Evidence map›Paper›PMID 41278799›Full record

ArticlebioRxiv : the preprint server for biology2025

Single-cell RNA-seq using UltraMarathonRT expands the known transcriptome.

Chia-Ling Chou, Anastasiya Grinko, Li-Tao Guo, Alexander M Leipold, Teresa Rummel, Florian Erhard, Anna Marie Pyle, Antoine-Emmanuel Saliba

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. 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.

Chia-Ling ChouHelmholtz Institute for RNA-based Infection Research (HIRI), Helmholtz Centre for Infection Research (HZI), Würzburg, Germany.
Anastasiya GrinkoHelmholtz Institute for RNA-based Infection Research (HIRI), Helmholtz Centre for Infection Research (HZI), Würzburg, Germany.ORCID 0009-0007-3539-1637
Li-Tao GuoDepartment of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, Connecticut 06520, USA.
Alexander M LeipoldHelmholtz Institute for RNA-based Infection Research (HIRI), Helmholtz Centre for Infection Research (HZI), Würzburg, Germany.
Teresa RummelFaculty for Informatics and Data Science, University of Regensburg, Regensburg, Germany.ORCID 0000-0002-9434-0836
Florian ErhardFaculty for Informatics and Data Science, University of Regensburg, Regensburg, Germany.ORCID 0000-0002-3574-6983
Anna Marie PyleDepartment of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, Connecticut 06520, USA.
Antoine-Emmanuel SalibaHelmholtz Institute for RNA-based Infection Research (HIRI), Helmholtz Centre for Infection Research (HZI), Würzburg, Germany.

Funding

High-throughput detection of transcriptomic and epitranscriptomic variation and kinetics using MarathonRTR01HG011868 · NHGRI · YALE UNIVERSITY · PI GRAVELEY, BRENTON R., PYLE, ANNA MARIE · 2021 to 2024
$3.9M
NHGRI NIH HHS R01 HG011868
6 · The paper itself

Abstract

The ability to map messenger RNA (mRNA) molecules from individual cells using next-generation sequencing technologies, known as single-cell RNA-seq (scRNA-seq), is transforming biology by redefining cellular identities with unmatched detail. However, all current protocols depend on copying RNA into complementary DNA with a single reverse transcriptase (RT) derived from murine leukemia virus, which is an RT enzyme known for low processivity and limited ability to unfold complex RNA structures. Here, for the first time, we introduce a group II intron reverse transcriptase, UltraMarathonRT (uMRT), to perform scRNA-seq. We demonstrate that this enzyme reveals an unexpected transcriptomics landscape by capturing additional genes and other genomic features that conventional RTs miss. We also combined uMRT with metabolic RNA labeling, nucleoside conversion and scRNA-seq to explore genome-wide transcriptome dynamics at the single-cell level. Overall, we establish uMRT as a transformative biotechnological tool for single-cell transcriptomics.

Indexed as

metabolic labelingreverse transcriptasescSLAM-seqsingle-cell RNA-seqtemplate switchingUltraMarathonRT

Identifiers

PMID41278799
PMCPMC12632341

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.