Evidence map›Paper›PMID 39267057›Full record

ArticleBMC biology2024

The subcellular distribution of miRNA isoforms, tRNA-derived fragments, and rRNA-derived fragments depends on nucleotide sequence and cell type.

Tess Cherlin, Yi Jing, Siddhartha Shah, Anne Kennedy, Aristeidis G Telonis, Venetia Pliatsika, Haley Wilson, Lily Thompson, Panagiotis I Vlantis, Phillipe Loher and 2 more

Abstract read
In one paragraph

Article in BMC biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. MicroRNAs and Their Profiling via Single-Cell Sequencing Technologies.Methods in molecular biology (Clifton, N.J.) · 2026
    Review
  5. Article
  6. Emerging roles of transfer RNA fragments in the CNS.Brain : a journal of neurology · 2025
    Review
  7. Review
  8. Review
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.

Tess CherlinComputational Medicine Center, Thomas Jefferson University, Philadelphia, PA, 19017, USA.
Yi JingComputational Medicine Center, Thomas Jefferson University, Philadelphia, PA, 19017, USA.
Siddhartha ShahComputational Medicine Center, Thomas Jefferson University, Philadelphia, PA, 19017, USA.
Anne KennedyComputational Medicine Center, Thomas Jefferson University, Philadelphia, PA, 19017, USA.
Aristeidis G TelonisComputational Medicine Center, Thomas Jefferson University, Philadelphia, PA, 19017, USA.
Venetia PliatsikaComputational Medicine Center, Thomas Jefferson University, Philadelphia, PA, 19017, USA.
Haley WilsonComputational Medicine Center, Thomas Jefferson University, Philadelphia, PA, 19017, USA.
Lily ThompsonComputational Medicine Center, Thomas Jefferson University, Philadelphia, PA, 19017, USA.
Panagiotis I VlantisComputational Medicine Center, Thomas Jefferson University, Philadelphia, PA, 19017, USA.
Phillipe LoherComputational Medicine Center, Thomas Jefferson University, Philadelphia, PA, 19017, USA.
Benjamin LeibyDivision of Biostatistics, Thomas Jefferson University, Philadelphia, PA, 19017, USA.
Isidore RigoutsosComputational Medicine Center, Thomas Jefferson University, Philadelphia, PA, 19017, USA. isidore.rigoutsos@jefferson.edu.ORCID http://orcid.org/0000-0003-1529-8631

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMicroRNA isoforms (isomiRs), tRNA-derived fragments (tRFs), and rRNA-derived fragments (rRFs) represent most of the small non-coding RNAs (sncRNAs) found in cells. Members of these three classes modulate messenger RNA (mRNA) and protein abundance and are dysregulated in diseases. Experimental studies to date have assumed that the subcellular distribution of these molecules is well-understood, independent of cell type, and the same for all isoforms of a sncRNA.

resultsWe tested these assumptions by investigating the subcellular distribution of isomiRs, tRFs, and rRFs in biological replicates from three cell lines from the same tissue and same-sex donors that model the same cancer subtype. In each cell line, we profiled the isomiRs, tRFs, and rRFs in the nucleus, cytoplasm, whole mitochondrion (MT), mitoplast (MP), and whole cell. Using a rigorous mathematical model we developed, we accounted for cross-fraction contamination and technical errors and adjusted the measured abundances accordingly. Analyses of the adjusted abundances show that isomiRs, tRFs, and rRFs exhibit complex patterns of subcellular distributions. These patterns depend on each sncRNA's exact sequence and the cell type. Even in the same cell line, isoforms of the same sncRNA whose sequences differ by a few nucleotides (nts) can have different subcellular distributions.

conclusionsSncRNAs with similar sequences have different subcellular distributions within and across cell lines, suggesting that each isoform could have a different function. Future computational and experimental studies of isomiRs, tRFs, and rRFs will need to distinguish among each molecule's various isoforms and account for differences in each isoform's subcellular distribution in the cell line at hand. While the findings add to a growing body of evidence that isomiRs, tRFs, rRFs, tRNAs, and rRNAs follow complex intracellular trafficking rules, further investigation is needed to exclude alternative explanations for the observed subcellular distribution of sncRNAs.

Indexed as

MicroRNAsRNA, RibosomalRNA, TransferBase SequenceCell LineCell Line, TumorHumansRNA IsoformsMicroRNAsRNA IsoformsRNA, RibosomalRNA, TransferisomiRsmicroRNAsmiRNA isoformsmiRNAsrRFsrRNA-derived fragmentsSmall non-coding RNAssncRNAssubcellular distributiontRFstRNA-derived fragments

Identifiers

PMID39267057
PMCPMC11397057

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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.