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. 2016 Oct 3;77:1.27.1-1.27.21.
doi: 10.1002/cpns.16.

Automatic Dendritic Spine Quantification From Confocal Data With Neurolucida 360

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Free PMC article

Automatic Dendritic Spine Quantification From Confocal Data With Neurolucida 360

Dara L Dickstein et al. Curr Protoc Neurosci. .
Free PMC article

Abstract

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. © 2016 by John Wiley & Sons, Inc.

Keywords: automated quantification; confocal microscopy; dendritic spines; neurons.

Figures

Figure 1
Figure 1
Neuronal map for determining the dendritic segments to image. (A) 3D reconstruction of a CA1 pyramidal neuron with superimposed Sholl rings created in Neurolucida Explorer (MBF Bioscience). (B) Low magnification confocal image of a CA1 pyramidal neuron with superimposed concentric circles at measured distances from the center of the cell body using the Zeiss Zen 780 software. Note: the neurons in this figure are not the same.
Figure 2
Figure 2. Example of optimally set gain and offset of dendritic segments
Images were acquired on a Zeiss 780 laser scanning confocal microscope equipped with the Zen software. (A) Image demonstrates an open Zen operating window for high-resolution dendritic Z-stack imaging. Note the longitudinal dendritic segment with the presence of spines. Within this image, the gain (red) and offset (blue) are properly set to acquire the optimal finalized Z-stack image. The gain is set so there are virtually no saturated pixels (red) present prior to imaging. The offset (blue) appears somewhat mottled within a black background. This adjusted level of background noise is necessary to facilitate successful post-processing deconvolution. All other parameters (frame resolution, averaging number, frame direction, etc) are set prior to imaging. The standard ZEN operating window is open, along with some of the more advanced functions needed for high-resolution 3D imaging (e.g., Z-stacks, focus, etc). (B) A vertical dendritic representation with incorrect offset (blue) settings. While there is no over saturation of the gain (red), the offset (blue) is too high (not enough blue demonstrated). The effect on image reconstruction would be altered.
Figure 3
Figure 3. Deconvolution of dendritic segments
XY and ZY maximal projections of a typical image stack before (A) and after (B) deconvolution with AutoDeblur. Compared to the raw data (A), the deconvolved data exhibit good relative intensity equalization of spines and dendrites, and significantly reduced Z-axis “stretching” from optical smear, in the ZY projection (B). Adapted from (Rodriguez et al., 2008).XY and ZY maximal projections of a typical image stack before (A) and after (B) deconvolution with AutoDeblur. Compared to the raw data (A), the deconvolved data exhibit good relative intensity equalization of spines and dendrites, and significantly reduced Z-axis “stretching” from optical smear, in the ZY projection (B). (Adapted from (Rodriguez et al., 2008).
Figure 4
Figure 4. Tracing the backbone of the dendritic segment
Dendritic segment seen here is from a mouse pyramidal CA1 neuron filled with Lucifer Yellow. A cursor (red +) is moved along the dendritic segment to see the path and estimated thickness provided by the tracing algorithm. The open yellow circles provide a preview of dendritic branch thickness. It is important to confirm that large spines do not over-influence the thickness of the dendrite. Scale bar = 2 µm.
Figure 5
Figure 5. Inspection of dendritic segment thickness
(A) Neurolucida 360 showing inspection and correction of points (green) from the dendritic branch (yellow) that were drawn off center by the large spine (arrow). (B) To edit, view the branch in point mode, select the point, and move it to the correct, centered location on the dendritic branch. Scale bar = 0.5 µm.
Figure 6
Figure 6. Merging dendritic spines
Individual spine detections can be merged to create a single model for a dendritic spine. Inspecting the underlying image data (A) can show that a single spine was inaccurately modeled as two discrete objects (B). To correct, select each spine object in edit mode and select merge. The software remodels the spine to include all voxels previously split between the two objects as a single spine (C). Scale bar = 1 µm.
Figure 7
Figure 7. Dendritic spine detection parameters
The software has four detection parameters to set the conditions for modeling dendritic spines. The parameters, which will vary based on the imaging settings and experimental paradigm being tested, should be chosen based on empirical data, and remain constant during the study. Do not choose parameters arbitrarily since this will introduce bias and may affect the data and interpretation.
Figure 8
Figure 8. Representation of dendritic spines in Neurolucida 360
The dendritic spine is modeled with a mesh to represent the surface and volume of the spine (A). The spine backbone (B) is represented with five points. The most distal point in the backbone indicates the furthest voxel from the dendritic surface, the centroid of the spine head (green) is the second point, and the last point represents where the spine connects with the dendrite. The shape of the dendritic spine is more accurately modeled, leading to better metrics and more complex spine classes. Spines can be re-assigned to nearby branches by dragging the last point from the original dendrite location to the desired location on the alternate branch.
Figure 9
Figure 9. Spine classification using default parameters
Dendritic spines are first modeled on the basis of a number of detection parameters, including distance from dendritic surface and apparent size. After detection, spines are colored to differentiate each modeled spine in close proximity (A). If desired, the detected spines can be classified using classification parameters as established by Rodriguez et al., 2008 (B) or through custom specifications. It is important that the same detection parameters and classification settings (if chosen) are used for all images in the experimental study. Scale bar = 2 µm.
Figure 10
Figure 10. Setting spine classification parameters
Different metrics can be entered to define the spine classes according to values specific to the species or condition under study. Parameters should not be changed during the study, and should be chosen on the basis of empirical evidence.
Figure 11
Figure 11. Complete neuron image montage of a hippocampal pyramidal neuron acquired after whole-cell recording
A single image was created using Neurolucida 360 to montage multiple 3D images of a mouse hippocampal pyramidal neuron labeled with biocytin. Multiple 2-channel z-series images were collected with a Zeiss LSM 710 confocal microscope, mounted on an AxioImager Z2, with a 20× Plan-apochromat objective, and a 25 mW multi-wavelength (458/488/514) argon laser and a 20 mW 561 nm diode DPSS laser (Alexa-549 displayed here). Dr. Piskorowski provided the image data from an experiment performed by Vincent Robert and Ludivine Therreau in accordance with European guidelines for the care and use of laboratory animals at the Université Paris Descartes. Note: this neuron was not imaged at a resolution high enough for concurrent spine analysis. Scale bar = 100 µm.
Figure 12
Figure 12. Complete neuron reconstruction created with Neurolucida 360
Using Neurolucida 360, the pyramidal cell shown in Figure 11 was reconstructed using user-guided tracing and soma modeling. Scale bar = 100 µm.
Figure 13
Figure 13. Setting the origin of branches as the closest point to the soma
While it is important for branch analyses to have each tree begin at the soma, it is not required to trace in any particular direction. Confirm the root of each tree in edit mode, by drawing a marquee around the soma small enough so that it does not contain full dendritic trees, but large enough to contain all the points closest to the soma. Once selected, the option to set all endings to “origins” becomes available only if some trees are initiated at a different location. Select the button to reset all trees to have their origin closest to the soma.

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