Raman Labeled Nanoparticles: Characterization of Variability and Improved Method for Unmixing

Advances in uremic toxin detection and monitoring in the management of chronic kidney disease progression to end-stage renal disease

Patients with end-stage kidney disease (ESKD) rely on dialysis to remove toxins and stay alive. However, hemodialysis alone is insufficient to completely remove all/major uremic toxins, resulting in the accumulation of specific toxins over time. The complexity of uremic toxins and their varying clearance rates across different dialysis modalities poses significant challenges, and innovative approaches such as microfluidics, biomarker discovery, and point-of-care testing are being investigated. This review explores recent advances in the qualitative and quantitative analysis of uremic toxins and highlights the use of innovative methods, particularly label-mediated and label-free surface-enhanced Raman spectroscopy, primarily for qualitative detection. The ability to analyze uremic toxins can optimize hemodialysis settings for more efficient toxin removal. Integration of multiple omics disciplines will also help identify biomarkers and understand the pathogenesis of ESKD, provide deeper understanding of uremic toxin profiling, and offer insights for improving hemodialysis programs. This review also highlights the importance of early detection and improved understanding of chronic kidney disease to improve patient outcomes.

Multiplexed Optical Imaging of Tumor-Directed Nanoparticles: A Review of Imaging Systems and Approaches

In recent decades, various classes of nanoparticles have been developed for optical imaging of cancers. Many of these nanoparticles are designed to specifically target tumor sites, and specific cancer biomarkers, to facilitate the visualization of tumors. However, one challenge for accurate detection of tumors is that the molecular profiles of most cancers vary greatly between patients as well as spatially and temporally within a single tumor mass. To overcome this challenge, certain nanoparticles and imaging systems have been developed to enable multiplexed imaging of large panels of cancer biomarkers. Multiplexed molecular imaging can potentially enable sensitive tumor detection, precise delineation of tumors during interventional procedures, and the prediction/monitoring of therapy response. In this review, we summarize recent advances in systems that have been developed for the imaging of optical nanoparticles that can be heavily multiplexed, which include surface-enhanced Raman-scattering nanoparticles (SERS NPs) and quantum dots (QDs). In addition to surveying the optical properties of these various types of nanoparticles, and the most-popular multiplexed imaging approaches that have been employed, representative preclinical and clinical imaging studies are also highlighted.

Surgical Guidance via Multiplexed Molecular Imaging of Fresh Tissues Labeled with SERS-Coded Nanoparticles

The imaging of dysregulated cell-surface receptors (or biomarkers) is a potential means of identifying the presence of cancer with high sensitivity and specificity. However, due to heterogeneities in the expression of protein biomarkers in tumors, molecular imaging technologies should ideally be capable of visualizing a multiplexed panel of cancer biomarkers. Recently, surface-enhanced Raman-scattering (SERS) nanoparticles (NPs) have attracted wide interest due to their potential for sensitive and multiplexed biomarker detection. In this review, we focus on the most recent advances in tumor imaging using SERS-coded NPs. A brief introduction of the structure and optical properties of SERS NPs is provided, followed by a detailed discussion of key imaging issues such as the administration of NPs in tissue (topical versus systemic), the optical configuration and imaging approach of Raman imaging systems, spectral demultiplexing methods for quantifying NP concentrations, and the disambiguation of specific vs. nonspecific sources of contrast through ratiometric imaging of targeted and untargeted (control) NP pairs. Finally, future challenges and directions are briefly outlined.

A hybrid least squares and principal component analysis algorithm for Raman spectroscopy

Raman spectroscopy is a powerful technique for detecting and quantifying analytes in chemical mixtures. A critical part of Raman spectroscopy is the use of a computer algorithm to analyze the measured Raman spectra. The most commonly used algorithm is the classical least squares method, which is popular due to its speed and ease of implementation. However, it is sensitive to inaccuracies or variations in the reference spectra of the analytes (compounds of interest) and the background. Many algorithms, primarily multivariate calibration methods, have been proposed that increase robustness to such variations. In this study, we propose a novel method that improves robustness even further by explicitly modeling variations in both the background and analyte signals. More specifically, it extends the classical least squares model by allowing the declared reference spectra to vary in accordance with the principal components obtained from training sets of spectra measured in prior characterization experiments. The amount of variation allowed is constrained by the eigenvalues of this principal component analysis. We compare the novel algorithm to the least squares method with a low-order polynomial residual model, as well as a state-of-the-art hybrid linear analysis method. The latter is a multivariate calibration method designed specifically to improve robustness to background variability in cases where training spectra of the background, as well as the mean spectrum of the analyte, are available. We demonstrate the novel algorithm’s superior performance by comparing quantitative error metrics generated by each method. The experiments consider both simulated data and experimental data acquired from in vitro solutions of Raman-enhanced gold-silica nanoparticles.