Evidence map›Paper›PMID 34697345›Full record

ArticleScientific reports2021

Optimization of RNA extraction methods from human metabolic tissue samples of the COMET biobank.

Agathe Nouvel, Jonas Laget, Flore Duranton, Jérémy Leroy, Caroline Desmetz, Marie-Dominique Servais, Nathalie de Préville, Florence Galtier, David Nocca, Nicolas Builles and 2 more

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 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
1.7field-weighted citation impact, top 15% 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

14 citing papers in PubMed, 30 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. 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 at 6 institutions in 2 countries.

Agathe NouvelBiocommunication in Cardio-Metabolism (BC2M), University of Montpellier, 15 avenue Charles Flahault, 34093, Montpellier Cedex 5, France.
Jonas LagetBiocommunication in Cardio-Metabolism (BC2M), University of Montpellier, 15 avenue Charles Flahault, 34093, Montpellier Cedex 5, France.
Flore DurantonBiocommunication in Cardio-Metabolism (BC2M), University of Montpellier, 15 avenue Charles Flahault, 34093, Montpellier Cedex 5, France.
Jérémy LeroyBiocommunication in Cardio-Metabolism (BC2M), University of Montpellier, 15 avenue Charles Flahault, 34093, Montpellier Cedex 5, France.
Caroline DesmetzBiocommunication in Cardio-Metabolism (BC2M), University of Montpellier, 15 avenue Charles Flahault, 34093, Montpellier Cedex 5, France.
Marie-Dominique ServaisServier, 50 rue Carnot, 92284, Suresnes Cedex, France.
Nathalie de PrévilleServier, 50 rue Carnot, 92284, Suresnes Cedex, France.
Florence GaltierBiocommunication in Cardio-Metabolism (BC2M), University of Montpellier, 15 avenue Charles Flahault, 34093, Montpellier Cedex 5, France.
David NoccaDepartment of Digestive Surgery, University Hospital of Montpellier, 80 Avenue Augustin Fliche, 34295, Montpellier Cedex 5, France.
Nicolas BuillesBiological Resources Center, Tissue Bank, University Hospital of Montpellier, 80 Avenue Augustin Fliche, 34295, Montpellier Cedex 5, France.
Sandra Rebuffat *Biocommunication in Cardio-Metabolism (BC2M), University of Montpellier, 15 avenue Charles Flahault, 34093, Montpellier Cedex 5, France.
Anne-Dominique Lajoix *Biocommunication in Cardio-Metabolism (BC2M), University of Montpellier, 15 avenue Charles Flahault, 34093, Montpellier Cedex 5, France. anne-dominique.lajoix@umontpellier.fr.
Biocommunication en Cardio-MétaboliqueBiocom · USCentre Hospitalier Universitaire de Montpellier · FRServier (France) · FRFrench Clinical Research Infrastructure Network · FRUniversité de Montpellier · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Constitution of biobank of human tissues requires careful handling and storage of biological material, to guarantee the quality of samples. Tissue preparation is also critical for further applications such as transcriptomic profiling. In this study, our aim was to evaluate the impact of different disruption techniques (FastPrep-24 instrument, GentleMACS dissociator, and syringe/needle) and homogenizing buffers (RLT versus QIAzol) on RNA purity and quality of metabolic tissues (adipose tissues, liver and skeletal muscle) present in the COMET Biobank. For all homogenization methods used and tissue types, the A260/280 ratios reached values ≥ 1.8, which are in the range of what is found in human tissues and cell lines, while the A260/230 ratios were however ≤ 1.8, with the lowest value obtained with GentleMACS Dissociator. In addition, GentleMACS Dissociator combined with QIAzol reagent gave the highest RIN value and 28S/18S ratio for all tissues tested, except for muscle. Performing RT-qPCR, Ct values for different housekeeping genes can be influenced by extraction methods and RNA quality of samples. In conclusion, we have demonstrated that different disruption techniques and homogenizing buffers impact the purity and some quality markers of RNA, and can also impact quantification of mRNAs by RT-qPCR in human metabolic tissues.

Indexed as

Adipose TissueBiological Specimen BanksGene Expression ProfilingGenetic TechniquesHumansLiverMuscle, SkeletalReal-Time Polymerase Chain ReactionRNA, MessengerSpecimen HandlingRNA, Messenger

Identifiers

PMID34697345
PMCPMC8545963
OpenAlexW3208256252

What OpenQuestion holds

Textmetadata
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.