Huntington's disease (HD) is an autosomal neurodegenerative disorder, characterized by severe behavioral, cognitive, and motor deficits. Since the discovery of the huntingtin gene (HTT) mutation that causes the disease, several mouse lines have been developed using different gene constructs of Htt. Recently, a new model, the zQ175 knock-in (KI) mouse, was developed (see description by Menalled et al, [1]) in an attempt to have the Htt gene in a context and causing a phenotype that more closely mimics HD in humans. Here we confirm the behavioral phenotypes reported by Menalled et al [1], and extend the characterization to include brain volumetry, striatal metabolite concentration, and early neurophysiological changes. The overall reproducibility of the behavioral phenotype across the two independent laboratories demonstrates the utility of this new model. Further, important features reminiscent of human HD pathology are observed in zQ175 mice: compared to wild-type neurons, electrophysiological recordings from acute brain slices reveal that medium spiny neurons from zQ175 mice display a progressive hyperexcitability; glutamatergic transmission in the striatum is severely attenuated; decreased striatal and cortical volumes from 3 and 4 months of age in homo- and heterozygous mice, respectively, with whole brain volumes only decreased in homozygotes. MR spectroscopy reveals decreased concentrations of N-acetylaspartate and increased concentrations of glutamine, taurine and creatine + phosphocreatine in the striatum of 12-month old homozygotes, the latter also measured in 12-month-old heterozygotes. Motor, behavioral, and cognitive deficits in homozygotes occur concurrently with the structural and metabolic changes observed. In sum, the zQ175 KI model has robust behavioral, electrophysiological, and histopathological features that may be valuable in both furthering our understanding of HD-like pathophyisology and the evaluation of potential therapeutic strategies to slow the progression of disease.

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Determining the density and morphology of dendritic spines is of high biological significance given the role of spines in synaptic plasticity and in neurodegenerative and neuropsychiatric disorders. Precise quantification of spines in three dimensions (3D) is essential for understanding the structural determinants of normal and pathological neuronal function. However, this quantification has been restricted to time- and labor-intensive methods such as electron microscopy and manual counting, which have limited throughput and are impractical for studies of large samples. While there have been some automated software packages that quantify spine number, they are limited in terms of their characterization of spine structure. This unit presents methods for objective dendritic spine morphometric analysis by providing image acquisition parameters needed to ensure optimal data series for proper spine detection, characterization, and quantification with Neurolucida 360. These protocols will be a valuable reference for scientists working towards quantifying and characterizing spines.

Stereologic cell counting has had a major impact on the field of neuroscience. A major bottleneck in stereologic cell counting is that the user must manually decide whether or not each cell is counted according to three-dimensional (3D) stereologic counting rules by visual inspection within hundreds of microscopic fields-of-view per investigated brain or brain region. Reliance on visual inspection forces stereologic cell counting to be very labor-intensive and time-consuming, and is the main reason why biased, non-stereologic two-dimensional (2D) "cell counting" approaches have remained in widespread use. We present an evaluation of the performance of modern automated cell detection and segmentation algorithms as a potential alternative to the manual approach in stereologic cell counting. The image data used in this study were 3D microscopic images of thick brain tissue sections prepared with a variety of commonly used nuclear and cytoplasmic stains. The evaluation compared the numbers and locations of cells identified unambiguously and counted exhaustively by an expert observer with those found by three automated 3D cell detection algorithms: nuclei segmentation from the FARSIGHT toolkit, nuclei segmentation by 3D multiple level set methods, and the 3D object counter plug-in for ImageJ. Of these methods, FARSIGHT performed best, with true-positive detection rates between 38 and 99% and false-positive rates from 3.6 to 82%. The results demonstrate that the current automated methods suffer from lower detection rates and higher false-positive rates than are acceptable for obtaining valid estimates of cell numbers. Thus, at present, stereologic cell counting with manual decision for object inclusion according to unbiased stereologic counting rules remains the only adequate method for unbiased cell quantification in histologic tissue sections.

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