Comparing Automated Morphology Quantification Software on Dendrites of Uninjured and Injured Drosophila Neurons

Abstract

Dendrites shape inputs and integration of depolarization that controls neuronal activity in the nervous system. Neuron pathologies can damage dendrite architecture and cause abnormalities in morphologies after injury. Dendrite regeneration can be quantified by various parameters, including total dendrite length and number of dendrite branches using manual or automated image analysis approaches. However, manual quantification is tedious and time consuming and automated approaches are often trained using wildtype neurons, making them poorly suited for analysis of genetically manipulated or injured dendrite arbors. In this study, we tested how well automated image analysis software performed on class IV Drosophila neurons, which have several hundred individual dendrite branches. We applied each software to automatically quantify features of uninjured neurons and neurons that regenerated new dendrites after injury. Regenerated arbors exhibit defects across multiple features of dendrite morphology, which makes them challenging for automated pipelines to analyze. We compared the performances of three automated pipelines against manual quantification using Simple Neurite Tracer in ImageJ: one that is commercially available (Imaris) and two developed by independent research groups (DeTerm and Tireless Tracing Genie). Out of the three software tested, we determined that Imaris is the most efficient at reconstructing dendrite architecture, but does not accurately measure total dendrite length even after intensive manual editing. Imaris outperforms both DeTerm and Tireless Tracing Genie for counting dendrite branches, and is better able to recreate previous conclusions from this same dataset. This thorough comparison of strengths and weaknesses of each software demonstrates their utility for analyzing regenerated neuron phenotypes in future studies.

Optimization of Neurite Tracing and Further Characterization of Human Monocyte-Derived-Neuronal-like Cells

“…Unfortunately, there is no universal tracing method that can efficiently extract neurostructural data under all research conditions. Instead, experts recommend testing several tracing approaches to determine which is the best suited for each laboratory [ 69 , 70 ]. Currently, there is a plethora of automated tracing methods, but the gold standard continues to be manual tracing via semi-automated approaches [ 69 ].…”

“…The difficulties arose because in many instances Volocity did not identify the entire neuritic length. Students, therefore, had to piece together sections of neurites, similar to what other research teams have described using different automated softwares [ 70 ]. This task was more time consuming than even tracing the entire cell using a semi-automated approach ( Figure 3 D).…”

“…It is possible that other automated applications would recognize the entire structure of MDNCs, even when analyzing photographs of live cells using light microscopy. However, if automated software products become a viable alternative, cost will have to be factored in, as semi-automated methods usually do not bear any cost to the researcher, while most automated software have to be purchased [ 69 , 70 ].…”

“…In summary, selecting a tracing method is a complicated process that depends on the specific research conditions to be tested [ 69 , 70 ]. For instance, analyzing neurons in culture (2-dimensional) versus brain slices (3-dimensional) or studying intact neurites versus damaged neurites would each generate its own set of intricacies for which only a couple of tracing approaches would be suitable.…”

“…For instance, analyzing neurons in culture (2-dimensional) versus brain slices (3-dimensional) or studying intact neurites versus damaged neurites would each generate its own set of intricacies for which only a couple of tracing approaches would be suitable. It is also essential to determine which aspects of the neuronal structure will be studied, as some tracing paradigms are better at measuring neurite length, while others excel at counting number of extensions [ 69 , 70 ]. Therefore, experts recommend testing different tracing approaches to determine the most efficient method for each laboratory [ 69 , 70 ].…”