ABSTRACT
A series of tamoxifen-like metallocifens of the group-8 metals (Fe, Ru, and Os) has strong antiproliferative activity on the triple-negative breast cancer cells (MDA-MB-231). To shed light on the mechanism of action of these molecules, synchrotron radiation X-ray fluorescence nanoimaging studies were performed on cells exposed to osmocenyl-tamoxifen (Oc-OH-Tam) to disclose its intracellular distribution. High-resolution mapping of the lipophilic Oc-OH-Tam in cells revealed its preferential accumulation in the endomembrane system. This is consistent with the ability of the amino nitrogen chain of the compounds to be protonated at physiological pH and responsible for electrostatic interactions between Oc-OH-Tam and membranes. A comprehensive scenario is proposed that provides new insight into the cellular behavior and activation of Oc-OH-Tam and advances the understanding of its mechanism of action.
Subject(s)
Antineoplastic Agents/chemistry , Coordination Complexes/chemistry , Organometallic Compounds/chemistry , Tamoxifen/chemistry , Triple Negative Breast Neoplasms/drug therapy , Antineoplastic Agents/metabolism , Cell Line, Tumor , Cell Membrane/metabolism , Coordination Complexes/metabolism , Female , Humans , Hydrogen-Ion Concentration , Iron/chemistry , Ligands , Molecular Imaging/methods , Molecular Probes/chemistry , Nanostructures/chemistry , Nanostructures/ultrastructure , Osmium/chemistry , Radiography , Ruthenium/chemistry , Static Electricity , Synchrotrons , X-RaysABSTRACT
In propagation based phase contrast imaging, intensity patterns are recorded on a x-ray detector at one or multiple propagation distances, called in-line holograms. They form the input of an inversion algorithm that aims at retrieving the phase shift induced by the object. The problem of phase retrieval in in-line holography is an ill-posed inverse problem. Consequently an adequate solution requires some form of regularization with the most commonly applied being the classical Tikhonov regularization. While generally satisfying this method suffers from a few issues such as the choice of the regularization parameter. Here, we offer an alternative to the established method by applying the principles of Bayesian inference. We construct an iterative optimization algorithm capable of both retrieving the unknown phase and determining a multi-dimensional regularization parameter. In the end, we highlight the advantages of the introduced algorithm, chief among them being the unsupervised determination of the regularization parameter(s). The proposed approach is tested on both simulated and experimental data and is found to provide robust solutions, with improved response to typical issues like low frequency noise and the twin-image problem.
