Integrating spatial and genetic information via automated image analysis and interactive visualization of tissue data

Project Information

TissueMaps

Grant agreement ID: 682810

DOI

10.3030/682810

Project closed

EC signature date 14 March 2016

Start date 1 April 2016

End date 31 March 2022

Funded under

• EXCELLENT SCIENCE - European Research Council (ERC)

Total cost

€ 1 738 690,00

EU contribution

€ 1 738 690,00

Coordinated by

UPPSALA UNIVERSITET
Sweden

Periodic Reporting for period 4 - TissueMaps (Integrating spatial and genetic information via automated image analysis and interactive visualization of tissue data)

Reporting period: 2020-10-01 to 2022-03-31

Summary of the context and overall objectives of the project

The key question of the TissUUmaps project is the how the relationship between single cell gene expression and tissue morphology is involved in health and disease. Digital imaging of tissue samples (digital pathology) and genetic analysis by next generation sequencing are two rapidly emerging fields in pathology. 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. Within the TissUUmaps project we develop computational methods that bridge single cell gene expression with tissue morphology by combining spatially resolved high-throughput genomics analysis (in situ RNA sequencing) of tissue sections with digital image analysis of tissue morphology. 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.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

Currently ongoing work in the TissUUmaps project includes (i) development of new robust and efficient decoding of in situ RNA sequencing signals in 2D and 3D, (ii) development of tools for visualization of decoded signals in relation to the underlying tissue, in large scale, (iii) development of tools for combining patterns from multiple molecular detection methods, and (iv) development of deep learning-based approaches to automatically detect and classify cells and local tissue morphology.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

The TissUUmaps project has already contributed to the development of new techniques for signal detection and web-based visualization and annotations of large whole slide images of tissue. Large focus is on relating gene expression in health and disease to tissue morphology, and deep learning approaches.

Mapping of gene expression, each colore representing one pattern, using TissUUmaps and spage2vec.