Jan 4, 2018

Microfluidic isoform sequencing shows widespread splicing coordination in the human transcriptome

Sheila Clark

Hagen Tilgner
Hagen Tilgner

Hagen Tilgner, Ph.D.

Assistant Professor, Weil Cornell Medical College

Hagen is an Assistant Professor at the Brain and Mind Research Institute at Weill Cornell Medical College. Prior to his current appointment, Hagen’s Postdoc was completed at Stanford University in the laboratory of Michael Snyder and he received his Ph.D. from The Universidad Pompeu Fabra (Barcelona) in Roderic Guigó’s lab. His team is focused on answering how the genome gives rise to functionally diverse cell types by investigating RNA isoform usage patterns. Specifically, he is interested providing a full-length view of RNA molecules and applies this information to investigate the brain and its diseases.

Much can be said about the life of a new Assistant Professor; drive, hard work, passion, and a thirst for knowledge can describe only part of the day-to-day and ultimately what motivates Hagen Tilgner, Ph.D. We were recently able to sit down and catch up with Hagen about his team’s new manuscript that provides a more comprehensive understanding of the human transcriptome, "Microfluidic isoform sequencing shows widespread splicing coordination in the human transcriptome", published in Genome Research. Speaking to him was eye-opening—from learning about how a human-machine interaction class made him realize that building smart coffee-makers was not in his future, and how answering the door when his team members’ knock is something he is excited about, to tackling how RNA processing is coordinated and how these coordination events can possibly have wide-ranging implications in disease states. We think you will enjoy reading our newest research spotlight interview with Dr. Tilgner below!

10x: Tell us a little bit about your career story: What area of study did you pursue and what got you interested in it? What ultimately led to you to focus on the RNA processing field?

HT: My father was a physicist so I grew up with tales of Einstein, which shaped my interests at an early age and led me to a path in pursuing science. I ended up studying computer science in college because it can be applied to multiple scientific fields. I realized when I was taking a human-machine interaction class, where we spent a whole class on learning about building a smart coffee-maker that learns the needs of its user, that I didn’t want to just build coffee-makers; I wanted to do something more impactful. I then spent some time studying abroad and it was at the Sanger Institute that, during a conference, I met my future graduate advisor, paving my path in bioinformatics. After my Ph.D., I decided that studying in the U.S. would be my next step, and chose Stanford University and Mike Snyder’s lab. I was interested in technology development Mike was involved in and that’s where I was introduced to long-range sequencing. We realized quickly that this would be a great way to get an unbiased and deep view of the transcriptome.

10x: You have been Assistant Professor at Weil Cornell Medical College for a little under 2 years, how has life as a new Assistant Professor been treating you?

HT: My experience so far has really pivoted on the fact that I was lucky enough to hire 2 great team members who both love science and contribute a lot to our research. I’m in a fortunate situation where I really look forward to answering their knocks on the door! They really ‘live science’, which is something I do as well and it makes things easier. This job is very stressful of course, many fires to put out, but you always have to ultimately move things forward. Once you publish a paper, you can rest for about a half an hour, then it’s off to the next thing!

10x: What challenges has your current field, RNA processing, faced in the past?

HT: Well, we are interested in RNA molecules, but a lot of the technologies that had been available essentially shoot the RNA to pieces. The analogy I like to use is that if you have an Egyptologist interested in studying the a newly found tomb and the first thing they did was to throw a hand grenade at it; the Egyptologist would find themselves in a situation where on the right side of the room they may have a lot of gold dust, and on the left side there may be burned wood. They would have to infer that the gold dust was likely treasure and the wood was less valuable. What the Egyptologist should be doing is going into the tomb and taking pictures, where they can map out what objects are in the tomb and how the gold relates to the wood to form the entire tomb. It’s our hope with long read sequencing technologies, we can get a more wholistic view. Really, any method that allows a complete view of the RNA molecule would be critical for these types of studies.

10x: You are one of the lead authors on a recent Genome Research paper that uses Linked-Reads to analyze if distant coordinated exon pairs affect coding regions and whether the majority of the human transcriptome is affected by coordinated exon usage. What inspired you and your team to ask these questions?

HT: This work was based on previous knowledge that indicated that one complex gene could potentially produce more isoforms than there are genes in the genome! We want to get to the point where we understand if all of these potential combinations of variants are made or which ones are being made preferentially. In essence, we have a state of exponential possibilities and this can get complicated, so we are trying to get to the rules of isoform production. Linked-Reads are very useful because you can specify how many molecules you want to investigate and what kind of coverage you want for each molecule. We’re able to fine tune the coverage and the outputs and that makes this very valuable for us.

10x: Did you face any challenges in trying to answer these questions? How did the team overcome them?

HT: The challenge right now is the human transcriptome is extremely divergent in terms of expression levels. Some genes produce many molecules and other genes barely produce any and we have to really deeply sequence in order to capture those rare isoforms. In the beginning we also had to perform some optimization to test out how the 10x Genomics system behaved for the questions we were trying to answer. Fareshteh (the co-lead author on this manuscript) found an input amount that worked nicely on the system. We found that we may have even overestimated how many molecules we would have, but in science, if you planned everything perfectly then you probably didn’t ask a hard enough question!

10x: What future potential applications might this research hold and what do you personally find most exciting or important about your work?

HT: The most important statement from a biological point of view is that this coordination of alternative exons seems to be extremely widespread. The implications of this is that researchers should consider long reads for isoform sequencing. If everything was independent and not coordinated, then you could just figure out probabilities of each variable site and get the probability of isoforms by multiplying the probabilities of the single variable sites. That would be much cheaper. However, it turns out that there are lots of non-random combinations. If we really want to understand what kind of RNA and protein molecules the cell produces, the only way of assessing the true nature is by using long-reads. Long-range sequencing gives you the rule of combinations.

10x: Are there any other notable projects that you are currently working on or plan to start in the future that you think would benefit from 10x technology?

HT: We’re in the Neuroscience department and we are really trying to take the brain apart and understand which cell types produce which kind of isoforms and when. The implications of this type of research is that we don’t really know how splicing affects brain diseases. We know that it matters, but to what extent does this coordination change from a healthy to disease state and if the rules of coordination change or not, is a long-term vision for this work.

Read the full publication in Genome Research here.

Learn more about Linked-Reads in the blog post "Everything you wanted to know about Linked-Reads".