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.

Notch3 Deficiency Attenuates Pulmonary Fibrosis and Impedes Lung-Function Decline.

Fibroblast activation includes differentiation to myofibroblasts and is a key feature of organ fibrosis. The Notch pathway has been involved in myofibroblast differentiation in several tissues, including the lung. Here, we identify a subset of collagen-expressing cells in the lung that exhibit Notch3 activity at homeostasis. After injury, this activation increases, being found in αSMA-expressing myofibroblasts in the mouse and human fibrotic lung. Although previous studies suggest a contribution of Notch3 in stromal activation, in vivo evidence of the role of Notch3 in lung fibrosis remains unknown. In this study, we examine the effects of Notch3 deletion in pulmonary fibrosis and demonstrate that Notch3-deficient lungs are protected from lung injury with significantly reduced collagen deposition after bleomycin administration. The induction of profibrotic genes is reduced in bleomycin-treated Notch3-knockout lungs that consistently present fewer αSMA-positive myofibroblasts. As a result, the volume of healthy lung tissue is higher and lung function is improved in the absence of Notch3. Using in vitro cultures of lung primary fibroblasts, we confirmed that Notch3 participates in their survival and differentiation. Thus, Notch3 deficiency mitigates the development of lung fibrosis because of its role in mediating fibroblast activation. Our findings reveal a previously unidentified mechanism underlying lung fibrogenesis and provide a potential novel therapeutic approach to target pulmonary fibrosis.

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.

Surface-Enhanced Raman Scattering Monitoring of Tryptophan Dynamics in 3D Pancreatic Tumor Models.

In the intricate landscape of the tumor microenvironment, both cancer and stromal cells undergo rapid metabolic adaptations to support their growth. Given the relevant role of the metabolic secretome in fueling tumor progression, its unique metabolic characteristics have gained prominence as potential biomarkers and therapeutic targets. As a result, rapid and accurate tools have been developed to track metabolic changes in the tumor microenvironment with high sensitivity and resolution. Surface-enhanced Raman scattering (SERS) is a highly sensitive analytical technique and has been proven efficient toward the detection of metabolites in biological media. However, profiling secreted metabolites in complex cellular environments such as those in tumor-stroma 3D in vitro models remains challenging. To address this limitation, we employed a SERS-based strategy to investigate the metabolic secretome of pancreatic tumor models within 3D cultures. We aimed to monitor the immunosuppressive potential of stratified pancreatic cancer-stroma spheroids as compared to 3D cultures of either pancreatic cancer cells or cancer-associated fibroblasts, focusing on the metabolic conversion of tryptophan into kynurenine by the IDO-1 enzyme. We additionally sought to elucidate the dynamics of tryptophan consumption in correlation with the size, temporal evolution, and composition of the spheroids, as well as assessing the effects of different drugs targeting the IDO-1 machinery. As a result, we confirm that SERS can be a valuable tool toward the optimization of cancer spheroids, in connection with their tryptophan metabolizing capacity, potentially allowing high-throughput spheroid analysis.

Prospects of Surface-Enhanced Raman Spectroscopy for Biomarker Monitoring toward Precision Medicine.

Future precision medicine will be undoubtedly sustained by the detection of validated biomarkers that enable a precise classification of patients based on their predicted disease risk, prognosis, and response to a specific treatment. Up to now, genomics, transcriptomics, and immunohistochemistry have been the main clinically amenable tools at hand for identifying key diagnostic, prognostic, and predictive biomarkers. However, other molecular strategies, including metabolomics, are still in their infancy and require the development of new biomarker detection technologies, toward routine implementation into clinical diagnosis. In this context, surface-enhanced Raman scattering (SERS) spectroscopy has been recognized as a promising technology for clinical monitoring thanks to its high sensitivity and label-free operation, which should help accelerate the discovery of biomarkers and their corresponding screening in a simpler, faster, and less-expensive manner. Many studies have demonstrated the excellent performance of SERS in biomedical applications. However, such studies have also revealed several variables that should be considered for accurate SERS monitoring, in particular, when the signal is collected from biological sources (tissues, cells or biofluids). This Perspective is aimed at piecing together the puzzle of SERS in biomarker monitoring, with a view on future challenges and implications. We address the most relevant requirements of plasmonic substrates for biomedical applications, as well as the implementation of tools from artificial intelligence or biotechnology to guide the development of highly versatile sensors.