Evidence map›Paper›PMID 41676689›Full record

ArticlebioRxiv : the preprint server for biology2026

Integrative Single-Cell Epigenomic Atlas Annotates the Regulatory Genome of the Adult Mouse Brain.

Zhaoning Wang, Songpeng Zu, Ethan J Armand, Timothy H Loe, Jonathan A Rink, Wanying Wu, Yang Xie, Lei Chang, Chenxu Zhu, Nicholas D Johnson and 17 more

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

27 authors.

Zhaoning WangDepartment of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA.ORCID 0000-0002-0290-4746
Songpeng ZuDepartment of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA.
Ethan J ArmandDepartment of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA.
Timothy H LoeDepartment of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA.
Jonathan A RinkComputational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA.
Wanying WuDepartment of Genetics, Washington University School of Medicine, St. Louis, MO.
Yang XieDepartment of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA.
Lei ChangDepartment of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA.
Chenxu ZhuNew York Genome Center, New York, NY.
Nicholas D JohnsonComputational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA.
Jasper LeeComputational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA.
Jackson K WillierComputational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA.
Silvia ChoComputational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA.
Stella CaoComputational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA.
Ariana S BarcomaComputational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA.
Nora EmersonComputational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA.
Hanqing LiuGenomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA.
Kangli WangDepartment of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA.
Zane A GibbsDepartment of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA.
Xiaomeng GaoDepartment of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA.
Sunan XuDepartment of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA.
David GuoDepartment of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA.
Zhuowen TuDepartment of Cognitive Science, University of California, San Diego, La Jolla, CA.
Yang E LiDepartment of Genetics, Washington University School of Medicine, St. Louis, MO.
Joseph R EckerGenomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA.
M Margarita BehrensComputational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA.
Bing RenDepartment of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA.

Funding

Ultra-high Throughout Single Cell Multi-omic Analysis of Histone Modifications and Transcriptome in Mouse and Human BrainsRF1MH128838 · NIMH · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI BEHRENS, M MARGARITA, REN, BING · 2021 to 2021
$3.2M
Illumina NovaSeq 6000 Sequencing SystemS10OD026929 · OD · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI JEPSEN, KRISTEN LYNN · 2019 to 2019
$600k
NIH HHS S10 OD026929NIMH NIH HHS RF1 MH128838
6 · The paper itself

Abstract

Histone modifications underpin the cell-type-specific gene regulatory networks that drive the remarkable cellular heterogeneity of the adult mammalian brain. Here, we profiled four histone modifications jointly with transcriptome in 2.5 million nuclei across multiple adult mouse brain regions. By integrating these data with existing maps of chromatin accessibility, DNA methylation, and 3D genome organization, we established a unified regulatory framework for over 100 brain cell subclasses. This integrative epigenomic atlas annotates 81% of the genome, defining distinct active, primed, and repressive states. Notably, active chromatin states marked by combinatorial histone modifications more precisely identify functional enhancers than chromatin accessibility alone, while Polycomb- and H3K9me3-mediated repression contributes prominently to cell-type-specific regulation. Finally, this multi-modal resource enables deep learning models to predict epigenomic features and gene expression from DNA sequences. This work provides a comprehensive annotation of the mouse brain regulatory genome and a framework for interpreting non-coding variation in complex tissues.

Identifiers

PMID41676689
PMCPMC12889674

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.