Voltage imaging enables monitoring neural activity at sub-millisecond and sub-cellular scale, unlocking the study of subthreshold activity, synchrony, and network dynamics with unprecedented spatio-temporal resolution. However, high data rates (>800MB/s) and low signal-to-noise ratios create bottlenecks for analyzing such datasets. Here we present VolPy, an automated and scalable pipeline to pre-process voltage imaging datasets. VolPy features motion correction, memory mapping, automated segmentation, denoising and spike extraction, all built on a highly parallelizable, modular, and extensible framework optimized for memory and speed. To aid automated segmentation, we introduce a corpus of 24 manually annotated datasets from different preparations, brain areas and voltage indicators. We benchmark VolPy against ground truth segmentation, simulations and electrophysiology recordings, and we compare its performance with existing algorithms in detecting spikes. Our results indicate that VolPy's performance in spike extraction and scalability are state-of-the-art.

Cells contain hundreds of organelles and macromolecular assemblies. Obtaining a complete understanding of their intricate organization requires the nanometre-level, three-dimensional reconstruction of whole cells, which is only feasible with robust and scalable automatic methods. Here, to support the development of such methods, we annotated up to 35 different cellular organelle classes-ranging from endoplasmic reticulum to microtubules to ribosomes-in diverse sample volumes from multiple cell types imaged at a near-isotropic resolution of 4 nm per voxel with focused ion beam scanning electron microscopy (FIB-SEM). We trained deep learning architectures to segment these structures in 4 nm and 8 nm per voxel FIB-SEM volumes, validated their performance and showed that automatic reconstructions can be used to directly quantify previously inaccessible metrics including spatial interactions between cellular components. We also show that such reconstructions can be used to automatically register light and electron microscopy images for correlative studies. We have created an open data and open-source web repository, 'OpenOrganelle', to share the data, computer code and trained models, which will enable scientists everywhere to query and further improve automatic reconstruction of these datasets.

Cells in many tissues, such as bone, muscle, and placenta, fuse into syncytia to acquire new functions and transcriptional programs. While it is known that fused cells are specialized, it is unclear whether cell-fusion itself contributes to programmatic-changes that generate the new cellular state. Here, we address this by employing a fusogen-mediated, cell-fusion system to create syncytia from undifferentiated cells. RNA-Seq analysis reveals VSV-G-induced cell fusion precedes transcriptional changes. To gain mechanistic insights, we measure the plasma membrane surface area after cell-fusion and observe it diminishes through increases in endocytosis. Consequently, glucose transporters internalize, and cytoplasmic glucose and ATP transiently decrease. This reduced energetic state activates AMPK, which inhibits YAP1, causing transcriptional-reprogramming and cell-cycle arrest. Impairing either endocytosis or AMPK activity prevents YAP1 inhibition and cell-cycle arrest after fusion. Together, these data demonstrate plasma membrane diminishment upon cell-fusion causes transient nutrient stress that may promote transcriptional-reprogramming independent from extrinsic cues.