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The workload posed by image analysis remains a major bottleneck for advances across the life sciences. To address this challenge, we have developed a fully automated workflow for processing complex 3D multi-channel microscopy images. Specifically, our workflow addresses the analysis of photoreceptor synapses in confocal images of the Drosophila melanogaster optic lobe. The workflow consists of multiple stages, combining traditional and machine learning–based approaches for image analysis and visual computing. It performs segmentation of brain regions, photoreceptor instance identification, and precise localization of synapses. The key novelty of the workflow is an automatic alignment of synapses into a cylindrical reference coordinate system, enabling comparative synaptic analysis across photoreceptors. To demonstrate the workflow’s applicability, preliminary biological results and their interpretation based on 50 images are presented. While the workflow is still being improved further, here, we showcase its capacity for efficient and objective data processing for high-throughput neurobiological analyses.
Background: Filopodia are thin and dynamic membrane protrusions that play a crucial role in cell migration, axon guidance, and other processes where cells explore and interact with their surroundings. Historically, filopodial dynamics have been studied in great detail in 2D in cultured cells, and more recently in 3D culture as well as living brains. However, there is a lack of efficient tools to trace and track filopodia in 4D images of complex brain cells.
Results: To address this issue, we have developed a semi-automatic workflow for tracing filopodia in 3D images and tracking the traced filopodia over time. The workflow was developed based on high-resolution data of photoreceptor axon terminals in the in vivo context of normal Drosophila brain development, but devised to be applicable to filopodia in any system, including at different temporal and spatial scales. In contrast to the pre-existing methods, our workflow relies solely on the original intensity images without the requirement for segmentation or complex preprocessing. The workflow was realized in C++ within the Amira software system and consists of two main parts, dataset pre-processing, and geometrical filopodia reconstruction, where each of the two parts comprises multiple steps. In this paper, we provide an extensive workflow description and demonstrate its versatility for two different axo-dendritic morphologies, R7 and Dm8 cells. Finally, we provide an analysis of the time requirements for user input and data processing.
Conclusion: To facilitate simple application within Amira or other frameworks, we share the source code, which is available at https://github.com/zibamira/filopodia-tool.
Elephants use their trunks, muscular hydrostats, to perform a plethora of tasks. Trunk tip morphology as well as grasping behavior differ between elephant species. While African savanna elephants (Loxodonta africana) use their dorsal and ventral finger for pinching movements, Asian elephants (Elephas maximus) prefer to wrap around objects with their one dorsal finger and ventral bulb trunk tip lip. Moreover, E. maximus can flip their ventral bulb backwards to clamp objects behind the trunk tip. Whether trunk tip musculature differs between elephant species and muscle architecture is reflected by preferred grasping behavior is, however, not clear. In this study, we performed dense muscle fascicle reconstruction of three L. africana and three E. maximus hemi-trunk tips using a combination of manual and automated segmentation of high-resolution microfocus tomography (microCT) scans. We distinguish three types of muscle fascicles: longitudinal (bending and shortening), radial (elongating) and transversal muscle fascicles (elongating). We found that trunk tips of L. africana consist to one third of longitudinal and two thirds radial/transversal muscle fascicles, likely aiding in their grasping behavior, while E. maximus trunk tips consist to two thirds of longitudinal and one third radial/transversal muscle fascicles, which is advantageous for their wrapping and backward clamping behavior.
The analysis of complex imaging data often represents a significant barrier to progress in neurobiology. This is particularly true for the analysis of cells with a complex morphology, and when the imaged data is multi-dimensional. Here, we present a holistic workflow for 4D quantitative analysis of the morphogenesis of a columnar neuron in the developing Drosophila melanogaster brain. The example data shows a neuron that simultaneously contacts tens of columns in the visual neuropil, posing a significant challenge of resolving spatially heterogeneous contact dynamics across individual columns. For successful quantification, the 4D multi-channel imaging data undergoes several processing steps, including data standardization, alignment, discretization, and imaging angle correction. From the processed data, contact dynamics at the level of individual columns are extracted. To facilitate the analysis of results, we developed the Column Explorer tool, which enables the user to visualize and explore the results of the workflow. In addition, analysis of the time requirements for the execution of the workflow is provided for three datasets. The quantitative framework presented here is adaptable to other neural systems with columnar organization, thus providing a generalizable tool for studying contact dynamics during neural circuit development.
Background
Synapses, as specialised cell–cell contacts, allow for a faithful and controlled signal transmission between a neuron and a target cell. Presynapses, the sites of neurotransmitter release, form de novo throughout the development of an organism. Although this process is fundamental to the development and function of synaptic circuits, how developing neurons control number and distribution of individual synapses remains poorly understood. In-vivo imaging analysis of synapse formation at the neuromuscular junction of anaesthetised Drosophila third instar larvae allows for spatial and temporal resolution of the underlying molecular processes. However, high-throughput, comprehensive analysis are hampered by the manual and time-consuming imaging analysis methods applied hitherto. Here, we focus on the early presynaptic formation steps, that is, the presynaptic seeding, initiated by the formation of transient Liprin-α/SYD1 seeding sites, either stabilised or disintegrated over a time span of 30–90 min.
Results
To investigate the dynamics of the Liprin-α/SYD1 seeding sites, we developed an automated analysis pipeline for 3D confocal images from in-vivo imaging at distinct time points to analyse fluorescently labelled presynaptic protein dynamics during early synapse formation. The workflow is realised in the data analysis software Amira, utilising the hierarchical watershed algorithm, and was designed for automatic processing with an option for manual proofreading. Compared to the previous 2D manual quantification, this automated approach provides a higher sensitivity in single Liprin-α seeding site detection in low-intensity areas and in regions of dense seeding sites. In addition, it substantially reduces the work time. To account for possible errors occurring in the automated processing, we implemented an additional proofreading step allowing for a manual correction of Liprin-α seeding site segmentation and assignment, thus greatly improving the analysis while only marginally increasing work time by 10% to a total work time reduction of 70% compared to the 2D manual analysis paradigm.
Conclusion
The process of synaptogenesis underlies the general principles of locomotion, learning and memory formation. The developed fast and accurate semi-automated 3D workflow will provide a substantial progress in the analysis of this molecular process.
