Raman spectroscopy shows great potential for practical clinical applications. By analyzing the structure and composition of molecules through real-time, non-destructive measurements of the scattered light from living cells and tissues, it offers valuable insights. The Raman spectral data directly link to the molecular composition of the cells and tissues and provides a “molecular fingerprint” for various disease states. This review focuses on the practical and clinical applications of Raman spectroscopy, especially in the early detection of human diseases. Identifying predisease, which marks the transition from a healthy to a disease state, is crucial for effective interventions to prevent disease onset. Raman spectroscopy can reveal biological processes occurring during the transition states and may eventually detect the molecular dynamics in predisease conditions.

Multiple myeloma (MM) is a cancer of plasma cells. Normal (NL) cells are considered to pass through a precancerous state, such as monoclonal gammopathy of undetermined significance (MGUS), before transitioning to MM. In the present study, we acquired Raman spectra at three stages—834 NL, 711 MGUS, and 970 MM spectra—and applied the dynamical network biomarker (DNB) theory to these spectra. The DNB analysis identified MGUS as the unstable pre-disease state of MM and extracted Raman shifts at 1149 and 1527–1530 ${\mathrm{cm}}^{-1}$ as DNB variables. The distribution of DNB scores for each patient showed a significant difference between the mean values for MGUS and MM patients. Furthermore, an energy landscape (EL) analysis showed that the NL and MM stages were likely to become stable states. Raman spectroscopy, the DNB theory, and, complementarily, the EL analysis will be applicable to the identification of the pre-disease state in clinical samples.

The dynamical network biomarker (DNB) theory detects the early warning signals of state transitions utilizing fluctuations in and correlations between variables in complex systems. Although the DNB theory has been applied to gene expression in several diseases, destructive testing by microarrays is a critical issue. Therefore, other biological information obtained by non-destructive testing is desirable; one such piece of information is Raman spectra measured by Raman spectroscopy. Raman spectroscopy is a powerful tool in life sciences and many other fields that enable the label-free non-invasive imaging of live cells and tissues along with detailed molecular fingerprints. Naïve and activated T cells have recently been successfully distinguished from each other using Raman spectroscopy without labeling. In the present study, we applied the DNB theory to Raman spectra of T cell activation as a model case. The dataset consisted of Raman spectra of the T cell activation process observed at 0 (naïve T cells), 2, 6, 12, 24 and 48 h (fully activated T cells). In the DNB analysis, the F-test and hierarchical clustering were used to detect the transition state and identify DNB Raman shifts. We successfully detected the transition state at 6 h and related DNB Raman shifts during the T cell activation process. The present results suggest novel applications of the DNB theory to Raman spectra ranging from fundamental research on cellular mechanisms to clinical examinations.

East Asians are known to develop diabetes mellitus at a lower body weight than Caucasians, potentially because of the different mechanisms underlying disease development. This study aimed to evaluate the variation in weight transition leading to diabetes onset in two subtypes of individuals (obese and non-obese) in a Japanese population. We conducted a retrospective, observational, longitudinal cohort study using health checkup data from 9, 260 participants in Japan. Individuals who developed diabetes within three years of the start of the observation period were excluded. Among the participants, 61.4% were men, and 259 developed diabetes. In the obesity group (body mass index [BMI] ≥25 kg/m2), the average BMI increased prior to the diabetes onset and subsequently decreased. Conversely, in the non-obesity group (BMI <25 kg/m2), the average BMI decreased and then stabilized before the onset of diabetes. Notably, a greater number of participants in the non-obesity group exhibited a BMI change of ≤–0.15 kg/m2 per year compared with those with a BMI change of ≥0.15 kg/m2 per year before diabetes onset (p = 0.003). Our findings indicate that body weight loss precedes the onset of diabetes in the non-obesity group. We recommend that non-obese individuals with elevated blood glucose levels who do not meet the criteria for diabetes should be considered a high-risk group for diabetes development. Therefore, it is imperative to identify these individuals and provide lifestyle guidance that does not focus on weight loss to prevent the onset of diabetes.

The dynamical network biomarker (DNB) theory detects early warning signals at the transition state (pre-disease state) in complex biological systems. The DNB theory has been applied to microarray data in several diseases. However, this theory has not yet been extensively to limited single-cell RNA sequencing (scRNA-seq) data. The main problem arises from missing values causing a standard deviation of zero, resulting in the calculation of correlation coefficients impossible in the DNB theory. The present study introduces pseudobulk and Gaussian noise to missing values in the scRNA-seq data to avoid division by zero. Without compromising the data characteristics, these two techniques detected previously missed genes for the DNB analysis and, thus, successfully expanded the scope of the DNB theory.

Raman spectroscopy is a non-destructive spectroscopic technique that provides complex molecular information. It is used to examine the physiological and pathological responses of living cells, such as differentiation, malignancy, and inflammation. The responses of two cellular states, initial and full-blown inflammation, have mainly been investigated using a comparative analysis with Raman spectra. However, the tipping point of the inflammatory state transition remains unclear. Therefore, the present study attempted to identify the tipping point of inflammation using a cell model. We stimulated RAW264.7 mouse macrophages with lipopolysaccharide (LPS) and continuously collected Raman spectra every 2 h for 24 h from the initial and full-blown inflammation states. A Partial Least Squares analysis and Principal Component Analysis—Linear Discriminant Analysis predicted the tipping point as 14 h after the LPS stimulation. In addition, a Dynamical Network Biomarker (DNB) analysis, identifying the tipping point of a state transition in various phenomena, indicated that the tipping point was 14 h and identified tryptophan as a biomarker. The results of a multivariate analysis and DNB analysis show the cellular tipping point.