ABSTRACT
<p><b>OBJECTIVE</b>To isolate and identify the cancer stem cells from primary human ovarian cancer tissues.</p><p><b>METHODS</b>Fresh tumor tissues from five cases of pathologically diagnosed ovarian cancers were taken, minced and then digested with collagenase and hyaluronidase to obtain single cell suspension. The erythrocytes were removed with ACK Lysis buffer. The suspensions were sorted by magnetic activated cell sorting (MACS) using CD133-binding microbeads. Then the sorted CD133(+) cells were verified by flow cytometry. The cells were cultured in serum-free medium supplemented with EGF, bFGF, insulin and BSA, and grew into spheroids. Immunofluorescence, differentiation and tumor formation tests of the cells were performed to characterize the properties of cancer stem cells.</p><p><b>RESULTS</b>The ovarian cancer stem cells were successfully isolated from primary human ovarian tumors, which formed typical spheroids in serum-free medium and had stronger ability of tumorigenesis. The results of related experiments verified that CD133 positive cells owned the properties of cancer stem cells.</p><p><b>CONCLUSIONS</b>The ovarian cancer stem cells presenting the characteristics of stemness in vitro and in vivo, have been successfully isolated from primary human ovarian tumor tissues by MACS. The isolated ovarian cancer stem cells could be used in future researches of their biological functions.</p>
Subject(s)
Animals , Female , Humans , Mice , AC133 Antigen , Antigens, CD , Metabolism , Cell Differentiation , Cell Separation , Methods , Flow Cytometry , Methods , Glycoproteins , Metabolism , Immunomagnetic Separation , Methods , Mice, Inbred NOD , Mice, SCID , Neoplasm Transplantation , Neoplastic Stem Cells , Metabolism , Pathology , Ovarian Neoplasms , Metabolism , Pathology , Peptides , MetabolismABSTRACT
The interaction between recombinant Fab57P and the coat protein of tobacco mosaic virus was studied using quantitative structure-activity relationship (QSAR) method. The development of quantitative multivariate model has shown to be a promising approach for unraveling protein-protein interactions by designed mutations in peptide sequence. This approach makes it possible to stereo-chemically determine which residue properties contribute most to the interaction. A set of side-chain descriptors was proposed and applied in structural characterization of three positions (positions 142, 145 and 146) in the peptide antigen. Quantitative sequence-kinetics relationship (QSKR) models describing the dissociation rates (log k(d) ) were developed successfully using orthogonal signal correction-partial least squares method. The results showed that peptides will have high log k(d) values when the amino acids in position 142 and 145 have high net charge index, and when residue 145 has high hydrophobicity and residue 146 has low hydrophobicity.
