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Visual Proteomics SIGNED

Biomarker discovery by AI-guided, image based single-cell isolation proteomics

Total Cost €

0

EC-Contrib. €

0

Partnership

0

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 Visual Proteomics project word cloud

Explore the words cloud of the Visual Proteomics project. It provides you a very rough idea of what is the project "Visual Proteomics" about.

optimize    correlate    acquisition    career    perform    intelligence    cells    receive    tissue    world    discoveries    cellular    stimulate    expression    host    proteomics    critically    microscopy    isolated    workflow    clinical    competitive    samples    candidates    proteome    early    molecular    ubiquitous    descriptions    prediction    outcome    individually    resolution    impede    training    ffpe    identity    individual    microscopic    limited    expertise    image    disease    cell    severe    edge    inspire    heterogeneity    class    pathology    malignancies    guided    techniques    shown    biomarker    proteins    prospective    critical    retrospective    learning    tumor    complement    microdissection    outside    diagnosis    followed    treatment    pipeline    laboratory    biobank    map    personalized    patient    laser    niche    fertile    exploits    sensitivity    ground    tissues    artificial    profiling    solid    me    automated    composition    unprecedented    unresolved    averaged    archival    therapies    diseases    collaborative    detection    cancer    populations    array    biomarkers    machine    survival    morphology    cutting    attempt    protein    form   

Project "Visual Proteomics" data sheet

The following table provides information about the project.

Coordinator
KOBENHAVNS UNIVERSITET 

Organization address
address: NORREGADE 10
city: KOBENHAVN
postcode: 1165
website: www.ku.dk

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 Denmark [DK]
 Total cost 207˙312 €
 EC max contribution 207˙312 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2018
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2019
 Duration (year-month-day) from 2019-04-01   to  2021-03-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    KOBENHAVNS UNIVERSITET DK (KOBENHAVN) coordinator 207˙312.00

Map

 Project objective

Early detection of severe malignancies such as cancer is the most effective way to increase patient survival, but early diagnosis and prediction of treatment outcome critically depend on disease-specific biomarkers. However, molecular and cellular disease heterogeneity provide a ubiquitous and unresolved challenge to this important task, and therefore impede any attempt to develop personalized therapies. Past and current approaches provide “averaged” descriptions of the tumor composition and have shown very limited success to identify biomarkers. This is likely due to the failure of these methods to identify the critical disease promoting cell populations within the tumor. Therefore, I will develop a new workflow that exploits automated microscopic image acquisition and artificial-intelligence-guided image analysis to identify specific cell populations in patient samples. These cells are then individually isolated by laser microdissection, followed by high-sensitivity proteome profiling, to identify proteins that define the identity of individual cells in a given tumor and thus represent the most promising biomarker candidates. To apply my approach to wide array of diseases, I will optimize it for archival biobank tissues (FFPE), the most common form of solid tissues in pathology. Applied to FFPE samples, my approach will allow me to perform both prospective and retrospective studies, correlate disease state and tissue morphology to protein expression and clinical outcome, and map tumor heterogeneity with unprecedented resolution. To achieve this, I will receive world-class training in cutting-edge microscopy and machine learning techniques in my host laboratory, which I complement with my expertise in high-sensitivity proteomics. My new pipeline will offer a highly fertile ground for new biomarker discoveries, inspire and stimulate collaborative research within and outside the host institute and allow me to establish a highly competitive niche for my future career.

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The information about "VISUAL PROTEOMICS" are provided by the European Opendata Portal: CORDIS opendata.

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