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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 (CA) 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 we describe an approach to accurately quantify centrosomes in tissue sections and tumors. Considering proliferation and baseline amplification rate the resulting population based ratio of centrosomes per nucleus allow the approximation of the proportion of cells with CA. Using this technique we show that 20-30 percent of cells have amplified centrosomes in Tp53 null mammary tumors. Combining fluorescence detection, deconvolution microscopy and a mathematical algorithm applied to a maximum intensity projection we show that this approach is superior to traditional investigator based visual analysis or threshold-based techniques.
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