of transcripts), and cooler colors represent higher Ct values (lower abundance of transcripts). Black indicates no amplification. Data of genes labeled in red were quantified in B. B, Ct differences of Atp13a4, Cpne7, Fam70b, Tmem88b, and Rcsd1 compared with Gapdh were plotted on a log2 scale. Error bar represents SD. RNA-Seq analysis showed that Atp13a4, Cpne7, Fam70b, Tmem88b, and Rcsd1 are enriched in astrocytes, neurons, OPCs, oligodendrocytes, and microglia, respectively. qRT-PCR validated these results. C–G, In situ hybridization validated novel cell type-specific genes. Left, Low-magnification image of the cortex and hippocampus. Scale bar, 200 μm. Right, High-magnification image of the cortex. Scale bar, 50 μm. Fluorescence in situ hybridization signals with probes against novel cell type-specific genes (red) and known cell type-specific markers (green) are shown. The regions in the yellow boxes are enlarged and shown as single channel and merged images on the right.

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    Figure 7.

    Energy metabolism differences between astrocytes and neurons. The expression of several regulatory enzymes by astrocytes but not neurons allows astrocytes to adapt their metabolic flux to the energy state of the cell and to perform higher rate of aerobic glycolysis. Left, Diagram of energy metabolism pathways. Glycogen metabolism and glycolysis, which occur in the cytosol, and the tricarboxylic acid cycle, which occurs in the mitochondria, are shown. Steps highlighted with red asterisks are differentially regulated in astrocytes and neurons. Right, Detailed diagram of energy metabolism differences between astrocytes and neurons. Metabolic steps with key differences are labeled with numbers 1–4 and explained below the diagram. The rate of reactions is represented by the width of the arrows. The predominant metabolic products converted from pyruvate (lactate in astrocytes and acetyl-CoA in neurons) are highlighted in green.

Tables

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    Table 1.

    Top cell type-enriched lncRNAs

    Gene nameExpression (FPKM)Enrichment
    AstrocyteNeuronOPCNFOMOMicrogliaEndothelialPericyte
    Astrocyte
        Gm3764773.478.391.521.52.16.51.1103.423.1
        Malat1419.9161.244.863.012.225.032.4120.87.4
        C130071C03Rik156.848.442.39.52.00.30.525.19.1
        AW047730100.39.742.85.52.31.90.540.19.6
        Gm2692454.90.10.10.10.11.74.97.546.8
    Neuron
        Meg36.2729.748.611.23.324.91.5105.545.7
        Rian3.8446.835.416.626.75.46.172.728.5
        6330403K07Rik23.4141.169.314.06.52.51.227.87.2
        Dlx1os2.029.00.90.40.20.10.42.344.6
        Dlx6os10.128.00.10.10.10.10.22.463.5
    OPC
        2810468N07Rik5.93.397.4199.9249.52.40.44.732.7
        Gm116501.00.829.16.10.10.10.10.659.1
        Gm162771.911.428.114.96.50.20.16.78.3
        Gm48761.50.825.819.31.40.11.22.728.7
        3110043A19Rik0.60.918.23.00.10.20.70.430.5
    NFO
        2810468N07Rik5.93.397.4199.9249.52.40.44.767.1
        9630013A20Rik0.10.22.4110.726.40.20.10.1739.3
        2410006H16Rik20.28.92.767.030.18.13.119.06.7
        Sox2ot8.821.720.949.433.20.20.15.06.4
        1700047M11Rik0.10.13.230.924.40.20.10.2240.9
    MO
        2810468N07Rik5.93.397.4199.9249.52.40.44.783.8
        Gm106870.50.20.816.026.50.11.50.845.2
        9630013A20Rik0.10.22.4110.726.40.20.10.1176.2
        1700047M11Rik0.10.13.230.924.40.20.10.2190.2
        Gm219840.22.40.33.118.40.20.10.425.3
    Microglia
        Gm1388921.714.143.017.229.7575.624.366.023.0
        Gm265328.03.15.20.50.2101.66.69.825.7
        A430104N18Rik0.10.21.40.10.160.30.10.3181.2
        Gm1197421.612.69.25.03.049.36.611.05.1
        Gm134761.41.00.14.43.438.70.10.122.2
    Endothelial cell
        Gm204600.90.30.30.20.20.414.92.340.4
        Gm142070.20.10.10.10.10.112.81.2106.7
        Gm207480.20.10.10.10.20.310.91.270.3
        Gm161040.30.20.20.10.10.29.41.451.8
        Gm95810.20.10.40.70.90.59.13.019.6
    Pericyte
        Mir22hg12.65.13.62.21.311.27.948.17.7
        Gm1775017.929.09.91.50.50.20.547.55.6
        Gm171200.10.21.60.10.10.13.721.024.5
        Gm149649.23.10.80.20.30.21.317.38.0
        H192.00.70.70.20.10.21.716.320.1
    • The top expressed lncRNAs are listed for each cell type with an enrichment threshold >5.

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    Table 2.

    Cell-specific transmembrane receptors and ligands

    AstrocyteNeuronOPCNFOMOMicrogliaEndothelialPericyte
    Enriched transmembrane receptorsPtprz1Gpc1PdgfraGpr17Efnb3Csf1rTfrcPdgfrb
    EdnrbPtprnGpr17Sema4dGpr37Cd83Pglyrp1Colec12
    S1pr1CalyItgavPlxnb3Sema4dTyrobpH2-D1Sfrp1
    Fgfr3Grin1OmgDdr1Lpar1Ccrl2Eltd1S1pr3
    Gabbr1Ptprn2Gfra1Efnb3Ddr1B2mKdrAbcc9
    Tnfrsf19Gria1Gria3Gpr37OmgTrem2EngRarres2
    Vcam1Cnr1Sstr1Lpar1Ephb1SirpaTie1Pdgfrl
    Adcyap1r1OpcmlIl1rapPdgfraGprc5bFcer1gCav1Mrc2
    Gria2Stx1bAdora1OmgS1pr5Cd14Flt1Fas
    F3CxadrLypd1Ephb1Gpr17Icam1FcgrtEdnra
    Grm3NptxrGria4Sema5aPlxnb3Cx3cr1Sema7aLepr
    Dag1Grm2Chrna4Il1rapGpr62Lag3LsrOsmr
    Plxnb1Robo2Sema5aS1pr5Il1rapGpr56Acvrl1Gprc5
    Ntsr2KitCalcrlPrkczPrkczItgamTekIfitm1
    Fgfr1Gabrb3Gabra3Erbb3Itgb4Fcgr3Gpr116Ddr2
    Ptch1Gabrg2Grin3aGpr62Erbb3P2ry12Fzd6Scarf2
    Fgfr2Sarm1Oprl1PtpreSema5aP2ry13PtprbTgfbr3
    Gpr19Gabra2Grik4Grik3Tnfrsf12aAplnrFzd5
    Itga6DarcPlxnb3Grik2H2-K1PtprgNpy1r
    Gabbr2Celsr3Grik3CasrGpr183Plxnd1Celsr1
    Enriched ligandsSparcl1RelnMatn4GsnTrfCst3SparcIgf2
    CpeSstScrg1Lgi3GsnC1qaSepp1Vtn
    Cyr61NpyOlfm2Scrg1ApodCcl4PltpCxcl12
    Mfge8Olfm1VcanEnpp6Lgi3Ccl3Igfbp7Col4a1
    CluDkk3Emid1Matn4MetrnC1qbSpock2Col1a2
    Htra1Ccl27aTnrTnrEndod1C1qcCtla2aBgn
    Igfbp2Cx3cl1Nxph1Ddr1Adamts4SelpigPglyrp1Dcn
    VegfaCckTimp4Adamts4Cntn2CtsbCol4a1Ptgds
    Scg3VgfSpon1MetrnEnpp6B2mEgfl7Cxcl1
    NcanVstm2lIgsf21Fam3cHapln2Gdf15AU021092Col1a1
    Pla2g7ChgbGsnC1ql1Il1rapOlfm3SrgnFstl1
    Fjx1Scg2Fam5cVcanErbb3TnfFn1Col3a1
    Timp3C1qtnf4QpctTimp4SlpiPla2g15KdrMdk
    Il18CxadrC1ql3Il1rapKlk6Tcn2AplnIgfbp5
    Btbd17Col6a2Smoc1Col11a2Col11a2Ly86Wfdc1Serpinf1
    Itih3Resp18Gpc5Bmp4Matn4Plod1Angptl4Nbl1
    Hapln1Vstm2aIl1rapElfn2Dlk2Il1aHtra3Nid2
    LcatCar11DscamDlx2Il23aTgfb1Smpdl3aIslr
    Chrdl1Igfbpl1ChgaSpon1Wnt3Lgals9Lama4Ptx3
    Pla2g3NppcNptx2DcamNpbCcl2EmcnVasn
    • The top 20 transmembrane receptors and top 20 ligands ranked by fold enrichment of each cell type are listed here. Fold enrichment of astrocytes, neurons, microglia, and endothelial cells are calculated as FPKM of one cell type divided by the average FPKM of all other cell types. Fold enrichment of OPCs, NFOs, and MOs are calculated as FPKM of one cell type divided by the average FPKM of all non-oligodendrocyte-lineage cells to highlight top genes specifically expressed by a particular maturation stage during oligodendrocyte development.

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    Table 3.

    Examples of genes differentially spliced in different cell types

    GeneCoveragedIp valueFDR
    AstrocytesFyn771−0.271.15 E-1761.95 E-174
    Prom12510.861.12 E-1334.59 E-131
    Ncam135760.241.63 E-1325.93 E-130
    Ptprf480−0.581.03 E-1202.98 E-118
    Srgap33080.691.99 E-841.44 E-82
    Kif1a1800.753.12 E-661.76 E-64
    Ptk2134−0.471.33 E-636.73 E-62
    Cam2kg903−0.445.75 E-562.43 E-54
    Mapk8270−0.556.98 E-372.36 E-35
    Pkm21112−0.232.85 E-256.58 E-24
    NeuronsAgrn907−0.5<1 E-300<1 E-300
    App51810.2<1 E-300<1 E-300
    Atp6v0a11815−0.66<1 E-300<1 E-300
    Clta3032−0.81<1 E-300<1 E-300
    Dync1i21618−0.82<1 E-300<1 E-300
    Nfasc821−0.94<1 E-300<1 E-300
    Rab630580.34<1 E-300<1 E-300
    Mtss11244−0.65.19 E-2167.02 E-214
    Srgap38340.621.69 E-1321.52 E-130
    Lrp8328−0.753.23 E-1192.51 E-117
    OligodendrocytesPhldb12947−0.58<1 E-300<1 E-300
    Aplp213580.48<1 E-300<1 E-300
    Capzb1350−0.585.97 E-2633.54 E-260
    Add11515−0.511.41 E-2315.95 E-229
    Mpzl111650.454.37 E-2273.15 E-223
    Cldnd11187−0.62.49 E-2098.2 E-207
    Enpp215500.231.63 E-2092.35 E-191
    H2afy3200.431.31 E-355.11 E-34
    Mtss11810.441.58 E-244.54 E-23
    Snap251000.398.8 E-222.1 E-20
    MicrogliaClstn15880.91<1 E-300<1 E-300
    H1312630.26.2 E-2963.94 E-283
    Sena4d7680.61.19 E-2826.79 E-280
    App705−0.65.33 E-2402.04 E-237
    Add15090.579.97 E-1883.17 E-185
    Lass54080.652.49 E-1747.12 E-172
    Rapgef13550.452.22 E-1735.79 E-171
    Fmnl14930.441.32 E-1583.15 E-156
    Fez28530.361.61 E-1393.08 E-137
    Fyn1310.687.07 E-899.31 E-87
    EndothelialAdam158930.7<1 E-300<1 E-300
    Mcf2l6290.74<1 E-300<1 E-300
    Palm942−0.65<1 E-300<1 E-300
    Ablim110250.47<1 E-300<1 E-300
    Mprip3292−0.51<1 E-300<1 E-300
    Actn41805−0.31<1 E-300<1 E-300
    Ktn1809−0.73.84 E-2261.02 E-223
    Arhgef18650.362.23 E-2195.66 E-217
    Eif4 h11990.461.14 E-1972.29 E-195
    Pkp45770.631.68 E-1953.26 E-193
    • The coverage, delta inclusion (dI), p value, and false discovery rate (FDR) are listed. To obtain dI values, we first calculated the ratio of inclusion junction tags to inclusion plus skipping junction tags in each cell type and then determined the differences of the ratios between cell types. dI essentially quantifies the magnitude of the difference between the splicing of the two groups being compared (1 or −1 represents maximum difference, whereas 0 represents no difference).

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The Journal of Neuroscience: 34 (36)
Journal of Neuroscience
Vol. 34, Issue 36
3 Sep 2014
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An RNA-Sequencing Transcriptome and Splicing Database of Glia, Neurons, and Vascular Cells of the Cerebral Cortex
Ye Zhang, Kenian Chen, Steven A. Sloan, Mariko L. Bennett, Anja R. Scholze, Sean O'Keeffe, Hemali P. Phatnani, Paolo Guarnieri, Christine Caneda, Nadine Ruderisch, Shuyun Deng, Shane A. Liddelow, Chaolin Zhang, Richard Daneman, Tom Maniatis, Ben A. Barres, Jia Qian Wu
Journal of Neuroscience 3 September 2014, 34 (36) 11929-11947; DOI: 10.1523/JNEUROSCI.1860-14.2014

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An RNA-Sequencing Transcriptome and Splicing Database of Glia, Neurons, and Vascular Cells of the Cerebral Cortex
Ye Zhang, Kenian Chen, Steven A. Sloan, Mariko L. Bennett, Anja R. Scholze, Sean O'Keeffe, Hemali P. Phatnani, Paolo Guarnieri, Christine Caneda, Nadine Ruderisch, Shuyun Deng, Shane A. Liddelow, Chaolin Zhang, Richard Daneman, Tom Maniatis, Ben A. Barres, Jia Qian Wu
Journal of Neuroscience 3 September 2014, 34 (36) 11929-11947; DOI: 10.1523/JNEUROSCI.1860-14.2014
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Keywords

  • alternative splicing
  • astrocytes
  • microglia
  • oligodendrocytes
  • transcriptome
  • vascular cells

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