Multiplex Determination of Glycan Profiles on Urinary Prostate-Specific Antigen by Quartz-Crystal Microbalance Combined with Surface-Enhanced Raman Scattering.

Prostate cancer remains a major health concern, with prostate-specific antigen (PSA) being a key biomarker for its detection and monitoring. However, PSA levels often fall into a "gray zone", where PSA levels are not clearly indicative of cancer, thus complicating early diagnosis and treatment decisions. Glycosylation profiles, which often differ between healthy and diseased cells, have emerged as potential biomarkers to enhance the specificity and sensitivity of cancer diagnosis in these ambiguous cases. We propose the integration of two complementary techniques, namely quartz-crystal microbalance with dissipation (QCM-D) and surface-enhanced Raman scattering (SERS) to study PSA glycan profiles. QCM-D offers real-time operation, PSA mass quantification, and label-free detection with high sensitivity, as well as enhanced specificity and reduced cross-reactivity when using nucleic acid aptamers as capture ligands. Complementary SERS sensing enables the determination of the glycosylation pattern on PSA, at low concentrations and without the drawbacks of photobleaching, thereby facilitating multiplexed glycosylation pattern analysis. This integrated setup could retrieve a data set comprising analyte concentrations and associated glycan profiles in relevant biological samples, which may eventually improve early disease detection and monitoring. Prostate-specific antigen (PSA), a glycoprotein secreted by prostate epithelial cells, serves as our proof-of-concept analyte. Our platform allows multiplex targeting of PSA multiplex glycosylation profiles of PSA at "gray zone" concentrations for prostate cancer diagnosis. We additionally show the use of SERS for glycan analysis in PSA secreted from prostate cancer cell lines after androgen-based treatment. Differences in PSA glycan profiles from resistant cell lines after androgen-based treatment may eventually improve cancer treatment.

A Scaffold-Assisted 3D Cancer Cell Model for Surface-Enhanced Raman Scattering-Based Real-Time Sensing and Imaging.

Despite recent advances in the development of scaffold-based three-dimensional (3D) cell models, challenges persist in imaging and monitoring cell behavior within these complex structures due to their heterogeneous cell distribution and geometries. Incorporating sensors into 3D scaffolds provides a potential solution for real-time, in situ sensing and imaging of biological processes such as cell growth and disease development. We introduce a 3D printed hydrogel-based scaffold capable of supporting both surface-enhanced Raman scattering (SERS) biosensing and imaging of 3D breast cancer cell models. The scaffold incorporates plasmonic nanoparticles and SERS tags, for sensing and imaging, respectively. We demonstrate the scaffold's adaptability and modularity in supporting breast cancer spheroids, thereby enabling spatial and temporal monitoring of tumor evolution.

SERS analysis of cancer cell-secreted purines reveals a unique paracrine crosstalk in MTAP-deficient tumors.

The tumor microenvironment (TME) is a dynamic pseudoorgan that shapes the development and progression of cancers. It is a complex ecosystem shaped by interactions between tumor and stromal cells. Although the traditional focus has been on the paracrine communication mediated by protein messengers, recent attention has turned to the metabolic secretome in tumors. Metabolic enzymes, together with exchanged substrates and products, have emerged as potential biomarkers and therapeutic targets. However, traditional techniques for profiling secreted metabolites in complex cellular contexts are limited. Surface-enhanced Raman scattering (SERS) has emerged as a promising alternative due to its nontargeted nature and simplicity of operation. Although SERS has demonstrated its potential for detecting metabolites in biological settings, its application in deciphering metabolic interactions within multicellular systems like the TME remains underexplored. In this study, we introduce a SERS-based strategy to investigate the secreted purine metabolites of tumor cells lacking methylthioadenosine phosphorylase (MTAP), a common genetic event associated with poor prognosis in various cancers. Our SERS analysis reveals that MTAP-deficient cancer cells selectively produce methylthioadenosine (MTA), which is taken up and metabolized by fibroblasts. Fibroblasts exposed to MTA exhibit: i) molecular reprogramming compatible with cancer aggressiveness, ii) a significant production of purine derivatives that could be readily recycled by cancer cells, and iii) the capacity to secrete purine derivatives that induce macrophage polarization. Our study supports the potential of SERS for cancer metabolism research and reveals an unprecedented paracrine crosstalk that explains TME reprogramming in MTAP-deleted cancers.

Machine Learning-Assisted High-Throughput SERS Classification of Cell Secretomes.

During the response to different stress conditions, damaged cells react in multiple ways, including the release of a diverse cocktail of metabolites. Moreover, secretomes from dying cells can contribute to the effectiveness of anticancer therapies and can be exploited as predictive biomarkers. The nature of the stress and the resulting intracellular responses are key determinants of the secretome composition, but monitoring such processes remains technically arduous. Hence, there is growing interest in developing tools for noninvasive secretome screening. In this regard, it has been previously shown that the relative concentrations of relevant metabolites can be traced by surface-enhanced Raman scattering (SERS), thereby allowing label-free biofluid interrogation. However, conventional SERS approaches are insufficient to tackle the requirements imposed by high-throughput modalities, namely fast data acquisition and automatized analysis. Therefore, machine learning methods were implemented to identify cell secretome variations while extracting standard features for cell death classification. To this end, ad hoc microfluidic chips were devised, to readily conduct SERS measurements through a prototype relying on capillary pumps made of filter paper, which eventually would function as the SERS substrates. The developed strategy may pave the way toward a faster implementation of SERS into cell secretome classification, which can be extended even to laboratories lacking highly specialized facilities.

Nanocomposite Scaffolds for Monitoring of Drug Diffusion in Three-Dimensional Cell Environments by Surface-Enhanced Raman Spectroscopy.

Monitoring dynamic processes in complex cellular environments requires the integration of uniformly distributed detectors within such three-dimensional (3D) networks, to an extent that the sensor could provide real-time information on nearby perturbations in a non-invasive manner. In this context, the development of 3D-printed structures that can function as both sensors and cell culture platforms emerges as a promising strategy, not only for mimicking a specific cell niche but also toward identifying its characteristic physicochemical conditions, such as concentration gradients. We present herein a 3D cancer model that incorporates a hydrogel-based scaffold containing gold nanorods. In addition to sustaining cell growth, the printed nanocomposite inks display the ability to uncover drug diffusion profiles by surface-enhanced Raman scattering, with high spatiotemporal resolution. We additionally demonstrate that the acquired information could pave the way to designing novel strategies for drug discovery in cancer therapy, through correlation of drug diffusion with cell death.

Double-Edged Lipid Nanoparticles Combining Liposome-Bound TRAIL and Encapsulated Doxorubicin Showing an Extraordinary Synergistic Pro-Apoptotic Potential.

Although TRAIL (TNF-related apoptosis-inducing ligand, also known as Apo2L) was described as capable of inducing apoptosis in transformed cells while sparing normal cells, limited results obtained in clinical trials has limited its use as an anti-tumor agent. Consequently, novel TRAIL formulations with enhanced bioactivity are necessary for overcoming resistance to conventional soluble TRAIL (sTRAIL) exhibited by many primary tumors. Our group has generated artificial liposomes with sTRAIL anchored on their surface (large unilamellar vesicle (LUV)-TRAIL), which have shown a greater cytotoxic activity both in vitro and in vivo when compared to sTRAIL against distinct hematologic and epithelial carcinoma cells. In this study, we have improved LUV-TRAIL by loading doxorubicin (DOX) in its liposomal lumen (LUVDOX-TRAIL) in order to improve their cytotoxic potential. LUVDOX-TRAIL killed not only to a higher extent, but also with a much faster kinetic than LUV-TRAIL. In addition, the concerted action of the liposomal DOX and TRAIL was specific of the liposomal DOX and was not observed when with soluble DOX. The cytotoxicity induced by LUVDOX-TRAIL was proven to rely on two processes due to different molecular mechanisms: a dynamin-mediated internalization of the doxorubicin-loaded particle, and the strong activation of caspase-8 exerted by the liposomal TRAIL. Finally, greater cytotoxic activity of LUVDOX-TRAIL was also observed in vivo in a tumor xenograft model. Therefore, we developed a novel double-edged nanoparticle combining the cytotoxic potential of DOX and TRAIL, showing an exceptional and remarkable synergistic effect between both agents.