A Multimodal Single-Cell Epigenomic and 3D Genome Atlas of the Human Basal Ganglia
The basal ganglia are a group of forebrain nuclei critical for motor control and reward processing, and their dysfunction contributes to neurological and neuropsychiatric disorders. Here, we present the first multimodal single-cell epigenomic atlas of the human basal ganglia across major subregions and cell types. We jointly profiled DNA methylation and 3D chromatin conformation in 197,003 nuclei from eight basal ganglia subregions using multi-omic sequencing (snm3C-seq), and integrated these data with existing DNA methylation and chromatin conformation sequencing datasets to build a unified atlas of 261,331 cells spanning 31 subclasses and 59 groups. This atlas reveals extensive cell-type- and region-specific differential methylation, enriched for distinct transcription factor motifs, and validated by MERFISH spatial transcriptomics, which uncovered epigenetic gradients linked to transcriptional output. Compared to neuronal cells, non-neuronal cells exhibit distinct 3D genome organization including smaller chromatin compartments, increased long-range inter-compartment contacts, shorter loops, and stronger CG hypomethylation in A compartments. We further identified genes that display compartment switches, are strongly correlated with compartment scores, and exhibit differential domain boundaries and chromatin looping across basal ganglia cell types. We identified multiple medium spiny neuron subtypes defined by distinct hypomethylated signature genes, with 3D genome embeddings emphasizing dorsal, ventral, and hybrid populations. By integrating chromatin accessibility and histone modification profiles, we reconstructed cell-type-resolved enhancer-promoter links and gene regulatory networks, providing a comprehensive epigenomic framework for interpreting genetic risk loci and regulatory architecture in the human basal ganglia.
Authors
- Nelson Johansen (ORCID: https://orcid.org/0000-0002-4436-969X)
- M. Margarita Behrens
- Jessica Arzavala
- Michelle Liem (ORCID: https://orcid.org/0009-0004-0542-7166)
- Silvia Cho (ORCID: https://orcid.org/0009-0002-0967-4469)
- Carissa Young
- Jin Luo (ORCID: https://orcid.org/0000-0001-7655-7663)
- Chumo Chen
- Daofeng Li (ORCID: https://orcid.org/0000-0003-4851-2163)
- Natalie M. Schenker-Ahmed (ORCID: https://orcid.org/0000-0002-2651-1576)
- Sarah M. Velázquez
- Cindy Tatiana Báez-Becerra
- William S. Owens
- Shuai Fu (ORCID: https://orcid.org/0000-0002-0298-8433)
- Zoey Zhao (ORCID: https://orcid.org/0009-0005-3822-7985)
- X Xu
- Lei Chang (ORCID: https://orcid.org/0000-0002-4508-3853)
- Yue Wu (ORCID: https://orcid.org/0009-0004-2428-0905)
- Yuanyuan Fu (ORCID: https://orcid.org/0000-0001-5934-6573)
- Quan Zhu
- Wenjin Zhang (ORCID: https://orcid.org/0000-0002-5979-0102)
- Jackson Willier
- Ariana Barcoma
- Andrea Saldana Acerbo
- Eshaan Soma
- Mikayla Marrin
- Amit Klein
- Ruoxuan Wang
- Manoj Hariharan
- Joseph R. Nery
- Chu-Yi Tai
- Colin Kern
- Kai Li
- Yang Xie
- Aleksandra Bikkina
- Kaitlyn G. Russo
- Shane N. Alt
- Yareli Sanchez
- Cynthia Rose
- Jesus Flores
- Kyle W. Knutson
- Eric Boone
- Emma Osgood
- Austin C. Manning
- Guha V. Sundaram
- Trygve E. Bakken
- Ed S. Lein
- Derek Chan
- Wubin Ding
- Ting Wang
- Jordan Altshul
- Cesar Barragan
- Rosa Gomez Castanon
- Jacquelin Olness
- Carolyn OConnor
- Wei Tian
- Jonathan A. Rink
- Huamin Chen
- Manya Jain
- Qiurui Zeng
- Bing Ren
- Joseph R. Ecker
- Alaina Petrella
- Anna Bartlett
- Rebecca D. Hodge
Institutions
- Salk Institute for Biological Studies (US)
- Allen Institute for Brain Science (US)
- Howard Hughes Medical Institute (US)
- University of California, Irvine (US)
- Washington University in St. Louis (US)
- Columbia University Irving Medical Center (US)
- University of California San Diego (US)
- Allen Institute (US)
- New York Genome Center (US)
Publication Details
- Journal
- bioRxiv (Cold Spring Harbor Laboratory)
- Published
- 2026-02-14
- DOI
- https://doi.org/10.64898/2026.02.12.705594
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 0.00