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Diagnostic drug response-profiling using SLAMseq

Total Cost €


EC-Contrib. €






 SLAM-Dx project word cloud

Explore the words cloud of the SLAM-Dx project. It provides you a very rough idea of what is the project "SLAM-Dx" about.

guiding    secondary    day    translational    chromatin    efficacy    personalised    tool    economy    select    direct    probing    quantification    time    microarrays    input    therapeutics    sequencing    hampers    precision    gene    selectivity    drug    one    techniques    profiling    scalable    tools    greatest    limited    co    patient    variety    thiol    erc    predicting    protocol    probe    perturbations    preclinical    deciphering    diagnostic    survival    seq    distinction    mrna    expression    successful    336860    lack    limitation    vivo    slam    metabolic    commercial    preparation    therapy    rapid    dx    proof    leukemia    right    stg    severe    transcriptional    cells    clinical    unbiased    optimization    disease    implications    unprecedented    alkylation    primary    candidate    first    resolution    clinic    revolutionize    sh    precluding    utility    treatment    linked    function    96    slamseq    poc    systematic    action    qualify    medicine    format    rna    tumor    cell    decisions    overcome    library   

Project "SLAM-Dx" data sheet

The following table provides information about the project.


Organization address
city: WIEN
postcode: 1030

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 Austria [AT]
 Total cost 150˙000 €
 EC max contribution 150˙000 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2019-PoC
 Funding Scheme ERC-POC-LS
 Starting year 2019
 Duration (year-month-day) from 2019-10-01   to  2021-03-31


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 


 Project objective

One of the greatest challenges today is to select the right drug for the right patient at the right time. A lack of diagnostic tools for predicting therapy response currently hampers patient-tailored treatment decisions in the clinic with severe implications for economy and – most importantly – patient survival.

Profiling transcriptional responses to drug treatment is a key method for probing the activity of targeted therapeutics and guiding their use in the clinic. A major limitation of established gene expression profiling techniques (such as microarrays and RNA-seq) is their limited time resolution precluding the distinction of direct from secondary transcriptional responses to drug therapy. This hampers their utility for deciphering drug action and guiding patient-tailored treatment decisions.

To overcome this problem, as part of an ERC-StG project (Systematic in-vivo analysis of chromatin-associated targets in leukemia, 336860) I have co-developed thiol(SH)-linked alkylation for the metabolic sequencing of RNA (SLAMseq) as a rapid, robust and highly scalable method for the unbiased quantification of changes in mRNA production upon cell perturbations. Its unique features (low input cell numbers, short treatment-to-sample time, 1-day protocol library preparation in 96-well format) may qualify SLAMseq as the first method to probe the function, efficacy and selectivity of candidate therapeutics in primary (tumor) cells with unprecedented precision and on a large scale.

In this ERC-PoC project, I propose to establish technical and commercial proof-of-concept for SLAMseq’s application in preclinical and clinical drug development and optimization, and for patient-tailored treatment selection. Upon the successful proof-of-concept for SLAMseq’s application as diagnostic tool, SLAM-Dx has the potential to revolutionize translational research and personalised medicine in a variety of disease areas.

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The information about "SLAM-DX" are provided by the European Opendata Portal: CORDIS opendata.

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