Understanding nano-engineered particle–cell interactions: biological insights from mathematical models

Johnston, Sue; Faria, Matthew; Crampin, Edmund J.

doi:10.1039/d0na00774a

Cited by 21 publications

(23 citation statements)

References 165 publications

(407 reference statements)

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“…Calculating the penetration kinetics of nanoparticles in cancer spheroids or organoids is an important step towards robust evaluation of nanoparticle uptake and falls under the drive toward consistent and reproducible reporting in bio-nano literature, for the direct comparison of nanoparticle designs between different studies [19,35]. We further validated the nanoparticle kinetic findings by incorporating mathematical modelling into our analysis, which has previously been used to investigate how nanoparticle characteristics influence cell uptake, or how tumor spheroid development may impact drug delivery, both in a high throughput and iterative manner [48][49][50][51].…”

Section: Discussionmentioning

confidence: 94%

Spatial-temporal analysis of nanoparticles in live tumor spheroids impacted by cell origin and density

Ahmed-Cox

Pandžić

Johnston

et al. 2021

Preprint

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Nanoparticles hold great preclinical promise in cancer therapy but continue to suffer attrition through clinical trials. Advanced, three dimensional (3D) cellular models such as tumor spheroids can recapitulate elements of the tumor environment and are considered the superior model to evaluate nanoparticle designs. However, there is an important need to better understand nanoparticle penetration kinetics and determine how different cell characteristics may influence this nanoparticle uptake. A key challenge with current approaches for measuring nanoparticle accumulation in spheroids is that they are often static, losing spatial and temporal information which may be necessary for effective nanoparticle evaluation in 3D cell models. To overcome this challenge, we developed an analysis platform, termed the Determination of Nanoparticle Uptake in Tumor Spheroids (DONUTS), which retains spatial and temporal information during quantification, enabling evaluation of nanoparticle uptake in 3D tumor spheroids. Outperforming linear profiling methods, DONUTS was able to measure silica nanoparticle uptake to 10 µm accuracy in both isotropic and irregularly shaped cancer cell spheroids. This was then extended to determine penetration kinetics, first by a forward-in-time, center-in-space model, and then by mathematical modelling, which enabled the direct evaluation of nanoparticle penetration kinetics in different spheroid models. Nanoparticle uptake was shown to inversely relate to particle size and varied depending on the cell type, cell stiffness and density of the spheroid model. The automated analysis method we have developed can be applied to live spheroids in situ, for the advanced evaluation of nanoparticles as delivery agents in cancer therapy.

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Section: Discussionmentioning

confidence: 94%

Spatial-temporal analysis of nanoparticles in live tumor spheroids impacted by cell origin and density

Ahmed-Cox

Pandžić

Johnston

et al. 2021

Preprint

Self Cite

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“…Endocytosis is the primary means by which cells uptake, or internalize, drugs, viruses and nanoparticles [1][2][3][4][5]. Single-cell in vitro analysis of internalization and similar dynamical processes reveals significant cell-to-cell variability in otherwise homogeneous cell populations [6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Identifying cell-to-cell variability in internalization using flow cytometry

Browning

Ansari

Drovandi

et al. 2022

J. R. Soc. Interface.

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Biological heterogeneity is a primary contributor to the variation observed in experiments that probe dynamical processes, such as the internalization of material by cells. Given that internalization is a critical process by which many therapeutics and viruses reach their intracellular site of action, quantifying cell-to-cell variability in internalization is of high biological interest. Yet, it is common for studies of internalization to neglect cell-to-cell variability. We develop a simple mathematical model of internalization that captures the dynamical behaviour, cell-to-cell variation, and extrinsic noise introduced by flow cytometry. We calibrate our model through a novel distribution-matching approximate Bayesian computation algorithm to flow cytometry data of internalization of anti-transferrin receptor antibody in a human B-cell lymphoblastoid cell line. This approach provides information relating to the region of the parameter space, and consequentially the nature of cell-to-cell variability, that produces model realizations consistent with the experimental data. Given that our approach is agnostic to sample size and signal-to-noise ratio, our modelling framework is broadly applicable to identify biological variability in single-cell data from internalization assays and similar experiments that probe cellular dynamical processes.

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“…Endocytosis is the primary means by which cells uptake, or internalise, drugs, viruses, and nanoparticles [1][2][3][4][5]. Single-cell in vitro analysis of internalisation and similar dynamical processes reveals significant cell-to-cell variability in otherwise isogenic cell populations [6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Identifying cell-to-cell variability in internalisation using flow cytometry

Browning

Ansari

Drovandi

et al. 2021

Preprint

View full text Add to dashboard Cite

SummaryBiological heterogeneity is a primary contributor to the variation observed in experiments that probe dynamical processes, such as internalisation. Given that internalisation is the primary means by which cells absorb drugs, viruses and other material, quantifying cell-to-cell variability in internalisation is of high biological interest. Yet, it is common for studies of internalisation to neglect cell-to-cell variability. We develop a simple mathematical model of internalisation that captures the dynamical behaviour, cell-to-cell variation, and extrinsic noise introduced by flow cytometry. We calibrate our model through a novel distribution-matching approximate Bayesian computation algorithm to flow cytometry data collected from an experiment that probes the internalisation of antibody by transferrin receptors in C1R cells. Our model reproduces experimental observations, identifies cell-to-cell variability in the internalisation and recycling rates, and, importantly, provides information relating to inferential uncertainty. Given that our approach is agnostic to sample size and signal-to-noise ratio, our modelling framework is broadly applicable to identify biological variability in single-cell data from experiments that probe a range of dynamical processes.

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Understanding nano-engineered particle–cell interactions: biological insights from mathematical models

Abstract: Understanding the interactions between nano-engineered particles and cells is necessary for the rational design of particles for therapeutic, diagnostic and imaging purposes. In particular, the informed design of particles relies...

Cited by 21 publications

References 165 publications

Spatial-temporal analysis of nanoparticles in live tumor spheroids impacted by cell origin and density

Spatial-temporal analysis of nanoparticles in live tumor spheroids impacted by cell origin and density

Identifying cell-to-cell variability in internalization using flow cytometry

Identifying cell-to-cell variability in internalisation using flow cytometry

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