Name: identifying transcription factor olig2 genomic binding sites in acutely purified pdgfr&#945;+ cells by low-cell chromatin immunoprecipitation sequencing analysis
Uploaded: 2018-04-16T14:00:00
Description: Watch this Scientific Journal Video about Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFRÃŽÂ±+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysi

JQW、XD、RCDD 和 YY 得到了国立卫生研究院 R01 NS088353 的赠款的支持;NIH 赠款 1R21AR071583-01;Staman 奥格尔维基金-纪念赫尔曼基金会;UTHealth 脑主动和 CTSA UL1 TR000371;还有德克萨斯大学神经系统科学和 Neurotechnology 研究所的补助金 (赠款 #362469)。

所有动物的使用和实验性的协议都是按照《动物护理和使用指南》进行的, 并由德克萨斯大学卫生科学中心的机构生物安全委员会和动物福利委员会批准。休斯顿。
1. 从小鼠脑中纯化 PDGFRα阳性少突胶质谱系细胞 (从先前描述的 immunopanning 协议修改为5, 6, 7)
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PDGFRα阳性细胞选择 immunopanning 板的?...

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哺乳动物基因调控网络是非常复杂的。芯片序列是研究全基因组蛋白质-DNA 相互作用的有力方法。该协议包括如何使用小鼠大脑中的少量纯化 OPCs (每反应低2万细胞) 来执行 Olig2 芯片序列。该协议的第一个关键步骤是用 PDGFRα抗体 immunopanning 纯化小鼠大脑中的 OPCs。对于 PDGFRα涂覆板的 OPCs 的阳性选择, OPCs 通常与 PDGFRα涂层板紧密结合。即使经过几次洗涤, 仍然有一些非黏附的细胞留下。在显微镜下,...

重要的是研究蛋白质 (或蛋白质复合物) DNA 绑定和表观遗传标记建立转录调控网络参与各种生物过程。特别是转录因子与基因组 DNA 的绑定, 在基因调控、细胞分化、组织发育等方面都有重要作用。研究转录调节和表观遗传学机制的有力工具是染色质免疫沉淀 (芯片)。由于下一代测序技术的飞速进步, 芯片结合高通量 dna 测序 (芯片序列) 被用于蛋白质 DNA 绑定和表观遗传标记的分析1。然而, 一个标准的芯片序列协议需要大约2000万细胞每反应, 这使得这种技术的应用困难, 当细胞数量有限, 如孤立的原生细胞和稀有细胞群体。
少突胶质谱系细胞包括少突胶质前体细胞 (OPCs) 和少突胶质广泛分布于整个大脑, 对大脑的发育和功能至关重要。OPCs 作为前体细胞的一种, 具有自我更新和分化的能力。OPCs 不仅作为少突胶质的始祖, 而且在神经元信号的传播中起着重要作用, 通过与其他类型的脑细胞进行交流2。以前的研究表明, 少突胶质的发展是由沿袭特定的转录因子 (如 Olig2 和Sox103, 4) 调节的.这些转录因子被发现绑定到一些关键基因的启动子或增强区, 以影响其表达在少突胶质规范和分化。然而, 要确定蛋白质 (或蛋白质复合物) 在急性纯化的初级 OPCs 中的 DNA 结合有很大的挑战性, 细胞数量非常有限。
本协议描述了如何利用芯片序列技术对纯化小鼠 OPCs 中 Olig2 基因组 DNA immunoprecipitated 进行系统的研究。OPCs 从小鼠的大脑被 immunopanning 的敏锐纯化, 并用于芯片实验, 不扩散在体外。有限数量的 OPCs 可以得到 immunopanning, 是不够的标准芯片序列实验。本文介绍了一种低细胞芯片序列协议, 其每片反应的转录因子为2万细胞。简而言之, 交联细胞裂解和微气泡由超声波装置剪切染色质。用 Olig2 抗体和蛋白 A 包覆的微珠对剪切的染色质进行孵化, 沉淀 Olig2 抗体绑定基因组 DNA。从蛋白 A 包膜珠和反向交联洗脱后, 用苯酚-氯仿萃取纯化 Olig2 抗体沉淀的基因组 DNA。该产品经过定量化, 并受到 T 尾矿、底漆退火模板的切换和扩展, 增加了适配器和放大器, 库的大小选择和净化步骤, 为芯片序列库的建设。
在测序后, 分析了用 Olig2 转录因子抗体和对照样品制备的样品的原始读数质量。剪裁了包含读取片段的低质量基对和适配器。接下来, 修剪的读数与鼠标参考基因组对齐。与对照样品相比, 对芯片读数显著丰富的基因组区域被检测为峰值。显著的峰值, 代表潜在的转录因子结合点, 被过滤和可视化的基因组浏览器。
值得注意的是, 本协议中描述的方法可以广泛地用于其他转录因子的芯片序列, 并具有任何细胞类型的有限数目。

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			<td>Reagent/ Equipment</td>
			<td></td>
			<td></td>
			<td></td>
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		<tr>
			<td>Banderiaea simplicifolia lectin 1</td>
			<td>Vector Laboratories</td>
			<td># L-1100</td>
			<td></td>
		</tr>
		<tr>
			<td>Rat anti-PDGFRa antibody</td>
			<td>BD Bioscience</td>
			<td># 558774</td>
			<td></td>
		</tr>
		<tr>
			<td>Neural tissue dissociation Kit (P)</td>
			<td>MACS Miltenyi Biotec</td>
			<td># 130-092-628</td>
			<td></td>
		</tr>
		<tr>
			<td>Accutase</td>
			<td>STEMCELL technologies</td>
			<td># 07920</td>
			<td></td>
		</tr>
		<tr>
			<td>TRIzol</td>
			<td>Thermo Fisher</td>
			<td># 15596026</td>
			<td></td>
		</tr>
		<tr>
			<td>Anti-NG2 Chondroitin Sulfate Proteoglycan Antibody</td>
			<td>Millipore</td>
			<td># AB5320</td>
			<td></td>
		</tr>
		<tr>
			<td>Bioruptor Pico sonication device</td>
			<td>Diagenode</td>
			<td># B01060001</td>
			<td></td>
		</tr>
		<tr>
			<td>True MicroChIP kit</td>
			<td>Diagenode</td>
			<td># C01010130</td>
			<td></td>
		</tr>
		<tr>
			<td>Phenol:Chloroform:Isoamyl Alcohol (25:24:1, v/v)</td>
			<td>Thermo Fisher</td>
			<td># 15593031</td>
			<td></td>
		</tr>
		<tr>
			<td>NucleoSpin Gel and PCR Clean-Up kit</td>
			<td>MACHEREY-NAGEL</td>
			<td># 740609</td>
			<td></td>
		</tr>
		<tr>
			<td>Quant-iT PicoGreen dsDNA Assay Kit</td>
			<td>Thermo Fisher</td>
			<td># P11496</td>
			<td></td>
		</tr>
		<tr>
			<td>DNA SMART ChIP-Seq kit</td>
			<td>Clontech Laboratories</td>
			<td># 634865</td>
			<td></td>
		</tr>
		<tr>
			<td>Agencourt AMPure XP</td>
			<td>Beckman Voulter</td>
			<td># A63880</td>
			<td></td>
		</tr>
		<tr>
			<td>GlycoBlue</td>
			<td>Thermo Fisher</td>
			<td># AM9516</td>
			<td></td>
		</tr>
		<tr>
			<td>pico-green</td>
			<td>Thermo Fisher</td>
			<td># P11496</td>
			<td></td>
		</tr>
		<tr>
			<td>D-PBS</td>
			<td>Thermo Fisher</td>
			<td># 14190-144</td>
			<td></td>
		</tr>
		<tr>
			<td>SYBR Green master mix</td>
			<td>Bio-Rad Laboratories</td>
			<td># 1725124</td>
			<td></td>
		</tr>
		<tr>
			<td>DMEM/F12</td>
			<td>Fisher Scientific</td>
			<td># 11-320-033</td>
			<td></td>
		</tr>
		<tr>
			<td>Penicillin-Streptomycin (P/S)</td>
			<td>Fisher Scientific</td>
			<td># 15140122</td>
			<td></td>
		</tr>
		<tr>
			<td>N-2 Supplement</td>
			<td>Fisher Scientific</td>
			<td># 17502048</td>
			<td></td>
		</tr>
		<tr>
			<td>B-27 Supplement</td>
			<td>Fisher Scientific</td>
			<td># 17504044</td>
			<td></td>
		</tr>
		<tr>
			<td>insulin</td>
			<td>Sigma</td>
			<td># I6634</td>
			<td>Prepare 0.5 mg/ml insulin solution by dissolving 5 mg insulin in 10 ml water and 50 &#956;l of 1 N HCl.</td>
		</tr>
		<tr>
			<td>Bovine Serum Albumin, suitable for cell culture (BSA)</td>
			<td>Sigma</td>
			<td># A4161</td>
			<td>Prepare 4% BSA solution by dissolving 4 g BSA in 100 ml D-PBS and adjust the pH to 7.4</td>
		</tr>
		<tr>
			<td>Fibroblast Growth Factor basic Protein, Human recombinant (bFGF)</td>
			<td>EMD Millipore</td>
			<td># GF003</td>
			<td></td>
		</tr>
		<tr>
			<td>HUMAN PDGF-AA</td>
			<td>VWR</td>
			<td># 102061-188</td>
			<td></td>
		</tr>
		<tr>
			<td>poly-D-lysine</td>
			<td>VWR</td>
			<td># IC15017510</td>
			<td>Prepare 1 mg/ml poly-D-lysine solution by dissolving 10 mg poly-D-lysine in 10 ml water and dilute 100 times when using.</td>
		</tr>
		<tr>
			<td>Falcon Disposable Petri Dishes, Sterile, Corning, 100x15mm</td>
			<td>VWR</td>
			<td># 25373-100</td>
			<td></td>
		</tr>
		<tr>
			<td>Falcon Disposable Petri Dishes, Sterile, Corning, 150x15mm</td>
			<td>VWR</td>
			<td># 25373-187</td>
			<td></td>
		</tr>
		<tr>
			<td>HBSS, 10X, no Calcium, no Magnesium, no Phenol Red</td>
			<td>Fisher Scientific</td>
			<td># 14185-052</td>
			<td></td>
		</tr>
		<tr>
			<td>Trypan Blue</td>
			<td>Stemcell Technologies</td>
			<td># 07050</td>
			<td></td>
		</tr>
		<tr>
			<td>protease inhibitor cocktail</td>
			<td>Sigma</td>
			<td># 11697498001</td>
			<td></td>
		</tr>
		<tr>
			<td>Software</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>FastQC</td>
			<td>[10] http://www.bioinformatics.babraham.ac.uk/projects/fastqc/</td>
			<td></td>
			<td>Obtaining quality control metrics of raw and trimmed reads</td>
		</tr>
		<tr>
			<td>Trimmomatic 0.33</td>
			<td>[11] http://www.usadellab.org/cms/?page=trimmomatic</td>
			<td></td>
			<td>Trimming and filtering raw reads</td>
		</tr>
		<tr>
			<td>GENCODE mm10</td>
			<td>ftp://ftp.sanger.ac.uk/pub/gencode/Gencode_mouse/release_M15/GRCm38.primary_assembly.genome.fa.gz</td>
			<td></td>
			<td>Mouse reference genome</td>
		</tr>
		<tr>
			<td>Bowtie2 2.2.4</td>
			<td>[12] http://bowtie-bio.sourceforge.net/bowtie2/index.shtml</td>
			<td></td>
			<td>Aligning reads to a reference genome</td>
		</tr>
		<tr>
			<td>SAMTools 1.5</td>
			<td>[13] http://samtools.sourceforge.net/</td>
			<td></td>
			<td>Converting SAM file into BAM format</td>
		</tr>
		<tr>
			<td>HOMER 4.9.1</td>
			<td>[14] http://homer.ucsd.edu/homer/</td>
			<td></td>
			<td>Creating a tag directory and annotating enriched genomic regions with gene symbols</td>
		</tr>
		<tr>
			<td>R 3.2.2</td>
			<td>[15] https://www.R-project.org/</td>
			<td></td>
			<td>Programming scripts and running functions</td>
		</tr>
		<tr>
			<td>SPP 1.13</td>
			<td>[16] https://github.com/hms-dbmi/spp</td>
			<td></td>
			<td>Creating a strand cross-correlation plot</td>
		</tr>
		<tr>
			<td>MACS2 2.2.4</td>
			<td>[17] https://github.com/taoliu/MACS</td>
			<td></td>
			<td>Finding regions of ChIP enrichment over control</td>
		</tr>
		<tr>
			<td>BEDTools 2.25</td>
			<td>[18] http://bedtools.readthedocs.io/en/latest/</td>
			<td></td>
			<td>Genome arithmetics</td>
		</tr>
		<tr>
			<td>bedGraphToBigWig</td>
			<td>http://hgdownload.cse.ucsc.edu/admin/exe/</td>
			<td></td>
			<td>Converting bedGraph file into bigwig</td>
		</tr>
		<tr>
			<td>IGV browser 2.3.58</td>
			<td>[19] http://software.broadinstitute.org/software/igv/</td>
			<td></td>
			<td>Visualization and browsing of significant ChIP-seq peaks</td>
		</tr>
		<tr>
			<td>Microsoft Excel</td>
			<td></td>
			<td></td>
			<td>Spreasheet program</td>
		</tr>
		<tr>
			<td>ENCODE&#39;s blacklist</td>
			<td>https://sites.google.com/site/anshulkundaje/projects/blacklists</td>
			<td></td>
			<td>Filtering peaks</td>
		</tr>
		<tr>
			<td>mm10.chrom.sizes</td>
			<td>http://hgdownload.cse.ucsc.edu/goldenPath/mm10/bigZips/mm10.chrom.sizes.</td>
			<td></td>
			<td>Converting bedGraph file into bigwig</td>
		</tr>
		<tr>
			<td>ENCODE&#39;s motif database</td>
			<td>[25] http://compbio.mit.edu/encode-motifs/</td>
			<td></td>
			<td>Comprehensive motif database required for motif enrichment</td>
		</tr>
		<tr>
			<td>MEME-ChIP</td>
			<td>[20] http://meme-suite.org/index.html</td>
			<td></td>
			<td>Motif enrichment analysis and motif discovery</td>
		</tr>
		<tr>
			<td>Primer names used for qPCR</td>
			<td></td>
			<td></td>
			<td>Primer sequences used for qPCR</td>
		</tr>
		<tr>
			<td>Mbp-F:</td>
			<td></td>
			<td></td>
			<td>CTATAAATCGGCTCACAAGG</td>
		</tr>
		<tr>
			<td>Mbp-R:</td>
			<td></td>
			<td></td>
			<td>AGGCGGTTATATTAAGAAGC</td>
		</tr>
		<tr>
			<td>Iba1-F:</td>
			<td></td>
			<td></td>
			<td>ACTGCCAGCCTAAGACAACC</td>
		</tr>
		<tr>
			<td>Iba1-R:</td>
			<td></td>
			<td></td>
			<td>GCTTTTCCTCCCTGCAAATCC</td>
		</tr>
		<tr>
			<td>Mog-F:</td>
			<td></td>
			<td></td>
			<td>GGCTTCTTGGAGGAAGGGAC</td>
		</tr>
		<tr>
			<td>Mog-R:</td>
			<td></td>
			<td></td>
			<td>TGAATTGTCCTGCATAGCTGC</td>
		</tr>
		<tr>
			<td>GAPDH-F</td>
			<td></td>
			<td></td>
			<td>ATGACATCAAGAAGGTGGTG</td>
		</tr>
		<tr>
			<td>GAPDH-R</td>
			<td></td>
			<td></td>
			<td>CATACCAGGAAATGAGCTTG</td>
		</tr>
		<tr>
			<td>Tuj1-F</td>
			<td></td>
			<td></td>
			<td>TTTTCGTCTCTAGCCGCGTG</td>
		</tr>
		<tr>
			<td>Tuj1-R</td>
			<td></td>
			<td></td>
			<td>GATGACCTCCCAGAACTTGGC</td>
		</tr>
		<tr>
			<td>PDGFR&#945;-F</td>
			<td></td>
			<td></td>
			<td>AGAGTTACACGTTTGAGCTGTC</td>
		</tr>
		<tr>
			<td>PDGFR&#945;-R</td>
			<td></td>
			<td></td>
			<td>GTCCCTCCACGGTACTCCT</td>
		</tr>
	</tbody>
</table>

identifying transcription factor olig2 genomic binding sites in acutely purified pdgfr&#945;+ cells by low-cell chromatin immunoprecipitation sequencing analysis

在哺乳动物细胞中, 基因转录通过转录因子与基因组 DNA 的相互作用来调节细胞类型的特定方式。谱系特异转录因子被认为在细胞规格和分化过程中发挥重要作用。芯片结合高通量 DNA 测序 (芯片序列) 被广泛用于分析基因组范围内转录因子 (或其关联的复合体) 与基因组 DNA 的结合点。然而, 一个标准的芯片反应需要大量的细胞, 这使得研究孤立的原生细胞或稀有细胞群体的数量有限是困难的。为了了解 Olig2 在急性纯化小鼠 OPCs 中少突胶质谱系特异转录因子的调控机制, 本文给出了用芯片序列识别 Olig2 (或 Olig2 复合体) 全基因组结合点的详细方法。首先, 该协议解释了如何从小鼠脑中纯化血小板衍生生长因子受体α (PDGFRα) 阳性 OPCs。其次, 对 Olig2 抗体介导芯片和库结构进行了实验。最后一部分介绍了用于 Olig2 芯片序列分析的生物信息学软件和程序。总之, 本文报道了一种分析急性纯化脑 OPCs 转录因子 Olig2 的全基因组绑定的方法。

采用低细胞芯片序列进行生物信息学分析, 研究转录因子 Olig2 与基因组 DNA 在急性纯化脑 OPCs 中的潜在相互作用。图 1显示了实验和数据分析过程的一般工作流。在这个协议中, 产后老鼠的大脑被分离成单细胞悬浮。组织分离后, 采用 PDGFRα抗体对 OPCs 进行纯化, immunopanning。图 2显示了 PDGFRα的显著丰富性、神经元标记 Tuj1 的小表?...

Watch this Scientific Journal Video about Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFRÃŽÂ±+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysi

Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFRÃƒÆ’Ã…Â½Ãƒâ€šÃ‚Â±+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis

在这里, 我们提出一个协议, 旨在分析少突胶质转录因子 2 (Olig2) 在急性纯化脑少突胶质前体细胞 (OPCs) 的全基因组结合执行低细胞染色质免疫沉淀 (芯片)、库准备、高通量测序和生物信息学数据分析。

低细胞染色质免疫沉淀测序分析 Olig2 基因组结合位点在急性纯化 PDGFRα + 细胞中的转录因子鉴定

In mammalian cells, gene transcription is regulated in a cell type specific manner by the interactions of transcriptional factors with genomic DNA. ...

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Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFR&#945;+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis

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