The Raman spectra of biological materials always exhibit complex profiles, constituting several peaks and/or bands which arise due to the large variety of biomolecules. The extraction of quantitative information from these spectra is not a trivial task. While qualitative information can be retrieved from the changes in peaks frequencies or from the appearance/disappearance of some peaks, quantitative analysis requires an examination of peak intensities. Unfortunately in biological samples it is not easy to identify a reference peak for normalizing intensities, and this makes it very difficult to study the peak intensities. In the last decades a more refined mathematical tool, the extended multiplicative signal correction (EMSC), has been proposed for treating infrared spectra, which is also capable of providing quantitative information. From the mathematical and physical point of view, EMSC can also be applied to Raman spectra, as recently proposed. In this work the reliability of the EMSC procedure is tested by application to a well defined biological system: the 20 standard amino acids and their combination in peptides. The first step is the collection of a Raman database of these 20 amino acids, and subsequently EMSC processing is applied to retrieve quantitative information from amino acids mixtures and peptides. A critical review of the results is presented, showing that EMSC has to be carefully handled for complex biological systems.

Inflammation is a common hallmark in several diseases including atherosclerosis, cancer, obesity and neurodegeneration. In Alzheimer’s disease, growing evidence directly correlates neuronal damage with inflammation of myeloid brain cells, such as microglia. Here, polymeric nanoparticles were engineered and characterized for the delivery of anti-inflammatory molecules to macrophages stimulated via direct incubation with amyloid-β fibers. 200 nm spherical polymeric nanoparticles (SPNs) and 1,000 nm discoidal polymeric nanoconstructs (DPNs) were synthesized using poly(lactic-co-glycolic acid) (PLGA), polyethylene glycol (PEG), and lipid chains as building blocks. First, the internalization propensity in macrophages of both nanoparticles was assessed via cytofluorimetric and confocal microscopy analyses, demonstrating that SPNs are by far more rapidly taken up as compared to DPNs (99.6±0,11% vs 14.4±0.06%, within 24h). Then, Curc-SPNs were realized by encapsulating curcumin, a natural anti-inflammatory molecule, within the PLGA core of SPNs. Finally, Curc-SPNs were shown to diminish up to 6.5-fold the production of pro-inflammatory cytokines – IL-1β; IL-6 and TNF-α – in macrophages stimulated via amyloid-β fibers. Although more sophisticated in vitro models and systematic analyses on the blood brain barrier permeability are critically needed, these preliminary findings hold potential in the development of nanoparticles for modulating inflammation in Alzheimer’s disease.