TY - JOUR
T1 - Automated analysis of neuronal morphology, synapse number and synaptic recruitment
AU - Schmitz, S.K.
AU - Hjorth, J.J.J.
AU - Joemai, R.M.S.
AU - Wijntjes, R.
AU - Eijgenraam, S.
AU - de Bruijn, P.
AU - Georgiou, C.
AU - de Jong, A.P.H.
AU - van Ooyen, A.
AU - Verhage, M.
AU - Cornelisse, L.N.
AU - Toonen, R.F.
AU - Veldkamp, W.J.H.
PY - 2011
Y1 - 2011
N2 - The shape, structure and connectivity of nerve cells are important aspects of neuronal function. Genetic and epigenetic factors that alter neuronal morphology or synaptic localization of pre- and post-synaptic proteins contribute significantly to neuronal output and may underlie clinical states. To assess the impact of individual genes and disease-causing mutations on neuronal morphology, reliable methods are needed. Unfortunately, manual analysis of immuno-fluorescence images of neurons to quantify neuronal shape and synapse number, size and distribution is labor-intensive, time-consuming and subject to human bias and error.We have developed an automated image analysis routine using steerable filters and deconvolutions to automatically analyze dendrite and synapse characteristics in immuno-fluorescence images. Our approach reports dendrite morphology, synapse size and number but also synaptic vesicle density and synaptic accumulation of proteins as a function of distance from the soma as consistent as expert observers while reducing analysis time considerably. In addition, the routine can be used to detect and quantify a wide range of neuronal organelles and is capable of batch analysis of a large number of images enabling high-throughput analysis. © 2010 Elsevier B.V.
AB - The shape, structure and connectivity of nerve cells are important aspects of neuronal function. Genetic and epigenetic factors that alter neuronal morphology or synaptic localization of pre- and post-synaptic proteins contribute significantly to neuronal output and may underlie clinical states. To assess the impact of individual genes and disease-causing mutations on neuronal morphology, reliable methods are needed. Unfortunately, manual analysis of immuno-fluorescence images of neurons to quantify neuronal shape and synapse number, size and distribution is labor-intensive, time-consuming and subject to human bias and error.We have developed an automated image analysis routine using steerable filters and deconvolutions to automatically analyze dendrite and synapse characteristics in immuno-fluorescence images. Our approach reports dendrite morphology, synapse size and number but also synaptic vesicle density and synaptic accumulation of proteins as a function of distance from the soma as consistent as expert observers while reducing analysis time considerably. In addition, the routine can be used to detect and quantify a wide range of neuronal organelles and is capable of batch analysis of a large number of images enabling high-throughput analysis. © 2010 Elsevier B.V.
U2 - 10.1016/j.jneumeth.2010.12.011
DO - 10.1016/j.jneumeth.2010.12.011
M3 - Article
SN - 0165-0270
VL - 195
SP - 185
EP - 193
JO - Journal of Neuroscience Methods
JF - Journal of Neuroscience Methods
IS - 2
ER -