Single Cell Sequencing Principles and Applications
Sequencing nucleic acid molecules (such as DNA or RNA) provides information about the sequence of their nucleotides (adenine, thymine, cytosine, and guanine), thereby offering the genetic instructions necessary for life. The development of sequencing technology originated from pioneering work by Walter Fiers, Frederick Sanger, and Ray Wu in the 1970s. Over the decades, the methods and techniques initially used for nucleic acid sequencing have rapidly evolved: from reading individual RNA molecules to making it possible to sequence the entire genome of an organism. The first draft of the human genome was published in 2001 as part of the Human Genome Project and was completed two years later.
Commercial DNA sequencers first appeared in the late 1990s. They were named second-generation or next-generation sequencers to distinguish them from the first-generation experimental sequencers and allowed for whole-genome sequencing. Over the next two decades, sequencers were continuously updated and iterated, optimizing parameters such as speed, accuracy, sequencing depth, and read length to varying degrees. Various sequencing methods have now been developed for specific applications. Although relatively new, one of the most interesting is single-cell sequencing. This article explores how this technology works and its application areas.
What is single-cell sequencing? What is single-cell RNA sequencing?
Traditional second-generation sequencing (NGS) targets the genomes of cell populations, such as cell cultures, tissues, organs, or entire organisms. Its output is the 'average genome' of a cell population. However, single-cell sequencing targets the genome of individual cells from a cell population. Today, traditional sequencing methods are referred to as bulk sequencing to differentiate them from single-cell technologies.
Single-cell sequencing technology is currently available to measure the genome (scDNA-seq), DNA methylome, or transcriptome (scRNA-seq) of each cell in a population. This technology has been used to identify new mutations in cancer cells, explore progressive epigenomic variations occurring during embryonic development, and assess how seemingly homogeneous cell populations express specific genes.
What is the principle of single-cell sequencing?
All single-cell sequencing technologies require four main steps:
- Isolation of individual cells from a cell population;
- Extraction, processing, and amplification of the genetic material of each isolated cell;
- Preparation of the 'sequencing library' of the isolated cell's genetic material;
- Sequencing the library using next-generation sequencers.
Cell isolation and basic sample processing
Different methods can be used to isolate cells, and the choice of method mainly depends on the nature of the sample and the processing steps required after cell isolation. The performance of each method depends on its efficiency (how many cells can be isolated per unit time), purity (the proportion of target cells collected), and recovery rate (the proportion of target cells collected out of the total available target cells initially). Common methods are shown as follows:
- Fluorescence-activated cell sorting (FACS)—involves labeling cells with fluorescent molecules attached to target-specific antibodies based on cell proteins. The markers inside cells can be accessed through cell permeability. Therefore, this technology allows for the selection of cells based on multiple parameters. However, FACS requires at least 10,000 starting cells, and rapid flow may compromise cell viability.
- Magnetic-activated cell sorting (MACS)—uses antibody-mediated superparamagnetic nanoparticles to label specific proteins on target cells. This means that, unlike FACS, only cell surface molecules can be used as targets to label live cells. An external magnetic field is then used to isolate the labeled cells while others are washed away. Therefore, the purity of MACS separation depends on the specificity and affinity of the antibodies used for labeling.
- Laser capture microdissection (LCM)—uses lasers to isolate target cells from solid tissue samples placed on microscope slides. This isolation can be performed in two ways: direct isolation when infrared light transfers laser energy to a heat-sensitive polymer that specifically binds only to target cells; indirect isolation when ultraviolet light ablates cells. Unlike FACS and MACS, LCM can be used with intact tissues. It is also fast and reliable. However, LCM requires visual identification of target cells by their morphology. Additionally, cells may be sliced during isolation, and ultraviolet light may damage DNA and RNA molecules.
- Manual cell collection or micromanipulation—requires an inverted microscope combined with a micropipette to select and isolate target cells. Micromanipulation has been used for the isolation of live cultures and embryonic cells. However, its throughput is limited, and like LCM, this technique requires skilled professionals to correctly identify target cells.
Before proceeding with library preparation, the quality of cell isolation needs to be assessed, and cell viability should be evaluated through imaging. RNA integrity can also be assessed, which is especially important for scRNA-seq analysis. The isolated cells are then lysed. The genetic material of interest (DNA or RNA) is isolated and amplified to provide sufficient quantity for subsequent detection, as single cells typically produce only minuscule amounts of DNA or RNA. Even for scRNA-seq analysis, the final product obtained through the above steps is single-stranded DNA, as described in the next section. Many protocols have been developed to address the requirements and limitations of specific studies, such as the availability of few cells.
Library preparation
Amplified DNA must be made into a sequencing library to be sequenced. A sequencing library is a collection of single-stranded DNA fragments from a cell population or, in the case of single-cell sequencing, from a specific cell. After amplification, DNA fragments are uniquely barcoded to determine which starting cell they belong to, and specific adapter sequences are added to the 5' and 3' ends. At this point, the DNA portion to be sequenced is usually called the insert fragment. The barcodes and adapters cap one or both ends of the insert fragment. All DNA fragments belonging to the same sequencing library are barcoded with the same oligonucleotide sequence. This allows different libraries to be pooled together and sequenced during the same sequencing run. Adapters depend on the platform and are required for fragment sequencing. Commercial kits are available for all sample and library preparation steps. Several quality control methods have been developed to ensure that the generated library accurately reproduces the original cell state.
Sequencing
Various commercial sequencing platforms have developed slightly different methods. Here, we mainly introduce sequencing by synthesis, including pyrosequencing and reversible terminator sequencing. Before sequencing, an amplification step typically generates multiple sets of DNA fragment clones (usually through bridge amplification or emulsion PCR). Since each set of clones emits the same signal during sequencing, the resulting cluster or well signal is sufficient for detection. This type of sequencing is typically performed within chips that may contain microwells. Adapters and other molecules (such as polymerases) bind to the chip (or the bottom of the microwells) and interact with the adapters attached to the insert fragments. Sequencing of insert fragments requires multiple replication steps performed by polymerases and fluorescently labeled nucleotides. In each cycle, a single fluorescently labeled nucleotide is added, and if bound by the polymerase, it triggers characteristic luminescence of the specific nucleotide. Before the next cycle begins, the spectra emitted simultaneously by all fragments are recorded by a camera. As each nucleotide emits a different light, the sequencer reconstructs the sequences of all insert fragments cycle by cycle. The sequencer also reads the tags of the insert fragments and assigns each measurement to the corresponding library.
Proton detection sequencing uses a different sequencing-by-synthesis method. Fragments are typically bound to beads and amplified to cover each bead (similar to pyrosequencing). However, instead of releasing fluorescent labels and characteristic light when bases are added during sequencing, a single proton is released, which is then detected and recorded.
Another less common sequencing method is ligation sequencing. This method uses DNA ligase instead of DNA polymerase, which attaches short, fluorescently labeled sequences instead of nucleotides. Before sequencing, DNA fragments are usually amplified using emulsion PCR chemistry. Since each inserted nucleotide is subsequently sequenced twice, the results of this sequencing method are very accurate. However, ligation sequencing only outputs shorter reads and is incompatible with palindromic sequences.
Types of single-cell sequencing
Single-cell sequencing technology can measure different types of genetic material—genome, transcriptome, or methylome—of individual cells. The following introduces the differences in sample preparation for these three sequencing methods and their applications.
Single-cell genome sequencing
By determining the genome of individual cells, ScDNA-seq can study the genomic heterogeneity of cell populations. Therefore, it is primarily used to study microbiomes and cancer. Microbiomes are communities of single-celled organisms, and ScDNA-seq sequencing does not require their prior isolation and cultivation. Sequencing data can be used to study the composition of microbiomes, thereby investigating their ecology, evolution, and changes. In cancer research, ScDNA-seq is used to study intratumoral genetic heterogeneity or to identify new oncogenic mutations. ScDNA-seq has significantly advanced the development of precision medicine.
For ScDNA-seq, the most commonly used amplification methods for DNA extracted from isolated cells are multiple displacement amplification (MDA) and multiple annealing and looping-based amplification cycles (MALBAC). MDA can rapidly amplify trace amounts of DNA, providing good but uneven genome coverage. MALBAC has poorer genome coverage than MDA but is more uniform, making it more suitable for detecting copy number variations. As mentioned earlier, libraries are generated from amplified DNA. For this method to succeed, uniform and efficient amplification is crucial. However, no amplification method is perfect. In particular, detecting single nucleotide polymorphisms (SNPs) and copy number variations is often very inefficient. Therefore, different amplification methods have been developed to improve the detection of specific mutation types.
Single-cell transcriptome sequencing (single-cell RNA sequencing, single-cell RNA-seq, or scRNA seq)
ScRNA-seq sequences RNA molecules within each cell of a given sample. This information provides a snapshot of the transcriptome (genes being transcribed) at the time of cell harvest. Since its development, ScRNA-seq has been widely applied. Although the final product of gene expression is a protein, detecting its messenger RNA (mRNA) indicates that the gene is in an active state and may subsequently be translated and expressed. Additionally, co-transcribed genes can be used to infer gene regulatory networks that constitute specific cell phenotypes. Transcriptional differences in cell populations help identify subpopulations, such as malignant cells in tumors. ScRNA-seq is also used to study important gene transcription features, such as splicing patterns and monoallelic transcription. Due to low mRNA copy numbers and RNA instability in bacterial cells, there are challenges in prokaryotic ScRNA-seq. Of course, these issues are slowly being overcome.
Isolated cells are lysed, and their mRNA molecules, which are polyadenylated, are enriched using poly[T] primers. This step is crucial because most RNA molecules in cells are ribosomal (rRNA): they are very large and are usually not the target of transcriptome sequencing. Next, reverse transcriptase converts poly[T]-primed single-stranded mRNA into complementary DNA (cDNA). The cDNA molecules are then amplified using PCR or in vitro transcription (IVT). Finally, barcode tags and other short sequences required by the sequencing platform are added to the cDNA molecules.
The sequencing quality of this technology is influenced by several factors, primarily the total number of libraries that can be obtained from the cell population and the detected reads. The ideal cell number depends on the expected number of different cell subpopulations or states. The number of reads indicates the depth of transcriptome sequencing, which depends on the size of the genome: the higher the read depth, the more reliable the details provided. Sample and library preparation also affect the quality of the results.
Single-cell DNA methylome sequencing
DNA methylation involves transferring a methyl group to the carbon of cytosine (usually C5). Methylation is an epigenetic mechanism that can alter DNA activity without affecting its sequence: in promoters, DNA methylation typically inhibits gene transcription. Therefore, single-cell DNA methylome sequencing (ScDNA-Met-seq) can be used to study epigenetic changes within cell populations with the same genes, resulting in different phenotypes. DNA methylation is crucial for cell characteristics and is key to X chromosome inactivation, genomic imprinting, transposable element repression, aging, and carcinogenesis. This technology is primarily used in developmental research but has also been used to explore rare and highly active tumor cell subpopulations.
The sample and library preparation for ScDNA-Met-seq is similar to ScDNA-seq, with the addition of one step. Before amplification, the DNA is subjected to bisulfite conversion, which converts only unmethylated cytosine residues to uracil, while 5-methylcytosine residues remain unaffected. However, bisulfite treatment often leads to DNA fragmentation and degradation. Although other methods, such as methylation-sensitive restriction endonucleases, have been developed, they are still not applicable to single-cell sequencing.
Single-cell sequencing data analysis
The final raw output of the sequencer is first directly processed within the sequencer, returning binary base call (BCL) files and quality scores. BCL files are the raw sequencing output in binary format. For further analysis, BCL files are subsequently converted to FASTQ files, which are text files containing sequence information and quality scores. This step is usually performed on a Linux server using barcode tags to demultiplex the data of different libraries.
FASTQ files can be processed to align sequences with a template genome, annotate them, detect variants, perform differential transcription analysis, and visualize the data. Some third-party academic scripts can be used to perform these preliminary analyses. Considering the size of FASTQ data files (typically 10-200 GB each), most of these analyses are computationally expensive and are usually performed on a Linux server using their command line. The resulting data can be further studied using data analysis and statistical tools, such as data normalization, principal component analysis, t-distributed stochastic neighbor embedding analysis, clustering analysis, and pathway or gene set enrichment analysis. These tools are helpful in exploring large datasets as they can identify unexpected patterns and biological behaviors, as well as the genes or transcripts that most significantly drive specific phenotypes. Especially Bioconductor—a variety of packages developed for the R statistical programming language provides free open-source software for analyzing genomic data. The tools in this package are designed to perform the aforementioned analyses and visualize their results. Certain workflows and functions have been specifically optimized for single-cell sequencing analysis.
Why is single-cell sequencing important?
Each cell in a tissue or organ affects the physiological or pathological aspects of the entire organism in different ways. With single-cell technology, we can probe each cell and study its specific impact on the entire cell population and its organism or ecosystem. This unique technology is particularly valuable when studying rare cells or exploring phenotypic variation within populations of the same cell type. For example, ScRNA-seq has been used to study rare antigen-specific T or B cells, measure the composition and structure of the human microbiome, explore the origins and development of drug-resistant tumor subpopulations, discover previously unknown gene functions in plant tissues, study tumor progression mechanisms, and predict prognosis based on cellular heterogeneity within tumors. These and more applications have become possible across various fields due to the uniqueness of single-cell sequencing technology.
Combining single-cell sequencing with other technologies and single-cell techniques
Various omics technologies are now often combined to study the multi-layered states of single cells. By combining the sequencing technologies described earlier, the genome, epigenome, and transcriptome within the same cell population can be studied. Bulk sequencing technologies and single-cell sequencing technologies are also often combined with proteomics methods, including metabolomics, phosphoproteomics, acetylomics, and glycoproteomics. Combining different single-cell omics methods can provide a deeper understanding of the heterogeneity of cell populations, such as identifying more subpopulations, as other techniques may discover different types of variations. It is also possible to infer connections between changes observed by one omics technique and changes observed by another omics technique. This information can help identify new causal relationships, thereby determining the mechanisms behind a known phenotype.
Some computational methods have also been developed to integrate different omics datasets, including innovative, machine learning-based approaches. However, algorithms for integrating multi-omics single-cell data are often still inadequate. While library preparation protocols and sequencing technologies have been greatly improved, data analysis tools are still lagging, which may be the biggest challenge currently facing the field.
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