Researchers from Sinai Health have published a study providing an ultra-detailed look at the organization of a living human cell, providing a new tool that can help scientists around the world better understand what happens during disease.
The new study, out today in the journal Nature, was conducted in the laboratory of Dr. Anne-Claude Gingras, a senior investigator at the LTRI and professor in the Department of Molecular Genetics at the University of Toronto.
The human body is composed of trillions of cells that are each further separated into different compartments with dedicated functions, much like a house has different rooms for sleeping or preparing food. These compartments, which are called organelles, each contain different proteins that perform specific activities associated with the compartment. Prominent examples of organelles include the mitochondria, responsible for generating the cell's primary energy supply, and the endoplasmic reticulum, a key structure for protein production.
Knowing which proteins are residing in which organelles is an important first step towards understanding the role of each cellular protein. However, approaches taken previously have most often relied on methods that first kill the cells before trying to separate the organelles from one another (akin to taking the house apart and trying to isolate each of the individual rooms). These approaches tend to provide only crude views of the organization of a cell.
The new study, called the “Human Cell Map”, was undertaken by co-first authors Christopher D. Go and Dr. James D. R. Knight, who surveyed the human cell landscape using 192 markers for proteins known to reside in specific organelles that can “tag” neighbouring proteins in the same compartment.
“The Human Cell Map was able to predict the localization of 4000 proteins across all compartments in living human cells,” explains Go. “We sampled all the major organelles of the human cell and used innovative analysis to create the highest resolution map to date, with high accuracy in predicting novel localizations for many unmapped proteins.
The Gingras lab develops tools to detect proteins using instruments known as mass spectrometers. In the new study, they have purified the proteins that are “tagged” by organelle markers and identified each of them by mass spectrometry. Computational tools were next used to reconstruct the organization of a human cell.
“Through our research, we have shown that we can precisely localize thousands of proteins at a time with relatively little effort,” said Dr. Knight, a bioinformatician in the Gingras Lab at the LTRI. “Previous methods for localizing a protein required each protein to be investigated individually or required a limited focus.” Given the expansive nature of the Human Cell Map, the team also created an analysis portal to allow researchers around the world to delve deeper into the data. Users can scan each of the 192 markers in detail and compare their own data on protein localization to predictions made in the Human Cell Map.
Knight said while this work provides a greater understanding of the organization within the human cell, it also can be leveraged to better understand what happens during disease.
“Human diseases are typically characterized at the molecular level by proteins with aberrant behaviour that cause the cell to behave in pathological ways. In these situations, proteins will often change where they reside in the cell,” said Dr. Knight. “Our research is a first step in addressing this challenge in normal cells and we can use it for comparisons against altered cell states, such as disease conditions, to identify proteins with unexpected localizations that may help us understand a diseased cell better.”
The team said the map will now be used in a variety of projects to help shed additional light on protein localization in human cells. Future efforts will include using chemical, viral and disease conditions to better characterize how cells adapt structurally to these stressors. This can inform future research efforts towards a mechanistic understanding of diseased states and the development of future therapeutics.
This work was made possible by a Canadian Institutes of Health Research grant, as well as funding to the Network Biology Collaborative Centre at the LTRI, a facility supported by the Canada Foundation for Innovation, the Ontario Government, Genome Canada and Ontario Genomics. Funding for the analysis portal was provided by Compute Canada. Funding of Go’s stipend was partially provided through a CIHR Banting studentship.
