Evidence map›Paper›PMID 26691212›Full record

ArticleEMBO reports2016

Single-cell transcriptomes reveal characteristic features of human pancreatic islet cell types.

Jin Li, Johanna Klughammer, Matthias Farlik, Thomas Penz, Andreas Spittler, Charlotte Barbieux, Ekaterine Berishvili, Christoph Bock, Stefan Kubicek

Abstract read
In one paragraph

Article in EMBO reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 145 papers.

0numbers the graph read from it
0cells of the map it votes in
145citing 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

145 citing papers in PubMed.

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  10. Advancements in the Application of scRNA-Seq in Breast Research: A Review.International journal of molecular sciences · 2024
    Review
  11. Article
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  17. scEM: A New Ensemble Framework for Predicting Cell Type Composition Based on scRNA-Seq Data.Interdisciplinary sciences, computational life sciences · 2024
    Article
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  20. Article

85 more citing papers are in PubMed but not listed here.

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.

Jin LiCeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria.
Johanna KlughammerCeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria.
Matthias FarlikCeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria.
Thomas PenzCeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria.
Andreas SpittlerMedical University of Vienna Anna Spiegel Forschungsgebäude, Vienna, Austria.
Charlotte BarbieuxDepartment of Surgery, Cell Isolation and Transplantation Center, Geneva University Hospitals University of Geneva, Geneva, Switzerland.
Ekaterine BerishviliDepartment of Surgery, Cell Isolation and Transplantation Center, Geneva University Hospitals University of Geneva, Geneva, Switzerland.
Christoph BockCeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria Department of Laboratory Medicine, Medical University of Vienna, Vienna, Austria Max Planck Institute for Informatics, Saarbrücken, Germany cbock@cemm.oeaw.ac.at skubicek@cemm.oeaw.ac.at.
Stefan KubicekCeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria Christian Doppler Laboratory for Chemical Epigenetics and Antiinfectives, CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria cbock@cemm.oeaw.ac.at skubicek@cemm.oeaw.ac.at.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pancreatic islets of Langerhans contain several specialized endocrine cell types, which are commonly identified by the expression of single marker genes. However, the established marker genes cannot capture the complete spectrum of cellular heterogeneity in human pancreatic islets, and existing bulk transcriptome datasets provide averages across several cell populations. To dissect the cellular composition of the human pancreatic islet and to establish transcriptomes for all major cell types, we performed single-cell RNA sequencing on 70 cells sorted from human primary tissue. We used this dataset to validate previously described marker genes at the single-cell level and to identify specifically expressed transcription factors for all islet cell subtypes. All data are available for browsing and download, thus establishing a useful resource of single-cell expression profiles for endocrine cells in human pancreatic islets.

Indexed as

TranscriptomeAdultCells, CulturedGene Expression ProfilingGenetic MarkersHumansInsulin-Secreting CellsMaleSingle-Cell AnalysisGenetic Markersalpha cellsbeta cellsdiabetesmarker genessingle‐cell RNA‐seq

Identifiers

PMID26691212
PMCPMC4784001

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.