November 1, 2014 (Vol. 34, No. 19)

As a Cell Line Tool, NGS May Aid Reconstruction of Disease Models and Expression Systems

Next-generation sequencing (NGS) provides a broader, deeper view into an organism’s genome than ever before. Because NGS is faster and cheaper than traditional sequencing, bioprocessors can now ask increasingly profound questions. They can investigate not only what sequences reveal about the genome, but also what they imply about the transcriptome and the epigenome.

More practically, NGS offers bioprocessors a way to improve cell line authentication. Intense interest in cell line authentication reflects the demands journals and funding agencies are placing on investigators to demonstrate that their cells are indeed as advertised.

“NGS offers a much stronger validation tool for engineered cells regardless of type, down to the nucleotide level,” says Peter Tolias, Ph.D., director of the center for healthcare innovation at Stevens Institute of Technology. “In some instances, even commercially purchased cells may be mistakenly packaged or contaminated with other cells, or they may have experienced some sort of mutation. To the degree that NGS becomes cost-effective, it will serve as a higher level validation method for such cells.”

Another area where NGS will contribute is in synthetic biology, where organisms may contain entirely novel genetic sequences or deliberate mutations. Here NGS will assist in clonal analysis, processing clones in numbers far beyond what would be practical, given time and money constraints, via conventional Sanger sequencing.

Digging Deeper

Dr. Tolias especially likes the opportunities in the sequencing of the transcriptome, of all coding and noncoding RNAs downstream of the genome. “If you’re creating an expression system or a unique cell-based disease model, you’d want to dive deeply into this level of gene expression.”

DNA microarray technology, which has been around for at least 15 years, works adequately. “But its sensitivity is limited,” Dr. Tolias observes. “With NGS, you can sequence and count, or tabulate, all mRNAs expressed by the genome, which provides a very deep view over many orders of magnitude in sensitivity.”

By contrast, microarrays offer a 3–4-log sensitivity range.

Dr. Tolias is optimistic about NGS because of the pace of improvements during the past few years. He also anticipates upgrades moving forward. For example, new methods are emerging that do not rely on the polymerase chain reaction (PCR). Avoiding PCR techniques, Dr. Tolias believes, could eventually lower the cost of a genomic analysis to $100. “If computing power keeps up with sequencing technology, this could be a real game-changer that turns sequencing into a commodity analysis tool.”

But even at today’s prices of about $5,000 per genome, NGS has become routine and reliable. Biomanufacturers can hardly ignore it as a means of assuring that cell lines behave as intended for the entire lifecycle of a product or process. “Pharmaceutical companies lose a lot of time and money due to simple molecular biology mistakes,” insists Dr. Tolias. “It makes no sense not to use NGS.”

He does not see NGS immediately revolutionizing the biopharmaceutical industry. Many innovative applications thus far have occurred in medical diagnostics and disease characterization, particularly in oncology, where sequencing will one day guide diagnosis, prognosis, and treatment. Because of its high throughput and parallelism, NGS is especially suited to the molecular evaluation of complex disorders such as heart disease.

As medical applications become routine, as methods are standardized, and as prices fall, bioprocessors may eventually adopt NGS techniques that were fine-tuned elsewhere.

Elegance through Parallelization

“The elegance of NGS is parallelization,” says Shawn Levy, Ph.D., faculty investigator at the HudsonAlpha Institute for Biotechnology. Parallelization introduces the ability to generate enough individual sequencing reads to allow counting-type applications, such as CHiP-Seq (chromatin immunoprecipitation sequencing) or, more relevant to bioprocessing, RNA-seq, also known as whole transcriptome shotgun sequencing. “NGS opens up a broad, deep toolbox for [investigators in biology], including those in bioprocessing,” declares Dr. Levy.

NGS examines RNA transcription profiles uniquely associated with conditions a cell experiences over a long culture, to provide insight into transcription and regulation under growth conditions. For the first time, bioprocessors can use those genetic markets to optimize media/feed conditions and cell densities while watching for markers of stress, cell death, and relevant cell cycle events.

Epigenetic surveillance—monitoring the impact of regulatory factors, often environmental, on gene regulation—would appear to be a slam-dunk NGS application during cell line development. Many epigenetic changes occurring in cultured cells are reversible over time, but NGS creates a snapshot of the current epigenome under precise conditions. This may provide insight, for example, into why a culture becomes less productive at one time point during generation of protein X, but at another time point for protein Y. Thus, NGS freezes the entire regulatory mechanism of a culture.

“It can monitor transcriptional markers and help assure that the genomic sequence remains stable during culture,” Dr. Levy explains. “It provides a new level of resolution compared with traditional sequencing.”

No Revolution

Like Dr. Tolias from Stevens, Dr. Levy believes that as costs come down and techniques improve, bioprocessors would be mistaken not to include NGS in routine, lifecycle-long characterization of cells from clone selection through harvest. NGS could easily become a standard quality control and optimization measure while complimenting and in some cases replacing assays for cell viability, density, productivity, and others.

“But to be fair,” Dr. Tolias admits, “I would not say it will revolutionize bioprocessing.” Instead, NGS may simply be viewed as another tool in the box, provided it is ever adopted to the degree it has been in the alcoholic beverages industry, which is beginning to embrace genomics. Manufacturers with whom Dr. Tolias has worked view NGS as an new capability for correlating quality attributes with what occurs in a yeast cell’s genes during a fermentation.

Then there are the outright doubters who refuse to see even a minor insurrection, let alone a revolution. Florian M. Wurm, Ph.D., of the Swiss Federal Institute of Technology, Lausanne—no stranger to GEN readers—credits most improvements in cell culture titer to media and feed strategies rather than to cell line engineering.

Besides, as Dr. Wurm recently suggested, it would not be radical to suggest that CHO represents many truly different cell species, or at least “quasispecies,” a term coined during the 1970s for families of related genomic sequences exposed to high mutation rate environments.

“While I see a certain opportunity in sequencing an undisturbed diploid organism, or a well-defined prokaryote, sequencing of CHO cells will, in my humble opinion, run into the same problems we have when we try to sequence tumor cells from a cancer patient,” offers Dr. Wurm. “There is continuing genomic dynamics that will make it very difficult to identify a single genome, even with a relatively well-defined host system in the hands of one company. So to believe that sequencing will solve many problems is in my mind an overstatement and a misguided expectation.”

He admits he is “pretty alone” regarding his view on NGS’ impact on biomanufacturing, but nevertheless adds that “we are already at 5–10 g/L without genetically modified CHO cells, without targeting into specific sites.” Even quality, which has overtaken yield as a major obsession, is strongly influenced through appropriate medium and process modifications.

“I’m a geneticist,” quips Dr. Wurm. “I like genes. I like the genome, but the CHO system is so weird. It’s not definable.”

Furthermore, the question of a “perfect” CHO cell, and what development groups might do to achieve that ideal expression system, is protein-specific, not cell-specific. More to the point, the question is not about who might engage in next-generation sequencing, which is becoming cheaper and faster. Rather, it is about who has the time to interpret the data, correlate the genetic profiles with performance, and take appropriate corrective action at the gene level.

If epigenomics is indeed operative, as many geneticists believe, then these changes may be induced through thorough design of the culture environment—with or without deep sequencing.

Dr. Wurm does not necessarily trivialize cell line engineering, but he believes that small to midsized companies (and perhaps large firms) are better served by focusing on innovative molecules—good old fashioned drug discovery, protein-style, focused at key disease targets.

“Consider that 100 companies want to block TNF, 50 are going after IL2, and another 50 focus on her2neu,” he says. “Your goal is to identify protein sequences that do a better job than your competitors,” not to revolutionize bioprocessing through genetics.

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