Abstract
Recent advances in brain imaging allow producing large amounts of 3-D volumetric data from which morphometry data is reconstructed and measured. Fine detailed structural morphometry of individual neurons, including somata, dendrites, axons, and synaptic connectivity based on digitally reconstructed neurons, is essential for cataloging neuron types and their connectivity. To produce quality morphometry at large scale, it is highly desirable but extremely challenging to efficiently handle petabyte-scale high-resolution whole brain imaging database. Here, we developed a multi-level method to produce high quality somatic, dendritic, axonal, and potential synaptic morphometry, which was made possible by utilizing necessary petabyte hardware and software platform to optimize both the data and workflow management. Our method also boosts data sharing and remote collaborative validation. We highlight a petabyte application dataset involving 62 whole mouse brains, from which we identified 50,233 somata of individual neurons, profiled the dendrites of 11,322 neurons, reconstructed the full 3-D morphology of 1,050 neurons including their dendrites and full axons, and detected 1.9 million putative synaptic sites derived from axonal boutons. Analysis and simulation of these data indicate the promise of this approach for modern large-scale morphology applications.
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Acknowledgements
This work is funded by Southeast University (SEU). SEU supported the development of the methods and informatics data management and analysis pipeline at the SEU-Allen Joint Center. We are grateful to the Allen Institute for imaging datasets collected through multiple grant awards from institutes under the National Institutes of Health (NIH), including award number R01EY023173 from The National Eye Institute to H.Z., U01MH105982 from the National Institute of Mental Health and Eunice Kennedy Shriver National Institute of Child Health & Human Development to H.Z., and U19MH114830 from the National Institute Of Mental Health to H.Z. Y.W. acknowledges National Science Foundation of China Grant number 32071367 and Natural Science Foundation Shanghai Grant 20ZR1420100. The content is solely the responsibility of the authors and does not necessarily represent the official views of NIH and its subsidiary institutes. The Allen Institute affiliated authors wish to thank the Allen Institute founder, Paul G. Allen, for his vision, encouragement, and support. G.A.A. acknowledges NIH grants R01NS39600, U01MH14829, and R01NS86082. We thank Zhi Zhou, Yuanyuan Song, Lulu Yin, Shichen Zhang, Jintao Pan, Yanting Liu, Guodong Hong, Jia Yuan, Yanjun Duan, Yaping Wang, Qiang Ouyang, Zijun Zhao, Wan Wan, Peng Wang, Ping He, Lingsheng Kong, Feng Xiong, and other team members for the support of data production.
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H.P. conceptualized the study, envisioned and led the development of this platform and various analyses. S.J., Y.W., and L.L. co-developed the MorphoHub system and the multi-level neuron reconstruction protocols. L.L. led the reconstruction of R1050. Y.W. provided the support of TeraVR. M.C. led the identification of somata in D62. S.Z., X.Z. assisted in MorphoHub development. P.X. and L.D. assisted in data analysis. Z.R. and H.P. constructed the hardware platform for this study. H.Z. provided the raw imaging dataset for producing D62 and advised on neurobiology. M.H. advised on morphometry and data management. H.D. and G.A. advised on whole brain neuronal structure analysis. H.P. led the writing of the manuscript in consultation with all authors.
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Shengdian Jiang, Yimin Wang, and Lijuan Liu contributed equally to this study.
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Jiang, S., Wang, Y., Liu, L. et al. Petabyte-Scale Multi-Morphometry of Single Neurons for Whole Brains. Neuroinform 20, 525–536 (2022). https://doi.org/10.1007/s12021-022-09569-4
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DOI: https://doi.org/10.1007/s12021-022-09569-4