Evidence map›Paper›PMID 27600341›Full record

ArticleMicroarrays (Basel, Switzerland)2014

Application of Tissue Microarray Technology to Stem Cell Research.

Alberto La Spada, Barnaba Rainoldi, Andrea De Blasio, Ida Biunno

Open access · goldAbstract read
In one paragraph

Article in Microarrays (Basel, Switzerland), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.8field-weighted citation impact, top 28% 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

2 citing papers in PubMed, 5 citations in OpenAlex.

  1. Cell Line Macroarray: An Alternative High-Throughput Platform to Analyze hiPSC Lines.The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society · 2016
    Article
  2. Living Cell Microarrays: An Overview of Concepts.Microarrays (Basel, Switzerland) · 2016
    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

4 authors at 4 institutions in 1 country.

Alberto La SpadaUOS-IRGB-CNR, Via Fantoli 16/15, 20138, Milano, Italy. laspada.alberto@gmail.com.
Barnaba RainoldiIRCCS-Multimedica, Via Fantoli 16/15, 20138, Milano, Italy. barnaba.rainoldi@multimedica.it.
Andrea De BlasioIntegrated Systems Engineering, Via Fantoli 16/15, 20138, Milano, Italy. andrea.deblasio@isenet.it.
Ida BiunnoUOS-IRGB-CNR, Via Fantoli 16/15, 20138, Milano, Italy. ida.biunno@irgb.cnr.it.
Eni (Italy) · ITInstitute of Genetic and Biomedical Research · ITIstituti di Ricovero e Cura a Carattere Scientifico · ITMultiMedica · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There is virtually an unlimited number of possible Tissue Microarray (TMA) applications in basic and clinical research and ultimately in diagnostics. However, to assess the functional importance of novel markers, researchers very often turn to cell line model systems. The appropriate choice of a cell line is often a difficult task, but the use of cell microarray (CMA) technology enables a quick screening of several markers in cells of different origins, mimicking a genomic-scale analysis. In order to improve the morphological evaluations of the CMA slides we harvested the cells by conventional trypsinization, mechanical scraping and cells grown on coverslips. We show that mechanical scraping is a good evaluation method since keeps the real morphology very similar to those grown on coverslips. Immunofluorescence images are of higher quality, facilitating the reading of the biomarker cellular and subcellular localization. Here, we describe CMA technology in stem cell research.

Indexed as

cell microArray (CMA)stem cellstissue microArray (TMA)

Identifiers

PMID27600341
PMCPMC4996362
OpenAlexW2012144722

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.