At MIT, Pippin Enables Splice Variant Detection and MicroRNA Analysis

At the BioMicro Center at MIT, Director Stuart Levine, PhD, recently introduced the Pippin Prep from Sage Science to enable key applications — including splice variant analysis with RNA-seq and microRNA analysis — that were not possible on other platforms.

Levine joined the BioMicro Center about four years ago; in that time, he has transformed it from a two-person lab to a major genomics and bioinformatics core facility with a dozen full-time employees serving more than 80 MIT faculty members. “My goal was to create an integrated core which would work with labs on everything from experimental design through to experimental analysis and anything they need help on anywhere along that chain,” Levine says.

His team of technology experts supports faculty members from a number of departments and institutes, covering scientific areas as diverse as cancer, environmental health sciences, biological engineering, and more. Levine’s core lab colleagues are professional scientists who focus on tools and methods, so they can translate that expertise across scientific areas and tailor experiments to each customer. “Ultimately what comes into our lab is nucleic acid, and what we do with nucleic acid is relatively constant,” Levine says. “The technology improvements that are useful for, say, understanding the evolution of ocean ecosystems can also apply to cancer research.”

In the last few years, Levine added sequencing sample preparation steps, and says that he realized early on he would have to find alternatives to manual gels. As a chargeback facility, Levine has to base prices on the fully-loaded costs of any service. When it came to setting prices for manual gel procedures, “I had to budget how long it takes to pour a gel, run out the sample — one per gel to avoid contamination — cut out the band, isolate the band. And when I put a price tag on it, I know that price tag is high enough that absolutely no one will pay for it. Once you start adding in the labor costs, the economics don’t make any sense at all,” he says.

Levine has been using the SPRIworks System from Beckman Coulter Genomics, which has been very successful as a gel alternative. However, certain applications require tighter sizing than was possible with that system, which is why he decided to look at the Pippin platform.

“What Pippin let us do was get into areas that we hadn’t been able to before,” Levine says. Two of those areas were RNA-seq — particularly identifying splice variants — and miRNAs, he adds. On the miRNA front, his team would otherwise have had to use manual gels and cut out bands, but “we wouldn’t be able to do that with any kind of economy of scale,” he says. “The high percentage gels on the Pippin allow us to cut out bands of the right size for microRNAs.”

When it comes to splice variant analysis, Levine says that his team recommends very tight sizing. “Some of the RNA-seq methodologies, when you’re doing de novo sequencing of transcriptomes and want to do assemblies, tend to perform better when the size distribution of the library inserts is very tight,” he says.

The reason this is helpful for splice detection is in adding another dimension of data in the alignment step to allow for the inference of structure between known nucleotides. “If you know where the left read is, and where the right read is, and if you know the size of the fragments, then you can infer based on known exons the entire pattern in between,” Levine says. “You can calculate the likelihood that the exon is included or not based on where pairs of reads are.” The same holds for de novo assembly in general, he notes. With very tight size distribution, “you have a much more constrained situation when you’re assembling,” he adds.

Levine also believes that the Pippin platform is well suited for planning ahead as sequencing technologies evolve to offer longer reads. “As we need longer inserts, we’re going to be limited in terms of what we can get off the SPRIworks machine,” he says. Pippin’s ability to work with longer ranges means that “it’s extremely useful both for the ability to do sample preparation now and for the ability to work with these future technologies.”

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