Selective photodynamic effects on cervical adenocarcinoma cells provided by F127 Pluronic®-based micelles modulating hypericin delivery

Abstract Cervical cancer is a leading cause of death among women. The endocervical adenocarcinoma (ECA) represents an aggressive and metastatic type of cancer with no effective treatment options currently available. We evaluated the antitumoral and anti-migratory effects of hypericin (HYP) encapsulated on Pluronic F127 (F127/HYP) photodynamic therapy (PDT) against a human cell line derived from invasive cervical adenocarcinoma (HeLa) compared to a human epithelial cell line (HaCaT). The phototoxicity and cytotoxicity of F127/HYP were evaluated by the following assays: colorimetric assay, MTT, cellular morphological changes by microscopy and long-term cytotoxicity by clonogenic assay. In addition, we performed fluorescence microscopy to analyze cell uptake and subcellular distribution of F127/HYP, cell death pathway and reactive oxygen species (ROS) production. The PDT mechanism was determined with sodium azide and D-mannitol and cell migration by wound-healing assay. The treatment with F127/HYP promoted a phototoxic result in the HeLa cells in a dose-dependent and selective form. Internalization of F127/HYP was observed mainly in the mitochondria, causing cell death by necrosis and ROS production especially by the type II PDT mechanism. Furthermore, F127/HYP reduced the long-term proliferation and migration capacity of HeLa cells. Overall, our results indicate a potentially application of F127/HYP micelles as a novel approach for PDT with HYP delivery to more specifically treat ECA.

Characterization of a new case of XMLV (Bxv1) contamination in the human cell line Hep2 (clone 2B).

The use of misidentified cell lines contaminated by other cell lines and/or microorganisms has generated much confusion in the scientific literature. Detailed characterization of such contaminations is therefore crucial to avoid misinterpretation and ensure robustness and reproducibility of research. Here we use DNA-seq data produced in our lab to first confirm that the Hep2 (clone 2B) cell line (Sigma-Aldrich catalog number: 85011412-1VL) is indistinguishable from the HeLa cell line by mapping integrations of the human papillomavirus 18 (HPV18) at their expected loci on chromosome 8. We then show that the cell line is also contaminated by a xenotropic murine leukemia virus (XMLV) that is nearly identical to the mouse Bxv1 provirus and we characterize one Bxv1 provirus, located in the second intron of the pseudouridylate synthase 1 (PUS1) gene. Using an RNA-seq dataset, we confirm the high expression of the E6 and E7 HPV18 oncogenes, show that the entire Bxv1 genome is moderately expressed, and retrieve a Bxv1 splicing event favouring expression of the env gene. Hep2 (clone 2B) is the fourth human cell line so far known to be contaminated by the Bxv1 XMLV. This contamination has to be taken into account when using the cell line in future experiments.

Boosting accuracy of automated classification of fluorescence microscope images for location proteomics.

BACKGROUND: Detailed knowledge of the subcellular location of each expressed protein is critical to a full understanding of its function. Fluorescence microscopy, in combination with methods for fluorescent tagging, is the most suitable current method for proteome-wide determination of subcellular location. Previous work has shown that neural network classifiers can distinguish all major protein subcellular location patterns in both 2D and 3D fluorescence microscope images. Building on these results, we evaluate here new classifiers and features to improve the recognition of protein subcellular location patterns in both 2D and 3D fluorescence microscope images. RESULTS: We report here a thorough comparison of the performance on this problem of eight different state-of-the-art classification methods, including neural networks, support vector machines with linear, polynomial, radial basis, and exponential radial basis kernel functions, and ensemble methods such as AdaBoost, Bagging, and Mixtures-of-Experts. Ten-fold cross validation was used to evaluate each classifier with various parameters on different Subcellular Location Feature sets representing both 2D and 3D fluorescence microscope images, including new feature sets incorporating features derived from Gabor and Daubechies wavelet transforms. After optimal parameters were chosen for each of the eight classifiers, optimal majority-voting ensemble classifiers were formed for each feature set. Comparison of results for each image for all eight classifiers permits estimation of the lower bound classification error rate for each subcellular pattern, which we interpret to reflect the fraction of cells whose patterns are distorted by mitosis, cell death or acquisition errors. Overall, we obtained statistically significant improvements in classification accuracy over the best previously published results, with the overall error rate being reduced by one-third to one-half and with the average accuracy for single 2D images being higher than 90% for the first time. In particular, the classification accuracy for the easily confused endomembrane compartments (endoplasmic reticulum, Golgi, endosomes, lysosomes) was improved by 5-15%. We achieved further improvements when classification was conducted on image sets rather than on individual cell images. CONCLUSIONS: The availability of accurate, fast, automated classification systems for protein location patterns in conjunction with high throughput fluorescence microscope imaging techniques enables a new subfield of proteomics, location proteomics. The accuracy and sensitivity of this approach represents an important alternative to low-resolution assignments by curation or sequence-based prediction.