Biology subjects

Kasthuri, N.

Publications and source records attributed to Kasthuri, N..

16 recordsLinked to original sources

bioRxiv2025

Local Dendritic Landscape of Mouse V1

The principles that govern where excitatory synapses form on cortical pyramidal neurons remain unclear. A long-standing hypothesis is that connectivity mirrors neuronal geometry--with apical dendrites dominating in layer 1 and basal dendrites in deeper layers. Leveraging the MiCRONS cubic-millimetre serial electron-microscopy dataset ([1, 2]), we mapped 4968 synapses onto reconstructed apical and basal arbors across layers 1-6 of mouse visual cortex. Contrary to a simple gradient model, synaptic targeting is overwhelmingly biased toward basal dendrites in layers 2/3-6, even where apical shafts are abundant. Layer 1 is the sole exception, exhibiting the expected apical bias. Basal predominance scales with local soma density and the relative arbor length available for contact, such that 66% (61101/92445) of all synapses land within 130 {micro}m of the soma, placing most excitatory drive near the cell body. Axonal analysis revealed specificity beyond mere geometric opportunity: individual axon segments preferentially innervated either basal or apical compartments far more often than predicted by chance, indicating compartment-selective wiring rules. Together, our results show that cortical connectivity is shaped by both neuronal geometry and axon-level targeting preferences, redefining how pyramidal cells integrate information across layers and apical and basal dendrites.

neuroscience↗

bioRxiv2024

Dense, Continuous Membrane Labeling and Expansion Microscopy Visualization of Ultrastructure in Tissues

Lipid membranes are key to the nanoscale compartmentalization of biological systems, but fluorescent visualization of them in intact tissues, with nanoscale precision, is challenging to do with high labeling density. Here, we report ultrastructural membrane expansion microscopy (umExM), which combines a novel membrane label and optimized expansion microscopy protocol, to support dense labeling of membranes in tissues for nanoscale visualization. We validated the high signal-to-background ratio, and uniformity and continuity, of umExM membrane labeling in brain slices, which supported the imaging of membranes and proteins at a resolution of [~]60 nm on a confocal microscope. We demonstrated the utility of umExM for the segmentation and tracing of neuronal processes, such as axons, in mouse brain tissue. Combining umExM with optical fluctuation imaging, or iterating the expansion process, yielded [~]35 nm resolution imaging, pointing towards the potential for electron microscopy resolution visualization of brain membranes on ordinary light microscopes.

bioengineering↗

bioRxiv2023

Synchrotron-source micro-x-ray computed tomography for examining butterfly eyes

1. Comparative anatomy is an important tool for investigating evolutionary relationships amongst species, but the lack of scalable imaging tools and stains for rapidly mapping the microscale anatomies of related species poses a major impediment to using comparative anatomy approaches for identifying evolutionary adaptations. 2. We describe a method using synchrotron source micro-x-ray computed tomography (syn-{micro}XCT) combined with machine learning algorithms for high-throughput imaging of Lepidoptera (i.e., butterfly and moth) eyes. Our pipeline allows for imaging at rates of [~] 15 min/mm3 at 600 nm3 resolution. Image contrast is generated using standard electron microscopy labeling approaches (e.g., osmium tetroxide) that unbiasedly labels all cellular membranes in a species independent manner thus removing any barrier to imaging any species of interest. 3. To demonstrate the power of the method, we analyzed the 3D morphologies of butterfly crystalline cones, a part of the visual system associated with acuity and sensitivity and found significant variation within six butterfly individuals. Despite this variation, a classic measure of optimization, the ratio of interommatidial angle to resolving power of ommatidia, largely agrees with early work on eye geometry across species. 4. We show that this method can successfully be used to determine compound eye organization and crystalline cone morphology. Our novel pipeline provides for fast, scalable visualization and analysis of eye anatomies that can be applied to any arthropod species, enabling new questions about evolutionary adaptations of compound eyes and beyond.

ecology↗

bioRxiv2023

A Pipeline for a Primate Projectome: mapping every individual myelinated axon across the whole brain

1We developed a pipeline to detail the morphology and complete trajectory of every long distance projecting neuron in large volumes of the non-human primate brain - a projectome. A projectome would organize how the hundreds of brain regions in a primate brain communicate with each other and provide a comprehensive classification of long distance neurons. The reason such projectomes do not exist already is that there is no single imaging technique with the resolution and sampling depth capable of producing one in primates. Here we address that gap by combining new ways to cut brains with minimal tissue loss, nanoscale x-ray microscopy imaging that reveals every individual myelinated axon (MA) and the location, size, and morphology of every neuron, and algorithms for tracing individual myelinated axons for millimeters to centimeters, across tissue gaps.

neuroscience↗

bioRxiv2023

Photoemission electron microscopy for connectomics

1Detailing the physical basis of neural circuits with large-volume serial electron microscopy (EM), connectomics, has emerged as an invaluable tool in the neuroscience armamentarium. However, imaging synaptic resolution connectomes is currently limited to either transmission electron microscopy (TEM) or scanning electron microscopy (SEM). Here, we describe a third way, using photoemission electron microscopy (PEEM) which illuminates ultra-thin brain slices collected on solid substrates with UV light and images the photoelectron emission pattern with a wide-field electron microscope. PEEM works with existing sample preparations for EM and routinely provides sufficient resolution and contrast to reveal myelinated axons, somata, dendrites, and sub-cellular organelles. Under optimized conditions, PEEM provides synaptic resolution; and simulation and experiments show that PEEM can be transformatively fast, at Gigahertz pixel rates. We conclude that PEEM imaging leverages attractive aspects of SEM and TEM, namely reliable sample collection on robust substrates combined with fast wide-field imaging, and could enable faster data acquisition for next-generation circuit mapping.

neuroscience↗

bioRxiv2023

The Development of Synapses in Mouse and Macaque Primary Sensory Cortices

We report that the rate of synapse development in primary sensory cortices of mice and macaques is unrelated to lifespan, as was previously thought. We analyzed 28,084 synapses over multiple developmental time points in both species and find, instead, that net excitatory synapse development of mouse and macaque neurons primarily increased at similar rates in the first few postnatal months, and then decreased over a span of 1-1.5 years of age. The development of inhibitory synapses differed qualitatively across species. In macaques, net inhibitory synapses first increase and then decrease on excitatory soma at similar ages as excitatory synapses. In mice, however, such synapses are added throughout life. These findings contradict the long-held belief that the cycle of synapse formation and pruning occurs earlier in shorter-lived animals. Instead, our results suggest more nuanced rules, with the development of different types of synapses following different timing rules or different trajectories across species.

neuroscience↗

bioRxiv2022

Model-free analysis in the spectral domain of postmortem mouse brain EPSI reveals inconsistencies with model-based analyses of the free induction decay

PurposeDysmyelinating disorders lead to abnormalities in myelin structure that produce detectable effects in an echo-planar spectroscopic imaging (EPSI) signal. To estimate the voxel-wise proportion of myelin, data are typically fit to compartmental models in the time domain. This work characterizes limitations in these models by comparing high-resolution water spectra measured in postmortem fixed mouse brains to spectra predicted from time-domain models fit to the same data, specifically by comparing spectra from control and shiverer mice, a model for dysmyelination. MethodsPerfusion-fixed, resected control (n = 5) and shiverer (n = 4) mouse brains were imaged using 3D EPSI with 100 {micro}m isotropic resolution. The free induction decay (FID) was sampled every 2.74 ms over 192 echoes and Fourier transformed to produce water spectra with 1.9 Hz resolution. FIDs were also fit to two biophysical models and the resulting fits were converted to spectra with a Fourier transform. Spectral asymmetry was computed and compared before and after fitting the data to models. ResultsSpectra derived from both models did not show the magnitude of asymmetric broadening observed in the raw data. Correlations between data- and model-derived asymmetries and estimated frequency shifts are weak, leading to a reduction in spectral sensitivity to changes in white-matter structure after fitting the data to models. ConclusionThe results demonstrate spectral inconsistencies between biophysical model predictions and measured data, promoting the further incorporation of spectral analysis methods to develop and benchmark new model-based approaches.

biophysics↗

bioRxiv2021

Large-scale dendritic spine extraction and analysis through petascale computing

The synapse is a central player in the nervous system serving as the key structure that permits the relay of electrical and chemical signals from one neuron to another. The anatomy of the synapse contains important information about the signals and the strength of signal it transmits. Because of their small size, however, electron microscopy (EM) is the only method capable of directly visualizing synapse morphology and remains the gold standard for studying synapse morphology. Historically, EM has been limited to small fields of view and often only in 2D, but recent advances in automated serial EM ("connectomics") have enabled collecting large EM volumes that capture significant fractions of neurons and the different classes of synapses they receive (i.e. shaft, spine, soma, axon). However, even with recent advances in automatic segmentation methods, extracting neuronal and synaptic profiles from these connectomics datasets are difficult to scale over large EM volumes. Without methods that speed up automatic segmentation over large volumes, the full potential of utilizing these new EM methods to advance studies related to synapse morphologies will never be fully realized. To solve this problem, we describe our work to leverage Argonne leadership-scale supercomputers for segmentation of a 0.6 terabyte dataset using state of the art machine learning-based segmentation methods on a significant fraction of the 11.69 petaFLOPs supercomputer Theta at Argonne National Laboratory. We describe an iterative pipeline that couples human and machine feedback to produce accurate segmentation results in time frames that will make connectomics a more routine method for exploring how synapse biology changes across a number of biological conditions. Finally, we demonstrate how dendritic spines can be algorithmically extracted from the segmentation dataset for analysis of spine morphologies. Advancing this effort at large compute scale is expected to yield benefits in turnaround time for segmentation of individual datasets, accelerating the path to biology results and providing population-level insight into how thousands of synapses originate from different neurons; we expect to also reap benefits in terms of greater accuracy from the more compute-intensive algorithms these systems enable.

neuroscience↗

bioRxiv2021

Regional cytoarchitecture of the adult and developing mouse enteric nervous system

The enteric nervous system (ENS) populates the gastrointestinal (GI) tract and controls GI function. In contrast to the central nervous system, macrostructure of the ENS has been largely overlooked. Here, we visually and computationally demonstrate that the ENS is organized in circumferential stripes that regionally differ in development and neuronal composition. This characterization provides a blueprint for future understanding of region-specific GI function and identifying ENS structural correlates of GI disorders.

neuroscience↗

bioRxiv2021

Ultrastructural analysis of dendritic spine necks reveals a continuum of spine morphologies

Dendritic spines are membranous protrusions, with a bulbous head connected to the dendrite by a thin neck, and receive essentially all excitatory inputs in most mammalian neurons. Spines have a wide variety of morphologies that likely have a significant effect on their biochemical and electrical properties. The question of whether spines belong to distinct morphological or functional subtypes or constitute a continuum is still open. To discern this, it is important to measure spine necks objectively. Recent advances in electron microscopy enable automatic reconstructions of 3D spines with nanometer precision. Analyzing ultrastructural reconstructions from mouse neocortical neurons with computer vision algorithms, we demonstrate that the vast majority of spines can be rigorously separated into head and neck components. Analysis of the head and neck morphologies reveals a continuous distribution of parameters. The spine neck diameter, but not the neck length, was correlated with the head volume. Spines with larger head volumes often had a spine apparatus and pairs of spines in a post-synaptic cell contacted by the same axon had similar head volumes. Our data are consistent with a lack of morphological categories of spines and indicate that the morphologies of the spine neck and head are independently regulated. These results have repercussions for our understanding of the function of dendritic spines in neuronal circuits.

neuroscience↗

bioRxiv2020

Multi-modal imaging of a single postmortem mouse brain over five orders of magnitude of resolution

Mammalian neurons operate at length scales spanning five orders of magnitude; micron-scale-diameter myelinated axons project millimeters across brain regions, ultimately forming nanometer scale synapses on individual post-synaptic neurons. Capturing these anatomical features across that breadth of scale has required imaging samples with multiple independent imaging modalities (e.g. MRI, electron microscopy, etc.). Translating between the different modalities, however, requires imaging the same brain with each. Here, we imaged the same postmortem mouse brain over five orders of spatial resolution using MRI, whole brain micron-scale synchrotron x-ray tomography (CT), and large volume automated serial electron microscopy. Using this pipeline, we can track individual myelinated axons previously relegated to axon bundles in diffusion tensor MRI or arbitrarily trace neurons and their processes brain-wide and identify individual synapses on them. This pipeline provides both an unprecedented look across a single brains multi-scaled organization as well as a vehicle for studying the brains multi-scale pathologies.

neuroscience↗

bioRxiv2020

Cocaine causes rapid remodeling of dopaminergic axons, synapses, and mitochondria

Dopaminergic (DA) neurons exert profound influences on behavior including addiction. However, how DA axons communicate with target neurons and how those communications change, for example, with drug exposure, remains poorly understood. We combine recent advances in cell type specific labeling for electron microscopy with large volume three-dimensional serial electron microscopy - connectomics- to detail DA connections in the Nucleus Accumbens (NAc) across multiple animals and after exposure to cocaine. We find that DA axonal varicosities are of four general types: 38% are empty, 25% contain few small ([~]50 nm diameter) vesicles, 19% contain few large ([~]133 nm diameter) vesicles, and 18% have mixed small and large vesicles, suggesting that DA axons may use multiple types of neurotransmitters. Individual DA axons were significantly more likely to contain multiple varicosities of the same type relative to chance, suggesting a new method of DA axon classification. Across all categories, we find only rare examples (<2%, 6/410) of varicosities making specific synapses with any neighboring neuron with the few examples being made exclusively on the shafts and soma of resident NAc neurons. Instead, we find much more frequently (15%) that DA varicosities form spinule-like structures: physical membrane interdigitations with nearby dendrites or excitatory and inhibitory axons. Days after a brief exposure to cocaine, DA axons were extensively branched relative to controls but with similar densities of varicosities and spinules. Additionally, cocaine exposure results in the formation of blind-ended "bulbs" in DA axons, filled with mitochondria, and reminiscent of axonal retraction in the developing and damaged brain. Every bulb was surrounded by elaborated glia further suggestive of active remodeling. Finally, mitochondrial lengths increased by [~]2.2 times relative to control throughout DA axons and NAc spiny dendrites after cocaine exposure but not in DA soma or DA dendrites. We conclude that DA axonal transmission is unlikely to be mediated via classical synapses in the NAc and that the major locus of anatomical plasticity of DA circuits after exposure to cocaine are large scale axonal rearrangements with correlated changes in mitochondria.

neuroscience↗

bioRxiv2020

Primate neuronal connections are sparse as compared to mouse

The mouse and macaque primary visual cortices are foundational models of cortical functioning, particularly at the level of single neurons. Therefore, detailing differences in how individual neurons connect across these species would inform models of cortical functioning and of how brains evolve. However, existing comparisons are limited, measuring synapse density without regard to where synapses are made or on what types of neurons. We use large volume electron microscopy to address this gap, reconstructing a total of 7735 synapses across 160 total neurons (146 excitatory, 14 inhibitory) from adult Rhesus macaque and mouse Layer 2/3 of primary visual cortex (V1). We find that primate connections are broadly sparse: primate excitatory and inhibitory neurons received 3-5 times fewer spine and somatic synapses with lower ratios of excitatory to inhibitory synapses than mouse equivalents. However, despite reductions in absolute synapse number, patterns of axonal innervation were preserved: inhibitory axons sparsely innervated neighboring excitatory neurons in macaque and mouse at similar rates and proportions. On the output side, most excitatory axons in mice myelinated close to the soma (81%) while most primate axons (68%) did not. Interestingly, primate axons, but not mouse axons, that myelinated had 3.3 fold more axon initial segment synapses than axons that did not myelinate, suggesting differential inhibitory control of long distance output in primate brains. Finally, we discover that when artificial recurrent neural networks (RNNs) are constrained by the metabolic cost of creating and maintaining synapses, increasing the number of nodes (e.g. neurons) as networks optimize for a cognitive task, reduces the number of connections per node, similar to primate neurons as compared to mice. One Sentence SummaryUsing large volume serial electron microscopy, we show that primate cortical neural networks are sparser than mouse and using recursive neural nets, we show that energetic costs of synaptic maintenance could underlie this difference.

neuroscience↗

bioRxiv2020

A three-dimensional thalamocortical dataset for characterizing brain heterogeneity

Neural cytoarchitecture is heterogeneous, varying both across and within brain regions. The consistent identification of regions of interest is one of the most critical aspects in examining neurocircuitry, as these structures serve as the vital landmarks with which to map brain pathways. Access to continuous, three-dimensional volumes that span multiple brain areas not only provides richer context for identifying such landmarks, but also enables a deeper probing of the microstructures within. Here, we describe a three-dimensional X-ray microtomography imaging dataset of a well-known and validated thalamocortical sample, encompassing a range of cortical and subcortical structures. In doing so, we provide the field with access to a micron-scale anatomical imaging dataset ideal for studying heterogeneity of neural structure.

neuroscience↗

bioRxiv2019

Expansion Microscopy of Lipid Membranes

Lipids are fundamental building blocks of cells and their organelles, yet nanoscale resolution imaging of lipids has been largely limited to electron microscopy techniques. We introduce and validate a chemical tag that enables lipid membranes to be imaged optically at nanoscale resolution via a lipid-optimized form of expansion microscopy, which we call membrane expansion microscopy (mExM). mExM, via a novel post-expansion antibody labeling protocol, enables protein-lipid relationships to be imaged in organelles such as mitochondria, the endoplasmic reticulum, the nuclear membrane, and the Golgi apparatus. mExM may be of use in a variety of biological contexts, including the study of cell-cell interactions, intracellular transport, and neural connectomics.

bioengineering↗

bioRxiv2018

Flexible Learning-Free Segmentation and Reconstruction for Sparse Neuronal Circuit Tracing

Imaging is a dominant strategy for data collection in neuroscience, yielding stacks of images that often scale to gigabytes of data for a single experiment. Machine learning algorithms from computer vision can serve as a pair of virtual eyes that tirelessly processes these images, automatically constructing more complete and realistic circuits. In practice, such algorithms are often too error-prone and computationally expensive to be immediately useful. We address these shortcomings with a new fast, flexible, learning-free method for sparse segmentation and reconstruction of neural volumes. Unlike learning methods, our Flexible Learning-free Reconstruction of Imaged Neural volumes (FLoRIN) pipeline exploits structure-specific contextual clues and requires no training. This approach generalizes across different modalities, including serially-sectioned scanning electron microscopy (sSEM) of genetically labeled and contrast enhanced processes, spectral confocal reflectance (SCoRe) microscopy, and high-energy synchrotron X-ray microtomography (CT) of large tissue volumes. We deploy the FLoRIN pipeline on newly published and novel mouse datasets, demonstrating the high biological fidelity of the pipelines reconstructions, which are of sufficient quality for preliminary biological study. Compared to existing supervised learning methods, it is both significantly faster (up to several orders of magnitude) and produces high-quality reconstructions that are robust to noise and artifacts.

neuroscience↗