Immunofluorescence staining and confocal imaging

The neurons were grown on glass coverslips as described above. The neurons were then washed with HBSS solution (Life Technologies, Grand Island, NY) and fixed with freshly prepared 3.7% paraformaldehyde (Electron Microscopy Sciences, Hatfield, PA) for 20 min at room temperature and washed twice with PBS solution. For immunolabeling, the cells were then permeabilized as described previously [22, 75, 94, 110]. Washes, blocking, and incubation with primary and secondary antibodies were carried out in cell collection chambers on the stage of an orbital shaker. The neurons were incubated in blocking solution containing 10% normal goat serum (LAMPIRE Biological Laboratories, Pipersville, PA) and 0.5% Triton X-100 in PBS (Sigma,St. Louis, MO) at room temperature for one hour. The neurons were then incubated with a solution containing primary antibody (1:1000), 0.1% Triton X-100 and 5% normal goat serum at 4 °C overnight. On the following day, the primary antibody was removed, and cells were subjected to three 20 min washes prior to addition of Alexa-Fluor 488- or 647 conjugated secondary antibodies (Life Technologies, Grand Island, NY) diluted at 1:200–500 in PBS. Cells were incubated in secondary antibody for one hour in the dark, at room temperature followed by three 20 min washes. Coverslips were then mounted on the Superfrost Excell Microscope Slides (VWR,West Chester, PA) using Flouromount-G (SouthernBiotech, Birmingham, AL). Slides were stored in the dark at 4 °C until imaging. Confocal images were collected using a Nikon A1 laser-scanning confocal microscope. Excitation wavelengths were set at 488 and 647-nm, for the respective fluorescent markers. Images were acquired using 488-nm excitation with a 514-nm long pass filter, 647- nm excitation with a 668-nm long pass filter. Images were taken using a 20X or a 60X Nikon objective with 1.40 NA (numerical aperture).

All imaging analyses were carried out via an automated graphic plugin for the public domain image analysis software ImageJ (Wayne Rasband; Research Services Branch, National Institutes of Mental Health, National Institutes of Health, Bethesda MD). The morphological analysis were performed from DIC images. For RGB images, average gray value within the selection as image intensity is the sum of the gray values of all the pixels in the selection divided by the number of pixels. The mean is calculated by converting each pixel to grayscale using the formula gray = (red + green + blue)/3. Area of selection was in square pixels or in calibrated square units (μm²). The perimeter of a composite selection is calculated by decomposing it into individual selections. The Neurite Tracing is based on multiscale image processing using both first-order (edge) and second-order (ridge) filters and combining the extracted information in a cost function that is globally minimized algorithms to detect elongated image structures and determine their centerlines [88]. The images were processed using ImageJ and the NeuronJ plugin for ImageJ which highlights and traces neurites and somas [49, 101]. The trace was manually initiated and ended of each neurite, using a crosshair pointer, and map the length and shape of each neurite with a series of connected straight lines. The tracing algorithm computed the optimal path between the selected starts and end points. Soma size was measured using the “selection brush” tool on ImageJ to carefully outline the shape of the soma. Requirements for counting a projection as a dendrite included: (1) Neurons being analyzed were at least one soma away from another neuron. (2) Projections were counted as dendrites if the length were at least the diameter of the soma. Fluorescence colocalization analysis was used to determine whether two proteins associate with the same subnuclear structures or with the same plasma membrane domains. We used three of frequently used colocalization coefficients to express the intensity correlation of colocalizing objects in each component of a dual-color image (see Additional file 2: Data): (1) Pearson's correlation coefficient: The Pearson's correlation coefficient is not sensitive to differences in mean signal intensities or range, or a zero offset between the two components. The result is + 1 for perfect correlation, 0 for no correlation, and − 1 for perfect anti-correlation. Noise makes the value closer to 0 than it should be [97]. (2) Spearman's rank correlation coefficient: The Spearman correlation between two variables is equal to the Pearson correlation between the rank values of those two variables, while Pearson's correlation assesses linear relationships, Spearman's correlation assesses monotonic relationships (whether linear or not). If there are no repeated data values, a perfect Spearman correlation of + 1 or − 1 occurs when each of the variables is a perfect monotone function of the other [120]. (3) Kendall rank correlation coefficient: the Kendall correlation between two variables will be high when observations have a similar (or identical for a correlation of 1) rank (i.e., relative position label of the observations within the variable: 1st, 2nd, 3rd, etc.) between the two variables, and low when observations have a dissimilar (or fully different for a correlation of − 1) rank between the two variables. Contrary to the Spearman correlation, the Kendall correlation is not affected by how far from each other ranks are but only by whether the ranks between observations are equal or not and is thus only appropriate for discrete variables but not defined for continuous variables [61].