TissueMaps SIGNED
Integrating spatial and genetic information via automated image analysis and interactive visualization of tissue data
Total Cost €
0EC-Contrib. €
0Partnership
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0TissueMaps project word cloud
Explore the words cloud of the TissueMaps project. It provides you a very rough idea of what is the project "TissueMaps" about.
Project "TissueMaps" data sheet
The following table provides information about the project.
| Coordinator |
UPPSALA UNIVERSITET
Organization address contact info |
| Coordinator Country | Sweden [SE] |
| Project website | https://tissuumaps.research.it.uu.se |
| Total cost | 1˙738˙690 € |
| EC max contribution | 1˙738˙690 € (100%) |
| Programme |
1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC)) |
| Code Call | ERC-2015-CoG |
| Funding Scheme | ERC-COG |
| Starting year | 2016 |
| Duration (year-month-day) | from 2016-04-01 to 2021-03-31 |
Partnership
Take a look of project's partnership.
| # | ||||
|---|---|---|---|---|
| 1 | UPPSALA UNIVERSITET | SE (UPPSALA) | coordinator | 1˙738˙690.00 |
Map
Project objective
Digital 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.
Publications
| year | authors and title | journal | last update |
|---|---|---|---|
| 2017 |
Jordi Carreras-Puigvert, Marinka Zitnik, Ann-Sofie Jemth, Megan Carter, Judith E. Unterlass, Björn Hallström, Olga Loseva, Zhir Karem, José Manuel Calderón-Montaño, Cecilia Lindskog, Per-Henrik Edqvist, Damian J. Matuszewski, Hammou Ait Blal, Ronnie P. A. Berntsson, Maria Häggblad, Ulf Martens, Matthew Studham, Bo Lundgren, Carolina Wählby, Erik L. L. Sonnhammer, Emma Lundberg, Pål Stenmar A comprehensive structural, biochemical and biological profiling of the human NUDIX hydrolase family published pages: , ISSN: 2041-1723, DOI: 10.1038/s41467-017-01642-w |
Nature Communications 8/1 | 2019-10-09 |
| 2017 |
Damian J. Matuszewski, Anders Hast, Carolina Wählby, Ida-Maria Sintorn A short feature vector for image matching: The Log-Polar Magnitude feature descriptor published pages: e0188496, ISSN: 1932-6203, DOI: 10.1371/journal.pone.0188496 |
PLOS ONE 12/11 | 2019-10-09 |
| 2018 |
Giorgia Milli Improving recall of In situ sequencing by self-learned features and classical image analysis techniques published pages: , ISSN: , DOI: |
2019-10-09 | |
| 2017 |
Sajith Kecheril Sadanandan, Petter Ranefall, Sylvie Le Guyader, Carolina Wählby Automated Training of Deep Convolutional Neural Networks for Cell Segmentation published pages: , ISSN: 2045-2322, DOI: 10.1038/s41598-017-07599-6 |
Scientific Reports 7/1 | 2019-06-18 |
| 2019 |
Anindya Gupta, Philip J. Harrison, HÃ¥kan Wieslander, Nicolas Pielawski, Kimmo Kartasalo, Gabriele Partel, Leslie Solorzano, Amit Suveer, Anna H. Klemm, Ola Spjuth, Idaâ€Maria Sintorn, Carolina Wählby Deep Learning in Image Cytometry: A Review published pages: 366-380, ISSN: 1552-4922, DOI: 10.1002/cyto.a.23701 |
Cytometry Part A 95/4 | 2019-06-06 |
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