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Jul 2017

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Preparing Single-cell DNA Library Using Nextera for Detection of CNV
利用Nextera制备检测CNV的单细胞DNA文库   

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Abstract

Single-cell DNA sequencing is a powerful tool to evaluate the state of heterogeneity of heterogeneous tissues like cancer in a quantitative manner that bulk sequencing can never achieve. DOP-PCR (Degenerate Oligonucleotide-Primed Polymerase Chain Reaction), MDA (Multiple Displacement Amplification), MALBAC (Multiple Annealing and Looping-Based Amplification Cycles), LIANTI (Linear Amplification via Transposon Insertion) and TnBC (Transposon Barcoded) have been the primary choices to prepare single-cell libraries. TnBC library prep method is a simple and versatile methodology, to detect copy number variations or to obtain the absolute copy numbers of genes per cell.

Keywords: Single-cell (单细胞), Transposon (转座子), Nextera (Nextera), Copy number variation (拷贝数变异), Shallow sequencing (浅测序)

Background

Bulk DNA sequencing, although being widely used nowadays, has been proven to be inadequate in analysis of heterogeneous systems, such as cancer tissues, which contain cancer cells of genetic aberrations at various degrees among normal cells with little or no genetic aberrations. Noncancerous cells can contribute a significant portion of the total DNA extracted from tumors, potentially masking important genetic aberrations (Alioto et al., 2015). Even when normal cells are removed, bulk sequencing of cancerous cells still averages out both the heterogeneity of cancerous cells in a tumor tissue and genomic instability over time (Yang et al., 2013; Francis et al., 2014). Single-cell DNA sequencing is believed to be the only method to reveal unequivocally the dynamics of mutations of tumor cell subpopulations in detail over space and time (Navin, 2015). Copy number variations (CNV) is under-detected in bulk sequencing, while they are found to be early events in tumorigenesis (Navin, 2015).

In the past years, several single-cell library preparation methods have been reported, which include DOP-PCR (Baslan and Hicks, 2014), MDA (Fan et al., 2011), MALBAC (Zong et al., 2012), LIANTI (Chen et al., 2017) and TnBC (Xi et al., 2017). Due to the fact that the minute amount of DNA from a single cell is not sufficient for NGS directly for most purposes, the single-cell genome needs to be amplified. Reflecting this requirement, almost all single-cell library preparation methodologies are named after an amplification method. As amplification is involved, the biases and errors associated with amplification inevitably need to be addressed (Xi, 2018). Biased amplification will require deeper sequencing to gain coverages. In an extreme case, under-amplified regions can be falsely identified as deletions. Amplification errors introduced by polymerases may overwhelm authentic mutations, which adds difficulty in mutation-calls. As TnBC methodology employs unique fragment index (UFI), it can handle amplification biases and polymerase-introduced errors better than other methods do (Xi et al., 2017; Xi, 2018).

An engineered Mu transposase was used in our original paper of TnBC library preparation (Xi et al., 2017). Since preparing custom-made transposases is technically demanding and time-consuming, here we report a protocol that utilizes Nextera, a commercially available Tn5 transposase. This protocol is intended to obtain single cell libraries that will be good for CNV detection through shallow sequencing. Due to the proof-reading DNA polymerase that is used in our library amplification, the error rate can be significantly smaller than that from DOP-PCR, MALBAC, or LIANTI. Therefore, the aggregate of sequences from multiple single cells can be used to detect global SNV of the source tissue of the single cells (Knouse et al., 2016).

Materials and Reagents

  1. Pipette tips
  2. 96-well plate
  3. Read1: TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG (suggested source: custom order from IDT)
  4. Read2: GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG (suggested source: custom order from IDT)
  5. TBE buffer: Make 1x from 10x TBE (suggested source: Tris-borate-EDTA Buffer from Fisher Scientific, catalog number: B52)
  6. Nextera DNA Library Preparation Kit (24 samples) (suggested source: Illumina, catalog number: FC-121-1030)
  7. Nextera Index Kit (96 Indices) (suggested source: Illumina, catalog number: FC-121-1012)
  8. Phusion Hot Start II High-Fidelity PCR Master Mix (Thermo Fisher, catalog number: F565L)
  9. UltraPureTM 0.5 M EDTA, pH 8.0 (suggested source: Thermo Fisher, catalog number: 15575020)
    Note: Make 10 fold dilution as working stock (100 mM).
  10. 20x EvaGreen (Biotium, catalog number: 31000)
  11. Agencourt AMPure XP (Beckmen Courtier, catalog number: A63882 )
  12. Proteinase K, Molecular Biology Grade (New England Biolabs, catalog number: P81075)
  13. Chymostatin (5 mg) (Sigma-Aldrich, catalog number: C7268-5MG)
    Note: Dissolve in 0.83 ml of DMSO to make 6 mg/ml stock solution.
  14. Double-distilled H2O (ddH2O)
  15. DMSO (Dimethyl sulfoxide)
  16. Ethanol, freshly make 75% ethanol for Agencourt AMPure XP bead washes
  17. Tris-HCl, 1 M, pH 8 (suggested source: Thermo Fisher, catalog number: AM9856)
  18. MgCl2, 1 M (suggested source: Thermo Fisher, catalog number: AM9530G), or equivalent
  19. DMF (Dimethylformamide)
  20. CLOROX bleach
  21. 5x Transposase buffer (see Recipes)

Equipment

  1. Pipettes
  2. DNA-free hood
  3. VeritiTM 96-Well Thermal Cycler (Thermo Fisher, catalog number: 4375786)
  4. QuantStudio 3 Real-Time PCR System (Thermo Fisher)
  5. DynaMagTM-96 Side Magnet (Thermo Fisher, catalog number: 12331D)
  6. Agilent BioAnalyzer

Procedure

  1. This library prep procedure starts with a 96-well plate with each well containing one cell in 5 μl TBE or ddH2O. (Single cells can be dispensed with a cell sorter according to manuals of manufacturers or by manual means [Knouse et al., 2017]). The overall workflow is shown in the table below:


  2. To each of the well, add 3 μl solution that contains 1 μl of 5x Transposase buffer, 0.5 μl of Proteinase K (NEB), and 1.5 μl of H2O. Spin down to make sure that the protease solution enters cell suspension. Incubate the suspension at 55 °C for 5 h to break the cell open and remove histone proteins, and then 65 °C for 20 min to denature Proteinase K. 
  3. Add 1 μl of 1.5 mg/ml Chymostatin to each well, spin down, incubate at room temperature for 10 min to inactivate remaining Proteinase K activity.
  4. Add 1.5 μl solution that contains 1 μl of 5x Transposase buffer and 0.5 μl of Tagment DNA Enzyme (TDE) from Illumina’s Nextera DNA Library Preparation Kit. Spin down.
  5. Keep the tagmentation reaction at 55 °C for 20 min.
  6. Add 1 μl solution that contains 0.5 μl of 100 mM EDTA and 0.5 μl of Proteinase K. Vortex and spin down. Incubate at 37 °C for 1 h and then 65 °C for 20 min.
  7. To each well, add 12.5 μl of 2x Hot-start Phusion Master Mix and 1.25 μl of 100 μM each of Read1 and Read2. Mix well, and run the following PCR protocols: 65 °C for 30 min and 95 °C for 2 min followed by 6 cycles between 15 s at 98 °C and 30 min at 65 °C, plus 7 cycles between 15 s at 98 °C and 3 min at 65 °C. The thermal cycling protocol is summarized in the table below:


  8. After PCR, add 45 μl AMPure beads to purify the PCR product according to the manufacturer’s instruction. Elute in 10 μl ddH2O and purify with 18 μl of AMPure beads, and then elute in 10 μl ddH2O. Detailed steps are listed in the table below:


  9. Use 2.5 μl from last step in 20 μl sample-barcoding PCR which contains 1x Hot-start Phusion Master Mix, 1x EvaGreen, 1 μM each of 5xx and 7xx barcodes for Illumina sequencing, one unique pair for each cell. Amplification is carried out by heating at 95 °C for 2 min, then followed by 8 cycles between 15 s at 98 °C and 3 min at 65 °C, Reactions is optionally monitored at 65 °C. The thermal cycling protocol is summarized in the table below:


  10. Use 36 μl of AMPure beads to purify barcoded product according to manufacturer’s instruction, and elute in 10 μl ddH2O.
  11. Take 1 μl for analyses on BioAnalyzer.
  12. If both the profile and the yield meet the requirements (see Notes), it is ready for sequencing using Illumina’s system.

Notes

  1. It is critical to avoid contamination and cross-contamination for single-cell operations (Knouse et al., 2017). We use DNA-free hood when pipetting, and eject and submerge pipette tips immediately into about 1% bleach water to minimize the chance of cross-contamination.
  2. Saturated transposition (Step 5) is critical for the procedure (Xi et al., 2017). Saturated transpositions minimize sequence bias of transposase. 
  3. Nick translation (Step 7) is necessary to generate “tags” that the libraries can be amplified. The polymerase activity from Hot-start Phusion Master Mix is leaky enough at 65 °C to accomplish the task based on the instruction of the manufacturer.
  4. We use EvaGreen to monitor the amplification of every single-cell library in barcoding reactions (Step 9). A significant increase in fluorescence starts at Cycle 4. 
  5. We analyze every single cell library on BioAnalyzer (Step 11). We use the profiles to evaluate our procedures. First, the concentration of the library reflects (1) the quantity of starting DNA and (2) amplification efficiency. Starting from 6 pg of DNA, we expect the concentration is at least 10 nM for a library of 10 μl in volume. Using together with the real-time PCR amplification curves, we can optimize cell lysis step and amplification. Second, we expect the profile of the library that is constructed under saturated transposition to look like what is shown in the left panel of Figure 1. This profile is different from what is recommended by Illumina for bulk sequencing. The fragment size is best to range between 170 to 350 base pairs (Xi et al., 2017). Several factors may lead to flat profiles as shown in the right panel of Figure 1: (1) insufficient removal of histone proteins, (2) insufficient amount of transposase, (3) incomplete denaturing of Proteinase K in Steps 2 and 3, leading to degrading of transposase in Step 5, (4) incomplete removal of transposons in Step 6. Larger fragments have relatively lower efficiencies to be sequenced by Illumina’s systems (Xi et al., 2017), leading to undercounting in these regions.


    Figure 1. Using BioAnalyzer to examine if transposition is saturated (left panel) or unsaturated (right panel) in addition to the quantitation

  6. After BioAnalyzer analysis, the concentration of the library can be adjusted for next-gen sequencing. A sequencing depth of 0.5%x to 5%x of genome is good for CNV analysis. The resolution (i.e., bin size) depends on the number of unique fragments (counts) per bin. To obtain statistically sound copy number call, we use at least 50 counts per bin. For example, if the intended resolution is 1,000 kilobases, for a library with the average insert size of 100 bases, the minimal coverage = (50 counts x 100 bases)/1,000,000 = 0.5%. If the resolution is set to be 100 kilobases, the minimal coverage = (50 counts x 100 bases)/100,000 = 5%. 
  7. We have used the protocol on single cells of K562 cell line, BJ, human lymphocyte GM01202 cell line, human intestine cells, human skin cells, mouse skin cells, and mouse brain cells.

Recipes

  1. 5x Transposase buffer (Picelli et al., 2014)
    50 mM Tris-HCl, 25 mM MgCl2, 50% (v/v) DMF (pH 8.0) at 25°C

Competing interests

Digenomix is commercializing the technology.

References

  1. Alioto, T. S., Buchhalter, I., Derdak, S., Hutter, B., Eldridge, M. D., Hovig, E., Heisler, L. E., Beck, T. A., Simpson, J. T., Tonon, L., Sertier, A. S., Patch, A. M., Jager, N., Ginsbach, P., Drews, R., Paramasivam, N., Kabbe, R., Chotewutmontri, S., Diessl, N., Previti, C., Schmidt, S., Brors, B., Feuerbach, L., Heinold, M., Grobner, S., Korshunov, A., Tarpey, P. S., Butler, A. P., Hinton, J., Jones, D., Menzies, A., Raine, K., Shepherd, R., Stebbings, L., Teague, J. W., Ribeca, P., Giner, F. C., Beltran, S., Raineri, E., Dabad, M., Heath, S. C., Gut, M., Denroche, R. E., Harding, N. J., Yamaguchi, T. N., Fujimoto, A., Nakagawa, H., Quesada, V., Valdes-Mas, R., Nakken, S., Vodak, D., Bower, L., Lynch, A. G., Anderson, C. L., Waddell, N., Pearson, J. V., Grimmond, S. M., Peto, M., Spellman, P., He, M., Kandoth, C., Lee, S., Zhang, J., Letourneau, L., Ma, S., Seth, S., Torrents, D., Xi, L., Wheeler, D. A., Lopez-Otin, C., Campo, E., Campbell, P. J., Boutros, P. C., Puente, X. S., Gerhard, D. S., Pfister, S. M., McPherson, J. D., Hudson, T. J., Schlesner, M., Lichter, P., Eils, R., Jones, D. T. and Gut, I. G. (2015). A comprehensive assessment of somatic mutation detection in cancer using whole-genome sequencing. Nat Commun 6: 10001.
  2. Baslan, T. and Hicks, J. (2014). Single cell sequencing approaches for complex biological systems. Curr Opin Genet Dev 26: 59-65.
  3. Chen, C., Xing, D., Tan, L., Li, H., Zhou, G., Huang, L. and Xie, X. S. (2017). Single-cell whole-genome analyses by Linear Amplification via Transposon Insertion (LIANTI). Science 356(6334): 189-194.
  4. Fan, H. C., Wang, J., Potanina, A. and Quake, S. R. (2011). Whole-genome molecular haplotyping of single cells. Nat Biotechnol 29(1): 51-57.
  5. Francis, J. M., Zhang, C. Z., Maire, C. L., Jung, J., Manzo, V. E., Adalsteinsson, V. A., Homer, H., Haidar, S., Blumenstiel, B., Pedamallu, C. S., Ligon, A. H., Love, J. C., Meyerson, M. and Ligon, K. L. (2014). EGFR variant heterogeneity in glioblastoma resolved through single-nucleus sequencing. Cancer Discov 4(8): 956-971.
  6. Knouse, K. A., Wu, J. and Amon, A. (2016). Assessment of megabase-scale somatic copy number variation using single-cell sequencing. Genome Res 26(3): 376-384.
  7. Knouse, K. A., Wu, J. and Hendricks, A. (2017). Detection of copy number alterations using single cell sequencing. J Vis Exp(120). Doi: 10.3791/55143.
  8. Navin, N. E. (2015). Delineating cancer evolution with single-cell sequencing. Sci Transl Med 7(296): 296fs229.
  9. Picelli, S., Bjorklund, A. K., Reinius, B., Sagasser, S., Winberg, G. and Sandberg, R. (2014). Tn5 transposase and tagmentation procedures for massively scaled sequencing projects. Genome Res 24(12): 2033-2040.
  10. Xi, L., Belyaev, A., Spurgeon, S., Wang, X., Gong, H., Aboukhalil, R. and Fekete, R. (2017). New library construction method for single-cell genomes. PLoS One 12(7): e0181163.
  11. Xi, L. (2018). Single-Cell DNA Sequencing: From Analog to Digital. Cancer Research Frontiers. 3(1): 161-169.
  12. Yang, L., Luquette, L. J., Gehlenborg, N., Xi, R., Haseley, P. S., Hsieh, C. H., Zhang, C., Ren, X., Protopopov, A., Chin, L., Kucherlapati, R., Lee, C. and Park, P. J. (2013). Diverse mechanisms of somatic structural variations in human cancer genomes. Cell 153(4): 919-929.
  13. Zong, C., Lu, S., Chapman, A. R. and Xie, X. S. (2012). Genome-wide detection of single-nucleotide and copy-number variations of a single human cell. Science 338(6114): 1622-1626.

简介

单细胞DNA测序是一种强有力的工具,可以定量方式评估异质组织如癌症的异质性状态,即批量测序无法实现。 DOP-PCR(简并寡核苷酸 - 引物聚合酶链反应),MDA(多位移扩增),MALBAC(多重退火和基于循环的扩增循环),LIANTI(通过转座子插入的线性扩增)和TnBC(转座子条形码)一直是主要的 选择准备单细胞库。 TnBC文库制备方法是一种简单且通用的方法,用于检测拷贝数变异或获得每个细胞的基因的绝对拷贝数。
【背景】批量DNA测序虽然目前被广泛使用,但已被证明不足以分析异质系统,例如癌组织,其在正常细胞中含有不同程度的遗传畸变的癌细胞,具有很少或没有遗传畸变。非癌细胞可以贡献从肿瘤中提取的总DNA的重要部分,可能掩盖重要的遗传畸变(Alioto et al。,2015)。即使正常细胞被移除,癌细胞的大量测序仍然可以平均肿瘤组织中癌细胞的异质性和基因组不稳定性(Yang et al。,2013; Francis et al。,2014)。单细胞DNA测序被认为是在空间和时间上详细揭示肿瘤细胞亚群突变动态的唯一方法(Navin,2015)。在批量测序中未发现拷贝数变异(CNV),而发现它们是肿瘤发生的早期事件(Navin,2015)。

在过去几年中,已经报道了几种单细胞库制备方法,包括DOP-PCR(Baslan和Hicks,2014),MDA(Fan et al。,2011),MALBAC(Zong et al。,2012),LIANTI(Chen et al。,2017)和TnBC(Xi et al。,2017)。由于来自单个细胞的微量DNA对于大多数目的而言不能直接用于NGS,因此需要扩增单细胞基因组。反映这一要求,几乎所有单细胞文库制备方法都以扩增方法命名。当涉及扩增时,不可避免地需要解决与扩增相关的偏差和误差(Xi,2018)。有偏差的扩增将需要更深的测序以获得覆盖。在极端情况下,未充分扩增的区域可能被错误地识别为缺失。聚合酶引入的扩增错误可能会破坏真实的突变,这增加了突变调用的难度。由于TnBC方法采用独特的片段指数(UFI),它可以比其他方法更好地处理扩增偏差和聚合酶引入的错误(Xi et al。,2017; Xi,2018)。

在我们的TnBC文库制备的原始论文中使用工程化的Mu转座酶(Xi et al。,2017)。由于制备定制的转座酶技术要求高且耗时,因此我们在此报告使用Nextera的方案,Nextera是市售的Tn5转座酶。该方案旨在获得通过浅层测序有利于CNV检测的单细胞文库。由于我们的文库扩增中使用的校对DNA聚合酶,错误率可以显着小于来自DOP-PCR,MALBAC或LIANTI的错误率。因此,来自多个单细胞的序列的聚集体可用于检测单个细胞的源组织的全局SNV(Knouse 等人,,2016)。

关键字:单细胞, 转座子, Nextera, 拷贝数变异, 浅测序

材料和试剂

  1. 移液器吸头
  2. 96孔板
  3. 阅读1:TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG(建议来源:IDT的定制订单)
  4. 阅读2:GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG(建议来源:IDT的定制订单)
  5. TBE缓冲液:从10x TBE中提取1倍(建议来源:Fisher Scientific的Tris-borate-EDTA Buffer,目录号:B52)
  6. Nextera DNA文库制备试剂盒(24个样品)(建议来源:Illumina,目录号:FC-121-1030)
  7. Nextera Index Kit(96 Indices)(建议来源:Illumina,目录号:FC-121-1012)
  8. Phusion Hot Start II高保真PCR Master Mix(Thermo Fisher,目录号:F565L)
  9. UltraPure TM 0.5 M EDTA,pH 8.0(建议来源:Thermo Fisher,目录号:15575020)
    注意:将10倍稀释液作为工作原液(100 mM)。
  10. 20x EvaGreen(Biotium,目录号:31000)
  11. Agencourt AMPure XP(Beckmen Courtier,目录号:A63882)
  12. Proteinase K,Molecular Biology Grade(New England Biolabs,目录号:P81075)
  13. Chymostatin(5 mg)(Sigma-Aldrich,目录号:C7268-5MG)
    注意:溶于0.83 ml DMSO中,制成6 mg / ml原液。
  14. 双蒸H 2 O(ddH 2 O)
  15. DMSO(二甲基亚砜)
  16. 乙醇,新鲜生产75%乙醇,用于Agencourt AMPure XP洗涤
  17. Tris-HCl,1M,pH 8(建议来源:Thermo Fisher,目录号:AM9856)
  18. MgCl 2 ,1M(建议来源:Thermo Fisher,目录号:AM9530G)或
  19. DMF(二甲基甲酰胺)
  20. CLOROX漂白剂
  21. 5x转座酶缓冲液(见食谱)

设备

  1. 移液器
  2. 无DNA罩
  3. Veriti TM 96孔热循环仪(Thermo Fisher,目录号:4375786)
  4. QuantStudio 3实时PCR系统(Thermo Fisher)
  5. DynaMag TM -96 Side Magnet(Thermo Fisher,目录号:12331D)
  6. Agilent BioAnalyzer

程序

  1. 该文库制备程序以96孔板开始,每个孔含有5μlTBE或ddH 2 O中的一个细胞。 (根据制造商的手册或通过手动方式[Knouse et al。,2017],可以用细胞分选器除去单细胞)。整个工作流程如下表所示:


  2. 向每个孔中加入3μl含有1μl5x转座酶缓冲液,0.5μl蛋白酶K(NEB)和1.5μlH 2 O的溶液。旋转以确保蛋白酶溶液进入细胞悬浮液。将悬浮液在55℃孵育5小时以打开细胞并除去组蛋白,然后在65℃孵育20分钟以使蛋白酶K变性。 
  3. 向每个孔中加入1μl1.5mg/ ml的胰凝乳蛋白酶抑制剂,旋转,在室温下孵育10分钟以灭活剩余的蛋白酶K活性。
  4. 添加1.5μl含有1μl5x转座酶缓冲液和0.5μl来自Illumina的Nextera DNA文库制备试剂盒的Tagment DNA酶(TDE)的溶液。旋转下来。
  5. 将标记反应保持在55℃下20分钟。
  6. 加入1μl含有0.5μl100mM EDTA和0.5μl蛋白酶K的溶液。涡旋并旋转。在37°C孵育1小时,然后在65°C孵育20分钟。
  7. 向每个孔中加入12.5μl的2x热启动Phusion Master Mix和1.25μl的100μMRead1和Read2。充分混合,并运行以下PCR方案:65°C 30分钟,95°C 2分钟,然后6个循环,15秒,98°C,30分钟,65°C,加上7个循环,15秒,98秒°C,65°C下3分钟。热循环方案总结在下表中:


  8. PCR后,根据制造商的说明添加45μlAmpure珠粒以纯化PCR产物。洗脱10μlddH 2 O并用18μlAMPure珠纯化,然后在10μlddH 2 O中洗脱。详细步骤列于下表:


  9. 使用来自最后一步的2.5μl进行20μl样品条形码PCR,其包含1x热启动Phusion Master Mix,1x EvaGreen,1μM,每种5xx和7xx条形码用于Illumina测序,每个细胞一对独特。通过在95℃加热2分钟进行扩增,然后在85℃下15秒和65℃下3分钟之间进行8个循环。任选地在65℃下监测反应。热循环方案总结在下表中:


  10. 根据制造商的说明使用36μl的AMPure珠子纯化条形码产物,并在10μlddH 2 O中洗脱。
  11. 取1μl进行BioAnalyzer分析。
  12. 如果配置文件和产量均符合要求(参见注释),则可以使用Illumina的系统进行测序。

笔记

  1. 对于单细胞操作,避免污染和交叉污染至关重要(Knouse et al。,2017)。我们在移液时使用无DNA罩,并立即将移液管吸头浸入并浸入约1%的漂白水中,以尽量减少交叉污染的可能性。
  2. 饱和转座(步骤5)对于该程序是至关重要的(Xi et al。,2017)。饱和转座可最大限度地减少转座酶的序列偏倚。 
  3. 尼克翻译(步骤7)对于生成可以扩增文库的“标签”是必要的。来自Hot-start Phusion Master Mix的聚合酶活性在65°C时足够渗漏,以根据制造商的说明完成任务。
  4. 我们使用EvaGreen监测条形码反应中每个单细胞文库的扩增(步骤9)。荧光显着增加从第4周期开始。 
  5. 我们在BioAnalyzer上分析每个单细胞库(步骤11)。我们使用配置文件来评估我们的程序。首先,文库的浓度反映了(1)起始DNA的量和(2)扩增效率。从6pg DNA开始,我们预计对于10μl体积的文库,浓度至少为10nM。与实时PCR扩增曲线一起使用,我们可以优化细胞裂解步骤和扩增。其次,我们期望在饱和转置下构建的文库的轮廓看起来如图1的左图所示。该轮廓与Illumina推荐的批量测序不同。片段大小最好在170到350个碱基对之间(Xi et al。,2017)。有几个因素可能导致扁平剖面,如图1右图所示:(1)组蛋白去除不充分,(2)转座酶量不足,(3)步骤2和3中蛋白酶K不完全变性,导致步骤5中转座酶的降解,(4)步骤6中转座子的不完全去除。较大的片段具有相对较低的效率,可通过Illumina的系统进行测序(Xi et al。,2017),导致计数不足这些地区。


    图1.除定量外,使用BioAnalyzer检查转座是否饱和(左图)或不饱和(右图)

  6. BioAnalyzer分析后,可以调整文库浓度以进行下一代测序。基因组的0.5%x至5%x的测序深度对于CNV分析是有益的。分辨率(即,bin大小)取决于每个bin的唯一片段(计数)的数量。要获得统计上合理的拷贝数调用,我们每个bin使用至少50个计数。例如,如果预期的分辨率是1,000千碱基,对于平均插入物大小为100个碱基的文库,最小覆盖度=(50个计数×100个碱基)/ 1,000,000个= 0.5%。如果分辨率设置为100千碱基,则最小覆盖率=(50个计数×100个碱基)/ 100,000 = 5%。 
  7. 我们已经在K562细胞系,BJ,人淋巴细胞GM01202细胞系,人肠细胞,人皮肤细胞,小鼠皮肤细胞和小鼠脑细胞的单细胞上使用该方案。

食谱

  1. 5x转座酶缓冲液(Picelli et al。,2014)
    在25°C下加入50 mM Tris-HCl,25 mM MgCl 2 ,50%(v / v)DMF(pH 8.0)

利益争夺

Digenomix正在将该技术商业化。

参考

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Copyright: © 2019 The Authors; exclusive licensee Bio-protocol LLC.
引用:Xi, L., Leong, P. and Mihajlovic, A. (2019). Preparing Single-cell DNA Library Using Nextera for Detection of CNV. Bio-protocol 9(4): e3175. DOI: 10.21769/BioProtoc.3175.
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