Statistical prediction of nanoparticle delivery: from culture media to cell

Abstract

The application of nanoparticles (NPs) within medicine is of great interest; their innate physicochemical characteristics provide the potential to enhance current technology, diagnostics and therapeutics. Recently a number of NP-based diagnostic and therapeutic agents have been developed for treatment of various diseases, where judicious surface functionalization is exploited to increase efficacy of administered therapeutic dose. However, quantification of heterogeneity associated with absolute dose of a nanotherapeutic (NP number), how this is trafficked across biological barriers has proven difficult to achieve. The main issue being the quantitative assessment of NP number at the spatial scale of the individual NP, data which is essential for the continued growth and development of the next generation of nanotherapeutics. Recent advances in sample preparation and the imaging fidelity of transmission electron microscopy (TEM) platforms provide information at the required spatial scale, where individual NPs can be individually identified. High spatial resolution however reduces the sample frequency and as a result dynamic biological features or processes become opaque. However, the combination of TEM data with appropriate probabilistic models provide a means to extract biophysical information that imaging alone cannot. Previously, we demonstrated that limited cell sampling via TEM can be statistically coupled to large population flow cytometry measurements to quantify exact NP dose. Here we extended this concept to link TEM measurements of NP agglomerates in cell culture media to that encapsulated within vesicles in human osteosarcoma cells. By construction and validation of a data-driven transfer function, we are able to investigate the dynamic properties of NP agglomeration through endocytosis. In particular, we statistically predict how NP agglomerates may traverse a biological barrier, detailing inter-agglomerate merging events providing the basis for predictive modelling of nanopharmacology.

Evaluation of Tumor Treatment of Magnetic Nanoparticles Driven by Extremely Low Frequency Magnetic Field

“…Owing to their superior physiochemical properties, nanoparticles have been applied in various biomedical fields as a promising and viable technology, with intense researches focused on their potential applications in tumor intervention12345. Due to their small size in nanoscale, nanoparticles can pass through various physiological barriers and/or penetrate into cells678.…”

Quantifying the cellular uptake of semiconductor quantum dot nanoparticles by analytical electron microscopy

“…(3) A simple minimization algorithm is employed to optimize the magnitude of λ (Walton et al, 2011) to minimize the L2-norm between the cumulative distribution of the collected and simulated data (Brown et al, 2015).…”

“…As already stated, 102 cells within a thin slice of a pellet of cells exposed to quantum dots for 1 h were imaged by TEM, producing a data set of 625 vesicles, where the number of quantum dot nanoparticles per vesicle was quantified using automated procedures in MATLAB (Summers et al, 2013;Brown et al, 2015). This vesicular uptake is presumably to endosomes, however it is not possible to establish the exact cellular uptake mechanism via TEM imaging (Mühlfeld et al, 2007).…”

“…In recent work, we have focussed on the characterization of cellular uptake of quantum dots using analytical electron microscopy. Commercially available quantum dots, CdSe/ZnS core/shell particles coated in arginine-rich peptides to target cellular uptake by endocytosis, have been investigated in terms of the agglomeration state in typical cell culture media , the traverse of particle agglomerates across U-2 OS cell membranes during endocytosis (Brown et al, 2015) and in the correlation of imaging flow cytometry and TEM to measure the final nanoparticle dose internalized by the U-2 OS cells (Summers et al, 2013). This comprehensive description of how an initial exposure dose of nanoparticles is internalized by an endocytically active cell population (whose cell cycle is not perturbed by these quantum dots ) and how the internalized, membrane bound nanoparticle load is processed by the cells required correlation of thin-section TEM analysis to whole cell data.…”

Nanomaterials

“…link to measured biological endpoints as discussed above and; 2. model the DDD relationship using a series of mathematical transformations (Figure 8(c)). 147,148 Step 1 will enable us to understand the key steps in the exposure pathway that may enhance a hazard presented by an NM type, for example, excessive particle uptake or gorging. 18 Step 2 will enable us to predict NM dose internalized by cells or organisms exposed to NM of a known primary particle size distribution in a given media.…”