Opendata, web and dolomites

Visual Proteomics SIGNED

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

Total Cost €

0

EC-Contrib. €

0

Partnership

0

Views

0

 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.

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

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.

Are you the coordinator (or a participant) of this project? Plaese send me more information about the "VISUAL PROTEOMICS" project.

For instance: the website url (it has not provided by EU-opendata yet), the logo, a more detailed description of the project (in plain text as a rtf file or a word file), some pictures (as picture files, not embedded into any word file), twitter account, linkedin page, etc.

Send me an  email (fabio@fabiodisconzi.com) and I put them in your project's page as son as possible.

Thanks. And then put a link of this page into your project's website.

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

More projects from the same programme (H2020-EU.1.3.2.)

NSTree (2020)

Understanding substrate delivery for cell wall biosynthesis in plants

Read More  

EngPTC2 (2019)

Exploring new technologies for the next generation pulse tube cryocooler below 2K

Read More  

BB-SLM (2020)

Polychromatic digital optics for structured light

Read More