Evidence map›Paper›PMID 28534222›Full record

ArticleImmunogenetics2017

Identification of innate lymphoid cells in single-cell RNA-Seq data.

Madeleine Suffiotti, Santiago J Carmona, Camilla Jandus, David Gfeller

Abstract read
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In one paragraph

Article in Immunogenetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
0.6field-weighted citation impact, top 36% 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

14 citing papers in PubMed, 19 citations in OpenAlex.

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  6. (Multi-) omics studies of ILC2s in inflammation and metabolic diseases.Frontiers in cell and developmental biology · 2024
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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

4 authors at 3 institutions in 1 country.

Madeleine SuffiottiLudwig Centre for Cancer Research, University of Lausanne, 1066, Epalinges, Switzerland.
Santiago J CarmonaLudwig Centre for Cancer Research, University of Lausanne, 1066, Epalinges, Switzerland.
Camilla JandusLudwig Centre for Cancer Research, University of Lausanne, 1066, Epalinges, Switzerland.
David GfellerLudwig Centre for Cancer Research, University of Lausanne, 1066, Epalinges, Switzerland. david.gfeller@unil.ch.
Ludwig Cancer Research · CHSIB Swiss Institute of Bioinformatics · CHUniversity of Lausanne · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Innate lymphoid cells (ILCs) consist of natural killer (NK) cells and non-cytotoxic ILCs that are broadly classified into ILC1, ILC2, and ILC3 subtypes. These cells recently emerged as important early effectors of innate immunity for their roles in tissue homeostasis and inflammation. Over the last few years, ILCs have been extensively studied in mouse and human at the functional and molecular level, including gene expression profiling. However, sorting ILCs with flow cytometry for gene expression analysis is a delicate and time-consuming process. Here we propose and validate a novel framework for studying ILCs at the transcriptomic level using single-cell RNA-Seq data. Our approach combines unsupervised clustering and a new cell type classifier trained on mouse ILC gene expression data. We show that this approach can accurately identify different ILCs, especially ILC2 cells, in human lymphocyte single-cell RNA-Seq data. Our new model relies only on genes conserved across vertebrates, thereby making it in principle applicable in any vertebrate species. Considering the rapid increase in throughput of single-cell RNA-Seq technology, our work provides a computational framework for studying ILC2 cells in single-cell transcriptomic data and may help exploring their conservation in distant vertebrate species.

Indexed as

AnimalsDatabases, GeneticGene Expression ProfilingHigh-Throughput Nucleotide SequencingHumansImmunity, InnateKiller Cells, NaturalLymphocytesMiceSequence Analysis, RNASingle-Cell AnalysisCell type predictionsImmune cell type evolutionImmunogenomicsInnate lymphoid cellsSingle-cell RNA-Seq

Identifiers

PMID28534222
OpenAlexW2618587325

What OpenQuestion holds

Textmetadata
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.