JQW, XD, RCDD, and YY were supported by grants from the National Institutes of Health R01 NS088353; NIH grant 1R21AR071583-01; the Staman Ogilvie Fund-Memorial Hermann Foundation; the UTHealth BRAIN Initiative and CTSA UL1 TR000371; and a grant from the University of Texas System Neuroscience and Neurotechnology Research Institute (Grant #362469).

<ol>
	<li>Wu, J. Q., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tcf7+is+an+important+regulator+of+the+switch+of+self-renewal+and+differentiation+in+a+multipotential+hematopoietic+cell+line.">Tcf7 is an important regulator of the switch of self-renewal and differentiation in a multipotential hematopoietic cell line.</a> PLoS Genet. 8 (3), e1002565 (2012).</li><li>Zuchero, J. B., Barres, B. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Intrinsic+and+extrinsic+control+of+oligodendrocyte+development.">Intrinsic and extrinsic control of oligodendrocyte development.</a> Curr Opin Neurobiol. 23 (6), 914-920 (2013).</li><li>Liu, Z., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Induction+of+oligodendrocyte+differentiation+by+Olig2+and+Sox10:+evidence+for+reciprocal+interactions+and+dosage-dependent+mechanisms.">Induction of oligodendrocyte differentiation by Olig2 and Sox10: evidence for reciprocal interactions and dosage-dependent mechanisms.</a> Dev Biol. 302 (2), 683-693 (2007).</li><li>Dong, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comprehensive+Identification+of+Long+Non-coding+RNAs+in+Purified+Cell+Types+from+the+Brain+Reveals+Functional+LncRNA+in+OPC+Fate+Determination.">Comprehensive Identification of Long Non-coding RNAs in Purified Cell Types from the Brain Reveals Functional LncRNA in OPC Fate Determination.</a> PLoS Genet. 11 (12), e1005669 (2015).</li><li>Hilgenberg, L. G., Smith, M. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Preparation+of+dissociated+mouse+cortical+neuron+cultures.">Preparation of dissociated mouse cortical neuron cultures.</a> J Vis Exp. (10), e562 (2007).</li><li>Emery, B., Dugas, J. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Purification+of+oligodendrocyte+lineage+cells+from+mouse+cortices+by+immunopanning.">Purification of oligodendrocyte lineage cells from mouse cortices by immunopanning.</a> Cold Spring Harb Protoc. 2013 (9), 854-868 (2013).</li><li>Cahoy, J. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+transcriptome+database+for+astrocytes,+neurons,+and+oligodendrocytes:+a+new+resource+for+understanding+brain+development+and+function.">A transcriptome database for astrocytes, neurons, and oligodendrocytes: a new resource for understanding brain development and function.</a> J Neurosci. 28 (1), 264-278 (2008).</li><li>Zhang, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+RNA-sequencing+transcriptome+and+splicing+database+of+glia,+neurons,+and+vascular+cells+of+the+cerebral+cortex.">An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex.</a> J Neurosci. 34 (36), 11929-11947 (2014).</li><li>Cheng, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Bone+morphogenetic+protein+signaling+and+olig1/2+interact+to+regulate+the+differentiation+and+maturation+of+adult+oligodendrocyte+precursor+cells.">Bone morphogenetic protein signaling and olig1/2 interact to regulate the differentiation and maturation of adult oligodendrocyte precursor cells.</a> Stem Cells. 25 (12), 3204-3214 (2007).</li><li>Bolger, A. M., Lohse, M., Usadel, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Trimmomatic:+a+flexible+trimmer+for+Illumina+sequence+data.">Trimmomatic: a flexible trimmer for Illumina sequence data.</a> Bioinformatics. 30 (15), 2114-2120 (2014).</li><li>Langmead, B., Salzberg, S. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fast+gapped-read+alignment+with+Bowtie+2.">Fast gapped-read alignment with Bowtie 2.</a> Nat Methods. 9 (4), 357-359 (2012).</li><li>Li, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Sequence+Alignment/Map+format+and+SAMtools.">The Sequence Alignment/Map format and SAMtools.</a> Bioinformatics. 25 (16), 2078-2079 (2009).</li><li>Heinz, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Simple+combinations+of+lineage-determining+transcription+factors+prime+cis-regulatory+elements+required+for+macrophage+and+B+cell+identities.">Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities.</a> Mol Cell. 38 (4), 576-589 (2010).</li><li>Team, R. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+language+and+environment+for+statistical+computing.">A language and environment for statistical computing.</a> R Foundation for Statistical Computing. , (2015).</li><li>Kharchenko, P. V., Tolstorukov, M. Y., Park, P. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Design+and+analysis+of+ChIP-seq+experiments+for+DNA-binding+proteins.">Design and analysis of ChIP-seq experiments for DNA-binding proteins.</a> Nat Biotechnol. 26 (12), 1351-1359 (2008).</li><li>Zhang, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Model-based+analysis+of+ChIP-Seq+(MACS).">Model-based analysis of ChIP-Seq (MACS).</a> Genome Biol. 9 (9), R137 (2008).</li><li>Quinlan, A. R., Hall, I. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=BEDTools:+a+flexible+suite+of+utilities+for+comparing+genomic+features.">BEDTools: a flexible suite of utilities for comparing genomic features.</a> Bioinformatics. 26 (6), 841-842 (2010).</li><li>Robinson, J. T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Integrative+genomics+viewer.">Integrative genomics viewer.</a> Nat Biotechnol. 29 (1), 24-26 (2011).</li><li>Bailey, T. L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MEME+SUITE:+tools+for+motif+discovery+and+searching.">MEME SUITE: tools for motif discovery and searching.</a> Nucleic Acids Res. 37 (Web Server issue), W202-W208 (2009).</li><li>Sims, D., Sudbery, I., Ilott, N. E., Heger, A., Ponting, C. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sequencing+depth+and+coverage:+key+considerations+in+genomic+analyses.">Sequencing depth and coverage: key considerations in genomic analyses.</a> Nat Rev Genet. 15 (2), 121-132 (2014).</li><li>Landt, S. G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ChIP-seq+guidelines+and+practices+of+the+ENCODE+and+modENCODE+consortia.">ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia.</a> Genome Res. 22 (9), 1813-1831 (2012).</li><li>Consortium, E. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+integrated+encyclopedia+of+DNA+elements+in+the+human+genome.">An integrated encyclopedia of DNA elements in the human genome.</a> Nature. 489 (7414), 57-74 (2012).</li><li>Mazzoni, E. O., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Embryonic+stem+cell-based+mapping+of+developmental+transcriptional+programs.">Embryonic stem cell-based mapping of developmental transcriptional programs.</a> Nat Methods. 8 (12), 1056-1058 (2011).</li><li>Kheradpour, P., Kellis, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Systematic+discovery+and+characterization+of+regulatory+motifs+in+ENCODE+TF+binding+experiments.">Systematic discovery and characterization of regulatory motifs in ENCODE TF binding experiments.</a> Nucleic Acids Res. 42 (5), 2976-2987 (2014).</li><li>Dugas, J. C., Tai, Y. C., Speed, T. P., Ngai, J., Barres, B. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Functional+genomic+analysis+of+oligodendrocyte+differentiation.">Functional genomic analysis of oligodendrocyte differentiation.</a> J Neurosci. 26 (43), 10967-10983 (2006).</li><li>Gilfillan, G. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Limitations+and+possibilities+of+low+cell+number+ChIP-seq.">Limitations and possibilities of low cell number ChIP-seq.</a> BMC Genomics. 13, 645 (2012).</li><li>Raskatov, J. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Modulation+of+NF-kappaB-dependent+gene+transcription+using+programmable+DNA+minor+groove+binders.">Modulation of NF-kappaB-dependent gene transcription using programmable DNA minor groove binders.</a> Proc Natl Acad Sci U S A. 109 (4), 1023-1028 (2012).</li><li>Cheneby, J., Gheorghe, M., Artufel, M., Mathelier, A., Ballester, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ReMap+2018:+an+updated+atlas+of+regulatory+regions+from+an+integrative+analysis+of+DNA-binding+ChIP-seq+experiments.">ReMap 2018: an updated atlas of regulatory regions from an integrative analysis of DNA-binding ChIP-seq experiments.</a> Nucleic Acids Res. , (2017).</li><li>Yevshin, I., Sharipov, R., Valeev, T., Kel, A., Kolpakov, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=GTRD:+a+database+of+transcription+factor+binding+sites+identified+by+ChIP-seq+experiments.">GTRD: a database of transcription factor binding sites identified by ChIP-seq experiments.</a> Nucleic Acids Res. 45 (D1), D61-D67 (2017).</li><li>Kulakovskiy, I. V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=HOCOMOCO:+a+comprehensive+collection+of+human+transcription+factor+binding+sites+models.">HOCOMOCO: a comprehensive collection of human transcription factor binding sites models.</a> Nucleic Acids Res. 41 (Database issue), D195-D202 (2013).</li><li>Jain, D., Baldi, S., Zabel, A., Straub, T., Becker, P. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Active+promoters+give+rise+to+false+positive+'Phantom+Peaks'+in+ChIP-seq+experiments.">Active promoters give rise to false positive 'Phantom Peaks' in ChIP-seq experiments.</a> Nucleic Acids Res. 43 (14), 6959-6968 (2015).</li><li>Gerstein, M. B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Architecture+of+the+human+regulatory+network+derived+from+ENCODE+data.">Architecture of the human regulatory network derived from ENCODE data.</a> Nature. 489 (7414), 91-100 (2012).</li></ol>

The authors declare that they have no competing financial interests.

Mammalian gene regulation networks are very complex. ChIP-seq is a powerful method to investigate genome-wide protein-DNA interactions. This protocol includes how to perform Olig2 ChIP-seq by using a low number of purified OPCs from mouse brains (as low as 20 thousand cells per reaction). The first key step for this protocol is the purification of OPCs from mouse brains by immunopanning with PDGFR&#945; antibody. For positive selection of OPCs with PDGFR&#945; coated plates, OPCs often weakly bind to the PDGFR&#945; coat...

It is important to study the protein (or protein complex) DNA bindings and the epigenetic marks to build transcriptional regulatory networks involved in various biological processes. Particularly, the bindings of transcription factors to the genomic DNA can play an important role in the gene regulation, the cell differentiation, and the tissue development. A powerful tool for studying the transcriptional regulation and epigenetic mechanisms is chromatin immunoprecipitation (ChIP). Owing to the rapid advancements in the next generation sequencing technology, ChIP coupled with high-throughput DNA sequencing (ChIP-seq) is used for the analyses of protein-DNA bindings and epigenetic marks1. However, a standard ChIP-seq protocol requires about 20 million cells per reaction, which makes the application of this technique difficult when the cell number is limited, such as isolated primary cells and rare cell populations.
Oligodendrocyte lineage cells including oligodendrocyte precursor cells (OPCs) and oligodendrocytes are widely distributed throughout the brain and are essential for the development and function of the brain. As a type of precursor cells, OPCs are capable of both self-renewal and differentiation. OPCs not only serve as progenitors for oligodendrocytes but also play an important role in the propagation of neuronal signaling by communicating with other types of brain cells2. Previous studies have suggested that oligodendrocyte development is regulated by lineage-specific transcription factors such as Olig2 and Sox103,4. These transcription factors were found to bind to the promoter or enhancer regions of some crucial genes to influence their expression during oligodendrocyte specification and differentiation. However, it is challenging to identify DNA binding of protein (or protein complex) of interest in acutely purified primary OPCs with a very limited number of cells.
This protocol describes how to systematically investigate the genomic DNA immunoprecipitated by Olig2 in purified mouse OPCs at genome-wide scale using ChIP-seq technique. OPCs from mouse brains were acutely purified by immunopanning and used in a ChIP experiment without proliferation in vitro. A limited number of OPCs can be obtained by immunopanning and is insufficient for standard ChIP-seq experiments. Herein, a low-cell ChIP-seq protocol with as low as 20 thousand cells per ChIP reaction for transcription factors is described. In brief, cross-linked cells were lysed and sonicated by a sonication device to shear the chromatin. The sheared chromatin was incubated with Olig2 antibody as well as protein A-coated beads to precipitate Olig2 antibody bound genomic DNA. After elution from protein A-coated beads and reverse cross-linking, genomic DNA precipitated by Olig2 antibody was purified by phenol-chloroform extraction. The resulting product was quantified and subjected to T-tailing, primer annealing template switching and extension, the addition of adapters and amplification, library size selection and purification steps for ChIP-seq library construction.
After sequencing, the quality of the raw reads from both the sample prepared with Olig2 transcription factor antibody and the control sample was analyzed. Low-quality base pairs and adapter containing read fragments were trimmed. Next, trimmed reads were aligned to the mouse reference genome. The genomic regions that were significantly enriched for ChIP reads, compared to the control sample, were detected as peaks. Significant peaks, representing potential transcription factor binding sites, were filtered and visualized in a genome browser.
Notably, the method described in this protocol can be broadly used for ChIP-seq of other transcription factors with any cell type of limited numbers.

<table><tbody><tr><td>&lt;strong&gt;Reagent/ Equipment&lt;/strong&gt;</td><td></td><td></td><td></td></tr><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 &amp;mu;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>&lt;strong&gt;Software&lt;/strong&gt;</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&#039;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&#039;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>&lt;strong&gt;Primer names used for qPCR&lt;/strong&gt;</td><td></td><td></td><td>&lt;strong&gt;Primer sequences used for qPCR&lt;/strong&gt;</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&amp;alpha;-F</td><td></td><td></td><td>AGAGTTACACGTTTGAGCTGTC</td></tr><tr><td>PDGFR&amp;alpha;-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

In mammalian cells, gene transcription is regulated in a cell type specific manner by the interactions of transcriptional factors with genomic DNA. Lineage-specific transcription factors are considered to play essential roles in cell specification and differentiation during development. ChIP coupled with high-throughput DNA sequencing (ChIP-seq) is widely used to analyze genome-wide binding sites of transcription factors (or its associated complex) to genomic DNA. However, a large number of cells are required for one standard ChIP reaction, which makes it difficult to study the limited number of isolated primary cells or rare cell populations. In order to understand the regulatory mechanism of oligodendrocyte lineage-specific transcription factor Olig2 in acutely purified mouse OPCs, a detailed method using ChIP-seq to identify the genome-wide binding sites of Olig2 (or Olig2 complex) is shown. First, the protocol explains how to purify the platelet-derived growth factor receptor alpha (PDGFR&#945;) positive OPCs from mouse brains. Next, Olig2 antibody mediated ChIP and library construction are performed. The last part describes the bioinformatic software and procedures used for Olig2 ChIP-seq analysis. In summary, this paper reports a method to analyze the genome-wide bindings of transcriptional factor Olig2 in acutely purified brain OPCs.

Low-cell ChIP-seq was performed and bioinformatic analyses were done to investigate the potential interactions of transcriptional factor Olig2 with genomic DNA in acutely purified brain OPCs. Figure 1 shows a general workflow of both the experimental and the data analysis procedures. In this protocol, postnatal mice brains were dissociated into a single-cell suspension. After tissue dissociation, immunopanning was performed to purify OPCs by using PDGFR&#945;...

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

Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFRα+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis | Neuroscience | Video

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

Here we present a protocol which is designed to analyze the genome-wide binding of the oligodendrocyte transcription factor 2 (Olig2) in acutely purified brain oligodendrocyte precursor cells (OPCs) by performing low-cell chromatin immunoprecipitation (ChIP), library preparation, high-throughput sequencing and bioinformatic data analysis.

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

identifying-transcription-factor-olig2-genomic-binding-sites-acutely

Education

Research

JoVE Core

JoVE Journal

Molecular Biology

Biology

Neuroscience

Genetics

Unit 1

DNA Structure and Function

Gene Expression

SCIENTISTS IN ACTION

9.2K Views.  University of Texas Health Science Center. Hier presenteren we een protocol dat is ontworpen voor het analyseren van de genoom-brede binding van de transcriptiefactor Oligodendrocyt 2 (Olig2) in acuut gezuiverde oligodendrocyte voorloper hersencellen (OPCs) door het uitvoeren van lage-cel chromatine immunoprecipitation (ChIP) , bibliotheek voorbereiding, high-throughput sequencing en bioinformatic data-analyse.

Video: Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFRα+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis

Genome-wide Snapshot of Chromatin Regulators and States in Xenopus Embryos by ChIP-Seq

The recruitment of chromatin regulators and the assignment of chromatin states to specific genomic loci are pivotal to cell fate decisions and tissue and organ formation during development. Determining the locations and levels of such chromatin features in vivo will provide valuable information about the spatio-temporal regulation of genomic elements, and will support aspirations to mimic embryonic tissue development in vitro. The most commonly used method for genome-wide and high-resolution profiling is chromatin immunoprecipitation followed by next-generation sequencing (ChIP-Seq). This protocol outlines how yolk-rich embryos such as those of the frog Xenopus can be processed for ChIP-Seq experiments, and it offers simple command lines for post-sequencing analysis. Because of the high efficiency with which the protocol extracts nuclei from formaldehyde-fixed tissue, the method allows easy upscaling to obtain enough ChIP material for genome-wide profiling. Our protocol has been used successfully to map various DNA-binding proteins such as transcription factors, signaling mediators, components of the transcription machinery, chromatin modifiers and post-translational histone modifications, and for this to be done at various stages of embryogenesis. Lastly, this protocol should be widely applicable to other model and non-model organisms as more and more genome assemblies become available.

Genome-wide Snapshot of Chromatin Regulators and States in Xenopus Embryos by ChIP-Seq

The question of how chromatin regulators and chromatin states affect the genome in vivo is key to our understanding of how early cell fate decisions are made in the developing embryo. ChIP-Seq&#8212;the most popular approach to investigate chromatin features at a global level&#8212;is outlined here for Xenopus embryos.

The recruitment of chromatin regulators and the assignment of chromatin states to specific genomic loci are pivotal to cell fate decisions and tissue and ...

Developmental Biology

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Upon activation, cells rapidly change their functional programs and, thereby, their gene expression profile. Massive changes in gene expression occur, for example, during cellular differentiation, morphogenesis, and functional stimulation (such as activation of immune cells), or after exposure to drugs and other factors from the local environment. Depending on the stimulus and cell type, these changes occur rapidly and at any possible level of gene regulation. Displaying all molecular processes of a responding cell to a certain type of stimulus/drug is one of the hardest tasks in molecular biology. Here, we describe a protocol that enables the simultaneous analysis of multiple layers of gene regulation. We compare, in particular, transcription factor binding (Chromatin-immunoprecipitation-sequencing (ChIP-seq)), de novo transcription (4-thiouridine-sequencing (4sU-seq)), mRNA processing, and turnover as well as translation (ribosome profiling). By combining these methods, it is possible to display a detailed and genome-wide course of action.
Sequencing newly transcribed RNA is especially recommended when analyzing rapidly adapting or changing systems, since this depicts the transcriptional activity of all genes during the time of 4sU exposure (irrespective of whether they are up- or downregulated). The combinatorial use of total RNA-seq and ribosome profiling additionally allows the calculation of RNA turnover and translation rates. Bioinformatic analysis of high-throughput sequencing results allows for many means for analysis and interpretation of the data. The generated data also enables tracking co-transcriptional and alternative splicing, just to mention a few possible outcomes.
The combined approach described here can be applied for different model organisms or cell types, including primary cells. Furthermore, we provide detailed protocols for each method used, including quality controls, and discuss potential problems and pitfalls.

This protocol describes the combinatorial use of ChIP-seq, 4sU-seq, total RNA-seq, and ribosome profiling for cell lines and primary cells. It enables tracking changes in transcription-factor binding, de novo transcription, RNA processing, turnover and translation over time, and displaying the overall course of events in activated and/or rapidly changing cells.

Upon activation, cells rapidly change their functional programs and, thereby, their gene expression profile. Massive changes in gene expression occur, for ...

A Chromatin Immunoprecipitation Assay to Identify Novel NFAT2 Target Genes in Chronic Lymphocytic Leukemia

Chronic lymphocytic leukemia (CLL) is characterized by the expansion of malignant B cell clones and represents the most common leukemia in western countries. The majority of CLL patients show an indolent course of the disease as well as an anergic phenotype of their leukemia cells, referring to a B cell receptor unresponsive to external stimulation. We have recently shown that the transcription factor NFAT2 is a crucial regulator of anergy in CLL. A major challenge in the analysis of the role of a transcription factor in different diseases is the identification of its specific target genes. This is of&#160;great significance for the elucidation of pathogenetic mechanisms and potential therapeutic interventions. Chromatin immunoprecipitation (ChIP) is a classic technique to demonstrate protein-DNA interactions and can, therefore, be used to identify direct target genes of transcription factors in mammalian cells. Here, ChIP was used to identify LCK as a direct target gene of NFAT2 in human CLL cells. DNA and associated proteins are crosslinked using formaldehyde and subsequently sheared by sonication into DNA fragments of approximately 200-500 base pairs (bp). Cross-linked DNA fragments associated with NFAT2 are then selectively immunoprecipitated from cell debris using an &#945;NFAT2 antibody. After purification, associated DNA fragments are detected via quantitative real-time PCR (qRT-PCR). DNA sequences with evident enrichment represent regions of the genome which are targeted by NFAT2 in vivo. Appropriate shearing of the DNA and the selection of the required antibody are particularly crucial for the successful application of this method. This protocol is ideal for the demonstration of direct interactions of NFAT2 with target genes. Its major limitation is the difficulty to employ ChIP in large-scale assays analyzing the target genes of multiple transcription factors in intact organisms.

Chronic lymphocytic leukemia (CLL) is the most common leukemia in the western world. NFAT transcription factors are important regulators of development and activation in numerous cell types. Here, we present a protocol for the use of chromatin immunoprecipitation (ChIP) in human CLL cells to identify novel target genes of NFAT2.

Chronic lymphocytic leukemia (CLL) is characterized by the expansion of malignant B cell clones and represents the most common leukemia in western ...

Cancer Research

Discovering CsgD Regulatory Targets in Salmonella Biofilm Using Chromatin Immunoprecipitation and High-Throughput Sequencing (ChIP-seq)

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is a technique that can be used to discover the regulatory targets of transcription factors, histone modifications, and other DNA-associated proteins. ChIP-seq data can also be used to find differential binding of transcription factors in different environmental conditions or cell types. Initially, ChIP was performed through hybridization on a microarray (ChIP-chip); however, ChIP-seq has become the preferred method through technological advancements, decreasing financial barriers to sequencing, and massive amounts of high-quality data output. Techniques of performing ChIP-seq with bacterial biofilms, a major source of persistent and chronic infections, are described in this protocol. ChIP-seq is performed on Salmonella enterica serovar Typhimurium biofilm and planktonic cells, targeting the master biofilm regulator, CsgD, to determine differential binding in the two cell types. Here, we demonstrate the appropriate amount of biofilm to harvest, normalizing to a planktonic control sample, homogenizing biofilm for cross-linker access, and performing routine ChIP-seq steps to obtain high quality sequencing results.

Discovering CsgD Regulatory Targets in Salmonella Biofilm Using Chromatin Immunoprecipitation and High-Throughput Sequencing ChIP-seq

Chromatin immunoprecipitation coupled with next-generation sequencing (ChIP-seq) is a method used to establish interactions between transcription factors and the genomic sequences they control. This protocol outlines techniques for performing ChIP-seq with bacterial biofilms, using Salmonella enterica serovar Typhimurium bacterial biofilm as an example.

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is a technique that can be used to discover the regulatory targets of transcription ...

Genome-wide Analysis using ChIP to Identify Isoform-specific Gene Targets

Recruitment of transcriptional and epigenetic factors to their targets is a key step in their regulation. Prominently featured in recruitment are the protein domains that bind to specific histone modifications. One such domain is the plant homeodomain (PHD), found in several chromatin-binding proteins. The epigenetic factor RBP2 has multiple PHD domains, however, they have different functions (Figure 4). In particular, the C-terminal PHD domain, found in a RBP2 oncogenic fusion in human leukemia, binds to trimethylated lysine 4 in histone H3 (H3K4me3)1. The transcript corresponding to the RBP2 isoform containing the C-terminal PHD accumulates during differentiation of promonocytic, lymphoma-derived, U937 cells into monocytes2. Consistent with both sets of data, genome-wide analysis showed that in differentiated U937 cells, the RBP2 protein gets localized to genomic regions highly enriched for H3K4me33. Localization of RBP2 to its targets correlates with a decrease in H3K4me3 due to RBP2 histone demethylase activity and a decrease in transcriptional activity. In contrast, two other PHDs of RBP2 are unable to bind H3K4me3. Notably, the C-terminal domain PHD of RBP2 is absent in the smaller RBP2 isoform4. It is conceivable that the small isoform of RBP2, which lacks interaction with H3K4me3, differs from the larger isoform in genomic location. The difference in genomic location of RBP2 isoforms may account for the observed diversity in RBP2 function. Specifically, RBP2 is a critical player in cellular differentiation mediated by the retinoblastoma protein (pRB). Consistent with these data, previous genome-wide analysis, without distinction between isoforms, identified two distinct groups of RBP2 target genes: 1) genes bound by RBP2 in a manner that is independent of differentiation; 2) genes bound by RBP2 in a differentiation-dependent manner.
To identify differences in localization between the isoforms we performed genome-wide location analysis by ChIP-Seq. Using antibodies that detect both RBP2 isoforms we have located all RBP2 targets. Additionally we have antibodies that only bind large, and not small RBP2 isoform (Figure 4). After identifying the large isoform targets, one can then subtract them from all RBP2 targets to reveal the targets of small isoform. These data show the contribution of chromatin-interacting domain in protein recruitment to its binding sites in the genome.

Here we are presenting a chromatin immunoprecipitation (ChIP) procedure for genome-wide location analysis of protein isoforms that differ in a histone-binding domain. We are applying it to ChIP-Seq analysis to identify the targets of the KDM5A/JARID1A/RBP2 histone demethylase.

Recruitment of transcriptional and epigenetic factors to their targets is a key step in their regulation. Prominently featured in recruitment are the ...

Repressing Gene Transcription by Redirecting Cellular Machinery with Chemical Epigenetic Modifiers

Regulation of chromatin compaction is an important process that governs gene expression in higher eukaryotes. Although chromatin compaction and gene expression regulation are commonly disrupted in many diseases, a locus-specific, endogenous, and reversible method to study and control these mechanisms of action has been lacking. To address this issue, we have developed and characterized novel gene-regulating bifunctional molecules. One component of the bifunctional molecule binds to a DNA-protein anchor so that it will be recruited to an allele-specific locus. The other component engages endogenous cellular chromatin-modifying machinery, recruiting these proteins to a gene of interest. These small molecules, called chemical epigenetic modifiers (CEMs), are capable of controlling gene expression and the chromatin environment in a dose-dependent and reversible manner. Here, we detail a CEM approach and its application to decrease gene expression and histone tail acetylation at a Green Fluorescent Protein (GFP) reporter located at the Oct4 locus in mouse embryonic stem cells (mESCs). We characterize the lead CEM (CEM23) using fluorescent microscopy, flow cytometry, and chromatin immunoprecipitation (ChIP), followed by a quantitative polymerase chain reaction (qPCR). While the power of this system is demonstrated at the Oct4 locus, conceptually, the CEM technology is modular and can be applied in other cell types and at other genomic loci.

Regulation of the chromatin environment is an essential process required for proper gene expression. Here, we describe a method for controlling gene expression through the recruitment of chromatin-modifying machinery in a gene-specific and reversible manner.

Regulation of chromatin compaction is an important process that governs gene expression in higher eukaryotes. Although chromatin compaction and gene ...

Bioengineering

Chromatin Isolation by RNA Purification (ChIRP)

Long noncoding RNAs are key regulators of chromatin states for important biological processes such as dosage compensation, imprinting, and developmental gene expression 1,2,3,4,5,6,7. The recent discovery of thousands of lncRNAs in association with specific chromatin modification complexes, such as Polycomb Repressive Complex 2 (PRC2) that mediates histone H3 lysine 27 trimethylation (H3K27me3), suggests broad roles for numerous lncRNAs in managing chromatin states in a gene-specific fashion 8,9. While some lncRNAs are thought to work in cis on neighboring genes, other lncRNAs work in trans to regulate distantly located genes. For instance, Drosophila lncRNAs roX1 and roX2 bind numerous regions on the X chromosome of male cells, and are critical for dosage compensation 10,11. However, the exact locations of their binding sites are not known at high resolution. Similarly, human lncRNA HOTAIR can affect PRC2 occupancy on hundreds of genes genome-wide 3,12,13, but how specificity is achieved is unclear. LncRNAs can also serve as modular scaffolds to recruit the assembly of multiple protein complexes. The classic trans-acting RNA scaffold is the TERC RNA that serves as the template and scaffold for the telomerase complex 14; HOTAIR can also serve as a scaffold for PRC2 and a H3K4 demethylase complex 13.
Prior studies mapping RNA occupancy at chromatin have revealed substantial insights 15,16, but only at a single gene locus at a time. The occupancy sites of most lncRNAs are not known, and the roles of lncRNAs in chromatin regulation have been mostly inferred from the indirect effects of lncRNA perturbation. Just as chromatin immunoprecipitation followed by microarray or deep sequencing (ChIP-chip or ChIP-seq, respectively) has greatly improved our understanding of protein-DNA interactions on a genomic scale, here we illustrate a recently published strategy to map long RNA occupancy genome-wide at high resolution 17. This method, Chromatin Isolation by RNA Purification (ChIRP) (Figure 1), is based on affinity capture of target lncRNA:chromatin complex by tiling antisense-oligos, which then generates a map of genomic binding sites at a resolution of several hundred bases with high sensitivity and low background. ChIRP is applicable to many lncRNAs because the design of affinity-probes is straightforward given the RNA sequence and requires no knowledge of the RNA's structure or functional domains.

Chromatin Isolation by RNA Purification ChIRP

ChIRP is a novel and rapid technique to map genomic binding sites of long noncoding RNAs (lncRNAs). The method takes advantage of the specificity of anti-sense tiling oligonucleotides to allow the enumeration of lncRNA-bound genomic sites.

Long noncoding RNAs are key regulators of chromatin states for important biological processes such as dosage compensation, imprinting, and developmental ...

Efficient Chromatin Immunoprecipitation using Limiting Amounts of Biomass

Chromatin immunoprecipitation (ChIP) is a widely-used method for determining the interactions of different proteins with DNA in chromatin of living cells. Examples include sequence-specific DNA binding transcription factors, histones and their different modification states, enzymes such as RNA polymerases and ancillary factors, and DNA repair components. Despite its ubiquity, there is a lack of up-to-date, detailed methodologies for both bench preparation of material and for accurate analysis allowing quantitative metrics of interaction. Due to this lack of information, and also because, like any immunoprecipitation, conditions must be re-optimized for new sets of experimental conditions, the ChIP assay is susceptible to inaccurate or poorly quantitative results.
Our protocol is ultimately derived from seminal work on transcription factor:DNA interactions1,2 , but incorporates a number of improvements to sensitivity and reproducibility for difficult-to-obtain cell types. The protocol has been used successfully3,4 , both using qPCR to quantify DNA enrichment, or using a semi-quantitative variant of the below protocol.
This quantitative analysis of PCR-amplified material is performed computationally, and represents a limiting factor in the assay. Important controls and other considerations include the use of an isotype-matched antibody, as well as evaluation of a control region of genomic DNA, such as an intergenic region predicted not to be bound by the protein under study (or anticipated not to show changes under the experimental conditions). In addition, a standard curve of input material for every ChIP sample is used to derive absolute levels of enrichment in the experimental material. Use of standard curves helps to take into account differences between primer sets, regardless of how carefully they are designed, and also efficiency differences throughout the range of template concentrations for a single primer set. Our protocol is different from others that are available5-8 in that we extensively cover the later, analysis phase.

We describe a robust method for chromatin immunoprecipitation using primary T cells. The method is founded on standard approaches, but uses a specific set of conditions and reagents that improve efficiency for limited a quantities of cells. Importantly, a detailed description of the data analysis phase is presented.

Chromatin immunoprecipitation (ChIP) is a  widely-used method for determining the interactions of different proteins with  DNA in chromatin of living ...

Generation of High Quality Chromatin Immunoprecipitation DNA Template for High-throughput Sequencing (ChIP-seq)

ChIP-sequencing (ChIP-seq) methods directly offer whole-genome coverage, where combining chromatin immunoprecipitation (ChIP) and massively parallel sequencing can be utilized to identify the repertoire of mammalian DNA sequences bound by transcription factors in vivo. "Next-generation" genome sequencing technologies provide 1-2 orders of magnitude increase in the amount of sequence that can be cost-effectively generated over older technologies thus allowing for ChIP-seq methods to directly provide whole-genome coverage for effective profiling of mammalian protein-DNA interactions.
For successful ChIP-seq approaches, one must generate high quality ChIP DNA template to obtain the best sequencing outcomes. The description is based around experience with the protein product of the gene most strongly implicated in the pathogenesis of type 2 diabetes, namely the transcription factor transcription factor 7-like 2 (TCF7L2). This factor has also been implicated in various cancers.
Outlined is how to generate high quality ChIP DNA template derived from the colorectal carcinoma cell line, HCT116, in order to build a high-resolution map through sequencing to determine the genes bound by TCF7L2, giving further insight in to its key role in the pathogenesis of complex traits.

Generation of High Quality Chromatin Immunoprecipitation DNA Template for High-throughput Sequencing ChIP-seq

The combination of chromatin immunoprecipitation and ultra-high-throughput sequencing (ChIP-seq) can identify and map protein-DNA interactions in a given tissue or cell line. Outlined is how to generate a high quality ChIP template for subsequent sequencing, using experience with the transcription factor TCF7L2 as an example.

ChIP-sequencing (ChIP-seq) methods directly  offer whole-genome coverage, where combining chromatin immunoprecipitation  (ChIP) and massively parallel ...

Chromatin Immunoprecipitation to Identify Target Protein Binding Sites on Genomic DNA

Source: Dong, X., et.al., <a href="https://app.jove.com/v/57547">Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFR&#945;+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis.</a>&#160;J. Vis. Exp.&#160;(2018)
In this video, we demonstrate the chromatin immunoprecipitation technique to identify protein binding sites on specific regions of the genomic DNA of oligodendrocyte precursor cells via the selective immunoprecipitation of protein-bound chromatin fragments. This method helps to study the interaction of several regulatory proteins, including transcription factors involved in gene regulation.

Source: Dong, X., et.al., Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFR&#945;+ Cells by Low-cell Chromatin ...

Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFRα+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis

Samenvatting

Meer video's verkennen