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TissueMaps SIGNED

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

Total Cost €

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EC-Contrib. €

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Partnership

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Project "TissueMaps" data sheet

The following table provides information about the project.

Coordinator
UPPSALA UNIVERSITET 

Organization address
address: VON KRAEMERS ALLE 4
city: UPPSALA
postcode: 751 05
website: www.uu.se

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 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.

# participants  country  role  EC contrib. [€] 
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
List of publications.
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, Håkan Wieslander, Nicolas Pielawski, Kimmo Kartasalo, Gabriele Partel, Leslie Solorzano, Amit Suveer, Anna H. Klemm, Ola Spjuth, Ida‐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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The information about "TISSUEMAPS" are provided by the European Opendata Portal: CORDIS opendata.

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