Evidence map›Paper›PMID 34856921›Full record

ArticleBMC bioinformatics2021

Exploring cell-specific miRNA regulation with single-cell miRNA-mRNA co-sequencing data.

Junpeng Zhang, Lin Liu, Taosheng Xu, Wu Zhang, Chunwen Zhao, Sijing Li, Jiuyong Li, Nini Rao, Thuc Duy Le

Abstract read
In one paragraph

Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Modeling ncRNA Synergistic Regulation in Cancer.Methods in molecular biology (Clifton, N.J.) · 2025
    Review
  5. Review
  6. Article
  7. Article
  8. Article
  9. miRNAs in pancreatic cancer progression and metastasis.Clinical & experimental metastasis · 2024
    Review
  10. Article
  11. Article
  12. Review
  13. Review
  14. Article
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

9 authors.

Junpeng ZhangCenter for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, Sichuan, China. zhangjunpeng411@gmail.com.
Lin LiuUniSA STEM, University of South Australia, Mawson Lakes, SA, 5095, Australia.
Taosheng XuInstitute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China.
Wu ZhangSchool of Agriculture and Biological Sciences, Dali University, Dali, 671003, Yunnan, China.
Chunwen ZhaoSchool of Engineering, Dali University, Dali, 671003, Yunnan, China.
Sijing LiSchool of Engineering, Dali University, Dali, 671003, Yunnan, China.
Jiuyong LiUniSA STEM, University of South Australia, Mawson Lakes, SA, 5095, Australia.
Nini RaoCenter for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, Sichuan, China. raonn@uestc.edu.cn.
Thuc Duy LeUniSA STEM, University of South Australia, Mawson Lakes, SA, 5095, Australia. thuc.le@unisa.edu.au.

Funding

Australian Research Council Discovery Grant DP170101306National Natural Science Foundation of China 61702069, 61963001National Natural Science Foundation of China 61872405, 61720106004National Natural Science Foundation of China 61902372NHMRC Grant 1123042Yunnan Fundamental Research Projects 202001AT070024
6 · The paper itself

Abstract

backgroundExisting computational methods for studying miRNA regulation are mostly based on bulk miRNA and mRNA expression data. However, bulk data only allows the analysis of miRNA regulation regarding a group of cells, rather than the miRNA regulation unique to individual cells. Recent advance in single-cell miRNA-mRNA co-sequencing technology has opened a way for investigating miRNA regulation at single-cell level. However, as currently single-cell miRNA-mRNA co-sequencing data is just emerging and only available at small-scale, there is a strong need of novel methods to exploit existing single-cell data for the study of cell-specific miRNA regulation.

resultsIn this work, we propose a new method, CSmiR (Cell-Specific miRNA regulation) to combine single-cell miRNA-mRNA co-sequencing data and putative miRNA-mRNA binding information to identify miRNA regulatory networks at the resolution of individual cells. We apply CSmiR to the miRNA-mRNA co-sequencing data in 19 K562 single-cells to identify cell-specific miRNA-mRNA regulatory networks for understanding miRNA regulation in each K562 single-cell. By analyzing the obtained cell-specific miRNA-mRNA regulatory networks, we observe that the miRNA regulation in each K562 single-cell is unique. Moreover, we conduct detailed analysis on the cell-specific miRNA regulation associated with the miR-17/92 family as a case study. The comparison results indicate that CSmiR is effective in predicting cell-specific miRNA targets. Finally, through exploring cell-cell similarity matrix characterized by cell-specific miRNA regulation, CSmiR provides a novel strategy for clustering single-cells and helps to understand cell-cell crosstalk.

conclusionsTo the best of our knowledge, CSmiR is the first method to explore miRNA regulation at a single-cell resolution level, and we believe that it can be a useful method to enhance the understanding of cell-specific miRNA regulation.

Indexed as

MicroRNAsCluster AnalysisGene Expression ProfilingGene Regulatory NetworksRNA, MessengerMicroRNAsRNA, MessengerCell–cell crosstalkCell-specific miRNA regulationChronic myelogenous leukemiamiRNAmRNAPseudo-cell interpolationSingle-cell clustering analysisSingle-cell miRNA-mRNA co-sequencing

Identifiers

PMID34856921
PMCPMC8641245

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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.