A complete guide to imaging and analyzing spines and neurons with Neurolucida 360


Following a well-designed protocol is essential to achieving accurate and consistent results in scientific research. Now, scientists using Neurolucida 360 for dendritic spine and neuron analysis can follow a published set of guidelines to ensure optimal confocal data series for proper dendritic spine quantification and neuron reconstruction. The paper, written by MBF Bioscience scientists and researchers from the Icahn School of Medicine at Mount Sinai in New York, was published in Current Protocols in Neuroscience.

The four protocols describe best practices for imaging and analyzing dendritic spines and entire neurons. Clearly laid out procedures specify necessary materials, image acquisition techniques, and analysis procedures with Neurolucida 360.

Imaging technique is crucial to obtaining unbiased, reproducible results. Clear, crisp images captured with an appropriate z-interval will make analysis with Neurolucida 360 easier and more accurate. Throughout the paper, the authors emphasize the importance of image scaling parameters and unbiased sampling for achieving repeatable results. They also discuss the benefits of correcting optical distortion, especially in the Z-plane, with deconvolution to acquire clear images – a process critical to getting the most accurate representation of dendrites and spines.

Dendritic spine analysis is traditionally performed through tedious, time-consuming manual techniques. According to the paper, this has spawned a growing interest in a more efficient solution for spine quantification and morphological analysis like the one Neurolucida 360 provides. A software platform for automatic neuron reconstruction and spine detection in a 3D environment, Neurolucida 360 offers a variety of benefits, including:

 

  • Fast and accurate spine detection and neuron reconstruction
  • Accurate spine classification and length quantification using a five-point segment that more accurately models the spine backbone.
  • 3 user-guided and automatic algorithms to accurately model neurons visualized with multiple methodologies and imaging techniques.
  • A large number of metrics, including volume, length, and surface area.

 

“We believe that the new quantitative software package, Neurolucida 360, provides the neuroscience research community with the ability to perform higher throughput automated 3D quantitative light microscopy spine analysis under standardized conditions to accelerate the characterization of dendritic spines with greater objectivity and reliability,” (Dickstein, et al. 2016)

The full paper can be found here.

An infographic quickly outlines Protocol 1: Imaging of fluorescently labeled dendritic segments. Use this as a quick reference tool in your lab (right-click on it to save as an image):

Dickstein, D.L., Dickstein, D.R., Janssen, W.G.M., Hof, P.R., Glaser, J.R., Rodriguez, A., O’Connor, N., Angstman, P., and Tappan, S.J. 2016. Automatic dendritic spine quantification from confocal data with Neurolucida 360. Curr. Protoc. Neurosci. 77:1.27.1-1.27.21. doi: 10.1002/cpns.16

Dendritic Spine Loss Reported in Schizophrenia and Bipolar Disorder

Golgi-stained human brain tissue from the dorsolateral prefrontal cortex.

Golgi-stained human brain tissue from the dorsolateral prefrontal cortex.

Schizophrenia and bipolar disorder are very different mental illnesses, but researchers are discovering evidence that the two disorders have some common pathologies. According to a recent study, a shared characteristic appears to be dendritic spine loss.

The researchers used Neurolucida to study pyramidal cells in human brain tissue from individuals with schizophrenia (n=14), individuals with bipolar disorder (n=9) and unaffected control participants (n=19). The pyramidal cells were located in the dorsolateral prefrontal cortex – a region that plays a key role in working memory. Bipolar patients showed significantly reduced spine density (10.5 percent) compared to control. Schizophrenic patients also showed lower spine density (6.5 percent), but this number just missed significance when compared to control patients. Individuals with both illnesses showed a lower number of spines per dendrite, as well as reduced dendritic length compared to controls.

To obtain these results, researchers analyzed 15 Golgi-stained pyramidal cells in each tissue sample. They used Neurolucida to reconstruct the longest dendrite on the pyramidal cells and to mark spines. After the researchers finished reconstructing the cells, Neurolucida provided them with important data about the dendrites and spines.

Continue reading “Dendritic Spine Loss Reported in Schizophrenia and Bipolar Disorder” »

AutoSpine™—A Groundbreaking New Tool for Identifying Dendritic Spines

The branches of a dendrite extend gracefully across the computer screen. Circles of florescent fuchsia and violet hover near the branch surfaces like colorful confetti. So much more than just a pretty picture, this superb image depicts some of the tiniest elements of a neuron, its dendritic spines.

Scientists studying neurological disorders such as Alzheimer’s disease, Huntington’s disease, and Parkinson’s disease require detailed information about the central nervous system to carry out their research. With the release AutoSpine by MBF Bioscience, scientists can access more information than ever before.

AutoSpine lets researchers easily identify and count a neuron’s dendritic spines. Using images of dendritic branches obtained from Neurolucida® and AutoNeuron®, AutoSpine offers an accurate assessment of the population and position of the hundreds to thousands of spines that protrude from the dendritic branches of a neuron. Once the spines have been identified, AutoSpine helps facilitate a variety of analyses. With AutoSpine, scientists can determine the number of spines along a dendritic branch, the density of the spines, the spine head volume, and the distance from the spine head to the dendritic branch. Scholl analysis and spine classification can also be performed.

“For the first time, researchers have access to fast, accurate, automated quantification of dendritic spines. We expect this will be a powerful tool for researchers investigating anatomical aspects of important neurological diseases,” said MBF Bioscience President Jack Glaser.

Watch our Instructional Webinar “Automatic Interactive Neuron Tracing and Dendritic Spine Detection” led by staff scientist Susan Hendricks, Ph.D., and Vice President of Research Jeff Sprenger, to learn more about how AutoSpine works.

Using Neurolucida for Dendritic Spine Analysis

Dendritic Spines

by Julie Simpson, Ph.D.

Neurolucida is used for reconstruction and morphometric analysis of neurons and anatomical regions of interest. Neurolucida can also be used for quantitative and qualitative analysis of dendritic spines. Spines are small, often bulbous protrusions that emerge from the dendritic shaft. They are the main site of excitatory synapses in the brain [1], and they range in length from 0.001 to 1mm and range in diameter from 0.5 to 2mms [2, 3]. Alterations in spine number and shape have been implicated in learning and memory, long-term potentiation, and synaptic efficacy [1-3]. Abnormalities in dendritic spine number and shape have been observed in pathological conditions such as Alzheimer’s disease, Parkinson’s disease, epilepsy, and trauma [2, 3]. The aging brain also exhibits a decrease in dendritic spines on cortical pyramidal neurons, leading to a decline in cognitive function, learning, and memory [2].

In Neurolucida there are currently two methods used to trace spines for quantitative analysis. The first method involves tracing spines as the neuron is reconstructed either directly from a tissue slide using a microscope and motorized stage or from an image stack. If spines are traced on an image stack, the 3D rendering of the image stack plus the tracing can be viewed using the 3D Solid Modeling module. This rendering allows the user to validate the placement of spines and the neuronal processes. In the other method, spines can be added after the entire neuron has been reconstructed using Neurolucida editing tools. Once the neuronal reconstruction is complete, morphometric analysis of the neuronal processes and dendritic spines is done in Neurolucida Explorer.

Neurolucida Explorer offers a number of quantitative spine measurements under Branched Structure Analysis. The first analysis is ‘neuron summary’, which lists the total number of spines placed. The second is ‘segment-dendrite analysis’ which lists the number of spines per tree or segment. This number can then be used to calculate spine density per tree. The next analysis, ‘spine report’, lists the spine number and density per branch order. The final analysis, ‘spine details,’ lists the spine length (distance from spine base to tip), area, volume (assumes the spine is cylindrical), distance of the spine from the origin of the neuronal process, and the X,Y,Z coordinates at the base of the spine.

Version 9.0 of Neurolucida improves on the previous versions’ process of classifying spine morphology. Before, the user manually marked the spines with markers named for each spine type, and marker totals indicated the number of each type of spine indentified in the trace. In the new version, users have the ability to efficiently classify spines according to five classes: thin, stubby, mushroom, branched, and filopodia. The quantitative spine analyses that are available have expanded to include spine morphology, and spine detection, tracing, and editing are more effective and user-friendly.

1. Zito, K. and V.N. Murthy, Dendritic spines. Curr Biol, 2002. 12(1): p. R5.

2. Calabrese, B., M.S. Wilson, and S. Halpain, Development and regulation of dendritic spine synapses. Physiology (Bethesda), 2006. 21: p. 38-47.

3. Lee, K.J., et al., Morphological changes in dendritic spines of Purkinje cells associated with motor learning. Neurobiol Learn Mem, 2007. 88(4): p. 445-50.

Julie Simpson is a staff scientist at MBF Bioscience.

{Confocal image of dendritic spines featured above, courtesy of Jakub Jedynak}

First published in The Scope, fall 2008.

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