000188459 001__ 188459
000188459 005__ 20230214173430.0
000188459 0247_ $$2CORDIS$$aG:(EU-Grant)682810$$d682810
000188459 0247_ $$2CORDIS$$aG:(EU-Call)ERC-2015-CoG$$dERC-2015-CoG
000188459 0247_ $$2originalID$$acorda__h2020::682810
000188459 035__ $$aG:(EU-Grant)682810
000188459 150__ $$aIntegrating spatial and genetic information via automated image analysis and interactive visualization of tissue data$$y2016-04-01 - 2022-03-31
000188459 371__ $$aUppsala University$$bUppsala University$$dSweden$$ehttps://www.uu.se/en/$$vCORDIS
000188459 372__ $$aERC-2015-CoG$$s2016-04-01$$t2022-03-31
000188459 450__ $$aTissueMaps$$wd$$y2016-04-01 - 2022-03-31
000188459 5101_ $$0I:(DE-588b)5098525-5$$2CORDIS$$aEuropean Union
000188459 680__ $$aDigital imaging of tissue samples and genetic analysis by next generation sequencing are two rapidly emerging fields in pathology. The exponential growth in digital imaging in pathology is catalyzed by more advanced imaging hardware, comparable to the complete shift from analog to digital images that took place in radiology a couple of decades ago: Entire glass slides can be digitized at near the optical resolution limits in only a few minutes’ time, and fluorescence as well as bright field stains can be imaged in parallel. 

Genetic analysis, and particularly transcriptomics, is rapidly evolving thanks to the impressive development of next generation sequencing technologies, enabling genome-wide single-cell analysis of DNA and RNA in thousands of cells at constantly decreasing costs. However, most of today’s available technologies result in a genetic analysis that is decoupled from the morphological and spatial information of the original tissue sample, while many important questions in tumor- and developmental biology require single cell spatial resolution to understand tissue heterogeneity.

The goal of the proposed project is to develop computational methods that bridge these two emerging fields. We want to combine spatially resolved high-throughput genomics analysis of tissue sections with digital image analysis of tissue morphology. Together with collaborators from the biomedical field, we propose two approaches for spatially resolved genomics; one based on sequencing mRNA transcripts directly in tissue samples, and one based on spatially resolved cellular barcoding followed by single cell sequencing. Both approaches require development of advanced digital image processing methods. Thus, we will couple genetic analysis with digital pathology. Going beyond visual assessment of this rich digital data will be a fundamental component for the future development of histopathology, both as a diagnostic tool and as a research field.
000188459 909CO $$ooai:juser.fz-juelich.de:808324$$pauthority$$pauthority:GRANT
000188459 909CO $$ooai:juser.fz-juelich.de:808324
000188459 970__ $$aoai:dnet:corda__h2020::9082fc3596e91b1f81369bdf5a07461f
000188459 980__ $$aG
000188459 980__ $$aCORDIS
000188459 980__ $$aAUTHORITY