Biology subjects

Meirovitch, Y.

Publications and source records attributed to Meirovitch, Y..

13 recordsLinked to original sources

bioRxiv2026

RAEM: random-access electron microscopy for revisitable 3D imaging

Volume electron microscopy is essential for understanding cells, tissues, and neural circuits in their native 3D context, but many biological specimens are too large to image exhaustively at nanometer resolution. Researchers therefore must choose between broad anatomical context and ultrastructural detail. We introduce random-access electron microscopy (RAEM), a framework for studying fixed tissue repeatedly across scales rather than imaging it once at a single resolution. RAEM first builds a lower resolution 3D survey of the specimen, then uses accumulated human or AI-derived knowledge of that volume to guide the microscope back to selected physical sites for high resolution imaging. By linking reconstructed 3D coordinates to precise electron-beam positions on the original sections, RAEM enables targeted imaging of membranes, vesicles, and other nanoscale structures within specimens that would be impractical to image exhaustively. We demonstrate RAEM with vesicle-resolved imaging of synaptic boutons in human cortex, targeted imaging of more than one million human cortical mitochondria, hierarchical imaging of a nematode nervous system, and retrospective targeting of a previously published petabyte-scale human cortical volume. RAEM turns serial-section EM into a query-driven, multi-resolution approach for scalable biomedical discovery.

neuroscience↗

bioRxiv2025

Human brain organoids record the passage of time over multiple years in culture

The human brain develops and matures over an exceptionally prolonged period of time that spans nearly two decades of life. Processes that govern species-specific aspects of human postnatal brain development are difficult to study in animal models. While human brain organoids offer a promising in vitro model, they have thus far been shown to largely mimic early stages of brain development. Here, we developed human brain organoids for an unprecedented 5 years in culture, optimizing growth conditions able to extend excitatory neuron viability beyond previously-known limits. Using module scores of maturation-associated genes derived from a time course of endogenous human brain maturation, we show that brain organoids transcriptionally age with cell type-specificity through these many years in culture. Whole-genome methylation profiling reveals that the predicted epigenomic age of organoids sampled between 3 months and 5 years correlates precisely with time spent in vitro, and parallels epigenomic aging in vivo. Notably, we show that in chimeric organoids generated by mixing neural progenitors derived from "old" organoids with progenitors from "young" organoids, old progenitors rapidly produce late neuronal fates, skipping the production of earlier neuronal progeny that are instead produced by their young counterparts in the same co-cultures. The data indicate that human brain organoids can mature and record the passage of time over many years in culture. Progenitors that age in organoids retain a memory of the time spent in culture reflected in their ability to execute age-appropriate, late developmental programs.

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bioRxiv2025

Developmental Connectomics of the Mouse Cerebellum

To uncover the developmental processes that establish the precise patterns of synaptic connectivity in the CNS, we employed a connectomic approach in the mouse cerebellar cortex between birth and 2 weeks of age. There were dramatic quantitative and qualitative changes in the structure and connectivity of cerebellar cells. Parallel fiber synapses onto Purkinje cells increased [~]500-fold, with the most rapid growth taking place a week after birth. To support this profound synaptogenesis, Purkinje cells generated thousands of transient parallel fiber-oriented filopodia that received nascent synapses from parallel fibers. Importantly, we find that granule cells initiate synaptic output onto Purkinje cells only after receiving mossy fiber input, revealing a sequential, input-dependent logic for circuit assembly. In sharp contrast to the concurrent pruning of climbing fiber inputs, parallel fiber connectivity expanded and became highly individualized during development. Despite anatomical overlap, neighboring Purkinje cells share significantly fewer parallel fiber inputs than expected by chance. Moreover, parallel fibers themselves diverged spatially, further enforcing selective input allocation and resulting in highly specific parallel fiber cohorts for each Purkinje cell. Our findings uncover a mechanistic sequence in which early afferent activity and transient cellular structures guide the selective wiring and expansion of parallel fiber input to Purkinje cells, establishing developmental principles that ensure functional specificity in the mature brain.

neuroscience↗

bioRxiv2025

Cellular and synaptic organization of the Octopus vertical lobe

Understanding memory formation and its influence on behavior is a central challenge in neuroscience. Associative learning networks, including the mushroom body in insects, the cerebellum in mammals, and the vertical lobe (VL) in cephalopods, typically exhibit a 3-layered architecture, characterized by divergence (fan-out) followed by convergence (fan-in), facilitating sparse sensory coding (Babadi and Sompolinsky, 2014; Lin et al., 2014; Litwin-Kumar et al., 2017; Turchetti-Maia et al., 2017). Previously, using volumetric electron microscopy, we showed that the VL uniquely comprises 22 million simple amacrine (SAM) interneurons, each receiving a singular input subject to activity-dependent long-term potentiation, contrasting with typical middle-layer interneurons (Bidel, Meirovitch et al., 2023). We also demonstrated that these SAMs provide excitatory feedforward input to the output cell layer, balanced by approximately 400,000 inhibitory complex amacrines (CAM), which are morphologically diverse and integrate numerous inputs (Bidel, Meirovitch et al., 2023). Here, we leverage the same digital tissue to explore the CAMs morphological diversity, identifying correlations between structure, postsynaptic site density, and synaptic input proportions, which led to the classification of CAMs into distinct groups. Further analysis of the input layer in the VL revealed a meticulous structural and synaptic compartmentalization, with distinct synaptic bouton types forming three zones that integrate different inputs towards CAMs. Additionally, we identify the potential presence of a neurogenic niche in the VL, hinting at parallels with neurogenic processes in other species and warranting further investigation, particularly in the context of learning and memory. This study deepens our understanding of the VLs cellular and synaptic architecture, revealing both shared and unique features compared to other associative networks, and highlighting the intricate interplay of structural and functional elements in memory formation. Highlights (85 characters including space)O_LIThe VL both aligns with and deviates from standard associative network features C_LIO_LIThe VL input layer presents a unique compartmentalization by axonal bouton type C_LIO_LIThe minority inhibitory interneurons differentiate into unique functional subtypes C_LIO_LIMirroring the mammalian hippocampus, the VL may contain an adult neurogenic niche C_LI

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bioRxiv2024

EM-Compressor: Electron Microscopy Image Compression in Connectomics with Variational Autoencoders

The ongoing pursuit to map detailed brain structures at high resolution using electron microscopy (EM) has led to advancements in imaging that enable the generation of connectomic volumes that have reached the petabyte scale and are soon expected to reach the exascale for whole mouse brain collections. To tackle the high costs of managing these large-scale datasets, we have developed a data compression approach employing Variational Autoencoders (VAEs) to significantly reduce data storage requirements. Due to their ability to capture the complex patterns of EM images, our VAE models notably decrease data size while carefully preserving important image features pertinent to connectomics-based image analysis. Through a comprehensive study using human EM volumes (H01 dataset), we demonstrate how our approach can reduce data to as little as 1/128th of the original size without significantly compromising the ability to subsequently segment the data, outperforming standard data size reduction methods. This performance suggests that this method can greatly alleviate requirements for data management for connectomics applications, and enable more efficient data access and sharing. Additionally, we developed a cloud-based application named EM-Compressor on top of this work to enable on-thefly interactive visualization: https://em-compressor-demonstration.s3.amazonaws.com/EM-Compressor+App.mp4.

neuroscience↗

bioRxiv2023

A large-scale volumetric correlated light and electron microscopy study localizes Alzheimer's disease-related molecules in the hippocampus

Connectomics using volume-electron-microscopy enables mapping and analysis of neuronal networks, revealing insights into neural circuit function and dysfunction. In Alzheimers disease (AD), where amyloid-{beta} (A{beta}) and hyperphosphorylated-Tau (pTau) are implicated, connectomics offers an approach to unravel how these molecules contribute to circuit alterations by enabling the study of these molecules within the context of the complete local neuronal and glial milieu. We present a volumetric-correlated-light-and-electron microscopy (vCLEM) protocol using fluorescent nanobodies to localize A{beta} and pTau within a large-scale connectomics dataset from the hippocampus of the 3xTg AD mouse model. A key outcome of this work is a publicly accessible vCLEM dataset, featuring fluorescent labeling of A{beta} and pTau in the ultrastructural context with segmented neurons, glia, and synapses. This dataset provides a unique resource for exploring AD pathology in the context of connectomics and fosters collaborative opportunities in neurodegenerative disease research. As a proof-of-principle, we uncovered new localizations of A{beta} and pTau, including pTau-positive spine-like protrusions at the axon initial segment and changes in the number and size of synapses near A{beta} plaques. Our vCLEM approach facilitates the discovery of both molecular and structural alterations within large-scale EM data, advancing connectomics research in Alzheimers and other neurodegenerative diseases.

neuroscience↗

bioRxiv2023

SmartEM: machine-learning guided electron microscopy

Connectomics provides nanometer-resolution, synapse-level maps of neural circuits to understand brain activity and behavior. However, few researchers have access to the high-throughput electron microscopes necessary to generate enough data for whole brain or even whole circuit reconstruction. To date, machine-learning methods have been used after the collection of images by electron microscopy (EM) to accelerate and improve neuronal segmentation, synapse reconstruction and other data analysis. With the computational improvements in processing EM images, acquiring EM images has now become the rate-limiting step in automated connectomics. Here, in order to speed up EM imaging, we integrate machine-learning into real-time image acquisition in a single-beam scanning electron microscope. This SmartEM approach allows an electron microscope to perform intelligent, data-aware imaging of specimens. SmartEM saves time by allocating the proper imaging time for each region of interest - first scanning all pixels rapidly, then rescanning more slowly only the small subareas where a higher quality signal is required. We demonstrate that SmartEM achieves up to a ~7-fold acceleration of image acquisition time for connectomic samples using a commercial single-beam SEM in samples from nematodes, mice and human brain. We apply this fast imaging method to reconstruct a portion of mouse cerebral cortex with an accuracy comparable to traditional electron microscopy.

neuroscience↗

bioRxiv2023

A Scalable Staining Strategy for Whole-Brain Connectomics

Mapping the complete synaptic connectivity of a mammalian brain would be transformative, revealing the pathways underlying perception, behavior, and memory. Serial section electron microscopy, via membrane staining using osmium tetroxide, is ideal for visualizing cells and synaptic connections but, in whole brain samples, faces significant challenges related to chemical treatment and volume changes. These issues can adversely affect both the ultrastructural quality and macroscopic tissue integrity. By leveraging time-lapse X-ray imaging and brain proxies, we have developed a 12-step protocol, ODeCO, that effectively infiltrates osmium throughout an entire mouse brain while preserving ultrastructure without any cracks or fragmentation, a necessary prerequisite for constructing the first comprehensive mouse brain connectome.

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bioRxiv2023

Multiplexed Volumetric CLEM enabled by antibody derivatives provides new insights into the cytology of the mouse cerebellar cortex

Mapping neuronal networks that underlie behavior has become a central focus in neuroscience. While serial section electron microscopy (ssEM) can reveal the fine structure of neuronal networks (connectomics), it does not provide the molecular information that helps identify cell types or their functional properties. Volumetric correlated light and electron microscopy (vCLEM) combines ssEM and volumetric fluorescence microscopy to incorporate molecular labeling into ssEM datasets. We developed an approach that uses small fluorescent single-chain variable fragment (scFv) immuno-probes to perform multiplexed detergent-free immuno-labeling and ssEM on the same samples. We generated eight such fluorescent scFvs that targeted useful markers for brain studies (green fluorescent protein, glial fibrillary acidic protein, calbindin, parvalbumin, voltage-gated potassium channel subfamily A member 2, vesicular glutamate transporter 1, postsynaptic density protein 95, and neuropeptide Y). To test the vCLEM approach, six different fluorescent probes were imaged in a sample of the cortex of a cerebellar lobule (Crus 1), using confocal microscopy with spectral unmixing, followed by ssEM imaging of the same sample. The results show excellent ultrastructure with superimposition of the multiple fluorescence channels. Using this approach we could document a poorly described cell type in the cerebellum, two types of mossy fiber terminals, and the subcellular localization of one type of ion channel. Because scFvs can be derived from existing monoclonal antibodies, hundreds of such probes can be generated to enable molecular overlays for connectomic studies.

neuroscience↗

bioRxiv2023

mEMbrain: an interactive deep learning MATLAB tool for connectomic segmentation on commodity desktops

Connectomics is fundamental in propelling our understanding of the nervous systems organization, unearthing cells and wiring diagrams reconstructed from volume electron microscopy (EM) datasets. Such reconstructions, on the one hand, have benefited from ever more precise automatic segmentation methods, which leverage sophisticated deep learning architectures and advanced machine learning algorithms. On the other hand, the field of neuroscience at large, and of image processing in particular, has manifested a need for user-friendly and open source tools which enable the community to carry out advanced analyses. In line with this second vein, here we propose mEMbrain, an interactive MATLAB-based software which wraps algorithms and functions that enable labeling and segmentation of electron microscopy datasets in a user-friendly user interface compatible with Linux and Windows. Through its integration as an API to the volume annotation and segmentation tool VAST, mEMbrain encompasses functions for ground truth generation, image preprocessing, training of deep neural networks, and on-the-fly predictions for proofreading and evaluation. The final goals of our tool are to expedite manual labeling efforts and to harness MATLAB users with an array of semi-automatic approaches for instance segmentation. We tested our tool on a variety of datasets that span different species at various scales, regions of the nervous system and developmental stages. To further expedite research in connectomics, we provide an EM resource of ground truth annotation from 4 different animals and 5 datasets, amounting to around 180 hours of expert annotations, yielding more than 1.2 GB of annotated EM images. In addition, we provide a set of 4 pre-trained networks for said datasets. All tools are available from https://lichtman.rc.fas.harvard.edu/mEMbrain/. With our software, our hope is to provide a solution for lab-based neural reconstructions which does not require coding by the user, thus paving the way to affordable connectomics.

neuroscience↗

bioRxiv2022

Connectomics of the Octopus vulgaris vertical lobe provides insight into conserved and novel principles of a memory acquisition network

We present the first analysis of the connectome of the vertical lobe (VL) of Octopus vulgaris, a brain structure mediating acquisition of long-term memory in this behaviorally advanced mollusk. Serial section electron microscopy revealed new types of interneurons, cellular components of extensive modulatory systems and multiple synaptic motifs. The sensory input to the VL is conveyed via ~1,800,000 axons that sparsely innervate two parallel and interconnected feedforward networks formed by the two types of amacrine interneurons (AM), simple AMs (SAMs) and complex AMs (CAMs). SAMs make up 89.3% of the ~25,000,000 VL cells, each receiving a synaptic input from only a single input neuron on its non-bifurcating primary neurite, suggesting that each input neuron is represented in only ~12 SAMs. This synaptic site is likely a "memory site" as it is endowed with LTP. The CAMs, a newly described AM type, comprise 1.6% of the VL cells. Their bifurcating neurites integrate multiple inputs from the input axons and SAMs. While the SAM network appears to feedforward sparse "memorizable" sensory representations into the VL output layer, the CAMs appear to monitor global activity and feedforward a balancing inhibition for "sharpening" the stimulus-specific VL output. While sharing morphological and wiring features with circuits supporting associative learning in other animals, the VL has evolved a unique circuit that enables associative learning based strictly on feedforward information flow.

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bioRxiv2021

Neuromuscular connectomes across development reveal synaptic ordering rules

In mammals, the connections between motor neurons and muscle fibers profoundly reorganize in the early postnatal period. To better understand this synaptic rewiring we traced out all the connectivity in muscles at successive ages in the mouse using serial section scanning electron microscopy in a muscle at birth and Brainbow-based and XFP-based fluorescent reconstructions in neonatal and older muscles respectively. Our data indicate that axons prune about 85% of their branches in the first two weeks of postnatal life, and that while much of this pruning leaves neuromuscular junctions with only one remaining axon (a [~]8-fold reduction), it also causes a [~]6-fold reduction in the number of muscle fibers that possess more than one neuromuscular junction. Unexpectedly, the simplification of the wiring diagram was not haphazard but rather was constrained by the tendency for neurons to maintain co-innervation the longest with other neurons based on their proximity in an abstract rank order. This synaptic ordering preference was even significant at birth when connectivity was the most overlapping but became more striking as development proceeded and was even obvious in the few adult muscle fibers that retained more than one axon at different neuromuscular junctions. Analysis of properties of muscle fibers sharing axons at developing ages and changes in the physical distance between neuromuscular junctions that were maintained in young versus older muscles suggests that the rank order of motor neurons is based on their relative similarity in activity patterns. This same ranking governs both the close-proximity synaptic competitions within neuromuscular junctions and the long-distance competitions that remove or maintain synapses millimeters apart meaning that all neuromuscular rewiring is based on the same global activity ordering rule. We think it is likely that this ranking is related to the ultimate recruitment order of motor axon activity as first described by (Henneman, 1957). Thus the emerging structure of neuromuscular circuitry is a product of its function: initial nearly all-to-all connectivity gives rise to a well-organized system of axons, allowing for the orderly recruitment of neurons during a smoothly graded behavior.

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bioRxiv2020

Connectomes across development reveal principles of brain maturation in C. elegans

From birth to adulthood, an animals nervous system changes as its body grows and its behaviours mature. The form and extent of circuit remodelling across the connectome is unknown. We used serial-section electron microscopy to reconstruct the full brain of eight isogenic C. elegans individuals across postnatal stages to learn how it changes with age. The overall geometry of the brain is preserved from birth to adulthood. Upon this constant scaffold, substantial changes in chemical synaptic connectivity emerge. Comparing connectomes among individuals reveals substantial connectivity differences that make each brain partly unique. Comparing connectomes across maturation reveals consistent wiring changes between different neurons. These changes alter the strength of existing connections and create new connections. Collective changes in the network alter information processing. Over development, the central decision-making circuitry is maintained whereas sensory and motor pathways substantially remodel. With age, the brain progressively becomes more feedforward and discernibly modular. Developmental connectomics reveals principles that underlie brain maturation.

neuroscience↗