FIGURE SUMMARY
Title

Image analysis of neural stem cell division patterns in the zebrafish brain

Authors
Lupperger, V., Buggenthin, F., Chapouton, P., Marr, C.
Source
Full text @ Cytometry A

NSCs in the zebrafish telencephalon. (A) In all experiments, four‐month‐old zebrafish of a gfap:GFP transgenic strain were used, where green fluorescent protein (GFP) is expressed under the control of gfap enhancer elements. Average length of adult fish is ∼3 cm (B) Top view on a zebrafish brain showing the telencephalon, optic tectum, and cerebellum. We image one hemisphere of the telencephalon (marked with a green rectangle). Scale bar: 2 mm. (C) Reconstructed 3D image stack from confocal microscopy.

SCIP for NSCs in the adult zebrafish brain. Raw 3D data (A) is transformed into 2D images (B) via 2D maximum intensity projection. Cell somata are touching each other on the surface, without intermediate space. Cell centers display a high GFP intensity and are used for identification. A blob detection using LoG identifies cell candidates (C). A Gaussian curve is fitted to the intensity profile of a cylinder with 4 µm diameter along z of every cell candidate (D). The mean of the Gaussian is taken as the z‐coordinate of cell candidate centroid. A surface based on a 3rd order polynomial regression model is fitted to all centroids (E). Cells that are further away than two cell diameters (∼16 µm) are excluded step by step by removing iteratively the most distant outlier and recalculate the surface (F). To remove remaining image artifacts an envelope is placed in 20 µm distance around the surface (G). All pixels outside this envelope are set to background intensity. Afterwards the pipeline starts over again at (B) using the filtered image stack without image artifacts. (H) The resulting cell centroids can now be used for further analyses. Scale bars: 50 µm.

Three metrics are defined on the hemispheres. NSCs are identified (A) and then used to calculate the Euclidean distance dE (blue line), the surface distance dS (orange line) and the graph distance dG (red lines) between all pairs of identified cells on the 2D surface (B). For the surface distance, the shortest path between two cells in 3D is projected on the surface fitted with SCIP. For the graph distance, the shortest path in a network derived from a Delaunay triangulation is calculated. Identified NSCs are shown in green on top of the gfap:GFP signal.

Spatial pattern of NSC divisions. NSCs are identified in the gfap:GFP channel (A) using SCIP. Cells in S‐phase were automatically identified by EdU signal and manually verified (B). S‐phase NSCs are identified as cells that appear both in the gfap:GFP and in the EdU channel (C). Scale bar: 100 µm.

Simulated attractive spatial influence (A), no spatial influence (B), and repulsive spatial influence (C) of S‐phase NSCs result in visually distinct patterns.

Evaluation of SCIP. We use manually (by P.C.) identified NSCs in different image regions on the 2D maximum intensity projection as ground truth (green circles). We evaluate SCIP (A), the 3D object counter plugin in ImageJ (B), and Imaris (C) by comparison of the identified cells to the manually detected ones. Scale bar: 30 µm.

Acknowledgments
This image is the copyrighted work of the attributed author or publisher, and ZFIN has permission only to display this image to its users. Additional permissions should be obtained from the applicable author or publisher of the image. Full text @ Cytometry A