G&T-Seq

Last updated

G&T-seq (short for single cell genome and transcriptome sequencing) is a novel form of single cell sequencing technique allowing one to simultaneously obtain both transcriptomic and genomic data from single cells, allowing for direct comparison of gene expression data to its corresponding genomic data in the same cell... [1]

Contents

Background

The advent of single-cell sequencing has provided researchers with the tools to resolve genotypically and phenotypically distinct cells within a mixed population. [2] In cases where such heterogeneity is relevant, such as in tumours, this technique enables the study of clonal relationships and tumour evolution. [3] As well, rare cell types and samples otherwise containing low cell numbers, such as in the case of circulating tumour cells, can also be studied in greater detail. [4] However, previous methods of library preparation typically involve the capture of either mRNA or genomic DNA (gDNA), but not both. [5] By simultaneously capturing and sequencing both DNA and RNA through a method called G&T sequencing, researchers are able to obtain sequence information for both genome and transcriptome analysis from single cell libraries, thereby allowing integrated studies involving both networks. As a proof of concept, the authors of G&T-seq demonstrated its ability to acquire both the messenger RNA (mRNA) and genomic DNA (gDNA) by using paramagnetic beads with biotinylated oligo-deoxy-Thymine(dT) primer to separate the polyadenylated (Poly-A) RNA from its gDNA prior to amplification and library preparation. Validation experiments on G&T-seq performed using cell lines with previous sequencing data available show that sequencing coverage, gene expression profile, and DNA copy number profiles were reliably reproduced by G&T sequencing, and that this method was able to call a majority (87%) of all previously annotated single nucleotide variants (SNVs) in these cell lines. The authors have argued on this basis that the process of physically separating mRNA from gDNA did not negatively affect the yield or quality of sequencing data. [1]

Methods

This workflow figures describes the sequential steps for performing G&T-seq: Cell sorting and lysis, separation of mRNA and gDNA, genome and transcriptom amplification, and sequencing and analysis. MEDG505 wiki workflow finished.pdf
This workflow figures describes the sequential steps for performing G&T-seq: Cell sorting and lysis, separation of mRNA and gDNA, genome and transcriptom amplification, and sequencing and analysis.

Similar to conventional single-cell sequencing, G&T-seq involves the harvesting and lysis of desired cells. However, both gDNA and polyA-mRNA are captured and physically separated prior to amplification and library construction for analysis using sequencing platforms.

Separation of poly-adenylated RNA from genomic DNA

G&T sequencing separates the mRNA from the gDNA using an unbiased global amplification procedure described previously. [6] First, mRNA is isolated on specialized oligo-dT (5’-biotin-triethyleneglycol-AAGCAGTGGTATCAACGCAGAGTAC(T)30VN-3’) conjugated to streptavidin-coupled paramagnetic beads. [7] The oligo-dT binds to the poly-A tails of processed mRNA, fishing them out from the pool of genomic material. Next, the paramagnetic beads are spatially isolated by magnetization. The genomic material remaining in the supernatant is extracted and physically separated from the mRNA. [1]

Amplification and Sequencing

The authors that developed G&T-seq utilized and validated two methods for whole-genome amplification: Multiple displacement amplification and PicoPlex. Other methods, such as MALBAC, may be applicable but have yet to be validated. [1] [8]

Multiple Displacement Amplification

MDA amplification technique can be used to generate long, high quality reads that produce sequencing data of comparable quality to bulk sequencing using PCR amplification. [9] This method involves the use of hexamer primers that bind randomly to the template, followed by DNA elongation using phi29 DNA polymerase. Upon reaching the 5’ end of a downstream primer, the polymerase displaces that elongating strand to continue synthesis. The displaced strand becomes open for pairing with more primers, allowing for amplification of the displaced strand. The process continues and produces a branched DNA library that can be cut and sequenced. The authors of the G&T technique found that, though MDA used in G&T-seq yielded genomic coverage of similar breadth as MDA performed in conventional single cell sequencing, the distribution of read coverage was less even across the genome. [1]

PicoPlex

Though MDA produces higher quality reads suitable for SNP analysis, DNA copy number profiles generated by such a technique are not highly accurate and reproducible due to its non-uniform amplification. [5] [10] An alternate technique called PicoPlex, developed by Rubicon Genomics, has been shown to produce better results. [1] Here, elongation of random primers ligated to an adapter creates a complementary strand with an adapter that, when denatured and randomly reprimed, produces a double stranded fragment with complementary adapters. Denaturation into single strands allows for the formation of hairpin loops due to the complementary nature of their adapters, creating a hairpin loop library that cannot be used for subsequent amplification, thereby preventing exponential amplification of initial bias. [11] [12]

cDNA amplification

The process of mRNA isolation and amplification of cDNA Strand-seq MY.pdf
The process of mRNA isolation and amplification of cDNA

Messenger RNA bound to oligo-dT is reverse transcribed into cDNA using the oligo-dT primers with the addition of Template-Switching Oligo (TSO, 5"-AAGCAGTGGTATCAACGCAGAGTACrGrG+G-3’) and Superscript II reverse transcriptase. [13] [14] Superscript II reverse transcriptase has additional terminal transferase activity which adds a variable number of cytosine residues to the end the 3’ terminal cDNA molecule. The overhang of 3’ cytosine residues bind to the TSO, creating an extended template. The Superscript II reverse transcriptase switches templates and continues transcribing to complete the 3’ end of the cDNA. This results in a full length cDNA containing the 5’ oligo-dT primer, cDNA transcribed from mRNA, and the 3’ universal priming site for second-strand synthesis. The cDNA undergoes amplification using the universal primer (5’- AAGCAGTGGTATCAACGCAGAGT-3’) for 18 cycles of PCR before it undergoes library preparation using the Nextera XT Kit from Illumina and sequencing by the Illumina HiSeq platform. [1] [15]

Alternatives Techniques

A similar method to G&T-seq, developed months earlier, is DR-seq (DNA and RNA sequencing). The primary difference between the two techniques is the amplification step, where DNA and polyA-RNA amplification occurs without their prior separation. [16] DR-seq uses random priming, where primers containing a common 27-nucleotide sequence along with a variable 8-nucleotide (ad2 primers) bind to different locations on the cDNA. [12] Despite there being multiple (50-250) primer binding sites on most cDNA, each original (i.e. not the product of amplification/in vitro transcription) cDNA molecule is usually primed only once during the initial amplification step, thus creating a single amplicon of a unique length, containing the ad2 primer on the 5' end. The 3' end contains the ad1 primer, which is the original poly-dT primer used for reverse transcription. This unique amplicon is termed the length-based identifier. Importantly, the length-based identifier is created, but not amplified by this quasilinear PCR step. The number of unique length-based identifiers for each gene can then be used to infer the number of original cDNA (and thus mRNA) molecules present for the gene, providing a method of estimating gene expression that avoids the effect of amplification bias. To further amplify the cDNA for RNA-seq, the cDNA amplicons generated by the original PCR step undergoes in vitro transcription using the T7 promoter incorporated in the ad1 primer to ensure RNA transcripts come from cDNA, not gDNA.

Advantages of the DR-seq technique include the reduction of the possibility for contamination and RNA loss, since the extra step of DNA/RNA separation is skipped. As well, amplification bias is reduced due to the use of the aforementioned length-based identifiers. However, since DNA and polyA-RNA is not separated prior to amplification and subsequent sequencing, the exonic regions must be computationally masked, leaving only reads that originate from gDNA, in order to determine copy number. This creates issues for accurately determining copy number counts from gDNA. The authors note, though, that copy number count over large genomic regions is apparently not impacted by masking as a result because coding regions compose a relatively small portion of the genome. [16]

Applications

Dual genome and transcriptome sequencing allows researchers to establish high resolution correlations of genomic aberrations with alterations to levels of transcription. For example, the authors of this technique were able to detect single cells with chromosomal aneuploidies, and establish that these aneuploidies corresponded with increased or decreased overall chromosomal gene expression when there was a respective chromosomal gain (e.g. Trisomy) or loss. Subchromosomal changes could also be correlated with changes in expression of genes at affected loci. As well, the authors were able to find a fusion transcript and locate the chromosomal breakpoint in the same cell resulting in the fusion. [1]

G&T-seq also provides a strategy for establishing causative links between genotype and phenotype associations in single cells (e.g. Non-coding SNVs). While bulk sequencing of genome and transcriptome may allow one to associate a collection of genotypic features with mean expression patterns in a population of cells, it overlooks subtle or temporal differences between individual cells that may arise due to cell ecology. [17] This presents an obstacle for researchers trying to pinpoint the genomic causes underlying transcript alterations, especially when compounded with tumour samples where heterogeneity is widespread and background genetic variation could confound relevant mutations. [3] [18] [19] Conventional single cell sequencing, on the other hand, prevents one from making direct associations between mutations and changes in the transcriptome because either the DNA or the RNA is lost in the process. Traditionally, researchers would have to use other methods, such as classification based on cell markers. However, such methods of discrimination rely on the availability of specific antibodies, and provide relatively coarse discrimination compared to sequencing since expression of cell surface markers constitute only a fraction of its overall phenotype [20] [21]

Finally, separation of DNA from RNA paves the way for dual sequencing of the epigenome and transcriptome, two components of the cell that are intricately linked to each other. However, this would require validation with conventional single cell bisulphite sequencing to ensure separation of DNA and RNA doesn’t affect the DNA methylation status.

Considerations

GC bias

The MDA amplification has an inherent bias against repeat sequences which were underrepresented in MDA products. In the context of G&T sequencing, this results in a reduced read count as the % of GC content increases for a particular region.

Distribution of read coverage

Comparing the amplification of single cell residual genomic DNA after mRNA isolation by MDA to amplification of single cell genomic DNA without mRNA isolation by MDA, showed a less evenly distributed coverage across the genome after mRNA isolation. Although there was a reduction in coverage distribution, it was not by a large proportion.

Exclusion of alternate RNA

Isolation of mRNA by the G&T-seq technique described is only capable of capturing mRNAs which have a sufficient length poly-A tail which can be captured by the oligo-dT bait. [6] This is not a complete representation of the mRNA present in the cell. Some mRNAs have crucial roles in phenotypic expression but do not present the standard polyA tail length due to alternative polyadenylation. [22] Therefore, G&Ts comparison of genotype–phenotype correlation does not necessarily represent the best causal link between the two.

Protein expression correlation

The mRNA isolation is not the only hurdle in establishing genotype–phenotype relation. It is not sufficient to use mRNA as a surrogate to total protein expression, because other RNA species exist which also play important roles in phenotypic expression. Another auxiliary technique which can bolster the claims made by G&T sequencing is a total proteome analysis by mass spectrometry, giving a better presentation of the relation between genomic changes and phenotypic presentation [15]

Related Research Articles

<span class="mw-page-title-main">Complementary DNA</span> Single-stranded DNA synthesized from RNA

In genetics, complementary DNA (cDNA) is DNA synthesized from a single-stranded RNA template in a reaction catalyzed by the enzyme reverse transcriptase. cDNA is often used to express a specific protein in a cell that does not normally express that protein, or to sequence or quantify mRNA molecules using DNA based methods. cDNA that codes for a specific protein can be transferred to a recipient cell for expression, often bacterial or yeast expression systems. cDNA is also generated to analyze transcriptomic profiles in bulk tissue, single cells, or single nuclei in assays such as microarrays, qPCR, and RNA-seq.

The transcriptome is the set of all RNA transcripts, including coding and non-coding, in an individual or a population of cells. The term can also sometimes be used to refer to all RNAs, or just mRNA, depending on the particular experiment. The term transcriptome is a portmanteau of the words transcript and genome; it is associated with the process of transcript production during the biological process of transcription.

Rapid amplification of cDNA ends (RACE) is a technique used in molecular biology to obtain the full length sequence of an RNA transcript found within a cell. RACE results in the production of a cDNA copy of the RNA sequence of interest, produced through reverse transcription, followed by PCR amplification of the cDNA copies. The amplified cDNA copies are then sequenced and, if long enough, should map to a unique genomic region. RACE is commonly followed up by cloning before sequencing of what was originally individual RNA molecules. A more high-throughput alternative which is useful for identification of novel transcript structures, is to sequence the RACE-products by next generation sequencing technologies.

<span class="mw-page-title-main">Serial analysis of gene expression</span> Molecular biology technique

Serial Analysis of Gene Expression (SAGE) is a transcriptomic technique used by molecular biologists to produce a snapshot of the messenger RNA population in a sample of interest in the form of small tags that correspond to fragments of those transcripts. Several variants have been developed since, most notably a more robust version, LongSAGE, RL-SAGE and the most recent SuperSAGE. Many of these have improved the technique with the capture of longer tags, enabling more confident identification of a source gene.

Multiple displacement amplification (MDA) is a DNA amplification technique. This method can rapidly amplify minute amounts of DNA samples to a reasonable quantity for genomic analysis. The reaction starts by annealing random hexamer primers to the template: DNA synthesis is carried out by a high fidelity enzyme, preferentially Φ29 DNA polymerase. Compared with conventional PCR amplification techniques, MDA does not employ sequence-specific primers but amplifies all DNA, generates larger-sized products with a lower error frequency, and works at a constant temperature. MDA has been actively used in whole genome amplification (WGA) and is a promising method for application to single cell genome sequencing and sequencing-based genetic studies.

<span class="mw-page-title-main">RNA-Seq</span> Lab technique in cellular biology

RNA-Seq is a sequencing technique that uses next-generation sequencing (NGS) to reveal the presence and quantity of RNA in a biological sample, representing an aggregated snapshot of the cells' dynamic pool of RNAs, also known as transcriptome.

Cap analysis of gene expression (CAGE) is a gene expression technique used in molecular biology to produce a snapshot of the 5′ end of the messenger RNA population in a biological sample. The small fragments from the very beginnings of mRNAs are extracted, reverse-transcribed to cDNA, PCR amplified and sequenced. CAGE was first published by Hayashizaki, Carninci and co-workers in 2003. CAGE has been extensively used within the FANTOM research projects.

Paired-end tags (PET) are the short sequences at the 5’ and 3' ends of a DNA fragment which are unique enough that they (theoretically) exist together only once in a genome, therefore making the sequence of the DNA in between them available upon search or upon further sequencing. Paired-end tags (PET) exist in PET libraries with the intervening DNA absent, that is, a PET "represents" a larger fragment of genomic or cDNA by consisting of a short 5' linker sequence, a short 5' sequence tag, a short 3' sequence tag, and a short 3' linker sequence. It was shown conceptually that 13 base pairs are sufficient to map tags uniquely. However, longer sequences are more practical for mapping reads uniquely. The endonucleases used to produce PETs give longer tags but sequences of 50–100 base pairs would be optimal for both mapping and cost efficiency. After extracting the PETs from many DNA fragments, they are linked (concatenated) together for efficient sequencing. On average, 20–30 tags could be sequenced with the Sanger method, which has a longer read length. Since the tag sequences are short, individual PETs are well suited for next-generation sequencing that has short read lengths and higher throughput. The main advantages of PET sequencing are its reduced cost by sequencing only short fragments, detection of structural variants in the genome, and increased specificity when aligning back to the genome compared to single tags, which involves only one end of the DNA fragment.

<span class="mw-page-title-main">Digital transcriptome subtraction</span>

Digital transcriptome subtraction (DTS) is a bioinformatics method to detect the presence of novel pathogen transcripts through computational removal of the host sequences. DTS is the direct in silico analogue of the wet-lab approach representational difference analysis (RDA), and is made possible by unbiased high-throughput sequencing and the availability of a high-quality, annotated reference genome of the host. The method specifically examines the etiological agent of infectious diseases and is best known for discovering Merkel cell polyomavirus, the suspect causative agent in Merkel-cell carcinoma.

<span class="mw-page-title-main">Illumina dye sequencing</span> DNA sequencing method

Illumina dye sequencing is a technique used to determine the series of base pairs in DNA, also known as DNA sequencing. The reversible terminated chemistry concept was invented by Bruno Canard and Simon Sarfati at the Pasteur Institute in Paris. It was developed by Shankar Balasubramanian and David Klenerman of Cambridge University, who subsequently founded Solexa, a company later acquired by Illumina. This sequencing method is based on reversible dye-terminators that enable the identification of single nucleotides as they are washed over DNA strands. It can also be used for whole-genome and region sequencing, transcriptome analysis, metagenomics, small RNA discovery, methylation profiling, and genome-wide protein-nucleic acid interaction analysis.

<span class="mw-page-title-main">Single-cell analysis</span> Testbg biochemical processes and reactions in an individual cell

In the field of cellular biology, single-cell analysis and subcellular analysis is the study of genomics, transcriptomics, proteomics, metabolomics and cell–cell interactions at the single cell level. The concept of single-cell analysis originated in the 1970s. Before the discovery of heterogeneity, single-cell analysis mainly referred to the analysis or manipulation of an individual cell in a bulk population of cells at a particular condition using optical or electronic microscope. To date, due to the heterogeneity seen in both eukaryotic and prokaryotic cell populations, analyzing a single cell makes it possible to discover mechanisms not seen when studying a bulk population of cells. Technologies such as fluorescence-activated cell sorting (FACS) allow the precise isolation of selected single cells from complex samples, while high throughput single cell partitioning technologies, enable the simultaneous molecular analysis of hundreds or thousands of single unsorted cells; this is particularly useful for the analysis of transcriptome variation in genotypically identical cells, allowing the definition of otherwise undetectable cell subtypes. The development of new technologies is increasing our ability to analyze the genome and transcriptome of single cells, as well as to quantify their proteome and metabolome. Mass spectrometry techniques have become important analytical tools for proteomic and metabolomic analysis of single cells. Recent advances have enabled quantifying thousands of protein across hundreds of single cells, and thus make possible new types of analysis. In situ sequencing and fluorescence in situ hybridization (FISH) do not require that cells be isolated and are increasingly being used for analysis of tissues.

Single-cell sequencing examines the nucleic acid sequence information from individual cells with optimized next-generation sequencing technologies, providing a higher resolution of cellular differences and a better understanding of the function of an individual cell in the context of its microenvironment. For example, in cancer, sequencing the DNA of individual cells can give information about mutations carried by small populations of cells. In development, sequencing the RNAs expressed by individual cells can give insight into the existence and behavior of different cell types. In microbial systems, a population of the same species can appear genetically clonal. Still, single-cell sequencing of RNA or epigenetic modifications can reveal cell-to-cell variability that may help populations rapidly adapt to survive in changing environments.

<span class="mw-page-title-main">Epitranscriptomic sequencing</span>

In epitranscriptomic sequencing, most methods focus on either (1) enrichment and purification of the modified RNA molecules before running on the RNA sequencer, or (2) improving or modifying bioinformatics analysis pipelines to call the modification peaks. Most methods have been adapted and optimized for mRNA molecules, except for modified bisulfite sequencing for profiling 5-methylcytidine which was optimized for tRNAs and rRNAs.

Single-cell transcriptomics examines the gene expression level of individual cells in a given population by simultaneously measuring the RNA concentration of hundreds to thousands of genes. Single-cell transcriptomics makes it possible to unravel heterogeneous cell populations, reconstruct cellular developmental pathways, and model transcriptional dynamics — all previously masked in bulk RNA sequencing.

Transcriptomics technologies are the techniques used to study an organism's transcriptome, the sum of all of its RNA transcripts. The information content of an organism is recorded in the DNA of its genome and expressed through transcription. Here, mRNA serves as a transient intermediary molecule in the information network, whilst non-coding RNAs perform additional diverse functions. A transcriptome captures a snapshot in time of the total transcripts present in a cell. Transcriptomics technologies provide a broad account of which cellular processes are active and which are dormant. A major challenge in molecular biology is to understand how a single genome gives rise to a variety of cells. Another is how gene expression is regulated.

<span class="mw-page-title-main">Spatial transcriptomics</span> Range of methods designed for assigning cell types

Spatial transcriptomics is a method for assigning cell types to their locations in the histological sections and can also be used to determine subcellular localization of mRNA molecules. First described in 2016 by Ståhl et al., it has since undergone a variety of improvements and modifications.

CITE-Seq is a method for performing RNA sequencing along with gaining quantitative and qualitative information on surface proteins with available antibodies on a single cell level. So far, the method has been demonstrated to work with only a few proteins per cell. As such, it provides an additional layer of information for the same cell by combining both proteomics and transcriptomics data. For phenotyping, this method has been shown to be as accurate as flow cytometry by the groups that developed it. It is currently one of the main methods, along with REAP-Seq, to evaluate both gene expression and protein levels simultaneously in different species.

Deterministic Barcoding in Tissue for Spatial Omics Sequencing (DBiT-seq) was developed at Yale University by Rong Fan and colleagues in 2020 to create a multi-omics approach for studying spatial gene expression heterogenicity within a tissue sample. This method can used for the co-mapping mRNA and protein levels at a near single-cell resolution in fresh or frozen formaldehyde-fixed tissue samples. DBiT-seq utilizes next generation sequencing (NGS) and microfluidics. This method allows for simultaneous spatial transcriptomic and proteomic analysis of a tissue sample. DBiT-seq improves upon previous spatial transcriptomics applications such as High-Definition Spatial Transcriptomics (HDST) and Slide-seq by increasing the number of detectable genes per pixel, increased cellular resolution, and ease of implementation.

References

  1. 1 2 3 4 5 6 7 8 Macaulay, I. C.; Haerty, W.; Kumar, P.; Li, Y. I.; Hu, T. X.; Teng, M. J.; Voet, T. (2015). "G&T-seq: Parallel sequencing of single-cell genomes and transcriptomes". Nature Methods. 12 (6): 519–22. doi:10.1038/nmeth.3370. PMID   25915121. S2CID   969246.
  2. ↑ Wang, X. Single Cell Sequencing and Systems Immunology (Vol. 5). Springer
  3. 1 2 Navin, N.; Kendall, J.; Troge, J.; Andrews, P.; Rodgers, L.; McIndoo, J.; Wigler, M. (2011). "Tumour evolution inferred by single-cell sequencing". Nature. 472 (7341): 90–4. Bibcode:2011Natur.472...90N. doi:10.1038/nature09807. PMC   4504184 . PMID   21399628.
  4. ↑ Xi-Xi Chen, Fan Bai (2015). "Single-cell analyses of circulating tumor cells". 癌症生物学与医学：英文版. 12 (3): 184–192. doi:10.7497/j.issn.2095-3941.2015.0056. PMC   4607822 . PMID   26487963.
  5. 1 2 Grün, D.; van Oudenaarden, A. (2015). "Design and analysis of single-cell sequencing experiments". Cell. 163 (4): 799–810. doi: 10.1016/j.cell.2015.10.039 . PMID   26544934.
  6. 1 2 Klein, C. A.; Seidl, S.; Petat-Dutter, K.; Offner, S.; Geigl, J. B.; Schmidt-Kittler, O.; Baeuerle, P. A. (2002). "Combined transcriptome and genome analysis of single micrometastatic cells". Nature Biotechnology. 20 (4): 387–92. doi:10.1038/nbt0402-387. PMID   11923846. S2CID   22474647.
  7. ↑ Picelli, S.; Faridani, O. R.; Björklund, Å. K.; Winberg, G.; Sagasser, S.; Sandberg, R. (2014). "Full-length RNA-seq from single cells using Smart-seq2". Nature Protocols. 9 (1): 171–81. doi:10.1038/nprot.2014.006. PMID   24385147. S2CID   20439681.
  8. ↑ Chapman, A. R.; He, Z.; Lu, S.; Yong, J.; Tan, L.; Tang, F.; Xie, X. S. (2015). "Single cell transcriptome amplification with MALBAC". PLOS ONE. 10 (3): e0120889. Bibcode:2015PLoSO..1020889C. doi: 10.1371/journal.pone.0120889 . PMC   4378937 . PMID   25822772.
  9. ↑ Blanco, L.; Bernad, A.; Lázaro, J. M.; MartÃn, G.; Garmendia, C.; Salas, M. (1989). "Highly efficient DNA synthesis by the phage phi 29 DNA polymerase. Symmetrical mode of DNA replication". Journal of Biological Chemistry. 264 (15): 8935–8940. doi: 10.1016/S0021-9258(18)81883-X . PMID   2498321.
  10. ↑ Voet, T.; Kumar, P.; Van Loo, P.; Cooke, S. L.; Marshall, J.; Lin, M.; Campbell, P. J. (2013). "Single-cell paired-end genome sequencing reveals structural variation per cell cycle". Nucleic Acids Research. 41 (12): 6119–6138. doi:10.1093/nar/gkt345. PMC   3695511 . PMID   23630320.
  11. ↑ "PGS/PGD." Rubicon Genomics. N.p., n.d. Web. 25 Feb. 2016
  12. 1 2 Zong, C.; Lu, S.; Chapman, A. R.; Xie, X. S. (2012). "Genome-wide detection of single-nucleotide and copy-number variations of a single human cell" (PDF). Science. 338 (6114): 1622–1626. Bibcode:2012Sci...338.1622Z. doi:10.1126/science.1229164. PMC   3600412 . PMID   23258894.
  13. ↑ Goetz, J. J.; Trimarchi, J. M. (2012). "Transcriptome sequencing of single cells with Smart-Seq". Nature Biotechnology. 30 (8): 763–765. doi:10.1038/nbt.2325. PMID   22871714. S2CID   10977972.
  14. ↑ "Nextera XT DNA Library Preparation Kit." Nextera XT DNA Library Prep Kit. N.p., n.d. Web. 25 Feb. 2016.
  15. 1 2 Maier, T.; Güell, M.; Serrano, L. (2009). "Correlation of mRNA and protein in complex biological samples". FEBS Letters. 583 (24): 3966–3973. doi:10.1016/j.febslet.2009.10.036. PMID   19850042.
  16. 1 2 Dey, S. S.; Kester, L.; Spanjaard, B.; Bienko, M.; van Oudenaarden, A. (2015). "Integrated genome and transcriptome sequencing of the same cell". Nature Biotechnology. 33 (3): 285–289. doi:10.1038/nbt.3129. PMC   4374170 . PMID   25599178.
  17. ↑ Shapiro, E.; Biezuner, T.; Linnarsson, S. (2013). "Single-cell sequencing-based technologies will revolutionize whole-organism science". Nature Reviews Genetics. 14 (9): 618–630. doi:10.1038/nrg3542. PMID   23897237. S2CID   500845.
  18. ↑ Xu, X.; Hou, Y.; Yin, X.; Bao, L.; Tang, A.; Song, L.; He, W. (2012). "Single-cell exome sequencing reveals single-nucleotide mutation characteristics of a kidney tumor". Cell. 148 (5): 886–895. doi: 10.1016/j.cell.2012.02.025 . PMC   7458411 . PMID   22385958.
  19. ↑ Patel, A. P.; Tirosh, I.; Trombetta, J. J.; Shalek, A. K.; Gillespie, S. M.; Wakimoto, H.; Louis, D. N. (2014). "Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma". Science. 344 (6190): 1396–1401. Bibcode:2014Sci...344.1396P. doi:10.1126/science.1254257. PMC   4123637 . PMID   24925914.
  20. ↑ Vaughan, Christopher. "New Way to Sort Cells without Limitations of Traditional Methods." News Center. Stanford Medicine, 30 Mar. 2015. Web. 25 Feb. 2016
  21. ↑ Bidlingmaier, S.; Zhu, X.; Liu, B. (2008). "The utility and limitations of glycosylated human CD133 epitopes in defining cancer stem cells". Journal of Molecular Medicine. 86 (9): 1025–1032. doi:10.1007/s00109-008-0357-8. PMC   2585385 . PMID   18535813.
  22. ↑ De Klerk, E.; AC; Hoen, P. (2015). "Alternative mRNA transcription, processing, and translation: insights from RNA sequencing". Trends in Genetics. 31 (3): 128–139. doi:10.1016/j.tig.2015.01.001. PMID   25648499.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.