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 Table of Contents  
REVIEW ARTICLE
Year : 2017  |  Volume : 1  |  Issue : 1  |  Page : 30-35

Application of Single-cell Sequencing Technologies in Reproductive Medicine


1 Department of Obstetrics and Gynecology, Peking University Third Hospital; Key Laboratory of Assisted Reproduction, Ministry of Education; Beijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Beijing 100191, China
2 Department of Obstetrics and Gynecology, Peking University Third Hospital; Key Laboratory of Assisted Reproduction, Ministry of Education; Beijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology; Beijing Advanced Innovation Center for Genomics; Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China

Date of Web Publication17-Jul-2017

Correspondence Address:
Jie Qiao
Department of Obstetrics and Gynecology, Peking University Third Hospital, No. 49 North Garden Road, Haidian District, Beijing 100191
China
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/2096-2924.210691

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  Abstract 

We have recently witnessed the rapid development of various single-cell sequencing technologies. Pioneering single cell assays are now available for the profiling of genome, epigenome, and transcriptome. Single-cell sequencing technologies provide multi-dimensional information on early embryonic development in humans and in clinical contexts where specimens are scarce. Here, we review several available techniques and their applications in reproductive medicine. Continuing development of multimodal single-cell sequencing measurement techniques in combination with multi-omics assays will provide comprehensive profiles of an individual cell and lead to the targeted therapy for reproductive disorders and infertility.

Keywords: Early Embryonic Development; Multi-omics Assay; Reproductive Medicine; Single-cell Sequencing


How to cite this article:
Yuan P, Yan LY, Qiao J. Application of Single-cell Sequencing Technologies in Reproductive Medicine. Reprod Dev Med 2017;1:30-5

How to cite this URL:
Yuan P, Yan LY, Qiao J. Application of Single-cell Sequencing Technologies in Reproductive Medicine. Reprod Dev Med [serial online] 2017 [cited 2020 Aug 13];1:30-5. Available from: http://www.repdevmed.org/text.asp?2017/1/1/30/210691




  Introduction Top


Single-cell sequencing investigates the sequence information from individual cells and provides a higher resolution of intercellular differences.[1],[2] Using advanced technology approaches, such as next generation sequencing, the omics studies of the individuality of the cells help us to better understand the characterization and functional diversity of an individual cell within its microenvironmental context.[3] The field of single-cell sequencing is evolving promptly and generating many new insights into complex biological systems, ranging from the diversity of microbial ecosystems to the heterogeneity in cell populations. Pioneering single cell assays are now available for detecting genome, epigenome, transcriptome sequencing at a single-cell level.[4],[5],[6],[7],[8] By employing single cell multi-omics technologies, researchers can reveal the fundamental processes of the cell-fate specification and establish an atlas of differentiation in which every tissue type can be traced back to its embryonic origins.[9],[10] Notably, the technologies provide a platform to measure and quantitatively manipulate the transcriptome using low cell numbers and small sample input, which will allow us to modulate cell phenotypes for the studies in reproductive medicine.[11]

In this review, we summarize the current and emerging single-cell sequencing technologies, as well as the challenges these methods present. We also highlight the use of these technologies in studies of development and disease.


  Single-Cell Sequencing Technologies Top


Single omic sequencing technologies

Single-cell genome sequencing

Single-cell genome sequencing can track mutations, copy number variations (CNVs), and chromosomal aberrations at the single-cell level, which are particularly useful in studies that involve limited cells such as oocytes, embryos, and neurons, as well as cancer cells.[4],[12],[13],[14] Single-cell genome sequencing involves single cell whole-genome amplification (WGA), which includes several methods, namely, degenerate oligonucleotide primed polymerase chain reaction (DOP-PCR), multiple displacement amplification (MDA), and multiple annealing and looping-based amplification cycles (MALBAC).[5],[15],[16],[17] WGA performance is assessed by genome coverage, uniformity, reproducibility, the chimera rate, the allele dropout rate, the false positive rate for calling single nucleotide variations, and the ability to call CNVs.

In DOP-PCR, the genome is amplified by degenerate primers containing a random six-base sequence at the 3'-end and a fixed sequence at the 5'-end. DOP-PCR can amplify a picogram amount of DNA. However, random priming, followed by PCR amplification, can lead to an amplification bias of different sequences,[18] resulting in incomplete genomic information. Regardless, DOP-PCR is useful in identifying large-scale genomic CNVs of 106 bases.[19]

MDA, a widely used WGA method, utilizes ϕ29 polymerase to enable isothermal random priming and extension with high fidelity and processivity.[17] MDA provides greater genome coverage than DOP-PCR because ϕ29 has a high replication fidelity due to its 3'→5' exonuclease and proofreading activity.[20] However, exponential amplification can result in the overrepresentation of several loci.[21]

MALBAC uses quasilinear pre-amplification to reduce the bias associating with nonlinear amplification.[5] A picogram amount of DNA (~10–100 kb) from a single human cell can serve as a template for amplification by MALBAC. Genome amplification is initiated with a random primer pair, each having a common 27-nucleotide sequence and eight variable nucleotides that can evenly hybridize to the template. MALBAC achieves quasilinear pre-amplification by copying the original genomic DNA. Unfortunately, MALBAC gives rise to sequence-dependent bias. Unlike the sequence-dependent bias of MDA, MALBAC is relatively reproducible along the genome from cell to cell. Therefore, it is possible to deduce the CNVs through population-based normalization. After signal normalization with a reference cell population, MALBAC offers a better CNVs prediction accuracy.

According to two recent reports, which compared the three WGA methods, MALBAC and DOP-PCR were better at determining CNVs (>1 MB) than MDA. Specifically, MDA had a better genome coverage than MALBAC (84% versus 52%) and DOP-PCR (84% vs. 6%) at high sequencing depth, which resulted in a higher detection rate of single nucleotide variants (SNV) than MALBAC (88% VS. 52%).[22],[23] Thus, the amplification method should be carefully selected based on the type of genetic variation to be analyzed.

Single-cell epigenome sequencing

Chemical modifications to DNA and histone proteins form a sophisticated regulatory network that modulates chromatin structure and genome function. The epigenome refers to these potentially heritable changes across the genome, falls into two major types: DNA methylation and histone modifications.[24] DNA methylation involves the addition of a methyl group to the C5 carbon residue (5 mC) of cytosine by ubiquitous DNA methyltransferases.[25] DNA methylation is a key epigenetic mechanism that regulates several important biological processes, such as gene expression, X-chromosome inactivation, imprinting, and silencing of germline-specific genes and repetitive elements.[26],[27],[28],[29],[30] Using an accurate method to measure normal and aberrant DNA methylation patterns is of great importance to understand the role of epigenetics in regulating gene expression and the related cell phenotypes. Quite recently, considerable progress has been achieved in the development of methodologies used to investigate the DNA methylation. Nowadays, several methods have been utilized to perform a single-cell DNA methylation sequencing.

In 2013, Guo et al. described a methylome analysis technique that enabled single-cell and single-base resolution DNA methylation analysis based on reduced representation bisulfite sequencing (scRRBS) in a one-tube reaction.[6] The technique is highly sensitive and can detect approximately 10% of all CpG sites. With the restriction digestion enzyme MspI, the scRBBS approach can enrich CpG-dense regions (such as CpG islands [CGI], i.e., ~70% of CGIs can be captured), which are probably the most informative elements for DNA methylation. ScRRBS is such a powerful tool that it allows assessment of a large fraction of promoters with relatively low sequencing costs, but due to its enzyme digestion enrichment, there is poor coverage of many important regulatory regions such as enhancers.

Postbisulfite adapter-tagging (PBAT) is a whole-genome bisulfite sequencing approach in which adaptor tagging is carried out after bisulfite treatment to bypass the bisulfite-induced loss of intact sequencing templates, thereby requiring only 100 ng of DNA for amplification-free whole-genome bisulfite sequencing (WGBS) of mammalian genomes.[31] Based on PBAT, single-cell bisulfite sequencing (scBS-seq) was brought up, and DNA methylation can be measured up to 48.4% of CpG sites in a single cell. In summary, scBS-seq is a versatile tool to explore DNA methylation in rare cells and heterogeneous populations, and it is unrivaled in assessing epigenetic state and cellular heterogeneity.

Compared with scRRBS, scBS-seq can identify more CpGs and CGIs, which are distributed throughout the genome, and provide more information in all genomic contexts. On the other hand, scBS-seq associates with high variability with regard to the DNA methylation of distal regulatory sites in single cells. Although scRRBS covers fewer CpG sites, it provides good coverage for CGIs, which allows assessment of a large percentage of promoters with considerably low sequencing costs.

Chromatin immunoprecipitation (ChIP), followed by high-throughput DNA sequencing (ChIP-seq), has become an indispensable and extensively used method for charting the genomic loci of transcription factor (TF) binding and histone modifications in living cells. ChIP-seq provides a versatile way to study the function of genomic elements and their regulation on genes.[32],[33] The major obstacles of using ChIP-seq in single cells are that a large quantity of input material are required and “averaged” profiles are insensitive to cellular heterogeneity. By combining microfluidics, DNA barcoding and sequencing, drop-ChIP can profile H3 lysine 4 trimethylation and dimethylation to collect chromatin profiles of single cells.[34] The single cell chromatin data are sparse, with only about 1,000 peaks detected in each individual cell due to low coverage. Nonetheless, the specificity is high. For example, drop-ChIP data generated for a mixed population of cells can effectively distinguish the “cell type” of each single-cell profile with almost 100% accuracy. Finally, aggregate profiles obtained for each unbiased cluster closely matched the conventional profiles for the respective substituents of the mixed population. However, the success of this approach relies on the existence of a coherent chromatin state in a sufficient number of sample cells. That is, the power to distinguish such subpopulations comes from a large number of cells and high throughput of microfluidics systems. This technology is fit for histone markers with high abundance, but not appropriate for profiling TFs-binding mapping.

Single-cell transcriptome sequencing

Single-cell transcriptome sequencing began with the development of single cell quantitative PCR and microarray.[35],[36] By adapting bulk cell RNA-sequencing, Tang et al.[8] developed the first transcriptome sequencing method, single-cell RNA-sequencing (scRNA-seq) in 2009. This scRNA-seq assay greatly enhanced the ability to analyze transcriptome complexity in individual cells and cell heterogeneity in rare cell populations (developing early embryonic cells). ScRNA-seq is unbiased in its amplification of complementary DNA (cDNA) from single cells, which allows investigators to identify novel transcripts and to estimate their abundance quantitatively in the cell. The assay can also identify new transcripts and alternatively spliced isoforms in studies on early embryonic development. Nonetheless, scRNA-seq associates with several drawbacks. First, the single cell cDNA amplification method relies on the poly(T) primer, which can only capture mRNA with a poly(A) tail. Second, for most mRNAs longer than 3 kb, a 5' end that is more than 3 kb away from the 3' end of the mRNA will not be detected. Third, the assay cannot discriminate between sense and antisense transcripts.

Smart-seq2, which relies on template switching, provides more even read coverage across transcripts than poly(A)-tailing methods, consistent with the widespread use of template switching in applications designed to capture RNA 5'-ends.[37] The key advantage of Smart-seq2 is that the full-length transcript can be acquired, leading to fewer 3'coverage biases originating from incomplete reverse transcription. Smart-seq2 is also limited to poly(A)+ tail RNAs and does not retain strand or molecule information.

Since RNA sequencing has achieved single-cell resolution, it is important to identify effective ways to routinely isolate and process large numbers of individual cells for quantitative in-depth sequencing, which allows the assessment of heterogeneity in cellular subpopulations. Recently, several methods that improve the quantitative sequencing ability of scRNA-seq using unique molecular identifiers (UMIs), which barcode each individual mRNA molecule within a cell during reverse transcription, have been developed.[38],[39] These methods are based on the fact that sequenced reads arising from PCR-duplicated tags possess the same barcode sequence. The number of copies of a transcript in a given cell is hence equivalent to the number of UMIs associating with all tags that map to the transcript, if one takes into account the initial RNA capture rate of this method.

To completely reveal transcriptome complexity, transcriptome analysis should be performed with individual cells and cover utmost RNA species within the cell. Previously developed single-cell RNA-seq protocols for eukaryotic cells are limited to detect mRNAs with poly(A) tails (poly(A)+ RNAs). A novel single-cell transcriptome profiling method, single-cell universal poly(A)-independent RNA sequencing, which uses random primers with fixed anchor sequences, can detect both poly(A)+ and poly(A)-RNAs within a single cell. This method is suitable to decipher the regulatory mechanisms of circRNAs during mammalian early embryonic development.[40],[41]

Parallel single-cell sequencing

Single-cell sequencing of genome, methylome and transcriptome is well established and widely used. The physical separation of intact RNA and DNA after cell lysis makes it possible to perform parallel sequencing in single cells. There is now proof-of-concept data which show that several omics dimensions (e.g., genome/transcriptome or transcriptome/methylome) can be analyzed in parallel within the same cell.

Co-measurement of both genomic and transcriptional signatures at the single-cell level

Single cell genome and single cell methylome technologies are powerful tools to study RNA and DNA profiles of single cells at the genome-wide level. Currently, there are two parallel sequencing technologies, namely, gDNA-mRNA sequencing (DR-seq) and genome and transcriptome sequencing (G&T-seq), that allow investigators to examine genomic variations and transcriptome heterogeneity.[42],[43]

G&T-seq is used to separate single cell's polyadenylated (poly(A)) RNA from the genomic DNA using a biotinylated oligo-dT primer.[43] After separation, both the genome and transcriptome are amplified and sequenced in parallel. This method largely depends on the quality of the separation because material found in the wrong fraction is lost, especially for the only copy of genomic DNA in a single cell. On the other hand, DR-seq is a quasilinear amplification strategy that quantifies genomic DNA and mRNA from the same cell by bypassing the physical separation of nucleic acids before amplification. In DR-seq, hand-picked single cells are lysed, single-cell DNA, and mRNA are pre-amplified within a single tube, and pre-amplified amplicons are subsequently split for the further independent amplification of the genomic DNA and cDNA.[42] DR-seq is similar to CEL-Seq, and the additional steps involved in amplifying of gDNA do not adversely affect the mRNA sequencing results. The genome sequencing results from DR-Seq are similar with those for MALBAC.

DR-seq amplifies DNA and mRNA without the need for physical separation. However, it requires in silico masking of the exonic regions at the genome to determine DNA CNVs. Furthermore, the RNA sequence reads obtained from DR-seq are biased toward the 3' end. By contrast, G&T-seq can be used to study the genome of a cell with any WGA method of choice, and without the need to mask coding sequences during analysis. It also provides access to full-length transcripts from the same cell. G&T-seq requires accurate biochemical separation of the genome and the transcriptome.

Simultaneous profiling of transcriptome and DNA methylome from a single cell

Parallel profiling of the genome-wide methylome and transcriptome of the same cell is an overwhelming tool in the dissection of transcriptional and epigenetic variations. ScM&T-seq requires physical separation of RNA and DNA, which allows for the bisulfite conversion of DNA without disruption of the transcriptome.[44] The parallel ScM&T-seq method yields results that are in agreement with data from methods profiling either feature in isolation. In addition, ScM&T-seq enables detailed studies of the complex relationships between DNA methylation and transcription in heterogeneous cell populations, which may provide multidimensional information in clinical contexts where the material is relatively scarce, such as in assisted reproductive technology. ScM&T-seq is a good approach to investigate correlations between transcription and DNA-methylation in single cells and to identify the factors regulating this relationship.

Triple omics sequencing

Besides the sequencing strategies delineated above, there is complementary option to predict the others omics dimensions using in silico n methods. Based on the read depth calculation, which deduces CNVs, scTrio-seq is a type of single cell triple omics sequencing, which can simultaneously analyze genomic CNVs, the DNA methylome, and the transcriptome of a single mammalian cell.[45] In other words, scTrio-seq can be used to identify subpopulations of cells based on CNVs, the DNA methylome, or the transcriptome and to investigate the mechanism that regulates the transcriptome, genome, and DNA methylome.


  Application of Single-Cell Sequencing in Reproductive Medicine Top


Gene expression networks, which shape the identity and behavior of a cell, are regulated by genetic and epigenetic mechanisms. Thus, deciphering the temporal and spatial three-dimensional patterns (genome, transcriptome, and methylome) of cells is a crucial step toward understanding gametogenesis, early development, and cell lineage relationships in normal and diseased tissues.

Reproductive tissues and cells are heterogeneous and typically difficult to obtain. Thus, single-cell sequencing is important in reproductive medicine, thus enabling a more comprehensive understanding of the extent, function and evolution of cellular heterogeneity during normal development and diseases.

Human primordial germ cells (PGCs) are embryonic founder cells that give rise to the mature gametes, oocytes and sperms.[46] Mature gametes are vital for transmitting both genetic and epigenetic information from one generation to the next and for maintaining the continuation of a species through fertilization.[47],[48] Therefore, it is important to understand the epigenetic processes of this most intriguing lineage in vivo. Recently, the transcriptome and DNA methylome of human PGCs from multiple developmental stages were analyzed at single cell and single-base levels. The researchers found that human PGCs exhibit unique features that were very different from mouse PGCs.[9]

Similar to mouse PGCs, human PGCs at early stages of development express pluripotency genes such as OCT4, NANOG,and REX1. However, human PGCs do not express SOX2. Instead, they express SOX15 and SOX17. In contrast to the mitotic PGCs, meiotic PGCs exhibit strong gene expression heterogeneity, probably because they are non-synchronized when entering meiosis arrest. Second, one of the two X-chromosomes is randomly inactivated after implantation in female embryos to maintain the same dose of gene expression level on X-chromosomes in both sexes; the inactivated X-chromosomes is already reactivated in the 4 weeks female PGCs. Third, global DNA demethylation occurs during the development of human PGCs. The DNA methylation level is reduced from 92% in postimplantation embryos to about 7% in PGCs at 10–11 weeks after gestation, thus reaching the lowest DNA methylation level in any type of normal human cell. Fourth, although most of the functionally important genomic elements are essentially free of methylation during the development of human PGCs, some repeat elements still possess high levels of residual DNA methylation. These observations form a basis for the potential transgenerational inheritance of epigenetic memory. Finally, PGCs maintain relatively stable global RNA expression patterns and a constitutive heterochromatin status when DNA methylation is globally decreased more than 10-fold, which indicates that other key components of epigenetic regulation, such as histone modifications, probably play an important role in this process.

For the first time, the human PGC transcriptome was comprehensively analyzed at single cell and single-base levels, which provided new insights on the epigenetic reprogramming and development of human PGCs. This research also has the potential to impact the safety of assisted reproductive technology, the clinical assessment of epigenetic-related diseases and germ cell abnormalities.

In 2013, Guo et al.[6] described the reduced representation bisulfite sequencing (scRRBS) method. Based on this “one-tube-reaction” method, they devised methylation landscapes of mouse embryonic stem cells and early embryos. After combining scRRBS and WGBS, the landscape of human early embryos was outlined, which systematically profiled the methylome of human early embryos from the zygotic stage to postimplantation.[10] The major wave of genome-wide demethylation is complete at the 2-cell stage, contrary to previous observations in mouse. Moreover, the demethylation of the paternal genome is much faster than that of the maternal genome, and by the end of the zygotic stage, the genome-wide methylation level in male pronuclei is already lower than that in female pronuclei. This work shed light on the critical features of the methylome of human early embryos, as well as the regulation of gene expression and repression of transposable elements.

Pioneering single-cell transcriptional research on embryonic development was published in 2013. A comprehensive set of transcriptome landscapes of human preimplantation embryos and embryonic stem cells were explored using the single-cell RNA-seq technique, including the expression data of known RefSeq genes and lncRNAs, as well as novel lncRNAs in 124 individual embryonic cells.[49] For the first time, maternally expressed lncRNAs were systematically analyzed in human early embryos. This work provided insights on early embryonic development, pluripotency and the molecular identity of hESCs. Another related study used single-cell RNA-seq to analyze transcriptomes from oocytes to morulae in both human and mouse embryos. Interestingly, the majority of human stage-specific genes were stable in the cross-species comparisons with mouse pre-implantation embryos.[11]

At present, MALBAC is one of the most successful applications of single-cell sequencing in clinical reproductive medicine. It is extensively used in preimplantation genetic diagnosis and preimplantation genetic screening to select embryos free of monogenic diseases and aneuploidy (chromosome abnormality). Recently, a mutated allele with aneuploidy revealed by sequencing and linkage analyses (MARSALA) combined with next-generation sequencing and MALBAC, allowed embryo diagnosis at the single molecule resolution, considerably reducing false-positive or false-negative errors.[50] We reckon that more couples will benefit from MARSALA to avoid transmitting their mutant genes and genetic diseases to descendants.


  Future Investigative Needs Top


During single-cell sequencing, the input template must be amplified several times to provide enough material for sequencing. Due to low, variable capture efficiencies and biases introduced during the PCR amplification step, it is difficult to determine whether an observed difference is due to a biological factor or technical noise.[51] Furthermore, low input templates can also lead to the loss of information at the single cell level, in spite of the fact that the missing information can be imputed to reduce the effect of low coverage.[1]

Although the initial single cell multi-omics parallel sequencing methods are accessible and beneficial for reproductive medicine research, improvements are needed to couple multi-omics methods for practical use in biotechnology and biomedicine, such as the isolation of single cells, with single-cell sequencing techniques and bioinformatics tools.


  Conclusions Top


In this review, we have presented an overview of the current state of the feld of single cell sequencing in reproductive medicine. Although further technical improvements are still needed, the initial application of these techniques to study the gametes and early embryo development is highly promising and has already provided great insight into the normal physiology and certain developmental disorders. New advanced methods are developing rapidly, and it will be able to dissect the complex interactions between the genomic, epigenomic, and the transcriptomic heterogeneity in the near future. Furthermore, the single-cell sequencing technologies have begun to be applied to the clinical practice in early detection, diagnosis and targeting therapy for reproductive disorders and infertility. The ultimate goal of the single cell sequencing studies is to transform our understanding of how the identity of the cell is maintained and how it is perturbed in disease into the practical use in biotechnology and biomedicine.

Financial support and sponsorship

This study was supported by the National High Technology Research and Development Program (grant number 2015AA020407) and the National Natural Science Foundation of China (grant numbers 81521002 and 31522034).

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Clark SJ, Lee HJ, Smallwood SA, Kelsey G, Reik W. Single-cell epigenomics: Powerful new methods for understanding gene regulation and cell identity. Genome Biol 2016;17:72. doi: 10.1186/s13059-016-0944-x.  Back to cited text no. 1
    
2.
Tanay A, Regev A. Scaling single-cell genomics from phenomenology to mechanism. Nature 2017;541:331-8. doi: 10.1038/nature21350.  Back to cited text no. 2
    
3.
Gawad C, Koh W, Quake SR. Single-cell genome sequencing: Current state of the science. Nat Rev Genet 2016;17:175-88. doi: 10.1038/nrg.2015.16.  Back to cited text no. 3
    
4.
Hou Y, Fan W, Yan L, Li R, Lian Y, Huang J, et al. Genome analyses of single human oocytes. Cell 2013;155:1492-506. doi: 10.1016/j.cell. 2013.11.040.  Back to cited text no. 4
    
5.
Zong C, Lu S, Chapman AR, Xie XS. Genome-wide detection of single-nucleotide and copy-number variations of a single human cell. Science 2012;338:1622-6. doi: 10.1126/science.1229164.  Back to cited text no. 5
    
6.
Guo H, Zhu P, Wu X, Li X, Wen L, Tang F. Single-cell methylome landscapes of mouse embryonic stem cells and early embryos analyzed using reduced representation bisulfite sequencing. Genome Res 2013;23:2126-35. doi: 10.1101/gr.161679.113.  Back to cited text no. 6
    
7.
Smallwood SA, Lee HJ, Angermueller C, Krueger F, Saadeh H, Peat J, et al. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat Methods 2014;11:817-20. doi: 10.1038/nmeth.3035.  Back to cited text no. 7
    
8.
Tang F, Barbacioru C, Wang Y, Nordman E, Lee C, Xu N, et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods 2009;6:377-82. doi: 10.1038/nmeth.1315.  Back to cited text no. 8
    
9.
Guo F, Yan L, Guo H, Li L, Hu B, Zhao Y, et al. The transcriptome and DNA methylome landscapes of human primordial germ cells. Cell 2015;161:1437-52. doi: 10.1016/j.cell.2015.05.015.  Back to cited text no. 9
    
10.
Guo H, Zhu P, Yan L, Li R, Hu B, Lian Y, et al. The DNA methylation landscape of human early embryos. Nature 2014;511:606-10. doi: 10.1038/nature13544.  Back to cited text no. 10
    
11.
Xue Z, Huang K, Cai C, Cai L, Jiang CY, Feng Y, et al. Genetic programs in human and mouse early embryos revealed by single-cell RNA sequencing. Nature 2013;500:593-7. doi: 10.1038/nature12364.  Back to cited text no. 11
    
12.
Lodato MA, Woodworth MB, Lee S, Evrony GD, Mehta BK, Karger A, et al. Somatic mutation in single human neurons tracks developmental and transcriptional history. Science 2015;350:94-8. doi: 10.1126/science.aab1785.  Back to cited text no. 12
    
13.
Navin NE. The first five years of single-cell cancer genomics and beyond. Genome Res 2015;25:1499-507. doi: 10.1101/gr.191098.115.  Back to cited text no. 13
    
14.
Lu S, Zong C, Fan W, Yang M, Li J, Chapman AR, et al. Probing meiotic recombination and aneuploidy of single sperm cells by whole-genome sequencing. Science 2012;338:1627-30. doi: 10.1126/science.1229112.  Back to cited text no. 14
    
15.
Telenius H, Carter NP, Bebb CE, Nordenskjöld M, Ponder BA, Tunnacliffe A. Degenerate oligonucleotide-primed PCR: General amplification of target DNA by a single degenerate primer. Genomics 1992;13:718-25. doi: 10.1016/0888-7543(92)90147-K.  Back to cited text no. 15
    
16.
Zhang L, Cui X, Schmitt K, Hubert R, Navidi W, Arnheim N. Whole genome amplification from a single cell: Implications for genetic analysis. Proc Natl Acad Sci U S A 1992;89:5847-51. doi: 10.1073/pnas.89.13.5847.  Back to cited text no. 16
    
17.
Dean FB, Nelson JR, Giesler TL, Lasken RS. Rapid amplification of plasmid and phage DNA using Phi 29 DNA polymerase and multiply-primed rolling circle amplification. Genome Res 2001;11:1095-9. doi: 10.1101/gr.180501.  Back to cited text no. 17
    
18.
Cheung VG, Nelson SF. Whole genome amplification using a degenerate oligonucleotide primer allows hundreds of genotypes to be performed on less than one nanogram of genomic DNA. Proc Natl Acad Sci U S A 1996;93:14676-9. doi: 10.1073/pnas.93.25.14676.  Back to cited text no. 18
    
19.
Navin NE. Cancer genomics: One cell at a time. Genome Biol 2014;15:452. doi: 10.1186/s13059-014-0452-9.  Back to cited text no. 19
    
20.
Garmendia C, Bernad A, Esteban JA, Blanco L, Salas M. The bacteriophage phi 29 DNA polymerase, a proofreading enzyme. J Biol Chem 1992;267:2594-9.  Back to cited text no. 20
    
21.
Pugh TJ, Delaney AD, Farnoud N, Flibotte S, Griffith M, Li HI, et al. Impact of whole genome amplification on analysis of copy number variants. Nucleic Acids Res 2008;36:e80. doi: 10.1093/nar/gkn378.  Back to cited text no. 21
    
22.
Huang L, Ma F, Chapman A, Lu S, Xie XS. Single-cell whole-genome amplification and sequencing: Methodology and applications. Annu Rev Genomics Hum Genet 2015;16:79-102. doi: 10.1146/annurev-genom-090413-025352.  Back to cited text no. 22
    
23.
Hou Y, Wu K, Shi X, Li F, Song L, Wu H, et al. Comparison of variations detection between whole-genome amplification methods used in single-cell resequencing. Gigascience 2015;4:37. doi: 10.1186/s13742-015-0068-3.  Back to cited text no. 23
    
24.
Bernstein BE, Meissner A, Lander ES. The mammalian epigenome. Cell 2007;128:669-81. doi:10.1016/cell.2007.01.033.  Back to cited text no. 24
    
25.
Hashimoto H, Horton JR, Zhang X, Bostick M, Jacobsen SE, Cheng X. The SRA domain of UHRF1 flips 5-methylcytosine out of the DNA helix. Nature 2008;455:826-9. doi: 10.1101/gad.947102.  Back to cited text no. 25
    
26.
Bird A. DNA methylation patterns and epigenetic memory. Genes Dev 2002;16:6-21. doi: 10.1101/gad.947102.  Back to cited text no. 26
    
27.
Fulka H, Mrazek M, Tepla O, Fulka J Jr. DNA methylation pattern in human zygotes and developing embryos. Reproduction 2004;128:703-8. doi: 10.1530/rep.1.00217.  Back to cited text no. 27
    
28.
Li E, Beard C, Jaenisch R. Role for DNA methylation in genomic imprinting. Nature 1993;366:362-5. doi: 10.1038/366362a0.  Back to cited text no. 28
    
29.
Hackett JA, Surani MA. DNA methylation dynamics during the mammalian life cycle. Philos Trans R Soc Lond B Biol Sci 2013;368:20110328. doi: 10.1098/rstb.2011.0328.  Back to cited text no. 29
    
30.
Lister R, Pelizzola M, Dowen RH, Hawkins RD, Hon G, Tonti-Filippini J, et al. Human DNA methylomes at base resolution show widespread epigenomic differences. Nature 2009;462:315-22. doi: 10.1038/nature08514.  Back to cited text no. 30
    
31.
Miura F, Enomoto Y, Dairiki R, Ito T. Amplification-free whole-genome bisulfite sequencing by post-bisulfite adaptor tagging. Nucleic Acids Res 2012;40:e136. doi: 10.1093/nar/gks454.  Back to cited text no. 31
    
32.
Kharchenko PV, Tolstorukov MY, Park PJ. Design and analysis of ChIP-seq experiments for DNA-binding proteins. Nat Biotechnol 2008;26:1351-9. doi: 10.1038/nbt.1508.  Back to cited text no. 32
    
33.
Park PJ. ChIP-seq: Advantages and challenges of a maturing technology. Nat Rev Genet 2009;10:669-80. doi: 10.1038/nrg2641.  Back to cited text no. 33
    
34.
Rotem A, Ram O, Shoresh N, Sperling RA, Goren A, Weitz DA, et al. Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state. Nat Biotechnol 2015;33:1165-72. doi: 10.1038/nbt.3383.  Back to cited text no. 34
    
35.
Bengtsson M, Hemberg M, Rorsman P, Ståhlberg A. Quantification of mRNA in single cells and modelling of RT-qPCR induced noise. BMC Mol Biol 2008;9:63. doi: 10.1186/1471-2199-9-63.  Back to cited text no. 35
    
36.
Subkhankulova T, Gilchrist MJ, Livesey FJ. Modelling and measuring single cell RNA expression levels find considerable transcriptional differences among phenotypically identical cells. BMC Genomics 2008;9:268. doi: 10.1186/1471-2164-9-268.  Back to cited text no. 36
    
37.
Picelli S, Björklund ŠK, Faridani OR, Sagasser S, Winberg G, Sandberg R. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat Methods 2013;10:1096-8. doi: 10.1038/nmeth.2639.  Back to cited text no. 37
    
38.
Jaitin DA, Kenigsberg E, Keren-Shaul H, Elefant N, Paul F, Zaretsky I, et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 2014;343:776-9. doi: 10.1126/science.1247651.  Back to cited text no. 38
    
39.
Klein AM, Mazutis L, Akartuna I, Tallapragada N, Veres A, Li V, et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 2015;161:1187-201. doi: 10.1016/j.cell.2015.04.044.  Back to cited text no. 39
    
40.
Fan X, Zhang X, Wu X, Guo H, Hu Y, Tang F, et al. Single-cell RNA-seq transcriptome analysis of linear and circular RNAs in mouse preimplantation embryos. Genome Biol 2015;16:148. doi: 10.1186/s13059-015-0706-1.  Back to cited text no. 40
    
41.
Dang Y, Yan L, Hu B, Fan X, Ren Y, Li R, Lian Y, et al. Tracing the expression of circular RNAs in human pre-implantation embryos. Genome Biol 2016;17:130. doi: 10.1186/s13059-016-0991-3.  Back to cited text no. 41
    
42.
Dey SS, Kester L, Spanjaard B, Bienko M, van Oudenaarden A. Integrated genome and transcriptome sequencing of the same cell. Nat Biotechnol 2015;33:285-9. doi: 10.1038/nbt.3129.  Back to cited text no. 42
    
43.
Macaulay IC, Haerty W, Kumar P, Li YI, Hu TX, Teng MJ, et al. G& T-seq: Parallel sequencing of single-cell genomes and transcriptomes. Nat Methods 2015;12:519-22. doi: 10.1038/nmeth.3370.  Back to cited text no. 43
    
44.
Angermueller C, Clark SJ, Lee HJ, Macaulay IC, Teng MJ, Hu TX, et al. Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity. Nat Methods 2016;13:229-32. doi: 10.1038/nmeth.3728.  Back to cited text no. 44
    
45.
Hou Y, Guo H, Cao C, Li X, Hu B, Zhu P, et al. Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas. Cell Res 2016;26:304-19. doi: 10.1038/cr.2016.23.  Back to cited text no. 45
    
46.
Fujimoto T, Miyayama Y, Fuyuta M. The origin, migration and fine morphology of human primordial germ cells. Anat Rec 1977;188:315-30. doi: 10.1002/ar.1091880305.  Back to cited text no. 46
    
47.
Leitch HG, Tang WW, Surani MA. Primordial germ-cell development and epigenetic reprogramming in mammals. Curr Top Dev Biol 2013;104:149-87. doi: 10.1016/B978-0-12-416027-9.00005-X.  Back to cited text no. 47
    
48.
Saitou M, Yamaji M. Primordial germ cells in mice. Cold Spring Harb Perspect Biol 2012;4. pii: a008375. doi: 10.1101/cshperspect.a008375.  Back to cited text no. 48
    
49.
Yan L, Yang M, Guo H, Yang L, Wu J, Li R, et al. Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells. Nat Struct Mol Biol 2013;20:1131-9. doi: 10.1038/nsmb.2660.  Back to cited text no. 49
    
50.
Yan L, Huang L, Xu L, Huang J, Ma F, Zhu X, et al. Live births after simultaneous avoidance of monogenic diseases and chromosome abnormality by next-generation sequencing with linkage analyses. Proc Natl Acad Sci U S A 2015;112:15964-9. doi: 10.1073/pnas.1523297113.  Back to cited text no. 50
    
51.
Wen L, Tang F. Computational biology: How to catch rare cell types. Nature 2015;525:197-8. doi: 10.1038/nature15204.  Back to cited text no. 51
    




 

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