Exploiting LBL-assembled Au nanoparticles to enhance Raman signals for point-of-care testing of osteoporosis with excreta sample

Due to the intrinsic lack of specific biomarkers, there is an increasing demand for degenerative diseases to develop a testing method independent upon the targeting biomolecules. In this paper, we proposed a novel idea for this issue which was to analyze the characteristic information of metabolites with Raman spectrum. First, we achieved the fabrication of stable, uniform and reproducible substrate to enhance the Raman signals, which is crucial to the following analysis of information. This idea was confirmed with the osteoporosis-modeled mice. Furthermore, the testing results with clinical samples also preliminarily exhibited the feasibility of this strategy. The substrate to enhance Raman signal was fabricated by the layer-by-layer assembly of Au nanoparticles. The osteoporosis modeling was made by bilateral ovariectomy. Ten female mice were randomly divided into two groups. The urine and dejecta samples of mice were collected every week. Clinic urine samples were collected from patients with osteoporosis while the controlled samples were from the young students in our university. The LBL-assembled substrate of Au nanoparticles was uniform, stable and reproducible to significantly enhance the Raman signals from tiny amount of samples. With a simple data processing technique, the Raman signal-based method can effectively reflect the development of osteoporosis by comparison with micro-CT characterization. Moreover, the Raman signal from samples of clinic patients also showed the obvious difference with that of the control. Raman spectrum may be a good tool to convey the pathological information of metabolites in molecular level. Our results manifested that the information-based testing is possibly feasible and promising. Our strategy utilizes the characteristic information rather than the biological recognition to test the diseases which are difficult to find specific biomarkers. This will be greatly beneficial to the prevention and diagnosis of degenerative diseases. Also, we believe the combination of big bio-data and characteristic recognition will change the current paradigm of medical diagnosis essentially.