Dataset Information

Dispersion and stability optimization of TiO2 nanoparticles in cell culture media.

ABSTRACT: Accurate evaluation of engineered nanomaterial toxicity requires not only comprehensive physical-chemical characterization of nanomaterials as produced, but also thorough understanding of nanomaterial properties and behavior under conditions similar to those used for in vitro and in vivo toxicity studies. In this investigation, TiO(2) nanoparticles were selected as a model nanoparticle and bovine serum albumin (BSA) was selected as a model protein for studying the effect of protein-nanoparticle interaction on TiO(2) nanoparticle dispersion in six different mammalian, bacteria, and yeast cell culture media. Great improvement in TiO(2) dispersion was observed upon the addition of BSA, even though the degree of dispersion varied from medium to medium and phosphate concentration in the cell culture media was one of the key factors governing nanoparticle dispersion. Fetal bovine serum (FBS) was an effective dispersing agent for TiO(2) nanoparticles in all six media due to synergistic effects of its multiple protein components, successfully reproduced using a simple "FBS mimic" protein cocktail containing similar concentrations of BSA, ?-globulin, and apo-transferrin.

SUBMITTER: Ji Z

PROVIDER: S-EPMC3971839 | biostudies-literature | 2010 Oct

REPOSITORIES: biostudies-literature

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Dispersion and stability optimization of TiO2 nanoparticles in cell culture media.

Ji Zhaoxia Z Jin Xue X George Saji S Xia Tian T Meng Huan H Wang Xiang X Suarez Elizabeth E Zhang Haiyuan H Hoek Eric M V EM Godwin Hilary H Nel André E AE Zink Jeffrey I JI

Environmental science & technology 20101001 19

Accurate evaluation of engineered nanomaterial toxicity requires not only comprehensive physical-chemical characterization of nanomaterials as produced, but also thorough understanding of nanomaterial properties and behavior under conditions similar to those used for in vitro and in vivo toxicity studies. In this investigation, TiO(2) nanoparticles were selected as a model nanoparticle and bovine serum albumin (BSA) was selected as a model protein for studying the effect of protein-nanoparticle ...[more]

PMID: 20536146

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Project description:The growing application of cell and gene therapies in humans leads to a need for cell type-optimized culture media. Design of Experiments (DoE) is a successful and well known tool for the development and optimization of cell culture media for bioprocessing. When optimizing culture media for primary cells used in cell and gene therapy, traditional DoE approaches that depend on interpretable models will not always provide reliable predictions due to high donor variability. Here we present the implementation of a machine learning pipeline into the DoE-based design of cell culture media to optimize T cell cultures in one experimental step (one-time optimization). We applied a definitive screening design from the DoE toolbox to screen 12 major media components, resulting in 25 (2k + 1) media formulations. T cells purified from a set of four human donors were cultured for 6 days and cell viability on day 3 and cell expansion on day 6 were recorded as response variables. These data were used as a training set in the machine learning pipeline. In the first step, individual models were created for each donor, evaluated and selected for each response variable, resulting in eight final statistical models (R 2 > 0.92, RMSE < 1.5). These statistical models were used to predict T cell viability and expansion for 105 random in silico-generated media formulations for each donor in a grid search approach. With the aim of identifying similar formulations in all donors, the 40 best performing media formulations of each response variable were pooled from all donors (n = 320) and subjected to unsupervised clustering using the k-means algorithm. The median of each media component in each cluster was defined as the cluster media formulation. When these formulations were tested in a new set of donor cells, they not only showed a higher T cell expansion than the reference medium, but also precisely matched the average expansion predicted from the donor models of the training set. In summary, we have shown that the introduction of a machine learning pipeline resulted in a one-time optimized T cell culture medium and is advantageous when working with heterogeneous biological material.

Dataset Information

Dispersion and stability optimization of TiO2 nanoparticles in cell culture media.

Publications

Dispersion and stability optimization of TiO2 nanoparticles in cell culture media.

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