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

Abstract

Proliferating stem cells in the adult body are the source of constant regeneration. In the brain, neural stem cells (NSCs) divide to maintain the stem cell population and generate neural progenitor cells that eventually replenish mature neurons and glial cells. How much spatial coordination of NSC division and differentiation is present in a functional brain is an open question. To quantify the patterns of stem cell divisions, one has to (i) identify the pool of NSCs that have the ability to divide, (ii) determine NSCs that divide within a given time window, and (iii) analyze the degree of spatial coordination. Here, we present a bioimage informatics pipeline that automatically identifies GFP expressing NSCs in three‐dimensional image stacks of zebrafish brain from whole‐mount preparations. We exploit the fact that NSCs in the zebrafish hemispheres are located on a two‐dimensional surface and identify between 1,500 and 2,500 NSCs in six brain hemispheres. We then determine the position of dividing NSCs in the hemisphere by EdU incorporation into cells undergoing S‐phase and calculate all pairwise NSC distances with three alternative metrics. Finally, we fit a probabilistic model to the observed spatial patterns that accounts for the non‐homogeneous distribution of NSCs. We find a weak positive coordination between dividing NSCs irrespective of the metric and conclude that neither strong inhibitory nor strong attractive signals drive NSC divisions in the adult zebrafish brain. © 2017 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of ISAC.

Aggregated spatio-temporal division patterns emerge from reoccurring divisions of neural stem cells

“…Ripley's K statistics is limited: It does not allow integrating different datasets, and it cannot quantitatively infer the strength and extent of an observed pattern. To remedy these aspects, we use the temporal extension of a spatial model (Lupperger et al, 2018) that allows determining the most likely parameters for interaction radius and interaction strength for an arbitrary number of datasets. Applied to the four Δt=32h patterns shown in Figure 3A, we find that a model with an interaction radius of~100 μm and an interaction strength > 1 describes the data best ( Figure 3B, random patterns are indicated by 1, dispersed pattern by a strength < 1 and aggregated pattern by a strength > 1, see Methods).…”

“…NSCs that are labelled by GFP in the transgenic gfap :GFP line were identified using the Single Cell Identification Pipeline (SCIP, (Lupperger et al, 2018) ). In this pipeline single cells are automatically identified from an image 3D stack, exploiting the fact that all NSCs are located on top of the hemisphere on a 2D surface.…”

“…We determine the distance between any two cells by calculating the shortest path on the hemisphere manifold to account for the bending of the hemisphere surface. To this end we used the fitted hemisphere surface and calculated the stepwise shortest paths between two cell locations on it (see (Lupperger et al, 2018) for a detailed description). Ripley's K (Ripley, 1976) is a measure for the deviation of a point pattern from spatial homogeneity.…”

“…To determine the spatial extent and the nature of the temporal influence of divisions at different timepoints, we extend a spatial influence model (Lupperger et al, 2018) with two parameters: the influence strength ( g ), where g =1 means no dependencies, below 1 dispersion between the two populations, and above 1 aggregation. The second parameter the model fits is the influence radius ( r ) in μm .…”

“…The behavior of radial glia in steady state or injury conditions in this area of the brain hints to their function as NSCs giving rise to intermediate dividing progenitors and directly to neurons (Barbosa et al, 2015;Kroehne et al, 2011;März et al, 2010;Rothenaigner et al, 2011) . We previously found that cell cycle entries in NSCs occur with aggregated spatial patterns (Lupperger et al, 2018) . Here, we extend our analysis to the spatio-temporal domain and analyze emerging patterns with an interdisciplinary mix of experimental and computational methods.…”