Skip to content

From Genetic Mutation to Skeletal Fragility: Multiscale Profiling of Human Osteogenesis Imperfecta Bone

Sep 2026 · Utrecht University Repository (Utrecht University)
Connective tissue disorders research

Abstract

Osteogenesis imperfecta (OI) is a genetically heterogeneous connective tissue disorder characterized by bone fragility, recurrent fractures, skeletal deformities, and substantial variability in clinical severity. Although OI is primarily associated with defects in type I collagen or proteins involved in its processing, the mechanisms through which genetic abnormalities translate into heterogeneous bone phenotypes remain incompletely understood. This thesis investigated OI bone across multiple biological scales, integrating collagen biochemistry, histopathology, molecular characterization, single-cell transcriptomics, and evidence on emerging anabolic therapy. Biochemical analysis of pediatric cortical bone demonstrated markedly reduced collagen content per dry bone weight in OI and increased lysyl hydroxylation. The degree of lysyl hydroxylation was related to mutation position and substitution type, supporting delayed triple-helix folding as an important determinant of collagen overmodification. Histological and imaging evidence further showed that OI is characterized by abnormalities extending beyond collagen composition, including reduced cortical thickness, disturbed lamellar organization, persistence of woven bone, altered mineralization, increased osteocyte lacunar density, and intracortical porosity. Importantly, several genetic subtypes exhibited characteristic histological patterns. Integrated histological, polarized-light microscopic, and Raman spectroscopic analysis of human cortical bone confirmed disrupted lamellar formation, abnormal collagen organization, increased osteocyte lacunar area, and alterations in mineral- and matrix-related molecular signals. These abnormalities were generally more pronounced in severe OI subtypes. Subtype-associated features included a fish-scale lamellar pattern in OI type VI and extensive woven bone in severe forms such as types VIII and XIV. Single-cell RNA sequencing of human bone and bone marrow further demonstrated that OI affects the cellular microenvironment beyond collagen-producing cells. Mesenchymal stromal cell proportions were reduced, with downregulation of osteogenesis-related genes including RUNX1 and BMP5. Changes in osteoblast, osteoclast-lineage, monocyte, and other immune populations differed between OI subtypes, suggesting subtype-dependent disturbances in bone formation, resorption, and immune–skeletal interactions. Finally, a systematic review of anti-sclerostin therapy showed that sclerostin inhibition can substantially improve bone mass, cortical and trabecular architecture, and whole-bone strength. However, improvements in intrinsic tissue-level material properties and matrix quality were less consistent, particularly in severe OI, indicating that increasing bone quantity does not necessarily normalize bone quality. Together, these findings establish OI as a multiscale disorder in which genetic defects interact with collagen matrix abnormalities, disrupted tissue organization, and altered cellular remodeling environments to determine skeletal fragility. A multiscale, genotype-informed approach may therefore improve phenotypic stratification and support more personalized therapeutic strategies targeting both bone quantity and bone quality.

View source

Similar papers

#computer vision Review Open access May 2015

A survey study on major technical barriers affecting the decision to adopt cloud services

The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.

Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al. · 111 citations · ⚡8
#computer vision Open access Feb 2018

Lean Internal Startups for Software Product Innovation in Large Companies: Enablers and Inhibitors

This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.

Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al. · 78 citations · ⚡6
#computer vision Book Open access Jul 2015

Understanding the affect of developers: theoretical background and guidelines for psychoempirical software engineering

This paper highlights the challenges to conduct proper affect-related studies with psychology, provides a comprehensive literature review in affect theory, and proposes guidelines for conducting psychoempirical software engineering.

D. Graziotin, Xiaofeng Wang, P. Abrahamsson · 56 citations · ⚡4
#machine learning Open access May 2017

What Influences the Speed of Prototyping? An Empirical Investigation of Twenty Software Startups

This study conducts a multiple case study on twenty European software startups and proposes a prototype-centric learning model in early stage software startups, and identifies factors that occur as barriers but also facilitators for prototyping in earlystage software startups.

Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson · 44 citations · ⚡5
#protein folding Open access Sep 2026

Programmable design of functional proteins from natural language

Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...

Fengyuan Dai, Shiyang You, Yudian Zhu et al. · 31 citations · ⚡3

Related blog posts

Google DeepMind Blog Sep 30, 2026

Introducing SynthID Bio

Proof of concept for watermarking AI-generated proteins while preserving biological function.

MIT News · Artificial Intelligence Aug 27, 2026

Looking beyond natural sequences

A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.