title: Semi-automated Sholl analysis for quantifying changes in growth and differentiation of neurons and glia.
abstract: There is a need to develop therapies that promote growth or remyelination of mammalian CNS axons. Although the feasibility of pre-clinical treatment strategies should be tested in animal models, in vitro assays are usually faster and less expensive. As a result, in vitro models are ideal for screening large numbers of potential therapeutics prior to use in more complex in vivo systems. In 1953, Sholl introduced a technique that is a reliable and sensitive method for quantifying indices of neurite outgrowth. However, application of the technique is limited because it is labor-intensive. Several methods have been developed to reduce the analysis time for the Sholl technique; but these methods require extensive pre-processing of digital images, they introduce user bias or they have not been compared to manual analysis to ensure accuracy. Here we describe a new, semi-automated Sholl technique for quantifying neuronal and glial process morphology. Using MetaMorph, we developed an unbiased analysis protocol that can be performed approximately 3x faster than manual quantification with a comparable level of accuracy regardless of cell morphology. The laborious image processing typical of most computer-aided analysis is avoided by embedding image correction functions into the automated portion of the analysis. The sensitivity and validity of the technique was confirmed by quantifying neuron growth treated with growth factors or oligodendroglial maturation in the presence or absence of thyroid hormone. Thus, this technique provides a rapid and sensitive method for quantifying changes in cell morphology and screening for treatment effects in multiple cell types in vitro.
authors.affiliation: Department of Neuroscience, Center for Brain and Spinal Cord Repair, The Ohio State University College of Medicine and Public Health, Columbus, OH 43210, USA. gensel.1@osu.edu
authors.forename: John C
authors.initials: JC
authors.lastname: Gensel
authors.forename: David L
authors.initials: DL
authors.lastname: Schonberg
authors.forename: Jessica K
authors.initials: JK
authors.lastname: Alexander
authors.forename: Dana M
authors.initials: DM
authors.lastname: McTigue
authors.forename: Phillip G
authors.initials: PG
authors.lastname: Popovich
mesh_terms: D000818:Animals; D001331:Automation; D002454:Cell Differentiation; D049109:Cell Proliferation; D002478:Cells, Cultured; D002540:Cerebral Cortex / Q000166:cytology / Q000502:physiology; D053595:Embryonic Stem Cells / Q000166:cytology / Q000502:physiology; D005727:Ganglia, Spinal / Q000166:cytology / Q000502:physiology; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007150:Immunohistochemistry; D036341:Intercellular Signaling Peptides and Proteins / Q000378:metabolism; D051379:Mice; D008810:Mice, Inbred C57BL; D016501:Neurites / Q000502:physiology; D009474:Neurons / Q000166:cytology* / Q000502:physiology; D015588:Observer Variation; D009836:Oligodendroglia / Q000166:cytology* / Q000502:physiology; D051381:Rats; D012984:Software; D012985:Software Design; D013963:Thyroid Hormones / Q000172:deficiency / Q000378:metabolism; D013997:Time Factors
chemical_list: D036341:Intercellular Signaling Peptides and Proteins; D013963:Thyroid Hormones