The overexpression of immunomarker programmed cell death protein 1 (PD-1) and engagement of PD-1 to its ligand, PD-L1, are involved in the functional impairment of cluster of differentiation 8+ (CD8+) T cells, contributing to cancer progression. However, heterogeneities in PD-L1 expression and variabilities in biopsy-based assays render current approaches inaccurate in predicting PD-L1 status. Therefore, PD-L1 screening alone is not predictive of patient response to treatment, which motivates us to simultaneously detect multiple immunomarkers engaged in immune modulation. Here, we have developed multimodal probes, immunoactive gold nanostars (IGNs), that accurately detect PD-L1+ tumor cells and CD8+ T cells simultaneously in vivo, surpassing the limitations of current immunoimaging techniques. IGNs integrate the whole-body imaging of positron emission tomography with high sensitivity and multiplexing of Raman spectroscopy, enabling the dynamic tracking of both immunomarkers. IGNs also monitor response to immunotherapies in mice treated with combinatorial PD-L1 and CD137 agonists and distinguish responders from those nonresponsive to treatment. Our results showed a multifunctional nanoscale probe with capabilities that cannot be achieved with either modality alone, allowing multiplexed immunologic tumor profiling critical for predicting early response to immunotherapies.

Staphylococcus aureus ( S. aureus) is a leading cause of hospital-acquired infections, such as bacteremia, pneumonia, and endocarditis. Treatment of these infections can be challenging since strains of S. aureus, such as methicillin-resistant S. aureus (MRSA), have evolved resistance to antimicrobials. Current methods to identify infectious agents in hospital environments often rely on time-consuming, multistep culturing techniques to distinguish problematic strains (i.e., antimicrobial resistant variants) of a particular bacterial species. Therefore, a need exists for a rapid, label-free technique to identify drug-resistant bacterial strains to guide proper antibiotic treatment. Here, our findings demonstrate the ability to characterize and identify microbes at the subspecies level using Raman microspectroscopy, which probes the vibrational modes of molecules to provide a biochemical "fingerprint". This technique can distinguish between different isolates of species such as Streptococcus agalactiae and S. aureus. To determine the ability of this analytical approach to detect drug-resistant bacteria, isogenic variants of S. aureus including the comparison of strains lacking or expressing antibiotic resistance determinants were evaluated. Spectral variations observed may be associated with biochemical components such as amino acids, carotenoids, and lipids. Mutants lacking carotenoid production were distinguished from wild-type S. aureus and other strain variants. Furthermore, spectral biomarkers of S. aureus isogenic bacterial strains were identified. These results demonstrate the feasibility of Raman microspectroscopy for distinguishing between various genetically distinct forms of a single bacterial species in situ. This is important for detecting antibiotic-resistant strains of bacteria and indicates the potential for future identification of other multidrug resistant pathogens with this technique.

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A decline in the inherent quality of bone tissue is a contributor to the age-related increase in fracture risk. Although this is well known, the important biochemical factors of bone quality have yet to be identified using Raman spectroscopy (RS), a non-destructive, inelastic light scattering technique. To identify potential RS predictors of fracture risk, we applied principal component analysis (PCA) to 558 Raman spectra (370 cm−1 – 1720 cm−1) of human cortical bone acquired from 62 female and male donors (9 spectra each) spanning adulthood (21 – 101 yo). Spectra were analyzed prior to R-curve, non-linear fracture mechanics that delineates crack initiation (Kinit) from crack growth toughness (Kgrow). The traditional ν1Phosphate peak per Amide I peak (mineral-to-matrix ratio) weakly correlated with Kinit (r = 0.341, p =0.0067) and overall crack growth toughness (J-int: r = 0.331, p =0.0086). Sub-peak ratios of the Amide I band that are related to the secondary structure of type 1 collagen did not correlate with the fracture toughness properties. In the full spectrum analysis, one principal component (PC5) correlated with all of the mechanical properties (Kinit: r = −0.467, Kgrow: r = −0.375, and J-int: r = −0.428; p < 0.0067). More importantly, when known predictors of fracture toughness, namely age and/or volumetric bone mineral density (vBMD), were included in general linear models as covariates, several principal components helped explain 45.0% (PC5) to 48.5% (PC7), 31.4% (PC6), and 25.8% (PC7) of the variance in Kinit, Kgrow, and J-int, respectively. Deriving spectral features from full spectrum analysis may improve the ability of RS, a clinically viable technology, to assess fracture risk.

Raman microspectroscopy was used to characterize and identify the three main pathogens that cause acute otitis media (AOM) in vitro. Cultured middle ear effusion from patients was studied and results suggest the potential of using this technique to aid in accurately diagnosing AOM and providing physicians with bacterial identification to guide treatment.