The Next Era of Sequencing Technology
Third generation sequencing is the newest round of sequencing technologies hitting the omics market — and the future has never been brighter! Genetic research is now capable of analyzing individual cells, navigating very long and difficult DNA regions, and understanding cells in their native tissue context.
In this article, we’ll summarize existing sequencing technologies, then discuss current third generation sequencing technologies and their limitations.
Sanger Sequencing
Dr. Frederick Sanger developed Sanger sequencing over 40 years ago. This process sequences short DNA regions of interest (~800bp) using primers specific to a section of the target sequence. A polymerase moves along the single-stranded sequence, adding complementary nucleotides to the copy of the original DNA strand.
This method is the “gold standard” of sequencing methods because of its high accuracy. However, conducting larger sequencing projects with Sanger sequencing quickly becomes expensive and time consuming. Designing primers for entire chromosomes is not a viable option for high-throughput, large scale sequencing projects.
Next-Generation Sequencing
The first next-generation sequencing (NGS) technology was released in 2000, by companies who would later become part of Illumina. Massively parallel sequencing technologies ushered in an age of high-throughput DNA sequencing.
These technologies made it possible to sequence whole genomes in much less time. For context, Sanger sequencing can cover between 300 and 1,000 nucleotides at one time. The NovaSeq 6000 S4 flow cell is capable of sequencing 48 human genomes (144 billion base pairs) in about two days — that is the power of NGS.
With improved sequencing technologies, the cost of genetic research rapidly decreased. Today, a human genome can be sequenced for well under $1,000. In the year 2000, using Sanger sequencing for the same project cost $100 million — and when Dr. Sanger developed the method in 1977, a coordinated effort to sequence the first entire human genome was over a decade away!
Other NGS technologies have expanded our knowledge of cellular functions and disease mechanisms. Whole exome sequencing, transcriptomics, and proteomics tools give researchers insights into all levels of biology. These NGS tools have opened countless doors in disease research, helping us better understand things like inflammation, drug resistance, and oncology.
However, some research questions remain difficult to answer using next generation sequencing. The short reads of WGS and WES are not always able to traverse difficult DNA regions. These include GC-rich regions and areas with heavy repeats.
Next generation sequencing also typically uses a method called bulk sequencing. In bulk sequencing, the varying cells in a sample are used to create an average signature. The sequencing readout does not match the DNA content of any individual cell. In research areas like oncology, where the heterogeneity of a group of cells is important to understanding disease processes, this method does not paint a full picture.
Sample requirements for NGS are sometimes more stringent than in third generation sequencing. In many cases, library preparation includes an amplification step. This can lead to uneven coverage during sequencing. The library prep step also presents a challenge for small or damaged samples. They may not have enough quality genetic material for sequencing.
Single Cell Sequencing
Single cell sequencing provides more cell-specific information than bulk sequencing. In single-cell sequencing, researchers can sequence each cell type individually and explore their unique gene signature. This is particularly useful in fields like oncology research, where the heterogeneity of tumor environments can contribute to drug response and prognostic estimates.
For example, a team of Psomagen customers used single-cell RNA sequencing on CAR-T cells in non-Hodgkin lymphoma patients undergoing CAR-T therapy. They found that TIGIT expression is an indicator of treatment inefficacy and weakened CAR-T. Their research demonstrated that TIGIT blockade could improve treatment results in these patients. With insights like these, personalized medicine will better address patients' unique needs.
Spatial Transcriptomics
Spatial biology provides unprecedented context to cellular analysis. This technology allows researchers to understand RNA and proteins within their neighboring cellular environments. This technology goes beyond single-cell sequencing. Spatial can explore individual cells, while also considering cells' relationships to each other.
Even at this early stage of spatial technology development, the impacts on disease research have been pronounced. Early adoption by pathologists and oncologists has impacted clinical practices and is already a fully accepted scientific area of study.
Third Generation Sequencing
Third-generation sequencing (TGS) is the next wave of sequencing technologies. TGS offers improved resolution and read length for sequencing projects.
The first TGS capabilities were introduced in 2009, but recent advancements have brought these technologies to the forefront of omics research. Although TGS is still under development, TGS instruments have already been used to answer important biological questions.
Long-Read Sequencing
Long-read sequencing (LRS) technologies can analyze large, complex sections of DNA and RNA in a way that NGS technologies can’t. Although lengths vary by platform, LRS is capable of sequencing tens of thousands of base pairs at one time.
With longer reads, researchers can better understand difficult GC-rich regions of DNA and large insertions or deletions. More confidence in these regions helps researchers to assemble complex genomes and identify structural variants.
This technology is a major asset to medical research, where short-read sequencing sometimes fails to identify causative genetic variants. For example, a team of researchers used PacBio long-read sequencing technology to identify a pathogenic deletion in a patient showing symptoms of multiple neoplasia and cardiac myxoma. This patient was not successfully diagnosed via short-read sequencing.
LRS successfully identified a variation causing a decrease in PRKAR1A expression. These results made it possible to diagnose the patient with Carney complex, an autosomal dominant Mendelian disease. LRS has the potential to identify many other disease-causing pathogenic variants that are difficult to detect with other methods.
| PacBio Revio | Sequencing by Synthesis | Nanopore Sequencing | |
| Read Length | 15–20 kb | 2x150 bp | 10–100 kb |
| Accuracy | 99.95% | 99.92% | 99.26% |
| Yield | 90 Gb | 2,400–3,000 Gb | 50–110 Gb |
| Variant Calling | CNVs, large rearrangements, SNVs, InDels, SVs, 5mC methylation, phasing | SNVs, InDels | SNVs, SVs, 5mC methylation, phasing |
Limitations of Third Generation Sequencing
Third generation technologies are changing what’s possible for multiomics researchers. However, as with any new technology, challenges remain in implementing TGS in an accessible, meaningful way.
Throughput and Accuracy
NGS technologies typically have higher throughput and overall accuracy across non-complex regions than TGS. This means that an NGS platform will deliver deeper sequencing coverage in a shorter time. NGS will also provide base calls with greater confidence than TGS in some situations.
These quality indicators are constantly improving for TGS. Ebola researchers, for example, used nanopore sequencing technology to identify viruses in human blood samples. Their project recovered 90% of the chikungunya virus with 97-99% accuracy. Improvements to instruments and chemistry will further improve accuracy and throughput.
Cost
NGS is typically less expensive than TGS. Like past sequencing technologies, decreasing cost is a process that occurs over the course of years. As additional advancements and instruments become available, this cost will decrease.
Availability
Not every lab has the capacity to conduct third generation sequencing. Most labs and CROs have built up an arsenal of next generation sequencing instruments over the last two decades. The cost of TGS instruments can make it challenging for labs to acquire the technology.
There are also fewer data systems available to interpret TGS data. With more complicated genetic information, more advanced technology is necessary to conduct bioinformatic analysis.
Third generation sequencing technologies have opened the door to more challenging and significant omics research. With insights into difficult DNA regions, cellular relationships, and subcellular processes, we’ve only just begun to see how these capabilities will be used in research.
