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Related Experiment Video

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Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets

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Published on: February 23, 2024

RIDDEN: Data-driven inference of receptor activity from transcriptomic data.

Szilvia Barsi^1,2, Eszter Varga², Daniel Dimitrov³

¹Institute of Molecular Life Sciences, Centre of Excellence of the Hungarian Academy of Sciences, HUN-REN Research Centre for Natural Sciences, Budapest, Hungary.

Plos Computational Biology

|June 16, 2025

Summary

This summary is machine-generated.

RIDDEN predicts receptor activity by analyzing gene expression changes, not ligand or receptor levels. This computational tool aids in identifying cell-specific receptor alterations and understanding cell communication in disease.

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Area of Science:

Computational Biology
Systems Biology
Genomics

Background:

Receptor signaling is crucial for physiological regulation and disease, making receptors key drug targets.
Existing computational methods for ligand-receptor interactions often focus on ligands or gene co-expression, which may not reflect functional activity.
There is a need for tools that directly infer receptor activity from downstream gene expression changes.

Purpose of the Study:

To develop a computational tool, RIDDEN (Receptor actIvity Data Driven inferENce), for predicting receptor activity.
To infer receptor activity directly from receptor-regulated gene expression profiles.
To enable systems-level analysis of cell and disease-specific receptor activity alterations.

Main Methods:

Trained the RIDDEN model using 14,463 perturbation gene expression profiles across 229 receptors.
RIDDEN infers receptor activity from downstream gene expression, not ligand or receptor gene expression.
Validated the model on independent in vitro and in vivo receptor perturbation datasets.

Main Results:

RIDDEN effectively predicts receptor activity in bulk and single-cell transcriptomics data.
Model weights align with known receptor-transcription factor regulatory interactions.
Predicted receptor activities correlate with receptor and ligand expression in in vivo data.
RIDDEN identified mechanistic biomarkers in a cancer patient cohort treated with immune checkpoint blockade.

Conclusions:

RIDDEN is the largest transcriptomics-based receptor activity inference model to date.
The tool can identify cell populations with altered receptor activity.
RIDDEN facilitates the study of cell-cell communication using transcriptomics data.
This approach advances understanding of receptor function in physiological and disease states.