We thank Stuart Levine for comments on this manuscript. This work was supported by the National Institutes of Health Grant GM056800 and the Kathy and Curt Marble Cancer Research Fund to Angelika Amon and in part by the Koch Institute Support Grant P30-CA14051. Angelika Amon is also an investigator of the Howard Hughes Medical Institute and the Glenn Foundation for Biomedical Research. K.A.K. is supported by the NIGMS Training Grant T32GM007753.

1. Isolating Single Cells
<ol>
	<li>Prepare the microaspirator
	<ol>
		<li>Remove the clear plastic end from the aspirator tube assembly and insert the narrow end into one end of a 1 foot-long PVC tubing with 3/16&#34; inner diameter.</li>
		<li>Insert the outlet of a 0.2 &#181;m syringe filter into the other end of the 1 foot-long PVC tubing with 3/6&#34; inner diameter.</li>
		<li>Insert the inlet of the 0.2 &#181;m syringe filter into one end of a 6 inch-long PVC tubing with 5/16&#34; inner diameter.</li>
		<li>Optional: Cut the 6 inch-long PVC tubing with 5/16&#34; inner diameter in half and insert an in-line water trap between the two halves.</li>
		<li>Break a 5 mL plastic serological pipette at the 1 mL graduation and insert the broken end into the open end of the PVC tubing with 5/16&#34; inner diameter.</li>
		<li>Insert the outlet of the 5 mL plastic serological pipette into the flexible end of an aspirator tube assembly (where the clear plastic end had been removed).</li>
		<li>For storage, cover the red mouthpiece of the aspirator tube assembly with a sterile plastic tube.</li>
		<li>Draw out a glass capillary tube to an inner diameter of 10-30 &#181;m by bringing the middle of the tube near a Bunsen burner flame and applying tension on either end of the tube. Break the drawn tube in the middle to create two potential aspirator needles. Repeat this step with several tubes as only some tubes will end up with an inner diameter that is appropriate for picking single cells.</li>
		<li>For storage, place two strips of modeling clay in a 15 cm Petri dish and secure the aspirator needles across the strips.</li>
	</ol>
	</li>
	<li>Pick cells
	<ol>
		<li>Prepare a single cell suspension as appropriate for the cell type and experiment. For example, to prepare adherent cells such as human fibroblast cell lines, harvest cells by trypsinization and transfer cells to a conical tube containing appropriate media.</li>
		<li>Before, during, or after preparing the single cell suspension (depending on how long it takes to prepare the single cell suspension), setup the hood for whole genome amplification.
		<ol>
			<li>Spray down the surface of the hood, pipette tip boxes, and pipette tips with 10% bleach and wipe with a paper towel. Repeat this step with 70% ethanol.</li>
			<li>Add 8 &#181;L of water from the whole genome amplification (WGA) kit to individual wells of a 96-well PCR plate, one well for each cell to be sequenced. Cover the 96-well PCR plate with a lid from a 96-well tissue culture plate and place on ice.</li>
		</ol>
		</li>
		<li>Add 1,000 cells to 10 mL media or phosphate buffered saline (PBS) in a 15 cm Petri dish and place the dish on ice to prevent the cells from adhering to the dish.</li>
		<li>Bring the cells in the Petri dish and the 96-well PCR plate with lid on ice to a light microscope with 10X objective.</li>
		<li>Increase the opening of the aspirator needle by gently tapping the drawn out end of the aspirator needle on a hard surface such that the tip breaks off. Insert the wide end of the aspirator needle into the clear end of the microaspirator.</li>
		<li>Place the Petri dish containing cells on the microscope stage. Place the red mouthpiece of the aspirator in the mouth. Use one hand to move the aspirator needle and the other hand to move the Petri dish containing cells. Identify single cells to be sequenced.</li>
		<li>Using mouth suction, draw a single cell into the aspirator needle along with ~1-2 &#181;L of media or PBS. Transfer the cell into the 8 &#181;L of water inside a single well of the 96-well PCR plate. Avoid introducing bubbles when transferring the cell.</li>
		<li>Repeat step 1.2.7 until the desired number of cells have been isolated. Keep the PCR plate on ice and covered with lid in between picking cells. Mark the wells that have received cells.
		<ol>
			<li>While picking single cells, thaw the 10X single cell lysis and fragmentation buffer from the whole genome amplification kit. After picking the desired number of cells, proceed immediately to whole genome amplification.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
2. Whole Genome Amplification
<ol>
	<li>To prevent contamination during whole genome amplification, add all reagents inside a tissue culture hood, use pipette tips with filters, and change the pipette tip in between wells.</li>
	<li>Prepare a working lysis and fragmentation buffer solution from the whole genome amplification (WGA) kit. For each set of up to 32 cells, combine 32 &#181;L of 10x single cell lysis and fragmentation buffer and 2 &#181;L of proteinase K solution in a microcentrifuge tube. Vortex the tube to mix the solutions.</li>
	<li>Add 1 &#181;L of working lysis and fragmentation buffer solution to each well and pipette up and down to mix.</li>
	<li>Cover the plate and seal all wells with a plastic film. Briefly centrifuge the plate in a mini plate spinner.</li>
	<li>Thermal cycle as follows: 50 &#176;C, 1 h and 99 &#176;C, 4 min.</li>
	<li>Cool the plate on ice and briefly centrifuge the plate in a mini plate spinner.</li>
	<li>Prepare a working library preparation buffer solution from the whole genome amplification kit. For each cell, combine 2 &#181;L of 1x single cell library preparation buffer and 1 &#181;L of library stabilization solution in a microcentrifuge tube. Prepare the working solution for several cells in the same microcentrifuge tube.</li>
	<li>Remove the plastic film and add 3 &#181;L of working library preparation buffer solution to each well. Pipette the contents of the well up and down to mix. Replace the plastic film. 
	NOTE: The plastic film can be reused throughout the whole genome amplification process until the wells start to bore holes in the film. When this occurs, switch to a new plastic film.</li>
	<li>Briefly centrifuge the plate in a mini plate spinner and incubate at 95 &#176;C for 2 min.</li>
	<li>Cool the plate on ice and briefly centrifuge the plate in a mini plate spinner.</li>
	<li>Remove the plastic film, add 1 &#181;L library preparation enzyme to each well, and pipette the contents of the well up and down to mix. Keep the library preparation enzyme on ice or in a cold block throughout this step. Replace the plastic film.</li>
	<li>Briefly centrifuge the plate in mini plate spinner and thermal cycle as follows: 16 &#176;C, 20 min, 24 &#176;C, 20 min, 37 &#176;C, 20 min, 75 &#176;C, 5 min, and hold at 4 &#176;C.</li>
	<li>Cool the plate on ice and briefly centrifuge the plate in a mini plate spinner.</li>
	<li>Prepare a working amplification mix from the whole genome amplification kit. For each cell, combine 48.5 &#181;L water, 7.5 &#181;L 10x amplification master mix, and 5 &#181;L WGA DNA polymerase in a microcentrifuge tube. Prepare the working mix for several cells in the same microcentrifuge tube. Keep the working mix on ice.</li>
	<li>Remove the plastic film, add 61 &#181;L working amplification mix to each well, and pipette contents of well up and down to mix. Keep the working amplification mix on ice or in a cold block throughout this step. Replace the plastic film.</li>
	<li>Briefly centrifuge the plate in a mini plate spinner and thermal cycle as follows: 95 &#176;C, 3 min, 25 cycles of 94 &#176;C, 30 sec and 65 &#176;C, 5 min, and hold at 4 &#176;C.</li>
	<li>Transfer 60 &#181;L of each sample to a separate microcentrifuge tube and store at - 20 &#176;C for use in step 3.</li>
	<li>Add DNA loading dye to the remaining volume of each sample (i.e. 3 &#181;L of 6x DNA loading dye to 15 &#181;L of sample) and run 5 &#181;L from each reaction on a 1% agarose gel. 
	NOTE: Cells that successfully amplified will appear as a smear from 250 to 1,000 bp. Samples that do not show a smear or only show a faint smear are unlikely to yield useful sequencing data.</li>
</ol>
3. Sequencing
<ol>
	<li>Purify samples with paramagnetic beads.
	<ol>
		<li>Thaw DNA samples on ice. Vortex paramagnetic beads until the solution is homogenous and incubate the beads at room temperature for at least 30 min.</li>
		<li>Transfer 20 &#181;L of each DNA sample into a clean microcentrifuge tube. The remainder of the sample can be stored at -20 &#176;C.</li>
		<li>Add 30 &#181;L (1.5x) of paramagnetic beads to each DNA sample and vortex to mix. Incubate the samples at room temperature for 10 min. Leave the stock solution of beads at room temperature for use in step 3.4.</li>
		<li>Place the tubes on a magnetic tube strip for 2 min, or until the beads form a pellet and the supernatant is clear.</li>
		<li>Using a P200 pipette, remove as much of the supernatant as possible without disturbing the beads.</li>
		<li>Add 180 &#181;L of 80% ethanol to the DNA-bead mixture. Rotate the tubes several times relative to the magnet to &#34;rinse&#34; the beads through the ethanol solution.</li>
		<li>Using a P200 pipette, remove as much of the ethanol wash as possible without disturbing the beads.</li>
		<li>Repeat 3.1.6 and 3.1.7.</li>
		<li>Air dry the beads for approximately 10 min or until no more ethanol is visible. Proceed to the next step when the beads appear cracked or fall off the wall of the tube.</li>
		<li>Remove the samples from the magnetic strip. Add 40 &#181;L of 10 mM Tris pH 8.0 to elute the DNA from the beads and vortex the samples to resuspend the beads. Incubate for 2 min at room temperature.</li>
		<li>Briefly centrifuge the samples to collect the liquid at the bottom of the tube and place the tubes on a magnetic tube strip for at least two min. Transfer the eluent into clean microcentrifuge tubes without disturbing the beads.</li>
	</ol>
	</li>
	<li>Sample normalization
	<ol>
		<li>Quantify the concentration of each sample using a spectrophotometer. The concentrations should range from 10 to 30 ng/&#181;L.</li>
		<li>Dilute each sample to 0.2 ng/&#181;L using 10 mM Tris pH 8.0. The following steps will require a minimum input of 5 &#181;L from this dilution.</li>
	</ol>
	</li>
	<li>Library preparation
	<ol>
		<li>Prepare sequencing libraries by following the instructions of the library preparation kit13.</li>
	</ol>
	</li>
	<li>Final cleanup
	<ol>
		<li>Clean the samples according to step 3.1. For read lengths of 50 bp, 75 bp, or 150 bp, use a ratio of bead to sample volume of 1.5x, 1x, or 0.6x, respectively. Elute with 15 &#181;L of 10 mM Tris pH 8.0.</li>
		<li>Run the samples on a fragment analyzer to check the size distribution of the library14. The size distribution should be evenly spread from 150 to 900 bp.</li>
	</ol>
	</li>
	<li>Quantify samples using qPCR
	<ol>
		<li>Using a library quantification kit, set up a qPCR reaction according to the kit instructions15. Leave a column blank to add positive standards (DNA standards from the kit) and negative standards (water or other blank solution).</li>
		<li>Thermal cycle as follows: 95 &#176;C, 5 min (ramp rate of 4.8 &#176;C/sec) and 35 cycles of 95 &#176;C, 30 sec (ramp rate of 4.8 &#176;C/sec) and 60 &#176;C, 45 sec (ramp rate of 2.5 &#176;C/sec).</li>
	</ol>
	</li>
	<li>Pooling
	<ol>
		<li>Determine how many lanes on the flowcell are needed and divide the samples into groups, with one group per lane.</li>
		<li>For each group of samples, choose the sample with the lowest concentration based on the qPCR data from step 3.5.2 and normalize all samples in that group to that concentration using 10 mM Tris pH 8.0.</li>
		<li>Pool the samples in each group together.</li>
	</ol>
	</li>
	<li>Sequencing
	<ol>
		<li>Load pools on next-generation sequencing machine following standard procedures16.</li>
	</ol>
	</li>
</ol>
4. Data Analysis
NOTE: A Unix-based environment is required to run the programs and scripts in this section. Install the software mentioned in the protocol following their installation guides. All scripts can be found at https://sourceforge.net/projects/singlecellseqcnv/.
<ol>
	<li>Trim the reads to 40-nt using fastx_trimmer from FASTX-Toolkit version 0.0.1317. 
	fastx_trimmer -Q33 -i example.fastq -l 40 -o example.trim.fastq</li>
	<li>Align the reads to the appropriate reference genome (mm9 for mouse, hg19 for human) using BWA version 0.6.1 with default options18. 
	bwa aln mm9.fa example.trim.fastq &#62;example.sai 
	bwa samse mm9.fa example.sai example.trim.fastq &#62;example.sam</li>
	<li>Remove alignments to chrM and random chromosomes, then sort and index the resulting BAM file using SAMTools version 0.1.1919. 
	grep -v -w chrM example.sam |grep -v random &#62;example.filtered.sam 
	samtools view -uSh example.filtered.sam | samtools sort - example.filtered 
	samtools index example.filtered.bam</li>
	<li>Run HMMcopy to detect CNVs20
	<ol>
		<li>Use gcCounter in HMMcopy to generate a GC percentage reference file for the genome. Use option &#34;-w 500000&#34; to specify window size. Use the same version of the fasta reference file used in step 4.2, but make sure chrM and random chromosomes are removed from the fasta file. 
		gcCounter -w 500000 mm9.fa &#62;mm9_gc.wig</li>
		<li>Use generateMap.pl in HMMcopy to generate a wiggle file for mappability. Use option &#34;-w 40&#34; to specify read length. Use the same fasta reference file used in step 4.4.1. 
		generateMap.pl -b mm9.fa 
		generateMap.pl -w 40 -i mm9.fa mm9.fa -o mm9.bigwig</li>
		<li>Use mapCounter in HMMcopy to generate a mappability reference file for the genome. Use option &#34;-w 500000&#34; to specify window size. 
		mapCounter -w 500000 mm9.bigwig &#62;mm9_map.wig</li>
		<li>Use readCounter in HMMcopy to generate a wiggle file for each BAM file. 
		readCounter -w 500000 example.filtered.bam &#62;input.wig</li>
		<li>Modify the paths to the reference files in the R scripts (run_hmmcopy.mm9.r or run_hmmcopy.hg19.r), use the files generated above in 4.4.1 (e.g., mm9_gc.wig) and 4.4.3 (e.g., mm9_map.wig) for the variables gfile and mfile, which refer to the GC percentage reference file and mappability reference file, respectively. Then run the provided R script &#34;run_hmmcopy.mm9.r&#34; or &#34;run_hmmcopy.hg19.r&#34;. 
		R CMD BATCH run_hmmcopy.mm9.r 
		or 
		R CMD BATCH run_hmmcopy.hg19.r</li>
		<li>For batch processing of a set of BAM files, put the BAM files in a single folder and run the provided script &#34;HMMpipe.pl&#34; in the package to call segments and compute variability scores (VS). Follow the format: 
		perl HMMpipe.pl Folder_to_BAMfiles mm9</li>
	</ol>
	</li>
	<li>Run DNAcopy to detect CNVs21.
	<ol>
		<li>Use SAMtools version 0.1.19 to extract uniquely mapped reads from each BAM file. 
		samtools view -h -F 0x0004 example.filtered.bam | egrep -i &#34;^@|XT:A:U&#34; |samtools view -Shu - &#62;example.mapped.bam</li>
		<li>Use MarkDuplicates in Picard version 1.94 to mark PCR duplicates in the BAM file22. 
		java -jar MarkDuplicates.jar INPUT=example.mapped.bam OUTPUT=example.nondup.bam METRICS_FILE=example.dup REMOVE_DUPLICATES=true</li>
		<li>Use coveragebed in bedtools version 2.17.0 to count mapped reads in each of the predefined dynamic 500kb mappable windows in the provided BED file &#34;mm9.500k.dynamic.win.bed&#34; or &#34;hg19.500k.dynamic.win.bed&#34;23. 
		coverageBed -abam example.nondup.bam -b mm9.500k.dynamic.win.bed -counts | sort -k1,1 -k2,2n &#62;example.nondup.counts</li>
		<li>Use the provided Perl script &#34;normalizeGC.pl&#34; to normalize the read counts based on the provided GC content reference file &#34;mm9.500k.dynamic.win.fa.gc.txt&#34; or &#34;hg19.500k.dynamic.win.fa.gc.txt&#34;. 
		perl normalizeGC.pl mm9.500k.dynamic.win.fa.gc.txt example.nondup.counts &#62;example.bam.norm.counts</li>
		<li>Use the provided R script &#34;dnacopy.r&#34; to call the segments. 
		R CMD BATCH dnacopy.r</li>
	</ol>
	</li>
	<li>Identify CNVs
	<ol>
		<li>Exclude cells for which the VS calculated in 4.4.6 exceeds 0.26.</li>
		<li>Filter the segments called by HMMcopy in 4.4.6 to include only those for which the median log2 ratio is greater than 0.4 (putative gain) or less than -0.35 (putative loss).</li>
		<li>Filter the segments called by DNAcopy in 4.5.5 to include only those for which the segment mean is greater than 1.32 or less than 0.6.</li>
		<li>Overlap the segments from 4.6.2 and 4.6.3, calling CNVs only in regions where both HMMcopy and DNAcopy identify a putative gain or putative loss.</li>
	</ol>
	</li>
</ol>

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The authors have nothing to disclose.

Traditionally, identifying CNVs and aneuploidy at the level of single cells required cytologic methods such as FISH and SKY. Now, single cell sequencing has emerged as an alternative approach for such questions. Single cell sequencing has advantages over FISH and SKY as it is both genome-wide and high resolution. Moreover, when appropriate quality control methods are applied, single cell sequencing can provide a more reliable assessment of CNVs and aneuploidy as it is not susceptible to the hybridization and spreading artifacts inherent to FISH and SKY. However, many of the recent applications of single cell sequencing have not been substantiated by thorough assessment of the sensitivity and specificity of the methods and analyses. Indeed, some of the analytic approaches used by other studies are associated with high frequencies (&#62;50%) of false positive CNV calls12. The approach we describe has been rigorously tested using cells of known CNV burden in order to determine true and false discovery rates and optimize the sensitivity and specificity of CNV detection12. Using the quality control and analytic approaches described in this protocol, approximately 20% of 5 Mb gains, 75% of 5 Mb losses, and all CNVs exceeding 10 Mb can be detected. Though determining the false discovery rate of single cell sequencing is difficult, we have estimated it to be less than 25%. This protocol can be applied to cells from a variety of sources, the scripts can be modified to adjust the resolution of CNV detection, and the protocol can be adapted to identify other types of genomic alterations.
There are a variety of means of dissociating fresh tissues into single cells, and many publications describe procedures optimized for specific tissues, such as skin24 and brain25. We prefer to isolate single cells by microaspiration as it allows for visual assessment of each cell to be sequenced. However, it is also possible to isolate single cells by fluorescence-activated cell sorting (FACS)26 and microfluidic devices27. If single cell isolation and whole genome amplification is performed manually, it is reasonable to isolate and amplify up to forty cells in a single sitting. In order to obtain high quality single cell sequencing data, it is crucial that the amplification of single cell genomes is uniform and complete. We find that the quality of single cells isolated as well as the efficiency of lysis and amplification has a significant impact on the quality of sequencing data. As such, the cells should be harvested from their native environment just prior to isolation and whole genome amplification should begin immediately after cells are isolated. Moreover, the lysis and fragmentation step should be followed exactly as described in the steps 2.3-2.6.
The algorithms can be adjusted to change the resolution of CNV detection, with opposing effects on sensitivity and specificity12. It is also possible to adjust the thresholds to detect whole-chromosome aneuploidy in the setting of tetraploidy11. However, we find that our approach is limited to detecting CNVs greater than 5 Mb, as noise introduced during whole genome amplification complicates the detection of smaller variants12. Future improvements in whole genome amplification approaches should ultimately enhance the resolution of CNV detection using single cell sequencing.
Single cell sequencing allows for investigation of not only copy number alterations but also single nucleotide variations28,29 and structural variation30. Our protocol for single cell isolation can be applied to answer these other questions. However, the choice of whole genome amplification method depends on the specific application. The method described in this protocol, which is based on polymerase chain reaction, is best suited for detecting copy number alterations because it is associated with lower levels of amplification bias32. For investigating other types of genomic alterations, such as single nucleotide polymorphisms, other methods of whole genome amplification are believed to be more suitable31,32.

Alterations in DNA copy number can range in size from several base pairs (copy number variants) (CNVs) to entire chromosomes (aneuploidy). Copy number alterations affecting large regions of the genome can have significant phenotypic consequences by altering the expression of up to thousands of genes1,2. CNVs that are present in all cells of a population can be detected by bulk sequencing or microarray-based methods3,4. However, populations can also be genetically heterogeneous, with CNVs existing in a subset of the population or even single cells. Genetic heterogeneity is common in cancer, driving tumor evolution, and also present in normal tissues, with unknown consequence5,6,7,8,9,10.
Traditionally, genetic heterogeneity was assessed either by cytologic approaches or bulk sequencing. Cytologic approaches, such as fluorescence in situ hybridization (FISH), chromosome spreads, and spectral karyotyping (SKY), have the benefit of identifying alterations present in individual cells but have high error rates due to artifacts of hybridization and spreading11. These approaches are also limited in their resolution&#8212;only revealing copy number changes spanning several megabases. Sequencing or microarrays of bulk DNA, though higher in accuracy and resolution, is less sensitive. In order to detect genetic heterogeneity by population-based approaches, the variants must be present in a substantial fraction of cells in the population. The emergence of methods to amplify the genomic DNA from single cells has made it possible to sequence the genome of single cells. Single cell sequencing has the advantages of high resolution, high sensitivity, and, when appropriate quality control methods are applied, high accuracy12.
Here, we describe a method for detecting megabase-scale copy number alterations in single cells. We isolate single cells by microaspiration, amplify genomic DNA using linker-adaptor PCR, prepare libraries for next-generation sequencing, and detect copy number variants by both hidden Markov model and circular binary segmentation.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Aspirator tube assemblies for calibrated microcapillary pipettes</td>
			<td>Sigma</td>
			<td>A5177</td>
			<td></td>
		</tr>
		<tr>
			<td>PVC tubing (ID 3/16&#34;, OD 5/16&#34;, ~1 foot)</td>
			<td>VWR</td>
			<td>89068-500</td>
			<td></td>
		</tr>
		<tr>
			<td>PVC tubing (ID 5/16&#34;, OD 7/16&#34;, ~6 inches)</td>
			<td>VWR</td>
			<td>89068-508</td>
			<td></td>
		</tr>
		<tr>
			<td>Acrodisc Syringe Filter with HT Tuffryn Membrane (diameter 25 mm, pore size 0.2 um)</td>
			<td>VWR</td>
			<td>28144-040</td>
			<td></td>
		</tr>
		<tr>
			<td>Serological pipette (5 ml)</td>
			<td>BioExpress</td>
			<td>P-2837-5</td>
			<td>Any plastic 5 mL pipette should suffice</td>
		</tr>
		<tr>
			<td>In-Line Water Trap for Oxygen Use</td>
			<td>Amazon.com</td>
			<td>700220210813</td>
			<td></td>
		</tr>
		<tr>
			<td>Capillary Melting Point Tubes (ID 0.8-1.1 mm, 100 mm length)</td>
			<td>VWR</td>
			<td>34502-99</td>
			<td></td>
		</tr>
		<tr>
			<td>Modeling clay</td>
			<td>VWR</td>
			<td>470156-850</td>
			<td></td>
		</tr>
		<tr>
			<td>Petri dish (150 mm diameter)</td>
			<td>VWR</td>
			<td>25384-326</td>
			<td>Surface should be non-adherent to facilitate aspiration of single cells</td>
		</tr>
		<tr>
			<td>Hard-Shell&#160;Full-Height 96-Well Semi-Skirted PCR&#160;Plates</td>
			<td>Bio-Rad</td>
			<td>HSS9601</td>
			<td>Any 96-well PCR plate should suffice</td>
		</tr>
		<tr>
			<td>96-well tissue culture plate</td>
			<td>VWR</td>
			<td>62406-081</td>
			<td>Any 96-well tissue culture plate should suffice</td>
		</tr>
		<tr>
			<td>GenomePlex Single Cell Whole Genome Amplification Kit</td>
			<td>Sigma</td>
			<td>WGA4</td>
			<td></td>
		</tr>
		<tr>
			<td>Microseal &#39;A&#39; Film</td>
			<td>Bio-Rad</td>
			<td>MSA5001</td>
			<td></td>
		</tr>
		<tr>
			<td>Mini plate spinner</td>
			<td>Thomas Scientific</td>
			<td>1225Z37</td>
			<td></td>
		</tr>
		<tr>
			<td>Thermal cycler</td>
			<td>Bio-Rad</td>
			<td>1861096&#160;</td>
			<td>Any thermal cycler should suffice so long as it can accommodate a 96-well PCR plate</td>
		</tr>
		<tr>
			<td>Agencourt Ampure Beads</td>
			<td>Beckman Coulter</td>
			<td>A63880</td>
			<td></td>
		</tr>
		<tr>
			<td>Dynamag-2 Magnet</td>
			<td>Thermo Fisher Scientific</td>
			<td>12321D</td>
			<td>Any similar magnetic tube strip should suffice</td>
		</tr>
		<tr>
			<td>Nextera XT DNA Library Preparation Kit</td>
			<td>Illumina</td>
			<td>FC-131-1096</td>
			<td></td>
		</tr>
		<tr>
			<td>Nextera XT Index Kit</td>
			<td>Illumina</td>
			<td>FC-121-1012</td>
			<td></td>
		</tr>
		<tr>
			<td>Complete kit (optimized for Roche LightCycler 480)</td>
			<td>Kapa Biosystems</td>
			<td>KK4845&#160;</td>
			<td>This kit is optimized for the Roche LightCycler 480 real-time PCR machine. If using a different machine, substitute a kit optimized for your machine.</td>
		</tr>
	</tbody>
</table>

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.

The assembled aspirator should appear similar to the one in Figure 1A. The needle should be drawn such that it is sufficiently wide to accommodate a single cell but not so wide that a large volume is drawn up with the single cell. Aspirating single cells is easiest when there are between one and five cells in a 10X field (Figure 1B).
If whole genome amplification is successful, the sample will appear as a smear on an agarose gel (Figure 2, lanes 1, 2, 4, 5, 6, and 7). A faint or absent smear indicates a failed amplification reaction and the sample should not be sequenced (Figure 2, lanes 3 and 8).
Following library preparation, the fragment size distribution of the samples should be assessed by capillary electrophoresis on a fragment analyzer. Successfully prepared libraries will have a rather even distribution of fragment sizes from 150 to 900 bp (Figure 3, A,B). Failed library preparation will result in a skewed fragment size distribution and such libraries should not be sequenced (Figure 3C).
Processing sequencing data through the hidden Markov model (HMMcopy) and circular binary segmentation (DNAcopy) will parse the genome of each cell into segments of estimated copy number. These segments can then be filtered to identify those with an estimated copy number consistent with gain or loss in a single cell (Table 1). These filtered segments from HMMcopy and DNAcopy should then be overlapped to identify high-confidence CNVs.
<img alt="Figure 1" src="/files/ftp_upload/55143/55143fig1.jpg" /> 
Figure 1. Single cell isolation. (A) An assembled microaspirator. (B) A 10X field showing dissociated cells (arrows) and microaspirator needle (lower right corner). Aspirating single cells is easiest when there are between one and five cells in a 10X field. <a href="http://cloudflare.jove.com/files/ftp_upload/55143/55143fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/55143/55143fig2.jpg" /> 
Figure 2. Whole genome amplification. Agarose gel electrophoresis of 5 &#181;l of whole genome amplification products. Samples that successfully amplify will appear as bright smears from 100 bp to 1 kb (lanes 1, 2, 4, 5, 6, and 7) and can be sequenced. Samples that do not successfully amplify will produce faint smears or no smear (lanes 3 and 8) and should not be sequenced. <a href="http://cloudflare.jove.com/files/ftp_upload/55143/55143fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" src="/files/ftp_upload/55143/55143fig3.jpg" /> 
Figure 3. Library preparation. Representative results from a fragment analyzer. The graphs show fragment size (in bp) on X axis and relative fluorescence units (RFU) on Y axis. To the right of each graph is a simulated gel lane. (A) Results for an ideal sample, with an even distribution between 150 and 900 bp and without sharp peaks or bias toward one side. This sample is acceptable for sequencing. (B) Results from an okay sample, with a size distribution skewed toward lower fragment sizes. While this is not optimal, the sample can still be sequenced. (C) Results from a failed sample, with predominantly small fragment sizes. This is likely caused by prolonged incubation during the tagmentation step of library preparation. This sample should not be sequenced. <a href="http://cloudflare.jove.com/files/ftp_upload/55143/55143fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td colspan="7">HMMcopy</td>
		</tr>
		<tr>
			<td>Sample</td>
			<td>Segment</td>
			<td>Chr</td>
			<td>Start</td>
			<td>End</td>
			<td>State</td>
			<td>Median</td>
		</tr>
		<tr>
			<td>D15-4998</td>
			<td>23</td>
			<td>chr8</td>
			<td>144,500,001</td>
			<td>146,500,000</td>
			<td>6</td>
			<td>0.5008794</td>
		</tr>
		<tr>
			<td>D15-4998</td>
			<td>29</td>
			<td>chr10</td>
			<td>67,000,001</td>
			<td>134,500,000</td>
			<td>6</td>
			<td>0.4031945</td>
		</tr>
		<tr>
			<td>D15-4998</td>
			<td>52</td>
			<td>chr19</td>
			<td>1</td>
			<td>20,000,000</td>
			<td>6</td>
			<td>0.4616884</td>
		</tr>
		<tr>
			<td>D15-4998</td>
			<td>57</td>
			<td>chrY</td>
			<td>1</td>
			<td>59,500,000</td>
			<td>2</td>
			<td>-1.506532</td>
		</tr>
		<tr>
			<td colspan="8">DNAcopy</td>
		</tr>
		<tr>
			<td>Sample</td>
			<td>Chrom</td>
			<td>Start bin</td>
			<td>End bin</td>
			<td>Start</td>
			<td>End</td>
			<td>Seg mean</td>
			<td>Seg med</td>
		</tr>
		<tr>
			<td>D15-4998</td>
			<td>chr10</td>
			<td>88</td>
			<td>197</td>
			<td>62,612,945</td>
			<td>129,971,511</td>
			<td>1.4688</td>
			<td>0.157</td>
		</tr>
		<tr>
			<td>D15-4998</td>
			<td>chr19</td>
			<td>0</td>
			<td>31</td>
			<td>0</td>
			<td>28,416,392</td>
			<td>1.4141</td>
			<td>0.1674</td>
		</tr>
		<tr>
			<td>D15-4998</td>
			<td>chrX</td>
			<td>77</td>
			<td>126</td>
			<td>51,659,160</td>
			<td>95,343,369</td>
			<td>1.3548</td>
			<td>0.1874</td>
		</tr>
		<tr>
			<td>D15-4998</td>
			<td>chrY</td>
			<td>0</td>
			<td>14</td>
			<td>0</td>
			<td>23,805,358</td>
			<td>-2.7004</td>
			<td>0.3591</td>
		</tr>
	</tbody>
</table>
Table 1. Data analysis. Filtered segments generated by HMMcopy and DNAcopy from a single cell. Overlapping these two results reveals a gain on chromosome 10 from 67 to 130 Mb as well as a gain on chromosome 19 from 0 to 20 Mb. We have found that gains on the proximal portion of chromosome 19 are an artifact of single cell sequencing 12.

Watch this Scientific Journal Video about Detection of Copy Number Alterations Using Single Cell Sequencing at JoVE.com

Detection of Copy Number Alterations Using Single Cell Sequencing

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 ...

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11.5K Views.  Massachusetts Institute of Technology. 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.

Video: Detection of Copy Number Alterations Using Single Cell Sequencing

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 ...

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 ...

An Array-based Comparative Genomic Hybridization Platform for Efficient Detection of Copy Number Variations in Fast Neutron-induced Medicago truncatula Mutants

Mutants are invaluable genetic resources for gene function studies. To generate mutant collections, three types of mutagens can be utilized, including biological such as T-DNA or transposon, chemical such as ethyl methanesulfonate (EMS), or physical such as ionization radiation. The type of mutation observed varies depending on the mutagen used. For ionization radiation induced mutants, mutations include deletion, duplication, or rearrangement. While T-DNA or transposon-based mutagenesis is limited to species that are susceptible to transformation, chemical or physical mutagenesis can be applied to a broad range of species. However, the characterization of mutations derived from chemical or physical mutagenesis traditionally relies on a map-based cloning approach, which is labor intensive and time consuming. Here, we show that a high-density genome array-based comparative genomic hybridization (aCGH) platform can be applied to efficiently detect and characterize copy number variations (CNVs) in mutants derived from fast neutron bombardment (FNB) mutagenesis in Medicago truncatula, a legume species. Whole genome sequence analysis shows that there are more than 50,000 genes or gene models in M. truncatula. At present, FNB-induced mutants in M. truncatula are derived from more than 150,000 M1 lines, representing invaluable genetic resources for functional studies of genes in the genome. The aCGH platform described here is an efficient tool for characterizing FNB-induced mutants in M. truncatula.

An Array-based Comparative Genomic Hybridization Platform for Efficient Detection of Copy Number Variations in Fast Neutron-induced Medicago truncatula Mutants

This protocol provides experimental steps and information about reagents, equipment, and analysis tools for researchers who are interested in carrying out whole genome array-based comparative genomic hybridization (CGH) analysis of copy number variations in plants.

Mutants are invaluable genetic resources for gene function studies. To generate mutant collections, three types of mutagens can be utilized, including ...

Generating Transgenic Plants with Single-copy Insertions Using BIBAC-GW Binary Vector

When generating transgenic plants, generally the objective is to have stable expression of a transgene. This requires a single, intact integration of the transgene, as multi-copy integrations are often subjected to gene silencing. The Gateway-compatible binary vector based on bacterial artificial chromosomes (pBIBAC-GW), like other pBIBAC derivatives, allows the insertion of single-copy transgenes with high efficiency. As an improvement to the original pBIBAC, a Gateway cassette has been cloned into pBIBAC-GW, so that the sequences of interest can now be easily incorporated into the vector transfer DNA (T-DNA) by Gateway cloning. Commonly, the transformation with pBIBAC-GW results in an efficiency of 0.2&#8211;0.5%, whereby half of the transgenics carry an intact single-copy integration of the T-DNA. The pBIBAC-GW vectors are available with resistance to Glufosinate-ammonium or DsRed fluorescence in seed coats for selection in plants, and with resistance to kanamycin as a selection in bacteria. Here, a series of protocols is presented that guide the reader through the process of generating transgenic plants using pBIBAC-GW: starting from recombining the sequences of interest into the pBIBAC-GW vector of choice, to plant transformation with Agrobacterium, selection of the transgenics, and testing the plants for intactness and copy number of the inserts using DNA blotting. Attention is given to designing a DNA blotting strategy to recognize single- and multi-copy integrations at single and multiple loci.

Using a pBIBAC-GW binary vector makes generating transgenic plants with intact single-copy insertions, an easy process. Here, a series of protocols is presented that guide the reader through the process of generating transgenic Arabidopsis plants, and testing the plants for intactness and copy number of the inserts.

When generating transgenic plants, generally the objective is to have stable expression of a transgene. This requires a single, intact integration of the ...

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Conventional next-generation sequencing techniques (NGS) have allowed for immense genomic characterization for over a decade. Specifically, NGS has been used to analyze the spectrum of clonal mutations in malignancy. Though far more efficient than traditional Sanger methods, NGS struggles with identifying rare clonal and subclonal mutations due to its high error rate of ~0.5&#8211;2.0%. Thus, standard NGS has a limit of detection for mutations that are &#62;0.02 variant allele fraction (VAF). While the clinical significance for mutations this rare in patients without known disease remains unclear, patients treated for leukemia have significantly improved outcomes when residual disease is &#60;0.0001 by flow cytometry. In order to mitigate this artefactual background of NGS, numerous methods have been developed. Here we describe a method for Error-corrected DNA and RNA Sequencing (ECS), which involves tagging individual molecules with both a 16 bp random index for error-correction and an 8 bp patient-specific index for multiplexing. Our method can detect and track clonal mutations at variant allele fractions (VAFs) two orders of magnitude lower than the detection limit of NGS and as rare as 0.0001 VAF.

Next-generation sequencing (NGS) is a powerful tool for genomic characterization that is limited by the high error rate of the platform (~0.5&#8211;2.0%). We describe our methods of error-corrected sequencing that allow us to obviate the NGS error rate and detect mutations at variant allele fractions as rare as 0.0001.

Conventional next-generation sequencing techniques (NGS) have allowed for immense genomic characterization for over a decade. Specifically, NGS has been ...

Ultralow Input Genome Sequencing Library Preparation from a Single Tardigrade Specimen

Tardigrades are microscopic animals that enter an ametabolic state called anhydrobiosis when facing desiccation and can return to their original state when water is supplied. The genomic sequencing of microscopic animals such as tardigrades risks bacterial contamination that sometimes leads to erroneous interpretations, for example, regarding the extent of horizontal gene transfer in these animals. Here, we provide an ultralow input method to sequence the genome of the tardigrade, Hypsibius dujardini, from a single specimen. By employing rigorous washing and contaminant exclusion along with an efficient extraction of the 50 ~ 200 pg genomic DNA from a single individual, we constructed a library sequenced with a DNA sequencing instrument. These libraries were highly reproducible and unbiased, and an informatics analysis of the sequenced reads with other H. dujardini genomes showed a minimal amount of contamination. This method can be applied to unculturable tardigrades that could not be sequenced using previous methods.

Contamination during the genomic sequencing of microscopic organisms remains a large problem. Here, we show a method to sequence the genome of a tardigrade from a single specimen with as little as 50 pg of genomic DNA without whole genome amplification to minimize the risk of contamination.

Tardigrades are microscopic animals that enter an ametabolic state called anhydrobiosis when facing desiccation and can return to their original state ...

Sequencing of mRNA from Whole Blood using Nanopore Sequencing

Sequencing in remote locations and resource-poor settings presents unique challenges. Nanopore sequencing can be successfully used under such conditions, and was deployed to West Africa during the recent Ebola virus epidemic, highlighting this possibility. In addition to its practical advantages (low cost, ease of equipment transport and use), this technology also provides fundamental advantages over second-generation sequencing approaches, particularly the very long read length, ability to directly sequence RNA, and real-time availability of data. Raw read accuracy is lower than with other sequencing platforms, which represents the main limitation of this technology; however, this can be partially mitigated by the high read depth generated. Here, we present a field-compatible protocol for sequencing of the mRNAs encoding for Niemann-Pick C1, which is the cellular receptor for ebolaviruses. This protocol encompasses extraction of RNA from animal blood samples, followed by RT-PCR for target enrichment, barcoding, library preparation, and the sequencing run itself, and can be easily adapted for use with other DNA or RNA targets.

Nanopore sequencing is a novel technology that allows cost-effective sequencing in remote locations and resource-poor settings. Here, we present a protocol for sequencing of mRNAs from whole blood that is compatible with such conditions.

Sequencing in remote locations and resource-poor settings presents unique challenges. Nanopore sequencing can be successfully used under such conditions, ...

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 ...

Identification of Circular RNAs using RNA Sequencing

Circular RNAs (circRNAs) are a class of non-coding RNAs involved in functions including micro-RNA (miRNA) regulation, mediation of protein-protein interactions, and regulation of parental gene transcription. In classical next generation RNA sequencing (RNA-seq), circRNAs are typically overlooked as a result of poly-A selection during construction of mRNA libraries, or are found at very low abundance, and are therefore difficult to isolate and detect. Here, a circRNA library construction protocol was optimized by comparing library preparation kits, pre-treatment options and various total RNA input amounts. Two commercially available whole transcriptome library preparation kits, with and without RNase R pre-treatment, and using variable amounts of total RNA input (1 to 4 &#181;g), were tested. Lastly, multiple tissue types; including liver, lung, lymph node, and pancreas; as well as multiple brain regions; including the cerebellum, inferior parietal lobe, middle temporal gyrus, occipital cortex, and superior frontal gyrus; were compared to evaluate circRNA abundance across tissue types. Analysis of the generated RNA-seq data using six different circRNA detection tools (find_circ, CIRI, Mapsplice, KNIFE, DCC, and CIRCexplorer) revealed that a stranded total RNA library preparation kit with RNase R pre-treatment and 4 &#181;g RNA input is the optimal method for identifying the highest relative number of circRNAs. Consistent with previous findings, the highest enrichment of circRNAs was observed in brain tissues compared to other tissue types.

Circular RNAs (circRNAs) are non-coding RNAs that may have roles in transcriptional regulation and mediating interactions between proteins. Following assessment of different parameters for construction of circRNA sequencing libraries, a protocol was compiled utilizing stranded total RNA library preparation with RNase R pre-treatment and is presented here.

Circular RNAs (circRNAs) are a class of non-coding RNAs involved in functions including micro-RNA (miRNA) regulation, mediation of protein-protein ...

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

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

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

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

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