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Dual-emissive, oxygen-sensing boron nanoparticles quantify oxygen consumption rate in breast cancer cells.

ABSTRACT: SIGNIFICANCE:Decreasing the oxygen consumption rate (OCR) of tumor cells is a powerful method for ameliorating tumor hypoxia. However, quantifying the change in OCR is challenging in complex experimental systems. AIM:We present a method for quantifying the OCR of two tumor cell lines using oxygen-sensitive dual-emissive boron nanoparticles (BNPs). We hypothesize that our BNP results are equivalent to the standard Seahorse assay. APPROACH:We quantified the spectral emissions of the BNP and accounted for external oxygen diffusion to quantify OCR over 24 h. The BNP-computed OCR of two breast cancer cell lines, E0771 and 4T07, were compared with their respective Seahorse assays. Both cell lines were also irradiated to quantify radiation-induced changes in the OCR. RESULTS:Using a Bland-Altman analysis, our BNPs OCR was equivalent to the standard Seahorse assay. Moreover, in an additional experiment in which we irradiated the cells at their 50% survival fraction, the BNPs were sensitive enough to quantify 24% reduction in OCR after irradiation. CONCLUSIONS:Our results conclude that the BNPs are a viable alternative to the Seahorse assay for quantifying the OCR in cells. The Bland-Altman analysis showed that these two methods result in equivalent OCR measurements. Future studies will extend the OCR measurements to complex systems including 3D cultures and in vivo models, in which OCR measurements cannot currently be made.

SUBMITTER: Rickard AG

PROVIDER: S-EPMC7682476 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Dual-emissive, oxygen-sensing boron nanoparticles quantify oxygen consumption rate in breast cancer cells.

Rickard Ashlyn G AG Zhuang Meng M DeRosa Christopher A CA Zhang Xiaojie X Dewhirst Mark W MW Fraser Cassandra L CL Palmer Gregory M GM

Journal of biomedical optics 20201101 11

<h4>Significance</h4>Decreasing the oxygen consumption rate (OCR) of tumor cells is a powerful method for ameliorating tumor hypoxia. However, quantifying the change in OCR is challenging in complex experimental systems.<h4>Aim</h4>We present a method for quantifying the OCR of two tumor cell lines using oxygen-sensitive dual-emissive boron nanoparticles (BNPs). We hypothesize that our BNP results are equivalent to the standard Seahorse assay.<h4>Approach</h4>We quantified the spectral emissions ...[more]

PMID: 33231018

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Project description:Magnetic resonance imaging (MRI) offers the possibility to non-invasively map the brain's metabolic oxygen consumption (CMRO2), which is essential for understanding and monitoring neural function in both health and disease. However, in depth study of oxygen metabolism with MRI has so far been hindered by the lack of robust methods. One MRI method of mapping CMRO2 is based on the simultaneous acquisition of cerebral blood flow (CBF) and blood oxygen level dependent (BOLD) weighted images during respiratory modulation of both oxygen and carbon dioxide. Although this dual-calibrated methodology has shown promise in the research setting, current analysis methods are unstable in the presence of noise and/or are computationally demanding. In this paper, we present a machine learning implementation for the multi-parametric assessment of dual-calibrated fMRI data. The proposed method aims to address the issues of stability, accuracy, and computational overhead, removing significant barriers to the investigation of oxygen metabolism with MRI. The method utilizes a time-frequency transformation of the acquired perfusion and BOLD-weighted data, from which appropriate feature vectors are selected for training of machine learning regressors. The implemented machine learning methods are chosen for their robustness to noise and their ability to map complex non-linear relationships (such as those that exist between BOLD signal weighting and blood oxygenation). An extremely randomized trees (ET) regressor is used to estimate resting blood flow and a multi-layer perceptron (MLP) is used to estimate CMRO2 and the oxygen extraction fraction (OEF). Synthetic data with additive noise are used to train the regressors, with data simulated to cover a wide range of physiologically plausible parameters. The performance of the implemented analysis method is compared to published methods both in simulation and with in-vivo data (n=30). The proposed method is demonstrated to significantly reduce computation time, error, and proportional bias in both CMRO2 and OEF estimates. The introduction of the proposed analysis pipeline has the potential to not only increase the detectability of metabolic difference between groups of subjects, but may also allow for single subject examinations within a clinical context.

Dataset Information

Dual-emissive, oxygen-sensing boron nanoparticles quantify oxygen consumption rate in breast cancer cells.

Publications

Dual-emissive, oxygen-sensing boron nanoparticles quantify oxygen consumption rate in breast cancer cells.

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