Evidence map›Paper›PMID 41573905›Full record

ArticlebioRxiv : the preprint server for biology2026

Alternative probe chemistries for single-molecule analysis of long non-coding RNA.

Kalika R Pai, Aimee M Martin, Madison Kadrmas, Julia R Widom

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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.

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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

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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

5 · Who and what money

Authors and funding

4 authors.

Kalika R PaiDepartment of Chemistry and Biochemistry, University of Oregon, Eugene, OR 97403.ORCID 0009-0004-8821-5766
Aimee M MartinDepartment of Biology, Loyola University New Orleans, New Orleans, LA 70118.ORCID 0009-0007-0257-6096
Madison KadrmasDepartment of Chemistry and Biochemistry, University of Oregon, Eugene, OR 97403.ORCID 0000-0002-8466-5434
Julia R WidomDepartment of Chemistry and Biochemistry, University of Oregon, Eugene, OR 97403.ORCID 0000-0002-6357-2749

Funding

Mapping the sequence landscape of RNA structure, dynamics and protein interactions using high-throughput single-molecule FRETR35GM147229 · NIGMS · UNIVERSITY OF OREGON · PI Julia Reed Widom · 2022 to 2026
$1.8M
NIGMS NIH HHS R35 GM147229
6 · The paper itself

Abstract

Single-molecule microscopy has been widely used to study the structure and dynamics of RNA, but extension to larger systems such as long non-coding RNA (lncRNA) has proven challenging. Methods such as single-molecule kinetic analysis of RNA transient structure (SiM-KARTS), where the binding of a short, complementary oligonucleotide probe is used to determine accessibility of a specific region of the RNA, are promising. However, adapting SiM-KARTS to systems as complex as lncRNA requires careful optimization of experimental variables that have not been thoroughly explored. In this work, SiM-KARTS, thermal denaturation experiments, and circular dichroism spectroscopy were used to analyze the binding behaviors of probes with alternative backbone chemistries, specifically DNA with locked nucleic acid (LNA) residues incorporated and morpholinos. A segment of lncRNA that enabled control over the accessibility of the target sequence was used as a model. We show that optimizing probe backbone chemistry can allow for a more precise distinction between different structures of the target RNA, and for fine-tuning of probe binding stability without the structural impacts that other variables such as ionic concentration may have. Specifically, we demonstrate that LNA probes exhibit a high degree of structural sensitivity in both their binding and unbinding kinetics. We further show that when binding and unbinding rates are considered holistically, LNA probes allow traces arising from different target RNA structures to be individually classified with a high degree of accuracy. These results provide design principles for the application of SiM-KARTS to target RNAs of increased complexity such as lncRNA.

Indexed as

fluorescenceLong non-coding RNAmicroscopynucleic acidRNA structuresingle-molecule biophysics

Identifiers

PMID41573905
PMCPMC12822723

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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.