Automated identification of axonal growth cones in time-lapse image sequences

Abstract

The isolation and purification of axon guidance molecules has enabled in vitro studies of the effects of axon guidance molecule gradients on numerous neuronal cell types. In a typical experiment, cultured neurons are exposed to a chemotactic gradient and their growth is recorded by manual identification of the axon tip position from two or more micrographs. Detailed and statistically valid quantification of axon growth requires evaluation of a large number of neurons at closely spaced time points (e.g. using a time-lapse microscopy setup). However, manual tracing becomes increasingly impractical for recording axon growth as the number of time points and/or neurons increases. We present a software tool that automatically identifies and records the axon tip position in each phase-contrast image of a time-lapse series with minimal user involvement. The software outputs several quantitative measures of axon growth, and allows users to develop custom measurements. For, example analysis of growth velocity for a dissociated E13 mouse cortical neuron revealed frequent extension and retraction events with an average growth velocity of 0.05 +/- 0.14 microm/min. Comparison of software-identified axon tip positions with manually identified axon tip positions shows that the software's performance is indistinguishable from that of skilled human users.

Fig. 1. Image processing

(A) The user selects a rectangular region (dotted line) encompassing the entire neuron in the last image of the series. Debris and undesired image features can be excluded from subsequent analysis with a polygonal regioning tool (solid line). (B) The polygonal cropped image is then contrast-adjusted, thresholded, and filtered to generate the binary image used by the search algorithm.

Fig. 2. Box search

(A) and (B) are successive images (labeled n and n − 1, respectively) in the time-lapse series. The asterisk (*) marks the position of ATP(1). (C) The axon tip position ATP(n − 1) (marked with white ×) in (A) is used as the starting point for the search in (B). (D) The absence of a black pixel in the original 3 pixel × 3 pixel search box causes the box to increase by 2 pixel in each dimension. The 10th iteration finds the first non-perimeter black pixel (marked with white \), which is chosen as the axon tip for image n, ATP(n).

Fig. 3. Region search

The cleaned binary image is inverted to produce a white axon on a black background. MATLAB™ labels each region with a unique number (here, 1–4). The white asterisk (*) marks the position of the axon tip identified by the user in the last image, ATP(N). The white × marks the position of the axon tip identified in the previous image of the search, ATP(n − 1). The black × in each region marks the pixel region nearest to ATP(n − 1) in that region. Here, Region 4 contains the marked pixel that is nearest to ATP(n − 1), so Region 4 is assumed to contain the axon tip. The pixel in Region 4 (marked with ◆) nearest to ATP(N) is chosen as the axon tip position for the current image, ATP(n).

Fig. 4. Axon growth distance and velocity along final trajectory

(A) Plot of growth distance (left vertical axis, dots) and velocity (right vertical axis, continuous line) along the final axon trajectory as a function of time: (…) growth distance; (—) velocity. (B) Histogram of the growth velocities. Mean = 0.05 ± 0.14 µm/min.

Fig. 5. Manual vs. A³G tip identification

The distances between A³Gidentified axon tips and the center of mass of axon tips identified by 11 human operators (HCM) are shown for the box (dotted line) and region (continuous line) search analyses of a 90-image sequence. The shaded region represents the range of distances between manually identified tips (11 humans) and the HCM for each image.