Single-cell sequencing
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.
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.
Recent technical improvements make single-cell sequencing a promising tool for approaching a set of seemingly inaccessible problems.
Advancements in single-cell DNA sequencing have enabled collecting of genomic data from uncultivated prokaryotic species present in complex microbiomes.
[12] Multiple displacement amplification (MDA) is a widely used technique, enabling amplifying femtograms of DNA from bacterium to micrograms for sequencing.
[10] 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.
By encapsulating single-cells in droplets for DNA capture and amplification, this method offers reduced bias and enhanced throughput compared to conventional MDA.
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.
[17] 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,[18] 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 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.
[18] 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.
Fresh or frozen tumors may be analyzed and categorized with respect to SCNAs, SNVs, and rearrangements quite well using whole-genome DNAS approaches.
[29] 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.)
In eukaryotes, especially animals, 5mC is widespread along the genome and plays an important role in regulating gene expression by repressing transposable elements.
To validate these methods during their development, the single-cell methylome data of a mixed population were successfully classified by hierarchal clustering to identify distinct cell types.
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.
[46] Current scRNA-seq protocols involve isolating single cells and their RNA, and then following the same steps as bulk RNA-seq: reverse transcription (RT), amplification, library generation and sequencing.
[49] 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.
The processivity 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.
However, different PCR efficiency on particular sequences (for instance, GC content and snapback structure) may also be exponentially amplified, producing libraries with uneven coverage.
[57] These protocols differ in terms of strategies for reverse transcription, cDNA synthesis and amplification, and the possibility to accommodate sequence-specific barcodes (i.e., UMIs) or the ability to process pooled samples.
[60] Collecting cellular contents following electrophysiological recording using patch-clamp has also allowed development of the Patch-Seq method, which is steadily gaining ground in neuroscience.
[63] Overall, in a first stage individual cells are captured separately and lysed, then reverse transcription (RT) of mRNA is performed and cDNA library is obtained.
[70] 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.
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.
The first single-cell transcriptome analysis in a prokaryotic species was accomplished using the terminator exonuclease enzyme to selectively degrade rRNA and rolling circle amplification (RCA) of mRNA.
scRNA-Seq has provided considerable insight into the development of embryos and organisms, including the worm Caenorhabditis elegans,[88] and the regenerative planarian Schmidtea mediterranea[89][90] and axolotl Ambystoma mexicanum.
[102][103] 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.
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.
