Evidence map›Paper›PMID 38768139›Full record

ArticlePloS one2024

Identification of ligand and receptor interactions in CKD and MASH through the integration of single cell and spatial transcriptomics.

Jaime Moreno, Lise Lotte Gluud, Elisabeth D Galsgaard, Henning Hvid, Gianluca Mazzoni, Vivek Das

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Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. 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

6 authors.

Jaime MorenoDigital Science and Innovation, Computational Biology - AI & Digital Research, Novo Nordisk A/S, Maløv, Denmark.ORCID 0009-0006-6173-8879
Lise Lotte GluudGastro Unit, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark.ORCID 0000-0002-9462-4468
Elisabeth D GalsgaardGlobal Translation, Novo Nordisk A/S, Maløv, Denmark.
Henning HvidGlobal Drug Discovery, Novo Nordisk A/S, Maløv, Denmark.
Gianluca MazzoniDigital Science and Innovation, Computational Biology - AI & Digital Research, Novo Nordisk A/S, Maløv, Denmark.ORCID 0000-0003-2397-1064
Vivek DasDigital Science and Innovation, Computational Biology - AI & Digital Research, Novo Nordisk A/S, Maløv, Denmark.ORCID 0000-0003-0614-0373

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChronic Kidney Disease (CKD) and Metabolic dysfunction-associated steatohepatitis (MASH) are metabolic fibroinflammatory diseases. Combining single-cell (scRNAseq) and spatial transcriptomics (ST) could give unprecedented molecular disease understanding at single-cell resolution. A more comprehensive analysis of the cell-specific ligand-receptor (L-R) interactions could provide pivotal information about signaling pathways in CKD and MASH. To achieve this, we created an integrative analysis framework in CKD and MASH from two available human cohorts.

resultsThe analytical framework identified L-R pairs involved in cellular crosstalk in CKD and MASH. Interactions between cell types identified using scRNAseq data were validated by checking the spatial co-presence using the ST data and the co-expression of the communicating targets. Multiple L-R protein pairs identified are known key players in CKD and MASH, while others are novel potential targets previously observed only in animal models.

conclusionOur study highlights the importance of integrating different modalities of transcriptomic data for a better understanding of the molecular mechanisms. The combination of single-cell resolution from scRNAseq data, combined with tissue slide investigations and visualization of cell-cell interactions obtained through ST, paves the way for the identification of future potential therapeutic targets and developing effective therapies.

Indexed as

Renal Insufficiency, ChronicSingle-Cell AnalysisTranscriptomeCell CommunicationFatty LiverGene Expression ProfilingHumansLigandsSignal TransductionLigands

Identifiers

PMID38768139
PMCPMC11104622

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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.