Sammarco, Mimi C.

Craniosynostosis is a congenital disorder characterized by premature fusion of cranial sutures. Lambdoid synostosis is a rare and severe form of the disease, but its developmental etiology remains poorly understood. Although ectopic cartilage has been associated with lambdoid suture fusion, whether abnormal chondrocyte development is causative has not been directly tested. Here, we investigated the role of platelet-derived growth factor receptor alpha (PDGFRα) signaling in chondrocyte development and lambdoid suture morphogenesis. Mesoderm-specific expression of an autoactivated Pdgfra allele (Pdgfra+/K) caused premature fusion of the lambdoid and occipitointerparietal sutures, preceded by excessive cartilage expansion during embryogenesis. Spatial transcriptomic analysis identified a PDGFRα-dependent transcriptional program characterized by increased Col2a1, Sox9, and Sfrp2 expression, indicating maintenance of chondrocytes in a proliferative progenitor state. Consistently, PDGFRα-activated chondrocytes exhibited increased proliferation. Moreover, expression of Pdgfra+/K in the Col2a1Cre lineage selectively induced occipitointerparietal suture fusion. Together, these findings demonstrate that PDGFRα signaling promotes proliferative cartilage and impairs chondrocyte maturation, identifying dysregulated chondrogenesis as a developmental mechanism underlying lambdoid craniosynostosis.

ABSTRACT Macrophages play a central role in determining the outcomes of healing, coordinating regeneration in some injuries and scar formation in others. In both cases, this coordination involves the cross-talk between macrophages and surrounding cells. But what drives the different cross-communication pathways to determine healing outcomes is not well known. In this study, we make use of the mouse digit tip amputation model, in which an amputation through the third phalangeal element (P3) is able to completely regenerate whereas an amputation through the second phalangeal element (P2) forms a scar. We identify a population of macrophages that is specific to the P3 regenerating digit. By integrating single-cell RNAseq, spatial transcriptomics, and metabolomic analyses, we show that this population localizes specifically to the growing bone front, express BMP ligands that drive downstream BMP activation in neighboring osteoblasts and is governed by a two-part metabolic switch involving increased fatty acid oxidation coupled with reduced glycolytic activity. This spatially restricted, BMP-expressing macrophage population is entirely absent in the scar-forming P2 injury, and our data indicate that environmental conditions unique to the regenerating digit are responsible for its emergence. Together these findings identify a regeneration-specific macrophage signaling center for patterned bone formation and suggest that targeting the metabolic conditions that drive this population could improve the efficacy of regenerative therapies.

Background/Objectives: Accurate assessment of spinal bone density is essential for evaluating bone health, particularly in preoperative planning. Conventional manual methods for Hounsfield unit (HU) measurements rely on single-slice measurements within the region of interest, limiting their precision and reproducibility in patients with severe vertebral deformities. We hypothesize that a novel deep-reasoning and learning model (DR-AI) can fully automate spinal bone density assessment volumetrically, with high correlations to spinal bone mineral density (BMD) obtained from dual-energy X-ray absorptiometry (DXA), as well as to the T- and Z-scores. Methods: A cross-sectional study was conducted on patients who had BMD assessment of their lumbar spine and lumbar CT scans within 1 year. The fully-automated DR model was utilized to analyze the soft-tissue window of the lumbar vertebrae CT scans. Spearman correlation coefficients were calculated to assess the strength of relationships between the computed volumetric HUs and the BMD, T-, and Z-scores from each vertebra. Results: 84 patients (67 females, mean age 74.1 ± 10.3 years; 17 males, mean age 68.1 ± 12.4 years) meeting inclusion criteria. Correlation analyses for L1 to L4 showed significant positive relationships (p < 0.0001), with the strongest correlation at L2 between HU and BMD (ρ = 0.75). Conclusions: the DR model for automated assessment of volumetric HUs offers a highly reliable, efficient, and precise alternative to DXA measurements.