Evidence map›Paper›PMID 26242641›Full record

ArticleScientific reports2015

An optimised direct lysis method for gene expression studies on low cell numbers.

Anh Viet-Phuong Le, Dexing Huang, Tony Blick, Erik W Thompson, Alexander Dobrovic

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Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Anh Viet-Phuong Le1] University of Melbourne Department of Surgery, St Vincent's Hospital, Melbourne, Victoria, Australia [2] Translational Genomics &Epigenomics, Olivia Newton-John Cancer Research Institute, Melbourne, Victoria, Australia.
Dexing HuangUniversity of Melbourne Department of Surgery, St Vincent's Hospital, Melbourne, Victoria, Australia.
Tony BlickUniversity of Melbourne Department of Surgery, St Vincent's Hospital, Melbourne, Victoria, Australia.
Erik W Thompson1] University of Melbourne Department of Surgery, St Vincent's Hospital, Melbourne, Victoria, Australia [2] Institute of Health and Biomedical Innovation and School of Biomedical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia [3] St. Vincent's Institute, Melbourne, Victoria, Australia.
Alexander Dobrovic1] Translational Genomics &Epigenomics, Olivia Newton-John Cancer Research Institute, Melbourne, Victoria, Australia [2] Department of Pathology, University of Melbourne, Melbourne, Victoria, Australia [3] School of Cancer Medicine, La Trobe University, Melbourne, Victoria, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There is increasing interest in gene expression analysis of either single cells or limited numbers of cells. One such application is the analysis of harvested circulating tumour cells (CTCs), which are often present in very low numbers. A highly efficient protocol for RNA extraction, which involves a minimal number of steps to avoid RNA loss, is essential for low input cell numbers. We compared several lysis solutions that enable reverse transcription (RT) to be performed directly on the cell lysate, offering a simple rapid approach to minimise RNA loss for RT. The lysis solutions were assessed by reverse transcription quantitative polymerase chain reaction (RT-qPCR) in low cell numbers isolated from four breast cancer cell lines. We found that a lysis solution containing both the non-ionic detergent (IGEPAL CA-630, chemically equivalent to Nonidet P-40 or NP-40) and bovine serum albumin (BSA) gave the best RT-qPCR yield. This direct lysis to reverse transcription protocol outperformed a column-based extraction method using a commercial kit. This study demonstrates a simple, reliable, time- and cost-effective method that can be widely used in any situation where RNA needs to be prepared from low to very low cell numbers.

Indexed as

Cell FractionationCell Line, TumorGene ExpressionGene Expression ProfilingHumansRNATranscriptomeRNA

Identifiers

PMID26242641
PMCPMC4525356

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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.