Evidence map›Paper›PMID 25970338›Full record

ArticlePloS one2015

Systems mapping for hematopoietic progenitor cell heterogeneity.

Linghua Zhou, Yong Shen, Libo Jiang, Danni Yin, Jingxin Guo, Hui Zheng, Hao Sun, Rongling Wu, Yunqian Guo

Abstract read
In one paragraph

Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Digital Microfluidics for Manipulation and Analysis of a Single Cell.International journal of molecular sciences · 2015
    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

9 authors.

Linghua ZhouCenter for Computational Biology, Beijing Forestry University, Beijing, People's Republic of China.
Yong ShenCenter for Computational Biology, Beijing Forestry University, Beijing, People's Republic of China.
Libo JiangCenter for Computational Biology, Beijing Forestry University, Beijing, People's Republic of China.
Danni YinCenter for Computational Biology, Beijing Forestry University, Beijing, People's Republic of China.
Jingxin GuoCenter for Computational Biology, Beijing Forestry University, Beijing, People's Republic of China.
Hui ZhengCAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong, People's Republic of China.
Hao SunCAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong, People's Republic of China.
Rongling WuCenter for Computational Biology, Beijing Forestry University, Beijing, People's Republic of China; Center for Statistical Genetics, Pennsylvania State University, Hershey, Pennsylvania, United States of America.
Yunqian GuoCenter for Computational Biology, Beijing Forestry University, Beijing, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cells with the same genotype growing under the same conditions can show different phenotypes, which is known as "population heterogeneity". The heterogeneity of hematopoietic progenitor cells has an effect on their differentiation potential and lineage choices. However, the genetic mechanisms governing population heterogeneity remain unclear. Here, we present a statistical model for mapping the quantitative trait locus (QTL) that affects hematopoietic cell heterogeneity. This strategy, termed systems mapping, integrates a system of differential equations into the framework for systems mapping, allowing hypotheses regarding the interplay between genetic actions and cell heterogeneity to be tested. A simulation approach based on cell heterogeneity dynamics has been designed to test the statistical properties of the model. This model not only considers the traditional QTLs, but also indicates the methylated QTLs that can illustrate non-genetic individual differences. It has significant implications for probing the molecular, genetic and epigenetic mechanisms of hematopoietic progenitor cell heterogeneity.

Indexed as

AlgorithmsChromosome MappingDNA MethylationEpigenesis, GeneticGenotypeHematopoietic Stem CellsHumansModels, GeneticPhenotypePolymorphism, Single NucleotideQuantitative Trait Loci

Identifiers

PMID25970338
PMCPMC4430299

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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.