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Neuronal morphometry directly from bitmap images

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Figure 1: Sholl Analysis provides metrics of complex arbors in Brainbow-expressing mice and can be used to classify cortical interneurons, without tracing or reconstruction.

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Acknowledgements

The authors thank T. Maddock for implementing version 1.0 of the software. We also thank J. Schindelin, W. Rasband and M. Longair for code contributions, H. Nedelescu for deconvolution and alignment of Brainbow images before reconstruction, and B. Chen for advice. Work supported by operating and infrastructure grants to A.J.W., P.J.S. and D.J.v.M. from the Canadian Institutes of Health Research (CIHR), the Natural Sciences and Engineering Research Council of Canada, the Canada Foundation for Innovation and the UK Medical Research Council. Salary awards came from the Research Institute of McGill University Health Center (T.F.), the McGill Faculty of Medicine (D.J.v.M.), a Biotechnology and Biological Sciences Research Council Industrial CASE studentship (A.V.B.), a University College London Impact Award (J.O.), and a CIHR New Investigator Award (P.J.S.).

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Correspondence to Tiago A Ferreira or Donald J van Meyel.

Integrated supplementary information

Supplementary Figure 1 Overview of the Sholl Analysis software.

(a) User interface, version 3.4.1. Sub-modules noted in blue. (b) Maximum intensity projection of a Drosophila class IV dendritic arborization sensory neuron (ddaC) labeled by the ppk1.9-GAL4-driven reporter UAS-mCD8::GFP, a sample image distributed with the plug-in. The annulus formed by Starting radius (the first sampled distance) and Critical radius is indicated. Outer arc depicts Enclosing radius, the distance to the most distal dendritic tip. (c) Semi-log plot of the cell depicted in (b), where the number of intersections was normalized to the area of each sampled shell. Two regression lines are shown to demonstrate that curves can be fitted to all data points, or to a subset restricted to the 10th to 90th percentiles of the data (shaded in gray). R2 is the coefficient of determination, and k the Sholl regression coefficient. (d) Linear Sholl plot of the cell in (b). Key metrics include Critical value (Nm), Critical radius (rc) and Mean value (Nav). Differences between sampled and fitted maxima are shaded in gray. The centroid of the sampled profile is marked (×). Schoenen ramification index (RI) is the ratio between number of branches at the maximum and the number of primary branches, using either sampled data or the fitted Nm. The number of primary branches can be entered manually, or drawn from the number of intersections at Starting radius.

Supplementary Figure 2 Accuracy of bitmap image-based Sholl Analysis.

(a) Maximum intensity projection of an Alexa 594 dye-filled layer-5 pyramidal neuron of juvenile mouse visual cortex1. Arrowheads highlight the apical tuft (top) and the soma (bottom). (b) Manually reconstructed dendrites (blue) and axon (magenta) of the neuron in (a). (c) Linear Sholl plots from bitmap images (dots), following either manual segmentation ("user segm.") or automated segmentation of the image stack. For comparison, results from reconstruction-based analysis are plotted for the axon (dashed magenta line) and dendrites (solid blue line). The plot of the left corresponds to the basal region populated by both axonal and dendritic arbors, while the plot on the right corresponds to apical dendrites. (d) Tukey mean-difference plot to examine the agreement between the bitmap approach and traced reconstruction approach for three different neurons (numbered and colorcoded). Each data point represents the count of intersections at a particular distance from the apical bifurcation: pairwise differences between the two approaches at each distance are plotted against each mean. The 95% limits of agreement for individual cells are shown to the right, as is the average for all three cells and the average bias (dotted lines). 1. Buchanan, K.A. et al. Target-specific expression of presynaptic NMDA receptors in neocortical microcircuits. Neuron 75, 451-466 (2012).

Supplementary Figure 3 Resilience of bitmap image-based morphometry to mage degradation over a range of synthetic noise.

(a) Maximum intensity projection of an axonal arbor of a Drosophila olfactory projection neuron from the DIADEM Challenge dataset (OP_1)1,2. We contaminated the original stack (voxel size: 0.33×0.33×1.0μm) with Poisson noise, using increasing multiples of the stack standard deviation (σ = 9.55) as the probability mass function of the Poisson random variable. This noise was either added (+) or subtracted (-), over a range from -18σ to +18σ. Images are shown with the coefficient of determination below each image (R2, mean ± s.d.) to indicate the degree of similarity with the original; R2= 1 corresponds to identical images. Arrowheads indicate the Sholl analysis center (see b). (b) Each graph represents one of 7 metrics (3 from sampled data, 4 from fitted data) that were calculated from Sholl plots generated directly from bitmap images with Sholl Analysis ("Bitmap"), or from their respective reconstructions traced with Neural Circuit Tracer37 ("Reconstruction"). The parameters and routines used to retrieve the data were fixed (See Supplementary Methods and Supplementary Software for details). Shaded areas in each graph (light gray) indicate the range of noise (-14σ to +8σ) over which metrics were largely consistent with those calculated from the original image (zero noise, dark grey vertical bar). Dashed lines indicate two references calculated at zero noise: one from a user-derived manual segmentation of the stack ("Bitmap reference", blue) and one from the DIADEM gold standard reconstruction (red). For each metric, the concordance correlation coefficient (ρc)3 between the bitmap approach and reconstruction approach are shown. 1. Brown, K.M. et al. The DIADEM data sets: representative light microscopy images of neuronal morphology to advance automation of digital reconstructions. Neuroinformatics 9, 143-157 (2011). 2. Jefferis, G.S. et al. Comprehensive maps of Drosophila higher olfactory centers: spatially segregated fruit and pheromone representation. Cell 128, 1187-1203 (2007). 3. Lin, L.I. A concordance correlation coefficient to evaluate reproducibility. Biometrics 45, 255-268 (1989).

Supplementary Figure 4 Sholl-based metrics of Type-1 and Type-2 PV interneurons.

Metrics loading on the first principal component (71.6% of observed variation), used as clustering variable in Figure 1c: (a) Sholl regression coefficient, (b) Sum of intersections, (c) Distance associated with at least two intersections (a modified Enclosing radius), (d) Centroid radius, (e) Centroid value, (f) Critical value, (g) Mean value, (h) Critical radius. Values enclosed by brackets depict factor loadings. Because pipette fluorescence could not be entirely eliminated near the soma, the number of primary branches and Schoenen ramification indices were excluded from the analysis.

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Supplementary text and figures

Supplementary Figures 1–4, Supplementary Note and Supplementary Methods (PDF 2420 kb)

Supplementary Software

Processing routines for the OP_1 DIADEM dataset (ZIP 3 kb)

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Ferreira, T., Blackman, A., Oyrer, J. et al. Neuronal morphometry directly from bitmap images. Nat Methods 11, 982–984 (2014). https://doi.org/10.1038/nmeth.3125

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