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.
Background A typical human cell contains 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 Sanger sequencing or next-generation sequencing. 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 amplification, sequencing library preparation, sequencing, and bioinformatic 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 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.
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, mosaicism 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.
Genome (DNA) sequencing 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 Illumina, Ion Torrent). 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 sequencing include SPAdes, IDBA-UD, Cortex, and HyDA.
Methods
A list of more than 100 different single-cell omics methods has been published.
Multiple displacement amplification (MDA) is a widely used technique, enabling amplifying femtograms of DNA from bacterium to micrograms 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 polymerases 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 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.
Another common method is MALBAC. 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.
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 SNPs, 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.
A method particularly suitable for the discovery of genomic structural variation is Single-cell DNA template strand sequencing (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 structural variation 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 is less affected by drop outs. The choice of method depends on the goal of the sequencing because each method presents different advantages.
Limitations 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 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 fluorescent in situ hybridization (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.
Single-nucleotide polymorphisms (SNPs), which are a big part of genetic variation in the human genome, and copy number variation (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 allele 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 CNVs, algorithms can detect and eradicate this noise to produce true variants.""
Strand-seq 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 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 structural variation classes of at least 200kb in size, including breakage-fusion-bridge cycles and chromothripsis 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 haplotype, which provides additional variant calling specificity. As a current limitation, Strand-seq requires dividing cells for strand-specific labelling using bromodeoxyuridine (BrdU), and the method does not detect variants smaller than 200kb in size, such as mobile element insertions.
Applications Microbiomes 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, 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.
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 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.
DNA methylome sequencing
Single-cell DNA methylome sequencing quantifies DNA methylation. There are several known types of methylation that occur in nature, including 5-methylcytosine (5mC), 5-hydroxymethylcytosine (5hmC), 6-methyladenosine (6mA), and 4-methylcytosine (4mC). In eukaryotes, especially animals, 5mC is widespread along the genome and plays an important role in regulating gene expression by repressing transposable elements. 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.
Methods