M. Rowan Brown

Statistical analysis of nanoparticle dosing in a dynamic cellular system

The delivery of nanoparticles into cells is important in therapeutic applications and in nanotoxicology. Nanoparticles are generally targeted to receptors on the surfaces of cells and internalized into endosomes by endocytosis, but the kinetics of the process and the way in which cell division redistributes the particles remain unclear. Here we show that the chance of success or failure of nanoparticle uptake and inheritance is random. Statistical analysis of nanoparticle-loaded endosomes indicates that particle capture is described by an over-dispersed Poisson probability distribution that is consistent with heterogeneous adsorption and internalization. Partitioning of nanoparticles in cell division is random and asymmetric, following a binomial distribution with mean probability of 0.52-0.72. These results show that cellular targeting of nanoparticles is inherently imprecise due to the randomness of nature at the molecular scale, and the statistical framework offers a way to predict nanoparticle dosage for therapy and for the study of nanotoxins.

Quantitative characterization of nanoparticle agglomeration within biological media

Quantitative analysis of nanoparticle dispersion state within biological media is essential to understanding cellular uptake and the roles of diffusion, sedimentation, and endocytosis in determining nanoparticle dose. The dispersion of polymer-coated CdTe/ZnS quantum dots in water and cell growth medium with and without fetal bovine serum was analyzed by transmission electron microscopy (TEM) and dynamic light scattering (DLS) techniques. Characterization by TEM of samples prepared by plunge freezing the blotted solutions into liquid ethane was sensitive to the dispersion state of the quantum dots and enabled measurement of agglomerate size distributions even in the presence of serum proteins where DLS failed. In addition, TEM showed a reduced packing fraction of quantum dots per agglomerate when dispersed in biological media and serum compared to just water, highlighting the effect of interactions between the media, serum proteins, and the quantum dots. The identification of a heterogeneous distribution of quantum dots and quantum dot agglomerates in cell growth medium and serum by TEM will enable correlation with the previously reported optical metrology of in vitro cellular uptake of this quantum dot dispersion. In this paper, we present a comparative study of TEM and DLS and show that plunge-freeze TEM provides a robust assessment of nanoparticle agglomeration state.

Ghrelin inhibits LPS-induced release of IL-6 from mouse dopaminergic neurones

BackgroundGhrelin is an orexigenic stomach hormone that acts centrally to increase mid-brain dopamine neurone activity, amplify dopamine signaling and protect against neurotoxin-induced dopamine cell death in the mouse substantia nigra pars compacta (SNpc). In addition, ghrelin inhibits the lipopolysaccharide (LPS)-induced release of pro-inflammatory cytokines from peripheral macrophages, T-cells and from LPS stimulated microglia. Here we sought to determine whether ghrelin attenuates pro-inflammatory cytokine release from dopaminergic neurones.FindingsThe dopaminergic SN4741 cell-line, which derives from the mouse substantia nigra (SN) and expresses the ghrelin-receptor (growth hormone secretagogue receptor (GHS-R)) and the ghrelin-O-acyl transferase (GOAT) enzyme, was used to determine the neuro-immunomodulatory action of ghrelin. We induced innate immune activation via LPS challenge (1 μg/ml) of SN4741 neurones that had been pre-cultured in the presence or absence of ghrelin (1, 10, 100 nM) for 4 h. After 24 h supernatants were collected for detection of IL-1 beta (IL-1β ), TNF alpha (TNF-α) and IL-6 cytokines via enzyme linked immunosorbent assay (ELISA) analysis. Nuclear translocation of the transcription factor nuclear factor kappa B (NF-κB) was analyzed by Western blotting, and to determine viability of treatments a cell viability assay and caspase-3 immunohistochemistry were performed.We provide evidence that while IL-1β and TNF-α were not detectable under any conditions, SN4741 neurones constitutively released IL-6 under basal conditions and treatment with LPS significantly increased IL-6 secretion. Pre-treatment of neurones with ghrelin attenuated LPS-mediated IL-6 release at 24 h, an affect that was inhibited by the GHS-R antagonist [D-Lys3]-GHRP-6. However, while ghrelin pre-treatment attenuated the LPS-mediated increase in NF-κB, there was no alteration in its nuclear translocation. Cell viability assay and caspase-3 immunocytochemistry demonstrated that the results were independent from activation of cytotoxic and/or apoptotic mechanisms in the neuronal population, respectively.ConclusionOur results provide evidence that the gut-hormone, ghrelin, attenuates IL-6 secretion to LPS challenge in mid-brain dopaminergic neurones. These data suggest that ghrelin may protect against dopaminergic SN nerve cell damage or death via modulation of the innate immune response.

Nanoparticle vesicle encoding for imaging and tracking cell populations

For phenotypic behavior to be understood in the context of cell lineage and local environment, properties of individual cells must be measured relative to population-wide traits. However, the inability to accurately identify, track and measure thousands of single cells via high-throughput microscopy has impeded dynamic studies of cell populations. We demonstrate unique labeling of cells, driven by the heterogeneous random uptake of fluorescent nanoparticles of different emission colors. By sequentially exposing a cell population to different particles, we generated a large number of unique digital codes, which corresponded to the cell-specific number of nanoparticle-loaded vesicles and were visible within a given fluorescence channel. When three colors are used, the assay can self-generate over 17,000 individual codes identifiable using a typical fluorescence microscope. The color-codes provided immediate visualization of cell identity and allowed us to track human cells with a success rate of 78% across image frames separated by 8 h.

A study of microstructural templating in fibrin–thrombin gel networks by spectral and viscoelastic analysis

We report a study of the microstructural templating role of incipient fibrin–thrombin gels by analysis of rheological and confocal microscope measurements. Fractal analysis based on the spectral dimension is used, for the first time, to characterise fibrin gel microstructure in terms of the internal connectivity of gel networks. A significant correlation is found between the fractal characteristics of the incipient gel network and its eventual mature form, confirming that incipient gel microstructure templates ensuing gel development. We report an analytical basis for the study of this templating effect which reveals two different regimes of microstructural development. The first involves low thrombin concentration, in which increasing concentration decreases the gel formation time and alters the fractal characteristics of both incipient and mature gels. In the second regime, involving higher thrombin concentrations, the incipient gel formation time and the fractal characteristics of incipient and mature gels show little variation. The network formation is discussed in terms of computer simulations of incipient fractal networks by the activation-limited aggregation of clusters of rod-like particles. The significance of the work is discussed in terms of biomaterials design for applications involving controlled drug release and wound healing, and improved predictions of blood clot susceptibility to lysis.

Long-term time series analysis of quantum dot encoded cells by deconvolution of the autofluorescence signal

The monitoring of cells labeled with quantum dot endosome‐targeted markers in a highly proliferative population provides a quantitative approach to determine the redistribution of quantum dot signal as cells divide over generations. We demonstrate that the use of time‐series flow cytometry in conjunction with a stochastic numerical simulation to provide a means to describe the proliferative features and quantum dot inheritance over multiple generations of a human tumor population. However, the core challenge for long‐term tracking where the original quantum dot fluorescence signal over time becomes redistributed across a greater cell number requires accountability of background fluorescence in the simulation. By including an autofluorescence component, we are able to continue even when this signal predominates (i.e., >80% of the total signal) and obtain valid readouts of the proliferative system. We determine the robustness of the technique by tracking a human osteosarcoma cell population over 8 days and discuss the accuracy and certainty of the model parameters obtained. This systems biology approach provides insight into both cell heterogeneity and division dynamics within the population and furthermore informs on the lineage history of its members.

A Review of the Development and Applications of the Cuckoo Search Algorithm

The cuckoo search is a relatively new gradient free optimization algorithm, which has been growing in popularity. The algorithm aims to replicate the particularly aggressive breeding behavior of cuckoos and it makes use of the Levy flight, which is an efficient search pattern. In this chapter, the original development of the cuckoo search is discussed and a number of modifications that have been made to the basic procedure are compared. A number of applications of the cuckoo search are described and some possible future developments of the cuckoo search algorithm are summarized.

Interoperability of time series cytometric data: A cross platform approach for modeling tumor heterogeneity

The cell cycle, with its highly conserved features, is a fundamental driver for the temporal control of cell proliferation—while abnormal control and modulation of the cell cycle are characteristic of tumor cells. The principle aim in cancer biology is to seek anunderstanding of the origin and nature of innate and acquired heterogeneity at the cellular level, driven principally by temporal and functional asynchrony. A major bottleneck when mathematically modeling these biological systems is the lack of interlinked structured experimental data. This often results in the in silico models failing to translate the specific hypothesis into parameterized terms that enable robust validation and hence would produce suitable prediction tools rather than just simulation tools. The focus has been on linking data originating from different cytometric platforms and cell‐based event analysis to inform and constrain the input parameters of a compartmental cell cycle model, hence partly measuring and deconvolving cell cycle heterogeneity within a tumor population. Our work has addressed the concept that the interoperability of cytometric data, derived from different cytometry platforms, can complement as well as enhance cellular parameters space, thus providing a more broader and in‐depth view of the cellular systems. The initial aim was to enable the cell cycle model to deliver an improved integrated simulation of the well‐defined and constrained biological system. From a modeling perspective, such a cross platform approach has provided a paradigm shift from conventional cross‐validation approaches, and from a bioinformatics perspective, novel computational methodology has been introduced for integrating and mapping continuous data with cross‐sectional data. This establishes the foundation for developing predictive models and in silico tracking and prediction of tumor progression.

A transfer function approach to measuring cell inheritance

BackgroundThe inheritance of cellular material between parent and daughter cells during mitosis is highly influential in defining the properties of the cell and therefore the population lineage. This is of particular relevance when studying cell population evolution to assess the impact of a disease or the perturbation due to a drug treatment. The usual technique to investigate inheritance is to use time-lapse microscopy with an appropriate biological marker, however, this is time consuming and the number of inheritance events captured are too low to be statistically meaningful.ResultsHere we demonstrate the use of a high throughput fluorescence measurement technique e.g. flow cytometry, to measure the fluorescence from quantum dot markers which can be used to target particular cellular sites. By relating, the fluorescence intensity measured between two time intervals to a transfer function we are able to deconvolve the inheritance of cellular material during mitosis. To demonstrate our methodology we use CdTe/ZnS quantum dots to measure the ratio of endosomes inherited by the two daughter cells during mitosis in the U2-OS, human osteoscarcoma cell line. The ratio determined is in excellent agreement with results obtained previously using a more complex and computational intensive bespoke stochastic model.ConclusionsThe use of a transfer function approach allows us to utilise high throughput measurement of large cell populations to derive statistically relevant measurements of the inheritance of cellular material. This approach can be used to measure the inheritance of organelles, proteins etc. and also particles introduced to cells for drug delivery.

Flow-based cytometric analysis of cell cycle via simulated cell populations

We present a new approach to the handling and interrogating of large flow cytometry data where cell status and function can be described, at the population level, by global descriptors such as distribution mean or co-efficient of variation experimental data. Here we link the “real” data to initialise a computer simulation of the cell cycle that mimics the evolution of individual cells within a larger population and simulates the associated changes in fluorescence intensity of functional reporters. The model is based on stochastic formulations of cell cycle progression and cell division and uses evolutionary algorithms, allied to further experimental data sets, to optimise the system variables. At the population level, the in-silico cells provide the same statistical distributions of fluorescence as their real counterparts; in addition the model maintains information at the single cell level. The cell model is demonstrated in the analysis of cell cycle perturbation in human osteosarcoma tumour cells, using the topoisomerase II inhibitor, ICRF-193. The simulation gives a continuous temporal description of the pharmacodynamics between discrete experimental analysis points with a 24 hour interval; providing quantitative assessment of inter-mitotic time variation, drug interaction time constants and sub-population fractions within normal and polyploid cell cycles. Repeated simulations indicate a model accuracy of ±5%. The development of a simulated cell model, initialized and calibrated by reference to experimental data, provides an analysis tool in which biological knowledge can be obtained directly via interrogation of the in-silico cell population. It is envisaged that this approach to the study of cell biology by simulating a virtual cell population pertinent to the data available can be applied to “generic” cell-based outputs including experimental data from imaging platforms.