Osteoclasts are bone-resorbing polykaryons differentiated from monocyte/macrophage-lineage hematopoietic precursors. It remains unclear whether osteoclasts originate from circulating blood monocytes or from bone tissue–resident precursors. To address this question, we combined two different experimental procedures: 1) shared blood circulation “parabiosis” with fluorescently labeled osteoclast precursors, and 2) photoconversion-based cell tracking with a Kikume Green-Red protein (KikGR). In parabiosis, CX3CR1-EGFP knock-in mice in which osteoclast precursors were labeled with EGFP were surgically connected with wild-type mice to establish a shared circulation. Mature EGFP+ osteoclasts were found in the bones of the wild-type mice, indicating the mobilization of EGFP+ osteoclast precursors into bones from systemic circulation. Receptor activator for NF-κB ligand stimulation increased the number of EGFP+ osteoclasts in wild-type mice, suggesting that this mobilization depends on the bone resorption state. Additionally, KikGR+ monocytes (including osteoclast precursors) in the spleen were exposed to violet light, and 2 d later we detected photoconverted “red” KikGR+ osteoclasts along the bone surfaces. These results indicate that circulating monocytes from the spleen entered the bone spaces and differentiated into mature osteoclasts during a certain period. The current study used fluorescence-based methods clearly to demonstrate that osteoclasts can be generated from circulating monocytes once they home to bone tissues.
BackgroundAutomated image analysis methods are becoming more and more important to extract and quantify image features in microscopy-based biomedical studies and several commercial or open-source tools are available. However, most of the approaches rely on pixel-wise operations, a concept that has limitations when high-level object features and relationships between objects are studied and if user-interactivity on the object-level is desired.ResultsIn this paper we present an open-source software that facilitates the analysis of content features and object relationships by using objects as basic processing unit instead of individual pixels. Our approach enables also users without programming knowledge to compose “analysis pipelines“ that exploit the object-level approach. We demonstrate the design and use of example pipelines for the immunohistochemistry-based cell proliferation quantification in breast cancer and two-photon fluorescence microscopy data about bone-osteoclast interaction, which underline the advantages of the object-based concept.ConclusionsWe introduce an open source software system that offers object-based image analysis. The object-based concept allows for a straight-forward development of object-related interactive or fully automated image analysis solutions. The presented software may therefore serve as a basis for various applications in the field of digital image analysis.
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