Reconstruction of 1,000 Projection Neurons Reveals New Cell Types and Organization of Long-Range Connectivity in the Mouse Brain

Winnubst, Johan; Bas, Erhan; Ferreira, Tiago Alves; Wu, Zhuhao; Economo, Michael N.; Edson, Patrick; Arthur, Ben J.; Bruns, Christopher; Rokicki, Konrad; Schauder, David M.; Olbris, Donald J.; Murphy, Sean D.; Ackerman, David G.; Arshadi, Cameron; Baldwin, Perry; Blake, Regina; Elsayed, Ahmad; Hasan, Mashtura; Ramirez, Daniel; Santos, Bruno dos; Weldon, Monet; Zafar, Amina; Dudman, Joshua T.; Gerfen, Charles R.; Hantman, Adam W.; Korff, Wyatt; Sternson, Scott M.; Spruston, Nelson; Svoboda, Karel; Chandrashekar, Jayaram

doi:10.1016/j.cell.2019.07.042

Cited by 443 publications

(334 citation statements)

References 87 publications

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“…It already hosts a wealth of packages for general morphometric and graph theoretic analysis (Csardi and Nepusz, 2006;Duong, 2007;Lafarge et al, 2014;Schlager, 2017). An R package is a bundle of functions, documentation, data, tests and example code (Wickham, 2015). R packages are discrete, standardised and highly shareable units of code.…”

Section: R Packages For Neuroanatomymentioning

confidence: 99%

“…Recent technological advances have made acquiring large amounts of neuronal morphology data in their whole-brain contexts feasible across phyla (Chiang et al, 2011;Cook et al, 2019;Economo et al, 2016;Jenett et al, 2012;Kunst et al, 2019;Li et al, 2019;Oh et al, 2014;Ohyama et al, 2015;Ryan et al, 2016;Winnubst et al, 2019;Zheng et al, 2018). Image data are typically registered against a template space, allowing one to compare data from many brains directly and quantitatively.…”

Section: Introductionmentioning

confidence: 99%

“…A distinctive feature of the natverse, as compared with other available tools, is to analyse neurons within and across template spaces and to simplify access to a wide range of data sources. Neurons can be read from local files or from online repositories (Ascoli et al, 2007;Chiang et al, 2011;Economo et al, 2016;Jenett et al, 2012;Kunst et al, 2019;Winnubst et al, 2019) and web-based reconstruction environments (Katz and Plaza, 2019;Saalfeld et al, 2009;Schneider-Mizell et al, 2016). The natverse can be installed in one line of code as described on the project website (https://natverse.org).…”

Section: Introductionmentioning

confidence: 99%

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The natverse, a versatile toolbox for combining and analysing neuroanatomical data

Bates

Manton

Jagannathan

et al. 2020

eLife

189

159

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To analyse neuron data at scale, neuroscientists expend substantial effort reading documentation, installing dependencies and moving between analysis and visualisation environments. To facilitate this, we have developed a suite of interoperable open-source R packages called the <monospace>natverse</monospace>. The <monospace>natverse</monospace> allows users to read local and remote data, perform popular analyses including visualisation and clustering and graph-theoretic analysis of neuronal branching. Unlike most tools, the <monospace>natverse</monospace> enables comparison across many neurons of morphology and connectivity after imaging or co-registration within a common template space. The <monospace>natverse</monospace> also enables transformations between different template spaces and imaging modalities. We demonstrate tools that integrate the vast majority of Drosophila neuroanatomical light microscopy and electron microscopy connectomic datasets. The <monospace>natverse</monospace> is an easy-to-use environment for neuroscientists to solve complex, large-scale analysis challenges as well as an open platform to create new code and packages to share with the community.

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Section: R Packages For Neuroanatomymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The natverse, a versatile toolbox for combining and analysing neuroanatomical data

Bates

Manton

Jagannathan

et al. 2020

eLife

189

159

View full text Add to dashboard Cite

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“…Conventionally this is followed by serial sectioning, imaging of each section, and manual reconstruction of the labeled neurons across many consecutive sections. Although relatively few such studies exist due to the labor-intensive process, they reveal critical features of specific projection neuron types that likely have important functional implications 22,24,[32][33][34][35][36][37][38][39] . The recent development of high-throughput and highresolution fluorescent imaging platforms, such as fMOST 40 and MouseLight 29 , coupled with more efficient sparse viral labeling strategies, now enable the potential for large-scale generation of neuronal morphology datasets.…”

Section: Introductionmentioning

confidence: 99%

Brain-wide single neuron reconstruction reveals morphological diversity in molecularly defined striatal, thalamic, cortical and claustral neuron types

Peng

Xie

Liu

et al. 2019

Preprint

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32Ever since the seminal findings of Ramon y Cajal, dendritic and axonal morphology has been 33 recognized as a defining feature of neuronal types and their connectivity. Yet our knowledge 34 about the diversity of neuronal morphology, in particular its distant axonal projections, is still 35 extremely limited. To systematically obtain single neuron full morphology on a brain-wide scale 36in mice, we established a pipeline that encompasses five major components: sparse labeling, 37whole-brain imaging, reconstruction, registration, and classification. We achieved sparse, robust 38and consistent fluorescent labeling of a wide range of neuronal types across the mouse brain in 39 an efficient way by combining transgenic or viral Cre delivery with novel transgenic reporter 40 lines, and generated a large set of high-resolution whole-brain fluorescent imaging datasets 41containing thousands of reconstructable neurons using the fluorescence micro-optical sectioning 42 tomography (fMOST) system. We developed a set of software tools based on the visualization 43 and analysis suite, Vaa3D, for large-volume image data processing and computation-assisted 44 morphological reconstruction. In a proof-of-principle case, we reconstructed full morphologies 45 of 96 neurons from the claustrum and cortex that belong to a single transcriptomically-defined 46 neuronal subclass. We developed a data-driven clustering approach to classify them into multiple 47 morphological and projection types, suggesting that these neurons work in a targeted and 48coordinated manner to process cortical information. Imaging data and the new computational 49 reconstruction tools are publicly available to enable community-based efforts towards large-scale 50 full morphology reconstruction of neurons throughout the entire mouse brain. 51 52 53

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“…The morphology of axonal arborizations is highly variable and an essential feature for cell type classification [16]. Because of this high variability, however, models recreating axonal arborizations have been few in number.…”

Section: Introductionmentioning

confidence: 99%

A generative growth model for thalamocortical axonal branching in primary visual cortex

2020

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Axonal morphology displays large variability and complexity, yet the canonical regularities of the cortex suggest that such wiring is based on the repeated initiation of a small set of genetically encoded rules. Extracting underlying developmental principles can hence shed light on what genetically encoded instructions must be available during cortical development. Within a generative model, we investigate growth rules for axonal branching patterns in cat area 17, originating from the lateral geniculate nucleus of the thalamus. This target area of synaptic connections is characterized by extensive ramifications and a high bouton density, characteristics thought to preserve the spatial resolution of receptive fields and to enable connections for the ocular dominance columns. We compare individual and global statistics, such as a newly introduced length-weighted asymmetry index and the global segment-length distribution, of generated and biological branching patterns as the benchmark for growth rules. We show that the proposed model surpasses the statistical accuracy of the Galton-Watson model, which is the most commonly employed model for biological growth processes. In contrast to the Galton-Watson model, our model can recreate the log-normal segment-length distribution of the experimental dataset and is considerably more accurate in recreating individual axonal morphologies. To provide a biophysical interpretation for statistical quantifications of the axonal branching patterns, the generative model is ported into the physically accurate simulation framework of Cx3D. In this 3D simulation environment we demonstrate how the proposed growth process can be formulated as an interactive process between genetic growth rules and chemical cues in the local environment.

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Reconstruction of 1,000 Projection Neurons Reveals New Cell Types and Organization of Long-Range Connectivity in the Mouse Brain

Cited by 443 publications

References 87 publications

The natverse, a versatile toolbox for combining and analysing neuroanatomical data

The natverse, a versatile toolbox for combining and analysing neuroanatomical data

Brain-wide single neuron reconstruction reveals morphological diversity in molecularly defined striatal, thalamic, cortical and claustral neuron types

A generative growth model for thalamocortical axonal branching in primary visual cortex

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