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Direct monitoring of single-cell response to biomaterials by Raman spectroscopy.

ABSTRACT: There is continued focus on the development of new biomaterials and associated biological testing methods needed to reduce the time taken for their entry to clinical use. The application of Raman spectroscopy to the study of individual cells that have been in contact with biomaterials offers enhanced in vitro information in a potentially non-destructive testing regime. The work presented here reports the Raman spectral analysis of discreet U-2 OS bone cells after exposure to hydroxyapatite (HA) coated titanium (Ti) substrates in both the as-deposited and thermally annealed states. These data show that cells that were in contact with the bioactive HA surface for 7 days had spectral markers similar to those cultured on the Ti substrate control for the same period. However, the spectral features for those cells that were in contact with the annealed HA surface had indicators of significant differentiation at day 21 while cells on the as-deposited surface did not show these Raman changes until day 28. The cells adhered to pristine Ti control surface showed no spectral changes at any of the timepoints studied. The validity of these spectroscopic results has been confirmed using data from standard in vitro cell viability, adhesion, and proliferation assays over the same 28-day culture period. In this case, cell maturation was evidenced by the formation of natural bone apatite, which precipitated intracellularly for cells exposed to both types of HA-coated Ti at 21 and 28 days, respectively. The properties of the intracellular apatite were markedly different from that of the synthetic HA used to coat the Ti substrate with an average particle size of 230 nm, a crystalline-like shape and Ca/P ratio of 1.63 ± 0.5 as determined by SEM-EDX analysis. By comparison, the synthetic HA particles used as a control had an average size of 372 nm and were more-rounded in shape with a Ca/P ratio of 0.8 by XPS analysis and 1.28 by SEM-EDX analysis. This study shows that Raman spectroscopy can be employed to monitor single U-2 OS cell response to biomaterials that promote cell maturation towards de novo bone thereby offering a label-free in vitro testing method that allows for non-destructive analyses.

SUBMITTER: McIvor MJ

PROVIDER: S-EPMC8643295 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Tumour cells exhibit altered metabolic pathways that lead to radiation resistance and disease progression. Raman spectroscopy (RS) is a label-free optical modality that can monitor post-irradiation biomolecular signatures in tumour cells and tissues. Convolutional Neural Networks (CNN) perform automated feature extraction directly from data, with classification accuracy exceeding that of traditional machine learning, in cases where data is abundant and feature extraction is challenging. We are interested in developing a CNN-based predictive model to characterize clinical tumour response to radiation therapy based on their degree of radiosensitivity or radioresistance. In this work, a CNN architecture is built for identifying post-irradiation spectral changes in Raman spectra of tumour tissue. The model was trained to classify irradiated versus non-irradiated tissue using Raman spectra of breast tumour xenografts. The CNN effectively classified the tissue spectra, with accuracies exceeding 92.1% for data collected 3 days post-irradiation, and 85.0% at day 1 post-irradiation. Furthermore, the CNN was evaluated using a leave-one-out- (mouse, section or Raman map) validation approach to investigate its generalization to new test subjects. The CNN retained good predictive accuracy (average accuracies 83.7%, 91.4%, and 92.7%, respectively) when little to no information for a specific subject was given during training. Finally, the classification performance of the CNN was compared to that of a previously developed model based on group and basis restricted non-negative matrix factorization and random forest (GBR-NMF-RF) classification. We found that CNN yielded higher classification accuracy, sensitivity, and specificity in mice assessed 3 days post-irradiation, as compared with the GBR-NMF-RF approach. Overall, the CNN can detect biochemical spectral changes in tumour tissue at an early time point following irradiation, without the need for previous manual feature extraction. This study lays the foundation for developing a predictive framework for patient radiation response monitoring.

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Direct monitoring of single-cell response to biomaterials by Raman spectroscopy.

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