H2020Индивидуална стипендия2019–2021

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

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период: 2019-04-01 → 2021-03-31
Финансиране от ЕС: 207 312 €
Участници: 1
Схема: MSCA-IF-EF-ST

Линиите свързват координатора с партньорите.

Накратко на български

Протеините в отделните клетки се анализират чрез микроскопия, изкуствен интелект и масспектрометрия, например при ракови клетки, устойчиви на химиотерапия. Това помага да се разбере заболяването на молекулярно ниво и да се открият слабите места в него за по-добра терапия.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

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

Proteins are the functional building blocks of life that determine health and disease states and constitute important drug targets. If a person gets sick and needs to see a doctor, it is because of a protein imbalance in our cells. The doctor then looks for irregularities in certain proteins to find out if there is inflammation in our body. In cancer for example, when chemotherapy does not work, it is because some of the cancer cells show changes in their protein repertoire, which can make them chemoresistant. The analysis of all proteins in a diseased tissue, the so-called proteome, is therefore of enormous value to understand the disease on a molecular level and to identify therapeutic vulnerabilities. The most widely used method to comprehensively study all the proteins in a biological system is mass spectrometry (MS) based proteomics. Using MS based proteomics to dissect a chemoresistant tumor and compare the sick tissue with healthy tissue from the same patient allows us to identify the proteins, which are most critical to the disease. However, the disease related proteome is complex, and analytical challenges exist due to the presence of different cell types and cellular states that can differentially promote disease progression or drug resistance. Classic approaches only provide average proteome descriptions of the disease as no methods exist to dissect and analyze the tissue with respect to cellular subsets. The goal of the fellowship was therefore to develop an innovative MS based method that would address these limitations to obtain fine-resolved molecular maps of the disease related proteome. To achieve this, we married high-resolution microscopy with artificial intelligence guided image analysis and ultra-high sensitivity MS based proteomics. For the first time, this new ‘visual proteomics’ concept combines the visual dimension with the molecular phenotype and is generically applicable to cell cultures and patient biobank specimens. Our method represents an exciting new tool for biomedical research for biomarker discovery and next-level molecular disease profiling on the protein level.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

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.

Оригинален текст от CORDIS (на английски).

Участници

KOBENHAVNS UNIVERSITET · KOBENHAVNКоординаторДания