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

Summary

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.

Abstract

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 (PDGFRα) 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.

Introduction

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 Sox10^3,4. 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.

Protocol

All animal usage and experimental protocols were performed in accordance with the Guide for the Care and Use of Laboratory Animals and approved by the Institutional Biosafety Committee and Animal Welfare Committee at the University of Texas Health Science Center at Houston.

1. Purification of PDGFRα Positive Oligodendrocyte Lineage Cells from Mouse Brain (modified from previously described immunopanning protocols^5,6,7)

1. Preparation of an immunopanning plate for PDGFRα positive cell selection and 2 plates for depletion of endothelial cells and microglia.
   NOTE: Please note that Petri dishes but not cell culture dishes work for immunopanning experiment; if the purified cells will be used for culturing, immunopanning steps must be carried out in the biosafety cabinet
   1. Coat a 10 cm Petri plate with 30 µL goat anti-rat IgG in 10 mL of pH 9.5, 50 mM Tris-HCl overnight at 4 °C. Agitate the plate to make sure the surfaces of the plates are evenly and entirely covered by the coating solution.
   2. Prepare PDGFRα antibody solution by diluting 40 µL of rat anti-PDGFRα antibody with 12 mL of Dulbecco's phosphate-buffered saline (DPBS) containing 0.2% BSA.
   3. After 3 washes of IgG coated plate with 10 mL 1x DPBS each, incubate IgG-coated plate with PDGFRα antibody solution at room temperature for 4 h.
   4. Wash the rat anti-PDGFRα antibody coated plate 3 times with 10 mL 1x DPBS each. Gently add DPBS solution along the side wall of the plate and do not disturb the coated surfaces.
   5. Coat 2 new 15 cm Petri plates for depletion of endothelial cells and microglia with 20 mL of DPBS containing 2.3 µg/mL of Banderiaea simplicifolia lectin 1 (BSL-1) for 2 h.
   6. Wash the BSL-1 coated plates 3 times with 20 mL 1x DPBS. Gently add DPBS solution along the side wall of the plates and do not disturb the coated surfaces.
2. Purification of PDGFRα positive oligodendrocyte lineage cells is modified from previously published methods^6,7,8.
   1. Dissect the cortical tissues from 2 postnatal day 7 (P7) mouse brains according to previously published protocols^5,6.
   2. Dissociate the tissues to generate a single-cell suspension with neural tissue dissociation Kit (P) according to detailed manufacturer's instructions.
   3. Briefly, cut the dissected cortical tissues into pieces with a scalpel and subject them to enzymatic digestion at 37 °C. After digestion, manually dissociate the pieces with Fire-polished glass Pasteur pipettes into a single-cell suspension.
   4. Centrifuge the single-cell suspension at 300 x g for 10 min at room temperature and suspend cell pellet using 15 mL of immunopanning buffer (immunopanning buffer is DPBS with 0.02% BSA and 5 µg/mL insulin).
   5. Incubate the single-cell suspension from 2 mouse brains sequentially on 2 BSL-1 coated plates for 15 min at room temperature with gentle agitation of the plate every 5 min to ensure a better depletion of microglia and endothelial cells.
   6. Gently swirl the plate to collect the non-adherent cells in the cell suspension and incubate them on the rat-PDGFRα antibody-coated plate for 45 min at room temperature.
   7. After the incubation of the cell suspension on the rat-PDGFRα antibody-coated plate, gently swirl the plate to collect the cell suspension, and rinse the plate 8 times with DPBS to get rid of non-adherent cells. Gently add wash solution along the side wall of the plate and agitate the plate several times to get rid of non-adherent cells.
   8. Detach the cells from the rat-PDGFRα antibody coated plate using a 4 mL of cell detachment solution treatment for 10 min at 37 °C. Shake the plate to dislodge adherent cells.
   9. Collect the purified OPCs by centrifugation at 300 x g at room temperature, suspend the cell pellet with 2 mL OPC cell culture medium and count the cells by using trypan blue and a hemocytometer (500 mL cell culture medium is DMEM/F12 medium containing 5 mL penicillin-streptomycin solution (P/S), 5 mL N2, 10 mL B27, 5 µg/mL insulin, 0.1% BSA, 20 ng/mL bFGF and 10 ng/mL PDGFRα).
3. Validation of the purity of OPCs after immunopanning.
   1. In order to evaluate the enrichment of OPCs after immunopanning, use some of the purified OPCs for RNA extraction with a guanidium thiocyanate based extraction according to manufacturer's instructions.
   2. Perform qRT-PCR by using fluorescent green dye master mix to check for the enrichment of PDGFRα expression in purified OPCs as compared with dissociated brain cells according to the previously published materials⁴.
   3. Additionally, seed some purified OPCs into the Poly-D-Lysine coated 24 well plates for immunostaining with anti-NG2 chondroitin sulfate proteoglycan (NG2) antibody as previously published materials⁹.

2. Low-cell ChIP Preparation and ChIP Library Construction for High-Throughput Sequencing

1. Olig2 Low-cell ChIP preparation with a commercially available sonication system and a commercially available low cell number ChIP kit (see Table of Materials) by following the detailed standard procedures from manufacturer's instructions.
   1. After detaching from the rat anti-PDGFRα antibody-coated plate and the cell counting with trypan blue and a hemocytometer put 20,000 purified OPCs in 1 mL OPC cell culture medium for each ChIP reaction.
   2. Add 27 µL of 36.5% formaldehyde to fix cell suspension for 10 min at room temperature.
      Caution: Please note that formaldehyde must be used in the chemical fume hood for safety reasons.
   3. Stop DNA-protein cross-linking with 50 µL 2.5 M glycine for 5 min at room temperature.
      NOTE: All steps must be carried out on ice or in 4 °C cold room from this point.
   4. Wash the cross-linked cell pellets with 1 mL of ice-cold Hanks' Balanced Salt Solution (HBSS) with protease inhibitor cocktail and get cells pelleted by a pre-cooled centrifuge at 300 x g at 4 °C.
   5. Lyse the cell pellet in 25 µL complete Lysis Buffer for 5 min on ice.
      NOTE: Agitate the tube to suspend the cells in Lysis Buffer.
   6. Supplement the cell lysate with 75 µL ice-cold HBSS containing protease inhibitor cocktail and shear the chromatin of cell lysate by pre-cooled sonication system with 5 cycles of 30 s ON and 30 s OFF program. Always sonicate 6 tubes together and make sure the balance tubes contain 100 µL water.
   7. After shearing, centrifuge at 14,000 x g for 10 min 4 °C and collect supernatant in a new tube after centrifugation.
   8. Dilute 100 µL of sheared chromatin with the equal volume of ice-cold complete ChIP Buffer with the protease inhibitor.
   9. Save 20 µL of the diluted sheared chromatin as Input control sample.
      NOTE: Input control sample is also required as the comparison to Olig2 immunoprecipitated sample to identify Olig2 antibody immunoprecipitated DNA.
   10. Add 1 µL of rabbit anti-olig2 antibody to 180 µL of the diluted sheared chromatin and incubate the reaction tube on a rotating wheel at 40 rpm for 16 h at 4 °C.
       NOTE: Perform this step in a cold room.
   11. For each ChIP reaction, wash 11 µL magnetic protein A-coated beads with 55 µL Beads Wash Buffer and put the bead pellet in 11 µL of bead wash buffer after 2 washes.
       NOTE: Perform this step in a cold room.
   12. For each ChIP reaction, add 10 µL of pre-washed Protein A-coated beads to ChIP reaction tube. Perform this step in a cold room.
   13. Incubate the ChIP reaction tube at 4 °C for another 2 h on a rotating wheel.
   14. Put the ChIP reaction tube on the magnetic rack for 1 min.
       NOTE: Perform this step in a cold room.
   15. Remove the supernatant and don't dislodge the bead pellet.
   16. Wash the bead pellet with 100 µL for each of 4 wash buffers respectively for 4 min on a rotating wheel at 4 °C.
   17. After washes, add 200 µL of elution buffer to the bead pellet and incubate the ChIP reaction tube at 65 °C for 4 h to reverse cross-link protein-DNA.
   18. Additionally, add 180 µL of elution buffer to 20 µL Input control sample and incubate at 65 °C for 4 h to reverse cross-link protein-DNA as well.
   19. Add 200 µL 25:24:1 (v/v) mixture of phenol, chloroform, and isoamyl alcohol to each ChIP reaction tube.
   20. Vortex vigorously for 1 min and centrifuge at 13,000 x g for 15 min at room temperature. Transfer the upper phase to a new tube.
   21. Add 40 µL 3 M sodium acetate solution, 1,000 µL of 100% ethanol and 2 µL of glycogen precipitant covalently linked to a blue dye to each ChIP reaction tube overnight at -20 °C.
   22. Centrifuge at 13,000 x g for 20 min at 4 °C.
   23. Discard the supernatant, wash with 500 µL cold 70% ethanol and centrifuge at 13,000 x g for 20 min at 4 °C.
   24. Discard the supernatant, keep the pellet air dry for 10 min and dissolve the pellet with 50 µL water.
2. ChIP library construction for high-throughput sequencing
   1. Clean and concentrate the ChIP DNA with a clean-up kit according to the manufacturers' instructions.
   2. Quantify the ChIP sample by a pico-green according to the manufacturer's instructions.
   3. Use a ChIP-seq kit for T-tailing, replication and tailing, template switching and extension, the addition of adapters and amplification, library size selection and purification according to the manufacturer's instructions.
      1. After denaturation of dsDNA, dephosphorylate the 3' end of ssDNA by shrimp alkaline phosphatase and add a poly (T) tail to the ssDNA by terminal deoxynucleotidyl transferase.
      2. Anneal the DNA Poly (dA) Primer to the ssDNA template for the DNA replication and template switching.
      3. After template switching, amplify the ChIP-seq library by PCR with the forward and reverse primers for indexing.
      4. Select the PCR amplified ChIP-seq library with fragments ranging from 250 to 500 bp by paramagnetic beads by option 2 for double size selection.
      5. Examine the quality of the selected ChIP-seq library using a microfluidic chip-capillary electrophoresis device.

3. Data Analysis

1. Prepare directory structure.
   1. Create a directory for performing the ChIP-seq data analysis. Inside the newly created directory, create six subdirectories with the following names: raw.files, fastqc.output, bowtie.output, homer.output, macs2.output, motif.analysis, and reports.
2. Prepare data and perform quality control analysis of raw sequence data.
   1. Move to the "raw.files" directory and download the ChIP-seq data files.
   2. Verify the filenames. If the sequencing was paired-end, there will be 4 files (two for treatment and two for input) with similar base names: PDGFRα_Olig2.read1.fastq, PDGFRα_Olig2.read2.fastq, PDGFRα_input.read1.fastq, and PDGFRα_input.read2.fastq. If the filenames are different, rename them using the "mv" command.
   3. Move to the "fastqc.output" directory.
   4. Obtain quality control metrics from the raw reads using a fastq file quality control software¹⁰. Use the command in Figure S1A to run the quality control process separately for each fastq file. The software will display several quality control metrics in an html file.
   5. Open the html quality control file and verify the number of reads, read length, per base sequence quality, per sequence quality scores, sequence GC content, sequence duplication levels, adapter content and kmer content.
3. Trim trailing low-quality reads and adapter content.
   1. Observe the "Per base sequence quality" and "Adapter Content" plots. Determine the trimming length for both the head and tail of each read. An adequate length for trimming is where the base quality falls below 30 and there is evidence of adapter content.
   2. Move to the "raw.files" directory.
   3. Download and install a software for trimming reads¹¹. Use the command in Figure S1B for trimming both the treatment and the input separately. Parameter 'CROP' indicates the remaining read length after trimming bases from the end and 'HEADCROP' specifies the number of bases to be eliminated from the start of the read.
   4. The trimming command also includes the minimum read length to be accepted after filtering in the parameter 'MINLEN'. If reads are at least 50 bp, use 35 bp as the read length threshold.
   5. Move to the "fastqc.output" directory.
   6. Perform the fastq file quality control analysis after trimming the reads. Verify that the quality control issues have been resolved. Use the command in Figure S1C to obtain the quality control metrics.
4. Align the single-end or paired-end ChIP-seq reads to the mouse reference genome.
   1. Move to the "bowtie.output" directory for mapping.
   2. Download the GENCODE mm10 mouse reference genome. Rename the mm10 mouse reference genome as "mm10.fa".
   3. Download a read mapper¹² and install it in the system.
   4. Create an index file of the downloaded reference genome using the command in Figure S2A. Six files are automatically created: mm10.1.bt2, mm10.2.bt2, mm10.3.bt2, mm10.4.bt2, mm10.rev.1.bt2, and mm10.rev.2.bt2.
   5. Use the command in Figure S2B to execute the alignment and adjust parameter "-p" to the number of processing cores according to your system settings. If running paired-end mode, parameters "-1" and "-2" indicate the names of the trimmed fastq files.
      NOTE: Mapping is performed separately for the treatment and input samples and output metric log files are created for each sample.
   6. Download a software for manipulating files in SAM format¹³ and install it in the system. Convert the aligned SAM file into a BAM file using the command in Figure S2C.
5. Obtain quality control metrics of mapped reads before peak calling for both paired-end treatment and control samples.
   1. One of the most important quality control metrics to address is the sequencing depth. Open the files "log.bowtie.PDGFRα_Olig2.txt" and "log.bowtie.PDGFRα_input.txt " in the bowtie.output directory and verify that the number of uniquely mapped read pairs in each sample is greater than 10 million.
   2. Move to the "homer.output" directory.
   3. Download a software for motif discovery and next-generation sequencing analysis¹⁴ and install it in the system.
   4. Create a directory named "tagDir".
   5. To verify tag clonality, use the command depicted in Figure S3A. The "makeTagDirectory" command will generate four quality control output files:tagAutocorrelation.txt, tagCountDistribution.txt, tagInfo.txt, and tagLengthDistribution.txt.
   6. Move to the "tagDir" directory.
   7. Copy the "tagCountDistribution.txt" file into the "reports" directory.
   8. Move to the "reports" directory.
   9. Open the tagCountDistribution.txt file using a spreadsheet program and create a bar plot of the number of tags per genomic position. Store the histogram file with the name "tag.clonality.xlsx".
   10. Install R programming language¹⁵ in the system.
   11. In the terminal, type 'R' and press ENTER to access the R programming environment.
   12. Download an R package for processing ChIP-seq data¹⁶ and install it using the command depicted in Figure S3B.
   13. Use the R script in Figure S3C to plot the strand cross-correlation.
6. Detection of peaks using a peak mapper.
   1. Move to the "macs2.output" directory. Download and install a peak mapper¹⁷ in your system.
   2. Use the function "callpeak" with the treatment and control BAM files generated in Step 3.4.6.
      NOTE: Other parameters include "-f" for input file format, "-g" for genome size, "-name" for indicating the base name of all output files, and "-B" for storing the fragment pileup in bedgraph format. The complete peak calling command is included in Figure S4A. Use BEDPE for paired-end samples. 
   3. Verify that the peak mapper generated six files: PDGFRα_Olig2_vs_PDGFRα_input_peaks.xls, PDGFRα_Olig2_vs_PDGFRα_input_peaks.narrowPeak, PDGFRα_Olig2_vs_PDGFRα_input_summits.bed, PDGFRα_Olig2_vs_PDGFRα_input_model.r, PDGFRα_Olig2_vs_PDGFRα_input_control_lambda.bdg, and PDGFRα_Olig2_vs_PDGFRα_input_treat_pileup.bdg.
   4. Open file PDGFRα_Olig2_vs_PDGFRα_input_peaks.xls to view the called peaks, location, length, summit position, pileup, and the peak enrichment metrics: -log10(pvalue), fold enrichment, and -log10(q-value).
7. Filter and annotate called peaks.
   1. Using the file opened in the previous step, filter resulting peaks according to the fold enrichment, the p-value and/or the q-value. To decide adequate filtering thresholds, make a histogram plot to determine the density of each metric. Filter out values below a selected threshold to obtain significant peaks.
   2. Download the mm10 blacklist (regions are known to have artificially high signal) and filter out the ChIP-seq peaks which are inside any of those artifact regions.
   3. Create a bed file with the filtered peaks containing the following columns: chromosome, start, end, peakID, mock, and strand. Fill the mock column with a dot "." since it is not used. In the strand column, set all values to "+". Save the bed file as "filtered.peakData2.bed".
   4. Copy the "filtered.peakData2.bed" file in the "reports" directory.
   5. Move to the "reports" directory.
   6. To annotate filtered peaks to a specific gene region, use the function "annotatePeaks" with the bed file created in the previous step. The command used is shown in Figure S5A.
   7. Open the "filtered.annotatedPeaks.txt" file using a statistical software.
   8. Filter resulting peaks lying in an intergenic region at a distance greater than 5 kb from an annotated TSS. Use the "Distance to TSS" column in the file as the distance for filtering. Store the filtered peaks in an excel file named "5kbup.geneBody.peakData.txt".
   9. Store the resulting filtered peaks in a bedGraph file with columns: chromosome, start, end, and -log10(q-value). Name the file "5kbup.geneBody.peakData.bedGraph".
8. Generate bigwig files for browser visualization.
   1. Download and install a software for genome arithmetic¹⁸.
   2. Download a tool for converting bedgraph to bigwig and install in the system. Download the 'mm10.chrom.sizes' file.
   3. Use the command in Figure S6A to generate a bigwig file.
   4. Download and install a genome browser¹⁹ in the system.
   5. Open the genome browser and load the file "5kbup.geneBody.peakData.bigwig" to visualize the filtered significant peaks and their location with respect to known genes.
9. Motif Search.
   1. Move to the "motif.analysis" directory.
   2. Download a de novo motif scanning and motif enrichment program and install it into your system²⁰.
   3. Download a comprehensive motif database that includes motifs of Olig2.
   4. Obtain the genomic sequences of 500 bp regions centered at significant peak summits. Store the sequences as peak.sequences.txt. Use the command in Figure S7 to search for Olig2 motifs within each of the 500 bp peak regions.
   5. Filter the resulting motifs using an adequate E-value (default = 0.05).

Results

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 PDGFRα anti...

Discussion

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 PDGFRα antibody. For positive selection of OPCs with PDGFRα coated plates, OPCs often weakly bind to the PDGFRα coat...

Materials

List of materials used in this article

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