Single-cell genomics and regulatory networks for 388 human brains

Single-cell genomics and regulatory networks for 388 human brains. Emani, P. S., Liu, J. J., Clarke, D., Jensen, M., Warrell, J., Gupta, C., Meng, R., Lee, C. Y., Xu, S., Dursun, C., Lou, S., Chen, Y., Chu, Z., Galeev, T., Hwang, A., Li, Y., Ni, P., Zhou, X., Consortium, P., ..., Girgenti, M., Zhang, J., Wang, D., Geschwind, D., & Gerstein, M. Science, 384(6698):eadi5199, 2024.

Paper doi abstract bibtex

Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multiomics datasets into a resource comprising >2.8 million nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550,000 cell type–specific regulatory elements and >1.4 million single-cell expression quantitative trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized 250 disease-risk genes and drug targets with associated cell types.

@article{
doi:10.1126/science.adi5199,
author = {Prashant S. Emani  and Jason J. Liu  and Declan Clarke  and Matthew Jensen  and Jonathan Warrell  and Chirag Gupta  and Ran Meng  and Che Yu Lee  and Siwei Xu  and Cagatay Dursun  and Shaoke Lou  and Yuhang Chen  and Zhiyuan Chu  and Timur Galeev  and Ahyeon Hwang  and Yunyang Li  and Pengyu Ni  and Xiao Zhou and PsychENCODE Consortium and ... and Matthew Girgenti and Jing Zhang and Daifeng Wang and Daniel Geschwind and Mark Gerstein},
title = {Single-cell genomics and regulatory networks for 388 human brains},
journal = {Science},
volume = {384},
number = {6698},
pages = {eadi5199},
year = {2024},
doi = {10.1126/science.adi5199},
URL = {https://www.science.org/doi/abs/10.1126/science.adi5199},
eprint = {https://www.science.org/doi/pdf/10.1126/science.adi5199},
abstract = {Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multiomics datasets into a resource comprising \&gt;2.8 million nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified \&gt;550,000 cell type–specific regulatory elements and \&gt;1.4 million single-cell expression quantitative trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types.}}

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