Modern biological research relies heavily on microscopic imaging. The advanced genetic toolkit of Drosophila makes it possible to label molecular and cellular components with unprecedented level of specificity necessitating the application of the most sophisticated imaging technologies. Imaging in Drosophila spans all scales from single molecules to the entire populations of adult organisms, from electron microscopy to live imaging of developmental processes. As the imaging approaches become more complex and ambitious, there is an increasing need for quantitative, computer-mediated image processing and analysis to make sense of the imagery. Bioimage Informatics is an emerging research field that covers all aspects of biological image analysis from data handling, through processing, to quantitative measurements, analysis and data presentation. Some of the most advanced, large scale projects, combining cutting edge imaging with complex bioimage informatics pipelines, are realized in the Drosophila research community. In this review, we discuss the current research in biological image analysis specifically relevant to the type of systems level image datasets that are uniquely available for the Drosophila model system. We focus on how state-of-the-art computer vision algorithms are impacting the ability of Drosophila researchers to analyze biological systems in space and time. We pay particular attention to how these algorithmic advances from computer science are made usable to practicing biologists through open source platforms and how biologists can themselves participate in their further development.

In recent years, the deluge of complicated molecular and cellular microscopic images creates compelling challenges for the image computing community. There has been an increasing focus on developing novel image processing, data mining, database and visualization techniques to extract, compare, search and manage the biological knowledge in these data-intensive problems. This emerging new area of bioinformatics can be called ’bioimage informatics’. This article reviews the advances of this field from several aspects, including applications, key techniques, available tools and resources. Application examples such as high-throughput/high-content phenotyping and atlas building for model organisms demonstrate the importance of bioimage informatics. The essential techniques to the success of these applications, such as bioimage feature identification, segmentation and tracking, registration, annotation, mining, image data management and visualization, are further summarized, along with a brief overview of the available bioimage databases, analysis tools and other resources.

Few technologies are more widespread in modern biological laboratories than imaging. Recent advances in optical technologies and instrumentation are providing hitherto unimagined capabilities. Almost all these advances have required the development of software to enable the acquisition, management, analysis and visualization of the imaging data. We review each computational step that biologists encounter when dealing with digital images, the inherent challenges and the overall status of available software for bioimage informatics, focusing on open-source options.

Proprioception, the sense of body position and movement, is essential for effective motor control. Because proprioceptive sensory neurons are embedded in complex and dynamic tissues, it has been challenging to understand how they sense and encode mechanical stimuli. Here, we find that proprioceptor neurons in the Drosophila femur are organized into functional groups that are biomechanically specialized to detect features of tibia joint kinematics. The dendrites of position and vibration-tuned proprioceptors receive distinct mechanical signals via the arculum, an elegant mechanical structure that decomposes movement of the tibia joint into orthogonal components. The cell bodies of position-tuned proprioceptors form a goniotopic map of joint angle, whereas the dendrites of vibration-tuned proprioceptors form a tonotopic map of tibia vibration frequency. Our findings reveal biomechanical mechanisms that underlie proprioceptor feature selectivity and identify common organizational principles between proprioception and other topographically organized sensory systems.

Our ability to sense and move our bodies relies on proprioceptors, sensory neurons that detect mechanical forces within the body. Different subtypes of proprioceptors detect different kinematic features, such as joint position, movement, and vibration, but the mechanisms that underlie proprioceptor feature selectivity remain poorly understood. Using single-nucleus RNA sequencing (RNA-seq), we found that proprioceptor subtypes in the Drosophila leg lack differential expression of mechanosensitive ion channels. However, anatomical reconstruction of the proprioceptors and connected tendons revealed major biomechanical differences between subtypes. We built a model of the proprioceptors and tendons that identified a biomechanical mechanism for joint angle selectivity and predicted the existence of a topographic map of joint angle, which we confirmed using calcium imaging. Our findings suggest that biomechanical specialization is a key determinant of proprioceptor feature selectivity in Drosophila. More broadly, the discovery of proprioceptive maps reveals common organizational principles between proprioception and other topographically organized sensory systems.

Liquid-liquid phase separation (LLPS) has emerged in recent years as an important physicochemical process for organizing diverse processes within cells via the formation of membraneless organelles termed biomolecular condensates. Emerging evidence now suggests that the formation and regulation of biomolecular condensates are also intricately linked to cancer formation and progression. We review the most recent literature linking the existence and/or dissolution of biomolecular condensates to different hallmarks of cancer formation and progression. We then discuss the opportunities that this condensate perspective provides for cancer research and the development of novel therapeutic approaches, including the perturbation of condensates by small-molecule inhibitors.

Liquid-liquid phase separation (LLPS) has emerged in recent years as an important physicochemical process for organizing diverse processes within cells via the formation of membraneless organelles termed biomolecular condensates. Emerging evidence now suggests that the formation and regulation of biomolecular condensates are also intricately linked to cancer formation and progression. We review the most recent literature linking the existence and/or dissolution of biomolecular condensates to different hallmarks of cancer formation and progression. We then discuss the opportunities that this condensate perspective provides for cancer research and the development of novel therapeutic approaches, including the perturbation of condensates by small-molecule inhibitors.

We investigate rules that govern neuronal arborization, speci%cally the local geometry of the

bifurcation of a neurite into its sub-branches. In the present study we set out to determine

the relationship between branch diameter and angle. Existing theories are based on minimizing a

neuronal volume cost function, or, alternatively, on the equilibrium of mechanical tension forces,

whichdepend on branchdiameters. Our experimental results utilizing two-dimensional cultured

neural networks partly corroborate both the volume optimization principles and the tension theory.

Deviation from pure tension forces equilibrium is explained by an additional force exerted by

the anchoring of the junction to the substrate.

The lobula giant movement detector (LGMD) is a large-field visual interneuron believed to be involved in collision avoidance and escape behaviors in orthopteran insects, such as locusts. Responses to approaching—or looming—stimuli are highly stereotypical, producing a peak that signals an angular size threshold. Over the past several decades, investigators have elucidated many of the mechanisms underpinning this response, demonstrating that the LGMD implements a multiplication in log-transformed coordinates. Furthermore, the LGMD possesses several mechanisms that preclude it responding to non-looming stimuli. This chapter explores these biophysical mechanisms, as well as highlighting insights the LGMD provides into general principles of dendritic integration.