On computational methods for spatial mapping of the human proteome
Time: Fri 2022-12-16 10.09
Location: Samuelssonsalen, Tomtebodavägen 6, Solna
Subject area: Biotechnology
Doctoral student: Casper F. Winsnes , Cellulär och klinisk proteomik, Science for Life Laboratory, SciLifeLab
Opponent: Professor Ida-Maria Sintorn, Uppsala universitet
Supervisor: Professor Emma Lundberg, Science for Life Laboratory, SciLifeLab, Albanova VinnExcellence Center for Protein Technology, ProNova, Cellulär och klinisk proteomik
Proteins are complex molecules that are involved in almost every task in the body. In general, the role a protein fulfills is highly dependent on where in the cell it is located, its subcellular localization. In order to understand human biology, it is therefore imperative to gain insight into the world of proteins by examining their subcellular distribution and interaction with each other. This thesis focuses on the development of computational models capable of performing large scale spatial protein analysis on a subcellular level. Within that scope, we were able to develop models that classify the localization of proteins in immunofluorescence microscopy images as well as show how such models can integrate with other methods to gain novel insights and understanding into the roles and spatially dependent functions of proteins.
In Paper I, we present and combine two separate methods for large scale protein localization. The first method is an integration of a protein localization task as a mini-game within an established massively multiplayer online video game. The second method consists of the first image-based deep neural network learning model capable of multi-label subcellular localization classification. We show that both these methods enable accurate and scalable high-throughput analysis of subcellular protein localization that overcome many of the challenges associated with such a dataset. We also show that combining the two methods yield better results than either of them do on their own, resulting in a model that is nearing human performance.
In Paper II, based on the success of the neural network model from Paper I, we continue the investigation into usage of deep neural networks for the purpose of subcellular protein localization. In an effort to find the best possible model for such tasks, a machine learning image competition was developed. Over 2,000 teams participated with various kinds of architectures, resulting in a predictor that far outperforms the one presented in Paper I. The winning model is analyzed thoroughly, and we show that its internal feature representation contains biologically relevant information and that it can be used for quantitative analysis of protein patterns.
Paper III takes the feature representation of immunofluorescence images from the model developed in Paper II and integrates it with features extracted from affinity purification experiments to create a hierarchical map of the human cell’s architecture. This method creates a map of protein communities grouped by subcellular structures, of which approximately 54% are putatively novel. We show that the map is biologically significant by validating several of the novel findings using affinity purification experiments and in-situ fractionation.
In Paper IV, we apply what was learned in Paper I and II in order to create a model that identifies proteins residing within micronuclei. We apply the model on the image data from the Human Protein Atlas to create the first extensive mapping of the micronuclear proteome. Through enrichment analysis of the identified proteins, we propose that micronuclei harbor a more diverse set of functions than previously thought. We find that the micronuclear proteome is highly interconnected and contains many proteins that show visible variations across different micronuclei, and theorize on what this means for their role in the cell.
In conclusion, Paper I and II examine and establish the possibilities of using deep neural networks for systematic subcellular protein localization analysis. Paper III and IV build upon what was learned in Papers I and II and use their models to examine protein distribution patterns and provide novel biological insights.