Publications

Beyond Static Brain Atlases: AI-Powered Open Databasing and Dynamic Mining of Brain-Wide Neuron Morphometry

Published in BioRxiv, 2024

We introduce NeuroXiv (neuroxiv.org), a large-scale, AI-powered database that provides detailed 3D morphologies of individual neurons mapped to a standard brain atlas, designed to support a wide array of dynamic, interactive neuroscience applications.

Recommended citation: Jiang, S., Wang, L., Yun, Z., Chen, H., Yao, J., & Peng, H. (2024). Beyond Static Brain Atlases: AI-Powered Open Databasing and Dynamic Mining of Brain-Wide Neuron Morphometry (p. 2024.09.22.614319). bioRxiv. https://doi.org/10.1101/2024.09.22.614319
https://www.biorxiv.org/content/10.1101/2024.09.22.614319v1

Non-homogenous axonal bouton distribution in whole-brain single-cell neuronal networks

Published in Cell Reports, Volume 43, 2024

We examined the distribution of pre-synaptic contacts in axons of mouse neurons and constructed wholebrain single-cell neuronal networks using an extensive dataset of 1,891 fully reconstructed neurons. We found that bouton locations were not homogeneous throughout the axon and among brain regions. As our algorithm was able to generate whole-brain single-cell connectivity matrices from full morphology reconstruction datasets, we further found that non-homogeneous bouton locations have a significant impact on network wiring, including degree distribution, triad census, and community structure. By perturbing neuronal morphology, we further explored the link between anatomical details and network topology. In our in silico exploration, we found that dendritic and axonal tree span would have the greatest impact on network wiring, followed by synaptic contact deletion. Our results suggest that neuroanatomical details must be carefully addressed in studies of whole-brain networks at the single-cell level.

Recommended citation: Qian P, Manubens-Gil L, Jiang S, et al. Non-homogenous axonal bouton distribution in whole-brain single cell neuronal networks[J]. Cell reports 43, 113871
https://doi.org/10.1016/j.celrep.2024.113871

Full-Spectrum Neuronal Diversity and Stereotypy through Whole Brain Morphometry

Published in Research Square (Under Review), 2023

We conducted a large-scale study of whole-brain morphometry, analyzing 3.7 peta-voxels of mouse brain images at the single-cell resolution, producing one of the largest multi-morphometry databases of mammalian brains to date.

Recommended citation: Liu, Y., Jiang, S., Li, Y., Zhao, S., Yun, Z., Zhao, Z. H., ... & Peng, H. (2023). Full-Spectrum Neuronal Diversity and Stereotypy through Whole Brain Morphometry. Research Square.
http://sd-jiang.github.io/files/Full_Spectrum.pdf

A guide to the BRAIN Initiative Cell Census Network data ecosystem

Published in PLoS biology, 21(6), e3002133, 2023

Here, we provide a guide to the cellular and spatial approaches employed by the BICCN, and to accessing and using these data and extensive resources, including the BRAIN Cell Data Center (BCDC), which serves to manage and integrate data across the ecosystem.

Recommended citation: Hawrylycz, M., Martone, M. E., Ascoli, G. A., Bjaalie, J. G., Dong, H. W., Ghosh, S. S., ... & Zingg, B. (2023). A guide to the BRAIN Initiative Cell Census Network data ecosystem. PLoS biology, 21(6), e3002133.
http://sd-jiang.github.io/files/PLOS_BICCN_Guide.pdf

NRRS: A re-tracing strategy to refine neuron reconstruction

Published in Bioinformatics Advances, Volume 3, Issue 1, vbad054,, 2023

Digital tracing or reconstruction of a neural morphology is defined to geometrically model the backbone of neurites with countable topologically connected structure components like 3D spheres or lines segments, and stores the coordinate, thickness and graph connectivity information in SWC file. Here, we introduce a Neuron Reconstruction Refinement Strategy (NRRS) to address the deviation issues in neuron tracing.

Recommended citation: Li, Y., Jiang, S., Ding, L., & Liu, L. (2023). NRRS: A re-tracing strategy to refine neuron reconstruction. Bioinformatics Advances, 3(1), vbad054.
http://sd-jiang.github.io/files/NRRS.pdf

Petabyte-Scale Multi-Morphometry of Single Neurons for Whole Brains

Published in Neuroinformatics, Volume 20, pages 525–536, 2022

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.

Recommended citation: Jiang, S., Wang, Y., Liu, L., Ding, L., Ruan, Z., Dong, H. W., ... & Peng, H. (2022). Petabyte-scale multi-morphometry of single neurons for whole brains. Neuroinformatics, 20(2), 525-536.
http://sd-jiang.github.io/files/MorphoHub.pdf

Morphological diversity of single neurons in molecularly defined cell types

Published in Nature, 598, pages174–181, 2021

Here, to systematically examine complete single-neuron morphologies on a brain-wide scale, we established a pipeline encompassing sparse labelling, whole-brain imaging, reconstruction, registration and analysis. We fully reconstructed 1,741 neurons from cortex, claustrum, thalamus, striatum and other brain regions in mice. We identified 11 major projection neuron types with distinct morphological features and corresponding transcriptomic identities. Extensive projectional diversity was found within each of these major types, on the basis of which some types were clustered into more refined subtypes.

Recommended citation: Peng, H., Xie, P., Liu, L., Kuang, X., Wang, Y., Qu, L., ... & Zeng, H. (2021). Morphological diversity of single neurons in molecularly defined cell types. Nature, 598(7879), 174-181.
http://sd-jiang.github.io/files/Nature_morphological_diversity.pdf

A multimodal cell census and atlas of the mammalian primary motor cortex

Published in Nature, 598(7879), 86-102, 2021

The NIH’s Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative Cell Census Network (BICCN) was launched at 2017, aiming to identify and catalog the diverse cells types in human, monkey and mouse brain. The first installment of this ambitious endeavor is now complete, with the comprehensive mapping of mammalian primary motor cortical cell type identities on a molecular level. This collection features the reseach, datasets, methods and tools generated by this project. The flagship paper provides a comprehensive overview of the accomplishments while a variety of companion papers reveal the specifics of the data, the development of the tools and the application of the analytical tools. This dedicated collection also contains accompanying commentary in the form of an editorial, News and Views Forum and broad News Feature.

Recommended citation: Principal manuscript editors, Analysis coordination, Integrated data analysis Armand Ethan 42 Yao Zizhen 5, ATAC-seq data generation and processing Fang Rongxin 45 Hou Xiaomeng 10 Lucero Jacinta D. 18 Osteen Julia K. 18 Pinto-Duarte Antonio 18 Poirion Olivier 10 Preissl Sebastian 10 Wang Xinxin 10 97, Epi-retro-seq data generation and processing Dominguez Bertha 53 Ito-Cole Tony 1 Jacobs Matthew 1 Jin Xin 54 99 100 Lee Cheng-Ta 53 Lee Kuo-Fen 53 Miyazaki Paula Assakura 1 Pang Yan 1 Rashid Mohammad 1 Smith Jared B. 54 Vu Minh 1 Williams Elora 54, OLST/STPT and other data generation Narasimhan Arun 6 Palaniswamy Ramesh 6, ... & Project management Kelly Kathleen 6 Mok Stephanie 5 Sunkin Susan 5. (2021). A multimodal cell census and atlas of the mammalian primary motor cortex. Nature, 598(7879), 86-102.
‘http://sd-jiang.github.io/files/Nature_BICCN_flagship.pdf’

Image enhancement to leverage the 3D morphological reconstruction of single-cell neurons

Published in Bioinformatics, Volume 38, Issue 2, Pages 503–512, 2021

We developed a pipeline to enhance the neurite signal and to suppress the background, with the goal of high SBC and better within- and between-image homogeneity. The performance of the image enhancement was quantitatively verified according to the different figures of merit benchmarking the image quality. In addition, the method could improve the neuron reconstruction in approximately 1/3 of the cases, with very few cases of degrading the reconstruction. This significantly outperformed three other approaches of image enhancement.

Recommended citation: Guo, S., Zhao, X., Jiang, S., Ding, L., & Peng, H. (2022). Image enhancement to leverage the 3D morphological reconstruction of single-cell neurons. Bioinformatics, 38(2), 503-512.
http://sd-jiang.github.io/files/Bioinformatics_Image_enhancement.pdf

Binocular encoding in the damselfly pre-motor target tracking system

Published in Current Biology, 2020

Supple et al. link the divergent eye morphology and hunting strategies of dragonflies and damselflies. Targetselective descending neurons in these two groups are identified as homologous, but damselfly TSDNs face forward and encode information in a binocular-fused reference frame.

Recommended citation: Supple, J. A., Pinto-Benito, D., Khoo, C., Wardill, T. J., Fabian, S. T., Liu, M., ... & Gonzalez-Bellido, P. T. (2020). Binocular encoding in the damselfly pre-motor target tracking system. Current Biology, 30(4), 645-656.
http://sd-jiang.github.io/files/Current_biology_Binocular_encoding.pdf

SD-Jiang (Shengdian Jiang)

Publications