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Updated: Oct 1, 2026

Phenotypic Profiling of Human Stem Cell-Derived Midbrain Dopaminergic Neurons
Published on: July 7, 2023
Phenotyping and hierarchical clustering in genetically unresolved DSD identifies distinct multisystem clinical
Chrysanthi Kouri1,2, Malika Allimussina3,4, S Faisal Ahmed3,4
1Pediatric Endocrinology, Diabetology and Metabolism, Department of Pediatrics, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland.
Context:
A substantial proportion of individuals with differences of sex development (DSD) remain without an identifiable molecular diagnosis. Systematic phenotyping may reveal clinically relevant patterns and inform genomic investigation.
Objective:
To identify phenotypic patterns among individuals with genetically unresolved DSD and determine whether multisystem anomalies form distinct clinical clusters and exhibit overlap with established DSD-associated conditions.
Design:
Data from the I-DSD Registry were analyzed for individuals with 46,XY or 46,XX DSD lacking a molecular diagnosis. A 3-part approach was used: (1) unsupervised hierarchical clustering based on Gower distance; (2) supervised phenotype-guided classification using reference profiles for NR5A1/SF-1 deficiency and P450 oxidoreductase deficiency (PORD); and (3) exploratory comparison with Human Phenotype Ontology (HPO)-derived profiles of established DSD-associated genes.
Results:
Among 1391 screened individuals, 345 (24.8%) fulfilled inclusion criteria, including the presence of at least 1 additional organ anomaly, 83.8% had a 46,XY karyotype. The most frequent associated findings were small for gestational age (28.1%), cardiac anomalies (21.4%), central nervous system (CNS) (15.7%), and renal anomalies (14.5%). Unsupervised clustering identified 5 phenotypic groups, including craniofacial-dominant and CNS-dominant clusters. Phenotype-guided classification showed overlap with SF-1-deficiency-associated phenotypes in 11% of cases and with PORD-associated phenotypes in 22%. Comparison with HPO-derived profiles of 81 DSD-associated genes demonstrated phenotypic similarities between several clusters and known gene-associated anomaly spectra.
Conclusion:
Systematic phenotyping identifies recognizable patterns of multisystem involvement in genetically unresolved DSD. Several clusters overlap with established DSD-associated conditions and gene-specific anomaly profiles, supporting phenotype-driven surveillance, multidisciplinary care, and informing future genomic re-evaluation strategies.
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