Evidence map›Paper›PMID 32290510›Full record

ArticlePharmaceutics2020

miRNA Reference Genes in Extracellular Vesicles Released from Amniotic Membrane-Derived Mesenchymal Stromal Cells.

Enrico Ragni, Carlotta Perucca Orfei, Antonietta Rosa Silini, Alessandra Colombini, Marco Viganò, Ornella Parolini, Laura de Girolamo

Open access · goldAbstract read
In one paragraph

Article in Pharmaceutics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
0.8field-weighted citation impact, top 34% of its field
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

15 citing papers in PubMed, 19 citations in OpenAlex.

  1. Review
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  8. Optimizing tendon repair and regeneration: how does theFrontiers in bioengineering and biotechnology · 2024
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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

7 authors at 3 institutions in 1 country.

Enrico RagniIRCCS Istituto Ortopedico Galeazzi, Laboratorio di Biotecnologie Applicate all'Ortopedia, Via R. Galeazzi 4, I-20161 Milano, Italy.
Carlotta Perucca OrfeiIRCCS Istituto Ortopedico Galeazzi, Laboratorio di Biotecnologie Applicate all'Ortopedia, Via R. Galeazzi 4, I-20161 Milano, Italy.
Antonietta Rosa SiliniCentro di Ricerca E. Menni, Fondazione Poliambulanza Istituto Ospedaliero, Via Bissolati 57, I-25124 Brescia, Italy.
Alessandra ColombiniIRCCS Istituto Ortopedico Galeazzi, Laboratorio di Biotecnologie Applicate all'Ortopedia, Via R. Galeazzi 4, I-20161 Milano, Italy.
Marco ViganòIRCCS Istituto Ortopedico Galeazzi, Laboratorio di Biotecnologie Applicate all'Ortopedia, Via R. Galeazzi 4, I-20161 Milano, Italy.
Ornella ParoliniCentro di Ricerca E. Menni, Fondazione Poliambulanza Istituto Ospedaliero, Via Bissolati 57, I-25124 Brescia, Italy.
Laura de GirolamoIRCCS Istituto Ortopedico Galeazzi, Laboratorio di Biotecnologie Applicate all'Ortopedia, Via R. Galeazzi 4, I-20161 Milano, Italy.
Istituto Ortopedico Galeazzi · ITFondazione Poliambulanza Istituto Ospedaliero · ITUniversità Cattolica del Sacro Cuore · IT

Funding

Ministero della Salute Ricerca CorrenteMinistero dell'Istruzione, dell'Università e della Ricerca 5x1000 (2015 and 2016)
6 · The paper itself

Abstract

Human amniotic membrane and amniotic membrane-derived mesenchymal stromal cells (hAMSCs) have produced promising results in regenerative medicine, especially for the treatment of inflammatory-based diseases and for different injuries including those in the orthopedic field such as tendon disorders. hAMSCs have been proposed to exert their anti-inflammatory and healing potential via secreted factors, both free and conveyed within extracellular vesicles (EVs). In particular, EV miRNAs are considered privileged players due to their impact on target cells and tissues, and their future use as therapeutic molecules is being intensely investigated. In this view, EV-miRNA quantification in either research or future clinical products has emerged as a crucial paradigm, although, to date, largely unsolved due to lack of reliable reference genes (RGs). In this study, a panel of thirteen putative miRNA RGs (let-7a-5p, miR-16-5p, miR-22-5p, miR-23a-3p, miR-26a-5p, miR-29a-5p, miR-101-3p, miR-103a-3p, miR-221-3p, miR-423-5p, miR-425-5p, miR-660-5p and U6 snRNA) that were identified in different EV types was assessed in hAMSC-EVs. A validated experimental pipeline was followed, sifting the output of four largely accepted algorithms for RG prediction (geNorm, NormFinder, BestKeeper and ΔCt method). Out of nine RGs constitutively expressed across all EV isolates, miR-101-3p and miR-22-5p resulted in the most stable RGs, whereas miR-423-5p and U6 snRNA performed poorly. miR-22-5p was also previously reported to be a reliable RG in adipose-derived MSC-EVs, suggesting its suitability across samples isolated from different MSC types. Further, to shed light on the impact of incorrect RG choice, the level of five tendon-related miRNAs (miR-29a-3p, miR-135a-5p, miR-146a-5p, miR-337-3p, let-7d-5p) was compared among hAMSC-EVs isolates. The use of miR-423-5p and U6 snRNA did not allow a correct quantification of miRNA incorporation in EVs, leading to less accurate fingerprinting and, if used for potency prediction, misleading indication of the most appropriate clinical batch. These results emphasize the crucial importance of RG choice for EV-miRNAs in hAMSCs studies and contribute to the identification of reliable RGs such as miR-101-3p and miR-22-5p to be validated in other MSC-EVs related fields.

Indexed as

amniotic membranedelivery platformsextracellular vesiclesmesenchymal stromal cellsmiRNAsnanocarriersplacentareference genesregenerative medicinetendinopathy

Identifiers

PMID32290510
PMCPMC7238137
OpenAlexW3016030606

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.