Metabolic Footprinting-Based DNA-AuNP Encoders for Extracellular Metabolic Response Profiling.
Guangpei QiHaixia ZouXiaohong PengShiliang HeQiqi ZhangWei YeYizhou JiangWentao WangGuangli RenXiangmeng QuPublished in: Analytical chemistry (2023)
Metabolic footprinting as a convenient and non-invasive cell metabolomics strategy relies on monitoring the whole extracellular metabolic process. It covers nutrient consumption and metabolite secretion of in vitro cell culture, which is hindered by low universality owing to pre-treatment of the cell medium and special equipment. Here, we report the design and a variety of applicability, for quantifying extracellular metabolism, of fluorescently labeled single-stranded DNA (ssDNA)-AuNP encoders, whose multi-modal signal response is triggered by extracellular metabolites. We constructed metabolic response profiling of cells by detecting extracellular metabolites in different tumor cells and drug-induced extracellular metabolites. We further assessed the extracellular metabolism differences using a machine learning algorithm. This metabolic response profiling based on the DNA-AuNP encoder strategy is a powerful complement to metabolic footprinting, which significantly applies potential non-invasive identification of tumor cell heterogeneity.
Keyphrases
- single cell
- machine learning
- drug induced
- ms ms
- liver injury
- circulating tumor
- artificial intelligence
- cell therapy
- mass spectrometry
- deep learning
- induced apoptosis
- computed tomography
- endoplasmic reticulum stress
- smoking cessation
- mesenchymal stem cells
- combination therapy
- pet ct
- neural network
- bone marrow
- pet imaging
- replacement therapy