The spatiotemporal protein landscape of human cells
Time: Fri 2021-11-12 13.00
Location: K1, Teknikringen 56, Zoom: https://kth-se.zoom.us/j/67398971556?pwd=K3ZNM1I1T1Z6Y0RyLzExMUF6OHUvUT09, Stockholm (English)
Subject area: Biotechnology
Doctoral student: Lovisa Stenström , Cellulär och klinisk proteomik, Science for Life Laboratory, SciLifeLab
Opponent: Professor Björn Högberg, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
Supervisor: Professor Emma Lundberg, Cellulär och klinisk proteomik, Science for Life Laboratory, SciLifeLab, Albanova VinnExcellence Center for Protein Technology, ProNova
This thesis focuses on the spatiotemporal mapping of proteins at a subcellular level. In other words, determining the cellular location of proteins over time. From a biological point of view, knowledge about protein location is fundamental to understand protein function. In the longer run, this also means better understanding of cells in the context of health and disease, since protein malfunction and mislocalization are two important factors during disease development.
Using an antibody-based imaging approach, Paper I contains a subcellular map of 12 003 protein in 30 different cellular structures, freely accessible as part of the Human Protein Atlas (www.proteinatlas.org). Apart from enabling exploration of the organellar proteomes, we conclude that half of the human proteins localize to multiple compartments, and that almost one fifth display cell-to-cell variations in terms of protein expression. Paper II aimed to decrease the cumbersome work of manual protein location annotations by leveraging the power of the crowd through citizen science. By integrating the image-classification task into a video game with a massive player base, EVE online, protein location labels could be efficiently and rapidly assessed compared to manual curation from a few experts. To compare the performance of the players, a deep learning classifier was developed. The algorithm was capable of classifying protein location in images containing several challenging localization problems, such as multilocalizing proteins, cell line variations and rare classes. Using the protein location data from Paper I and Paper II, Paper III presents an image-based characterization of the nucleolar proteome. In total, 1 318 nucleolar proteins are included, of which 157 localizes to a fourth nucleolar compartment, the nucleolar rim. Additionally, 65 proteins were detected on the chromosomal periphery during mitosis, and these could be further divided into two recruitment phenotypes with different temporal profiles. Also, the mitotic chromosome proteins are enriched for intrinsically disordered domains, suggesting liquid-like properties of the perichromosomal layer. Paper IV presents a systematic dissection of the variable proteome drafted in Paper I. We show evidence for 539 proteins being correlated to cell cycle variations, of which a minority are also cycling at a transcriptional level, suggesting protein regulation at a translational or post-translational level. Additionally, we detected hundreds of proteins with previously unknown relations to mitosis and the cell cycle, many being linked to proliferation and oncogenic functions.
In conclusion, Paper I and Paper II provide a basis for further in-depth studies of proteins at a subcellular level, while Paper III and Paper IV show how this resource can be used to study proteins in space and time. The results enable system-level investigation of protein dynamics, as well as provide exciting insights into organellar organization, such as the nucleolus.