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This command will generate two files suffixed with '_val_1.fq.gz'and'_val_2.fq.gz'for paired-end sequencing data. We call these two files 'clean data' here. |
▲ CRITICAL STEP The parameter'--rrbs' used here will remove the artificial bases introduced in the end-repair step from the 3' ends of the reads. |
46| When clean data have been obtained, they can be mapped to the reference genome using the Bismark alignment tool35, For the first-time use of a reference genome and before alignment, ensure that the genome is bisulfite-converted in silico and indexed well using bismark genome preparation, which is attached with the ... |
bismark_genome_preparation --verbose -path_to_bowtie bowtie-1.0.0 <genome_folder> |
This command will create a transformed reference genome index for bisulfite data alignment. |
bismark -path_to_bowtie bowtie-1.0.0 -quiet -o <output_dir> --temp_dir <tmp_dir> <reference_folder> -1 <read1_val_1.fq.gz> -2 <read2_val_2.fq.gz> |
This command will generate a SAM file named 'read1_val_1.fq.gz_bismark_pe.sam' containing all the mapping outputs. A final alignment report will be generated upon completion of the Bismark alignment. In this report, the number of total sequencing reads, the number of uniquely mapped reads and the mapping ratio of the s... |
Because the samples were spiked with trace amounts of unmethylated λ-DNA, the cytosines in a non-CG (CHH and CHG) context in the λ-DNA genome are definitely unmethylated. The nonconversion rate of scRRBS is calculated as the number of sequenced cytosines in non-CG contexts divided by all the covered cytosines in non-CG... |
47| Once the alignment is complete, use SortSam.jar in the Picard toolkit to sort the mapping result in coordinate order to prepare for the next step. |
java -Djava.io.tmpdir=<TMP> SortSam.jar I=<read1_val_1.fq.gz_bismark_pe.sam> O=<read1_val_1.sort.bam> SORT_ORDER=coordinate VALIDATION_STRINGENCY=LENIENT VERBOSITY=ERROR TMP DIR=<TMP> |
This command will generate a sorted BAM file. |
48| Create a pileup file of mapped data prepared for DNA methylation-level calculation. This step will give a pileup result for each covered locus in the genome. Example command: |
samtools mpileup -f <genome.fa> <read1_val_1.sort.bam> > <read1_val_1.pileup> |
49| Calculate the DNA methylation level for each covered CpG and non-CpG site. The DNA methylation level of each covered cytosine is calculated as the number of reported C divided by the total number of reported C and T at the same genome position. Example command: |
Perl SingleC_MetLevel.pl <genome.fa> <read1_val_1.pileup> > <read1_val_1.SingleCmet> |
The output file is tab-delimited, and it contains the chromosome, base position, reference genome, chain, total coverage depth, number of methylated reads, number of unmethylated reads, methylation level and reference context (CpG, CHH or CHG). This output file contains well-processed data that can be used to calculate... |
## ?TROUBLESHOOTING |
Troubleshooting advice can be found in Table 3. |
TABLE 3 | Troubleshooting table. |
Step Problem Possible reason Solution |
7 Cell transfer failure Single cells may stick to the inside wall of the mouth pipette First, suck a small volume of PBA-BSA into the pipette, and then pick the single cell into it. Ensure that the cell is already in the pipette but near the tip of the pipette. Push all of the carryover PBA-BSA out of the pipette, toge... |
9 Incomplete cell lysis A single cell is not loaded into the lysis buffer, or the cell is not intact before transferring it by mouth pipette During cell transfer, ensure that the cell is seeded into the bottom of the tube. Centrifugation is crucial, and it cannot be omitted. In addition, ensure that the cells are of go... |
24, 28 Low PCR amplification efficiency Too much DNA loss before PCR amplification Poor quality of PCR reagents Avoid excessive pipetting, and use LoBind tubes during the whole procedure Ensure that the reagents for PCR have not expired. Divide them into small batches to avoid unnecessary freeze-thaw cycles, |
41 contamination Excessive primer-dimer Too many PCR cycles or excessive especially for the primers Perform another round of AMPure XP bead purification to remove contaminants. In addition, ensure that the concentrations of PCR |
## TIMING |
Steps 1-9, cell culture, single-cell isolation and cell lysis: 4-5 d |
Steps 10-12, MspI digestion: 3-4 h |
Steps 13-15, end-repair/dA-tailing reaction: 1-2 h |
Steps 16-19, adapter ligation: 9-10 h |
Steps 20-23, bisulfite conversion: 3-4 h |
Steps 24-27, first-round PCR amplification: 3-4 h (plus purification time; see Box 1) |
Steps 28-31, second-round PCR amplification: 3-4 h (plus purification time; see Box 1) |
Steps 32-41, size selection of the amplified DNA fragments: 9-10 h (plus purification time; see Box 1) |
Steps 42-44, quality control and high-throughput DNA sequencing: 10-18 d |
Steps 45-49, data analysis for single-cell RRBS data: 2-3 d |
Box 1, DNA purification using AMPure XP beads: 0.5-1 h |
Box 2, 'Crush and soak' method: 12-14 h |
## ANTICIPATED RESULTS |
The yields of scRRBS libraries from different sample types (haploid cells or diploid cells) do not vary substantially. The typical yield is ~20-30 ng (using a Qubit fluorometer for quantification) after gel-based size selection and AMPure XP beads purification, with <1 ng in the 'picking-buffer-only' negative controls ... |
We have performed scRRBS on individual mouse and human metaphase II oocytes, sperm, male and female pronuclei of zygotes, as well as individual mESCs10,17. The average mapping ratio of scRRBS is ~25%, which is lower than that observed with standard RRBS, which ranges from 50 to 70%. This low mapping ratio may be due to... |
For a mammalian individual diploid cell, scRRBS is expected to cover ~40% of the CpG sites (~1 million CpG sites in a mouse and human diploid cell) that can be recovered by standard RRBS using thousands of cells10. Coverage lower than this may be due to degradation of genomic DNA before cell lysis. |
In our studies, only scRRBS samples with a high bisulfite conversion rate (>98%) were used for further analysis. We recovered between 0.2 and 1.5 million CpG sites from each individual haploid or diploid cell (Fig. 4 and Supplementary Fig. 1). |
Figure 4 | Box plots of the mapping efficiencies and the total unique CpG sites covered in our scRRBS data. (a) Box plot of the mapping efficiencies of some scRRBS libraries. The six items on the left in a indicate different types of human cells, including single human sperm cells (n = 4), single metaphase II oocytes (... |
n = 4), single metaphase II oocytes (with polar bodies removed, diploid, n = 2), single female pronuclei (haploid, n = 11) and single male pronuclei (haploid, n = 11). The six items on the right in b indicate different types of mouse cells, including single mESCs (diploid, n = 8), single sperm cells (haploid, n = 4), s... |
Figure 5 | The methylation status of a representative locus of sperm-specific differentially methylated regions (DMRs). The methylation levels of most of the CpG sites at this locus in the four single human sperm cells are fully methylated (black filled circles), and most of the CpG sites at this locus in the three hum... |
The majority of the covered CpG sites should show digitized DNA methylation; i.e., they are either fully methylated or unmethylated (Fig. 5; Supplementary Figs. 2 and 3). |
Plotting the scRRBS data of individual cells across genes shows that methylation levels are high on gene bodies compared with neighboring genomic regions, and that there is an expected hypomethylation valley around the transcriptional start sites (TSSs) (Fig. 6). Moreover, methylation levels qradually increase from the... |
Figure 6 | The average DNA methylation levels across gene bodies and the flanking intergenic regions. (a,b) Average DNA methylation levels along the transcripts and 15 kb upstream and downstream of the TSSs and the TESs of all RefSeq genes in the scRRBS data set of human single metaphase II oocytes and single female pr... |
Note: Any Supplementary Information and Source Data files are available in the online version of the paper. |
ACKNOWLEDGMENTS We thank J. Qiao and L. Yan for their great help. The project was supported by the National Science Foundation of China (31322037 and 31271543) and the National Basic Research Program of China (2012CB966704 and 2011CB966303). This work is supported by a collaborative grant from the Center for Molecular ... |
AUTHOR CONTRIBUTIONS L.W. and F.T. conceived the experiments and supervised the project. H.G., F.G., X.L., X.W. and X.F. carried out all of the experiments. P.Z. conducted the bioinformatic analyses. H.G., P.Z., L.W. and F.T. wrote the manuscript with contributions from all of the authors. |
COMPETING FINANCIAL INTERESTS The authors declare no competing financial interests. |
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