Researchers have combined surface-enhanced Raman scattering sensors with 3D-printed breast cancer models to map cellular pH changes following drug treatment, chemical damage, and plasmonic heating. The approach could support laboratory studies of treatment response, cell viability, and communication within advanced tissue models, while providing a platform for evaluating how local chemical conditions change across 3D structures.
The in vitro system was developed to address a measurement challenge created by increasingly complex 3D tissue models. Although these models can reproduce aspects of tissue architecture more closely than two-dimensional cultures, monitoring chemical changes within them remains difficult because cells are distributed across several depths and locations.
Gail A. Vinnacombe-Willson and colleagues loaded breast cancer cells with pH-responsive SERS nanotags before placing the cells on a 3D-printed hydrogel scaffold. The nanotags consisted of polymer-coated gold nanostars functionalized with 4-mercaptobenzoic acid, a Raman reporter whose spectrum changes with pH.
SERS uses nanostructured metal surfaces to enhance Raman signals, allowing molecular information to be collected from small regions. In this study, changes in the reporter molecule’s spectral bands provided a ratiometric measure of local pH. Encapsulating the gold nanostars within a polymer layer helped create a cellular sensor that could be incorporated into the model before stimulation.
The researchers exposed the models to three types of stress: a chemotherapeutic agent, a cytotoxic surfactant, and light-induced plasmonic heating. The nanotags detected increases in pH after each treatment, although the timing varied from minutes to days depending on the stimulus.
Independent cytotoxicity testing linked these pH increases with damage to the cell membrane. This suggests that the sensor can report biological changes associated with loss of cell integrity rather than simply recording the presence of an external treatment.
The team acquired SERS maps with subcellular spatial resolution across three-dimensional regions measuring several millimeters. Machine-learning analysis was then used to identify pH-related changes within the Raman spectra and follow their distribution through the models.
The work shows how responsive SERS tags can be integrated into engineered tissue systems rather than applied only to flat cultures or isolated samples. The combination provides chemical, spatial, and time-dependent information from locations that may be difficult to examine using conventional probes.
Future work will need to assess calibration stability, sensor-to-sensor variation, biological effects of the nanotags, and performance in models containing additional cell types and extracellular components.
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