Transcriptomic Profiling of the Human Retina Reveals Inflammatory and Metabolic Signatures Associated With Clinical Severity After Retinal Detachment

Purpose: Retinal detachment (RD) remains an ophthalmologic emergency with high anatomical success rates after surgery but often suboptimal visual outcomes. This study aimed to identify transcriptomic signatures linked with clinical severity in human RD to uncover the molecular basis of variability in functional recovery. Methods: Full-length RNA sequencing (RNA-seq) was performed on freshly collected human retinas from patients with rhegmatogenous RD. Principal component analysis was used to derive a composite severity framework, which guided subsequent analysis (differential gene expression, protein-protein interaction, multivariable modeling, and functional enrichment) to identify potential biomarkers and pathways associated with disease severity. Results: Transcriptomic changes were primarily driven by a core severity axis, highlighting baseline best-corrected visual acuity and macular/foveal involvement as clinically interpretable proxies of severity. Severe RD was characterized by strong upregulation of immune and inflammatory genes and pathways, along with activation of Rho-GTPase pathways and G protein-coupled receptors-signaling, suggesting an active immune microenvironment. Consistent downregulation of metabolic and photoreceptor associated pathways, reflecting mitochondrial dysfunction and bioenergetic failure, was also observed. Transcriptomic shifts seemed to occur beyond clinically relevant severity thresholds rather than along linear gradients. PTPRC, FCGR3A, and SCARB1 emerged as central hub proteins with potential biomarker value. Unexpected enrichment of sensory and olfactory receptor pathways suggested a potential contribution to post-detachment neurodegeneration. Individual variables largely recapitulated these transcriptional signatures, reinforcing their applicability in stratification. Conclusions: Inflammation, immune dysregulation, and metabolic impairment emerged as key molecular indicators of severe RD, supporting the development of molecular-based stratification and potential adjuvant therapies.

Authors

Institutions

Publication Details

Journal
Investigative Ophthalmology & Visual Science
Published
2026-05-18
DOI
https://doi.org/10.1167/iovs.67.5.44
Primary Topic
Retinal and Macular Surgery
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Transcriptomic Profiling of the Human Retina Reveals Inflammatory and Metabolic Signatures Associated With Clinical Severity After Retinal Detachment

Salvador Pastor‐Idoate, Nadia Galindo‐Cabello, Eva Maria Sobas-Abad, Laura Molinero-Sicilia et al.
Investigative Ophthalmology & Visual Science
Retinal and Macular Surgery
article

Transcriptomic Profiling of the Human Retina Reveals Inflammatory and Metabolic Signatures Associated With Clinical Severity After Retinal Detachment

Salvador Pastor‐Idoate, Nadia Galindo‐Cabello, Eva Maria Sobas-Abad, Laura Molinero-Sicilia, Ricardo Usategui-Martín, Pablo Redruello-Guerrero, on behalf of the RICOR-REI PVR-Network
article en

Abstract

Purpose: Retinal detachment (RD) remains an ophthalmologic emergency with high anatomical success rates after surgery but often suboptimal visual outcomes. This study aimed to identify transcriptomic signatures linked with clinical severity in human RD to uncover the molecular basis of variability in functional recovery. Methods: Full-length RNA sequencing (RNA-seq) was performed on freshly collected human retinas from patients with rhegmatogenous RD. Principal component analysis was used to derive a composite severity framework, which guided subsequent analysis (differential gene expression, protein-protein interaction, multivariable modeling, and functional enrichment) to identify potential biomarkers and pathways associated with disease severity. Results: Transcriptomic changes were primarily driven by a core severity axis, highlighting baseline best-corrected visual acuity and macular/foveal involvement as clinically interpretable proxies of severity. Severe RD was characterized by strong upregulation of immune and inflammatory genes and pathways, along with activation of Rho-GTPase pathways and G protein-coupled receptors-signaling, suggesting an active immune microenvironment. Consistent downregulation of metabolic and photoreceptor associated pathways, reflecting mitochondrial dysfunction and bioenergetic failure, was also observed. Transcriptomic shifts seemed to occur beyond clinically relevant severity thresholds rather than along linear gradients. PTPRC, FCGR3A, and SCARB1 emerged as central hub proteins with potential biomarker value. Unexpected enrichment of sensory and olfactory receptor pathways suggested a potential contribution to post-detachment neurodegeneration. Individual variables largely recapitulated these transcriptional signatures, reinforcing their applicability in stratification. Conclusions: Inflammation, immune dysregulation, and metabolic impairment emerged as key molecular indicators of severe RD, supporting the development of molecular-based stratification and potential adjuvant therapies.

Investigative Ophthalmology & Visual ScienceVol. 67(5)
Universidad de Valladolid (ES), Unidad de Investigación en cuidados y servicios de salud (Investén-isciii) (ES), Instituto de Biomedicina y Genética Molecular de Valladolid (ES)
European Commission, Universidad de Valladolid, Junta de Castilla y León, Instituto de Salud Carlos III, Agencia Estatal de Investigación
Good health and well-being
Openalex Percentile: Top 5%
Retinal and Macular Surgery
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.