title: Intensity-based signal separation algorithm for accurate quantification of clustered centrosomes in tissue sections.
abstract: Centrosomes are small organelles that organize the mitotic spindle during cell division and are also involved in cell shape and polarity. Within epithelial tumors, such as breast cancer, and some hematological tumors, centrosome abnormalities (CAs) are common, occur early in disease etiology, and correlate with chromosomal instability and disease stage. In situ quantification of CA by optical microscopy is hampered by overlap and clustering of these organelles, which appear as focal structures. CA has been frequently associated with Tp53 status in premalignant lesions and tumors. Here the authors described an approach to accurately quantify centrosome frequencies in tissue sections and tumors, independently of background or noise levels. Applying simple optical rules in nondeconvolved conventional 3D images of stained tissue sections, the authors showed that they could evaluate more accurately and rapidly centrosome frequencies than with traditional investigator-based visual analysis or threshold-based techniques. The resulting population-based frequency of centrosomes per nucleus could then be used to approximate the proportion of cells with CA in that same population. This was done by taking into account baseline centrosome amplification and proliferation rates measured in the tissue. Using this technique, the authors showed that 20-30% of cells have amplified centrosomes in Tp53 null mammary tumors.
authors.affiliation: Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA.
authors.forename: Markus C
authors.initials: MC
authors.lastname: Fleisch
authors.forename: Christopher A
authors.initials: CA
authors.lastname: Maxwell
authors.forename: Claudia K
authors.initials: CK
authors.lastname: Kuper
authors.forename: Erika T
authors.initials: ET
authors.lastname: Brown
authors.forename: Mary Helen
authors.initials: MH
authors.lastname: Barcellos-Hoff
authors.forename: Sylvain V
authors.initials: SV
authors.lastname: Costes
mesh_terms: D000465:Algorithms; D000818:Animals; D002467:Cell Nucleus / Q000648:ultrastructure; D018385:Centrosome; D004847:Epithelial Cells / Q000648:ultrastructure*; D005260:Female; D005455:Fluorescent Antibody Technique; D007091:Image Processing, Computer-Assisted; D008321:Mammary Glands, Animal / Q000166:cytology / Q000648:ultrastructure*; D015674:Mammary Neoplasms, Animal / Q000648:ultrastructure*; D051379:Mice; D008807:Mice, Inbred BALB C; D016159:Tumor Suppressor Protein p53 / Q000172:deficiency
chemical_list: D016159:Tumor Suppressor Protein p53