Evidence map›Paper›PMID 42681329›Full record

ReviewMethods in molecular biology (Clifton, N.J.)2026

MicroRNAs and Their Profiling via Single-Cell Sequencing Technologies.

Chinmayee Goda, Shaopeng Gu, Anjun Ma, Mario Acunzo, Giulia Romano, Adrienne M Dorrance, Ramiro Garzon, Qin Ma, Giovanni Nigita

Abstract readReview
PubMed Publisher
In one paragraph

Review in Methods in molecular biology (Clifton, N.J.), 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

9 authors.

Chinmayee GodaDivision of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA. Chinmayee.Goda@hci.utah.edu.
Shaopeng GuDepartment of Biomedical Informatics, The James Comprehensive Cancer Center, College of Medicine, The Ohio State University, Columbus, OH, USA.
Anjun MaDepartment of Biomedical Informatics, The James Comprehensive Cancer Center, College of Medicine, The Ohio State University, Columbus, OH, USA.
Mario AcunzoDepartment of Medicine and Surgery, LUM "Giuseppe Degennaro", Casamassima, Bari, Italy.
Giulia RomanoDivision of Pulmonary Diseases and Critical Care Medicine, Virginia Commonwealth University, Richmond, VA, USA.
Adrienne M DorranceDivision of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA.
Ramiro GarzonDivision of Hematology and Hematological Malignancies, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA.
Qin MaDepartment of Biomedical Informatics, The James Comprehensive Cancer Center, College of Medicine, The Ohio State University, Columbus, OH, USA.
Giovanni NigitaPelotonia Institute for Immuno-Oncology, The Arthur G. James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA. Giovanni.Nigita@osumc.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

MicroRNAs (miRNAs) are small non-coding RNAs that play essential roles in gene regulation, cellular function, and disease pathogenesis. Advances in single-cell RNA sequencing technologies have enabled the profiling of miRNAs at single-cell resolution, providing unprecedented insight into cell-specific regulatory networks and heterogeneity. This chapter presents an overview of miRNA biology, technical approaches for single-cell miRNA sequencing, and recent bioinformatics tools developed for data analysis. We discuss challenges in library preparation, such as adapter biases and low RNA input, and highlight integrative strategies for co-profiling miRNAs with other omics layers. Finally, we outline in the conclusion the potential of single-cell miRNA profiling to contribute to precision medicine and therapeutic development, including its possible use in biomarker discovery, monitoring tumor heterogeneity, and informing personalized treatment strategies. As the field progresses, continued innovation will be critical to overcoming existing barriers and fully harnessing the power of single-cell miRNA analyses.

Indexed as

Gene Expression ProfilingMicroRNAsSequence Analysis, RNASingle-Cell AnalysisAnimalsComputational BiologyGene LibraryHigh-Throughput Nucleotide SequencingHumansSingle-Cell Gene Expression AnalysisMicroRNAsmiRNAsncRNAsscRNA-seq

Identifiers

PMID42681329

What OpenQuestion holds

Textmetadata
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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.