Automatic Dendritic Length Quantification for High Throughput Screening of Mature Neurons

Abstract

High-throughput automated fluorescent imaging and screening are important for studying neuronal development, functions, and pathogenesis. An automatic approach of analyzing images acquired in automated fashion, and quantifying dendritic characteristics is critical for making such screens high-throughput. However, automatic and effective algorithms and tools, especially for the images of mature mammalian neurons with complex arbors, have been lacking. Here, we present algorithms and a tool for quantifying dendritic length that is fundamental for analyzing growth of neuronal network. We employ a divide-and-conquer framework that tackles the challenges of high-throughput images of neurons and enables the integration of multiple automatic algorithms. Within this framework, we developed algorithms that adapt to local properties to detect faint branches. We also developed a path search that can preserve the curvature change to accurately measure dendritic length with arbor branches and turns. In addition, we proposed an ensemble strategy of three estimation algorithms to further improve the overall efficacy. We tested our tool on images for cultured mouse hippocampal neurons immunostained with a dendritic marker for high-throughput screen. Results demonstrate the effectiveness of our proposed method when comparing the accuracy with previous methods. The software has been implemented as an ImageJ plugin and available for use.

Keywords: Automatic quantification; High-throughput screening; Neuron dendrite.

Figure 1. Example of high-throughput images of cultured mature mouse hippocampal neurons

This is a 2*2 montage obtained by BD Pathway Imaging system. Size: 2688*2048. Scale: 0.15622 micron/pixel.

Figure 2. The schematic illustration of our methodology

A. The raw image. Green channel is the MAP2 stained dendrite channel. B. Preprocessing of the dendrite channel including soma removal and image enhancement. C. The divide-and-conquer framework for dendrite length quantification.

Figure 3

An illustrative example for boundary analysis that reduces duplicate estimation.

Figure 4. Dendrite length estimation Based on AET

A. Examples of grid window images. The detected initial points as indicated using blue arrows. B. Preprocessing to highlight the faint and/or broken dendrites using Hessian transform and histogram stretching. C. Stopping mask generation using RATS. D. Estimation algorithm flow. The branch on the right is traced first because it is thinner.

Figure 5. Dendrite length estimation Based on CSP

The flow starts from finding initial points on post-Hessian grid window, followed by calculating and storing paths from legitimate pairs of initial points. The path placement is then repeated and traces are removed until all points are processed.

Figure 6. Comparison of different search waves and choices of the cost function in CSP

A. Shortest path wavefront without threshold truncating. The yellow wavefront indicates the area where the pixels are searched. B. Shortest path wavefront with threshold truncating. C. Tracing results (in red) when using cost function with a logarithmic growth rate. D. Tracing results when using a cost function with linear growth rate. E. Tracing results when using a cost function with cubic growth rate. The branching curvature is better preserved compared with C and D.

Figure 7. Comparison of the LSK method with global alternative

A. Original image (raw image has been enhanced for better visualization). Blue masks indicate the area with dim branches. B. Global skeletonization. C. LSK with the divide-and-conquer strategy. The improved local adaptiveness allows better capturing of dim branches.

Figure 8. Examples of results

A. LSK works with a grid window with multiple branches; B. AET works with a grid window with dim and broken branches; C. CSP works with a grid window of big curve change. D. An example of tracing results overlaid in the original image. The dotted red line indicates the trace. The small gaps between dots represent the step size of exploration tracing algorithm (set to 3 by default).

Figure 9. Performance versus number of grids

The x coordinate is number of grids starting from 2*2 = 4, 3*3 =9, …, 20*20 = 400. The trendline is fitted using second order polynominal.