Population-scale three-dimensional reconstruction and quantitative profiling of microglia arbors - PubMed (original) (raw)

Population-scale three-dimensional reconstruction and quantitative profiling of microglia arbors

Murad Megjhani et al. Bioinformatics. 2015.

Abstract

Motivation: The arbor morphologies of brain microglia are important indicators of cell activation. This article fills the need for accurate, robust, adaptive and scalable methods for reconstructing 3-D microglial arbors and quantitatively mapping microglia activation states over extended brain tissue regions.

Results: Thick rat brain sections (100-300 µm) were multiplex immunolabeled for IBA1 and Hoechst, and imaged by step-and-image confocal microscopy with automated 3-D image mosaicing, producing seamless images of extended brain regions (e.g. 5903 × 9874 × 229 voxels). An over-complete dictionary-based model was learned for the image-specific local structure of microglial processes. The microglial arbors were reconstructed seamlessly using an automated and scalable algorithm that exploits microglia-specific constraints. This method detected 80.1 and 92.8% more centered arbor points, and 53.5 and 55.5% fewer spurious points than existing vesselness and LoG-based methods, respectively, and the traces were 13.1 and 15.5% more accurate based on the DIADEM metric. The arbor morphologies were quantified using Scorcioni's L-measure. Coifman's harmonic co-clustering revealed four morphologically distinct classes that concord with known microglia activation patterns. This enabled us to map spatial distributions of microglial activation and cell abundances.

Availability and implementation: Experimental protocols, sample datasets, scalable open-source multi-threaded software implementation (C++, MATLAB) in the electronic supplement, and website (www.farsight-toolkit.org). http://www.farsight-toolkit.org/wiki/Population-scale\_Three-dimensional\_Reconstruction\_and\_Quanti-tative\_Profiling\_of\_Microglia\_Arbors

Contact: broysam@central.uh.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

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Figures

Fig. 1.

Fig. 1.

Illustrating the problem of large-scale microglia reconstruction. (A) Maximum-intensity projection of a 3-D two-channel mosaic image (size 5903 × 9874 × 229 voxels, 3117 microglia, yellow: IBA1, blue: Hoechst). (A1) close-up of the boxed region. (B) Automated reconstructions with microglial soma in red and arbor reconstructions in green. (B1) Close-up of the reconstructions in the boxed region. (C1–C5) Close-ups of individual microglial arbor reconstructions illustrating the diversity of cellular morphologies

Fig. 2.

Fig. 2.

A subset of the 3-D image patch dictionary entries displayed as maximum-intensity projections. It is clear from this illustration that some patches (highlighted by red boxes) clearly belong to parts of microglial arbors, whereas the other patches are either ambiguous or part of the background

Fig. 3.

Fig. 3.

Illustrating 3-D seed points used for arbor reconstruction. (A) Sample IBA1+ cell (grayscale) overlaid with initially detected seed points (red). (B) Close-up of boxed region in Panel A. The white arrows indicate some valid seed points that lie on the cellular arbor, and the yellow arrows some indicate invalid seed points. Only a subset of the seed points are marked by arrows here to avoid clutter

Fig. 4.

Fig. 4.

Performance of our algorithm compared to manual reconstruction as measured by the DIADEM metric. (A) The mean DIADEM metric for our algorithm (0.77) was higher than the value for the algorithm of (Galbreath, 2011)[1](0.65) and the GVF based Vesselness-(Wang et al., 2011)[2] (0.68) based algorithm. (B) Our algorithm was insensitive to the size of the dictionary parameter K. (C) Our algorithm was insensitive to variations in the sparsity constraint T. (D) Increasing the cost threshold τ led to reduced performance, as expected. A threshold of 200 was chosen empirically for all the subsequent experiments based on this analysis

Fig. 5.

Fig. 5.

Quantitative analysis of the microglial cell population by harmonic co-clustering of the L-measure data. (A) 3-D rendering of the reconstructed microglial field in Figure 1, with the arbor traces color coded to indicate the automatically identified groups (red: Group 1, Yellow: Group 2, Green: Group 3, Blue: Group 4). (B) Heatmap representation of the co-clustering results. Each row in the heatmap corresponds to one cell, and each column corresponds to one feature in the L-measure. The co-clustering identified four principal groups. Representative cells from these groups are shown in Panels G1–G4, and correspond to ramified cells (G1), elongated cells (G2), activated cells (G3) and amoeboid/round cells (G4), respectively

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