There is strength in numbers
Bringing together hundreds of thousands of microscopy images from various experiments helps to understand how cells cope with changes
Cells are busy structures in which proteins move from one place to the other to perform their roles. To survive, organisms must constantly adapt to changes, such as mutations, new environments, or encountering drugs. Often, these adaptive responses involve groups of proteins traveling to new locations in the cell. Tracking these movements is useful to understand which biological processes cells activate to cope with modifications.
To study these mechanisms, scientists conduct experiments where they expose cells to one type of change — for example they mutate one gene, or give one drug. They then follow the proteins in the cell using powerful microscopes.
These instruments can take thousands of pictures of cells every day, which results in large amounts of data. The images are then analyzed, which is often subjective, time-consuming or cannot easily be repeated on other experiments. This prevents researchers from considering more than one experiment at the time, and it makes it difficult to compare how cells respond across many different types of changes.
Here, Lu et al. combine and analyze 400,000 images from 24 experiments conducted on yeasts; they use a computational method that automatically measures the differences in the locations of the proteins between experiments. This new large-scale approach reveals aspects of cell biology that are not obvious from the results of any one experiment alone. For example, responses that are specific to one type of change can be distinguished from the ones that occur each time cells are exposed to new conditions. It also becomes possible, for the first time, to group together proteins that move in the same way across different types of changes, and to infer their roles. For instance, a protein was shown to be ‘pulsing’ — moving quickly between two specific compartments in the cell — based on how it shared certain features with other pulsing proteins.
This computational approach is not limited to experiments on yeasts, and could be used on studies in human cells as well. Drug companies could then compare at a large scale how different treatments affect protein localization.
To find out more
Read the eLife research paper on which this eLife digest is based:
