Dissecting depression symptoms: Multi-omics clustering uncovers immune-related subgroups and cell-type specific...

Area of Science:

• Neuroscience
• Immunology
• Genetics

Background:

• Depression is linked to inflammation and altered immune markers in some patients.
• Current assessments often use limited data types and marker panels.
• A transdiagnostic, multi-omics approach is needed to understand depression heterogeneity.

Purpose of the Study:

• To define subgroups of depression symptoms using large-scale multi-omics clustering.
• To investigate immune alterations and their relationship with depression severity.
• To identify potential biomarkers for depression stratification.

Main Methods:

• Combined data from two cohorts (237 individuals) using a transdiagnostic approach.
• Incorporated age, BMI, 43 plasma immune markers, and RNA-seq from PBMCs.
• Utilized multi-omics clustering, including predicted cell type proportions.

Main Results:

• Identified four initial clusters, with two showing immune-related depression symptoms (elevated BMI, severity, IL-1RA, CRP, CCL2).
• RNA-seq data differentiated a low-severity cluster enriched in brain-related genes.
• Integrated data revealed a SERPINF1/VEGF-A pathway dysregulation specific to dendritic cells.

Conclusions:

• Combining multiple data modalities (immune markers, RNA-seq) offers advantages for understanding depression.
• Distinct immune profiles and cellular aspects of immune dysregulation in depression were identified.
• Highlights potential markers for future depression symptom stratification research.