Evidence map›Paper›PMID 42620363›Full record

ArticlebioRxiv : the preprint server for biology2026

Genomic repeats for single-cell molecular recording.

Rachel K Dveirin, Jessica D Lin, Preeti Vyas, Jiaxi Lu, Yuqing Yan, Jennifer J Lee, Xinzhong Dong, Sujatha Kannan, Ben Langmead, Sashank K Reddy and 2 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

12 authors.

Rachel K DveirinDepartment of Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.ORCID 0000-0003-0453-503X
Jessica D LinCenter for Epigenetics, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.ORCID 0000-0002-4093-0005
Preeti VyasDepartment of Anesthesiology and Critical Care Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.ORCID 0000-0002-1469-4678
Jiaxi LuDepartment of Materials Science and Engineering, The Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA.
Yuqing YanDepartment of Materials Science and Engineering, The Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA.
Jennifer J LeeDepartment of Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Xinzhong DongDepartment of Neuroscience, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Sujatha KannanDepartment of Anesthesiology and Critical Care Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.ORCID 0000-0002-9497-5786
Ben LangmeadDepartment of Computer Science, The Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA.ORCID 0000-0003-2437-1976
Sashank K ReddyDepartment of Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.ORCID 0000-0001-6323-3950
Dingchang LinDepartment of Materials Science and Engineering, The Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA.ORCID 0000-0002-9354-5952
Reza KalhorDepartment of Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.ORCID 0000-0002-5558-7545

Funding

Mapping the dynamics of mouse neurogenesis in autism models using high-resolution genomic barcoding technologiesR01HG012357 · NHGRI · JOHNS HOPKINS UNIVERSITY · PI KALHOR, REZA · 2022 to 2025
$2.1M
NHGRI NIH HHS R01 HG012357
6 · The paper itself

Abstract

Genomic recording enables transient biological signals to be indelibly captured through DNA alterations, creating a permanent record of cellular history retrievable by sequencing. However, current methods are limited by scarce writing space, typically targeting only one or a few amenable genomic sites and requiring large cell populations for signal reconstruction. Here, we establish Repeats for Genomic Recording (RGRs): sequences with up to 400 copies targetable by a single CRISPR guide RNA, readable with a common primer pair, and predicted to have minimal functional impact. We demonstrate that RGRs enable both signal deconvolution in single cells and high-resolution recording in cell populations. Individual RGR sites exhibit distinct response kinetics; thus, combining them improves recording resolution beyond what redundancy alone provides, analogous to diversity reception in wireless communication. We develop a computational pipeline for systematic RGR identification, revealing 15,000 to 25,000 candidates per species across human, mouse, and zebrafish, thereby markedly expanding recording capacity and enabling cell-type-specific applications. Finally, we validate RGRs in live mice by recording long-term immediate early gene activity across the brain following epilepsy induction. This work establishes genomic repeats as a high-capacity platform for single-cell molecular recording in vivo.

Identifiers

PMID42620363
PMCPMC13484776

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.