“…When corrected for cell area, the dose distributions for G1 and G2 cells are identical (null hypothesis accepted p value = 0.586 and two-distribution Kolmogorov–Smirnov stat = 0.028 at a 0.1% significance level). These results help clarify a debate presented in recent correspondence about the role of cell cycle in controlling nanoparticle dose 18,20 as the ambiguity introduced by confounding factors of different cell area and time integration of dose are removed in this study. Crucially λ , the instantaneous formation rate of nanoparticle-loaded endosomes, in terms of concentration per unit area is constant; therefore, the internal biological processes of the cell cycle do not impact endosome formation at the membrane.Fig.…”
Section: Resultssupporting
confidence: 75%
“…This prediction can be used to explore the effect of cell state on the uptake of nanoparticles, and we demonstrate this capability by clarifying the role of cell cycle on nanoparticle uptake—a subject of some current controversy 18–20 . The exposure times in the experiments herein were specifically chosen to isolate and hence allow investigation of the mechanisms of particle uptake.…”
Section: Resultsmentioning
confidence: 92%
“…At the current time, however, the model presented was developed and tested on the basis of short exposure durations (0.5–2.0 h) rather than on the more lengthy time-integrated exposures that are typical in the field. Whilst this was extremely helpful in showing that cell area, which is correlated with the cell cycle position, is the driver of any perceived cell-cycle dependence of nanoparticle uptake 18,20 , future development should consider further adaption to account for the dilution of the nanoparticle dose that occurs through endosome inheritance upon cell division and daughter–cell formation 13 . A further consideration for effective use of the model for nanosafety applications is the effect that cell shrinkage due to toxicity 15 will play over longer exposure times.…”
Understanding nanoparticle uptake by biological cells is fundamentally important to wide-ranging fields from nanotoxicology to drug delivery. It is now accepted that the arrival of nanoparticles at the cell is an extremely complicated process, shaped by many factors including unique nanoparticle physico-chemical characteristics, protein-particle interactions and subsequent agglomeration, diffusion and sedimentation. Sequentially, the nanoparticle internalisation process itself is also complex, and controlled by multiple aspects of a cell’s state. Despite this multitude of factors, here we demonstrate that the statistical distribution of the nanoparticle dose per endosome is independent of the initial administered dose and exposure duration. Rather, it is the number of nanoparticle containing endosomes that are dependent on these initial dosing conditions. These observations explain the heterogeneity of nanoparticle delivery at the cellular level and allow the derivation of simple, yet powerful probabilistic distributions that accurately predict the nanoparticle dose delivered to individual cells across a population.
“…When corrected for cell area, the dose distributions for G1 and G2 cells are identical (null hypothesis accepted p value = 0.586 and two-distribution Kolmogorov–Smirnov stat = 0.028 at a 0.1% significance level). These results help clarify a debate presented in recent correspondence about the role of cell cycle in controlling nanoparticle dose 18,20 as the ambiguity introduced by confounding factors of different cell area and time integration of dose are removed in this study. Crucially λ , the instantaneous formation rate of nanoparticle-loaded endosomes, in terms of concentration per unit area is constant; therefore, the internal biological processes of the cell cycle do not impact endosome formation at the membrane.Fig.…”
Section: Resultssupporting
confidence: 75%
“…This prediction can be used to explore the effect of cell state on the uptake of nanoparticles, and we demonstrate this capability by clarifying the role of cell cycle on nanoparticle uptake—a subject of some current controversy 18–20 . The exposure times in the experiments herein were specifically chosen to isolate and hence allow investigation of the mechanisms of particle uptake.…”
Section: Resultsmentioning
confidence: 92%
“…At the current time, however, the model presented was developed and tested on the basis of short exposure durations (0.5–2.0 h) rather than on the more lengthy time-integrated exposures that are typical in the field. Whilst this was extremely helpful in showing that cell area, which is correlated with the cell cycle position, is the driver of any perceived cell-cycle dependence of nanoparticle uptake 18,20 , future development should consider further adaption to account for the dilution of the nanoparticle dose that occurs through endosome inheritance upon cell division and daughter–cell formation 13 . A further consideration for effective use of the model for nanosafety applications is the effect that cell shrinkage due to toxicity 15 will play over longer exposure times.…”
Understanding nanoparticle uptake by biological cells is fundamentally important to wide-ranging fields from nanotoxicology to drug delivery. It is now accepted that the arrival of nanoparticles at the cell is an extremely complicated process, shaped by many factors including unique nanoparticle physico-chemical characteristics, protein-particle interactions and subsequent agglomeration, diffusion and sedimentation. Sequentially, the nanoparticle internalisation process itself is also complex, and controlled by multiple aspects of a cell’s state. Despite this multitude of factors, here we demonstrate that the statistical distribution of the nanoparticle dose per endosome is independent of the initial administered dose and exposure duration. Rather, it is the number of nanoparticle containing endosomes that are dependent on these initial dosing conditions. These observations explain the heterogeneity of nanoparticle delivery at the cellular level and allow the derivation of simple, yet powerful probabilistic distributions that accurately predict the nanoparticle dose delivered to individual cells across a population.
“…The work of Panet et al 1 is a welcome addition to earlier reports on nanoparticle uptake in proliferating cells 2,3 , and provides us the opportunity to further reinforce and clarify the main points of our paper 2 on the coupling of cell cycle progression and nanoparticle accumulation.…”
“…To determine whether the experimental apparent heterogeneity arises solely from heterogeneous nanoparticle-cell association, as suggested previously (10), we introduce cell heterogeneity into our modeling framework via the affinity parameter. A potential explanation for heterogeneous affinity is the heterogeneity in cell surface area due to the cell cycle (10,30,31). Specifically, we assume that a cell with higher surface area is more likely to interact and associate with nanoparticles (10).…”
Section: Cell Size Distribution Does Not Account For Time-dependent Hmentioning
Nanoparticles have the potential to enhance therapeutic success and reduce toxicitybased treatment side effects via the targeted delivery of drugs to cells. This delivery relies on complex interactions between numerous biological, chemical and physical processes. The intertwined nature of these processes has thus far hindered attempts to understand their individual impact. Variation in experimental data, such as the number of nanoparticles inside each cell, further inhibits understanding. Here we present a mathematical framework that is capable of examining the impact of individual processes during nanoparticle delivery. We demonstrate that variation in experimental nanoparticle uptake data can be explained by three factors: random nanoparticle motion; variation in nanoparticle-cell interactions; and variation in the maximum nanoparticle uptake per cell. Without all three factors, the experimental data cannot be explained. This work provides insight into biological mechanisms that cause heterogeneous responses to treatment, and enables precise identification of treatment-resistant cell subpopulations.
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