Caroline Woeffler-Maucler1, Anne Beghin2, Denis Ressnikoff2, Laurent Bezin3, Stéphane Marinesco4. 1. Université de Lyon, Lyon, France; INSERM, Institut National de la Santé et de la Recherche Médicale, U1028, Lyon Neuroscience Research Center, Lyon, France; CNRS, Centre National de la Recherche Scientifique, UMR5292, Lyon Neuroscience Research Center, Lyon, France. 2. Université de Lyon, Lyon, France; Centre Commun de Quantimétrie, Lyon F-69008, France. 3. Université de Lyon, Lyon, France; INSERM, Institut National de la Santé et de la Recherche Médicale, U1028, Lyon Neuroscience Research Center, Lyon, France; CNRS, Centre National de la Recherche Scientifique, UMR5292, Lyon Neuroscience Research Center, Lyon, France; IDÉE, Institut Des ÉpilepsiEs, Lyon, France. 4. Université de Lyon, Lyon, France; INSERM, Institut National de la Santé et de la Recherche Médicale, U1028, Lyon Neuroscience Research Center, Lyon, France; CNRS, Centre National de la Recherche Scientifique, UMR5292, Lyon Neuroscience Research Center, Lyon, France; Plate-forme technologique AniRA-Neurochem, Lyon F-69000, France. Electronic address: stephane.marinesco@univ-lyon1.fr.
Abstract
BACKGROUND: To study neurotoxic processes, it is necessary to quantify the number of neurons in a given brain structure and estimate neuronal loss. Neuronal densities can be estimated by immunohistochemical quantitation of a neuronal marker such as the protein NeuN. However, NeuN expression may vary, depending on certain pathophysiological conditions and bias such quantifications. NEW METHOD: We have developed a simple automatic quantification of neuronal densities in brain sections stained with DAPI and antibody to NeuN. This method determines the number of DAPI-positive nuclei also positive for NeuN in at least two adjacent sections within a Z-stack of optical sections. RESULTS: We tested this method in animals with induced status epilepticus (SE) a state of intractable persistent seizure that produces extensive neuronal injury. We found that SE significantly reduced neuronal density in the piriform cortex, the amygdala, the dorsal thalamus, the CA3 area of the hippocampus, the dentate gyrus and the hilus, but not in the somatosensory cortex or the CA1 area. SE resulted in increases in the total density of cellular nuclei within these brain structures, suggesting gliosis. COMPARISON WITH EXISTING METHODS: This automated method was more accurate than simply estimating the overall NeuN fluorescence intensity in the brain section, and as accurate, but less time-consuming, than manual cell counts. CONCLUSION: This method simplifies and accelerates the unbiased quantification of neuronal density. It can be easily applied to other models of brain injury and neurodegeneration, or used to screen the efficacy of neuroprotective treatments.
BACKGROUND: To study neurotoxic processes, it is necessary to quantify the number of neurons in a given brain structure and estimate neuronal loss. Neuronal densities can be estimated by immunohistochemical quantitation of a neuronal marker such as the protein NeuN. However, NeuN expression may vary, depending on certain pathophysiological conditions and bias such quantifications. NEW METHOD: We have developed a simple automatic quantification of neuronal densities in brain sections stained with DAPI and antibody to NeuN. This method determines the number of DAPI-positive nuclei also positive for NeuN in at least two adjacent sections within a Z-stack of optical sections. RESULTS: We tested this method in animals with induced status epilepticus (SE) a state of intractable persistent seizure that produces extensive neuronal injury. We found that SE significantly reduced neuronal density in the piriform cortex, the amygdala, the dorsal thalamus, the CA3 area of the hippocampus, the dentate gyrus and the hilus, but not in the somatosensory cortex or the CA1 area. SE resulted in increases in the total density of cellular nuclei within these brain structures, suggesting gliosis. COMPARISON WITH EXISTING METHODS: This automated method was more accurate than simply estimating the overall NeuN fluorescence intensity in the brain section, and as accurate, but less time-consuming, than manual cell counts. CONCLUSION: This method simplifies and accelerates the unbiased quantification of neuronal density. It can be easily applied to other models of brain injury and neurodegeneration, or used to screen the efficacy of neuroprotective treatments.