Alzheimer’s disease is a progressive neurodegenerative disorder, the early detection of which is crucial for timely intervention and enrollment in clinical trials. However, the preclinical diagnosis of Alzheimer’s encounters difficulties with gold-standard methods. The current definitive diagnosis of Alzheimer’s still relies on expensive instrumentation and post-mortem histological examinations. Here, we explore label-free Raman spectroscopy with machine learning as an alternative to preclinical Alzheimer’s diagnosis. A special feature of this study is the inclusion of patient samples from different cohorts, sampled and measured in different years. To develop reliable classification models, partial least squares discriminant analysis in combination with variable selection methods identified discriminative molecules, including nucleic acids, amino acids, proteins, and carbohydrates such as taurine/hypotaurine and guanine, when applied to Raman spectra taken from dried samples of cerebrospinal fluid. The robustness of the model is remarkable, as the discriminative molecules could be identified in different cohorts and years. A unified model notably classifies preclinical Alzheimer’s, which is particularly surprising because of Raman spectroscopy’s high sensitivity regarding different measurement conditions. The presented results demonstrate the capability of Raman spectroscopy to detect preclinical Alzheimer’s disease for the first time and offer invaluable opportunities for future clinical applications and diagnostic methods.

Vibrational spectroscopy techniques are widely used in analytical chemistry, physics and biology. The most prominent techniques are Raman and Fourier-transform infrared spectroscopy (FTIR). Combining both techniques delivers complementary information of the test sample. We present the design, construction, and calibration of a novel bimodal spectroscopy system featuring both Raman and infrared measurements simultaneously on the same sample without mutual interference. The optomechanical design provides a modular flexible system for solid and liquid samples and different configurations for Raman. As a novel feature, the Raman module can be operated off-axis for optical sectioning. The calibrated system demonstrates high sensitivity, precision, and resolution for simultaneous operation of both techniques and shows excellent calibration curves with coefficients of determination greater than 0.96. We demonstrate the ability to simultaneously measure Raman and infrared spectra of complex biological material using bovine serum albumin. The performance competes with commercial systems; moreover, it presents the additional advantage of simultaneously operating Raman and infrared techniques. To the best of our knowledge, it is the first demonstration of a combined Raman-infrared system that can analyze the same sample volume and obtain optically sectioned Raman signals. Additionally, quantitative comparison of confocality of backscattering micro-Raman and off-axis Raman was performed for the first time.

Metabolic heterogeneity within the cell microenvironment is a key driver of cancer progression and resistance to therapy. However, current approaches lack the spatial and temporal resolution required to capture its dynamics in living systems. While recent advances in 3D cell culture models and metabolomic profiling have improved our understanding of the tumor niche, their integration with real-time optical sensing remains underdeveloped. Here, we present an integrated platform combining a 3D-printed microreactor culture chamber with Raman spectroscopy to enable non-invasive, spatially resolved metabolic monitoring of living cell cultures. Our microreactor platform generates controlled oxygen and nutrient cues while simultaneously acquiring label-free Raman spectra, revealing extracellular metabolic fingerprints linked to cell catabolism (e.g., glucose and lactate shifts) and acidification. Analysis across four cell lines uncovered temporal evolution as the dominant source of metabolic variance, while spatial heterogeneity along oxygen gradients is a secondary factor. In particular, diffusion-limited regions exhibited localized acidification and accumulation of stress biomarkers—such as the release of nucleotides—features that cannot be detected using conventional bulk assays. By providing a versatile platform for real-time mapping, this work enables the mechanistic dissection of cell adaptation to microenvironmental stress and supports the prediction of metabolic signatures underlying drug response and treatment outcomes.