This work was supported by the BLUERPINT EU grant (BLUEPRINT &#8211; A BLUEPRINT of Haematopoietic Epigenomes). We also thank the Walloon Region (DG06) for its financial support.

1. Standard ChIP Experiments
<ol>
	<li>Cell collection and DNA-protein crosslinking.
	<ol>
		<li>Grow HeLa-S3 cells to a confluence of 80%-90%. Remove culture medium, wash the dish twice with 10 ml of 1x phosphate buffered saline (PBS), and add Trypsin-EDTA (1x) to the cultured plate. Incubate for a maximum of 2 min to detach the cells from the dish. Collect cells and wash twice with 10 ml PBS. 
		NOTE: Longer incubation times will lead to cell damage.</li>
		<li>Centrifuge the cells for 5 min at 500 x g and resuspend the cells in 20 ml of PBS. Proceed to count the cells.</li>
		<li>Centrifuge the cells for 5 min at 500 x g, discard the supernatant and add 500 &#956;l of PBS. The optimal number of cells for the fixation step is 10 million cells per 500 &#956;l of PBS.</li>
		<li>Add 13.5 &#956;l of fresh 37% formaldehyde into each aliquot of 500 &#956;l of cell suspension. Fixate the cells for 8 min at RT.</li>
		<li>Add 57 &#956;l of 1.25 M glycine solution to stop the fixation. Incubate for 5 min at RT with constant mixing by gentle vortex. Work on ice from this point onwards.</li>
		<li>Centrifuge the cells at 500 x g for 5 min at 4 &#176;C and discard the supernatant without disturbing the cell pellet.</li>
		<li>Wash the cells twice with 1 ml PBS. Discard gently the supernatant and keep the cell pellet on ice.</li>
	</ol>
	</li>
	<li>Cell lysis and chromatin shearing
	<ol>
		<li>Add 10 ml of ice cold lysis buffer iL1 to the cell pellet (1 ml of lysis buffer per 1 million cells is the optimal ratio). Pipette up and down several times and incubate for 10 min at 4 &#176;C with gentle mixing.</li>
		<li>Centrifuge the lysate for 5 min at 500 x g and 4 &#176;C. Discard the supernatant.</li>
		<li>Add 10 ml of ice cold lysis buffer iL2 to the lysates and mix gently by pipetting up and down. Incubate the lysates for 10 min at 4 &#176;C.</li>
		<li>Centrifuge for 5 min at 500 x g and 4 &#176;C and discard the supernatant.</li>
		<li>Prepare the complete shearing buffer adding the 200x protease inhibitor cocktail (PIC) to the iS1 shearing buffer. Keep the buffer on ice for 5 min and work on ice afterwards. Add 1 ml of the complete iS1 shearing buffer to each 10 million cells pellet and gently mix by pipetting up and down. Prior to sonication incubate the samples on ice for 10 min to reduce viscosity of the sample.</li>
		<li>Shear 300 &#181;l aliquots of chromatin by sonication using a water bath sonicator for 2 to 3 sets of 10 cycles each. A cycle consists of 30 sec &#8220;ON&#8221; and 30 sec &#8220;OFF&#8221; on a high power setting. Alternatively, use a pico-sonication device with a shorter sonication time of 5 to 10 cycles of 30 sec &#8220;ON&#8221;, 30 sec &#8220;OFF&#8221;. Briefly vortex and spin the tubes between the runs. When using other types of sonicators, follow corresponding manufacturer&#8217;s instructions for chromatin shearing.</li>
		<li>Centrifuge the sheared chromatin at 16,000 x g for 10 min and collect the supernatant to be used immediately in the IP step. Alternatively, store the chromatin at -80 &#176;C for up to 2 months for future use.</li>
		<li>Analyze the chromatin shearing efficiency prior to immunoprecipitation step using 1-1.5% TAE agarose gels or bionalyzer. Optimal chromatin fragment sizes range between 100-600 bp.</li>
	</ol>
	</li>
</ol>
2. Low Cell ChIP Experiments
<ol>
	<li>Cell collection and DNA-protein crosslinking
	<ol>
		<li>Grow HeLa-S3 cells to a confluence of 80%-90%. Remove culture medium, wash the dish twice with 10 ml of 1x phosphate buffered saline (PBS), and add 1x Trypsin-EDTA to the cultured plate. Incubate for a maximum of 2 min to detach the cells from the dish. 
		NOTE: Longer incubation times will lead to cell damage.</li>
		<li>Collect cells by adding 1 ml culture medium containing serum into a 1 ml centrifugation tube. Count the cells.</li>
		<li>Centrifuge the cells for 5 min at 500 x g. Bring the cell number to 10,000 cells per ml of culture medium for the fixation.</li>
		<li>Add 27 &#956;l of 36.5% fresh prepared formaldehyde into each tube for the fixation. Invert the tube two or three times and incubate 10 min at RT.</li>
		<li>Add 115 &#956;l of 1.25 M glycine solution to the sample, invert the tube two or three times and incubate 5 min at RT. Work on ice from this point onwards.</li>
		<li>Centrifuge the cells at 300 x g for 10 min at 4 &#176;C. Discard the supernatant slowly.</li>
		<li>Wash cells with 1 ml ice cold HBSS with PIC (200x, final concentration 1x). Invert the tube two or three times to resuspend the cells and centrifuge at 300 x g for 10 min at 4 &#176;C. Discard gently the supernatant and keep the cell pellet on ice.</li>
	</ol>
	</li>
	<li>Cell lysis and chromatin shearing
	<ol>
		<li>Add 25 &#956;l of complete Lysis Buffer tL1 (Lysis Buffer tL1 + PIC) per 10,000 cells and agitate manually the bottom of the tube to resuspend the cells. Incubate on ice for 5 min.</li>
		<li>Add 75 &#956;l of complete HBSS (HBSS + PIC) buffer into each aliquot containing 10,000 cells.</li>
		<li>Shear 100 &#956;l aliquots of 10,000 cells by sonication for 5 sets of 5 cycle each. A cycle consists of 30 sec &#8220;ON&#8221; and 30 sec &#8220;OFF&#8221; on the high power setting. Alternatively, use a pico-sonication device used with a shorter sonication time of 5 cycles of 30 sec &#8220;ON&#8221;, 30 sec &#8220;OFF&#8221;. Optimal chromatin fragment sizes range between 100-600 bp. Note that chromatin preparations, cell types and different sonicators require separate shearing optimization experiments.</li>
		<li>Centrifuge the sheared chromatin at 14,000 x g for 10 min to discard the insoluble material and collect the supernatant to be used immediately in the IP step. Alternatively, store the chromatin at -80 &#176;C for up to 2 months for future use.</li>
		<li>Analyze the chromatin shearing efficiency prior to immunoprecipitation step using 1-1.5% TAE agarose gels or the bionalyzer. Treat samples with RNase prior to agarose gel analysis in order to improve the visual assessment of shearing. Optimal chromatin fragment sizes range between 100-600 bp.</li>
	</ol>
	</li>
</ol>
3. Chromatin Immunoprecipitation and Library Prep
<ol>
	<li>For standard automated ChIP experiments
	<ol>
		<li>Add 120 &#956;l of ChIP Buffer H (ChIP Buffer H + PIC) to 100 &#956;l sheared chromatin. Use 200 &#956;l for the IP and keep 2 &#956;l to 20 &#956;l as input sample.</li>
		<li>Select the automated ChIP 200 &#956;l protocol in the automation instrument. The throughput of the protocol is 1 to 16 samples per run.</li>
		<li>Run an automated ChIP experiment using, chromatin corresponding to 1-2 million cells, 1-2 &#956;g of anti-H3K79me3, -H3K27me3, -H3K4me3, -H3K4me2, -H3K9ac, -H3K9/14ac, -H3K36me3 and H3K9me3 ChIP-seq grade rabbit polyclonal antibodies. Optimal antibody amounts vary depending on the histone modification and the affinity and specificity of the corresponding antibody.
		<ol>
			<li>Use equal amounts of non-immune rabbit IgG as an isotype control antibody. Alternatively, use uncoated beads or specific blocked antibody as ChIP controls. Add 20 &#956;l of Protein-A coated magnetic beads for each reaction.</li>
		</ol>
		</li>
		<li>Use the automated histone ChIP-seq kit reagents to perform automated ChIP experiments with anti-H3K79me3 and -H3K4me2 polyclonal antibodies. Use the ideal ChIP-seq kit reagents to perform ChIP experiments with anti-H3K27me3, -H3K4me3, -H3K9ac, -H3K9/14ac, -H3K36me3 and -H3K9me3.</li>
		<li>Select the automated ChIP protocol following the software instructions implemented in the automation device. Set the ChIP experimental parameters to 4 hr for the antibody coating step and 15 hr for the immunoprecipitation step. The reverse crosslinking step takes place in the automated instrument at 65 &#176;C for 4 hr.</li>
		<li>Purify the reverse cross-linked DNA on the automated system. Select automated protocols for DNA purification with a protocol or kit using magnetic bead based DNA purification. Elute the DNA in 25 &#956;l of water.</li>
		<li>Quantify the immunoprecipitated DNA by extracting 10% of the immunoprecipitated DNA. The immunoprecipitated DNA yield depends on the quality of the chromatin and antibody, cell type and the target histone modification. Quantify the DNA using an assay kit according to manufacturer&#8217;s instructions.</li>
		<li>Analyze the quality of the immunoprecipitated DNA by quantitative PCR using primers for at least 1 positive and 1 negative control genomic regions. Do not use more than 10% of the total immunoprecipitated DNA for assessing ChIP enrichments.
		<ol>
			<li>Prepare the qPCR reactions. Add 10 &#956;l of a 2x SyberGreen qPCR master mix, 1 &#956;l of primer mix, 1-5 &#956;l of immunoprecipitated or input DNA and sterile water up to 20 &#181;l final reaction volume. The qPCR program includes an initial denaturation step at 95 &#176;C for 5-10 min depending on the provider of the Taq polymerase and the annealing temperatures should be set according to the primers selected.</li>
		</ol>
		</li>
		<li>Use automated library prep protocols compatible with the commercially available Illumina ChIP-seq library preparation reagents to build the libraries using both ChIP DNA as well as the saved input DNA from the same chromatin preparation. Use 10-20 ng of immunoprecipitated DNA from each antibody for library preparation. Prepare up to 16 automated libraries per run.</li>
		<li>Sequence the libraries and generate clusters according to Illumina manufacturer&#8217;s instructions. Perform primary bioinformatics analysis (cluster filtering, base calling, etc.) following the standard Illumina pipeline, filter and align the reads to the latest human genome assembly (current version is GRCh38) with the ELAND aligner. Use SICER7 or MACS8 for peak calling and perform the downstream analyses of peaks with Homer9, BEDTools10 or preferred software.</li>
	</ol>
	</li>
	<li>For low cell number automated ChIP experiments
	<ol>
		<li>Add 120 &#956;l of complete ChIP buffer tC1 (ChIP Buffer tC1 + PIC) to 100 &#956;l sheared chromatin. Use 200 &#956;l for the IP and keep 20 &#956;l as input.</li>
		<li>Select automated ChIP 200 &#956;l protocol in the automation system. The throughput of the protocol is 1 to 16 samples per run.</li>
		<li>Run an automated ChIP-seq experiment using automated ChIP reagents and ChIP grade antibodies optimized to work on low chromatin quantities. Use chromatin corresponding to 10,000 cells and 100,000 cells, 0.5 &#956;g of anti-H3K27me3, 0.25 &#956;g -H3K4me3, 0.1 &#956;g -H3K27ac, 0.25 &#956;g -H3K9me3 rabbit Premium ChIP-seq grade rabbit polyclonal antibodies. Optimal antibody amounts vary depending on the histone modification and the affinity and specificity of the corresponding antibody.
		<ol>
			<li>Use equal amount of non-immune rabbit IgG as an isotype control antibody. Alternatively, use uncoated beads or specific blocked antibody as ChIP controls. Add 10 &#956;l of Protein-A coated magnetic beads for each reaction.</li>
		</ol>
		</li>
		<li>Select the automated ChIP protocol following the software instructions implemented in the automation device. Set the ChIP experimental parameters to 4 hr for the antibody coating step and 15 hr for the immunoprecipitation step. The reverse crosslinking step takes place in the automated instrument at 65 &#176;C for 4 hr.</li>
		<li>Purify reverse cross-linked DNA using spin columns following manufacturer&#8217;s instructions and elute in volumes from 6 &#956;l to 25 &#956;l of water.</li>
		<li>Quantify the DNA using a commercial assay kit. Analyze the results by qPCR using primers for positive and negative control regions to evaluate the ChIP quality.</li>
		<li>Use a library preparation kit with optimized library preparation reagents to prepare libraries with low DNA quantities. Use 30 pg and 300 pg of ChIP DNA (corresponding to 10,000 and 100,000 cells experiments respectively) for library preparation. Prepare libraries using the automated protocol compatible with library preparation reagents. The automated library prep throughput is 1 to 48 libraries per run.</li>
		<li>Following the end repair of the double-stranded DNA templates, ligate the cleavable stem-loop adaptors containing the sequencing primer sites. Following the DNA extension step, amplify the sample with the high fidelity amplification method described in the library preparation kit protocol.</li>
		<li>After library amplification, quantify and purify the libraries following the library preparation kit guidelines. Note that size selection after purification is not necessary.</li>
		<li>Sequence the libraries and generate clusters according to the manufacturer&#8217;s instructions. Perform primary bioinformatics analysis (cluster filtering, base calling, etc.) following the standard manufacturer pipeline, filter, and align the reads to the latest human genome assembly (current version is GRCh38) with the ELAND7 aligner. Use SICER8 or MACS9 for peak calling and perform the downstream analyses of peaks with Homer, BEDTools10, or any preferred software.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 1" src="/files/ftp_upload/52150/52150fig1highres.jpg" /> 
Figure 1. Screenshots of the software showing how to set up automated ChIP experiments in the IP-Star Compact. The software provides the flexibility to select the amount of samples per run as well as to change key experimental parameters (Antibody Coating, IP and washes) according to the researcher needs. The automated procedure allows testing different conditions in parallel (i.e., different types and amounts of antibodies, different types and amounts of cells and even different types and amounts of magnetic beads in the same run. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/52150/52150fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/52150/52150fig2highres.jpg" /> 
Figure 2. Screenshots of the software showing how to set up automated library preparation for next generation sequencing using the library kit in the automation system. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/52150/52150fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

<ol>
	<li>Dahl, J. A., Collas, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+rapid+micro+chromatin+immunoprecipitation+assay+(microChIP).">A rapid micro chromatin immunoprecipitation assay (microChIP).</a> Nature Protocols. 3, 1032-1045 (2008).</li><li>Adli, M., Bernstein, B. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Whole-genome+chromatin+profiling+from+limited+numbers+of+cells+using+nano-ChIP-seq.">Whole-genome chromatin profiling from limited numbers of cells using nano-ChIP-seq.</a> Nature Protocols. 6, 1656-1668 (2011).</li><li>Blecher-Gonen, R., 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=High-throughput+chromatin+immunoprecipitation+for+genome-wide+mapping+of+in+vivo+protein-DNA+interactions+and+epigenomic+states.">High-throughput chromatin immunoprecipitation for genome-wide mapping of in vivo protein-DNA interactions and epigenomic states.</a> Nature Protocols. 8, 539-554 (2013).</li><li>Aldridge, 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=AHT-ChIP-seq:+a+completely+automated+robotic+protocol+for+high-throughput+chromatin+immunoprecipitation.">AHT-ChIP-seq: a completely automated robotic protocol for high-throughput chromatin immunoprecipitation.</a> Genome Biology. 14, 124 (2013).</li><li>Farias-Hesson, E., 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=Semi-automated+library+preparation+for+high-throughput+DNA+sequencing+platforms.">Semi-automated library preparation for high-throughput DNA sequencing platforms.</a> Journal of Biomedicin., &amp; Biotechnology. 2010, (2010).</li><li>Callejas, S., Alvarez, R., Benguria, A., Dopazo, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=AG-NGS:+a+powerful+and+user-friendly+computing+application+for+the+semi-automated+preparation+of+next-generation+sequencing+libraries+using+open+liquid+handling+platforms.">AG-NGS: a powerful and user-friendly computing application for the semi-automated preparation of next-generation sequencing libraries using open liquid handling platforms.</a> BioTechniques. 56, 28-35 (2014).</li><li>Zang, C., 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+clustering+approach+for+identification+of+enriched+domains+from+histone+modification+ChIP-Seq+data.">A clustering approach for identification of enriched domains from histone modification ChIP-Seq data.</a> Bioinformatics. 25, 1952-1958 (2009).</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 Biology. 9, 137 (2008).</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> Molecular Cell. 38, 576-589 (2010).</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 (Oxford, England). 26, 841-842 (2010).</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 Research. 22, 1813-1831 (2012).</li></ol>

The authors of this article, at the time of its writing, are employed by Diagenode S.A. and Diagenode, Inc., the manufacturer of the automated system described.

Chromatin immunoprecipitation followed by sequencing is now a standard procedure. Here an automated ChIP-seq protocol that can generate chromatin epigenetic profiles with as few as 10,000 cells of starting material is presented.
Automating ChIP and library preparation assays allows standardizing the ChIP optimization procedure and reducing experimental variability. The liquid handling system presented here eliminates many of the manual procedures associated with ChIP reducing the hands on time to just 30 min, minimizes sample loss, and enables accurate ChIP-seq with just a few picograms of library input. In order to achieve successful automated ChIP-seq experiments, it is also crucial to use high quality sheared chromatin preparations and ChIP-seq grade antibodies in each experiment The system uses magnetic bead-based technology and offers flexibility to change main experimental parameters such as incubation time for the antibody coating and immunoprecipitation steps or modification of the washing conditions allowing the researcher to conduct all necessary experiments for ChIP-seq optimization. The automated system is an &#8220;open&#8221; platform that also allows comparison of multiple reagents in parallel for optimization of experimental conditions for each individual cell line and antibody and enables direct comparison of various types and concentrations of chromatin, different antibodies and even different types of magnetic beads.
One of the limitations of the automated system is the need of automating all protocols in volumes that range from 5 &#956;l to 200 &#956;l. However, the miniaturization of the experiments in this automated platform also enables saving costs in reagents.
In addition to the protocols described in this study, the system is adaptable and also automates a variety of other magnetic bead based applications such as immunoprecipitation and capture of methylated DNA (MeDIP and MethylCap technologies), immunoprecipitation of hydroxylmethylated DNA (hMEDIP), sequential chromatin immunoprecipitation (ReChIP), RNA immunoprecipitation (RNA-IP), bisulfite conversion, and DNA purification assays.

As Next-Generation Sequencing (NGS) technologies have become widespread and more accessible, the primary method for genome-wide mapping of protein-DNA interactions is now chromatin immunoprecipitation followed by NGS detection (ChIP-seq), which enables the discovery of transcription factor binding sites or patterns of histone modifications. ChIP-seq is advantageous in providing high-throughput data of the whole genome that can be used for quantitative and qualitative analysis of the protein-DNA interactions by the measurement of the enriched DNA fragments. However, there are some disadvantages in standard ChIP-seq experiments such as the difficulty in obtaining enough material to create a sequencing library.
ChIP experiments are divided into six basic steps including 1) crosslinking protein-DNA binding regions 2) sample preparation which includes cell lysis and shearing the chromatin by sonication, 3) formation of the immunocomplexes, 4) precipitation of the immunocomplexes, 5) washing of the immunocomplexes, and 6) elution of the enriched material and analysis by qPCR and NGS.
The success of a ChIP assay is dependent on three main factors: a good chromatin preparation, the amount of antigen in the original sample, and the specificity and affinity of the antibody for its cognate antigen. A major limitation is the requirement for high amounts of starting cell numbers in order to obtain enough enriched DNA to create a sequencing library. For scientists who work with limited sample amounts, such as biopsy samples or cell sub-populations, ChIP-seq experiments are very challenging. Recent studies have shown that ChIP-seq assays can be performed when working with a low amount of cells1, 2. Diagenode has developed a robotic liquid handling system that can fully automate ChIP-seq experiments when starting with a limited number of cells.
Automation provides many advantages over manual preparation of ChIP-seq samples as it diminishes human error, reduces variability, and reduces experimental cost. Semi-automated protocols for chromatin immunoprecipitation and library preparation have been reported but none of these studies has shown data when using low cell numbers3, 4, 5, 6.
In this paper a complete automated workflow is described for both chromatin immunoprecipitation and library preparation assays in a robotic liquid-handling system that uses magnetic bead-based technology and that can address multiple parameters in the protocol optimization. Here, automated ChIP-seq experiments were successfully performed on a limited number of cells with the goal of simplifying, standardizing, and providing a reliable solution to study epigenetic profiles in small cell populations. The automated ChIP protocol described in this paper has been optimized on HeLa cells using specific histone antibodies and reagents but the workflow can be adapted to other cell lines and antibodies with corresponding experimental optimization.

<table border="0" cellpadding="0" cellspacing="0" width="917">
	<tbody>
		<tr height="16">
			<td height="16" style="height:16px;width:333px;">Product Description</td>
			<td style="width:111px;">Company</td>
			<td style="width:107px;">Catalogue number</td>
			<td style="width:367px;">Comments</td>
		</tr>
		<tr height="16">
			<td height="16" style="height:16px;"></td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr height="16">
			<td height="16" style="height:16px;">PBS&#160;</td>
			<td>Life technologies</td>
			<td>14190-094</td>
			<td></td>
		</tr>
		<tr height="16">
			<td height="16" style="height:16px;">Trypsin-EDTA</td>
			<td>Sigma</td>
			<td>T3924-100ML</td>
			<td></td>
		</tr>
		<tr height="16">
			<td height="16" style="height:16px;">Formaldehyde 37%</td>
			<td>Sigma</td>
			<td>F8775-25</td>
			<td></td>
		</tr>
		<tr height="16">
			<td height="16" style="height:16px;">1,25M Glycine Solution</td>
			<td>Diagenode</td>
			<td>C01020010</td>
			<td>Component of the ideal ChIP-seq kit</td>
		</tr>
		<tr height="16">
			<td height="16" style="height:16px;">Lysis Buffer iL1</td>
			<td>Diagenode</td>
			<td>C01020010</td>
			<td>Component of the ideal ChIP-seq kit</td>
		</tr>
		<tr height="16">
			<td height="16" style="height:16px;">Lysis Buffer iL2</td>
			<td>Diagenode</td>
			<td>C01020010</td>
			<td>Component of the ideal ChIP-seq kit</td>
		</tr>
		<tr height="16">
			<td height="16" style="height:16px;">Shearing Buffer iS1</td>
			<td>Diagenode</td>
			<td>C01020010</td>
			<td>Component of the ideal ChIP-seq kit</td>
		</tr>
		<tr height="16">
			<td height="16" style="height:16px;">Protease Inhibitors Mix (200x)</td>
			<td>Diagenode</td>
			<td>C01010130</td>
			<td>Component of the Auto True Micro ChIP kit</td>
		</tr>
		<tr height="16">
			<td height="16" style="height:16px;">HBSS (no calcium, no magnesium, no phenol red)</td>
			<td>Life technologies</td>
			<td>14175-053</td>
			<td></td>
		</tr>
		<tr height="16">
			<td height="16" style="height:16px;">Lysis Buffer tL1</td>
			<td>Diagenode</td>
			<td>C01010130</td>
			<td>Component of the Auto True Micro ChIP kit</td>
		</tr>
		<tr height="16">
			<td height="16" style="height:16px;">ChIP Buffer tC1</td>
			<td>Diagenode</td>
			<td>C01010130</td>
			<td>Component of the Auto True Micro ChIP kit</td>
		</tr>
		<tr height="16">
			<td height="16" style="height:16px;">ideal ChIP-seq kit</td>
			<td>Diagneode</td>
			<td>C01010051</td>
			<td></td>
		</tr>
		<tr height="16">
			<td height="16" style="height:16px;">ChIP-Buffer H</td>
			<td>Diagenode</td>
			<td>C01010020</td>
			<td>Component of the Auto Histone ChIP-seq kit</td>
		</tr>
		<tr height="16">
			<td height="16" style="height:16px;">Auto Histone ChIP-seq kit</td>
			<td>Diagenode</td>
			<td>C01010020</td>
			<td></td>
		</tr>
		<tr height="16">
			<td height="16" style="height:16px;">Auto True Micro ChIP kit</td>
			<td>Diagenode</td>
			<td>C01010130</td>
			<td></td>
		</tr>
		<tr height="16">
			<td height="16" style="height:16px;">H3K79me3 polyclonal antibody-Classic</td>
			<td>Diagenode</td>
			<td>C15310068</td>
			<td></td>
		</tr>
		<tr height="16">
			<td height="16" style="height:16px;">H3K27me3 polyclonal antibody-Classic</td>
			<td>Diagenode</td>
			<td>C15410069</td>
			<td></td>
		</tr>
		<tr height="16">
			<td height="16" style="height:16px;">H3K4me3 polyclonal antibody-Classic</td>
			<td>Diagenode</td>
			<td>C15410030</td>
			<td></td>
		</tr>
		<tr height="16">
			<td height="16" style="height:16px;">H3K4me2 polyclonal antibody-Classic</td>
			<td>Diagenode</td>
			<td>C15410035</td>
			<td></td>
		</tr>
		<tr height="16">
			<td height="16" style="height:16px;">H3K9ac polyclonal antibody-Premium</td>
			<td>Diagenode</td>
			<td>C15410004</td>
			<td></td>
		</tr>
		<tr height="16">
			<td height="16" style="height:16px;">H3K9/14ac polyclonal antibody-Premium</td>
			<td>Diagenode</td>
			<td>C15410200</td>
			<td></td>
		</tr>
		<tr height="16">
			<td height="16" style="height:16px;">H3K36me3 polyclonal antibody-Premium</td>
			<td>Diagenode</td>
			<td>C15410058</td>
			<td></td>
		</tr>
		<tr height="16">
			<td height="16" style="height:16px;">H3K9me3 polyclonal antibody-Premium</td>
			<td>Diagenode</td>
			<td>C15410193</td>
			<td></td>
		</tr>
		<tr height="16">
			<td height="16" style="height:16px;">Rabbit IgG</td>
			<td>Diagenode</td>
			<td>C15410206</td>
			<td></td>
		</tr>
		<tr height="16">
			<td height="16" style="height:16px;">Protein-A coated paramagnetic beads</td>
			<td>Diagenode</td>
			<td>C01010020</td>
			<td></td>
		</tr>
		<tr height="16">
			<td height="16" style="height:16px;">Auto IPure</td>
			<td>Diagenode</td>
			<td>C03010010</td>
			<td></td>
		</tr>
		<tr height="16">
			<td height="16" style="height:16px;">MicroChIP DiaPure columns</td>
			<td>Diagenode</td>
			<td>C03040001</td>
			<td></td>
		</tr>
		<tr height="16">
			<td height="16" style="height:16px;">Universal SyberGreenMaster Mix 1.25ml</td>
			<td>Diagenode</td>
			<td>DMMLD2D100&#160;</td>
			<td></td>
		</tr>
		<tr height="16">
			<td height="16" style="height:16px;">Quant-IT dsDNA</td>
			<td>Invitrogen</td>
			<td>Q32854</td>
			<td></td>
		</tr>
		<tr height="16">
			<td height="16" style="height:16px;">Illumina Sample Preparation kit fro Genomic DNA</td>
			<td>Illumina</td>
			<td>FC-121-3001</td>
			<td></td>
		</tr>
		<tr height="16">
			<td height="16" style="height:16px;">Illumina True-seq kit ChIP library Prep kit</td>
			<td>Illumina</td>
			<td>IP-202-1012</td>
			<td></td>
		</tr>
		<tr height="16">
			<td height="16" style="height:16px;">MicroPlex Library Preparation Kit</td>
			<td>Diagenode</td>
			<td>C05010010</td>
			<td></td>
		</tr>
		<tr height="16">
			<td height="16" style="height:16px;">Agencourt AMPure XP beads</td>
			<td>Beckman Coulter</td>
			<td>A63881</td>
			<td></td>
		</tr>
		<tr height="16">
			<td height="16" style="height:16px;">Illumina Library prep Quantification kit</td>
			<td>Kapa Biosystems</td>
			<td>KK4844</td>
			<td></td>
		</tr>
		<tr height="16">
			<td height="16" style="height:16px;">IP-Star Compact&#160; Automated System</td>
			<td>Diagenode</td>
			<td>B03000002</td>
			<td></td>
		</tr>
		<tr height="16">
			<td height="16" style="height:16px;">Bioruptor Plus</td>
			<td>Diagenode</td>
			<td>B01020001</td>
			<td></td>
		</tr>
		<tr height="16">
			<td height="16" style="height:16px;">Bioruptor Pico</td>
			<td>Diagenode</td>
			<td>B01060001</td>
			<td></td>
		</tr>
		<tr height="16">
			<td height="16" style="height:16px;">Qubit system</td>
			<td>Invitrogen</td>
			<td>Q32857</td>
			<td></td>
		</tr>
		<tr height="16">
			<td height="16" style="height:16px;">Illumina Hiseq systems</td>
			<td>Illumina</td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>

automating chip-seq experiments to generate epigenetic profiles on 10,000 hela cells

Chromatin immunoprecipitation followed by next generation sequencing (ChIP-seq) is a technique of choice for studying protein-DNA interactions. ChIP-seq has been used for mapping protein-DNA interactions and allocating histones modifications. The procedure is tedious and time consuming, and one of the major limitations is the requirement for high amounts of starting material, usually millions of cells. Automation of chromatin immunoprecipitation assays is possible when the procedure is based on the use of magnetic beads. Successful automated protocols of chromatin immunoprecipitation and library preparation have been specifically designed on a commercially available robotic liquid handling system dedicated mainly to automate epigenetic assays. First, validation of automated ChIP-seq assays using antibodies directed against various histone modifications was shown, followed by optimization of the automated protocols to perform chromatin immunoprecipitation and library preparation starting with low cell numbers. The goal of these experiments is to provide a valuable tool for future epigenetic analysis of specific cell types, sub-populations, and biopsy samples.

Optimizing automated ChIP-seq experiments for eight different histone markers
In order to successfully develop and validate the automated ChIP protocols, ChIP-seq grade antibodies that were previously validated in manual ChIP-Seq experiments (data not shown) were selected. The following ChIP-seq grade antibodies were chosen for this study: anti-H3K79me3, -H3K27me3, -H3K4me3, -H3K4me2, -H3K9ac, -H3K9/14ac, -H3K36me3 and -H3K9me3. The specificity of all ChIP-seq grade antibodies was previously confirmed by dot blot, peptide arrays and Western Blot experiments (data not shown). Pilot ChIP-qPCR experiments with increasing antibody amounts were performed to determine the sensitivity of the antibodies (Figure 3). qPCR with at least two positive and two negative control targets were analyzed and profiles with enrichments of positive over negative target higher than fivefold are qualified for sequencing experiments. It is important to perform ChIP and ChIP-seq experiments with a high quality of sheared chromatin. All ChIP experiments shown in this publication were performed using fresh chromatin. It is also possible to freeze the fixed cells at -80 &#176;C and proceed with the chromatin preparation and shearing on a different day. However, chromatin prepared from frozen fixed cells may behave differently from chromatin freshly prepared and therefore sonication conditions may need to be optimized for each chromatin preparation. When working with different cell types, shearing buffers with different detergent compositions (SDS) can be used. Cell types such as primary cell lines or cell grown in suspension are difficult cells to shear and will require of high SDS concentrations (1%) whereas cell lines that are easy to shear such as HeLa will require low SDS concentrations (0.1%) in the shearing buffers.
<img alt="Figure 3" src="/files/ftp_upload/52150/52150fig3highres.jpg" /> 
Figure 3. Validation of ChIP-grade antibodies using the automation system. ChIP was performed with anti-H3K79me3, -H3K27me3, -H3K4me3, -H3K4me2, -H3K9ac, H3K9/14ac, -H3K36me3 and -H3K9me3 rabbit polyclonal antibodies on sheared chromatin from 1 million HeLa-S3 cells depending on the histone modifications. Automated ChIP protocols with 200 &#181;l working volumes were used in the automation instrument for antibody titration experiments. Antibody quantities of 1, 2, 5 and 10 &#956;g were tested per ChIP experiment and 2 &#956;g IgG were used as negative control in each experiment. Enrichments were assessed by qPCR. Results are shown as a % of input (the relative amount of immunoprecipitated DNA compared to input DNA after qPCR analysis). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/52150/52150fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
After validating and determining optimal amounts of ChIP-grade antibodies to be used in the automation system, automated ChIP-seq experiments were performed in order to generate sequencing profiles for each histone modification (Figure 4).
<img alt="Figure 4" src="/files/ftp_upload/52150/52150fig4highres.jpg" /> 
Figure 4. Histone ChIP-seq profiles generated by automated ChIP-seq experiments. The figure shows the ChIP-seq profiles in different genomic regions for H3K4me3, H3K9ac and H3K9/14acH3K4me2 H3K79me3, and H3K36me3. 4A shows the peak distribution along the complete X-chromosome and 4B the distribution in a 75 kb region surrounding the GAPDH gene. 4C shows the profiles of H3K27me3, H3K36me3 and H3K4me3 in a 500 kb region surrounding the MYT1 gene and 4D shows the distribution of H3K9me3 in a 200 kb region surrounding ZNF12. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/52150/52150fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Histone epigenetic profiles for six different histone modifications associated with gene expression were generated (H3K4me3, H3K9ac, H3K9/14ac, H3K4me2, H3K79me3 and H3K36me3). Figure 4A shows ChIP-seq profiles along the chromosome X for the different histone markers. The highly peak correlation observed between the 6 different histone profiles indicates the capabilities of automated system to generate accurate and reliable data. Figure 4B, 4C and 4D show the distribution of peaks for different histone modifications at specific genomic regions.
Automated chromatin immunoprecipitation experiments down to 200 cells
The minimum amount of cells that can be used in ChIP experiments depends on the quality of the chromatin, the specificity and sensitivity of the antibody and the abundance of the histone modification or protein studied. Selecting good ChIP-seq grade antibodies is important when working with limited amounts of sample and the selection of optimal reagents and different carriers improves the efficiency of the DNA recovery and contribute to the success of the ChIP experiment. To determine the minimum amount of cells that the automated ChIP protocol can process, different quantities of chromatin, antibody, and magnetic beads were tested in the IP-Star automated system using ChIP reagents specifically optimized to work with low quantities of chromatin.
First, chromatin from 10,000 cells was sonicated as described in the protocol. The ChIP results were confirmed by qPCR (Figure 5A) which shows significant enrichments with H3K4me3 antibody in positive control regions and negligible signal in negative control regions. For comparison and proof of consistency, additional data obtained with H3K27ac, H3K9me3, and H3K27me3 antibodies, using 10,000 cells is provided.
Automated ChIP experiments were performed then to demonstrate the capabilities of the automated system to work with low quantities of cells using the same H3K4me3 antibody. The automated ChIP performed well, demonstrated by a series of ten IP reactions that were reproducible and highly comparable with the manual ChIP results (Figure 5B). Manual and automated experiments were performed and benefits of the automated protocols were seen in reducing experiment to experiment variability (Figure 5C).
<img alt="Figure 5" src="/files/ftp_upload/52150/52150fig5highres.jpg" /> 
Figure 5. Optimization of ChIP and Auto ChIP experiments on 10,000 cells Manual ChIP experiments were performed on 10,000 cells and using 0.25 &#956;g of H3K4me3, 0.1 &#956;g of H3K27ac, 0.5 &#956;g of H3K9me3 and 0.25 &#956;g of H3K27me3 antibodies. Identical quantities of rabbit IgG were used as a control. The qPCR was performed with primers for two positive loci and two negative loci for each ChIP assay. Figure 5A shows the recovery, expressed as a percentage of input (the relative amount of immunoprecipitated DNA compared to input DNA after qPCR analysis). Figure 5B shows 10 ChIP reactions run on the IP-Star Compact using 0.25 &#956;g H3K4me3 polyclonal antibody and 0.25 &#956;g of rabbit IgG as negative control antibody. Then qPCR analysis was performed with primers for the positive loci EIF4A2 promoter and GAPDH TSS and the negative loci Myoglobin exon2 and Sat2. The figure shows the recovery, expressed as a percent of input (relative amount of immunoprecipitated DNA compared to input DNA after qPCR analysis). Figure 5C shows H3K4me3 ChIP data of 10 manual ChIP experiments in comparison to 10 automated ChIP experiments. Error bars represent standard deviations of each of the ten replicates. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/52150/52150fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
In order to understand the sensitivity of the automated ChIP protocols, experiments were performed using an amount of cells that ranged from 100,000 down to 200 cells per IP. The anti-H3K27me3 antibody was used as it is a very common histone modification. The use of other histone or non-histone antibodies may require more or less cells, depending on the abundance of the epitope and the quality of the antibody. The experiments were validated by quantitative PCR and it was observed that by reducing amounts of beads and antibody background in the experiments is reduced allowing successful ChIP-qPCR results with as little as 200 cells antibody (Figure 6).
<img alt="Figure 6" src="/files/ftp_upload/52150/52150fig6highres.jpg" /> 
Figure 6. Automated ChIP assays on 200 cells. Hela-S3 cells and antibody directed against H3K27me3. Chromatin was sheared from 1 million cells and serial dilutions of this chromatin (from 100,000 to 200 cell equivalent) were used per ChIP reaction. 1 &#181;g of H3K27me3 and 10 &#956;l of protein A-coated magnetic beads were used on 100,000 cells experiment, 0.5 &#181;g of H3K27me3 and 10 &#181;l of beads on 10,000 and 1,000 cells, and 0.25 &#181;g of H3K27me3 and 5 &#181;l of beads with 500 and 200 cells. 1 &#181;g and 0.5 of &#956;g of rabbit IgG were used as negative control antibody when performing experiments with 100,000 cells and 1,000 cells respectively. 6A shows the occupancy of TSH2B and GAPDH genes in % over input. 6B shows relative occupancy of TSH2B versus negative GAPDH control genomic region.
Downstream analysis of ChIP-seq results on 10,000 cells 
In order to assess the global quality of the automated ChIP-seq experiments with low starting numbers of cells, automated ChIP-seq assays were performed with 0.25 &#181;g of the H3K4me3 antibody on 10,000 HeLa cells and ChIP experiments on 100,000 HeLa-S3 cells were used as positive control for the experiment. The automated libraries were prepared using the MicroPlex library preparation kit reagents adapted to prepared libraries with low DNA amounts. Note that even though it is possible to perform successful automated ChIP-experiments with less than 10,000 cells, the amounts of pulled down DNA will not be enough to prepare libraries using the kit reagents. Cluster generation and sequencing were performed according to the manufacturer&#8217;s instructions. The bioinformatics analyses after the sequencing show outstanding results from the low cell number ChIP samples. The 30 pg dataset (corresponding to 10,000 cells of starting material) contain low background noise and highly reliable enrichment peaks which are confirmed by both the 300 pg dataset (corresponding to 100,000 cells of starting material) and the H3K4me3 dataset generated by the Broad Institute for the ENCODE project which was used as an external reference. It is important to note the Top 40 overlap ratio data, which refers to a standard method used in the ENCODE11 project in which the ChIP-seq is considered reproducible if comparing two datasets there is at least an 80% overlap of the best 40% of the peaks ranked by significance score. The 30 pg dataset fulfills these criteria when compared to both the 300 pg dataset (considering all of its peaks, not just the best 40%) and the Broad Institute data (Table 1). The 300 pg dataset shows almost identical peaks to Broad Institute data with a 98% Top 40 overlap ratio (Figure 7).
<img alt="Figure 7" src="/files/ftp_upload/52150/52150fig7highres.jpg" /> 
Figure 7. ChIP assays and library generation on 10,000 cells ChIP-seq experiments were generated on 10,000 and 100,000 HeLa-S3 cells using H3K4me3 antibody (0.25 &#181;g/&#181;l). The 35 bp tags were mapped to the human genome with the ELAND aligner. During the subsequent peak calling SICER could reliably identify the enrichments from low cell numbers as well as from millions of cells. The datasets were analyzed and compared with each other and to the reference data generated by the Broad Institute. The low cell samples are consistent and have very high similarity. The 30 pg sample fulfills the ENCODE criteria11 (min. 80% of the top 40% of the peaks should overlap). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/52150/52150fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Table 1" src="/files/ftp_upload/52150/52150table1.jpg" /> 
Table 1.

Watch this Scientific Journal Video about Automating ChIP-seq Experiments to Generate Epigenetic Profiles on 10,000 HeLa Cells at JoVE.com

Automating ChIP-seq Experiments to Generate Epigenetic Profiles on 10,000 HeLa Cells

Methods for mapping in vivo protein-DNA interactions are becoming crucial for every aspect of genomic research but they are laborious, costly, and time consuming. Here a commercially available robotic liquid handling system that automates chromatin immunoprecipitation for mapping in vivo protein-DNA interactions with limited amounts of cells is presented.

Chromatin immunoprecipitation followed by next generation sequencing (ChIP-seq) is a technique of choice for studying protein-DNA interactions. ChIP-seq ...

automating-chip-seq-experiments-to-generate-epigenetic-profiles-on

Nanyang Technological University

Research

JoVE Journal

Biology

18.6K Views.  Diagenode S.A.. Methods for mapping in vivo protein-DNA interactions are becoming crucial for every aspect of genomic research but they are laborious, costly, and time consuming. Here a commercially available robotic liquid handling system that automates chromatin immunoprecipitation for mapping in vivo protein-DNA interactions with limited amounts of cells is presented.

Video: Automating ChIP-seq Experiments to Generate Epigenetic Profiles on 10,000 HeLa Cells

Analyzing the Function of Small GTPases by Microinjection of Plasmids into Polarized Epithelial Cells

Epithelial cells polarize their plasma membrane into biochemically and functionally distinct apical and basolateral domains where the apical domain faces the 'free' surfaces and the basolateral membrane is in contact with the substrate and neighboring cells. Both membrane domains are separated by tight junctions, which form a diffusion barrier. Apical-basolateral polarization can be recapitulated successfully in culture when epithelial cells such as Madin-Darby Canine Kidney (MDCK) cells are seeded at high density on polycarbonate filters and cultured for several days 1 2. Establishment and maintenance of cell polarity is regulated by an array of small GTPases of the Ras superfamily such as RalA, Cdc42, Rab8, Rab10 and Rab13 3 4 5 6 7. Like all GTPases these proteins cycle between an inactive GDP-bound state and an active GTP-bound state. Specific mutations in the nucleotide binding regions interfere with this cycling 8. For example, Rab13T22N is permanently locked in the GDP-form and thus dubbed 'dominant negative', whereas Rab13Q67L can no longer hydrolyze GTP and is thus locked in a 'dominant active' state 7. To analyze their function in cells both dominant negative and dominant active alleles of GTPases are typically expressed at high levels to interfere with the function of the endogenous proteins 9. An elegant way to achieve high levels of overexpression in a short amount of time is to introduce the plasmids encoding the relevant proteins directly into the nuclei of polarized cells grown on filter supports using microinjection technique. This is often combined with the co-injection of reporter plasmids that encode plasma membrane receptors that are specifically sorted to the apical or basolateral domain. A cargo frequently used to analyze cargo sorting to the basolateral domain is a temperature sensitive allele of the vesicular stomatitis virus glycoprotein (VSVGts045) 10. This protein cannot fold properly at 39&deg;C and will thus be retained in the endoplasmic reticulum (ER) while the regulatory protein of interest is assembled in the cytosol. A shift to 31&deg;C will then allow VSVGts045 to fold properly, leave the ER and travel to the plasma membrane 11. This chase is typically performed in the presence of cycloheximide to prevent further protein synthesis leading to cleaner results. Here we describe in detail the procedure of microinjecting plasmids into polarized cells and subsequent incubations including temperature shifts that allow a comprehensive analysis of regulatory proteins involved in basolateral sorting.

This article details the procedures involved in overexpression and analysis of small GTPases in polarized epithelial cells using microinjection technique.

Epithelial cells polarize their plasma membrane into biochemically and functionally distinct apical and basolateral domains where the apical domain faces ...

Performing Custom MicroRNA Microarray Experiments

microRNAs (miRNAs) are a large family of &tilde; 22 nucleotides (nt) long RNA molecules that are widely expressed in eukaryotes 1. Complex genomes encode at least hundreds of miRNAs, which primarily inhibit the expression of a vast number of target genes post-transcriptionally 2, 3. miRNAs control a broad range of biological processes 1. In addition, altered miRNA expression has been associated with human diseases such as cancers, and miRNAs may serve as biomarkers for diseases and prognosis 4, 5. It is important, therefore, to understand the expression and functions of miRNAs under many different conditions.
Three major approaches have been employed to profile miRNA expression: real-time PCR, microarray, and deep sequencing. The technique of miRNA microarray has the advantage of being high-throughput, generally less expensive, and most of the experimental and analysis steps can be carried out in a molecular biology laboratory at most universities, medical schools and associated hospitals. Here, we describe a method for performing custom miRNA microarray experiments. A miRNA probe set will be printed on glass slides to produce miRNA microarrays. RNA is isolated using a method or reagent that preserves small RNA species, and then labeled with a fluorescence dye. As a control, reference DNA oligonucleotides corresponding to a subset of miRNAs are also labeled with a different fluorescence dye. The reference DNA will serve to demonstrate the quality of the slide and hybridization and will also be used for data normalization. The RNA and DNA are mixed and hybridized to a microarray slide containing probes for most of the miRNAs in the database. After washing, the slide is scanned to obtain images, and intensities of the individual spots quantified. These raw signals will be further processed and analyzed as the expression data of the corresponding miRNAs. Microarray slides can be stripped and regenerated to reduce the cost of microarrays and to enhance the consistency of microarray experiments. The same principles and procedures are applicable to other types of custom microarray experiments.

A simple procedure of performing custom microRNA microarray experiments is described. The steps include isolating RNA, labeling RNA and reference DNA, hybridizing the samples to microarrays, scanning the microarrays, quantifying and analyzing hybridization signals.

microRNAs (miRNAs) are a large family of &tilde; 22 nucleotides (nt) long RNA molecules that are widely expressed in eukaryotes 1.  Complex genomes encode ...

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 ...

Induction and Analysis of Epithelial to Mesenchymal Transition

Epithelial to mesenchymal transition (EMT) is essential for proper morphogenesis during development. Misregulation of this process has been implicated as a key event in fibrosis and the progression of carcinomas to a metastatic state. Understanding the processes that underlie EMT is imperative for the early diagnosis and clinical control of these disease states. Reliable induction of EMT in vitro is a useful tool for drug discovery as well as to identify common gene expression signatures for diagnostic purposes. Here we demonstrate a straightforward method for the induction of EMT in a variety of cell types. Methods for the analysis of cells pre- and post-EMT induction by immunocytochemistry are also included. Additionally, we demonstrate the effectiveness of this method through antibody-based array analysis and migration/invasion assays.

A straightforward method is described for the induction of epithelial to mesenchymal transition (EMT) in a variety of cell types. Methods for the analysis of cells in EMT states by immunocytochemistry are included.

Epithelial  to mesenchymal transition (EMT) is essential for proper morphogenesis during  development. Misregulation of this  process has been implicated ...

Generation of Myospheres From hESCs by Epigenetic Reprogramming

Generation of a homogeneous and abundant population of skeletal muscle cells from human embryonic stem cells (hESCs) is a requirement for cell-based therapies and for a "disease in a dish" model of human neuromuscular diseases. Major hurdles, such as low abundance and heterogeneity of the population of interest, as well as a lack of protocols for the formation of three-dimensional contractile structures, have limited the applications of stem cells for neuromuscular disorders. We have designed a protocol that overcomes these limits by ectopic introduction of defined factors in hESCs - the muscle determination factor MyoD and SWI/SNF chromatin remodeling complex component BAF60C - that are able to reprogram hESCs into skeletal muscle cells. Here we describe the protocol established to generate hESC-derived myoblasts and promote their clustering into tridimensional miniaturized structures (myospheres) that functionally mimic miniaturized skeletal muscles7.

Here, we describe a protocol based on epigenetic reprogramming of human embryonic stem cells (hESCs) toward generating a homogeneous population of skeletal muscle progenitors that under permissive culture conditions form three-dimensional clusters of contractile myofibers (myospheres), which recapitulate biological features of human skeletal muscles.

Generation of a homogeneous and abundant population of skeletal muscle cells from human embryonic stem cells (hESCs) is a requirement for cell-based ...

MicroRNA Expression Profiles of Human iPS Cells, Retinal Pigment Epithelium Derived From iPS, and Fetal Retinal Pigment Epithelium

The objective of this report is to describe the protocols for comparing the microRNA (miRNA) profiles of human induced-pluripotent stem (iPS) cells, retinal pigment epithelium (RPE) derived from human iPS cells (iPS-RPE), and fetal RPE. The protocols include collection of RNA for analysis by microarray, and the analysis of microarray data to identify miRNAs that are differentially expressed among three cell types. The methods for culture of iPS cells and fetal RPE are explained. The protocol used for differentiation of RPE from human iPS is also described. The RNA extraction technique we describe was selected to allow maximal recovery of very small RNA for use in a miRNA microarray. Finally, cellular pathway and network analysis of microarray data is explained. These techniques will facilitate the comparison of the miRNA profiles of three different cell types.

The microRNA (miRNA) profiles of human induced-pluripotent stem (iPS) cells, retinal pigment epithelium (RPE) derived from human induced-pluripotent stem (iPS) cells (iPS-RPE), and fetal RPE, were compared.

The objective of this report is to describe the protocols for comparing the microRNA (miRNA) profiles of human induced-pluripotent stem (iPS) cells, ...

Toxicological Assays for Testing Effects of an Epigenetic Drug on Development, Fecundity and Survivorship of Malaria Mosquitoes

Insecticidal resistance poses a major problem for malaria control programs. Mosquitoes adapt to a wide range of changes in the environment quickly, making malaria control an omnipresent problem in tropical countries. The emergence of insecticide resistant populations warrants the exploration of novel drug target pathways and compounds for vector mosquito control. Epigenetic drugs are well established in cancer research, however not much is known about their effects on insects. This study provides a simple protocol for examining the toxicological effects of 3-Deazaneplanocin A (DZNep), an experimental epigenetic drug for cancer therapy, on the malaria vector, Anopheles gambiae. A concentration-dependent increase in mortality and decrease in size was observed in immature mosquitoes exposed to DZNep, whereas the compound reduced the fecundity of adult mosquitoes relative to control treatments. In addition, there was a drug-dependent decrease in S-adenosylhomocysteine (SAH) hydrolase activity in mosquitoes following exposure to DZNep relative to control treatments. These protocols provide the researcher with a simple, step-by-step procedure to assess multiple toxicological endpoints for an experimental drug and, in turn, demonstrate a unique multi-prong approach for exploring the toxicological effects of water-soluble epigenetic drugs or compounds of interest against vector mosquitoes and other insects.

A protocol is developed to examine the effects of an epigenetic drug DZNep on the development, fecundity and survivorship of mosquitoes. Here we describe procedures for the aqueous exposure of DZNep to immature mosquitoes and a blood-based exposure of DZNep to adult mosquitoes in addition to measuring SAH hydrolase inhibition.

Insecticidal resistance poses a major problem for malaria control programs. Mosquitoes adapt to a wide range of changes in the environment quickly, making ...

HeLa Based Cell Free Expression Systems for Expression of Plasmodium Rhoptry Proteins

Malaria causes significant global morbidity and mortality. No routine vaccine is currently available. One of the major reasons for lack of a vaccine is the challenge of identifying suitable vaccine candidates. Malarial proteins expressed using prokaryotic and eukaryotic cell based expression systems are poorly glycosylated, generally insoluble and undergo improper folding leading to reduced immunogenicity. The wheat germ, rabbit reticulocyte lysate and Escherichia coli lysate cell free expression systems are currently used for expression of malarial proteins. However, the length of expression time and improper glycosylation of proteins still remains a challenge. We demonstrate expression of Plasmodium proteins in vitro using HeLa based cell free expression systems, termed &#8220;in vitro human cell free expression systems&#8221;. The 2 HeLa based cell free expression systems transcribe mRNA in 75 min and 3 &#181;l of transcribed mRNA is sufficient to translate proteins in 90 min. The 1-step expression system is a transcription and translation coupled expression system; the transcription and co-translation occurs in 3 hr. The process can also be extended for 6 hr by providing additional energy. In the 2-step expression system, mRNA is first transcribed and then added to the translation mix for protein expression. We describe how to express malaria proteins; a hydrophobic PF3D7_0114100 Maurer&#8217;s Cleft &#8211; 2 transmembrane (PfMC-2TM) protein, a hydrophilic PF3D7_0925900 protein and an armadillo repeats containing protein PF3D7_1361800, using the HeLa based cell free expression system. The proteins are expressed in micro volumes employing 2-step and 1-step expression strategies. An affinity purification method to purify 25 &#181;l of proteins expressed using the in vitro human cell free expression system is also described. Protein yield is determined by Bradford&#8217;s assay and the expressed and purified proteins can be confirmed by western blotting analysis. Expressed recombinant proteins can be used for immunizations, immunoassays and protein sequencing.

HeLa Based Cell Free Expression Systems for Expression of Plasmodium Rhoptry Proteins

Expression of malarial proteins in cell based systems remains challenging. We demonstrate two step and one step IVT (in vitro translation) cell free expression systems for expressing malarial recombinant rhoptry proteins from HeLa cells. We use a Ni-resin affinity based purification system to purify the rhoptry proteins.

Malaria causes significant global morbidity and mortality. No routine vaccine is currently available. One of the major reasons for lack of a vaccine is ...

Measurement and Analysis of Extracellular Acid Production to Determine Glycolytic Rate

Extracellular measurement of oxygen consumption and acid production is a simple and powerful way to monitor rates of respiration and glycolysis1. Both mitochondrial (respiration) and non-mitochondrial (other redox) reactions consume oxygen, but these reactions can be easily distinguished by chemical inhibition of mitochondrial respiration. However, while mitochondrial oxygen consumption is an unambiguous and direct measurement of respiration rate2, the same is not true for extracellular acid production and its relationship to glycolytic rate 3-6. Extracellular acid produced by cells is derived from both lactate, produced by anaerobic glycolysis, and CO2, produced in the citric acid cycle during respiration. For glycolysis, the conversion of glucose to lactate- + H+ and the export of products into the assay medium is the source of glycolytic acidification. For respiration, the export of CO2, hydration to H2CO3 and dissociation to HCO3- + H+ is the source of respiratory acidification. The proportions of glycolytic and respiratory acidification depend on the experimental conditions, including cell type and substrate(s) provided, and can range from nearly 100% glycolytic acidification to nearly 100% respiratory acidification 6. Here, we demonstrate the data collection and calculation methods needed to determine respiratory and glycolytic contributions to total extracellular acidification by whole cells in culture using C2C12 myoblast cells as a model.

Glycolysis is a defining metabolic marker in multiple biological systems. Monitoring glycolysis by measuring the extracellular flux of H+ is common, but requires correction to be quantitative and unambiguous. Here, we demonstrate how to gather and correct extracellular flux data to distinguish between respiratory and glycolytic sources of extracellular acidification.

Extracellular measurement of oxygen consumption and acid production is a simple and powerful way to monitor rates of respiration and glycolysis1. Both ...

Nephrotoxin Microinjection in Zebrafish to Model Acute Kidney Injury

The kidneys are susceptible to harm from exposure to chemicals they filter from the bloodstream. This can lead to organ injury associated with a rapid decline in renal function and development of the clinical syndrome known as acute kidney injury (AKI). Pharmacological agents used to treat medical circumstances ranging from bacterial infection to cancer, when administered individually or in combination with other drugs, can initiate AKI. Zebrafish are a useful animal model to study the chemical effects on renal function in vivo, as they form an embryonic kidney comprised of nephron functional units that are conserved with higher vertebrates, including humans. Further, zebrafish can be utilized to perform genetic and chemical screens, which provide opportunities to elucidate the cellular and molecular facets of AKI and develop therapeutic strategies such as the identification of nephroprotective molecules. Here, we demonstrate how microinjection into the zebrafish embryo can be utilized as a paradigm for nephrotoxin studies.

Renal injuries incurred from nephrotoxins, which include drugs ranging from antibiotics to chemotherapeutics, can result in complex disorders whose pathogenesis remains incompletely understood. This protocol demonstrates how zebrafish can be used for disease modeling of these conditions, which can be applied to the identification of renoprotective measures.

The kidneys are susceptible to harm from exposure to chemicals they filter from the bloodstream. This can lead to organ injury associated with a rapid ...

Automating ChIP-seq Experiments to Generate Epigenetic Profiles on 10,000 HeLa Cells

Summary

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Chapters in this video

Analyzing the Function of Small GTPases by Microinjection of Plasmids into Polarized Epithelial Cells

Performing Custom MicroRNA Microarray Experiments

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

Induction and Analysis of Epithelial to Mesenchymal Transition

Generation of Myospheres From hESCs by Epigenetic Reprogramming

MicroRNA Expression Profiles of Human iPS Cells, Retinal Pigment Epithelium Derived From iPS, and Fetal Retinal Pigment Epithelium

Toxicological Assays for Testing Effects of an Epigenetic Drug on Development, Fecundity and Survivorship of Malaria Mosquitoes

HeLa Based Cell Free Expression Systems for Expression of Plasmodium Rhoptry Proteins

Measurement and Analysis of Extracellular Acid Production to Determine Glycolytic Rate

Nephrotoxin Microinjection in Zebrafish to Model Acute Kidney Injury