scLearning

The pandemic caused by the SARS-CoV-2 coronavirus has forced the scientific community to accelerate the lines of research on the key molecular mechanisms caused by diseases, as well as to prepare the necessary tools for future pandemics.

SC-LEARN-CM focuses, first of all, on finding answers to still open questions about COVID-19, in particular, on finding the key molecular mechanisms that, due to their high relationship with aging and with previous cardiac pathologies, give rise to responses of the organism of very varied severity to the coronavirus. To carry it out, the project proposes a study with the highest level of detail that existing technologies allow to achieve, designing and applying supervised machine learning techniques from: (1) clinical patient data; and (2) data obtained by sequencing, at the single cell and multi-omic levels, samples of both heart tissue and human blood. The study covers a cohort of 200 individuals stratified by age and heart disease.

Massive data analysis will make it possible to find these molecular targets and biomarkers in blood for their diagnosis in humans. It is necessary to correlate these human mechanisms with those of the animal model corresponding to the subsequent preclinical phase, aimed at the design of possible treatments. For this reason, the project also applies the same multi-omic individual cell sequencing techniques and super massive data analysis previously mentioned on an experimental model of systemic inflammatory response stratified by age that mimics the acute complications and sequelae caused by COVID-19. 19, with the ultimate goal of finding differential and specific treatments to stop them.