The production of polyhydroxybutyrate (PHB) by autotrophic fermentation of cyanobacteria has received increasing interest in the light of carbon emission reducing process strategies. Biotechnological approaches are in development to optimize the yield of PHB, including adapted cultivation media, characterized by a limitation of key nutrients: cyanobacteria accumulate PHB as energy storage molecules under limited growth conditions. Since there is an increasing demand for fast, simple and reliable analytics, we report the establishment of surface enhanced Raman spectroscopy (SERS) as a suitable monitoring tool for up scaled PHB production processes. Both, pure Ag-colloids mixed with bacterial culture, and in situ prepared colloids (Ag-Synechocystis), generated on the cell surface directly, were successfully applied and evaluated for this purpose. SERS measurements with in situ prepared Ag-colloids improved the reproducibility of Raman signals from 54.8% to 93.9%. The measurement time could be reduced significantly, completing our secondary goal. The quality of classically and in situ prepared Ag-colloids was monitored by zeta potential measurements and scanning electron microscopy (SEM) respectively. For data interpretation and statistical model-building an in house written code in the open source software RStudio was implemented. It was applied for the differentiation of PHB producers at the cellular level, revealing heterogeneities within sample groups regarding the PHB amount accumulated. The results obtained using the statistical model were validated as well and were complementary to the reference HPLC analysis. Therefore, a fast and reliable identification in situ SERS tool for the selection of the most promising cyanobacterial PHB production was established.

This study investigates the use of quantitative LC-MS/MS-based proteomics and surface-enhanced Raman spectroscopy (SERS) for biomarker detection in classical Hodgkin lymphoma (HL). Two HL cell models with distinct TP53 status were utilized to evaluate the effects of etoposide, a DNA-damaging chemotherapeutic agent, and resveratrol, a polyphenolic compound with known chemosensitizing activity. For SERS, the best performance was achieved by applying logistic regression to classify different treatment conditions and identify discriminative spectral features in the data. Proteomics showed highly reproducible and accurate results with relative standard deviations of below 5% for the sample preparation and about 2% for the measurements. Proteomic profiling revealed a TP53-dependent organization of metabolic and stress-response pathways and demonstrated that cryopreserved aliquots yielded the most consistent proteomic signatures. Treatment-dependent regulation of key biomarker proteins showed direct correspondence to specific SERS features, such as reduced nucleotide/cytochrome-associated signals and enhanced amide and aromatic amino acid signals. Our findings highlight the strength of applying reproducible proteomic profiling with machine learning–guided SERS analysis to improve molecular interpretation and to validate potential biomarkers in cancer research. Future work will focus on refining the analytical workflow and extending it toward integrative multi-omics applications, enabling more comprehensive biomarker detection and mechanistic insight into classical Hodgkin lymphoma. This strategy will be extended to additional model systems—such as metastatic melanoma, melanocytes, and ultimately patient-derived leukemia cells—to help bridge the gap toward clinical translation. Graphical abstract An analytical approach that enables reproducible and complementary biomarker discovery in Hodgkin lymphoma cells by applying robust SERS-based spectral classification with quantitative LC-MS/MS proteomics for molecular validation. It emphasizes reproducibility and data complementarity while identifying treatment-specific molecular signatures.

Bone regeneration requires tight coordination between mesenchymal stem cells (MSCs), immune signaling, and extracellular matrix remodeling. Yet, how atypical immune receptors contribute to this process remains unclear. Here, we identify Toll-like receptor 10 (TLR10) as a key regulator of osteogenic differentiation in human adipose-derived MSCs. Herein, ASC/TERT1 MSCs were engineered to overexpress or silence TLR10 using lentiviral vectors, and osteogenic differentiation (0–14 days) was assessed by metabolic assays—RT-qPCR of COL1A2, ALPL and BGLAP—Alizarin Red S staining, and quantitative mass spectrometry. Enhancing TLR10 expression promoted osteogenic gene programs, extracellular matrix organization, metabolic adaptation, and robust matrix mineralization, whereas TLR10 suppression maintained proliferative states and impaired osteoblast maturation. Proteomic analyses revealed that TLR10 selectively activates osteogenic, ECM-remodeling, and vitamin D-responsive pathways, while restraining programs antagonistic to differentiation. Notably, active vitamin D induced TLR10 expression and partially restored osteogenesis in TLR10-deficient cells, indicating that TLR10 is associated with vitamin D-driven bone formation. Together, beyond its established role in innate immunity, TLR10 emerges as a vitamin D-responsive regulator of mesenchymal stem cell osteogenesis, highlighting a potential therapeutic axis to enhance bone regeneration and osteogenic outcomes.