Evidence map›Paper›PMID 41511333›Full record

ReviewCells2025

The Role of Metabolites in Cell-Cell Communication: A Review of Databases and Computational Tools.

Qi Song, Zhenchao Liu, Sen Liu

Abstract readReview
In one paragraph

Review in Cells, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

3 authors.

Qi SongKey Laboratory of Fermentation Engineering, Ministry of Education, Cooperative Innovation Center of Industrial Fermentation (Ministry of Education & Hubei Province), Hubei University of Technology, Wuhan 430068, China.ORCID 0000-0002-3025-4832
Zhenchao LiuKey Laboratory of Fermentation Engineering, Ministry of Education, Cooperative Innovation Center of Industrial Fermentation (Ministry of Education & Hubei Province), Hubei University of Technology, Wuhan 430068, China.
Sen LiuKey Laboratory of Fermentation Engineering, Ministry of Education, Cooperative Innovation Center of Industrial Fermentation (Ministry of Education & Hubei Province), Hubei University of Technology, Wuhan 430068, China.ORCID 0000-0001-5182-7241

Funding

Department of Science and Technology of the Hubei Provincial People's Government 2024AFA014National Natural Science Foundation of China 32400654
6 · The paper itself

Abstract

Cell-cell communication (CCC) is essential for multicellular organisms, enabling different cell types to coordinate their activities in both physiological and pathological contexts, such as cell growth, proliferation, tumorigenesis, and immune responses. Metabolites represent an important class of signaling molecules, though their signaling roles were long underappreciated. Growing evidence has highlighted the critical involvement of metabolites in CCC, and the advent of single-cell RNA sequencing (scRNA-seq) has enabled high-resolution exploration of CCC events. This review summarizes existing metabolite-sensor databases and computational tools developed to identify metabolite-mediated CCC using scRNA-seq data. Nonetheless, these databases exhibit considerable variability due to lack of unified collection standards. Most computational tools were adapted from methods used for general CCC inference and often estimate metabolite abundance based on the expression of one or several related genes. Therefore, such approaches are not fully suited to capturing metabolite-mediated CCC due to the complexity of interaction mechanisms between metabolites and their sensors. To address these challenges, improved computational methods and refined databases are needed for the reliable inference of metabolite-mediated CCC. This review discusses the current limitations in database construction and method development, and highlights potential directions for future improvement, including the incorporation of spatial omics and artificial intelligence (AI) approaches. Furthermore, the systematic inference and validation of metabolite-mediated CCC will pave the way for the discovery of novel drugs and therapeutic targets.

Indexed as

Cell CommunicationComputational BiologyDatabases, FactualAnimalsHumanscell–cell communicationmetabolite–sensor databasepolyaminescRNA-seqspatial omics

Identifiers

PMID41511333
PMCPMC12786037

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.