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1. VesiclePy: A Machine Learning Vesicle Analysis Toolbox for Volume Electron Microscopy

   https://doi.org/10.1101/2025.09.08.674799  

   Adhinarta, Jason K; Fan, Yutian; Gohain, Adam; Lin, Michael; Nurkin, Paige; Ren, Richard; Roth, Micaela; Zhang, Shulin; Yakobe, Ayal; Yuste, Rafael; et al (September 2025, bioRxiv)

   Vesicles are critical components of neurons that package neurotransmitters and neuropeptides for their release, in order to communicate with other neurons and cells. However, due to their small size, the reconstruction of the full vesicle endowment across an entire neuronal morphology remains challenging. To achieve this, we have used, as a tool to identify and visualize vesicles, Volume Electron Microscopy (vEM), a method that has the nanoscale resolution to detect individual vesicle boundaries, content, and 3D locations. However, the large volume of vEM datasets poses a challenge in the segmentation, classification, and spatial analysis of tens of thousands of vesicles and their target cell in 3D. Here we report the development of VesiclePy, an integrated pipeline for automated segmentation, classification, proofreading, and spatial analysis of vesicles, relative to neuron masks in large-volume electron microscopy data. Our package integrates the efficiency of deep learning and the accuracy of human proofreading and provides a streamlined package in chunked processing and accurate indexing, localization, and visualization of single vesicle resolution in large vEM data. We demonstrate the viability of VesiclePy using high-pressure frozen serial EM data ofHydra vulgarisand quantify the performance of the package using ground truth manual annotations. We show that VesiclePy can process a multiterabyte serial EM dataset, efficiently annotate 53,851 vesicles from 20 complete neurons, and classify vesicles into 5 types. Each vesicle has a unique ID and 3D location for further spatial analysis in relation to neuron or non-neuronal targets nearby. Finally, by combining vesicle data and morphological information of each neuron, we can quantitatively cluster neurons into subtypes. VesiclePy is available athttps://github.com/PytorchConnectomics/VesiclePyunder an MIT license. 

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2. Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex

   https://doi.org/10.1101/2023.01.23.525290  

   Schneider-Mizell, Casey M; Bodor, Agnes L; Brittain, Derrick; Buchanan, JoAnn; Bumbarger, Daniel J; Elabbady, Leila; Gamlin, Clare; Kapner, Daniel; Kinn, Sam; Mahalingam, Gayathri; et al (January 2023, bioRxiv)

   Mammalian cortex features a vast diversity of neuronal cell types, each with characteristic anatomical, molecular and functional properties. Synaptic connectivity powerfully shapes how each cell type participates in the cortical circuit, but mapping connectivity rules at the resolution of distinct cell types remains difficult. Here, we used millimeter-scale volumetric electron microscopy¹to investigate the connectivity of all inhibitory neurons across a densely-segmented neuronal population of 1352 cells spanning all layers of mouse visual cortex, producing a wiring diagram of inhibitory connections with more than 70,000 synapses. Taking a data-driven approach inspired by classical neuroanatomy, we classified inhibitory neurons based on the relative targeting of dendritic compartments and other inhibitory cells and developed a novel classification of excitatory neurons based on the morphological and synaptic input properties. The synaptic connectivity between inhibitory cells revealed a novel class of disinhibitory specialist targeting basket cells, in addition to familiar subclasses. Analysis of the inhibitory connectivity onto excitatory neurons found widespread specificity, with many interneurons exhibiting differential targeting of certain subpopulations spatially intermingled with other potential targets. Inhibitory targeting was organized into “motif groups,” diverse sets of cells that collectively target both perisomatic and dendritic compartments of the same excitatory targets. Collectively, our analysis identified new organizing principles for cortical inhibition and will serve as a foundation for linking modern multimodal neuronal atlases with the cortical wiring diagram. 

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3. Light microscopy based approach for mapping connectivity with molecular specificity

   https://doi.org/10.1038/s41467-020-18422-8  

   Shen, Fred Y.; Harrington, Margaret M.; Walker, Logan A.; Cheng, Hon Pong; Boyden, Edward S.; Cai, Dawen (December 2020, Nature Communications)

   null (Ed.)

   Abstract Mapping neuroanatomy is a foundational goal towards understanding brain function. Electron microscopy (EM) has been the gold standard for connectivity analysis because nanoscale resolution is necessary to unambiguously resolve synapses. However, molecular information that specifies cell types is often lost in EM reconstructions. To address this, we devise a light microscopy approach for connectivity analysis of defined cell types called spectral connectomics. We combine multicolor labeling (Brainbow) of neurons with multi-round immunostaining Expansion Microscopy (miriEx) to simultaneously interrogate morphology, molecular markers, and connectivity in the same brain section. We apply this strategy to directly link inhibitory neuron cell types with their morphologies. Furthermore, we show that correlative Brainbow and endogenous synaptic machinery immunostaining can define putative synaptic connections between neurons, as well as map putative inhibitory and excitatory inputs. We envision that spectral connectomics can be applied routinely in neurobiology labs to gain insights into normal and pathophysiological neuroanatomy. 

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4. Time-lapse imaging of cell death in cell culture and whole living organisms using turn-on deep-red fluorescent probes

   https://doi.org/10.1039/c8tb01495g  

   Jarvis, Tia S.; Roland, Felicia M.; Dubiak, Kyle M.; Huber, Paul W.; Smith, Bradley D. (January 2018, Journal of Materials Chemistry B)

   Cell death is a central process in developmental biology and also an important indicator of disease status and treatment efficacy. Two related fluorescent probes are described that are molecular conjugates of one or two zinc dipicolylamine (ZnDPA) coordination complexes with an appended solvatochromic benzothiazolium squaraine dye. The probes were designed to target the anionic phospholipid, phosphatidylserine (PS), that is exposed on the surface of dead and dying cells. A series of spectrometric and microscopy studies using liposomes and red blood cell ghosts as models showed that the probe with two ZnDPA targeting units produced higher affinity, stronger fluorescence “turn-on” effect, and better image contrast than the probe with one ZnDPA. Both fluorescent probes enabled “no-wash” time-lapse microscopic imaging of mammalian cell death within a culture. The probe with two ZnDPA units was used for non-invasive time-lapse imaging of cell death during the development of Xenopus laevis (frog) embryos. In vivo fluorescence micrographs revealed probe accumulation within the embryo tail, head and spine regions that were undergoing regression and apoptosis during growth and maturation. These new fluorescent probes are likely to be useful for time-resolved, non-invasive in vivo imaging of cell death process in range of living organisms. From a broader perspective, it should be possible to utilize the negative solvatochromism exhibited by benzothiazolium squaraine dyes for development of various “turn-on” deep-red fluorescent probes and materials that target cell surface biomarkers for in vitro and in vivo imaging. 

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5. Contextual High-Throughput 3D Volume Electron Microscopy Data Acquisition Using Artificial Intelligence

   https://doi.org/10.1093/mam/ozag014  

   Hurník_Konečná, Tereza; Jančík, Radek; Slamková, Daniela; Přibyl, Bronislav; Tomova, Cveta; Rotkina, Lolita; Burch-Smith, Tessa; Sviben, Sanja; Fitzpatrick, James_A J; Czymmek, Kirk J (March 2026, Microscopy and Microanalysis)

   Abstract One of the greatest challenges when generating large and/or high-resolution three-dimensional (3D) volume electron microscopy (vEM) datasets is long acquisition times. We developed a method that leverages Artificial intelligence algorithms to increase acquisition throughput for 3D datasets by creating an AI-derived mask for the targeted region of interest, referred to as Adaptive Scanning. This allowed for specific structures to be imaged at high resolution, with the surrounding area captured at lower resolution, without artificially generating or enhancing any raw data. We demonstrate that this dynamic-resolution scanning approach significantly reduced acquisition time depending on the number of pixels of interest and was sample agnostic, being compatible with a diverse array of organisms and tissues, including brains, parasites, cultured cells, and plants. This multiresolution strategy has the potential to enhance focused ion beam scanning electron microscopy and other forms of vEM, by increasing time savings by up to twofold or more, enabling routine generation of multiple and/or larger vEM datasets more efficiently, cost-effectively, and allowing more data collection to increase statistical power for comparative studies. 

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