Evidence map›Paper›PMID 33346944›Full record

ArticleMolecular systems biology2020

Using single-plant-omics in the field to link maize genes to functions and phenotypes.

Daniel Felipe Cruz, Sam De Meyer, Joke Ampe, Heike Sprenger, Dorota Herman, Tom Van Hautegem, Jolien De Block, Dirk Inzé, Hilde Nelissen, Steven Maere

Abstract read
In one paragraph

Article in Molecular systems biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Article
  2. Review
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  6. Bet-hedging and variability in plant development: seed germination and beyond.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024
    Review
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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

10 authors.

Daniel Felipe CruzDepartment of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium.
Sam De MeyerDepartment of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium.
Joke AmpeDepartment of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium.
Heike SprengerDepartment of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium.
Dorota HermanDepartment of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium.
Tom Van HautegemDepartment of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium.
Jolien De BlockDepartment of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium.
Dirk InzéDepartment of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium.
Hilde NelissenDepartment of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium.
Steven MaereDepartment of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium.ORCID 0000-0002-5341-136X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Most of our current knowledge on plant molecular biology is based on experiments in controlled laboratory environments. However, translating this knowledge from the laboratory to the field is often not straightforward, in part because field growth conditions are very different from laboratory conditions. Here, we test a new experimental design to unravel the molecular wiring of plants and study gene-phenotype relationships directly in the field. We molecularly profiled a set of individual maize plants of the same inbred background grown in the same field and used the resulting data to predict the phenotypes of individual plants and the function of maize genes. We show that the field transcriptomes of individual plants contain as much information on maize gene function as traditional laboratory-generated transcriptomes of pooled plant samples subject to controlled perturbations. Moreover, we show that field-generated transcriptome and metabolome data can be used to quantitatively predict individual plant phenotypes. Our results show that profiling individual plants in the field is a promising experimental design that could help narrow the lab-field gap.

Indexed as

Genes, PlantGenomicsCluster AnalysisData AnalysisDatabases, GeneticGene Expression Regulation, PlantGene OntologyMetabolomePhenotypePolymorphism, Single NucleotideStress, PhysiologicalTranscriptomeZea maysfield triallab-field gappredictive modelingsingle-plant -omicsZea mays

Identifiers

PMID33346944
PMCPMC7751767

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.