Imaging mass cytometry in biological research and innovation
Name of applicant
Søren Riis Paludan
Institution
Aarhus University
Amount
DKK 5,000,000
Year
2020
Type of grant
Research Infrastructure
What?
In this project we will use imaging mass cytometry (IMC) to acquire unpresedented temporal and spatial resolution in the information of how specific proteins and cell types localize in healthy and pathological tissue. Combined with other high-content technologies, such as single cell sequencing this will provide deep insight into biological processes in living tissues. The new equipment will be used in projects in both biology, biomedicine, and innovation, and hence aims to translate basic discoveries to the benefit of society.
Why?
With the development of single cell sequencing, we can now also get deep information on the expression profiles from such single cells. Combined with molecular mechanistic and functional studies this has allowed uncovering of fundamental new knowledge. However, there is a critical lack of knowledge on the spatial resolution of cellular responses in tissues. This knowledge-gap prevents important advances in understanding of biological processes, but is also obstructing fast progression from discovery into innovation and personalized medical treatment. With the development of imaging mass cytometry (IMC) there is now a unique opportunity to obtain deep and high spatial resolution information on cellular phenotypes in healthy and pathological tissues, tumors, and cell culture systems.
How?
The project will be performed by 4-5 interacting research groups, each using complementary cellular and animal model systems. The Imaging Mass Cytometry equipment will be run by an AC-TAP who will soon be employed. The groups involved and the AC-TAP will together optimize the antibodies to be used, and perform the experiments. The AC-TAP will take a leading role in analysing the data in the initial phase of the project, and will later train key personel from the labs involved. The results obtained will be intersected with other types of high-concent data (e.g. single cell RNA seq) to increase the depth of the analysis.