Revealing Population Heterogeneity in Vesicle-Based Nanomedicines Using Automated, Single Particle Raman Analysis

The intrinsic heterogeneity of many nanoformulations is currently challenging to characterize on both the single particle and population level. Therefore, there is great opportunity to develop advanced techniques to describe and understand nanomedicine heterogeneity, which will aid translation to the clinic by informing manufacturing quality control, characterization for regulatory bodies, and connecting nanoformulation properties to clinical outcomes to enable rational design. Here, we present an analytical technique to provide such information, while measuring the nanocarrier and cargo simultaneously with label-free, nondestructive single particle automated Raman trapping analysis (SPARTA). We first synthesized a library of model compounds covering a range of hydrophilicities and providing distinct Raman signals. These compounds were then loaded into model nanovesicles (polymersomes) that can load both hydrophobic and hydrophilic cargo into the membrane or core regions, respectively. Using our analytical framework, we characterized the heterogeneity of the population by correlating the signal per particle from the membrane and cargo. We found that core and membrane loading can be distinguished, and we detected subpopulations of highly loaded particles in certain cases. We then confirmed the suitability of our technique in liposomes, another nanovesicle class, including the commercial formulation Doxil. Our label-free analytical technique precisely determines cargo location alongside loading and release heterogeneity in nanomedicines, which could be instrumental for future quality control, regulatory body protocols, and development of structure-function relationships to bring more nanomedicines to the clinic.

Single Particle Automated Raman Trapping Analysis of Breast Cancer Cell-Derived Extracellular Vesicles as Cancer Biomarkers

Extracellular vesicles (EVs) secreted by cancer cells provide an important insight into cancer biology and could be leveraged to enhance diagnostics and disease monitoring. This paper details a high-throughput label-free extracellular vesicle analysis approach to study fundamental EV biology, toward diagnosis and monitoring of cancer in a minimally invasive manner and with the elimination of interpreter bias. We present the next generation of our single particle automated Raman trapping analysis─SPARTA─system through the development of a dedicated standalone device optimized for single particle analysis of EVs. Our visualization approach, dubbed dimensional reduction analysis (DRA), presents a convenient and comprehensive method of comparing multiple EV spectra. We demonstrate that the dedicated SPARTA system can differentiate between cancer and noncancer EVs with a high degree of sensitivity and specificity (>95% for both). We further show that the predictive ability of our approach is consistent across multiple EV isolations from the same cell types. Detailed modeling reveals accurate classification between EVs derived from various closely related breast cancer subtypes, further supporting the utility of our SPARTA-based approach for detailed EV profiling.