Single-cell sequencing - Knowledge (XXG)

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regulation. Small-seq is a single-cell method that captures small RNAs (<300 nucleotides) such as microRNAs, fragments of tRNAs and small nucleolar RNAs in mammalian cells. This method uses a combination of “oligonucleotide masks” (that inhibit the capture of highly abundant 5.8S rRNA molecules) and size selection to exclude large RNA species such as other highly abundant rRNA molecules. To target larger non-poly(A) RNAs, such as long non-coding mRNA, histone mRNA, circular RNA, and enhancer RNA, size selection is not applicable for depleting the highly abundant ribosomal RNA molecules (18S and 28s rRNA). Single-cell RamDA-Seq is a method that achieves this by performing reverse transcription with random priming (random displacement amplification) in the presence of “not so random” (NSR) primers specifically designed to avoid priming on rRNA molecule. While this method successfully captures full-length total RNA transcripts for sequencing and detected a variety of non-poly(A) RNAs with high sensitivity, it has some limitations. The NSR primers were carefully designed according to rRNA sequences in the specific organism (mouse), and designing new primer sets for other species would take considerable effort. Recently, a CRISPR-based method named scDASH (single-cell depletion of abundant sequences by hybridization) demonstrated another approach to depleting rRNA sequences from single-cell total RNA-seq libraries.

95:). Single-cell DNA sequencing has been widely applied in mammalian systems to study normal physiology and disease. Single-cell resolution can uncover the roles of genetic mosaicism or intra-tumor genetic heterogeneity in cancer development or treatment response. In the context of microbiomes, a genome from a single unicellular organism is referred to as a single amplified genome (SAG). Advancements in single-cell DNA sequencing have enabled collecting of genomic data from uncultivated prokaryotic species present in complex microbiomes. Although SAGs are characterized by low completeness and significant bias, recent computational advances have achieved the assembly of near-complete genomes from composite SAGs. Data obtained from microorganisms might establish processes for culturing in the future. Some of the genome assembly tools used in single cell single-cell sequencing include 328: 579:(RCA) of mRNA. In this method, the ends of single-stranded DNA were ligated together to form a circle, and the resulting loop was then used as a template for linear RNA amplification. The final product library was then analyzed by microarray, with low bias and good coverage. However, RCA has not been tested with RNA-seq, which typically employs next-generation sequencing. Single-cell RNA-seq for bacteria would be highly useful for studying microbiomes. It would address issues encountered in conventional bulk metatranscriptomics approaches, such as failing to capture species present in low abundance, and failing to resolve heterogeneity among cell populations. 292:

methylome readout, the bisulfite-treated sequence is aligned to an unmodified genome. Whole genome bisulfite sequencing was achieved in single cells in 2014. The method overcomes the loss of DNA associated with the typical procedure, where sequencing adapters are added prior to bisulfite fragmentation. Instead, the adapters are added after the DNA is treated and fragmented with bisulfite, allowing all fragments to be amplified by PCR. Using deep sequencing, this method captures ~40% of the total CpGs in each cell. With existing technology DNA cannot be amplified prior to bisulfite treatment, as the 5mC marks will not be copied by the polymerase.

146:. As done in MDA, this method begins with isothermal amplification, but the primers are flanked with a “common” sequence for downstream PCR amplification. As the preliminary amplicons are generated, the common sequence promotes self-ligation and the formation of “loops” to prevent further amplification. In contrast with MDA, the highly branched DNA network is not formed. Instead, the loops are denatured in another temperature cycle allowing the fragments to be amplified with PCR. MALBAC has also been implemented in a microfluidic device, but the amplification performance was not significantly improved by encapsulation in nanoliter droplets. 428:

may also be exponentially amplified, producing libraries with uneven coverage. On the other hand, while libraries generated by IVT can avoid PCR-induced sequence bias, specific sequences may be transcribed inefficiently, thus causing sequence drop-out or generating incomplete sequences. Several scRNA-seq protocols have been published: Tang et al., STRT, SMART-seq, SORT-seq, CEL-seq, RAGE-seq, Quartz-seq. , and C1-CAGE. These protocols differ in terms of strategies for reverse transcription, cDNA synthesis and amplification, and the possibility to accommodate sequence-specific barcodes (i.e.,

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DNA sequencing. In 2017, a major improvement to this technique, called WGA-X, was introduced by taking advantage of a thermostable mutant of the phi29 polymerase, leading to better genome recovery from individual cells, in particular those with high G+C content. MDA has also been implemented in a microfluidic droplet-based system to achieve a highly parallelized single-cell whole genome amplification. By encapsulating single-cells in droplets for DNA capture and amplification, this method offers reduced bias and enhanced throughput compared to conventional MDA.

416:(RT), amplification, library generation and sequencing. Early methods separated individual cells into separate wells; more recent methods encapsulate individual cells in droplets in a microfluidic device, where the reverse transcription reaction takes place, converting RNAs to cDNAs. Each droplet carries a DNA "barcode" that uniquely labels the cDNAs derived from a single cell. Once reverse transcription is complete, the cDNAs from many cells can be mixed together for sequencing, because transcripts from a particular cell are identified by the unique barcode. 108: 572:

devices have been used to sequence single malaria parasites or single yeast cells. The single-cell yeast study sought to characterize the heterogeneous stress tolerance in isogenic yeast cells before and after the yeast are exposed to salt stress. Single-cell analysis of the several transcription factors by scRNA-seq revealed heterogeneity across the population. These results suggest that regulation varies among members of a population to increase the chances of survival for a fraction of the population.

405: 196:(CNV), pose problems in single cell sequencing, as well as the limited amount of DNA extracted from a single cell. Due to scant amounts of DNA, accurate analysis of DNA poses problems even after amplification since coverage is low and is susceptible to errors. With MDA, average genome coverage is less than 80% and SNPs that are not covered by sequencing reads will be opted out. In addition, MDA shows a high ratio of 5826: 248:

compound mutations present in amplified therapeutic targets such as receptor tyrosine kinase genes (EGFR, PDGFRA etc.) where conventional population-level approaches of the bulk tumor are not able to resolve the co-occurrence patterns of these mutations within single cells of the tumor. Such overlap may provide redundancy of pathway activation and tumor cell resistance.

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Bulk bacterial studies typically apply general rRNA depletion to overcome the lack of polyadenylated mRNA on bacteria, but at the single-cell level, the total RNA found in one cell is too small. Lack of polyadenylated mRNA and scarcity of total RNA found in single bacteria cells are two important barriers limiting the deployment of scRNA-seq in bacteria.

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distinct cell types. Another application is studying single cells during the first few cell divisions in early development to understand how different cell types emerge from a single embryo. Single-cell whole-genome bisulfite sequencing has also been used to study rare but highly active cell types in cancer such as circulating tumor cells (CTCs).

633:(FACS) is a widely used approach. Individual cells can also be collected by micromanipulation, for example by serial dilution or by using a patch pipette or nanotube to harvest a single cell. The advantages of micromanipulation are ease and low cost, but they are laborious and susceptible to misidentification of cell types under microscope. 659:

Increasing the number of cells and decreasing the read depth increases the power of identifying major cell populations. However, low read depths may not always provide necessary information about the genes, and the difference in their expression between the cell populations is dependent on the stability and detection of the mRNA molecules.

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Some scRNA-seq methods have also been applied to single cell microorganisms. SMART-seq2 has been used to analyze single cell eukaryotic microbes, but since it relies on poly(A) tail capture, it has not been applied in prokaryotic cells. Microfluidic approaches such as Drop-seq and the Fluidigm IFC-C1

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Challenges for scRNA-Seq include preserving the initial relative abundance of mRNA in a cell and identifying rare transcripts. The reverse transcription step is critical as the efficiency of the RT reaction determines how much of the cell's RNA population will be eventually analyzed by the sequencer.

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classes ≥200kb in size. Strand-seq overcomes limitations of whole genome amplification based methods for identification of somatic genetic variation classes in single cells, because it is not susceptible against read chimers leading to calling artefacts (discussed in detail in the section below), and

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manufacture new strands, a strand displacement reaction takes place, synthesizing multiple copies from each template DNA. At the same time, the strands that were extended antecedently will be displaced. MDA products result in a length of about 12 kb and ranges up to around 100 kb, enabling its use in

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data analysis. It is more challenging to perform single-cell sequencing than sequencing from cells in bulk. The minimal amount of starting materials from a single cell makes degradation, sample loss, and contamination exert pronounced effects on the quality of sequencing data. In addition, due to the

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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

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The single-cell RNA-Seq protocols vary in efficiency of RNA capture, which results in differences in the number of transcripts generated from each single cell. Single-cell libraries are usually sequenced to a depth of 1,000,000 reads because a large majority of genes are detected with 500,000 reads.

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In the amplification step, either PCR or in vitro transcription (IVT) is currently used to amplify cDNA. One of the advantages of PCR-based methods is the ability to generate full-length cDNA. However, different PCR efficiency on particular sequences (for instance, GC content and snapback structure)

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method. This sequencing method is based on sequencing by synthesis (SBS) principle and the use of reversible dye-terminator that enables the identification of each single nucleotid. In order to read the transcript sequences on one end, and the barcode and UMI on the other end, paired-end sequencing

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In 2017, two approaches were introduced to simultaneously measure single-cell mRNA and protein expression through oligonucleotide-labeled antibodies known as REAP-seq, and CITE-seq. Collecting cellular contents following electrophysiological recording using patch-clamp has also allowed development

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also offers a route for direct methylation sequencing without fragmentation or modification to the original DNA. Nanopore sequencing has been used to sequence the methylomes of bacteria, which are dominated by 6mA and 4mC (as opposed to 5mC in eukaryotes), but this technique has not yet been scaled

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is applied to bulk samples, the majority of the CpG sites in gene promoters are detected, but site in gene promoters only account for 10% of CpG sites in the entire genome. In single cells, 40% of the CpG sites from the bulk sample are detected. To increase coverage, this method can also be applied

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Bacteria and other prokaryotes are currently not amenable to single-cell RNA-seq due to the lack of polyadenylated mRNA. Thus, the development of single-cell RNA-seq methods that do not depend on poly(A) tail capture will also be instrumental in enabling single-cell resolution microbiome studies.

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has become the gold standard in detecting and sequencing 5mC in single cells. Treatment of DNA with bisulfite converts cytosine residues to uracil, but leaves 5-methylcytosine residues unaffected. Therefore, DNA that has been treated with bisulfite retains only methylated cytosines. To obtain the

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overcomes limitations of methods based on whole genome amplification for genetic variant calling: Since Strand-seq does not require reads (or read pairs) transversing the boundaries (or breakpoints) of CNVs or copy-balanced structural variant classes, it is less susceptible to common artefacts of

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dropout, not detecting alleles from heterozygous samples. Various SNP algorithms are currently in use but none are specific to single-cell sequencing. MDA with CNV also poses the problem of identifying false CNVs that conceal the real CNVs. To solve this, when patterns can be generated from false

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MDA of individual cell genomes results in highly uneven genome coverage, i.e. relative overrepresentation and underrepresentation of various regions of the template, leading to loss of some sequences. There are two components to this process: a) stochastic over- and under-amplification of random

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mRNA molecules). Within each GEM reaction vesicle, a single cell is lysed and undergo reverse transcription. cDNA from the same cell are identified thanks to a common 10X barcode. In addition, the number of UMIs express the gene expression level and its analyse allows to detect highly variable

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While bisulfite sequencing remains the most widely used approach for 5mC detection, the chemical treatment is harsh and fragments and degrades the DNA. This effect is exacerbated when moving from bulk samples to single cells. Other methods to detect DNA methylation include methylation-sensitive

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Single-cell reduced representation bisulfite sequencing (scRRBS) is another method. This method leverages the tendency of methylated cytosines to cluster at CpG islands (CGIs) to enrich for areas of the genome with a high CpG content. This reduces the cost of sequencing compared to whole-genome

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capture to enrich mRNA and deplete abundant and uninformative rRNA. Thus, they are often restricted to sequencing polyadenylated mRNA molecules. However, recent studies are now starting to appreciate the importance of non-poly(A) RNA, such as long-noncoding RNA and microRNAs in gene expression

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Single-cell DNA methylation sequencing has been widely used to explore epigenetic differences in genetically similar cells. To validate these methods during their development, the single-cell methylome data of a mixed population were successfully classified by hierarchal clustering to identify

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Cancer sequencing is also an emerging application of scDNAseq. Fresh or frozen tumors may be analyzed and categorized with respect to SCNAs, SNVs, and rearrangements quite well using whole-genome DNAS approaches. Cancer scDNAseq is particularly useful for examining the depth of complexity and

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A molecular cell atlas of mice testes was established to define BDE47-induced prepubertal testicular toxicity using the ScRNA-seq approach, providing novel insight into our understanding of the underlying mechanisms and pathways involved in BDE47-associated testicular injury at a single-cell

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The droplet-based platform allows the detection of rare cell types thanks to its high throughput. In fact, 500 to 10,000 cells are captured per sample from a single cell suspension. The protocol is performed easily and allows a high cell recovery rate of up to 65%. The global workflow of the

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are among the main targets of single cell genomics due to the difficulty of culturing the majority of microorganisms in most environments. Single-cell genomics is a powerful way to obtain microbial genome sequences without cultivation. This approach has been widely applied on marine, soil,

396:. This can uncover rare cell types within a cell population that may never have been seen before. For example, one group of scientists performing scRNA-seq on neuroblastoma tumor tissue identified a rare pan-neuroblastoma cancer cell, which may be attractive for novel therapy approaches. 56:. By deep sequencing of DNA and RNA from a single cell, cellular functions can be investigated extensively. Like typical next-generation sequencing experiments, single-cell sequencing protocols generally contain the following steps: isolation of a single cell, nucleic acid extraction and 35:

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

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to a small pool of single cells. In a sample of 20 pooled single cells, 63% of the CpG sites from the bulk sample were detected. Pooling single cells is one strategy to increase methylome coverage, but at the cost of obscuring the heterogeneity in the population of cells.

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Quality control covariates serve as a strategy to analyze the number of cells. These covariates mainly include filtering based on count depth, the number of genes, and the fraction of counts from mitochondrial genes, which leads to the interpretation of cellular signals.

645:. Both FACS and microfluidics are accurate, automatic and capable of isolating unbiased samples. However, both methods require detaching cells from their microenvironments first, thereby causing perturbation to the transcriptional profiles in RNA expression analysis. 637:(LCM) can also be used for collecting single cells. Although LCM preserves the knowledge of the spatial location of a sampled cell within a tissue, it is hard to capture a whole single cell without also collecting the materials from neighboring cells. 5773:

Ziegenhain, Christoph; Vieth, Beate; Parekh, Swati; Reinius, Björn; Guillaumet-Adkins, Amy; Smets, Martha; Leonhardt, Heinrich; Heyn, Holger; Hellmann, Ines; Enard, Wolfgang (February 2017). "Comparative Analysis of Single-Cell RNA Sequencing Methods".

153:, whereas MALBAC is preferred for detecting copy number variants. While performing MDA with a microfluidic device markedly reduces bias and contamination, the chemistry involved in MALBAC does not demonstrate the same potential for improved efficiency. 391:

for defining cell states and phenotypes as of 2020. Although it is impossible to obtain complete information on every RNA expressed by each cell, due to the small amount of material available, gene expression patterns can be identified through gene

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Single cell transposase-accessible chromatin sequencing maps chromatin accessibility across the genome. A transposase inserts sequencing adapters directly into open regions of chromatin, allowing those regions to be amplified and sequenced.

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of somatic tissues, analyses of microbes that cannot be cultured, and disease evolution can all be elucidated through single-cell sequencing. Single-cell sequencing was selected as the method of the year 2013 by Nature Publishing Group.

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droplet-based platform takes 8 hours and so is faster than the Microwell-based method (BD Rhapsody), which takes 10 hours. However, it presents some limitations as the need of fresh samples and the final detection of only 10% mRNA.

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picogram level of the number of nucleic acids used, heavy amplification is often needed during sample preparation of single-cell sequencing, resulting in uneven coverage, noise, and inaccurate quantification of sequencing data.

164:(a.k.a. Strand-seq). Using the principle of single-cell tri-channel processing, which uses joint modelling of read-orientation, read-depth, and haplotype-phase, Strand-seq enables discovery of the full spectrum of somatic 86:

Single-cell DNA genome sequencing involves isolating a single cell, amplifying the whole genome or region of interest, constructing sequencing libraries, and then applying next-generation DNA sequencing (for example

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So, the first step of the method is the single cell encapsulation and library preparation. Cells are encapsulated into Gel Beads-in-emulsion (GEMs) thanks to an automate. To form these vesicle, the automate uses a

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Recent technical improvements make single-cell sequencing a promising tool for approaching a set of seemingly inaccessible problems. For example, heterogeneous samples, rare cell types, cell lineage relationships,

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The final step of the platform is the sequencing. Libraries generated can be directly used for single cell whole transcriptome sequencing or target sequencing workflows. The sequencing is performed by using the

276:(5hmC), 6-methyladenine (6mA), and 4mC 4-methylcytosine (4mC). In eukaryotes, especially animals, 5mC is widespread along the genome and plays an important role in regulating gene expression by repressing 47:

A typical human cell consists of about 2 x 3.3 billion base pairs of DNA and 600 million mRNA bases. Usually, a mix of millions of cells is used in sequencing the DNA or RNA using traditional methods like

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single-cell methods based on whole genome amplification, which include variant calling dropouts due to missing reads at the variant breakpoint and read chimera. Strand-seq discovers the full spectrum of

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for sequencing. Reagents required for MDA reactions include: random primers and DNA polymerase from bacteriophage phi29. In 30 degree isothermal reaction, DNA is amplified with included reagents. As the

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and combines all components with oil. Each functional GEM contains a single cell, a single Gel Bead, and RT reagents. On the Gel Bead, olignonucleotides composed by 4 distincts parts are bind:

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Kashtan N, Roggensack SE, Rodrigue S, Thompson JW, Biller SJ, Coe A, et al. (April 2014). "Single-cell genomics reveals hundreds of coexisting subpopulations in wild Prochlorococcus".

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Swan BK, Martinez-Garcia M, Preston CM, Sczyrba A, Woyke T, Lamy D, et al. (September 2011). "Potential for chemolithoautotrophy among ubiquitous bacteria lineages in the dark ocean".

280:. Sequencing 5mC in individual cells can reveal how epigenetic changes across genetically identical cells from a single tissue or population give rise to cells with different phenotypes. 244:

subsurface, organismal, and other types of microbiomes in order to address a wide array of questions related to microbial ecology, evolution, public health and biotechnology potential.

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Gu H, Smith ZD, Bock C, Boyle P, Gnirke A, Meissner A (April 2011). "Preparation of reduced representation bisulfite sequencing libraries for genome-scale DNA methylation profiling".

568:, and then have applied these on single cell gene expression profiles, obtaining a more robust method to detect the presence of mutations in individual cells using transcript levels. 5514:

Kurimoto K, Yabuta Y, Ohinata Y, Saitou M (2007). "Global single-cell cDNA amplification to provide a template for representative high-density oligonucleotide microarray analysis".

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Yoon HS, Price DC, Stepanauskas R, Rajah VD, Sieracki ME, Wilson WH, et al. (May 2011). "Single-cell genomics reveals organismal interactions in uncultivated marine protists".

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This platform of single cell RNA sequencing allows to analyze transcriptomes on a cell-by-cell basis by the use of microfluidic partitioning to capture single cells and prepare

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Armour CD, Castle JC, Chen R, Babak T, Loerch P, Jackson S, et al. (September 2009). "Digital transcriptome profiling using selective hexamer priming for cDNA synthesis".

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regions; and b) systematic bias against high %GC regions. The stochastic component may be addressed by pooling single-cell MDA reactions from the same cell type, by employing

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events, as well as balanced inversions, and copy-number balanced or imbalanced translocations." Structural variant calls made by Strand-seq are resolved by chromosome-length

182:(FISH) and/or post-sequencing confirmation. The bias of MDA against high %GC regions can be addressed by using thermostable polymerases, such as in the process called WGA-X. 5873: 149:

Comparing MDA and MALBAC, MDA results in better genome coverage, but MALBAC provides more even coverage across the genome. MDA could be more effective for identifying

487:(essential for the sequencing) ; 10X barcoded oligonucleotides ; Unique Molecular Identifier (UMI) sequence ; PolydT sequence (that enables capture of 424:

of reverse transcriptases and the priming strategies used may affect full-length cDNA production and the generation of libraries biased toward 3’ or 5' end of genes.

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analyze the RNA expression from large populations of cells. These measurements may obscure critical differences between individual cells in mixed-cell populations.

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methods, data from bulk RNA-Seq has been used to increase the signal/noise ratio in scRNA-Seq. Specifically, scientists have used gene expression profiles from

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Peterson VM, Zhang KX, Kumar N, Wong J, Li L, Wilson DC, et al. (October 2017). "Multiplexed quantification of proteins and transcripts in single cells".

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The first single-cell transcriptome analysis in a prokaryotic species was accomplished using the terminator exonuclease enzyme to selectively degrade rRNA and

456:. The droplets based platform enables massively parallel sequencing of mRNA in a large numbers of individual cells by capturing single cell in oil droplet. 297: 224:, which provides additional variant calling specificity. As a current limitation, Strand-seq requires dividing cells for strand-specific labelling using 204: 161: 3827: 1709:

Zhang K, Martiny AC, Reppas NB, Barry KW, Malek J, Chisholm SW, Church GM (June 2006). "Sequencing genomes from single cells by polymerase cloning".

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Shintaku H, Nishikii H, Marshall LA, Kotera H, Santiago JG (February 2014). "On-chip separation and analysis of RNA and DNA from single cells".

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Shapiro E, Biezuner T, Linnarsson S (September 2013). "Single-cell sequencing-based technologies will revolutionize whole-organism science".

2456:"Single-cell methylome landscapes of mouse embryonic stem cells and early embryos analyzed using reduced representation bisulfite sequencing" 169:

is less affected by drop outs. The choice of method depends on the goal of the sequencing because each method presents different advantages.

4215:"Circulating tumour cell (CTC) counts as intermediate end points in castration-resistant prostate cancer (CRPC): a single-centre experience" 604:. In each case multiple stages of the embryo were studied, allowing the entire process of development to be mapped on a cell-by-cell basis. 3141:

Tang F, Barbacioru C, Wang Y, Nordman E, Lee C, Xu N, et al. (May 2009). "mRNA-Seq whole-transcriptome analysis of a single cell".

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Dal Molin A, Di Camillo B (July 2019). "How to design a single-cell RNA-sequencing experiment: pitfalls, challenges and perspectives".

3431:"Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression heterogeneity" 2306:"A comparison of existing global DNA methylation assays to low-coverage whole-genome bisulfite sequencing for epidemiological studies" 5859: 179: 116: 508:

The major difference between the droplet-based method and the microwell-based method is the technique used for partitioning cells.

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modifications can reveal cell-to-cell variability that may help populations rapidly adapt to survive in changing environments.

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which bind specifically the poly(A) tail of mRNA molecules. Subsequently, the amplified cDNA library is used for sequencing.

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Current scRNA-seq protocols involve isolating single cells and their RNA, and then following the same steps as bulk RNA-seq:

150: 1084:"Obtaining high-quality draft genomes from uncultured microbes by cleaning and co-assembly of single-cell amplified genomes" 5467:"Characterization of 2,2',4,4'-tetrabromodiphenyl ether (BDE47)-induced testicular toxicity via single-cell RNA-sequencing" 3916:

Hagemann-Jensen M, Abdullayev I, Sandberg R, Faridani OR (October 2018). "Small-seq for single-cell small-RNA sequencing".

3374:"High-throughput targeted long-read single cell sequencing reveals the clonal and transcriptional landscape of lymphocytes" 6013: 2644:

Guo H, Zhu P, Yan L, Li R, Hu B, Lian Y, et al. (July 2014). "The DNA methylation landscape of human early embryos".

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This figure illustrates the workflow of single-cell genome sequencing. MDA stands for Multiple Displacement Amplification.

5816: 1277:"Improved genome recovery and integrated cell-size analyses of individual uncultured microbial cells and viral particles" 310:

restriction enzymes. Restriction enzymes also enable the detection of other types of methylation, such as 6mA with DpnI.

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Stoeckius M, Hafemeister C, Stephenson W, Houck-Loomis B, Chattopadhyay PK, Swerdlow H, et al. (September 2017).

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library is obtained. To select mRNA, the RT is performed with a single-stranded sequence of deoxythymine (oligo dT)

6150: 4865:"Single-cell RNA sequencing reveals intrinsic and extrinsic regulatory heterogeneity in yeast responding to stress" 576: 565: 492:

genes. Those data are often used for either cellular phenotype classification or new subpopulation identification.

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The two methods for library preparation in scATAC-Seq are based on split-pool cellular indexing and microfluidics.

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Yu Z, Lu S, Huang Y (October 2014). "Microfluidic whole genome amplification device for single cell sequencing".

638: 384: 5882: 609: 5175:"Transcriptomic landscape of the blastema niche in regenerating adult axolotl limbs at single-cell resolution" 582:

scRNA-Seq has provided considerable insight into the development of embryos and organisms, including the worm

4492: 6181: 6005: 5940: 697: 682: 497: 273: 229: 107: 88: 6191: 6159: 5996: 1033:"Genomes from uncultivated prokaryotes: a comparison of metagenome-assembled and single-amplified genomes" 587: 583: 4649:"Predicting bacterial infection outcomes using single cell RNA-sequencing analysis of human immune cells" 4403:"Single-cell RNA-seq of rheumatoid arthritis synovial tissue using low-cost microfluidic instrumentation" 5616:"Amplification of multiple genomic loci from single cells isolated by laser micro-dissection of tissues" 4863:

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Kildisiute G, Kholosy WM, Young MD, Roberts K, Elmentaite R, van Hooff SR, et al. (February 2021).

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There are several ways to isolate individual cells prior to whole genome amplification and sequencing.

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1915:"Prevalent genome streamlining and latitudinal divergence of planktonic bacteria in the surface ocean" 1543:"Single-cell analysis of structural variations and complex rearrangements with tri-channel processing" 1334:"Massively parallel whole genome amplification for single-cell sequencing using droplet microfluidics" 1145:

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Francis JM, Zhang CZ, Maire CL, Jung J, Manzo VE, Adalsteinsson VA, et al. (August 2014).

1486:"DNA template strand sequencing of single-cells maps genomic rearrangements at high resolution" 1183:"Distilled single-cell genome sequencing and de novo assembly for sparse microbial communities" 5894: 5801: 5755: 5696: 5647: 5596: 5531: 5496: 5428: 5379: 5320: 5271: 5214: 5155: 5096: 5043: 5002: 4945: 4896: 4845: 4788: 4737: 4686: 4629: 4580: 4562: 4507: 4440: 4383: 4334: 4285: 4236: 4195: 4146: 4095: 4036: 4001: 3933: 3879: 3809: 3757: 3697: 3646: 3589: 3554: 3519: 3462: 3411: 3354: 3313: 3264: 3215: 3158: 3122: 3070: 3021: 2972: 2890: 2839: 2789: 2726: 2669: 2626: 2577: 2520: 2485: 2433: 2384: 2353:

Smallwood SA, Lee HJ, Angermueller C, Krueger F, Saadeh H, Peat J, et al. (August 2014).

2335: 2278: 2229: 2180: 2131: 2074: 2013: 1972:

Rinke C, Schwientek P, Sczyrba A, Ivanova NN, Anderson IJ, Cheng JF, et al. (July 2013).

1954: 1895: 1828: 1777: 1726: 1691: 1633: 1572: 1515: 1466: 1431: 1394:"Genome-wide detection of single-nucleotide and copy-number variations of a single human cell" 1371: 1314: 1230: 1162: 1121: 1064: 1005: 965: 917: 876: 841: 792: 764: 738: 721:

Eberwine J, Sul JY, Bartfai T, Kim J (January 2014). "The promise of single-cell sequencing".

480: 468: 225: 74: 61: 49: 5114:

Gerber T, Murawala P, Knapp D, Masselink W, Schuez M, Hermann S, et al. (October 2018).

3282:

Muraro MJ, Dharmadhikari G, Grün D, Groen N, Dielen T, Jansen E, et al. (October 2016).

2304:

Crary-Dooley FK, Tam ME, Dunaway KW, Hertz-Picciotto I, Schmidt RJ, LaSalle JM (March 2017).

1484:

Falconer E, Hills M, Naumann U, Poon SS, Chavez EA, Sanders AD, et al. (November 2012).

1031:

Alneberg J, Karlsson CM, Divne AM, Bergin C, Homa F, Lindh MV, et al. (September 2018).

6128: 5960: 5791: 5783: 5745: 5735: 5686: 5678: 5637: 5627: 5586: 5578: 5523: 5486: 5478: 5418: 5410: 5369: 5359: 5310: 5302: 5261: 5253: 5204: 5194: 5145: 5135: 5086: 5078: 5033: 4992: 4984: 4935: 4927: 4886: 4876: 4835: 4827: 4778: 4768: 4727: 4717: 4676: 4668: 4619: 4611: 4570: 4554: 4497: 4487: 4479: 4430: 4422: 4373: 4365: 4324: 4316: 4275: 4267: 4226: 4185: 4177: 4136: 4126: 4085: 4075: 4028: 3991: 3983: 3925: 3869: 3861: 3799: 3791: 3747: 3687: 3677: 3636: 3628: 3581: 3546: 3509: 3501: 3452: 3442: 3401: 3393: 3344: 3303: 3295: 3254: 3246: 3235:"Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells" 3205: 3197: 3150: 3112: 3102: 3060: 3052: 3011: 3003: 2962: 2954: 2915: 2880: 2875: 2870: 2829: 2781: 2716: 2708: 2661: 2616: 2608: 2567: 2559: 2512: 2475: 2467: 2423: 2415: 2374: 2366: 2325: 2317: 2268: 2219: 2211: 2170: 2162: 2121: 2064: 2056: 2003: 1993: 1944: 1934: 1885: 1875: 1820: 1769: 1718: 1681: 1673: 1665: 1623: 1613: 1562: 1554: 1505: 1497: 1458: 1421: 1413: 1361: 1353: 1304: 1296: 1220: 1212: 1154: 1111: 1103: 1054: 1044: 997: 955: 907: 868: 831: 823: 784: 730: 605: 557: 437: 393: 331:

Comparison of single-cell methylation sequencing methods in terms of coverage as at 2015 on

269: 5397:

Briggs JA, Weinreb C, Wagner DE, Megason S, Peshkin L, Kirschner MW, Klein AM (June 2018).

5340:"Single-cell lineage tracing by integrating CRISPR-Cas9 mutations with transcriptomic data" 5173:

Leigh ND, Dunlap GS, Johnson K, Mariano R, Oshiro R, Wong AY, et al. (December 2018).

5022:"Cell type atlas and lineage tree of a whole complex animal by single-cell transcriptomics" 3482:"C1 CAGE detects transcription start sites and enhancer activity at single-cell resolution" 3186:"Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq" 3184:

Islam S, Kjällquist U, Moliner A, Zajac P, Fan JB, Lönnerberg P, Linnarsson S (July 2011).

6031: 4808:"Single-cell RNA sequencing reveals a signature of sexual commitment in malaria parasites" 4115:"Using single-cell genomics to understand developmental processes and cell fate decisions" 517: 265: 23: 5614:

Frumkin D, Wasserstrom A, Itzkovitz S, Harmelin A, Rechavi G, Shapiro E (February 2008).

5447: 5291:"Single-cell reconstruction of developmental trajectories during zebrafish embryogenesis" 4303:

Jerby-Arnon L, Shah P, Cuoco MS, Rodman C, Su MJ, Melms JC, et al. (November 2018).

5731: 5574: 5355: 5249: 5190: 5131: 4980: 4823: 4664: 4550: 4475: 4418: 4213:

Olmos D, Arkenau HT, Ang JE, Ledaki I, Attard G, Carden CP, et al. (January 2009).

3979: 3497: 3389: 2990:

Klein AM, Mazutis L, Akartuna I, Tallapragada N, Veres A, Li V, et al. (May 2015).

2950: 2657: 2404:"Amplification-free whole-genome bisulfite sequencing by post-bisulfite adaptor tagging" 2264: 2117: 2052: 1989: 1930: 1871: 1816: 1765: 1409: 1349: 1292: 1208: 1099: 780: 5976: 5830: 5750: 5715: 5691: 5666: 5642: 5615: 5591: 5558: 5491: 5466: 5423: 5399:"The dynamics of gene expression in vertebrate embryogenesis at single-cell resolution" 5398: 5374: 5339: 5315: 5290: 5266: 5234:"Single-cell mapping of gene expression landscapes and lineage in the zebrafish embryo" 5233: 5209: 5174: 5150: 5115: 5091: 5062: 4997: 4964: 4963:

Cao J, Packer JS, Ramani V, Cusanovich DA, Huynh C, Daza R, et al. (August 2017).

4940: 4915: 4891: 4864: 4840: 4807: 4783: 4756: 4732: 4705: 4681: 4648: 4624: 4599: 4575: 4534: 4502: 4459: 4435: 4402: 4378: 4353: 4329: 4305:"A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade" 4304: 4280: 4255: 4190: 4166:"Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain" 4165: 4141: 4114: 4090: 4063: 3996: 3963: 3874: 3849: 3804: 3779: 3752: 3735: 3692: 3665: 3641: 3616: 3514: 3481: 3457: 3430: 3406: 3373: 3308: 3283: 3259: 3234: 3210: 3185: 3117: 3090: 3065: 3040: 3016: 2991: 2967: 2934: 2885: 2858: 2721: 2697:"Circulating Tumor Cell Clustering Shapes DNA Methylation to Enable Metastasis Seeding" 2696: 2621: 2596: 2572: 2547: 2480: 2455: 2428: 2403: 2379: 2354: 2330: 2305: 2224: 2199: 2175: 2151:"EGFR variant heterogeneity in glioblastoma resolved through single-nucleus sequencing" 2150: 1949: 1914: 1890: 1855: 1686: 1653: 1628: 1601: 1567: 1542: 1510: 1485: 1426: 1393: 1366: 1333: 1309: 1276: 1225: 1182: 1116: 1083: 1059: 1032: 836: 811: 692: 600: 453: 217: 27: 4164:

Raj B, Wagner DE, McKenna A, Pandey S, Klein AM, Shendure J, et al. (June 2018).

3795: 3039:

Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M, et al. (May 2015).

6175: 5948: 5916: 4533:

Kuppe C, Flores RO, Li Z, Hannani M, Tanevski J, Halder M, et al. (2022-08-10).

4519: 3601: 3480:

Kouno T, Moody J, Kwon AT, Shibayama Y, Kato S, Huang Y, et al. (January 2019).

2935:"Tumor to normal single-cell mRNA comparisons reveal a pan-neuroblastoma cancer cell" 2355:"Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity" 2290: 1840: 1584: 642: 464: 65: 5851: 4706:"Pan-Cancer and Single-Cell Modeling of Genomic Alterations Through Gene Expression" 4048: 3945: 3664:

Tripathy SJ, Toker L, Bomkamp C, Mancarci BO, Belmadani M, Pavlidis P (2018-10-08).

3170: 2532: 2086: 1789: 1216: 750: 228:(BrdU), and the method does not detect variants smaller than 200kb in size, such as 5543: 4914:

Kang Y, Norris MH, Zarzycki-Siek J, Nierman WC, Donachie SP, Hoang TT (June 2011).

4757:"Single-cell RNA-seq reveals hidden transcriptional variation in malaria parasites" 2992:"Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells" 2681: 2025: 1738: 1017: 929: 459:

Overall, in a first stage individual cells are captured separately and lysed, then

421: 189: 5559:"Distant metastasis occurs late during the genetic evolution of pancreatic cancer" 3233:

Ramsköld D, Luo S, Wang YC, Li R, Deng Q, Faridani OR, et al. (August 2012).

2801: 2321: 1854:

Woyke T, Xie G, Copeland A, González JM, Han C, Kiss H, et al. (2009-04-23).

404: 5787: 5557:

Yachida S, Jones S, Bozic I, Antal T, Leary R, Fu B, et al. (October 2010).

5020:

Plass M, Solana J, Wolf FA, Ayoub S, Misios A, Glažar P, et al. (May 2018).

4965:"Comprehensive single-cell transcriptional profiling of a multicellular organism" 4881: 4600:"Pathogen Cell-to-Cell Variability Drives Heterogeneity in Host Immune Responses" 4271: 3828:"Chromium Single Cell Gene Expression Solution with Feature Barcoding technology" 3429:

Sasagawa Y, Nikaido I, Hayashi T, Danno H, Uno KD, Imai T, Ueda HR (April 2013).

3349: 3332: 2834: 2817: 2548:"N6-methyldeoxyadenosine marks active transcription start sites in Chlamydomonas" 2215: 2166: 1880: 788: 6048: 5968: 332: 135: 36: 5825: 5720:

Proceedings of the National Academy of Sciences of the United States of America

5667:"Single-cell dissection of transcriptional heterogeneity in human colon tumors" 5364: 5232:

Wagner DE, Weinreb C, Collins ZM, Briggs JA, Megason SG, Klein AM (June 2018).

5199: 4672: 4615: 4558: 4426: 4320: 3987: 3736:"A comprehensive review on droplet-based bioprinting: Past, present and future" 3505: 3397: 3299: 3056: 3007: 2712: 2563: 1919:

Proceedings of the National Academy of Sciences of the United States of America

1357: 1300: 1107: 268:. There are several known types of methylation that occur in nature, including 201:

CNVs, algorithms can detect and eradicate this noise to produce true variants.

6120: 6117: 5465:

Zhang W, Xia S, Zhong X, Gao G, Yang J, Wang S, et al. (September 2022).

5289:

Farrell JA, Wang Y, Riesenfeld SJ, Shekhar K, Regev A, Schier AF (June 2018).

4483: 3929: 3865: 2612: 1558: 1158: 1049: 546: 484: 373: 256: 240: 5482: 4722: 4566: 4369: 3962:

Hayashi T, Ozaki H, Sasagawa Y, Umeda M, Danno H, Nikaido I (February 2018).

3717:"Single cell RNA-seq: An introductory overview and tools for getting started" 3682: 3447: 6137: 5987: 5932: 5740: 5448:"Science's 2018 Breakthrough of the Year: tracking development cell by cell" 5414: 5306: 5257: 5140: 5082: 5038: 5021: 4988: 4231: 4214: 2273: 2248: 2126: 2101: 2060: 1939: 1824: 1773: 1618: 1417: 595: 538: 221: 130: 5805: 5759: 5700: 5651: 5632: 5600: 5535: 5500: 5432: 5383: 5324: 5275: 5218: 5159: 5100: 5047: 5006: 4949: 4916:"Transcript amplification from single bacterium for transcriptome analysis" 4900: 4849: 4792: 4741: 4690: 4633: 4584: 4511: 4444: 4387: 4338: 4289: 4240: 4199: 4150: 4099: 4064:"Effective ribosomal RNA depletion for single-cell total RNA-seq by scDASH" 4040: 4005: 3937: 3883: 3813: 3761: 3701: 3650: 3593: 3558: 3523: 3466: 3415: 3358: 3317: 3268: 3219: 3162: 3126: 3107: 3074: 3025: 2976: 2958: 2894: 2843: 2816:

Kolodziejczyk AA, Kim JK, Svensson V, Marioni JC, Teichmann SA (May 2015).

2793: 2730: 2673: 2630: 2581: 2524: 2516: 2489: 2437: 2388: 2339: 2282: 2233: 2184: 2135: 2078: 2017: 1974:"Insights into the phylogeny and coding potential of microbial dark matter" 1958: 1899: 1832: 1781: 1730: 1695: 1637: 1576: 1519: 1470: 1435: 1375: 1318: 1234: 1166: 1125: 1068: 1009: 969: 921: 880: 845: 796: 742: 5527: 4931: 4131: 3201: 2471: 533:

scRNA-Seq is becoming widely used across biological disciplines including

6096: 6039: 3550: 2419: 1677: 827: 542: 344: 5582: 4831: 4773: 4080: 2665: 2102:"Major role of nitrite-oxidizing bacteria in dark ocean carbon fixation" 2069: 1998: 1973: 1001: 5796: 4032: 3632: 3154: 2919: 2597:"Deciphering bacterial epigenomes using modern sequencing technologies" 2370: 2008: 1669: 1501: 1082:

Kogawa M, Hosokawa M, Nishikawa Y, Mori K, Takeyama H (February 2018).

960: 943: 912: 895: 734: 687: 377: 5061:

Fincher CT, Wurtzel O, de Hoog T, Kravarik KM, Reddien PW (May 2018).

3773: 3771: 1462: 1387: 1385: 872: 5682: 4181: 3850:"Direct Comparative Analyses of 10X Genomics Chromium and Smart-seq2" 3585: 3250: 197: 143: 96: 3617:"Simultaneous epitope and transcriptome measurement in single cells" 2909: 2785: 1722: 2546:

Fu Y, Luo GZ, Chen K, Deng X, Yu M, Han D, et al. (May 2015).

296:

bisulfite sequencing, but limits the coverage of this method. When

3333:"CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification" 1602:"Current challenges in the bioinformatics of single cell genomics" 1199: 403: 326: 255: 106: 2249:"Genome-wide evolutionary analysis of eukaryotic DNA methylation" 767:(April 2018). "Chronicling embryos, cell by cell, gene by gene". 5844: 5855: 5907: 1181:

Taghavi Z, Movahedi NS, Draghici S, Chitsaz H (October 2013).

594:. The first vertebrate animals to be mapped in this way were 4354:"Single-Cell Genomics: Approaches and Utility in Immunology" 4256:"Single-Cell Transcriptomic Analysis of Tumor Heterogeneity" 810:

Saliba AE, Westermann AJ, Gorski SA, Vogel J (August 2014).

156:

A method particularly suitable for the discovery of genomic

3331:

Hashimshony T, Wagner F, Sher N, Yanai I (September 2012).

3091:"Methods, Challenges and Potentials of Single Cell RNA-seq" 1332:

Hosokawa M, Nishikawa Y, Kogawa M, Takeyama H (July 2017).

2818:"The technology and biology of single-cell RNA sequencing" 440:

method, which is steadily gaining ground in neuroscience.

4460:"Decoding myofibroblast origins in human kidney fibrosis" 3848:

Wang, X; He, Y; Zhang, Q; Ren, X; Zhang, Z (April 2021).

3284:"A Single-Cell Transcriptome Atlas of the Human Pancreas" 2454:

Guo H, Zhu P, Wu X, Li X, Wen L, Tang F (December 2013).

188:(SNPs), which are a big part of genetic variation in the 117:

list of more than 100 different single-cell omics methods

2247:

Zemach A, McDaniel IE, Silva P, Zilberman D (May 2010).

339:

Transposase-accessible chromatin sequencing (scATAC-seq)

4535:"Spatial multi-omic map of human myocardial infarction" 3666:"Assessing Transcriptome Quality in Patch-Seq Datasets" 2402:

Miura F, Enomoto Y, Dairiki R, Ito T (September 2012).

5454:. American Association for the Advancement of Science. 1856:"Assembling the marine metagenome, one cell at a time" 260:

One method for single cell DNA methylation sequencing.

5814: 2911:

Single Cell Transcriptome Analysis in Prostate Cancer

2746:"Single-Cell Sequencing Sifts through Multiple Omics" 812:"Single-cell RNA-seq: advances and future challenges" 4352:

Neu KE, Tang Q, Wilson PC, Khan AA (February 2017).

4113:

Griffiths JA, Scialdone A, Marioni JC (April 2018).

383:

Single-cell RNA sequencing (scRNA-seq) provides the

5892: 2859:"Investigating Tumor Heterogeneity in Mouse Models" 16:

Examines sequence information from individual cells

5716:"High-throughput microfluidic single-cell RT-qPCR" 3734:Gudapati, H; Dey, M; Ozbolat, I (September 2016). 1654:"Dissecting genomic diversity, one cell at a time" 1392:Zong C, Lu S, Chapman AR, Xie XS (December 2012). 5063:"Cell type transcriptome atlas for the planarian 4493:20.500.11820/a0a63f61-934c-4036-80d7-79c2e3095885 1600:Ning L, Liu G, Li G, Hou Y, Tong Y, He J (2014). 444:Example of a droplet based platform - 10X method 264:Single-cell DNA methylome sequencing quantifies 125:is a widely used technique, enabling amplifying 2595:Beaulaurier J, Schadt EE, Fang G (March 2019). 3957: 3955: 5867: 212:classes of at least 200kb in size, including 8: 649:Number of cells to be sequenced and analyzed 432:) or the ability to process pooled samples. 5845:"Seurat R Toolkit for Single Cell Genomics" 3778:Gao, C; Zhang, M; Chen, L (December 2020). 5874: 5860: 5852: 5338:Zafar H, Lin C, Bar-Joseph Z (June 2020). 4254:Levitin HM, Yuan J, Sims PA (April 2018). 2766: 2764: 2762: 1535: 1533: 1531: 1529: 1249:"Single-Cell-Omics.v2.3.13 @albertvilella" 716: 714: 712: 387:of individual cells and is considered the 162:Single-cell DNA template strand sequencing 5795: 5749: 5739: 5690: 5641: 5631: 5590: 5490: 5422: 5373: 5363: 5314: 5265: 5208: 5198: 5149: 5139: 5090: 5037: 4996: 4939: 4890: 4880: 4839: 4782: 4772: 4731: 4721: 4680: 4623: 4574: 4501: 4491: 4434: 4377: 4328: 4279: 4230: 4189: 4140: 4130: 4089: 4079: 3995: 3873: 3854:Genomics, Proteomics & Bioinformatics 3803: 3751: 3691: 3681: 3640: 3513: 3456: 3446: 3405: 3348: 3307: 3258: 3209: 3116: 3106: 3064: 3015: 2966: 2884: 2874: 2833: 2720: 2620: 2571: 2479: 2427: 2378: 2329: 2272: 2223: 2174: 2125: 2068: 2007: 1997: 1948: 1938: 1889: 1879: 1685: 1627: 1617: 1566: 1509: 1425: 1365: 1308: 1224: 1198: 1139: 1137: 1135: 1115: 1058: 1048: 959: 911: 835: 123:Multiple displacement amplification (MDA) 5821: 4704:Mercatelli D, Ray F, Giorgi FM (2019). 2876:10.1146/annurev-cancerbio-030419-033413 708: 641:for single cell isolation also include 608:recognized these advances as the 2018 2449: 2447: 1652:Blainey PC, Quake SR (January 2014). 1270: 1268: 7: 983: 981: 979: 362:Transcriptome sequencing (scRNA-seq) 3670:Frontiers in Molecular Neuroscience 631:Fluorescence-activated cell sorting 408:Single-cell RNA sequencing workflow 4062:Loi DS, Yu L, Wu AR (2021-01-15). 3753:10.1016/j.biomaterials.2016.06.012 14: 3796:10.2174/1389202921999200625220812 586:, and the regenerative planarian 180:fluorescent in situ hybridization 5824: 2857:Tammela T, Sage J (March 2020). 3089:Hebenstreit D (November 2012). 2863:Annual Review of Cancer Biology 1147:Current Opinion in Microbiology 516:Most RNA-seq methods depend on 186:Single-nucleotide polymorphisms 549:, Cardiovascular research and 1: 2322:10.1080/15592294.2016.1276680 1217:10.1093/bioinformatics/btt420 635:Laser-capture microdissection 214:breakage-fusion-bridge cycles 99:, IDBA-UD, Cortex, and HyDA. 64:preparation, sequencing, and 6187:Molecular biology techniques 6077:CRISPR genome-editing method 5788:10.1016/j.molcel.2017.01.023 4882:10.1371/journal.pbio.2004050 4272:10.1016/j.trecan.2018.02.003 3899:"Single Cell (10X Genomics)" 3350:10.1016/j.celrep.2012.08.003 2914:(MSc). 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Genetics 2601:Nature Reviews. Genetics 990:Nature Reviews. Genetics 896:"Single-cell sequencing" 610:Breakthrough of the Year 252:DNA methylome sequencing 6006:Human genetic variation 5941:Whole genome sequencing 5741:10.1073/pnas.1019446108 5415:10.1126/science.aar5780 5307:10.1126/science.aar3131 5258:10.1126/science.aar4362 5141:10.1126/science.aaq0681 5083:10.1126/science.aaq1736 5039:10.1126/science.aaq1723 4989:10.1126/science.aam8940 2744:Stein RA (1 Jul 2019). 2274:10.1126/science.1186366 2127:10.1126/science.aan8260 2061:10.1126/science.1248575 1940:10.1073/pnas.1304246110 1825:10.1126/science.1203690 1774:10.1126/science.1203163 1619:10.3389/fonc.2014.00007 1418:10.1126/science.1229164 894:Nawy T (January 2014). 698:Whole genome sequencing 683:Single cell epigenomics 639:High-throughput methods 498:Illumina dye sequencing 274:5-hydroxymethylcytosine 82:Genome (DNA) sequencing 6110:Single-cell sequencing 6014:Cellular reprogramming 5633:10.1186/1472-6750-8-17 5483:10.1093/pcmedi/pbac016 5065:Schmidtea mediterranea 3108:10.3390/biology1030658 2959:10.1126/sciadv.abd3311 2517:10.1038/nprot.2010.190 2408:Nucleic Acids Research 954:(1): 1. January 2014. 816:Nucleic Acids Research 588:Schmidtea mediterranea 584:Caenorhabditis elegans 501:readers are required. 409: 335: 315:down to single cells. 261: 112: 20:Single-cell sequencing 5925:Accelerating universe 5528:10.1038/nprot.2007.79 5344:Nature Communications 5179:Nature Communications 4932:10.1101/gr.116103.110 4710:Frontiers in Genetics 4653:Nature Communications 4407:Nature Communications 4232:10.1093/annonc/mdn544 4132:10.15252/msb.20178046 3968:Nature Communications 3486:Nature Communications 3378:Nature Communications 3202:10.1101/gr.110882.110 2472:10.1101/gr.161679.113 1606:Frontiers in Oncology 1281:Nature Communications 566:coexpression networks 535:Developmental biology 461:reverse transcription 414:reverse transcription 407: 330: 278:transposable elements 259: 194:copy number variation 110: 6058:Cancer immunotherapy 6023:Ardipithecus ramidus 5671:Nature Biotechnology 4358:Trends in Immunology 4170:Nature Biotechnology 3574:Nature Biotechnology 3239:Nature Biotechnology 1711:Nature Biotechnology 1547:Nature Biotechnology 1451:Analytical Chemistry 861:Analytical Chemistry 673:Single-cell analysis 289:Bisulfite sequencing 210:structural variation 166:structural variation 158:structural variation 119:has been published. 6101:neutron star merger 6089:gravitational waves 5997:Poincaré conjecture 5732:2011PNAS..10813999W 5726:(34): 13999–14004. 5583:10.1038/nature09515 5575:2010Natur.467.1114Y 5569:(7319): 1114–1117. 5356:2020NatCo..11.3055Z 5250:2018Sci...360..981W 5191:2018NatCo...9.5153L 5132:2018Sci...362..681G 4981:2017Sci...357..661C 4832:10.1038/nature24280 4824:2017Natur.551...95P 4774:10.7554/eLife.33105 4665:2019NatCo..10.3266B 4551:2022Natur.608..766K 4476:2021Natur.589..281K 4419:2018NatCo...9..791S 4081:10.7717/peerj.10717 3980:2018NatCo...9..619H 3498:2019NatCo..10..360K 3390:2019NatCo..10.3120S 2951:2021SciA....7.3311K 2707:(1–2): 98–112.e14. 2666:10.1038/nature13544 2658:2014Natur.511..606G 2265:2010Sci...328..916Z 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913:10.1038/nmeth.2771 828:10.1093/nar/gku555 735:10.1038/nmeth.2769 551:Infectious disease 410: 336: 262: 113: 6169: 6168: 6129:COVID-19 vaccines 6085:First observation 5953:Molecular circuit 5782:(4): 631–643.e4. 5677:(12): 1120–1127. 5620:BMC Biotechnology 5244:(6392): 981–987. 4975:(6352): 661–667. 4545:(7924): 766–777. 4470:(7841): 281–286. 3924:(10): 2407–2424. 3294:(4): 385–394.e3. 2908:Harris C (2020). 2652:(7511): 606–610. 2466:(12): 2126–2135. 2259:(5980): 916–919. 2047:(6182): 416–420. 1984:(7459): 431–437. 1760:(6030): 714–717. 1496:(11): 1107–1112. 1463:10.1021/ac5032176 1457:(19): 9386–9390. 1193:(19): 2395–2401. 873:10.1021/ac4040218 822:(14): 8845–8860. 481:microfluidic chip 467:is performed and 226:bromodeoxyuridine 50:Sanger sequencing 6199: 6162: 6154: 6145: 6132: 6123: 6112: 6104: 6091: 6079: 6071: 6060: 6052: 6043: 6034: 6026: 6016: 6008: 6000: 5991: 5982: 5971: 5963: 5961:RNA interference 5955: 5943: 5935: 5927: 5919: 5911: 5876: 5869: 5862: 5853: 5848: 5829: 5828: 5820: 5810: 5809: 5799: 5770: 5764: 5763: 5753: 5743: 5711: 5705: 5704: 5694: 5683:10.1038/nbt.2038 5662: 5656: 5655: 5645: 5635: 5611: 5605: 5604: 5594: 5554: 5548: 5547: 5516:Nature Protocols 5511: 5505: 5504: 5494: 5462: 5456: 5455: 5452:Science Magazine 5443: 5437: 5436: 5426: 5394: 5388: 5387: 5377: 5367: 5335: 5329: 5328: 5318: 5286: 5280: 5279: 5269: 5229: 5223: 5222: 5212: 5202: 5170: 5164: 5163: 5153: 5143: 5111: 5105: 5104: 5094: 5058: 5052: 5051: 5041: 5017: 5011: 5010: 5000: 4960: 4954: 4953: 4943: 4911: 4905: 4904: 4894: 4884: 4875:(12): e2004050. 4860: 4854: 4853: 4843: 4803: 4797: 4796: 4786: 4776: 4752: 4746: 4745: 4735: 4725: 4701: 4695: 4694: 4684: 4644: 4638: 4637: 4627: 4610:(6): 1309–1321. 4595: 4589: 4588: 4578: 4530: 4524: 4523: 4505: 4495: 4455: 4449: 4448: 4438: 4398: 4392: 4391: 4381: 4349: 4343: 4342: 4332: 4300: 4294: 4293: 4283: 4260:Trends in Cancer 4251: 4245: 4244: 4234: 4210: 4204: 4203: 4193: 4182:10.1038/nbt.4103 4161: 4155: 4154: 4144: 4134: 4110: 4104: 4103: 4093: 4083: 4059: 4053: 4052: 4016: 4010: 4009: 3999: 3959: 3950: 3949: 3918:Nature Protocols 3913: 3907: 3906: 3894: 3888: 3887: 3877: 3845: 3839: 3838: 3832: 3824: 3818: 3817: 3807: 3784:Current Genomics 3775: 3766: 3765: 3755: 3731: 3725: 3724: 3712: 3706: 3705: 3695: 3685: 3661: 3655: 3654: 3644: 3612: 3606: 3605: 3586:10.1038/nbt.3973 3569: 3563: 3562: 3545:(4): 1384–1394. 3534: 3528: 3527: 3517: 3477: 3471: 3470: 3460: 3450: 3426: 3420: 3419: 3409: 3369: 3363: 3362: 3352: 3328: 3322: 3321: 3311: 3279: 3273: 3272: 3262: 3251:10.1038/nbt.2282 3230: 3224: 3223: 3213: 3196:(7): 1160–1167. 3181: 3175: 3174: 3138: 3132: 3130: 3120: 3110: 3085: 3079: 3078: 3068: 3051:(5): 1202–1214. 3036: 3030: 3029: 3019: 3002:(5): 1187–1201. 2987: 2981: 2980: 2970: 2939:Science Advances 2930: 2924: 2923: 2905: 2899: 2898: 2888: 2878: 2854: 2848: 2847: 2837: 2813: 2807: 2805: 2768: 2757: 2756: 2754: 2752: 2741: 2735: 2734: 2724: 2692: 2686: 2685: 2641: 2635: 2634: 2624: 2592: 2586: 2585: 2575: 2543: 2537: 2536: 2505:Nature Protocols 2500: 2494: 2493: 2483: 2451: 2442: 2441: 2431: 2399: 2393: 2392: 2382: 2350: 2344: 2343: 2333: 2301: 2295: 2294: 2276: 2244: 2238: 2237: 2227: 2210:(8): 1386–1397. 2195: 2189: 2188: 2178: 2155:Cancer Discovery 2146: 2140: 2139: 2129: 2097: 2091: 2090: 2072: 2036: 2030: 2029: 2011: 2001: 1969: 1963: 1962: 1952: 1942: 1910: 1904: 1903: 1893: 1883: 1851: 1845: 1844: 1800: 1794: 1793: 1749: 1743: 1742: 1706: 1700: 1699: 1689: 1649: 1643: 1641: 1631: 1621: 1596: 1590: 1588: 1570: 1537: 1524: 1523: 1513: 1481: 1475: 1474: 1446: 1440: 1439: 1429: 1389: 1380: 1379: 1369: 1329: 1323: 1322: 1312: 1272: 1263: 1262: 1260: 1259: 1245: 1239: 1238: 1228: 1202: 1178: 1172: 1170: 1141: 1130: 1129: 1119: 1079: 1073: 1072: 1062: 1052: 1028: 1022: 1021: 985: 974: 973: 963: 940: 934: 933: 915: 891: 885: 884: 867:(4): 1953–1957. 856: 850: 849: 839: 807: 801: 800: 761: 755: 754: 718: 558:machine learning 270:5-methylcytosine 6207: 6206: 6202: 6201: 6200: 6198: 6197: 6196: 6172: 6171: 6170: 6165: 6157: 6148: 6135: 6126: 6115: 6107: 6094: 6082: 6074: 6063: 6055: 6046: 6037: 6032:quantum machine 6029: 6019: 6011: 6003: 5994: 5985: 5974: 5966: 5958: 5946: 5938: 5930: 5922: 5917:Dolly the sheep 5914: 5905: 5898: 5888: 5880: 5843: 5840: 5835: 5823: 5815: 5813: 5772: 5771: 5767: 5713: 5712: 5708: 5664: 5663: 5659: 5613: 5612: 5608: 5556: 5555: 5551: 5513: 5512: 5508: 5464: 5463: 5459: 5445: 5444: 5440: 5396: 5395: 5391: 5337: 5336: 5332: 5288: 5287: 5283: 5231: 5230: 5226: 5172: 5171: 5167: 5113: 5112: 5108: 5060: 5059: 5055: 5019: 5018: 5014: 4962: 4961: 4957: 4920:Genome Research 4913: 4912: 4908: 4862: 4861: 4857: 4818:(7678): 95–99. 4805: 4804: 4800: 4754: 4753: 4749: 4703: 4702: 4698: 4646: 4645: 4641: 4597: 4596: 4592: 4532: 4531: 4527: 4457: 4456: 4452: 4400: 4399: 4395: 4351: 4350: 4346: 4302: 4301: 4297: 4253: 4252: 4248: 4212: 4211: 4207: 4163: 4162: 4158: 4112: 4111: 4107: 4061: 4060: 4056: 4018: 4017: 4013: 3961: 3960: 3953: 3915: 3914: 3910: 3896: 3895: 3891: 3847: 3846: 3842: 3835:10xgenomics.com 3830: 3826: 3825: 3821: 3777: 3776: 3769: 3733: 3732: 3728: 3721:10xgenomics.com 3715:Clark, Sheila. 3714: 3713: 3709: 3663: 3662: 3658: 3614: 3613: 3609: 3580:(10): 936–939. 3571: 3570: 3566: 3536: 3535: 3531: 3479: 3478: 3474: 3428: 3427: 3423: 3371: 3370: 3366: 3330: 3329: 3325: 3281: 3280: 3276: 3232: 3231: 3227: 3190:Genome Research 3183: 3182: 3178: 3140: 3139: 3135: 3088: 3086: 3082: 3038: 3037: 3033: 2989: 2988: 2984: 2945:(6): eabd3311. 2932: 2931: 2927: 2907: 2906: 2902: 2856: 2855: 2851: 2815: 2814: 2810: 2786:10.1038/nrg3542 2771: 2769: 2760: 2750: 2748: 2743: 2742: 2738: 2694: 2693: 2689: 2643: 2642: 2638: 2594: 2593: 2589: 2545: 2544: 2540: 2502: 2501: 2497: 2460:Genome Research 2453: 2452: 2445: 2401: 2400: 2396: 2352: 2351: 2347: 2303: 2302: 2298: 2246: 2245: 2241: 2197: 2196: 2192: 2148: 2147: 2143: 2099: 2098: 2094: 2038: 2037: 2033: 1971: 1970: 1966: 1912: 1911: 1907: 1853: 1852: 1848: 1802: 1801: 1797: 1751: 1750: 1746: 1723:10.1038/nbt1214 1708: 1707: 1703: 1651: 1650: 1646: 1599: 1597: 1593: 1540: 1538: 1527: 1483: 1482: 1478: 1448: 1447: 1443: 1391: 1390: 1383: 1331: 1330: 1326: 1274: 1273: 1266: 1257: 1255: 1247: 1246: 1242: 1180: 1179: 1175: 1144: 1142: 1133: 1081: 1080: 1076: 1030: 1029: 1025: 987: 986: 977: 942: 941: 937: 893: 892: 888: 858: 857: 853: 809: 808: 804: 763: 762: 758: 720: 719: 710: 706: 669: 656: 651: 627: 622: 531: 514: 489:poly-adenylated 446: 402: 370: 364: 356: 347: 341: 321: 307: 286: 266:DNA methylation 254: 238: 175: 105: 84: 45: 30:with optimized 17: 12: 11: 5: 6205: 6203: 6195: 6194: 6189: 6184: 6182:DNA sequencing 6174: 6173: 6167: 6166: 6164: 6163: 6155: 6146: 6133: 6124: 6113: 6105: 6092: 6080: 6072: 6061: 6053: 6044: 6042:clinical trial 6035: 6027: 6017: 6009: 6001: 5992: 5983: 5972: 5964: 5956: 5944: 5936: 5928: 5920: 5912: 5902: 5900: 5890: 5889: 5881: 5879: 5878: 5871: 5864: 5856: 5850: 5849: 5839: 5838:External links 5836: 5834: 5833: 5812: 5811: 5776:Molecular Cell 5765: 5706: 5657: 5606: 5549: 5522:(3): 739–752. 5506: 5477:(3): pbac016. 5457: 5438: 5389: 5330: 5281: 5224: 5165: 5106: 5053: 5012: 4955: 4926:(6): 925–935. 4906: 4855: 4798: 4747: 4696: 4639: 4590: 4525: 4450: 4393: 4364:(2): 140–149. 4344: 4295: 4266:(4): 264–268. 4246: 4205: 4176:(5): 442–450. 4156: 4105: 4054: 4027:(9): 647–649. 4021:Nature Methods 4011: 3951: 3908: 3889: 3860:(2): 253–266. 3840: 3819: 3790:(8): 602–609. 3767: 3726: 3707: 3656: 3627:(9): 865–868. 3621:Nature Methods 3607: 3564: 3529: 3472: 3435:Genome Biology 3421: 3364: 3343:(3): 666–673. 3323: 3274: 3245:(8): 777–782. 3225: 3176: 3149:(5): 377–382. 3143:Nature Methods 3133: 3101:(3): 658–667. 3080: 3031: 2982: 2925: 2900: 2849: 2828:(4): 610–620. 2822:Molecular Cell 2808: 2780:(9): 618–630. 2758: 2736: 2687: 2636: 2607:(3): 157–172. 2587: 2558:(4): 879–892. 2538: 2511:(4): 468–481. 2495: 2443: 2394: 2365:(8): 817–820. 2359:Nature Methods 2345: 2316:(3): 206–214. 2296: 2239: 2190: 2161:(8): 956–971. 2141: 2092: 2031: 1964: 1905: 1846: 1795: 1744: 1717:(6): 680–686. 1701: 1658:Nature Methods 1644: 1591: 1553:(3): 343–354. 1525: 1490:Nature Methods 1476: 1441: 1381: 1324: 1264: 1240: 1187:Bioinformatics 1173: 1153:(5): 510–516. 1131: 1074: 1023: 996:(3): 175–188. 975: 948:Nature Methods 935: 900:Nature Methods 886: 851: 802: 756: 723:Nature Methods 707: 705: 702: 701: 700: 695: 693:DNA sequencing 690: 685: 680: 675: 668: 665: 655: 652: 650: 647: 626: 623: 621: 620:Considerations 618: 601:Xenopus laevis 530: 527: 513: 510: 454:cDNA libraries 445: 442: 401: 398: 366:Main article: 363: 360: 355: 352: 343:Main article: 340: 337: 320: 317: 306: 303: 285: 282: 253: 250: 237: 234: 218:chromothripsis 174: 171: 104: 101: 83: 80: 44: 41: 15: 13: 10: 9: 6: 4: 3: 2: 6204: 6193: 6192:Biotechnology 6190: 6188: 6185: 6183: 6180: 6179: 6177: 6161: 6156: 6152: 6147: 6143: 6139: 6134: 6130: 6125: 6122: 6119: 6114: 6111: 6106: 6102: 6098: 6093: 6090: 6086: 6081: 6078: 6073: 6070: 6069:comet mission 6068: 6062: 6059: 6054: 6050: 6045: 6041: 6036: 6033: 6028: 6025: 6024: 6018: 6015: 6010: 6007: 6002: 5998: 5993: 5989: 5984: 5981: 5979: 5973: 5970: 5965: 5962: 5957: 5954: 5950: 5945: 5942: 5937: 5934: 5929: 5926: 5921: 5918: 5913: 5910:understanding 5909: 5904: 5903: 5901: 5897: 5896: 5891: 5887: 5885: 5877: 5872: 5870: 5865: 5863: 5858: 5857: 5854: 5846: 5842: 5841: 5837: 5832: 5827: 5822: 5818: 5807: 5803: 5798: 5793: 5789: 5785: 5781: 5777: 5769: 5766: 5761: 5757: 5752: 5747: 5742: 5737: 5733: 5729: 5725: 5721: 5717: 5710: 5707: 5702: 5698: 5693: 5688: 5684: 5680: 5676: 5672: 5668: 5661: 5658: 5653: 5649: 5644: 5639: 5634: 5629: 5625: 5621: 5617: 5610: 5607: 5602: 5598: 5593: 5588: 5584: 5580: 5576: 5572: 5568: 5564: 5560: 5553: 5550: 5545: 5541: 5537: 5533: 5529: 5525: 5521: 5517: 5510: 5507: 5502: 5498: 5493: 5488: 5484: 5480: 5476: 5472: 5468: 5461: 5458: 5453: 5449: 5442: 5439: 5434: 5430: 5425: 5420: 5416: 5412: 5408: 5404: 5400: 5393: 5390: 5385: 5381: 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all 6136:2021: 6127:2020: 6108:2018: 6095:2017: 6083:2016: 6075:2015: 6064:2014: 6056:2013: 6047:2012: 6038:2011: 6020:2009: 6012:2008: 6004:2007: 5995:2006: 5986:2005: 5978:Spirit 5975:2004: 5967:2003: 5959:2002: 5947:2001: 5939:2000: 5931:1999: 5923:1998: 5915:1997: 5906:1996: 5817:Portal 5804: 5758: 5748: 5699: 5689: 5650: 5640: 5599: 5589: 5563:Nature 5542: 5534: 5499: 5489: 5431: 5421: 5382: 5372: 5323: 5313: 5274: 5264: 5217: 5207: 5158: 5148: 5099: 5089: 5046: 5005: 4995: 4948: 4938: 4899: 4889: 4848: 4838: 4812:Nature 4791: 4781: 4740: 4730: 4689: 4679: 4632: 4622: 4583: 4573: 4565: 4539:Nature 4518: 4510: 4500: 4464:Nature 4443: 4433: 4386: 4376: 4337: 4327: 4288: 4278: 4239: 4198: 4188: 4149: 4139: 4098: 4088: 4047: 4039: 4004: 3994: 3944: 3936: 3882: 3872: 3812: 3802: 3760: 3700: 3690: 3649: 3639: 3600: 3592: 3557: 3522: 3512: 3465: 3455: 3414: 3404: 3357: 3316: 3306: 3267: 3257: 3218: 3208: 3169: 3161: 3125: 3115: 3073: 3063: 3024: 3014: 2975: 2965: 2893: 2883: 2842: 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