pH-sensing hybrid hydrogels for non-invasive metabolism monitoring in tumor spheroids.

The constant increase in cancer incidence and mortality pushes biomedical research towards the development of 3D systems able to faithfully reproduce and effectively probe the tumor microenvironment. Cancer cells interact with this complex and dynamic architecture, leading to peculiar tumor-associated phenomena, such as acidic pH conditions, rigid extracellular matrix, altered vasculature, hypoxic condition. Acidification of extracellular pH, in particular, is a well-known feature of solid tumors, correlated to cancer initiation, progression, and resistance to therapies. Monitoring local pH variations, non-invasively, during cancer growth and in response to drug treatment becomes extremely important for understanding cancer mechanisms. Here, we describe a simple and reliable pH-sensing hybrid system, based on a thermoresponsive hydrogel embedding optical pH sensors, that we specifically apply for non-invasive and accurate metabolism monitoring in colorectal cancer (CRC) spheroids. First, the physico-chemical properties of the hybrid sensing platform, in terms of stability, rheological and mechanical properties, morphology and pH sensitivity, were fully characterized. Then, the proton gradient distribution in the spheroids proximity, in the presence or absence of drug treatment, was quantified over time by time lapse confocal light scanning microscopy and automated segmentation pipeline, highlighting the effects of the drug treatment in the extracellular pH. In particular, in the treated CRC spheroids the acidification of the microenvironment resulted faster and more pronounced over time. Moreover, a pH gradient distribution was detected in the untreated spheroids, with more acidic values in proximity of the spheroids, resembling the cell metabolic features observed in the tumor microenvironment. These findings promise to shed light on mechanisms of regulation of proton exchanges by cellular metabolism being essential for the study of solid tumors in 3D models and the development of personalized medicine approaches.