3-Minute Thesis
The CNPN hosts an annual Three Minute Thesis (3MT) Competition, inviting Canadian graduate students (Master’s and Ph.D.) to present their proteomics research in a clear, compelling, and creative way, in just three minutes!
Presentations are tailored for a non-specialist audience, showcasing the impact and relevance of proteomics research beyond the scientific community.
Missed the event? Our seminars are available on-demande to our members. Make sure to log-in and look for "recordings" under the Spotlight Seminar Series tab.
CNPN is hosting its second annual Three Minute Thesis (3MT) Competition during the June Spotlight Seminar !
We invite graduate students (Master’s or Ph.D.) who are Canadian or enrolled at a Canadian institution to present their proteomics research in a creative and compelling way, in just 3 minutes. This is your chance to showcase the purpose and importance of your work to a general audience — using just one PowerPoint slide. (There will be prizes!)
Think you can rise to the challenge? Use the form below to apply.
Applications are due June 2nd 2025, 11:59 PM PDT.
The seminar will be held on June 17th at 3pm EDT on Zoom (you must be available to present at this time).
If you have any questions please email: info@cnpn.ca
CNPN Three Minute Thesis 2025 winners
Judges' Pick Winner
Iryna Abramchuk, University of Ottawa
Mapping the Unknown: Smart Protein Interaction Detection in Human Cells
About me
Iryna is a Ph.D. Candidate at the University of Ottawa studying bioinformatics in the Lavallée-Adam lab. Her work focuses on developing novel computational algorithms to improve the mass spectrometry technologies that are used to study protein interactions.
Audiance Pick Winner
Saya Sedighi, University of Toronto
A proximity-dependent approach to expand the human cell map across multiple different cancer lines
About me
Saya completed her Bachelor of Biomedical Sciences in 2022 at York University, where she studied RNA-binding proteins and phase-separated condensates in the Bayfield Lab. She is now a PhD student in Molecular Genetics at the University of Toronto under the supervision of Dr. Anne-Claude Gingras. Her research focuses on mapping subcellular organization across cancer cell lines using proximity-based proteomics. She has contributed to the Human Cell Map initiative by benchmarking biotin ligases for BioID and designing scalable bait selection strategies.
CNPN Three Minute Thesis 2024 winners
Judges' Pick Winner
Madison Shiyuk, University of Victoria
Combining Multiple Mass Spectrometry Imaging Modalities for Comprehensive Molecular Analysis of Tissues
About me
Madison completed her undergraduate degree at the University of Victoria, and during this time she completed an Honours project at the UVic Genome BC Proteomics Centre. She is now continuing her training as a Master’s student at the Proteomics Centre.
About the science
MALDI mass spectrometry imaging (MALDI-MSI) involves the detection of molecules across the surface of a tissue. Each MALDI-MSI experiment is specific for a single class of molecules (i.e. lipids or proteins), and this provides limited insight into biological processes. The aim of this project is to develop a workflow to enable several consecutive MALDI-MSI experiments on the same tissue section. The resulting information will reveal interactions between different molecular classes in addition to correlations with histological regions of interest. This technology can be used on any tissue, providing opportunity for complete molecular profiling in a broad range of applications.
Audiance Pick Winner
Davier Gutierrez-Gongora, University of Guelph
Proteogenomic and AI uncover new antifungal agents from mollusks
About me
I am from Cuba, where I did my bachelor’s in science with a mayor in Biochemistry and Molecular Biology. Currently, I am pursuing a PhD degree at the University of Guelph in Molecular and Cellular Biology. Specifically, my research focuses on the discovery of new peptidase inhibitors from mollusks as antifungal agents against the human fungal pathogen, Cryptococcus neoformans.
About the science
In this investigation, we use the combination of genomics and proteomics to characterize non-model species like invertebrates with potential as sources of new antifungals. Then we use artificial intelligence models to predict the antifungal properties of gene-encoded and protein-derived peptides. Likewise, we use AlphaFold multimer to predict the interaction between virulence-related enzymes and protein within the mollusk’s proteome to unveil new inhibitors.