3DeeCellTracker, a deep learning-based pipeline for segmenting and tracking cells in 3D time lapse images.

Despite recent improvements in microscope technologies, segmenting and tracking cells in three-dimensional time-lapse images (3D + T images) to extract their dynamic positions and activities remains a considerable bottleneck in the field. We developed a deep learning-based software pipeline, 3DeeCellTracker, by integrating multiple existing and new techniques including deep learning for tracking. With only one volume of training data, one initial correction, and a few parameter changes, 3DeeCellTracker successfully segmented and tracked ~100 cells in both semi-immobilized and 'straightened' freely moving worm's brain, in a naturally beating zebrafish heart, and ~1000 cells in a 3D cultured tumor spheroid. While these datasets were imaged with highly divergent optical systems, our method tracked 90-100% of the cells in most cases, which is comparable or superior to previous results. These results suggest that 3DeeCellTracker could pave the way for revealing dynamic cell activities in image datasets that have been difficult to analyze.

Microscopes have been used to decrypt the tiny details of life since the 17th century. Now, the advent of 3D microscopy allows scientists to build up detailed pictures of living cells and tissues. In that effort, automation is becoming increasingly important so that scientists can analyze the resulting images and understand how bodies grow, heal and respond to changes such as drug therapies. In particular, algorithms can help to spot cells in the picture (called cell segmentation), and then to follow these cells over time across multiple images (known as cell tracking). However, performing these analyses on 3D images over a given period has been quite challenging. In addition, the algorithms that have already been created are often not user-friendly, and they can only be applied to a specific dataset gathered through a particular scientific method. As a response, Wen et al. developed a new program called 3DeeCellTracker, which runs on a desktop computer and uses a type of artificial intelligence known as deep learning to produce consistent results. Crucially, 3DeeCellTracker can be used to analyze various types of images taken using different types of cutting-edge microscope systems. And indeed, the algorithm was then harnessed to track the activity of nerve cells in moving microscopic worms, of beating heart cells in a young small fish, and of cancer cells grown in the lab. This versatile tool can now be used across biology, medical research and drug development to help monitor cell activities.

An early endothelial cell-specific requirement for Glut1 is revealed in Glut1 deficiency syndrome model mice.

Paucity of the glucose transporter-1 (Glut1) protein resulting from haploinsufficiency of the SLC2A1 gene arrests cerebral angiogenesis and disrupts brain function to cause Glut1 deficiency syndrome (Glut1 DS). Restoring Glut1 to Glut1 DS model mice prevents disease, but the precise cellular sites of action of the transporter, its temporal requirements, and the mechanisms linking scarcity of the protein to brain cell dysfunction remain poorly understood. Here, we show that Glut1 functions in a cell-autonomous manner in the cerebral microvasculature to affect endothelial tip cells and, thus, brain angiogenesis. Moreover, brain endothelial cell-specific Glut1 depletion not only triggers a severe neuroinflammatory response in the Glut1 DS brain, but also reduces levels of brain-derived neurotrophic factor (BDNF) and causes overt disease. Reduced BDNF correlated with fewer neurons in the Glut1 DS brain. Controlled depletion of the protein demonstrated that brain pathology and disease severity was greatest when Glut1 scarcity was induced neonatally, during brain angiogenesis. Reducing Glut1 at later stages had mild or little effect. Our results suggest that targeting brain endothelial cells during early development is important to ensure proper brain angiogenesis, prevent neuroinflammation, maintain BDNF levels, and preserve neuron numbers. This requirement will be essential for any disease-modifying therapeutic strategy for Glut1 DS.

High-speed 3D imaging of cellular activity in the brain using axially-extended beams and light sheets.

As optical reporters and modulators of cellular activity have become increasingly sophisticated, the amount that can be learned about the brain via high-speed cellular imaging has increased dramatically. However, despite fervent innovation, point-scanning microscopy is facing a fundamental limit in achievable 3D imaging speeds and fields of view. A range of alternative approaches are emerging, some of which are moving away from point-scanning to use axially-extended beams or sheets of light, for example swept confocally aligned planar excitation (SCAPE) microscopy. These methods are proving effective for high-speed volumetric imaging of the nervous system of small organisms such as Drosophila (fruit fly) and D. Rerio (Zebrafish), and are showing promise for imaging activity in the living mammalian brain using both single and two-photon excitation. This article describes these approaches and presents a simple model that demonstrates key advantages of axially-extended illumination over point-scanning strategies for high-speed volumetric imaging, including longer integration times per voxel, improved photon efficiency and reduced photodamage.