We thank Drs. David Sanchez-Martin and Christopher B. Buck for comments during the conceptualization stage of the project. We also thank the Genomics Core, Center for Cancer Research, National Cancer Institute, National Institutes of Health for help in preliminary experiments, and the Collaborative Bioinformatics Resource, CCR, NCI, NIH for advice in computational analysis. We thank Ms. Anna Word for helping with the optimization of DNA polymerases used in the method. This work utilized the computational resources of the NIHHPC Biowulf cluster (http://hpc.nih.gov). This project is supported by the Intramural Program of the Center for Cancer Research, National Cancer Institute, National Institutes of Health, the NCI Director&#39;s Innovation Award (#397172), and Federal funds from the National Cancer Institute under Contract No. HHSN261200800001E. We thank Drs. Tom Misteli, Carol Thiele, Douglas R. Lowy, and all members of Laboratory of Cellular Oncology for productive comments.&#160;

<ol>
	<li>Perkel, J. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-cell+analysis+enters+the+multiomics+age.">Single-cell analysis enters the multiomics age.</a> Nature. 595 (7868), 614-616 (2021).</li><li>Clark, S. J., 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=scNMT-seq+enables+joint+profiling+of+chromatin+accessibility+DNA+methylation+and+transcription+in+single+cells.">scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells.</a> Nature Communications. 9 (1), 781 (2018).</li><li>Ma, 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=Chromatin+potential+identified+by+shared+single-cell+profiling+of+RNA+and+Chromatin.">Chromatin potential identified by shared single-cell profiling of RNA and Chromatin.</a> Cell. 183 (4), 1103-1116 (2020).</li><li>Zhu, 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=An+ultra+high-throughput+method+for+single-cell+joint+analysis+of+open+chromatin+and+transcriptome.">An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome.</a> Nature Structural &amp; Molecular Biology. 26 (11), 1063-1070 (2019).</li><li>Zhu, 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=Joint+profiling+of+histone+modifications+and+transcriptome+in+single+cells+from+mouse+brain.">Joint profiling of histone modifications and transcriptome in single cells from mouse brain.</a> Nature Methods. 18 (3), 283-292 (2021).</li><li>Mimitou, E. P., 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=Scalable,+multimodal+profiling+of+chromatin+accessibility,+gene+expression+and+protein+levels+in+single+cells.">Scalable, multimodal profiling of chromatin accessibility, gene expression and protein levels in single cells.</a> Nature Biotechnology. 39 (10), 1246-1258 (2021).</li><li>Ohnuki, H., Venzon, D. J., Lobanov, A., Tosato, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Iterative+epigenomic+analyses+in+the+same+single+cell.">Iterative epigenomic analyses in the same single cell.</a> Genome Research. 31 (10), 1819-1830 (2021).</li><li>Tosato, G., Ohnuki, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Methods of preparing a re-usable single cell and methods fro analyzing the epigenome, transcriptome, and genome of a single cell. , (2021).</li><li>Harada, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+chromatin+integration+labelling+method+enables+epigenomic+profiling+with+lower+input.">A chromatin integration labelling method enables epigenomic profiling with lower input.</a> Nature Cell Biology. 21 (2), 287-296 (2019).</li><li>Egelhofer, T. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+assessment+of+histone-modification+antibody+quality.">An assessment of histone-modification antibody quality.</a> Nature Structural &amp; Molecular Biology. 18 (1), 91-93 (2011).</li><li>Marina, R. J., 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=TET-catalyzed+oxidation+of+intragenic+5-methylcytosine+regulates+CTCF-dependent+alternative+splicing.">TET-catalyzed oxidation of intragenic 5-methylcytosine regulates CTCF-dependent alternative splicing.</a> EMBO Journal. 35 (3), 335-355 (2016).</li><li>Weast, R. C., Weast, R. C., Astle, M. J., Beyer, W. H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Handbook of chemistry and physics: a ready-reference book of chemical and physical data. 56th edn. , (1975).</li><li>Aydin, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+short+history,+principles,+and+types+of+ELISA,+and+our+laboratory+experience+with+peptide/protein+analyses+using+ELISA.">A short history, principles, and types of ELISA, and our laboratory experience with peptide/protein analyses using ELISA.</a> Peptides. 72, 4-15 (2015).</li><li>Gan, S. D., Patel, K. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Enzyme+immunoassay+and+enzyme-linked+immunosorbent+assay.">Enzyme immunoassay and enzyme-linked immunosorbent assay.</a> Journal of Investigative Dermatology. 133 (9), 12 (2013).</li><li>Porstmann, T., Kiessig, S. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Enzyme+immunoassay+techniques.+An+overview.">Enzyme immunoassay techniques. An overview.</a> Journal of Immunological Methods. 150 (1-2), 5-21 (1992).</li><li>Chao, H. P., 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=Systematic+evaluation+of+RNA-Seq+preparation+protocol+performance.">Systematic evaluation of RNA-Seq preparation protocol performance.</a> BMC Genomics. 20 (1), 571 (2019).</li><li>Song, 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=A+comparative+analysis+of+library+prep+approaches+for+sequencing+low+input+translatome+samples.">A comparative analysis of library prep approaches for sequencing low input translatome samples.</a> BMC Genomics. 19 (1), 696 (2018).</li><li>Rotem, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-cell+ChIP-seq+reveals+cell+subpopulations+defined+by+chromatin+state.">Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state.</a> Nature Biotechnology. 33 (11), 1165-1172 (2015).</li><li>Ku, W. L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-cell+chromatin+immunocleavage+sequencing+(scChIC-seq)+to+profile+histone+modification.">Single-cell chromatin immunocleavage sequencing (scChIC-seq) to profile histone modification.</a> Nature Methods. 16 (4), 323-325 (2019).</li><li>Carter, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mapping+histone+modifications+in+low+cell+number+and+single+cells+using+antibody-guided+chromatin+tagmentation+(ACT-seq).">Mapping histone modifications in low cell number and single cells using antibody-guided chromatin tagmentation (ACT-seq).</a> Nature Communications. 10 (1), 3747 (2019).</li><li>Maskell, D. P., 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=Structural+basis+for+retroviral+integration+into+nucleosomes.">Structural basis for retroviral integration into nucleosomes.</a> Nature. 523 (7560), 366-369 (2015).</li></ol>

Drs. Ohnuki and Tosato are co-inventors on a patent entitled "Methods for preparing a reusable single cell and methods for analyzing the epigenome, transcriptome and genome of a single cell" (EP3619307 and US20200102604). The patent application was filed in part based on preliminary results related to the technology described in the current manuscript. The invention or inventions described and claimed in this patent application were made while the inventors were full-time employees of the U.S. Government. Therefore, under 45 Code of Federal Regulations Part 7, all rights, title, and interest to this patent application have been or should by law be assigned to the U.S. Government. The U.S. Government conveys a portion of the royalties it receives to its employee inventors under 15 U.S. Code &#167; 3710c.

This article describes the step-by-step protocol for the recently reported single-cell multiepigenomic analysis using reusable single cells7. In the subsequent paragraphs, we discuss critical points, emphasizing potential limitations in the protocol. 
One of the critical points throughout the protocol (from steps 7.2-13) is avoiding DNase contamination. A single cell only has two copies of genomic DNA. Therefore, damaging genomic DNA critically reduces the signal number...

Single-cell technology is entering the era of single-cell multiomics, which integrates individual single-cell omics technologies1. Recently, single-cell transcriptomics has been combined with methods for detecting chromatin accessibility (scNMT-seq2 and SHARE-seq3) or histone modifications (Paired-seq4 and Paired-Tag5). More recently, single-cell transcriptomics and proteomics were integrated with chromatin accessibility (DOGMA-seq6). These methods use transposase-based tagging for detecting chromatin accessibility or histone modifications.
Transposase-based approaches cleave genomic DNA and add a DNA barcode at the end of the genomic DNA fragment. Each cleaved genomic fragment can only accept up to two DNA barcodes (= one epigenetic mark per cleavage site), and the genomic DNA at the cleavage site is lost. Therefore, cleavage-based approaches have a trade-off between the number of epigenetic marks tested and the signal density. This hampers the analysis of multiple epigenetic marks in the same single cell. A single-cell epigenomic method that does not cleave the genomic DNA was developed to overcome this issue7,8.
In addition to the cleavage-derived issue mentioned above, transposase-based approaches have other limitations. In single-cell epigenome analysis, it is critical to know the location of histones and DNA-associated proteins on the genome. In current approaches, this is accomplished by using unfixed single cells and retention of protein-DNA and protein-protein interactions. However, this generates strong bias to accessible chromatin regions, even in the analysis of histone modifications 9. The location of histones and genome-associated proteins on the genome can be preserved without crosslinking protein-DNA and protein-protein, using a polyacrylamide scaffold7,8. This approach reduces the structural bias observed in current approaches that depend upon protein-DNA and protein-protein interactions.
Transposase-based approaches can acquire signals only once from a single cell. Therefore, it is difficult to delineate the complete epigenome of a single cell due to the drop-off of the signals. Reusable single cells have been developed to overcome current limitations by allowing iterative epigenomic analysis in the same single cell.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>10x CutSmart buffer</td>
			<td>New England BioLabs</td>
			<td>B6004</td>
			<td>10x Digestion buffer</td>
		</tr>
		<tr>
			<td>200 proof ethanol</td>
			<td>Warner-Graham Company</td>
			<td>200 proof</td>
			<td>Ethanol</td>
		</tr>
		<tr>
			<td>5-Hydroxymethylcytosine (5-hmC) Monoclonal Antibody [HMC/4D9]</td>
			<td>Epigentek</td>
			<td>A-1018-100</td>
			<td>Anti-5hmC</td>
		</tr>
		<tr>
			<td>Acridine Orange/Propidium Iodide Stain</td>
			<td>Logos Biosystems</td>
			<td>F23001</td>
			<td>Cell counter</td>
		</tr>
		<tr>
			<td>Acrylamide solution, 40% in H2O, for molecular biology</td>
			<td>MilliporeSigma</td>
			<td>01697-500ML</td>
			<td>40% acrylamide solution</td>
		</tr>
		<tr>
			<td>All-in-One Fluorescence Microscope BZ-X710</td>
			<td>Keyence</td>
			<td>BZ-X710</td>
			<td>Scanning microscope</td>
		</tr>
		<tr>
			<td>Amicon Ultra-0.5 Centrifugal Filter Unit</td>
			<td>MilliporeSigma</td>
			<td>UFC510024</td>
			<td>Ultrafiltration cassette</td>
		</tr>
		<tr>
			<td>Ammonium persulfate for molecular biology</td>
			<td>MilliporeSigma</td>
			<td>A3678-100G</td>
			<td>Ammonium persulfate powder</td>
		</tr>
		<tr>
			<td>Anhydrous DMF</td>
			<td>Vector laboratories</td>
			<td>S-4001-005</td>
			<td>Anhydrous N,N-dimethylformamide (DMF)</td>
		</tr>
		<tr>
			<td>Anti-RNA polymerase II CTD repeat YSPTSPS (phospho S5) antibody [4H8]</td>
			<td>Abcam</td>
			<td>ab5408</td>
			<td>Anti-Pol II</td>
		</tr>
		<tr>
			<td>Anti-TRAP220/MED1 (phospho T1457) antibody</td>
			<td>Abcam</td>
			<td>ab60950</td>
			<td>Anti-Med1</td>
		</tr>
		<tr>
			<td>BciVI</td>
			<td>New England BioLabs</td>
			<td>R0596L</td>
			<td>BciVI</td>
		</tr>
		<tr>
			<td>Bovine Serum Albumin solution, 20 mg/mL in H2O, low bioburden, protease-free, for molecular biology</td>
			<td>MilliporeSigma</td>
			<td>B8667-5ML</td>
			<td>20% BSA (Table 7)</td>
		</tr>
		<tr>
			<td>Bst DNA Polymerase, Large Fragment</td>
			<td>New England BioLabs</td>
			<td>M0275L</td>
			<td>Bst DNA polymerase</td>
		</tr>
		<tr>
			<td>BT10 Series 10 &#181;l Barrier Tip</td>
			<td>NEPTUNE</td>
			<td>BT10</td>
			<td>P10 low-retention tip</td>
		</tr>
		<tr>
			<td>CellCelector</td>
			<td>Automated Lab Solutions</td>
			<td>N/A</td>
			<td>Automated single cell picking robot</td>
		</tr>
		<tr>
			<td>CellCelector 4 nl nanowell plates for single cell cloning, Plate S200-100 100K, 24 well,ULA</td>
			<td>Automated Lab Solutions</td>
			<td>CC0079</td>
			<td>4 nL nanowell plate</td>
		</tr>
		<tr>
			<td>Chloroform</td>
			<td>MilliporeSigma</td>
			<td></td>
			<td>Chloroform</td>
		</tr>
		<tr>
			<td>Corning Costar 96-Well, Cell Culture-Treated, Flat-Bottom Microplate</td>
			<td>Corning</td>
			<td>3596</td>
			<td>Flat-bottom 96-well plates</td>
		</tr>
		<tr>
			<td>Deep Vent (exo-) DNA Polymerase</td>
			<td>New England BioLabs</td>
			<td>M0259L</td>
			<td>Exo- DNA polymerase</td>
		</tr>
		<tr>
			<td>DNA LoBind Tubes, 0.5 mL</td>
			<td>Eppendorf</td>
			<td>30108035</td>
			<td>0.5 mL DNA low-binding tube</td>
		</tr>
		<tr>
			<td>DNA Oligo, 1st random primer</td>
			<td>Integrated DNA Technologies</td>
			<td>N/A, see Table 3</td>
			<td>1st random primer</td>
		</tr>
		<tr>
			<td>DNA Oligo, 2nd random primer Cell#01</td>
			<td>Integrated DNA Technologies</td>
			<td>N/A, see Table 3</td>
			<td>2nd random primer</td>
		</tr>
		<tr>
			<td>DNA Oligo, 2nd random primer Cell#02</td>
			<td>Integrated DNA Technologies</td>
			<td>N/A, see Table 3</td>
			<td>2nd random primer</td>
		</tr>
		<tr>
			<td>DNA Oligo, 2nd random primer Cell#03</td>
			<td>Integrated DNA Technologies</td>
			<td>N/A, see Table 3</td>
			<td>2nd random primer</td>
		</tr>
		<tr>
			<td>DNA Oligo, 2nd random primer Cell#04</td>
			<td>Integrated DNA Technologies</td>
			<td>N/A, see Table 3</td>
			<td>2nd random primer</td>
		</tr>
		<tr>
			<td>DNA Oligo, 2nd random primer Cell#05</td>
			<td>Integrated DNA Technologies</td>
			<td>N/A, see Table 3</td>
			<td>2nd random primer</td>
		</tr>
		<tr>
			<td>DNA Oligo, 2nd random primer Cell#06</td>
			<td>Integrated DNA Technologies</td>
			<td>N/A, see Table 3</td>
			<td>2nd random primer</td>
		</tr>
		<tr>
			<td>DNA Oligo, 2nd random primer Cell#07</td>
			<td>Integrated DNA Technologies</td>
			<td>N/A, see Table 3</td>
			<td>2nd random primer</td>
		</tr>
		<tr>
			<td>DNA Oligo, 2nd random primer Cell#08</td>
			<td>Integrated DNA Technologies</td>
			<td>N/A, see Table 3</td>
			<td>2nd random primer</td>
		</tr>
		<tr>
			<td>DNA Oligo, 2nd random primer Cell#09</td>
			<td>Integrated DNA Technologies</td>
			<td>N/A, see Table 3</td>
			<td>2nd random primer</td>
		</tr>
		<tr>
			<td>DNA Oligo, 2nd random primer Cell#10</td>
			<td>Integrated DNA Technologies</td>
			<td>N/A, see Table 3</td>
			<td>2nd random primer</td>
		</tr>
		<tr>
			<td>DNA Oligo, 2nd random primer Cell#11</td>
			<td>Integrated DNA Technologies</td>
			<td>N/A, see Table 3</td>
			<td>2nd random primer</td>
		</tr>
		<tr>
			<td>DNA Oligo, 2nd random primer Cell#12</td>
			<td>Integrated DNA Technologies</td>
			<td>N/A, see Table 3</td>
			<td>2nd random primer</td>
		</tr>
		<tr>
			<td>DNA Oligo, 2nd synthesis primer</td>
			<td>Integrated DNA Technologies</td>
			<td>N/A, see Table 3</td>
			<td>2nd synthesis primer</td>
		</tr>
		<tr>
			<td>DNA Oligo, Ligation Adaptor</td>
			<td>Integrated DNA Technologies</td>
			<td>N/A, see Table 3</td>
			<td>Ligation Adaptor</td>
		</tr>
		<tr>
			<td>DNA Oligo, Reverse Transcription primer</td>
			<td>Integrated DNA Technologies</td>
			<td>N/A, see Table 3</td>
			<td>Reverse Transcription primer</td>
		</tr>
		<tr>
			<td>DNase I (RNase-free)</td>
			<td>New England BioLabs</td>
			<td>M0303L</td>
			<td>DNase I (RNase-free, 4 U).</td>
		</tr>
		<tr>
			<td>DNase I Reaction Buffer</td>
			<td>New England BioLabs</td>
			<td>B0303S</td>
			<td>10x DNase I buffer (NEB)</td>
		</tr>
		<tr>
			<td>dNTP Mix (10 mM each)</td>
			<td>Thermo Fisher</td>
			<td>R0192</td>
			<td>10 mM dNTPs</td>
		</tr>
		<tr>
			<td>Fetal Bovine Serum, USA origin, Heat-inactivated</td>
			<td>MilliporeSigma</td>
			<td>F4135-500ML</td>
			<td>Fetal bovine serum</td>
		</tr>
		<tr>
			<td>HiScribe T7 High Yield RNA Synthesis Kit</td>
			<td>New England BioLabs</td>
			<td>E2040S</td>
			<td>In-vitro-transcription master mix</td>
		</tr>
		<tr>
			<td>Histone H3K27ac antibody</td>
			<td>Active motif</td>
			<td>39133</td>
			<td>Anti-H3K27ac</td>
		</tr>
		<tr>
			<td>Histone H3K27me3 antibody</td>
			<td>Active motif</td>
			<td>39155</td>
			<td>Anti-H3K27me3</td>
		</tr>
		<tr>
			<td>IgG from rabbit serum</td>
			<td>Millipore Sigma</td>
			<td>I5006-10MG</td>
			<td>Control IgG</td>
		</tr>
		<tr>
			<td>Iron oxide(II,III) magnetic nanopowder, 30 nm avg. part. size (TEM), NHS ester functionalized</td>
			<td>MilliporeSigma</td>
			<td>747467-1G</td>
			<td>NHS ester functionalized 30 nm iron oxide powder</td>
		</tr>
		<tr>
			<td>K-562</td>
			<td>American Type Culture Collection (ATCC)</td>
			<td>CCL-243</td>
			<td>cells</td>
		</tr>
		<tr>
			<td>Linear Acrylamide (5 mg/mL)</td>
			<td>Thermo Fisher</td>
			<td>AM9520</td>
			<td>Linear Acrylamide</td>
		</tr>
		<tr>
			<td>LUNA-FL Dual Fluorescence Cell Counter</td>
			<td>Logos Biosystems</td>
			<td>L20001</td>
			<td>Cell counter</td>
		</tr>
		<tr>
			<td>LUNA Cell Counting Slides, 50 Slides</td>
			<td>Logos Biosystems</td>
			<td>L12001</td>
			<td>Cell counter</td>
		</tr>
		<tr>
			<td>Mineral oil, BioReagent, for molecular biology, light oil</td>
			<td>MilliporeSigma</td>
			<td>M5904-500ML</td>
			<td>Mineral oil</td>
		</tr>
		<tr>
			<td>N,N,N&#8242;,N&#8242;-Tetramethylethylenediamine for molecular biology</td>
			<td>MilliporeSigma</td>
			<td>T7024-100ML</td>
			<td>N,N,N&#8242;,N&#8242;-Tetramethylethylenediamine</td>
		</tr>
		<tr>
			<td>NaCl (5 M), RNase-free</td>
			<td>Thermo Fisher</td>
			<td>AM9760G</td>
			<td>5M NaCl</td>
		</tr>
		<tr>
			<td>NanoDrop Lite</td>
			<td>Thermo Fisher</td>
			<td>2516</td>
			<td>&#160;Microvolume spectrophotometer</td>
		</tr>
		<tr>
			<td>NEST 2 mL 96-Well Deep Well Plate, V Bottom</td>
			<td>Opentrons</td>
			<td>N/A</td>
			<td>2 mL deep well 96-well plate</td>
		</tr>
		<tr>
			<td>Non-skirted 96-well PCR plate</td>
			<td>Genesee Scientific</td>
			<td>27-405</td>
			<td>96-well PCR plate</td>
		</tr>
		<tr>
			<td>NuSive GTG Agarose</td>
			<td>Lonza</td>
			<td>50081</td>
			<td>Agarose</td>
		</tr>
		<tr>
			<td>OmniPur Acrylamide: Bis-acrylamide 19:1, 40% Solution</td>
			<td>MilliporeSigma</td>
			<td>1300-500ML</td>
			<td>40%Acrylamide/Bis-acrylamide</td>
		</tr>
		<tr>
			<td>OT-2 lab robot</td>
			<td>Opentrons</td>
			<td>OT2</td>
			<td>Automated liquid handling robot</td>
		</tr>
		<tr>
			<td>Paraformaldehyde, EM Grade, Purified, 20% Aqueous Solution</td>
			<td>Electron Microscopy Sciences</td>
			<td>15713</td>
			<td>20% Pararmaldehyde</td>
		</tr>
		<tr>
			<td>PBS (10x), pH 7.4</td>
			<td>Thermo Fisher</td>
			<td>70011044</td>
			<td>10x PBS</td>
		</tr>
		<tr>
			<td>PIPETMAN Classic P1000</td>
			<td>GILSON</td>
			<td>F123602</td>
			<td>A P1000 pipette</td>
		</tr>
		<tr>
			<td>Protein LoBind Tubes, 1.5 mL</td>
			<td>Eppendorf</td>
			<td>925000090</td>
			<td>1.5 mL Protein low-binding tube</td>
		</tr>
		<tr>
			<td>QIAgen Gel Extraction kit</td>
			<td>Qiagen</td>
			<td>28706</td>
			<td>A P1000 pipette</td>
		</tr>
		<tr>
			<td>Quant-iT PicoGreen dsDNA Assay</td>
			<td>Thermo Fisher</td>
			<td>P11495</td>
			<td>&#160;dsDNA specific intercalator dye</td>
		</tr>
		<tr>
			<td>Quick Ligation kit</td>
			<td>New England BioLabs</td>
			<td>M2200L</td>
			<td>T4 DNA ligase (NEB)</td>
		</tr>
		<tr>
			<td>RNaseOUT Recombinant Ribonuclease Inhibitor</td>
			<td>Thermo Fisher</td>
			<td>10777019</td>
			<td>RNAse inhibitor</td>
		</tr>
		<tr>
			<td>S-4FB Crosslinker (DMF-soluble)</td>
			<td>Vector laboratories</td>
			<td>S-1004-105</td>
			<td>Succinimidyl 4-formylbenzoate (S-4FB)</td>
		</tr>
		<tr>
			<td>S-HyNic</td>
			<td>Vector laboratories</td>
			<td>S-1002-105</td>
			<td>Succinimidyl 6-hydrazinonicotinate acetone hydrazone (S-HyNic)</td>
		</tr>
		<tr>
			<td>Sodium Acetate, 3 M, pH 5.2, Molecular Biology Grade</td>
			<td>MilliporeSigma</td>
			<td>567422-100ML</td>
			<td>3M Sodium acetate (pH 5.2)</td>
		</tr>
		<tr>
			<td>Sodium bicarbonate, 1M buffer soln., pH 8.5</td>
			<td>Alfa Aesar</td>
			<td>J60408</td>
			<td>1M sodium bicarbonate buffer, pH 8.5</td>
		</tr>
		<tr>
			<td>Sodium phosphate dibasic for molecular biology</td>
			<td>MilliporeSigma</td>
			<td>S3264-250G</td>
			<td>Na2HPO4</td>
		</tr>
		<tr>
			<td>Sodium phosphate monobasic for molecular biology</td>
			<td>MilliporeSigma</td>
			<td>S3139-250G</td>
			<td>NaH2PO4</td>
		</tr>
		<tr>
			<td>SuperScript IV reverse transcriptase</td>
			<td>Thermo Fisher</td>
			<td>18090050</td>
			<td>Reverse transcriptase</td>
		</tr>
		<tr>
			<td>SYBR Gold Nucleic Acid Gel Stain (10,000x Concentrate in DMSO)</td>
			<td>Thermo Fisher</td>
			<td>S11494</td>
			<td>An intercalator dye for DNA</td>
		</tr>
		<tr>
			<td>T4 DNA Ligase Reaction Buffer</td>
			<td>New England BioLabs</td>
			<td>B0202S</td>
			<td>10x T4 DNA ligase reaction buffer</td>
		</tr>
		<tr>
			<td>ThermoPol Reaction Buffer Pack</td>
			<td>New England BioLabs</td>
			<td>B9004S</td>
			<td>10x TPM-T buffer (Tris-HCl/Pottasium chloride/Magnesium sulfate/Triton X-100)</td>
		</tr>
		<tr>
			<td>TRIzol LS reagent</td>
			<td>Thermo Fisher</td>
			<td>10296-028</td>
			<td>Guanidinium thiocyanate-phenol-chloroform extraction</td>
		</tr>
		<tr>
			<td>TruSeq Nano DNA library prep kit</td>
			<td>Illumina</td>
			<td>20015965</td>
			<td>A DNA library preparation kit (see also the manufacturer&#39;s instruction)</td>
		</tr>
		<tr>
			<td>Ultramer DNA Oligo, Anti-5hmC_Ab#005</td>
			<td>Integrated DNA Technologies</td>
			<td>N/A, see Table 3</td>
			<td>An amine-modified DNA probe for antibody</td>
		</tr>
		<tr>
			<td>Ultramer DNA Oligo, Anti-H3K27ac_Ab#002</td>
			<td>Integrated DNA Technologies</td>
			<td>N/A, see Table 3</td>
			<td>An amine-modified DNA probe for antibody</td>
		</tr>
		<tr>
			<td>Ultramer DNA Oligo, Anti-H3K27me3_Ab#003</td>
			<td>Integrated DNA Technologies</td>
			<td>N/A, see Table 3</td>
			<td>An amine-modified DNA probe for antibody</td>
		</tr>
		<tr>
			<td>Ultramer DNA Oligo, Anti-Med1_Ab#004</td>
			<td>Integrated DNA Technologies</td>
			<td>N/A, see Table 3</td>
			<td>An amine-modified DNA probe for antibody</td>
		</tr>
		<tr>
			<td>Ultramer DNA Oligo, Anti-Pol II_Ab#006</td>
			<td>Integrated DNA Technologies</td>
			<td>N/A, see Table 3</td>
			<td>An amine-modified DNA probe for antibody</td>
		</tr>
		<tr>
			<td>Ultramer DNA Oligo, Control IgG_Ab#001</td>
			<td>Integrated DNA Technologies</td>
			<td>N/A, see Table 3</td>
			<td>An amine-modified DNA probe for control IgG</td>
		</tr>
		<tr>
			<td>UltraPure 0.5 M EDTA, pH 8.0</td>
			<td>Thermo Fisher</td>
			<td>15575020</td>
			<td>0.5M EDTA, pH 8.0</td>
		</tr>
		<tr>
			<td>UltraPure DNase/RNase-Free Distilled Water</td>
			<td>Thermo Fisher</td>
			<td>10977023</td>
			<td>Ultrapure water</td>
		</tr>
		<tr>
			<td>Zeba Splin Desalting Columns, 7K MWCO, 0.5 mL</td>
			<td>Thermo Fisher</td>
			<td>89882</td>
			<td>Desalting column</td>
		</tr>
	</tbody>
</table>

reusable single cell for iterative epigenomic analyses

Current single-cell epigenome analyses are designed for single use. The cell is discarded after a single use, preventing analysis of multiple epigenetic marks in a single cell and requiring data from other cells to distinguish signal from experimental background noise in a single cell. This paper describes a method to reuse the same single cell for iterative epigenomic analyses. 
In this experimental method, cellular proteins are first anchored to a polyacrylamide polymer instead of crosslinking them to protein and DNA, alleviating structural bias. This critical step allows repeated experiments with the same single cell. Next, a random primer with a scaffold sequence for proximity ligation is annealed to the genomic DNA, and the genomic sequence is added to the primer by extension using a DNA polymerase. Subsequently, an antibody against an epigenetic marker and control IgG, each labeled with different DNA probes, are bound to the respective targets in the same single cell. 
Proximity ligation is induced between the random primer and the antibody by adding a connector DNA with complementary sequences to the scaffold sequence of the random primer and the antibody-DNA probe. This approach integrates antibody information and nearby genome sequences in a single DNA product of proximity ligation. By enabling repeated experiments with the same single cell, this method allows an increase in data density from a rare cell and statistical analysis using only IgG and antibody data from the same cell. The reusable single cells prepared by this method can be stored for at least a few months and reused later to broaden epigenetic characterization and increase data density. This method provides flexibility to researchers and their projects.

K562 single cells were generated using the protocol described in step 8 (see Figure 5). Single cells were embedded in the outer layer of the polyacrylamide bead. Cell DNA was stained and visualized using an intercalator dye for DNA staining.
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/63456/63456fig05.jpg" /> 
Figure 5: Generated reusable single cells. Cells are sta...

Watch this Scientific Journal Video about Reusable Single Cell for Iterative Epigenomic Analyses at JoVE.com

Reusable Single Cell for Iterative Epigenomic Analyses

The present protocol describes a single-cell method for iterative epigenomic analyses using a reusable single cell. The reusable single cell allows analyses of multiple epigenetic marks in the same single cell and statistical validation of the results.

Current single-cell epigenome analyses are designed for single use. The cell is discarded after a single use, preventing analysis of multiple epigenetic ...

reusable-single-cell-for-iterative-epigenomic-analyses

Research

JoVE Journal

Genetics

1.2K Views.  National Institutes of Health. The present protocol describes a single-cell method for iterative epigenomic analyses using a reusable single cell. The reusable single cell allows analyses of multiple epigenetic marks in the same single cell and statistical validation of the results. 

Video: Reusable Single Cell for Iterative Epigenomic Analyses

siRNA Transfection and EMSA Analyses on Freshly Isolated Human Villous Cytotrophoblasts

Human primary villous cytotrophoblasts are a very useful source of primary cells to study placental functions and regulatory mechanisms, and to comprehend diseases related to pregnancy. In this protocol, human primary villous cytotrophoblasts freshly isolated from placentas through a standard DNase/trypsin protocol are microporated with small interfering RNA (siRNA). This approach provided greater efficiency for siRNA transfection when compared to a lipofection-based method. Transfected cells can subsequently be analyzed by standard Western blot within a time frame of 3-4 days post-transfection. In addition, using cultured primary villous cytotrophoblasts, Electrophoretic Mobility Shift Assay (EMSA) analysis was optimized and performed on extracts from days 1 to 4. The use of these cultured primary cells and the protocol described allow for an evaluation of the implication of specific genes and transcription factors in the process of villous cytotrophoblast differentiation into a syncytiotrophoblast-like cell layer. However, the limited time span allowable in culture precludes the use of methods requiring more time, such as generation of a stable cell population. Therefore testing of this cell population requires highly optimized gene transfer protocols.

This protocol describes a method for efficiently transfecting siRNA in freshly isolated human villous cytotrophoblasts using microporation and identifying DNA-protein complexes in these cells. Transfected cells can be monitored by Western blot and EMSA analyses during the 4-day culture time.

Human primary villous cytotrophoblasts are a very useful source of primary cells to study placental functions and regulatory mechanisms, and to comprehend ...

Single-cell Gene Expression Using Multiplex RT-qPCR to Characterize Heterogeneity of Rare Lymphoid Populations

Gene expression heterogeneity is an interesting feature to investigate in lymphoid populations. Gene expression in these cells varies during cell activation, stress, or stimulation. Single-cell multiplex gene expression enables the simultaneous assessment of tens of genes1,2,3. At the single-cell level, multiplex gene expression determines population heterogeneity4,5. It allows for the distinction of population heterogeneity by determining both the probable mix of diverse precursor stages among mature cells and also the diversity of cell responses to stimuli.
Innate lymphoid cells (ILC) have been recently described as a population of innate effectors of the immune response6,7. In this protocol, cell heterogeneity of the ILC hepatic compartment is investigated during homeostasis.
Currently, the most widely used technique to assess gene expression is RT-qPCR. This method measures gene expression only one gene at a time. Additionally, this method cannot estimate heterogeneity of gene expression, since multiple cells are needed for one test. This leads to the measurement of the average gene expression of the population. When assessing large numbers of genes, RT-qPCR becomes a time-, reagent-, and sample-consuming method. Hence, the trade-offs limit the number of genes or cell populations that can be evaluated, increasing the risk of missing the global picture.
This manuscript describes how single-cell multiplex RT-qPCR can be used to overcome these limitations. This technique has benefited from recent microfluidics technological advances1,2. Reactions occurring in multiplex RT-qPCR chips do not exceed the nanoliter-level. Hence, single-cell gene expression, as well as simultaneous multiple gene expression, can be performed in a reagent-, sample-, and cost-effective manner. It is possible to test cell gene signature heterogeneity at the clonal level between cell subsets within a population at different developmental stages or under different conditions4,5. Working on rare populations with large numbers of conditions at the single-cell level is no longer a restriction.

This protocol describes how to assess the expression of a large array of genes at the clonal level. Single-cell RT-qPCR produces highly reliable results with a strong sensitivity for hundreds of samples and genes.

Gene expression heterogeneity is an interesting feature to investigate in lymphoid populations. Gene expression in these cells varies during cell ...

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of genomic changes at single cell resolution is important for characterizing genetic heterogeneity and evolution in normal tissues, cancers, and microbial populations. Traditional methods for assessing genetic heterogeneity have been limited by low resolution, low sensitivity, and/or low specificity. Single cell sequencing has emerged as a powerful tool for detecting genetic heterogeneity with high resolution, high sensitivity and, when appropriately analyzed, high specificity. Here we provide a protocol for the isolation, whole genome amplification, sequencing, and analysis of single cells. Our approach allows for the reliable identification of megabase-scale copy number variants in single cells. However, aspects of this protocol can also be applied to investigate other types of genetic alterations in single cells.

Single cell sequencing is an increasingly popular and accessible tool for addressing genomic changes at high resolution. We provide a protocol that uses single cell sequencing to identify copy number alterations in single cells.

Detection of genomic changes at single cell resolution is important for characterizing genetic heterogeneity and evolution in normal tissues, cancers, and ...

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Gene expression measurements from bulk populations of cells can obscure the considerable transcriptomic variation of individual cells within those populations. Single-cell gene expression measurements can help assess the role of noise in gene expression, identify correlations in the expression of pairs of genes, and reveal subpopulations of cells that respond differently to a stimulus. Here, we describe a procedure to measure the expression of up to 96 genes in single mammalian cells isolated from a population growing in tissue culture. Cells are sorted into lysis buffer by fluorescence-activated cell sorting (FACS), and the mRNA species of interest are reverse-transcribed and amplified. Gene expression is then measured using a microfluidic real-time PCR machine, which performs up to 96 qPCR assays on up to 96 samples at a time. We also describe the generation and use of PCR amplicon standards to enable the estimation of the absolute number of each transcript. Compared with other methods of measuring gene expression in single cells, this approach allows for the quantification of more distinct transcripts than RNA FISH at a lower cost than RNA-Seq.

We describe a method to sort single mammalian cells and to quantify the expression of up to 96 target genes of interest in each cell. This method includes the use of internal qPCR standards to enable the estimation of absolute transcript counts.

Gene expression measurements from bulk populations of cells can obscure the considerable transcriptomic variation of individual cells within those ...

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

Accurate transcription is required for the faithful expression of genetic information. Surprisingly though, little is known about the mechanisms that control the fidelity of transcription. To fill this gap in scientific knowledge, we recently optimized the circle-sequencing assay to detect transcription errors throughout the transcriptome of Saccharomyces cerevisiae, Drosophila melanogaster, and Caenorhabditis elegans. This protocol will provide researchers with a powerful new tool to map the landscape of transcription errors in eukaryotic cells so that the mechanisms that control the fidelity of transcription can be elucidated in unprecedented detail.

This protocol provides researchers with a new tool to monitor the fidelity of transcription in multiple model organisms.

Accurate transcription is required for the faithful expression of genetic information. Surprisingly though, little is known about the mechanisms that ...

Single Molecule Fluorescence In Situ Hybridization (smFISH) Analysis in Budding Yeast Vegetative Growth and Meiosis

Single molecule fluorescence in situ hybridization (smFISH) is a powerful technique to study gene expression in single cells due to its ability to detect and count individual RNA molecules. Complementary to deep sequencing-based methods, smFISH provides information about the cell-to-cell variation in transcript abundance and the subcellular localization of a given RNA. Recently, we have used smFISH to study the expression of the gene&#160;NDC80&#160;during meiosis in budding yeast, in which two transcript isoforms exist and the short transcript isoform has its entire sequence shared with the long isoform. To confidently identify each transcript isoform, we optimized known smFISH protocols and obtained high consistency and quality of smFISH data for the samples acquired during budding yeast meiosis. Here, we describe this optimized protocol, the criteria that we use to determine whether high quality of smFISH data is obtained, and some tips for implementing this protocol in&#160;other yeast strains and growth conditions.

Single Molecule Fluorescence In Situ Hybridization smFISH Analysis in Budding Yeast Vegetative Growth and Meiosis

This single molecule fluorescence in situ hybridization protocol is optimized to quantify the number of RNA molecules in budding yeast during vegetative growth and meiosis.

Single molecule fluorescence in situ hybridization (smFISH) is a powerful technique to study gene expression in single cells due to its ability to detect ...

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Immunophenotypic characterization and molecular analysis have long been used to delineate heterogeneity and define distinct cell populations. FACS is inherently a single-cell assay, however prior to molecular analysis, the target cells are often prospectively isolated in bulk, thereby losing single-cell resolution. Single-cell gene expression analysis provides a means to understand molecular differences between individual cells in heterogeneous cell populations. In bulk cell analysis an overrepresentation of a distinct cell type results in biases and occlusions of signals from rare cells with biological importance. By utilizing FACS index sorting coupled to single-cell gene expression analysis, populations can be investigated without the loss of single-cell resolution while cells with intermediate cell surface marker expression are also captured, enabling evaluation of the relevance of continuous surface marker expression. Here, we describe an approach that combines single-cell reverse transcription quantitative PCR (RT-qPCR) and FACS index sorting to simultaneously characterize the molecular and immunophenotypic heterogeneity within cell populations.
In contrast to single-cell RNA sequencing methods, the use of qPCR with specific target amplification allows for robust measurements of low-abundance transcripts with fewer dropouts, while it is not confounded by issues related to cell-to-cell variations in read depth. Moreover, by directly index-sorting single-cells into lysis buffer this method, allows for cDNA synthesis and specific target pre-amplification to be performed in one step as well as for correlation of subsequently derived molecular signatures with cell surface marker expression. The described approach has been developed to investigate hematopoietic single-cells, but have also been used successfully on other cell types.
In conclusion, the approach described herein allows for sensitive measurement of mRNA expression for a panel of pre-selected genes with the possibility to develop protocols for subsequent prospective isolation of molecularly distinct subpopulations.

Bulk gene expression measurements cloud individual cell differences in heterogeneous cell populations. Here, we describe a protocol for how single-cell gene expression analysis and index sorting by Florescence Activated Cell Sorting (FACS) can be combined to delineate heterogeneity and immunophenotypically characterize molecularly distinct cell populations.

Immunophenotypic characterization and molecular analysis have long been used to delineate heterogeneity and define distinct cell populations. FACS is ...

Generation of Cancer Cell Clones to Visualize Telomeric Repeat-containing RNA TERRA Expressed from a Single Telomere in Living Cells

Telomeres are transcribed, giving rise to telomeric repeat-containing long noncoding RNAs (TERRA), which have been proposed to play important roles in telomere biology, including heterochromatin formation and telomere length homeostasis. Recent findings revealed that TERRA molecules also interact with internal chromosomal regions to regulate gene expression in mouse embryonic stem (ES) cells. In line with this evidence, RNA fluorescence in situ hybridization (RNA-FISH) analyses have shown that only a subset of TERRA transcripts localize at chromosome ends. A better understanding of the dynamics of TERRA molecules will help define their function and mechanisms of action. Here, we describe a method to label and visualize single-telomere TERRA transcripts in cancer cells using the MS2-GFP system. To this aim, we present a protocol to generate stable clones, using the AGS human stomach cancer cell line, containing MS2 sequences integrated at a single subtelomere. Transcription of TERRA from the MS2-tagged telomere results in the expression of MS2-tagged TERRA molecules that are visualized by live-cell fluorescence microscopy upon co-expression of a MS2 RNA-binding protein fused to GFP (MS2-GFP). This approach enables researchers to study the dynamics of single-telomere TERRA molecules in cancer cells, and it can be applied to other cell lines.

Here, we present a protocol to generate cancer cell clones containing a MS2 sequence tag at a single subtelomere. This approach, relying on the MS2-GFP system, enables visualization of the endogenous transcripts of telomeric repeat-containing RNA (TERRA) expressed from a single telomere in living cells.

Telomeres are transcribed, giving rise to telomeric repeat-containing long noncoding RNAs (TERRA), which have been proposed to play important roles in ...

Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames

Genome annotation is central to today's proteomic research as it draws the outlines of the proteomic landscape. Traditional models of open reading frame (ORF) annotation impose two arbitrary criteria: a minimum length of 100 codons and a single ORF per transcript. However, a growing number of studies report expression of proteins from allegedly non-coding regions, challenging the accuracy of current genome annotations. These novel proteins were found encoded either within non-coding RNAs, 5' or 3' untranslated regions (UTRs) of mRNAs, or overlapping a known coding sequence (CDS) in an alternative ORF. OpenProt is the first database that enforces a polycistronic model for eukaryotic genomes, allowing annotation of multiple ORFs per transcript. OpenProt is freely accessible and offers custom downloads of protein sequences across 10 species. Using OpenProt database for proteomic experiments enables novel proteins discovery and highlights the polycistronic nature of eukaryotic genes. The size of OpenProt database (all predicted proteins) is substantial and need be taken in account for the analysis. However, with appropriate false discovery rate (FDR) settings or the use of a restricted OpenProt database, users will gain a more realistic view of the proteomic landscape. Overall, OpenProt is a freely available tool that will foster proteomic discoveries.

OpenProt is a freely accessible database that enforces a polycistronic model of eukaryotic genomes. Here, we present a protocol for the use of OpenProt databases when interrogating mass spectrometry datasets. Using OpenProt database for analysis of proteomic experiments allows for discovery of novel and previously undetectable proteins.

Genome annotation is central to today's proteomic research as it draws the outlines of the proteomic landscape. Traditional models of open reading frame ...

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Pancreatic islets comprise of endocrine cells with distinctive hormone expression patterns. The endocrine cells show functional differences in response to normal and pathological conditions. The goal of this protocol is to generate high-quality, large-scale transcriptome data of each endocrine cell type with the use of a droplet-based microfluidic single-cell RNA sequencing technology. Such data can be utilized to build the gene expression profile of each endocrine cell type in normal or specific conditions. The process requires careful handling, accurate measurement, and rigorous quality control. In this protocol, we describe detailed steps for human pancreatic islets dissociation, sequencing, and data analysis. The representative results of about 20,000 human single islet cells demonstrate the successful application of the protocol.

Here, we present a protocol to generate high-quality, large-scale transcriptome data of single cells from isolated human pancreatic islets using a droplet-based microfluidic single-cell RNA sequencing technology.

Pancreatic islets comprise of endocrine cells with distinctive hormone expression patterns. The endocrine cells show functional differences in response to ...