A Word From: Katherine Hajjar & Jacob Rand

Last week, Cold Spring Harbor Laboratory hosted Annexins 2017. This international conference is held in a different location every other year, and CSHL was chosen for the 2017 meeting because of its accessibility, on-campus amenities, and proximity to New York City. We are proud to have hosted the Annexins community and checked in with two of its three organizers – Katherine Hajjar and Jacob Rand – to talk about the conference. 

As the annexin community grows, the organizers do their part to ensure the “newcomers” take part in the conference: 

Speaking of abstracts, Annexins 2017 did adopt the CSHL Meetings & Courses tradition of selecting a majority of talks from the submitted abstracts.

We ended the chat talking about the annexin community: 

Thank you to the organizers for choosing Cold Spring Harbor Laboratory as the venue for Annexins 2017!

For more conversations with other meeting organizers, check out our A Word From... series. 

A Word From: Mark Reimers & Pascal Wallisch

2017 Neural Data Science instructors Mark Reimers and Pascal Wallisch (L to R)

We recently sat down with co-lead instructors and co-founders, Mark Reimers and Pascal Wallisch, to talk about their biennial course on Neural Data Science. The course was first offered in 2015 to help neuroscientists develop conceptual and practical capabilities for analyzing large datasets such as those resulting from single- and multi-electrode extracellular recordings, local field potentials and electroencephalograms (EEGs), as well as two-photon and wide-field optical imaging. Cutting edge data analysis methods are still very much the focus of the Neural Data Science course. Here, Mark and Pascal go into detail about the course’s importance to neuroscience.

Pascal: We now have a lot of new ways of recording, storing, and processing neural data but we don’t yet have a good understanding of how to analyze the data. The richness of the newly-available data demonstrates how much we still don’t understand about the brain and that one would be well-advised to be more modest and humble about what we actually understand about brain function. A lot of other courses focus on existing theories and models; whereas our course de-emphasizes modeling and theory. We won’t tell you what to think about how the brain works, but rather how to analyze your data. 

Mark: I would agree that this course really is very distinct from other computational neuroscience courses. We’re not focused on trying to simulate the brain or impose models on data. We’re exploring the data in a hypothesis-free context rather than use it to buttress or falsify a particular theory. I think the real imperative for neuroscientists now is to look closely at their data and to let it inform them, rather than impose their ideas on their data. Because the new technologies enable us to see more richly than before, I would argue that it’s appropriate in this stage of neuroscience to be more exploratory. 

Pascal: I think the students are loving the exploration aspect of it. It’s a very hands-on course. Someone came up to me this year and said, “This course instills in me confidence.” He is trained as a medical doctor with no mathematical background and was always afraid of equations. But our hands-on approach showed him that he can absolutely do this. It’s actually not that hard once one gets the hang of it.

Mark: The students are asking questions about their data that we can show them how to address. Many of them feel that some of the longstanding theories and ideas are not really giving them insight or traction on their data. So I think they’re welcoming of this new perspective.

Pascal: Regardless of whether someone takes this course or not, those continuing in neuroscience will, in my opinion, need a solid understanding of statistics and at least one relevant programming language to be successful in the field. 

Mark: I second that. I think it’s a fundamental transformation in all of biology, though it’s been particularly rapid and, perhaps, hard for some people at this point in neuroscience. It used to be that it was important for you to have understanding of the animals, ability to do careful surgery, really good microscope skills, be able to identify neurons, and be familiar with your technical apparatus and its artifacts. All of these are still very important but, increasingly, you also have to think quantitatively and pose questions that can be addressed by data analytic methods.

Pascal: Which may be challenging for someone who has no training in that kind of skillset. Biology used to be a field where you could have a long, productive and respectable career while skirting math and statistics for the most part. But this is no longer the case – at least not in neuroscience. So a course like this is desperately needed. 

For the second iteration of the course this summer, Mark and Pascal made revisions to the curriculum and schedule that maximized what the trainees learned. 

Pascal: We made four big changes to this year’s course that made things run much more smoothly. The first is that the course now runs a full two weeks instead of just ten days, with one week dedicated to data recorded with electrical methods and the other to data recorded with optical methods. The second change is that we cut topics that were not directly related to electrical and optical methods, like information theory and fMRI, as there are other courses that focus more directly on those topics. The third change we made is that we streamlined the way we teach, in the sense that we now work with much fewer data sets. In 2015, each invited lecturer brought his/her own data, and we spent hours and hours learning the structures of all the different data sets. Finally, we increased the number of teaching assistants in the course. 

Mark: Most of our theories are built on a kind of information processing model that increasingly seems unlikely to be true except for a few specific areas, such as the visual system, so we no longer emphasize information theory in the curriculum. In addition, we’ve oriented the course towards the new revolutionary technologies like optical imaging. Our plan for the future is to try and pivot toward emerging transformational technologies as they become important. They all pose dramatically different (and difficult) data analysis issues.

We then switched gears and talked about what a typical day for their trainees look like.

Mark: We typically have intense conversations about neuroscience over breakfast, occasionally about politics as well. Then starting at 9 o’clock, we have three hours of seminars, followed by a lunch break. We bring in one invited lecturer to give two talks each morning. The lab sessions then run from 1 to 4:30 and when we say lab, we don’t mean pipetting and test tubes.

Pascal: We mean MATLAB. A key feature of the afternoon lab sessions is that the students work in pairs, and the partners are assigned by us based on their experience with MATLAB. We purposely assign two students per computer so they have someone to talk to while they’re doing analyses and grappling with the data. It prevents the students from feeling isolated and it’s been working really well.

Mark: In the late afternoon, the students have free time when they can go to the tennis court or beach, or take a walk or run around the estate. Dinner is at 6 and, of course, we have even more intense conversations about neuroscience, politics, teaching, and universities. We resume again at 7:30 with evening sessions that are more of a diversion. For example, we’ll have a presentation on how to write a good scientific paper, a discussion about contentious issues in data analysis, or a presentation by someone from Mathworks on new capabilities in their program. The students typically stay up late to do more coding or work on what they didn’t finish during the day. 

Acceptance into the Neural Data Analysis course is competitive. We asked Mark and Pascal what they look for in potential course trainees and their application materials. 

Mark: We’re looking for someone who’s wrestling with fundamental questions about the brain, who’s exploring and using some of the cutting-edge technologies, and who is quantitatively-minded but doesn’t have the tools to analyze the data they’re getting in any detail. We would like to help our students formulate rigorous statistical and mathematical approaches for answering questions that may be lying underneath their experiments, that they want to address but don’t know how to formulate. 

Pascal: We look for three things: 1. Scientists who will benefit from the course. We’ve rejected some applicants because we felt they already knew too much about the material. 2. People who are working on data from electrical or optical methods. Some people who applied for the course were qualified, but were interested in clinical neurology, neurosurgery, or neuro-imaging, and there are other courses for developing those skills. 3. And, of course, we look for overall curiosity and a demonstrated record of achievement. 

Mark: For those whose applications were not accepted this year, I would encourage them to try again. The competition this year was stiff and there were many talented students we couldn’t accept. 

Pascal: We only have 20 spots and we’re already at maximum capacity, so we had to make some really tough choices, heartbreaking in some cases. 

We ended by asking what their favorite moments have been throughout the two iterations of the course.

Mark: During the 2015 course, I fondly remember singing math songs. 

Pascal: I remember that! That was definitely a community building moment. 

Mark: Several of our perhaps best-remembered moments were those where students see that someone is taking seriously a fundamental confusion or uncertainty of theirs, something they had but never articulated very clearly, or had never seen articulated explicitly. Before the course, they’d just had to cope, sort of make do, make stuff up, or do something to produce what’s expected. And now they see that yes, something is really problematic: that it’s not clear how to go from, let’s say, an electrical trace to a discrete set of times where a particular set of neurons has fired. We really need to make processes of the brain visible to researchers so they can come up with better ideas of how the brain works.

Make sure to read the rest of our A Word From... series.

A Word From: Maria Jasin

Last weekend, we hosted the third CSHL meeting on Genome Engineering: The CRISPR/Cas Revolution. We met with Maria Jasin, one of the three original organizers and a CSHL meeting veteran, to talk about the CRISPR meeting. Here is a quick overview of her research:

We study double strand break repair and genomic instability, with a particular interest in the breast cancer suppressors BRCA1 and BRCA2. We’ve also had a major interest in understanding meiotic recombination and how double strand breaks are repaired there, and maybe aberrantly repaired in other syndromes. 

Maria provided a great overview of the meeting, how it has evolved with the field, and the developments she’s most excited about. 

One major change is the expansion in the number of enzymes and nucleases that are being used. The first meeting focused primarily on Cas9 itself. Now, many different enzymes are used that have better applications than Cas9 in some contexts, which is an exciting development in the field that is much more represented in the meeting than it was in the past. It’s a recognition of how large the CRISPR repertoire is in bacteria, the almost limitless number of proteins that can be cloned and characterized, that have somewhat different specificities or other reasons that make them preferable in different situations. 

This year’s meeting started with a lot of CRISPR biology which I found really exciting, because it lets us non-CRISPR biologists understand this beautiful genetic system of adaptive immunity in bacteria. Also, there’s been an emergence of anti-CRISPRs - peptides that can halt CRISPR activity. These peptides are numerous and act differently by blocking different steps, which is a really fascinating system. It’s perhaps not surprising that there are some practical uses to them as well. 

One of the really exciting things for me was Kathy Niakan’s talk about using human zygotes to address important questions about embryonic development in humans. Obviously, it’s a very difficult system that needs to be heavily regulated. But as much as we know about mouse development and the very earliest stages of mouse embryos, one thing that’s clear is how different things are in human embryos. We have known for a long time that human oocytes are very prone to aneuploidy, and that results in miscarriages or birth defects. Trying to understand the genome instability that arises in the early stages of embryos is something that’s also important for human health and infertility. 

Another talk I was excited about was given by Danwei Huangfu, who uses human pluripotent stem cells to understand pancreatic development. For a long time, we’ve only been able to use cell lines in humans that are transformed and highly aberrant. The ability to use cells that can be differentiated into human lineages is really exciting and highlights the ability to understand, again, human embryonic development at a much later stage. It’s related to human genetics that in the past we wouldn’t have been able to do.  

Also, my student, Weiran Feng gave a talk about his work on homologous recombination – one of the pathways people like to use to modify the genome. It was very touching for me to see one of my students develop a beautiful story and present it in a beautiful way. 

These days, there’s no shortage of scientific meetings focused on CRISPR. We asked Maria what sets the CSHL meeting apart from others, and also who benefits most from attending it.

There certainly has been a large explosion of CRISPR and genome engineering meetings, but the one thing that’s particularly exciting about this meeting is its emphasis on biology. We, of course, have talks that are more technical, about improving the CRISPR systems or adapting new systems or doing screens. But we’ve balanced it by having a lot of biologists present who are trying to understand human development. We even had a talk this year on using killifish  as a new model for aging, so the meeting brings together a lot of biologists.   

Commercial interest companies love this meeting because they are able to showcase their work here. Most of the talks are from academic scientists though, who love this meeting for the basic biology and technology developments that are coming from and presented by academic labs. 

And Cold Spring Harbor is always a great place for graduate students and postdocs. We do have invited speakers, but the number of talks given by postdocs or grad students outnumber the invited talks. My first talk as a scientist was at CSH when I was a graduate student! Attending a Cold Spring Harbor meeting is an ideal way to start your career because you are able to not only interact with a number of scientists but also have the opportunity to speak or present a poster. 

We ended the conversation on how this year’s meeting turned out. 

I had thought that, with time, the meeting would get less exciting but this year’s meeting was just as exciting as the first one. It’s a testament to the growth of the field, the creative approaches people are taking, and the expansion of the number of nucleases involved. We brought in people with different expertise – stem cells, modifying human embryonic cells, etc. – which brought together people who don’t know or often see other. The meeting hits a lot of different areas in biology even if there’s a CRISPR coherence to it, and that promotes great scientific interactions.

The Genome Engineering: The CRISPR/Cas Revolution meeting will return to campus on August 22-25, 2018. Follow us on Facebook or Twitter for meeting updates. 

To read more conversations with CSHL meeting organizers and course instructors, browse through our A Word From... series. 

A Word From: Karla Kaun, Alex Keene, Chi-Hon Lee & Stefan Pulver

2017 Drosphila Neurobiology co-lead course instructors Karla Kaun, Stefan Pulver, Alex Keene, and Chi-Hon Lee (L to R)

A few days ago, another batch of alumni from the course on Drosophila Neurobiology: Genes, Circuits & Behavior were welcomed into the ever-expanding network. We met up with Karla Kaun, Alex Keene, Chi-Hon Lee, and Stefan Pulver to talk about the annual course and exchange fly-centric one-liners. Drosophila Neurobiology, aka “the fly course,” is among our longest running courses and has therefore evolved significantly over the last 34 years. Here is an overview of the course format today and the major updates Alex, Chi-Hon, Karla and Stefan have made to keep it current and engaging. 

Stefan: One of the things we try to do in the course is continually reinvent and change it in response to student needs and feedback. The content of the course is usually structured by us ahead of students applying. But we build in flexibility so we have avenues where students can explore – maybe improvise different modules and extend some of the modules we teach, all while having a solid framework of learning objectives. The course isn’t a static thing that moves from one year to the next. It’s a growing, living thing that can actually change – sometimes even within the three weeks of the course. We make adjustments “on the fly!” 

Chi-Hon: One of the most significant changes we made is the addition of the capstone project. The course was originally designed to teach students how to use Drosophila to study neuroscience. The capstone project allows the students to link what they learn at the course with their own research interests.  

Karla: At the end of the course, the students give chalk talks on their capstone projects and what they want to do in the future in their own labs using what they learned in the course. We really like chalk talks because they create a lot of back-and-forth discussions between the students and faculty. 

Stefan: Another new thing we’ve introduced over the last few years is a do-it-yourself (DIY) component. We have a few sessions where we teach students how to build apparatus and create their own equipment. These are systems students can create by using 3D printed materials or purchasing separate components. The systems we DIY – generally behavioral apparatus and opti-genetic LED controls – are expensive if you buy them outright. 

Alex: With the advent of 3D printing, DIY-ing is becoming more and more common across science and we wanted to make sure we integrated it into the course. 

Karla: And the students love it. One of the scientific advantages to DIY is it permits a lot more creativity. We try to reveal to our students that it’s not that expensive to do it yourself, so anybody can do it. You don’t have to buy a pre-built thing. The only limitations are your imagination and ability to order materials.

Alex: Another thing that’s been ramping up is the integration of computational biology into the course’s physiology and behavioral sections. Computational biology is something that’s growing within the neurobiology field, and we make sure our students get a healthy background in it. 

Chi-Hon: We aim to empower our students. In Drosophila neuroscience, there is a norm to use creativity to overcome hurdles. Most of what you want to do, you cannot buy the right tool: you have to invent it, or write your own program to achieve a goal. Our sessions are designed to help students get to the stage where they can build a tool or program and realize it’s not that hard. 

We next asked them to describe the day-to-day life of their trainees.

Chi-Hon: The day starts at 9 o’clock with a lecture. Students then spend the latter half of the morning, which sometimes runs into the afternoon, to explore and work on achieving a specific  goal in the laboratory. We come back to the lecture room in the afternoon for the students to present their observations and discuss how to process their data. In the evenings, students continue to work on and practice very difficult techniques, and also hear invited lectures from world-renowned scientists. 

Karla: The faculty we invite as lecturers are experts in many different things and can tailor what they’re teaching to the students’ interests. So it’s rather dynamic in that way.

Stefan: We bring in a range of faculty members. And oftentimes, they bring along course aides or assistants – like postdocs and graduate students – who contribute to the course too. So you don’t only have faculty but also members of their laboratories coming in to help teach course practicals.

Alex: Oftentimes, the invited  lecturers come in to give a two-hour talk but stay for 3 or 4 days. They want to spend as much time with the students as possible. That generates opportunities for students to informally interact with them, talk to them about their projects and get feedback, which I think is one of the more valuable things the students get out of this course. 

The course runs close to three weeks and during that time, the trainees also have access to the equipment and technology acquired for the course. 

Stefan: Although we study Drosophila, this really is an integrative neuroscience course. We use Drosophila in part because we can teach a whole bunch of different concepts in neuroscience, from genetics to cellular physiology to behavior, that are very tricky to do with other model organisms. Cold Spring Harbor Laboratory is exceptionally good at bringing in cutting-edge technologies and giving students access to it 24 hours a day, 7 days a week. It’s important to not underestimate the access to resources and equipment here; we have equipment that is not easy to access in a lot of institutions. So students can see the full array of possibilities for addressing a question, from two-photon microscopy to live imaging to electrophysiology to inexpensive ways of doing behavior experiments.

Karla: And some of the most complex software for analyzing behavior, too. Altogether, it’s $2-3 million dollars worth of equipment.

Stefan: We have chunks of time in the course where there is no scheduled learning. So students can do their own experiments and access this fantastic equipment any time of day or night. 

When it comes to the value of the course, here is what the instructors had to say: 

Alex: We had an analysis done and it showed that 64% of the graduates of this course go on to faculty positions. Part of that is accepting some of the best people as students but we like to believe that the course itself contributes to this. I really think it’s priceless.

Karla:  On a recent long-term survey to people who had previously taken the course, we got amazing feedback from a lot of them. For me, I’m an assistant professor fairly new to professor-ing and it’s pretty amazing when these really big names email you and say, “This course was amazing! Thank you so much for continuing it!” I think this course is something that sticks with you your whole life. 

Alex: Go online and look through the roll of honor. The people who’ve taken the course over the last 34 years include huge names and people who have started new areas in the field, like Karla. To come in and see this year’s students, I can’t help but think that at least half of them will go on to make their own big contributions. 

Chi-Hon: This course offers more than just skills, knowledge, and technology – we provide an environment to network.

Karla: The lifelong friendships that start in the course are priceless and make the biggest impacts. 

Stefan: Having a network of colleagues you can trust and go to with questions or problems is really helpful. Each year of students formulates that network.      

Chi-Hon: We have a mixer with the Advanced Techniques in Molecular Neuroscience course where we invite a world-renowned scientist to give a research talk to students of the two very different but overlapping fields. The students in both courses intermingle and discuss their field’s perspective on the same topic. It’s followed by a cheese and wine reception so the discussions continue for hours afterwards.

Alex: This year, we had Amita Sehgal of the National Academy give this talk. Amita’s in my field and I’ve seen her talk probably a dozen times, but it was neat to see her teaching to a small group.

Karla: We also do a mixer with the Frontiers and Techniques in Plant Science course. 

Alex: We “cross-pollinate!”

Acceptance to the Drosophila Neurobiology course is competitive. We asked the course instructors for tips on what they look for in applications and how they make their selections. 

Alex: I would advise applicants to clearly state what they hope to get out of the course. In addition to them being very talented, that’s one of the important things we look for in an application. 

Karla: Our goal is to create a really dynamic group of students that is diverse scientifically, by gender, and by nationality. It’s helpful for applicants to include a few sentences about their expertise so we know what they can contribute to the course and what they can take away from it. 

Stefan: We look for excellence in science and for people who, in a general sense, are going to be leaders in the field. Also, our makeup is international and we welcome people from Drosophila labs all over the world. Karla’s point about the students’ expertise is important in that the course can be considered a two-way street: Students teach other students about their own work, so those who can come in and contribute something new are actually really nice to have in the course.

Chi-Hon: We look for people who not only want to do their own thing, but are also willing to share. People who not only learn from lecturers and instructors, but those who can work with and learn from other students. 

Karla: It isn’t only graduate students and postdocs that apply to our course. We also receive applications from faculty at undergraduate institutions. And we welcome them because flies are particularly useful tools for doing undergraduate laboratories, so these faculty can have a really big impact on future generations of Drosophila scientists. 

Stefan: I would also say, if you apply to the course and don’t get in, don’t be afraid to try again. Don’t take an initial no as a permanent no. We have had students who, in our opinion, weren’t quite ready to come to the course one year, but then apply again and gain acceptance in another year. 

Here are a few more pieces of advice from the instructors:

Karla: Bring insect repellent!

Alex: And sunscreen!

Karla: And sleep a lot before you come!

Chi-Hon: I’ve been to many, many courses in different institutions and I always come back to Cold Spring Harbor. This is the best place.

If you'd like to learn of the Drosophila Neurobiology course from a trainee's perspective, read our Q&A with 2017 fly alum Tayfun Tumkaya. Also, for more on how to prepare for your time at CSHL, check out our course trainee informational guide series.

Make sure to read the rest of our A Word From... series. 

A Word From: Adam Rosebrock

Last week, we met with Adam Rosebrock to chat about the CSHL Metabolomics course. Founded in 2016 by Adam, Amy Caudy, and Eyal Gottlieb, Metabolomics is one of our newer courses. The second iteration of the course this summer saw the addition of a fourth lead instructor – Justin Cross – as well as a broader applicant pool and a refinement of how trainees rotate through the instruments.

It’s been a fun trajectory watching the students go from really thinking of metabolomics as a black box to understanding how it works. We have students from computational backgrounds, biology backgrounds, even chemists merging together in this one science. They’ve been working together as a team and it’s fun to watch!

I think of these courses like science summer camp: participants have great shared experiences, become disconnected from the outside world, and build a group of friends with whom they stay in contact for years to come. 

Are there any new developments in the field that are reflected in the course?  

Metabolomics is an actively changing and growing field. And ‘Metabolomics’ means many things to many people; it’s a catch-all word that is incredibly overloaded. Each time we set up this course, we have to think about what metabolomics means in that specific year. We’ve shifted from discovering new compounds every time we do an experiment to accurately measuring a known list of compounds. The changes in these known compounds -- that we thought we knew everything about -- are the meat of the biological story. The regulation of metabolism is really what defines different cell types and different physiological states. Students are now having to turn their thinking from “I want to discover a new compound” to “How do I measure a large swath of chemical matter that’s inside the cell?”

Another major change is the development of new software tools. One of the reasons students from computational backgrounds are great to have in the course is because they can get a better idea of how the data they analyze are generated. 

The course is very technology-driven. Metabolomics is underpinned by mass spectrometry, and instrument vendors are actively changing their offerings as this science becomes a larger part of what we do in the biological community. Every year we have to learn, as instructors, the new toys and tools out there to ensure the course stays current. It's a fantastic opportunity to see first-hand how vendors improve their offerings to suit the changing needs of science.

We have about $2.5 million worth of loaned instrumentation that students get to use hands-on during this course. So what we have is sort of like a flash-mob version of a core facility with high-end, top-of-the-line instruments. It’s a fantastic way for students to come in and have expert practitioners in the field – the instructors – set up machines for them to use. Although a lot of metabolomics is mass spectrometry, we have stuff that people who don’t have mass spectrometers can do, too; you don’t have to have a mass spec in your own lab to do the analyses we teach in the course.

We requested a description of a day-in-the-life of a Metabolomics trainee and, by the sound of it, they are kept quite busy!

The Metabolomics course is intense – science starts at 9 AM and many students don’t head home until nearly midnight. The goal this year was to have a common, defining thread: Students can see how the many different tools of metabolomics analysis play into a single experiment.   

At the beginning of the course, students are asked to give a two-slide pitch on why they chose the course, what metabolomics means to them, and what they want to get out of it in terms of the science. There’s a significant amount of lecture-based learning from my co-instructors and myself where students learn fundamental technologies, applications of different tools and algorithms to biological questions, hands-on time on high-end instruments, and the basic processes in designing and executing metabolomics experiments. The 16-student cohort is split off into smaller groups so that everybody has a lot more hands-on time with both instructors and instruments.

But that’s only part of what we do here. On top of the hands-on and lecture-based learning, students hear talks from more than a dozen invited speakers in metabolism. Each speaker gives a gloves-off chalk talk in the evening after dinner that is meant to be interactive, so the students are able ask questions and figure out tools the speaker used to enable his/her research. The students usually see these invited chalk talks at a time in the course when they’ve just learned the tools from us. The next day, the same speaker gives a more formal 50-minute talk that provides a distillate of the technologies, tools, and ideas into a formal scientific package. 

We’ve also designed a good amount of time for students to propose projects that they would like to execute back in their home institutions. The capstone of the course is for students to tell us what experiment they’re going to do first back home with the tools they currently have, as well as what they would like to do at their home institution but can’t. The idea is to foster collaboration; together, the cohort of students can critique ideas and designs given what they’ve all learned in the course.

We switched gears and talked about the students themselves:

This year, we wanted to bring in a wide range of scientists from different disciplines. I was really stunned by the quality of applications we received: they all asked how they could apply small-molecule metabolism analysis to their science. We have students from the National Institute of Standards and Technology, cancer biologists, and also scientists involved in microbiology and biofuel production. 

The applicant pool was also diverse in age this year. We have students who are as young as first-year graduates or MD-PhD students, and they bring a totally unbiased perspective to science, a real love for learning. They’re able to keep up with the senior graduate students, postdocs, and faculty who make up the rest of the class. They’re all getting along very well, and the age disparity that I initially thought might be a problem has turned out to be fantastic. It created a balance in the student body: the younger students add a spark while the older scientists provide perspective. 

We then looked forward to next year’s application process and applicant pool:

We would love to take twice as many students next year if we could. Unfortunately, we don’t have the bandwidth to do so. I think having a mix of computational and wet biologists is critical. Having a mix of young, fresh faces and grizzled scientists - like myself - is also critical. And certainly a mixture of model systems and kinds of biology is very important. So instead of just being a cancer metabolism course this really has, from the start, been a course about general methods for metabolism measurement and ways of computationally and directly measuring what happens biochemically inside the cell.  

For those interested in attending the 2018 iteration of this course, Adam offers the following advice:

The biggest criteria my fellow instructors and I use in evaluating student applications is “Can you make use of this in your current projects?” Or “Are you turning to projects that will immediately use these tools?” Cold Spring Harbor Laboratory Meetings & Courses Program has a very selective course admission process and we can only take roughly 16 students a year. There are many who would love to learn the theory and perhaps the practice behind this science just to have that added to their knowledge set. But as much as we love teaching and learning, our main goal is to train the next generation of scientists who can take these tools into the greater scientific community.

The Metabolomics course may have just finished its second iteration, but it has already been mentioned and recognized in publications.

I recently attended the American Society for Mass Spectrometry meeting which is an international meeting of a diverse range of mass spectrometrists, including metabolomists. At that meeting and many others I’ve been to over the last year, it’s been fantastic to see alumni from our 2016 course presenting talks and posters, demonstrating the power of this course and its effects on the greater science community. We’ve already been acknowledged in 1 publication, with another in the final phase of revisions.

We concluded our conversation discussing how Adam sees the field of metabolomics evolving, and the overall goal he and his co-instructors have for course alumni. 

While we do our best to present students with a wide range of different scientific approaches and technologies, there’s no way to encapsulate all of metabolomics into a 2- or 3- week course. Our main goal is to foster independent, metabolomics-empowered scientists.  

As metabolomics becomes a more mature field, it will be easier to have the actual measurements done by somebody else. Therefore, we try to teach students how to think about the design of an experiment -- to make a proper contrast to ask the question they want and then, with the raw data, figure out what’s inside and make their own scientific interpretation. That way, in a future where the mass spectrometry happens in some distant core facility, the students are still empowered to design proper experiments, generate biological samples that can be run through a mass spectrometer elsewhere, analyze the data, and make their own biological conclusions.

Thank you to Adam for taking the time to chat with us. For more conversations with our other meeting organizers and course instructors, go here. Also, to gain a trainee's perspective on the Metabolomics course, read our Q&A with Vita Stepanova.

Adam helping the Metabolomics course capture another Scavenger Hunt win.