An unsupervised map of excitatory neurons’ dendritic morphology in the mouse visual cortex

Weis, Marissa A.; Papadopoulos, Stelios; Hansel, Laura; Lüddecke, Timo; Celii, Brendan; Fahey, Paul G.; Bae, Jinsoo; Bodor, Ágnes L.; Brittain, Derrick; Buchanan, JoAnn; Bumbarger, Daniel J.; Castro, Manuel; Cobos, Erick; Collman, Forrest; Costa, Nuno Maçarico da; Dorkenwald, Sven; Elabbady, Leila; Froudarakis, Emmanouil; Halageri, Akhilesh; Jia, Zhen; Jordan, Chris; Kapner, Dan; Kemnitz, Nico; Kinn, Sam; Lee, Ki-Suk; Li, Kai; Lu, Ran; Macrina, Thomas; Mahalingam, Gayathri; Mitchell, Eric; Mondal, Shanka Subhra; Mu, Shang; Nehoran, Barak; Patel, Saumil S.; Pitkow, Xaq; Popovych, Sergiy; Reid, R. Clay; Schneider-Mizell, Casey M; Seung, H. Sebastian; Silversmith, William; Sinz, Fabian H.; Takeno, Marc; Torres, Russel; Turner, Nicholas L.; Wong, William; Wu, Jingpeng; Yin, Wenjing; Yu, Szi-chieh; Reimer, Jacob; Tolias, Andreas S.; Ecker, Alexander S.

doi:10.1101/2022.12.22.521541

Cited by 5 publications

(11 citation statements)

References 40 publications

(142 reference statements)

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“…5j-m and Fig. 20 replicate observations from previous studies (Elabbady et al, 2022;Kawaguchi et al, 2006;Villa and Nedivi, 2016). Additionally, using the spine metrics extracted by NEURD, we were able to replicate many of the findings of (Harris and Stevens, 1989;Arellano et al, 2007) concerning synapse size and spine head volume correlation, and we also show that these scaling rules and others depend on cell type (Fig.…”

Section: Discussionsupporting

confidence: 85%

“…b) Soma volume computed during the soma detection step in the NEURD mesh processing pipeline. As expected, 5P-NP, 4P and BPC generally have smaller somas than other cells from their same excitatory or inhibitory class while 5P-ET and BC are larger than other cells in the same class (Elabbady et al, 2022). c) Width measurements generated from the average distance of the inner skeleton to the mesh surface (radius approximation) at the beginning of the apical trunk protrusion.…”

Section: Supplementary Figuresmentioning

confidence: 71%

“…Those axon presyns located not on the main axon but on dendritic segments are filtered away and designated as "Presyn From Dendrite", which does not include the heuristic rule of "Axon on Dendrite" but instead just filters away any presyns located on dendritic segments that were not filtered away using the heuristic rules. (Elabbady et al, 2022). a) Spine density (number of spines per µm of skeletal length) distributions from automatic spine detection.…”

Section: Supplementary Figuresmentioning

confidence: 99%

“…Compared to light microscopy, the ultra-high resolution of EM reconstructions provides many additional features that can be used to classify cells by morphology. Previous studies have demonstrated that rich information enabling cell-type classification is available even in local nuclear and peri-somatic features (Elabbady et al, 2022;Al-Thelaya et al, 2021), small segments of neural processes (Dorkenwald et al, 2022c), and the shape of postsynaptic regions (Seshamani et al, 2020). NEURD provides an additional rich and interpretable feature set that can be used for cell-type classification via a number of different approaches.…”

Section: Summary Of Large-scale Dense Em Reconstructionsmentioning

confidence: 99%

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NEURD: automated proofreading and feature extraction for connectomics

Celii

Papadopoulos

Ding

et al. 2023

Preprint

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We are now in the era of millimeter-scale electron microscopy (EM) volumes collected at nanometer resolution. Dense reconstruction of cellular compartments in these EM volumes has been enabled by recent advances in Machine Learning (ML). Automated segmentation methods can now yield exceptionally accurate reconstructions of cells, but despite this accuracy, laborious post-hoc proofreading is still required to generate large connectomes free of merge and split errors. The elaborate 3-D meshes of neurons produced by these segmentations contain detailed morphological information, from the diameter, shape, and branching patterns of axons and dendrites, down to the fine-scale structure of dendritic spines. However, extracting information about these features can require substantial effort to piece together existing tools into custom workflows. Building on existing open-source software for mesh manipulation, here we present "NEURD", a software package that decomposes each meshed neuron into a compact and extensively-annotated graph representation. With these feature-rich graphs, we implement workflows for state-of-the art automated post-hoc proofreading of merge errors, cell classification, spine detection, axon-dendritic proximities, and other features that can enable many downstream analyses of neural morphology and connectivity. NEURD can make these new massive and complex datasets more accessible to neuroscience researchers focused on a variety of scientific questions.

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Section: Discussionsupporting

confidence: 85%

Section: Supplementary Figuresmentioning

confidence: 71%

Section: Supplementary Figuresmentioning

confidence: 99%

Section: Summary Of Large-scale Dense Em Reconstructionsmentioning

confidence: 99%

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NEURD: automated proofreading and feature extraction for connectomics

Celii

Papadopoulos

Ding

et al. 2023

Preprint

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“…Recent surveys aimed at discovering excitatory cell types have used morphology alone 88 , morphology and electrophysiology 13 , transcriptomics 19,89 , or all three together using Patch-seq 16 . The M-types found here from morphology and synaptic properties generally agree with these approaches, distinguishing cells in upper layer and lower layer 2/3, finding layer 4 diversity, and differentiating between projection subclasses.…”

Section: Comparison To Other Cell Type Definitionsmentioning

confidence: 99%

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

Schneider-Mizell

Bodor

Brittain

et al. 2023

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Mammalian cortex features a large diversity of neuronal cell types, each with characteristic anatomical, molecular and functional properties. Synaptic connectivity rules powerfully shape how each cell type participates in the cortical circuit, but comprehensively mapping connectivity at the resolution of distinct cell types remains difficult. Here, we used millimeter-scale volumetric electron microscopy to investigate the connectivity of inhibitory neurons across a dense neuronal population spanning all layers of mouse visual cortex with synaptic resolution. We classified all 1183 excitatory neurons within a 100 micron column into anatomical subclasses using quantitative morphological and synapse features based on full dendritic reconstructions, finding both familiar subclasses corresponding to axonal projections and novel intralaminar distinctions based on synaptic properties. To relate these subclasses to single-cell connectivity, we reconstructed all 164 inhibitory interneurons in the same column, producing a wiring diagram of inhibition with more than 70,000 synapses. We found widespread cell-type-specific inhibition, including interneurons selectively targeting certain excitatory subpopulations among spatially intermingled neurons in layer 2/3, layer 5, and layer 6. Globally, inhibitory connectivity was organized into "motif groups," heterogeneous collections of cells that collectively target both perisomatic and dendritic compartments of the same combinations of excitatory subtypes. We also discovered a novel category of disinhibitory-specialist interneurons that preferentially targets basket cells. Collectively, our analysis revealed new organizing principles for cortical inhibition and will serve as a powerful foundation for linking modern multimodal neuronal atlases with the cortical wiring diagram.

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Functional connectomics spanning multiple areas of mouse visual cortex

Bodor¹,

Halageri²,

Sterling³

et al. 2021

Preprint

152

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The value of an integrated approach for understanding the neocortex by combining functional characterization of single neuron activity with the underlying circuit architecture has been understood since the dawn of modern neuroscience. However, in practice, anatomical connectivity and physiology have been studied mostly separately. Following in the footsteps of previous studies that have combined physiology and anatomy in the same tissue, here we present a unique functional connectomics dataset that contains calcium imaging of an estimated 75,000 neurons from primary visual cortex (VISp) and three higher visual areas (VISrl, VISal and VISlm), that were recorded while a mouse viewed natural movies and parametric stimuli. The functional data were co-registered with electron microscopy (EM) data of the same volume which were automatically segmented, reconstructing more than 200,000 cells (neuronal and non-neuronal) and 524 million synapses. Subsequent proofreading of some neurons in this volume yielded reconstructions that include complete dendritic trees as well the local and inter-areal axonal projections. The largest proofread excitatory axon reached a length of 19 mm and formed 1,893 synapses, while the largest inhibitory axon formed 10,081 synapses. Here we release this dataset as an open access resource to the scientific community including a set of analysis tools that allows easy data access, both programmatically and through a web user interface.

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An unsupervised map of excitatory neurons’ dendritic morphology in the mouse visual cortex

Cited by 5 publications

References 40 publications

NEURD: automated proofreading and feature extraction for connectomics

NEURD: automated proofreading and feature extraction for connectomics

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

Functional connectomics spanning multiple areas of mouse visual cortex

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