Label-free mapping of cetuximab in multi-layered tumor oral mucosa models by atomic force-microscopy-based infrared spectroscopy.
Gregor GermerLeonie SchwartzeJill García-MillerRoberta Balansin-RigonLucie J GrothIsabel RühlPiotr PatokaChristian ZoschkeEckart RühlPublished in: The Analyst (2024)
Sensitive mapping of drugs and drug delivery systems is pivotal for the understanding and improvement of treatment options. Since labeling alters the physicochemical and potentially the pharmacological properties of the molecule of interest, its label-free detection by photothermal expansion is investigated. We report on a proof-of-concept study to map the cetuximab distribution by atomic-force microscopy-based infrared spectroscopy (AFM-IR). The monoclonal antibody cetuximab was applied to a human tumor oral mucosa model, consisting of a tumor epithelium on a lamina propria equivalent. Hyperspectral imaging in the wavenumber regime between 903 cm -1 and 1312 cm -1 and a probing distance between the data points down to 10 × 10 nm are used for determining the local drug distribution. The local distinction of cetuximab from the tissue background is gained by linear combination modeling making use of reference spectra of the drug and untreated models. The results from this approach are compared to principal component analyses, yielding comparable results. Even single molecule detection appears feasible. The results indicate that cetuximab penetrates the cytosol of tumor cells but does not bind to structures in the cell membrane. In conclusion, AFM-IR mapping of cetuximab proved to sensitively determine drug concentrations at an unprecedented spatial resolution without the need for drug labeling.
Keyphrases
- atomic force microscopy
- single molecule
- label free
- metastatic colorectal cancer
- high resolution
- high speed
- wild type
- locally advanced
- monoclonal antibody
- living cells
- high density
- endothelial cells
- photodynamic therapy
- adverse drug
- drug induced
- rectal cancer
- drug delivery
- machine learning
- fluorescence imaging
- cancer therapy
- deep learning
- fluorescent probe
- pluripotent stem cells