Evidence map›Paper›PMID 42863841›Full record

ArticleNucleic acids research2026

Development of CRISPR/Cas13-based analytical tools to study RNA-protein interactions.

Keita Yamamoto, Shiori Shikata, Yangying Hao, Takuya Tomita, Rina Kanai, Els Verhoeyen, Yasushi Saeki, Susumu Goyama

Abstract read
In one paragraph

Article in Nucleic acids research, 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

8 authors.

Keita YamamotoDepartment of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, 108-8639,Japan.ORCID 0009-0007-0225-9525
Shiori ShikataDepartment of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, 108-8639,Japan.
Yangying HaoDepartment of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, 108-8639,Japan.
Takuya TomitaDivision of Protein Metabolism, The Institute of Medical Science, The University of Tokyo, Tokyo, 108-8639,Japan.ORCID 0000-0003-4568-9940
Rina KanaiDepartment of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, 108-8639,Japan.
Els VerhoeyenCIRI-International Center for Infectiology Research, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, Ecole Normale Supérieure de Lyon, Université Lyon, 69007,Lyon, France.
Yasushi SaekiDivision of Protein Metabolism, The Institute of Medical Science, The University of Tokyo, Tokyo, 108-8639,Japan.
Susumu GoyamaDepartment of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, 108-8639,Japan.

Funding

Fostering Joint International Research 22KK0127Grant-in-Aid for Scientific Research 22H03100Grant-in-Aid for Scientific Research on Innovative Areas 21H00274Japan Agency for Medical Research and Development JP23ama221223Japanese Society of HematologyJapan Society for the Promotion of ScienceJSPS KAKEN JP22K16319JSPS KAKEN JP23H05479JSPS KAKEN JP24K19216Mochida Memorial Foundation for Medical and Pharmaceutical ResearchTokyo Biochemical Research FoundationUehara Memorial Foundation
6 · The paper itself

Abstract

RNA-protein interactions (RPIs) are as important as protein-protein interactions (PPIs) for the formation of membraneless organelles (MLOs) and play vital roles in diverse biological processes. Despite remarkable advances in PPI analysis technologies, the development of tools for RPI analysis has lagged behind. To address this challenge, we integrated three established PPI technologies-bimolecular fluorescence complementation (BiFC), NanoBiT, and split-TurboID-with RNA-targeting CRISPR/dCas13. We applied these platforms to paraspeckles, a representative MLO formed through interactions between the long non-coding RNA NEAT1 and the RNA-binding protein NONO. BiFC-dCas13 functions as a fluorescence-based recorder that enables quantitative detection of NEAT1-NONO interaction events. NanoBiT-dCas13 precisely monitors dynamic changes in the NEAT1-NONO interaction through a reversible luminescence readout. Split-TurboID-dCas13 induces proximity-dependent biotinylation of proteins surrounding paraspeckles, identifying the N6-methyladenosine reader YTHDC1 as a paraspeckle-associated protein. Furthermore, BiFC-dCas13 was readily reconfigured to detect additional RPI pairs, demonstrating the modularity of the platform. Together, these complementary systems provide versatile approaches for recording, monitoring, and discovering RPI-associated biology and molecular environments.

Indexed as

CRISPR-Cas SystemsRNARNA-Binding ProteinsRNA, Long NoncodingHEK293 CellsHumansParaspecklesProtein BindingNEAT1 long non-coding RNA, humanRNARNA-Binding ProteinsRNA, Long Noncoding

Identifiers

PMID42863841
PMCPMC13657679

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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.