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Getting Started in Academia

Cristina Savin: “In retrospect, I had very little idea what I was doing. After the first few rejections, I started doubting that I belonged in academia at all.”

CSM Lab

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Cristina Savin

[su_boxbox title=”About”]Prof. Cristina Savin received her PhD from Goethe University in Frankfurt, Germany, after doing theoretical work on the computational roles of different forms of plasticity in the group of Jochen Triesch at the Frankfurt Institute for Advanced Studies. She then moved to Cambridge University for a postdoc with Mate Lengyel, followed by a short research visit at ENS in Paris in the group of Sophie Deneve, and an independent research fellowship at IST Austria, working in collaboration with Gasper Tkacik and Joszef Csicsvari. Since 2017 she has been an Assistant Professor in the Center for Neural Science and the Center for Data Science at NYU. Her research sits at the intersection between neuroscience and machine learning, with a focus on learning at the level of circuits. Her group develops both theory and new data analysis tools for understanding how neural circuits do useful computation, in collaboration with several experimental partners. Cristina has also recently started an industry collaboration centered on nervous system computer interfaces for medical applications. Learn more about the Savin Lab. The story is co-published in collaboration with Growing up in Science. [/su_boxbox]

[su_boxnote note_color=”#d5ecb3″]Some Take Aways

  • Give yourself time to figure out what interests you.
  • Donโ€™t feel too proud/embarrassed to ask for help.
  • Be proud of your achievements but learn to also appreciate the mistakes made along the way.[/su_boxnote]

Dr. Cristina Savin

[dropcap]G[/dropcap]rowing up in a small town in Transylvania, my vision of possible career options was quite narrow. As someone who excelled academically but felt no passion for any particular subject (beyond reading indiscriminately and painting), I toyed with the idea of going to medical school before finally ending up, almost by chance, as a computer science (CS) major in high-school and then at the Technical University in Cluj-Napoca. There, I fell in love with both CS and university life; I continued to be top of the class while still painting and generally doing more things that should realistically fit in 24 hours.

In my third year, I briefly worked in industry as a software engineer, which helped confirm my choice of staying in academia. In Romania, this would usually mean staying on at the same university and working slowly through the ranks towards a permanent position. The Dean, one of my early mentors, pushed me to go abroad for my PhD instead. After some complicated family negotiations, I accepted a PhD position in Frankfurt, working with Jochen Triesch on computational roles of neuroplasticity. Despite struggling with living abroad for the first time and a rather unwelcoming environment, research proceeded relatively smoothly, resulting in several publications and one contributed Cosyne talk.

I handed in my thesis after three and a half years. While the questions I was asking during my PhD remained prominent in my mind, by the end of my stay in Frankfurt, I was feeling dissatisfied with the methodology. Prompted by a collaboration with Joerg Luecke, at the time a postdoc at the same institute, I started shifting towards machine learning based approaches to studying brain computation. ย Why? Well, traditional approaches involved too much trial and error and figuring with model parameters. The machine learning methods offered a neat mathematically clean way of thinking. They have their own limits which I eventually found out but machine learning provided a more natural way for me to think about computation in general and brain computation in particular.

I sat for the first time in an experimental lab and quickly discovered that none of the neuroscientists around were interested in talking to me.

I landed my dream postdoc in Cambridge University working in the lab of Mate Lengyel in collaboration with Peter Dayan. It quickly became apparent that I had a great deal to learn, but I persevered and managed to develop several interesting ideas, with corresponding publications.

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While external validation that I was on the right track was hard to come by, I did manage to get a selective Neural Information Processing Systems (NIPS) talk, and after two and a half years, I felt I was in a good position to go back on the job market. I had multiple offers, but eventually decided on an independent research fellowship in Vienna as I wanted to focus more on data analysis. I ended up deferring the start date to spend some time in Paris in the lab of Sophie Deneve to work on what became one of her first independent lines of research (which brought another NIPS spotlight and a second Cosyne talk).

In Vienna, I sat for the first time in an experimental lab and quickly discovered that none of the neuroscientists around were interested in talking to me. It took a few months of participating in journal clubs and research talks until finally someone came to me with a question, and I finally got a stamp of approval when one of my suggestions turned into an actual experiment. In parallel, I started looking into faculty jobs, initially restricting the search to Europe but eventually also sending a few applications in the US, despite strong family opposition. I struggled with understanding how the job marked worked, especially in the US; lack of mentorship partly because of me being too stubborn and proud to ask for help; and more generally figuring out the best way to pitch my research to a broad audience. It took a while before starting to get invited for interviews; even longer for an actual offer to materialize. In retrospect, I had very little idea what I was doing. After the first few rejections, I started doubting that I belonged in academia at all.

Fortunately, chance intervened. At the point when I was ready to give up, I received an email encouraging her to apply for a joint CNS-CDS position at NYU. Long story short, I eventually was offered the job. Getting started was not easy, but things are starting to come together thanks to some rather amazing students. I am excited about starting several new collaborations and developing new grad courses. I am excited about the challenges ahead.

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CivicSciTimes - Stories in Science

Unexpected Stories and Spindle Mistakes: Discovering that Wild-type Cells are Full of Surprises

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Natalie Nannas

Natalie Nannas is an Associate Professor of Biology at Hamilton College in Clinton, NY. She teaches courses in genetics, molecular biology, and bioethics. Dr. Nannas graduated from Grinnell College with bachelor’s degrees in biological chemistry and French. She received her Masterโ€™s and PhD from Harvard University in molecular biology and genetics. Dr. Nannas conducted her postdoctoral research at the University of Georgia where she won a National Science Foundation Plant Genome Postdoctoral Fellowship. At Hamilton College, Dr. Nannas enjoys teaching and sharing her passion for microscopy with her undergraduate research students. When not glued to a microscope, she loves spending time with her husband and two daughters. The narrative below by Natalie Nannas captures the human stories behind the science from a 2022 paper titled โ€œFrequent spindle errors require structural rearrangement to complete meiosis in Zea maysโ€ which was published by her group in 2022 in the International Journal of Molecular Sciences.

Science never works out the way we plan. As scientists, we ask questions, hypothesize and outline our goals โ€ฆ then reality of science occurs. The reality of science is often full of failed controls, endless troubleshooting, and sometimes strange findings that lead us in new and unpredictable directions. Our publications give the impression that we planned these scientific journeys from the beginning and do not tell the human side of the process with all of its twists and turns, dead-ends and U-turns. I want to tell you the real story behind my first publication as a faculty member with my own lab. It did not go as planned due to the COVID-19 pandemic. My lab was shut down in the middle of our investigation, and my students and I were unable to generate new data. In the beginning, it seemed like we were stranded with only control data and no story to tell, but the time away from the lab allowed us to spend more time looking carefully at wild-type cells. What seemed like a dead-end suddenly became its own story when we found something unexpected hiding within microscopy movies. Our wild-type cells were making mistakes, attempting fixes and changing directions, just like we do as scientists.

My scientific journey began with flickering green lights and a microscope (you can read more about it here). As an undergraduate, I was mesmerized by the beauty of watching living cells shuffle fluorescently labeled proteins throughout their cytoplasm. I followed this passion for microscopy into my doctoral dissertation research at Harvard University where I investigated how yeast cells build the machinery needed to pull their chromosomes apart. This machinery is a dynamic collection of long protein tubes called microtubules and other organizing proteins that help move and shuffle microtubules. I loved watching the delicate dance of chromosomes interacting with microtubules of the spindle, and I wanted to continue studying this process in my postdoctoral studies.

During postdoctoral studies at the University of Georgia, I won a fellowship from the National Science Foundation to develop a new technique in microscopy. No one had ever watched plants building their spindles in meiosis, the specialized cell division that produces egg and sperm. Other scientists had performed beautiful microscopy studies observing how mitotic spindles function inside of plant cells, but due to the technical challenges, no one had ever observed live plant cells building spindles in meiosis. I was thrilled to take on this challenge by using version of maize that had fluorescently labeled tubulin, the protein that makes up microtubules of the spindle. With this line of maize, spindles would glow fluorescent green, allowing me to image if only I could extract the meiotic cells.

Dr. Natalie Nannas

We were so busy collecting data and prepping for our mutant studies that we never really took time to analyze the wild-type cells.

After almost a year spent dissecting maize plants, I finally managed to develop a method to isolate these tiny cells and keep them alive in a growth media long enough to image them. This new method of live imaging was going to serve as the foundation of my new lab at Hamilton College, a primarily undergraduate institution. With my students, I planned to investigate the pathways governed spindle assembly. Most animal mitotic cells have a structure called a centrosome that dictates how spindles are formed; however, female animal meiotic cells lack these structures and must use other pathways to direct spindle assembly. Plants also lack centrosomes, and I wanted to inhibit these known animal pathways in our plant live imaging system.

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As I set up my lab, my students and I collected live movies of wild-type maize cells building their spindles. I told my students and myself that these movies were not the main event, they were just the control cells so we would have a baseline comparison for our experimental conditions. We were so busy collecting data and prepping for our mutant studies that we never really took the time to analyze the wild-type cells. At the surface level, they built spindles and segregated chromosomes in a generally expected amount of time, so we focused on preparing for our upcoming experimentsโ€ฆ. then March 2020 occurred.

The pandemic forced us to slow down and look more carefully at our wild-type data, and I am grateful for the detour.

My students headed home for spring break with a warning that there may be a delay in coming back to campus due to the spread of COVID-19. None of us were prepared for the shutdown that followed. Like many colleges and universities, our campus was closed for the remainder of the spring 2020 semester and the summer of 2020. My students and I began meeting on Zoom, trying to make a new plan for our research. The only data we had to work with were the microscopy of wild-type maize cells, so we decided to spend time digging more deeply into these movies. Originally, we had only measured the total time it took to build a spindle as it would be a baseline for comparison to our mutants. We had not looked carefully at any of the intermediate time points in the assembly process. When my students looked more closely at our movies, they discovered that wild-type cells built an incorrectly shaped spindle over 60% of the time!

We found that maize meiotic cells often built spindles with three poles instead of two, and they had to actively rearrange their spindle structure to correct this mistake. We also found that in these cells, there was a delay in meiosis as cells refused to progress until this correction had been made. This is an exciting discovery as it showed that plants are error-prone in their spindle assembly, much like human female meiotic cells. Our findings also suggested that meiotic cells were monitoring their spindle shape when determining if they should move forward in meiosis. Previous work has shown that cells monitor the attachment of chromosomes to the spindle to make this decision, but our work adds a new dimension, showing that they also monitor spindle shape. As we continued to analyze our videos, we also learned that cells corrected their spindle morphology in a predictable way. They always collapsed the two poles that were closest together, creating a single pole and resulting in a correct bipolar spindle.

The image shows the first page of the paper which can be accessed here.

My students and I had begun our scientific journey planning to breeze over wild-type cells, moving on to what we envisioned would be a more exciting story of spindle mutants. The pandemic forced us to slow down and look more carefully at our wild-type data, and I am grateful for the detour. I rediscovered my love of closely watching flickering green fluorescent lights, the dance of microtubules sliding into place or making missteps and shuffling into new arrangements. Watching life attempt a complicated process, make mistakes, and try again, is a lesson that never grows old. It reminds me that our scientific journeys are just the same, they start in one direction but are fluid and constantly changing, and hopefully, they end with a functional spindle!

Read the Published Paper

Weiss, J.D., McVey, S.L., Stinebaugh, S.E., Sullivan, C.F., Dawe, R.K., and N.J. Nannas. 2022. Frequent spindle errors require structural rearrangement to complete meiosis in Zea maysInternational Journal of Molecular Sciences, 23 (8):4293โ€“4312.

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ABOUT: Stories in Science is a special series on the Civic Science Times. The main aim is to document the first-hand accounts of the human stories behind the science being published by scientists around the world. Such stories are an important element behind the civic nature of science.

SUBMISSION: Click here to access the story guidelines and submission portal. Please note that not all stories are accepted for publication. After submission, we will let you know whether we have selected the story for the review process.

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