Evidence map›Paper›PMID 42039500›Full record

ArticlebioRxiv : the preprint server for biology2026

Whole-genome 3D architectural screen reveals modulators of brain DNA structure.

Bibudha Parasar, Achuthan Raja Venkatesh, Jonathan Perera, Lucas Sosnick, Siavash Moghadami, Yunji Seo, Jenny Shi, Lynette Chan, Satoshi Takenawa, Tetsuya Akiyama and 6 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

16 authors.

Bibudha ParasarDepartment of Neurobiology, Stanford University.ORCID 0000-0001-9042-543X
Achuthan Raja VenkateshDepartment of Neurobiology, Stanford University.ORCID 0009-0003-1868-4068
Jonathan PereraDepartment of Neurobiology, Stanford University.ORCID 0000-0003-3224-6075
Lucas SosnickDepartment of Neurobiology, Stanford University.ORCID 0000-0001-5821-2290
Siavash MoghadamiDepartment of Neurobiology, Stanford University.ORCID 0000-0003-1251-7384
Yunji SeoDepartment of Neurobiology, Stanford University.ORCID 0000-0003-0314-1805
Jenny ShiDepartment of Neurobiology, Stanford University.ORCID 0000-0003-1643-4789
Lynette ChanDepartment of Neurobiology, Stanford University.ORCID 0000-0002-4112-6211
Satoshi TakenawaDepartment of Neurobiology, Stanford University.ORCID 0009-0002-1220-6826
Tetsuya AkiyamaDepartment of Genetics, Stanford University.ORCID 0000-0002-8076-1242
Odilia SiantoDepartment of Genetics, Stanford University.
Takeshi UenakaInstitute for Stem Cell Biology and Regenerative Medicine, Stanford University.ORCID 0000-0001-5786-5038
Angela HadjipanayisTarget, Disease and Systems Biology, Sanofi.ORCID 0000-0002-9553-6523
Marius WernigInstitute for Stem Cell Biology and Regenerative Medicine, Stanford University.ORCID 0000-0002-5309-515X
Aaron D GitlerDepartment of Genetics, Stanford University.ORCID 0000-0001-8603-1526
Longzhi TanDepartment of Neurobiology, Stanford University.ORCID 0000-0002-5618-1299

Funding

Human iPSCs for Elucidating Intercellular Crosstalk Signaling in Dilated CardiomyopathyR01HL141371 · NHLBI · STANFORD UNIVERSITY · PI WONG, WING H., WU, JOSEPH C. · 2018 to 2025
$6.2M
Molecular Biophysics Training Program at StanfordT32GM136568 · NIGMS · STANFORD UNIVERSITY · PI Zev Bryant, KERWYN C. HUANG · 2021 to 2026
$3.7M
Innovating next generation technologies to define mechanisms of neurodegenerative disease and devise therapeutic strategiesR35NS137159 · NINDS · STANFORD UNIVERSITY · PI Aaron D. Gitler · 2025 to 2026
$2.3M
Molecular Pharmacology Training ProgramT32GM136631 · NIGMS · STANFORD UNIVERSITY · PI BOGYO, MATTHEW, CHEN, JAMES K · 2021 to 2025
$2.0M
What Are the Rules of 3D Genome Folding in Single Cells?R35GM162529 · NIGMS · STANFORD UNIVERSITY · PI Longzhi Tan · 2026 to 2026
$385k
NHLBI NIH HHS R01 HL141371NIGMS NIH HHS R35 GM162529NIGMS NIH HHS T32 GM136568NIGMS NIH HHS T32 GM136631NINDS NIH HHS R35 NS137159
6 · The paper itself

Abstract

Three-dimensional (3D) genome architecture is the foundation of gene regulation, and plays a critical role in normal physiology and disease. However, our understanding of its biochemical determinants has long been limited by technology: imaging-based screens only profile a small number of loci, while sequencing-based studies rarely exceed 100 samples or conditions. Here we present "in-plate chromosome conformation capture" (Plate-C), a high-throughput, cost-effective platform that profiles thousands of whole-genome architectures in a day. Plate-C enabled the first chemical screen for whole-genome structural changes-profiling 2,956 samples from 834 conditions across 5 neuronal and glial types, accompanied by 6,081 single cells using "easy diploid chromosome conformation capture" (Easy Dip-C) and 200,893 single-cell transcriptomes. We discovered that diverse, dose/time-dependent, and cell type/species-specific modes of DNA structural changes can be rapidly induced by manipulating epigenetic (HDAC, BET), metabolic (mTOR), proteostatic (UPR), developmental (GSK3/Wnt, Hedgehog), immune (cGAS/STING), and neurotransmission pathways. To validate our finding in vivo, we demonstrated in newborn mice that HDAC inhibition drives brain-wide genome rewiring within hours, highly correlated with changes in vitro and inducing a latent structural and transcriptional state orthogonal to normal differentiation. By enabling massively parallel profiling of whole-genome structures, Plate-C paves the way for systematic discovery of DNA folding principles to better understand and engineer the human genome in 3D.

Identifiers

PMID42039500
PMCPMC13104906

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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.