Human protein atlas single cell competition

Just like people, all cells are different in terms of their morphology (shape, size) and they can also differ in the spatial distribution of their proteins. This is called single cell variability (SCV). The phenomenon of how genetically identical cells can show functional heterogeneity (for example in response to drug treatment), is poorly understood (Human Protein Atlas, 2021). This competition focusses on getting a better understanding of the functional heterogeneity of cells.

There is a lot of cellular heterogeneity between humans. Proteins play essential roles in virtually all cellular processes, emphasising the importance of tracking proteins in a single human cell to enable the discovery of mechanisms too difficult to see with multi-cell research. Current machine learning models for classifying protein localization patterns in microscope images are aimed at summarizing entire cell populations. However, the single-cell revolution in biology demands machine learning models that can precisely classify patterns in each individual cell in an image. Therefore, the Human Protein Atlas initiative organized the single-cell localization challenge. The goal is to be able to more accurately model the spatial organization of the human cell and to provide new open-access cellular data to the scientific community.