Proteomics-based scoring of cellular response to stimuli for improved characterization of signaling pathway activity.
Elizaveta M KazakovaElizaveta M SolovyevaLev I LevitskyJulia A BubisDaria D EmekeevaAnastasia A AntonetsAlexey A NazarovMikhail V GorshkovIrina A TarasovaPublished in: Proteomics (2022)
Omics technologies focus on uncovering the complex nature of molecular mechanisms in cells and organisms, including biomarkers and drug targets discovery. Aiming at these tasks, we see that information extracted from omics data is still underused. In particular, characteristics of differentially regulated molecules can be combined in a single score to quantify the signaling pathway activity. Such a metric can be useful for comprehensive data interpretation to follow: 1) developing molecular responses in time; 2) potency of a drug on a certain cell culture; 3) ranking the signaling pathway activity in stimulated cells; and 4) integration of the omics data and assay-based measurements of cell viability, cytotoxicity and proliferation. With recent advances in ultrafast mass spectrometry for quantitative proteomics allowing data collection in a few minutes, proteomics score for cellular response to stimuli can become a fast, accurate and informative complement to bioassays. Here we utilized an interquartile-based selection of differentially regulated features and a variety of schemes for quantifying cellular responses to come up with the quantitative metric for total cellular response and pathway activity. Validation was performed using antiproliferative and virus assays and label-free proteomics data collected for cancer cells subjected to drug stimulation. This article is protected by copyright. All rights reserved.
Keyphrases
- signaling pathway
- mass spectrometry
- label free
- induced apoptosis
- electronic health record
- big data
- pi k akt
- high resolution
- cell cycle arrest
- epithelial mesenchymal transition
- high throughput
- single cell
- machine learning
- oxidative stress
- healthcare
- endoplasmic reticulum stress
- emergency department
- adverse drug
- cell proliferation
- data analysis
- capillary electrophoresis
- drug induced
- artificial intelligence
- ms ms