Automated analysis of neurite branching in cultured cortical neurons using HCA‐Vision

Abstract

Manual neuron tracing is a very labor-intensive task. In the drug screening context, the sheer number of images to process means that this approach is unrealistic. Moreover, the lack of reproducibility, objectivity, and auditing capability of manual tracing is limiting even in the context of smaller studies. We have developed fast, sensitive, and reliable algorithms for the purpose of detecting and analyzing neurites in cell cultures, and we have integrated them in software called HCA-Vision, suitable for the research environment. We validate the software on images of cortical neurons by comparing results obtained using HCA-Vision with those obtained using an established semi-automated tracing solution (NeuronJ). The effect of the Sez-6 deletion was characterized in detail. Sez-6 null neurons exhibited a significant increase in neurite branching, although the neurite field area was unchanged due to a reduction in mean branch length. HCAVision delivered considerable speed benefits and reliable traces. ' International Society for Analytical CytologyKey terms neurite tracing; neurite branching; neurite analysis; neurites; neurite outgrowth; neuron tracing; neuron analysis; cell morphology; neuron image analysis NEURITE tracing capabilities are invaluable in the drug development arena to identify compounds that display neuroprotective or neuroregenerative effects (1,2). Detailed morphological analyses of neurons are also vital for studying the normal development of dendritic and axonal arbors and for documenting neuropathological changes. Neurite arborization patterns established during development are characteristic for particular neuronal subtypes and relate to function. Neurite arbor size and shape influence the integration of synaptic inputs (3) and these, in turn, are regulated by both intrinsic developmental programs and external signals (4,5). Alterations in neurite arbors have been observed in a number of neuropathological conditions including mental retardation syndromes such as Down, Rett's and Fragile X syndromes (6), schizophrenia (7,8), and Alzheimer's disease (9).The simplest method for quantifying neurite arbors from digital micrographs involves intensity thresholding. Under controlled imaging conditions, good results can be obtained (10). However, most neurite images display noisy, low contrast areas that affect the ability to detect neurites. A recent implementation of neurite tracing by Xiong et al. used a multi-scale approach, with a Hessian matrix guiding the tracing (11). These authors report measurements of the total length of neurites and of the number of extremities. A system to study neurite dynamics has also been described (12). One of its most interesting features is the ability to correct for relatively frequent tracing mistakes by the use of a probabilistic model, making use of a mask associated with areas where pixel intensities change significantly from image to image.Here, we present a fully automated image analysis solution for generating neurite traces, for segmenting the ...

Neuron tracing in perspective

“…In phasecontrast microscopy too, the cell bodies often yield more contrast than their processes, in which case they can be segmented simply by means of intensity thresholding (33,34). If, on the other hand, the somas cannot be distinguished solely based on intensity, they may be discriminated by applying suitable (binary or grayscale) morphological filtering (35)(36)(37)(38). For example, repeated ''erosion'' operations remove all thin and small image structures (the neurites), after which the larger structures (the somas) can be restored by subsequent ''dilation'' operations (21,22).…”

“…Neurite centerlines may alternatively be obtained directly from the grayscale images, by applying Hessian (58)(59)(60)(61) or Jacobian (62) based analysis of critical points, or by nonmaximum suppression (37,63). The result of the skeletonization step often contains errors (such as spurious gaps or branches) and (especially in 2D) ambiguities (spurious loops or crossings).…”

“…The result of the skeletonization step often contains errors (such as spurious gaps or branches) and (especially in 2D) ambiguities (spurious loops or crossings). Various filling and pruning strategies have been developed to try to rectify these retrospectively (25,37,38,(46)(47)(48)53,57,59,62,63). Given the centerlines, it is easy to estimate the neurite diameter at each location, for example using the Rayburst algorithm (46,64).…”

“…Other prominent commercial software tools providing advanced manual and (semi)automatic tracing functionality include the FilamentTracer (formerly NeuronTracer) module of the Imaris package (Bitplane, Switzerland) and (especially for 2D high-content screening applications) HCA-Vision (CSIRO Biotech Imaging, Australia) (37). Nowadays, many commercial software packages for (live) cell imaging also contain modules for neuron segmentation and analysis (not mentioned explicitly here), with varying levels of sophistication.…”

“…For measuring neurite length parameters in 2D assays, a popular semiautomatic tool is the NeuronJ plugin to ImageJ, whose corresponding article (58) was the highest cited article in the past decade of all those discussed in the present survey (according to the ISI Web of Science). NeuronJ has been used as a reference tool for testing later 2D neuron tracing algorithms (32,37,48) and has inspired others to develop interactive tools such as Neuromantic (75) for neuron reconstruction in 3D. Higher levels of automation for 2D applications are found in tools such as NeuronMetrics (47), NeuriteTracer (48), and NeuronCyto (32), and for 3D applications in NeuronStudio (46), NeuronIQ (109), ORION (76), and FARSIGHT (101).…”