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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
Published on: November 15, 2017
Ontology-guided clustering enables proteomic analysis of rare pediatric disorders
Ericka C M Itang1,2, Vincent Albrecht1, Alicia-Sophie Schebesta1,2
1Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.
Insights
Analyzing rare pediatric disorders is challenging due to small patient numbers. This study integrates clinical ontologies and proteomic profiling to uncover molecular signatures in aggregated rare diseases, enabling new insights.
Area of Science:
- Biochemistry
- Genomics
- Pediatrics
Background:
- Studying rare pediatric disorders is hampered by limited patient cohorts, hindering robust biological analysis.
- Existing research often lacks the scale to identify significant molecular patterns in rare conditions.
Purpose of the Study:
- To develop and apply a novel framework for the systematic proteomic analysis of rare pediatric diseases in aggregate.
- To overcome the limitations of small patient numbers in rare disease research by integrating clinical and molecular data.
Main Methods:
- Developed a framework integrating clinical ontologies (SNOMED CT) with proteomic profiling.
- Analyzed urine and plasma samples from 1140 children and adolescents across 394 rare diseases using mass spectrometry.
- Quantified thousands of proteins in various sample types, including urine and plasma.
Main Results:
- Successfully grouped rare diseases based on clinical relationships using SNOMED CT, enabling analysis of conditions with as few as two patients.
- Identified molecular signatures across different developmental stages and disease clusters.
- Accounted for age- and sex-specific variations in the proteomic data.
Conclusions:
- The integrated framework offers a generalizable solution for studying heterogeneous rare pediatric populations.
- This approach bridges clinical classification and molecular profiling, overcoming the impracticality of traditional case-control studies for rare diseases.
- Provides a scalable method for discovering biological insights in rare conditions previously limited by patient numbers.
Abstract:
The study of rare pediatric disorders is fundamentally limited by small patient numbers, making it challenging to draw meaningful biological conclusions. To address this, we developed a framework integrating clinical ontologies with proteomic profiling, enabling the systematic analysis of rare conditions in aggregate. We applied this approach to urine and plasma samples from 1140 children and adolescents, encompassing 394 distinct disease conditions and healthy controls. Using advanced mass spectrometry workflows, we quantified over 5000 proteins in urine, 900 in undepleted (neat) plasma, and 1900 in perchloric acid-depleted plasma. Embedding SNOMED CT clinical terminology in a network structure allowed us to group rare conditions based on their clinical relationships, enabling statistical analysis even for diseases with as few as two patients. This approach revealed molecular signatures across developmental stages and disease clusters while accounting for age- and sex-specific variation. Our framework provides a generalizable solution for studying heterogeneous patient populations where traditional case-control studies are impractical, bridging the gap between clinical classification and molecular profiling of rare diseases.
