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Kun:LabNotes/Haplotyping/2011-5-12
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==Methylation haplotyping on a small number of cells== ===Idea=== *Directly lyse a small number of cells, remove all proteins, denature the gDNA, and trap the ssDNA in polyacrylamide gel. *Perform bisulfite conversion, followed by limited MDA and tagmentation all directly within the gel. *Dissect the gel into a number of small pieces, such that each piece contains less than haploid genome, followed by barcoded PCR in tubes. *The PCR amplicons are pooled for sequencing. ===Experiment protocol=== *Make dilution of GM20431 cells to 1 cell/ul. *Mix 10ul of cell solution with 10ul Cell Lysis Buffer(20M EDTA, 10mM Tris.HCl , 200mM NaCl, 0.2% Triton X-100, 0.2AU/ml Qiagen Protease), incubate at 37C for 30min, 75C for 15min. Slowly mix the cell lysate with a P20 pipette for 10 times (to break down chromosomes). *Prepare gel mix: H2O 10ul ABD 8ul 30% BSA 0.3ul Cell lysate 20ul 5% TEMED 0.8ul Heat at 94C for 5min, immediately transfer to ice Add 5% APS 0.8ul *Add 18ul each to two slides, wait ~15 minutes for the gel to polymerize. *Wash the slides with ddH2O for >5min. *Place a frameseal chamber on each slide, add 120 ul CT Conversion Reagent (Zymo EZ DNA Methylation Direct Kit), seal the chamber. *Incubate at 95C 4min -> 64C 4h -> 4C hold. Roughly half of the CT conversion reagent evaporated in less than 2 hours despite my best efforts in sealing the chamber. So I removed the chambers, put new ones and added new CT Conversion Reagent. 95C 1min -> 64C 2h 15min -> 4C hold. *Wash the slides twice with ddH2O for >5min. *Add ~50ul of M-desulphonation buffer onto the gel and let it stand at RT for 15min. *Wash the slides twice with ddH2O for >5min. *Set up limited MDA reaction: 10x RepliPhi Buffer 4.0ul 1mM N6 primer 2.0ul 25mM dNTP 0.4ul 2X SYBR Green I 2.0ul RepliPhi Phi29(100U/ul) 2.0ul Exo-minus Klenow (5U/ul) 1.0ul H2O 28.0ul 30% BSA 0.7ul After adding MDA mix to the gel and cover with coverslips, carefully pipette mineral oil to cover the edges of coverslips. Incubate one slide at 30C for 30 min, the other slide at 30C for 1h, head inactivation at 65C for 10min *Wash the slide with ddH2O for > 5min, air-dry inside PCR hood. *Prepare tagmentation mix: Dilute the enzyme: 1:5 5x LMW Buffer 8ul diluted enzyme 4ul H2O 28ul Add 20ul to each gel, cover with a coverslip, seal the edges with mineral oil, incubate at 55C for 10min *Wash the Wash the slide with ddH2O for > 5min, use a clean scalpel to cut the gel into 12 slices, transfer one into each PCR tube. *Set up PCR reaction: x 12 KAPA QPCR mix 25ul Orange Primer (10uM) 1ul Blue Primer (10uM) 1ul Bst Pol (5U/ul) 0.5ul H2O 23ul 65C 15min -> 95C 30 sec -> (95C 10sec -> 58C 30 sec -> 72 1min) x 22 -> 72C 3min. Monitor the reactions on a real-time thermal cycler and terminate them before the curves reach saturation. *If the amplification curves are good, check the amplicon size with 6% TBE gel. [[Image:In-Gel-biscvt-MDA-tagmentation-PCR-Plate.png|300px]] [[Image:In-Gel-biscvt-MDA-tagmentation-PCR-legend.png|300px]] [[Image:In-Gel-biscvt-MDA-tagmentation-PCR-AmpCurve.png|300px]] [[Image:In-Gel-biscvt-MDA-tagmentation-PCR-13May2011.png|320px]] *Asked Alice to perform AmpPure bead purification, then 2nd PCR to add barcodes and sequencing adaptors. ===Discussions=== #I terminated the PCR reactions at the end of 22th cycle when the amplification curves for the 7 reactions with gel came up whereas the NTC was still almost flat. This is a good indication that tagmentation worked to some extent. Based on the PAGE gel, there are faint smears in the 7 reactions. They are not strong enough, it's probably because the PCR reactions were terminated 1-2 cycles earlier. Overall this seems to be promising enough to proceed. #There are a few steps that could be improved in the future experiments. ##I wasn't very confident on the quality of cell lysis, because Alice got much better results with the other buffer using NP40. I did add fresh protease into this lysis buffer, and also used the buffer to lyse ~20,000 cells and checked with microscope. The cells seemed to be lysed, but I still think it worth trying the NP40-based buffer next time. ##On-gel bisulfite conversion is tricky, because it's hard to fill the FrameSeal chamber completely and prevent leaking during the high-temperature incubation. It is possible that the conversion rate is better in some area of the gel than the other. Covering the gel with coverslips and seal with mineral oil seems to be a better option. Definitely worth trying next time. ##I check the gel with microscrope at SYBG channel after MDA, but didn't see any signal except for some debris. I wasn't sure whether the MDA was working or not. However, if the amplification occurred evenly across the gel, I would not see anything either. Since I was able to get some amplicons after tagmentation/PCR, it's likely that MDA did work to some extent. ##There were a few bubble on the gel when I did in-gel tagmentation. Some small areas of the gel was damaged after the previous steps, such that the coverslip couldn't sit flat on the gel. One solution is to be more careful and try not to damage the gel. Another idea is to increase the volume of tagmentation reaction mix from 20ul to 25-30ul. ##Cutting gel into small piece and collecting the pieces individually is a little bit tricky. It's important not to do it when the gel is too dry. Transferring little gel pieces into PCR tubes is not easy either. I ended up pipetting 5ul of clean water to the scalpel tip that carries the gel piece, then transferring the water (with gel piece inside) to PCR tubes with the help of a P20 pipette. ===Sequencing=== *Alice helped me to add unique barcodes (Indx8-12) to six amplicons, which were pooled with another six libraries and sequenced in one HiSeq lane. Roughly 4 million 36bp SE reads were obtained. ===Data analysis=== ====Part I==== *Read mapping summary. {| {{table}} | align="center" style="background:#f0f0f0;"|'''Index''' | align="center" style="background:#f0f0f0;"|'''Sample Description''' | align="center" style="background:#f0f0f0;"|'''Total reads''' | align="center" style="background:#f0f0f0;"|'''# Uniquely mapped''' | align="center" style="background:#f0f0f0;"|'''# Failed to map''' | align="center" style="background:#f0f0f0;"|'''# Non-uniquely mapped''' | align="center" style="background:#f0f0f0;"|'''% Uniquely mapped''' | align="center" style="background:#f0f0f0;"|'''% Failed to map''' | align="center" style="background:#f0f0f0;"|'''% Non-uniquely mapped''' | align="center" style="background:#f0f0f0;"|'''# Bis_mapped''' | align="center" style="background:#f0f0f0;"|'''% Bis_mapped''' |- | Indx7||bisulfite-haplotyping-5-cells-1||4,535,639||1,148,608||2,983,032||403,999||25.3%||65.8%||8.9%||2,463,680||54.3% |- | Indx8||bisulfite-haplotyping-5-cells-2||5,908,247||1,350,371||4,062,938||494,938||22.9%||68.8%||8.4%||3,813,863||64.6% |- | Indx9||bisulfite-haplotyping-5-cells-3||4,281,883||689,409||3,333,689||258,785||16.1%||77.9%||6.0%||2,895,651||67.6% |- | Indx10||bisulfite-haplotyping-5-cells-4||3,922,281||953,993||2,630,259||338,029||24.3%||67.1%||8.6%||2,193,243||55.9% |- | Indx11||bisulfite-haplotyping-5-cells-5||3,353,075||451,253||2,737,656||164,166||13.5%||81.6%||4.9%||1,654,575||49.3% |- | Indx12||bisulfite-haplotyping-5-cells-6||4,385,132||678,617||3,449,632||256,883||15.5%||78.7%||5.9%||2,429,874||55.4% |- | |} *On average 27% of the reads can be mapped to the human genome, suggesting that there were contamination of human DNA after bisulfite conversion, most likely at the MDA step or the washing step before MDA. *An average of 58% of the reads can be mapped to the bisulfite converted genome, indicating that roughly 58%-27%=31% of the sequencing reads came from the bisulfite gel trapped chromosomes. Note that genomic reads are mappable to the bisulfite genome because during the mapping the sequencing reads were bisulfite converted in silico. *To further confirm that a significant fraction of sequencing reads are bisulfite converted reads, I did the base composition analysis on the raw reads. I took the top 10,000 reads from the raw fastq file, then calculate the ratio of C/T and A/G for each read. Then I made a scatter plot of C/T versus A/G. I also did the same analysis on a genomic library and a BSPP library for comparison. Clearly C/T or A/G is highly skewed in the bisulfite converted reads, compared with regular genomic reads. The scatter plot indicates that the in-gel methylHap reads is a mixture of regular genomic reads and bisulfite converted reads. [[Image:GenomicReadBaseComposition.png|300px]][[Image:BSPPReadBaseComposition.png|300px]][[Image:InGelMethylHapReadBaseComposition.png|300px]] *This above analysis led to the next question: can we separate bisulfite converted reads from regular genomic reads in the same sequence file based on the base composition? To do this I calculate a metric, which is the absolute log-transformed ratio of [C/T]/[A/G], and ranked 10000 genomic reads and BSPP reads based on the values. Then I created a scatter plot of this metric versus the rank. This plot can provide a good idea on where can I find a cutoff value that can best distinguish the two types of reads. [[Image:BaseBias_genomic_vs_BisReads.png]] Guided by this plot, I found that with a threshold of 2.5, I can keep 98.9% (sensitivity) of the bisulfite reads while 11% (false positives) of the genomic reads will be also included. If I raise the threshold to 3.0, I can still keep 96.4% of bisulfite reads, and the fraction of genomic reads included dropped to 7%. *Alternatively, I can map the read to the human genome first, than perform bisulfite mapping only on the unmappable reads. *To Do: **Extract bisulfite converted reads using the two methods mentioned above, perform bisulfite read mapping. **Compare the methylation level with GM20431 BSPP data. **Calculate bisulfite conversion rate. **Check whether reads from each library tend to concentrate to a subset of genomic regions. If that's the case, it's a good indication that large chromosome fragments were present in different gel slices and therefore we might be able to get haplotype data. ** Perform SNP calling, and compare the genotypes with the PGP1 CGI data. ====Part II==== *Subtraction of genomic reads on all six read files using bowtie: /home/kunzhang/softwares/bowtie-latest/bowtie -k 1 -l 32 -n 2 -p 4--un lane1_Indx7_bisReads.fastq /home/kunzhang/HsGenome/1KG.ref/human_b36_male.fa lane1_Indx7.fastq lane1_Indx7.hg18.bowtie.out *Call methylation on the subtracted bisReads. *I estimated the bisulfite conversion rates by checking the average methylation level on CHH sites. Since CHH methylation is very low, most of the Cs on CHH sites are due to incomplete conversion. [[Media:pileup2ChhMethylLevel.txt|pileup2ChhMethylLevel.pl]] lane1_Indx7_bisReads.fastq.fwd.pileup lane1_Indx7_bisReads.fastq.rev.pileup chr1 {| {{table}} | align="center" style="background:#f0f0f0;"|'''Index''' | align="center" style="background:#f0f0f0;"|'''Total reads''' | align="center" style="background:#f0f0f0;"|'''# reads after genomic filtering''' | align="center" style="background:#f0f0f0;"|'''# reads mapped to bisCvt hg18''' | align="center" style="background:#f0f0f0;"|'''% mapped''' | align="center" style="background:#f0f0f0;"|'''% bisCvt rate''' |- | Indx7||4,535,639||2,990,075||1,119,588||37.44%||78.79% |- | Indx8||5,908,247||4,075,544||2,227,644||54.66%||85.63% |- | Indx9||4,281,883||3,344,978||2,085,038||62.33%||84.94% |- | Indx10||3,922,281||2,635,968||1,066,949||40.48%||76.74% |- | Indx11||3,353,075||2,743,982||1,131,382||41.23%||77.95% |- | Indx12||4,385,132||3,456,344||1,636,709||47.35%||90.23% |} *Comparison of methylation level with BSPP data from four PGP lymphocyte lines. {| {{table}} | align="center" style="background:#f0f0f0;"|'''Correlation''' | align="center" style="background:#f0f0f0;"|'''Indx7''' | align="center" style="background:#f0f0f0;"|'''Indx8''' | align="center" style="background:#f0f0f0;"|'''Indx9''' | align="center" style="background:#f0f0f0;"|'''Indx10''' | align="center" style="background:#f0f0f0;"|'''Indx11''' | align="center" style="background:#f0f0f0;"|'''Indx12''' | align="center" style="background:#f0f0f0;"|'''PGP1''' | align="center" style="background:#f0f0f0;"|'''PGP5''' | align="center" style="background:#f0f0f0;"|'''PGP7''' | align="center" style="background:#f0f0f0;"|'''PGP8''' |- | Indx7||1.000||-||-||-||-||-||-||-||-||- |- | Indx8||0.374||1.000||-||-||-||-||-||-||-||- |- | Indx9||0.637||0.509||1.000||-||-||-||-||-||-||- |- | Indx10||0.035||0.185||0.414||1.000||||-||-||-||-||- |- | Indx11||0.349||0.689||1.000||-0.262||1.000||-||-||-||-||- |- | Indx12||-0.119||-0.465||-0.052||-0.225||-0.211||1.000||-||-||-||- |- | PGP1||0.694||0.511||0.591||0.473||0.526||0.615||1.000||-||-||- |- | PGP5||0.421||0.437||0.708||0.381||0.725||0.562||0.881||1.000||-||- |- | PGP7||0.590||0.368||0.645||0.470||0.715||0.445||0.893||0.904||1.000||- |- | PGP8||0.773||0.508||0.641||0.461||0.590||0.496||0.884||0.932||0.909||1.000 |- | |} *SNP calling. **Raw SNPs were called as part of the methylation mapping process, then filtered. I got between 293-703 SNPs per library. Note that only SNPs with the minimal read depth of 8 could passed the filter. The cut-off for read depth could be reduced in the future. **I wrote a script to compare SNPs called from bisulfite reads to Complete Genomics' data in var files. Roughly 2/3 of the SNPs could be mapped to CGI's data. The majority of such SNPs showed one allele in the bisulfite data, and two alleles in CGI's data, which is expected. A small percentage of SNPs are homozygous in both, but different. When I compared the CGI's genotypes generated from three different cell types of PGP1, it became obvious that these SNPs were called heterozygous in one data set, but homozygous in another, probably due to the lack of read depth or random allelic drops in CGI's data.
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