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Report

Teaser, summary, work performed and final results

Periodic Reporting for period 2 - TissueMaps (Integrating spatial and genetic information via automated image analysis and interactive visualization of tissue data)

Teaser

Summary

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 todayâ€™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

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.

Final results

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.