Preparing for careers in start-ups/spin-outs

For most scientists, it seems like our training focuses on just two career paths: tenure-track academics or industry scientists. Sometimes you’ll get a career discussion about patent law, but those two trajectories seem to be all that trainees hear about.

So when Alex Federation asked me if I wanted to help him launch Talus Bio, I absolutely had no idea what I was doing or getting myself into. All I knew was that I loved the science and I loved working with Fed, so we pushed ahead with the shared vision of what we wanted the company/lab to do.

Over the last year with Talus, I’ve had the opportunity to meet a ton of amazing people in the start-up/spin-out world, and I’ve been blown away at the range of career opportunities there are beyond company founders themselves. And also dismayed that I’ve never even heard of most of them because they sound like perfect fits for the skills that a PhD cultivates beyond churning out papers for your boss.

That’s why I organized the latest Career Explorations panel for US HUPO’s Early Career Researcher group and focused on some of these jobs. I invited four panelists, ranging from startup founders to tech transfer officers to due diligence scientists:

Emily: https://linkedin.com/in/emily-hartman-guthrie-0b013437/
Susan: https://linkedin.com/in/susanmockus/
Marco: https://linkedin.com/in/marco-lobba/
Steve: https://www.linkedin.com/in/steve-ouellette-b93b1561/

During the 1-hour conversation, we touched on many aspects of spin-outs and start-ups. Of course there was plenty of interest in how to launch a startup company from a university research project, but there was also discussion about how exactly patenting and IP plays into startup companies, how tech transfer and commercialization are handled from the university or research institute, and how funding is secured from venture capital and investment firms.

I’ve compiled four main highlights from our discussion, but US HUPO will be sharing the recording of the panel shortly, so check there for the full conversation!

Q1: How do you build non-lab skills, like management, leadership, etc, during your academic training?
(1) Read books on management and leadership skills (*see recommended reading below)
(2) Take courses from your university’s MBA program, both to expand your knowledge but also to meet people (potential co-founders) with complementary business skills
(3) Look for incubators or entrepreneur support programs at your university, which typically include workshops on leadership, management, and business development
(4) Internships at VC firms (commonly posted on LinkedIn, so have your LinkedIn profile up to date!)

Q2: How do you get started with a potential spin off?
(1) iCORPS program is a great place to start and will give you opportunities to practice business development
(2) Find product market fit through “user/customer” interviews (something that iCORPS will also help you with)
(3) Report all your inventions/discoveries to university patent/IP office BEFORE publishing a paper or sharing at a conference — having intellectual property will make it easier to fundraise for your company
(4) Draft a commercialization/business plan (again, something that iCORPS can help you with)
(5) Read Jared Friedman’s How to spin your scientific research out of a university and into a startup

Q3: Investment in proteomics is booming — pros and cons?
(Pro) Proteomics can solve problems that genomics couldn’t; next generation sequencing is cheap now so there’s more investment opportunities for proteomics; there’s more mainstream interest in proteomics these days
(Con) hard to explain proteomics and especially mass spectrometry; orthogonal validation for proteomics is tough; there’s a history of failed companies and overpromised/underdelivered projects; hard to define a proteomics product (methods and software patents are trickier than a product or chemical/compound, for example)

Q4: What business models seem to work for proteomics?
Hybrid platform/fee-for-service + internal therapeutics dev is a popular model now with early stage venture capital. However, it’s tough to stay lean and hard to stay focused when you’re essentially trying to run two businesses: one as a CRO and one as a pharmaceutical. In the future, maybe there will be more proteomics-based diagnostics companies?

* Recommended reading from the panelists:
Managing Up: How to Forge an Effective Relationship With Those Above You, Book by Roger Gittines and Rosanne Badowski
Radical Candor: Be a Kick-Ass Boss Without Losing Your Humanity, Book by Kim Scott
Biotechnology Entrepreneurship: Starting, Managing, and Leading Biotech Companies, Editor: Craig D. Shimasaki
Bio Design: Nature, Science, Creativity, Book by William Myers

The Bioinformatics Chat podcast: Calibrating signal in mass spectrometry and beyond

A few weeks ago, I had the absolute pleasure of catching up with Jacob Schreiber on his podcast, The Bioinformatics Chat. Jacob and I met as grad students in Bill Noble’s lab at UW. Chronologically, we were in the same cohort (started grad school in 2014) but he was in the Computer Science and Engineering program and I was in the Genome Sciences program.

From day one, he absolutely blew me away with his knowledge and as a baby bioinformatician, I was so intimidated! For a few years, I barely understood what, exactly, he did. All I knew was that you should always add MORE LAYERS to your neural net. It turned out he, too, barely understood what, exactly, I did. Imposter syndrome is a hell of a drug.

Anyway, you can catch our episode on signal calibration, what it means to be “quantitative”, and whether numbers are even real on Apple, Google, or Spotify now!

Basics of targeted mass spectrometry with Skyline

I kicked off the scientific presentations for the virtualized Northeastern University May Institute with a workshop on the basics of mass spectrometry proteomics. In this mixed-methods 1.5 hour session, I aimed to give biomedical researchers a crash course in all things quantitative mass spectrometry-based proteomics and even give some of the pros a few tips on the Skyline software ecosystem. By the end of this workshop, I wanted participants to come away with the ability to:

  1. Assess the experimental pros and cons of targeted proteomics, and compare to discovery proteomics.
  2. Explain the fundamentals of mass spectrometry proteomics, including peptide fragmentation and basic components of a mass spectrometer
  3. Describe the steps of a targeted proteomics workflow and the information required to build an assay
  4. Apply the Skyline software ecosystem to their own targeted proteomics experiments.

Throughout the lecture-based workshop, I mixed participant question-and-answer and examples of the concepts discussed in Skyline. Finally, I closed with three hands-on examples of using Skyline to build a Parallel Reaction Monitoring (PRM) mass spectrometry experiment.

You can check out the recording below:

Using Prosit predicted spectral libraries to build GPF chromatogram libraries

UPDATE: I learned that Searle et al 2020 includes a tutorial in the Supplementary Note 1!

Click here to go to my version of the tutorial

Unsurprisingly, my most common approach to proteome abundance measurements by mass spectrometry is data independent acquisition (DIA). Specifically I’ve been using the chromatogram library approach (Searle et al 2018) because, compared to spectral library-based approaches, it doesn’t take a lot of extra work. I just prepare my samples as usual, then pool a few uL of each sample into a “library” or consensus sample. I queue up my single-shot experimental samples, then I acquire the pooled library sample with multiple injections, each time spanning a 100 m/z range (gas phase fractionation, GPF) with very narrow isolation windows.

The next step up is to search the narrow window, GPF multi-injections against a spectral library. Recently, a team of researchers released “Prosit”, a tool to predict spectral libraries. Using Prosit predicted spectral libraries to search GPF chromatogram libraries gives detection numbers a boost (Searle et al 2020). Because it’s so easy to use predicted spectral libraries, I’ve been doing it for all my projects.

The tutorial above is a work in progress, so let me know if you have questions or suggestions to improve it!

Resources for beginner bioinformatics

drgab30vqaaoy2i

Learning to code, coming from an experimental background, can be a frustrating and intimidating experience. Although there are many free courses online that will teach you the basics of Python and programming, the best way to improve is to simply practice. The best practice is a project you are personally motivated and passionate about. For graduate students, this might be a component of your thesis or a side project that complements your research interests. Coming up with an interesting project might be daunting at first, however, so here are some resources for quick, achievable practice problems to help keep you coding.

Practice mathematical/computer programming problems
Python basics with Rosalind.info
Beginner bioinformatics with Rosalind.info

Additional resources:
Check your code style online

Digging Deeper into Gene Editing at Expanding Your Horizons (Edmonds, WA)

Students, teachers, parents — welcome! Thank you for your interest in following up on the Expanding Your Horizon’s workshop! Here’s a summary of what we talked about today, along with many links to videos and sites where you can learn more about gene editing.531408_bc42415de0c4c736e3651f94e97597c0

For the 2017 Expanding Your Horizons event in Edmonds, WA, the University of Washington’s Women in Genome Sciences group created a workshop around the topic of gene (genome) editing (find the lesson plan here, and the presentation here). Our goals for this workshop were to communicate the key terms and concepts of gene editing, and apply a scientific thought process to some familiar and not-so-familiar problems. Read more about our day below.

Continue reading “Digging Deeper into Gene Editing at Expanding Your Horizons (Edmonds, WA)”