Single cell sequencing

Single cell sequencing examines the sequence information from individual cells with optimized next generation sequencing (NGS) 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. Sequencing the DNA of individual cells can give information about mutations carried by small populations of cells, for example in cancer, while sequencing the RNAs expressed by individual cells can give insight into the existence and behavior of different cell types, for example in development. Single cell sequencing examines the sequence information from individual cells with optimized next generation sequencing (NGS) 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. Sequencing the DNA of individual cells can give information about mutations carried by small populations of cells, for example in cancer, while sequencing the RNAs expressed by individual cells can give insight into the existence and behavior of different cell types, for example in development. A typical human cell consists of about 2 x 3.3 billion base pairs of DNA and 600 million bases of mRNA. Usually a mix of millions of cells are used in sequencing the DNA or RNA using traditional methods like Sanger sequencing or Illumina sequencing. By using deep sequencing of DNA and RNA from a single cell, cellular functions can be investigated extensively. Like typical NGS experiments, the protocols of single cell sequencing 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 in comparison with sequencing from cells in bulk. The minimal amount of starting materials from a single cell make degradation, sample loss and contamination exert pronounced effects on quality of sequencing data. In addition, due to the picogram level of the amount of nucleic acids used, heavy amplification is often needed during sample preparation of single cell sequencing, resulting in the 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. Single cell DNA genome sequencing involves isolating a single cell, performing whole-genome-amplification (WGA), constructing sequencing libraries and then sequencing the DNA using a next-generation sequencer (ex. Illumina, Ion Torrent). A genome constructed in this fashion is commonly referred to as a single amplified genome or SAG. It can be used in microbiome studies, in order to obtain genomic data from uncultured microorganisms. In addition, it can be united with high throughput cell sorting of microorganisms and cancer. One popular method used for single cell genome sequencing is multiple displacement amplification and this enables research into various areas such as microbial genetics, ecology and infectious diseases. Furthermore, data obtained from microorganisms might establish processes for culturing in the future. Some of the genome assembly tools that can be used in single cell genome sequencing include: SPAdes, IDBA-UD, Cortex and HyDA. Multiple displacement amplification (MDA) is a widely used technique, enabling amplifying femtograms of DNA from bacterium to micrograms for the use of 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. Other methods include MALBAC. 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 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. 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.