Evidence map›Paper›PMID 29904728›Full record

ArticleWellcome open research2018

Transcriptomic profiling of human skin biopsies in the clinical trial setting: A protocol for high quality RNA extraction from skin tumours.

Marina Danilenko, Robert Stones, Neil Rajan

Abstract read
In one paragraph

Article in Wellcome open research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. 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

3 authors.

Marina DanilenkoInstitute of Genetic Medicine, University of Newcastle upon Tyne, Newcastle upon Tyne, NE1 3BZ, UK.
Robert StonesInstitute of Genetic Medicine, University of Newcastle upon Tyne, Newcastle upon Tyne, NE1 3BZ, UK.
Neil RajanInstitute of Genetic Medicine, University of Newcastle upon Tyne, Newcastle upon Tyne, NE1 3BZ, UK.ORCID https://orcid.org/0000-0002-5850-5680

Funding

Wellcome Trust
6 · The paper itself

Abstract

Transcriptomic profiling of skin disease using next generation sequencing allows for detailed information on aspects of RNA biology including gene expression, non-coding regulatory elements and gene splicing. The application of RNA sequencing to human skin disease and cancer is often hampered by degraded RNA. Here we describe a protocol that allows for consistently intact RNA to be extracted from snap frozen skin biopsy samples, which has been validated in a clinical trial setting. Human skin tumour punch biopsies (n=28) ranging from 4-6mm in diameter were obtained from 14 patients with an inherited skin tumour syndrome (CYLD cutaneous syndrome) and frozen in liquid nitrogen prior to being stored at -80°C. These samples were then subject to cyrostat sectioning, allowing for histological assessment, and were homogenised using a bead-based lysis platform. RNA extraction was performed using a silica column-based system. RNA concentration was measured using fluorescent quantitation and RNA integrity assessed using microfluidic gel electrophoresis. We also processed normal skin biopsies using the same protocol (n=10). The mean RNA integrity score of the tumour and normal samples was 9.5, and the quantity of RNA obtained from the small amounts of tissue used exceeded requirements for RNA-seq library generation. We propose that the method of RNA extraction suggested here allows for transcriptomic profiling from small pieces of human tissue without the need for PCR amplification during library preparation. This protocol could be utilised in healthy and diseased skin to improve mechanistic understanding in a range of human skin diseases.

Indexed as

cancerclinical trialCYLDRNA sequencingSkin biopsytranscriptomics.tumour

Identifiers

PMID29904728
PMCPMC5989147

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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.