Background: Oligodendrocytes (OL) are the myelinating cells of the central nervous system. OL differentiation from oligodendrocyte progenitor cells (OPC) is accompanied by characteristic stereotypical morphological changes. Quantitative imaging of those morphological alterations during OPC differentiation is commonly used for characterization of new molecules in cell differentiation and myelination and screening of new pro-myelinating drugs. Current available imaging analysis methods imply a non-automated morphology assessment, which is time-consuming and prone to user subjective evaluation.
New method: Here, we describe an automated high-throughput quantitative image analysis method entitled collar occupancy that allows morphometric ranking of different stages of in vitro OL differentiation in a high-content analysis format. Collar occupancy is based on the determination of the percentage of area occupied by OPC/OL cytoplasmic protrusions within a defined region that contains the protrusion network, the collar.
Results: We observed that more differentiated cells have higher collar occupancy and, therefore, this parameter correlates with the degree of OL differentiation.
Comparison with existing methods: In comparison with the method of manual categorization, we found the collar occupancy to be more robust and unbiased. Moreover, when coupled with myelin basic protein (MBP) staining to quantify the percentage of myelinating cells, we were able to evaluate the role of new molecules in OL differentiation and myelination, such as Dusp19 and Kank2.
Conclusions: Altogether, we have successfully developed an automated and quantitative method to morphologically characterize OL differentiation in vitro that can be used in multiple studies of OL biology.
Keywords: Automated; Collar occupancy; Dusp19; High-throughput; Imaging; Kank2; Morphometric analysis; Oligodendrocyte.
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