@article {69825, title = {HortaCloud: An Open and Collaborative Platform for Whole Brain Neuronal Reconstructions}, journal = {bioRxiv}, year = {2025}, month = {2025 Mar 13}, abstract = {

HortaCloud is a cloud-based, open-source platform designed to facilitate the collaborative reconstruction of long-range projection neurons from whole-brain light microscopy data. By providing virtual environments directly within the cloud, it eliminates the need for costly and time-consuming data downloads, allowing researchers to work efficiently with terabyte- scale volumetric datasets. Standardization of computational resources in the cloud make deployment easier and more predictable. The pay-as-you-go cloud model reduces adoption barriers by eliminating upfront investments in expensive hardware. Finally, HortaCloud{\textquoteright}s decentralized architecture enables global collaboration between researchers and between institutions.

}, doi = {10.1101/2025.03.13.642887}, url = {https://www.biorxiv.org/content/early/2025/03/14/2025.03.13.642887}, author = {Rokicki, Konrad and Schauder, David and Olbris, Donald J. and Goina, Cristian and Clements, Jody and Edson, Patrick and Kawase, Takashi and Svirskas, Robert and Arshadi, Cameron and Feng, David and Chandrashekar, Jayaram and Ferreira, Tiago A. and MouseLight Project Team} } @article {69710, title = {Discovery of neuronal cell types by pairing whole cell reconstructions with RNA expression profiles}, journal = {bioRxiv}, year = {2024}, month = {12/2024}, abstract = {

Effective classification of neuronal cell types requires both molecular and morphological descriptors to be collected in situ at single cell resolution. However, current spatial transcriptomics techniques are not compatible with imaging workflows that successfully reconstruct the morphology of complete axonal projections. Here, we introduce a new methodology that combines tissue clearing, submicron whole-brain two photon imaging, and Expansion-Assisted Iterative Fluorescence In Situ Hybridization (EASI-FISH) to assign molecular identities to fully reconstructed neurons in the mouse brain, which we call morphoFISH. We used morphoFISH to molecularly identify a previously unknown population of cingulate neurons projecting ipsilaterally to the dorsal striatum and contralaterally to higher-order thalamus. By pairing whole-brain morphometry, improved techniques for nucleic acid preservation and spatial gene expression, morphoFISH offers a quantitative solution for discovery of multimodal cell types and complements existing techniques for characterization of increasingly fine-grained cellular heterogeneity in brain circuits.Competing Interest StatementThe authors have declared no competing interest.

}, doi = {10.1101/2024.12.30.630829}, url = {https://www.biorxiv.org/content/early/2024/12/31/2024.12.30.630829}, author = {The MouseLight Project Team and Ferreira, Tiago A. and Eddison, Mark and Copeland, Monique and Lay, Mary and Tenshaw, Emily and Weldon, Monet and Schauder, David and Olbris, Donald J. and Rokicki, Konrad and Spruston, Nelson and Tillberg, Paul W. and Korff, Wyatt and Dudman, Joshua T.} } @article {65138, title = {Reconstruction of 1,000 projection neurons reveals new cell types and organization of long-range connectivity in the mouse brain.}, journal = {Cell}, volume = {179}, year = {2019}, month = {2019 Sep 19}, pages = {268-81}, abstract = {

Neuronal\ cell\ types\ are the nodes of neural circuits that determine the flow of information within the\ brain. Neuronal morphology, especially the shape of the axonal arbor, provides an essential descriptor of\ cell\ type and\ reveals\ how individual\ neurons\ route their output across the\ brain. Despite the importance of morphology, few\ projection\ neurons\ in the\ mouse\ brain\ have been reconstructed in their entirety. Here we present a robust and efficient platform for imaging and reconstructing complete neuronal morphologies, including axonal arbors that span substantial portions of the\ brain. We used this platform to reconstruct more than\ 1,000\ projection\ neurons\ in the motor cortex, thalamus, subiculum, and hypothalamus. Together, the reconstructed\ neurons\ constitute more than 85\ meters of axonal length and are available in a searchable online database. Axonal shapes revealed previously unknown subtypes of\ projection\ neurons\ and suggest organizational principles of\ long-range\ connectivity.

}, issn = {00928674}, doi = {10.1016/j.cell.2019.07.042}, author = {Winnubst, Johan and Bas, Erhan and Ferreira, Tiago A. and Wu, Zhuhao and Economo, Michael N. and Edson, Patrick and Arthur, Ben J. and Bruns, Christopher and Rokicki, Konrad and Schauder, David and Olbris, Donald J. and Murphy, Sean D. and Ackerman, David G. and Arshadi, Cameron and Baldwin, Perry and Blake, Regina and Elsayed, Ahmad and Hasan, Mashtura and Ramirez, Daniel and Dos Santos, Bruno and Weldon, Monet and Zafar, Amina and Dudman, Joshua T. and Gerfen, Charles R. and Hantman, Adam W. and Korff, Wyatt and Sternson, Scott M. and Spruston, Nelson and Svoboda, Karel and Chandrashekar, Jayaram} } @article {46226, title = {Annotating synapses in large EM datasets.}, journal = {arXiv}, year = {2014}, month = {2014 Sep 5}, pages = {arXiv:1409.1801 [q-bio.QM]}, abstract = {

Reconstructing neuronal circuits at the level of synapses is a central problem in neuroscience and becoming a focus of the emerging field of connectomics. To date, electron microscopy (EM) is the most proven technique for identifying and quantifying synaptic connections. As advances in EM make acquiring larger datasets possible, subsequent manual synapse identification ({\em i.e.}, proofreading) for deciphering a connectome becomes a major time bottleneck. Here we introduce a large-scale, high-throughput, and semi-automated methodology to efficiently identify synapses. We successfully applied our methodology to the Drosophila medulla optic lobe, annotating many more synapses than previous connectome efforts. Our approaches are extensible and will make the often complicated process of synapse identification accessible to a wider-community of potential proofreaders.

}, url = {http://arxiv.org/abs/1409.1801}, author = {Plaza, Stephen M and Parag, Toufiq and Huang, Gary and Olbris, Donald J. and Saunders, Mathew A. and Rivlin, Patricia K.} } @article {6178, title = {A visual motion detection circuit suggested by Drosophila connectomics.}, journal = {Nature}, volume = {500}, year = {2013}, month = {2013 Aug 7}, pages = {175{\textendash}81}, abstract = {

Animal behaviour arises from computations in neuronal circuits, but our understanding of these computations has been frustrated by the lack of detailed synaptic connection maps, or connectomes. For example, despite intensive investigations over half a century, the neuronal implementation of local motion detection in the insect visual system remains elusive. Here we develop a semi-automated pipeline using electron microscopy to reconstruct a connectome, containing 379 neurons and 8,637 chemical synaptic contacts, within the Drosophila optic medulla. By matching reconstructed neurons to examples from light microscopy, we assigned neurons to cell types and assembled a connectome of the repeating module of the medulla. Within this module, we identified cell types constituting a motion detection circuit, and showed that the connections onto individual motion-sensitive neurons in this circuit were consistent with their direction selectivity. Our results identify cellular targets for future functional investigations, and demonstrate that connectomes can provide key insights into neuronal computations.

}, issn = {1476-4687}, doi = {doi:10.1038/nature12450}, url = {http://www.nature.com/nature/journal/v500/n7461/full/nature12450.html}, author = {Takemura, Shin-ya and Bharioke, Arjun and Lu, Zhiyuan and Nern, Aljoscha and Vitaladevuni, Shiv and Rivlin, Patricia K. and Katz, William T. and Olbris, Donald J. and Plaza, Stephen M and Winston, Philip and Zhao, Ting and Horne, Jane Anne and Fetter, Richard D and Takemura, Satoko and Blazek, Katerina and Chang, Lei-Ann and Ogundeyi, Omotara and Saunders, Mathew A. and Shapiro, Victor and Sigmund, Christopher and Rubin, Gerald M and Scheffer, Louis K and Meinertzhagen, Ian A. and Chklovskii, Dmitri B} }