The biological activity of 24R,25-dihydroxyvitamin D₃ [24R,25(OH)₂D₃] remains controversial, but it has been suggested that it contributes to fracture healing. Cyp24a1^–/– mice, synthesizing no 24R,25(OH)₂D₃, show suboptimal endochondral ossification during fracture repair, with smaller callus and reduced stiffness. These defects were corrected by 24R,25(OH)₂D₃ treatment, but not by 1,25-dihydroxyvitamin D₃. Microarrays with Cyp24a1^–/– callus mRNA identified FAM57B2 as a mediator of the 24R,25(OH)₂D₃ effect. FAM57B2 produced lactosylceramide (LacCer) upon specific binding of 24R,25(OH)₂D₃. Fam57b inactivation in chondrocytes (Col2-Cre Fam57b^fl/fl) phenocopied the callus formation defect of Cyp24a1^–/– mice. LacCer or 24R,25(OH)₂D₃ injections restored callus volume, stiffness, and mineralized cartilage area in Cyp24a1-null mice, but only LacCer rescued Col2-Cre Fam57b^fl/fl mice. Gene expression in callus tissue suggested that the 24R,25(OH)₂D₃/FAM57B2 cascade affects cartilage maturation. We describe a previously unrecognized pathway influencing endochondral ossification during bone repair through LacCer production upon binding of 24R,25(OH)₂D₃ to FAM57B2. Our results identify potential new approaches to ameliorate fracture healing.

Mass spectrometry is an effective imaging tool for evaluating biological tissue to detect cancer. With the assistance of deep learning, this technology can be used as a perioperative tissue assessment tool that will facilitate informed surgical decisions. To achieve such a system requires the development of a database of mass spectrometry signals and their corresponding pathology labels. Assigning correct labels, in turn, necessitates precise spatial registration of histopathology and mass spectrometry data. This is a challenging task due to the domain differences and noisy nature of images. In this study, we create a registration framework for mass spectrometry and pathology images as a contribution to the development of perioperative tissue assessment. In doing so, we explore two opportunities in deep learning for medical image registration, namely, unsupervised, multi-modal deformable image registration and evaluation of the registration. We test this system on prostate needle biopsy cores that were imaged with desorption electrospray ionization mass spectrometry (DESI) and show that we can successfully register DESI and histology images to achieve accurate alignment and, consequently, labelling for future training. This automation is expected to improve the efficiency and development of a deep learning architecture that will benefit the use of mass spectrometry imaging for cancer diagnosis.

Colorectal cancer (CRC) is the second leading cause of cancer deaths. Despite recent advances, five-year survival rates remain largely unchanged. Desorption electrospray ionization mass spectrometry imaging (DESI) is an emerging nondestructive metabolomics-based method that retains the spatial orientation of small-molecule profiles on tissue sections, which may be validated by ‘gold standard’ histopathology. In this study, CRC samples were analyzed by DESI from 10 patients undergoing surgery at Kingston Health Sciences Center. The spatial correlation of the mass spectral profiles was compared with histopathological annotations and prognostic biomarkers. Fresh frozen sections of representative colorectal cross sections and simulated endoscopic biopsy samples containing tumour and non-neoplastic mucosa for each patient were generated and analyzed by DESI in a blinded fashion. Sections were then hematoxylin and eosin (H and E) stained, annotated by two independent pathologists, and analyzed. Using PCA/LDA-based models, DESI profiles of the cross sections and biopsies achieved 97% and 75% accuracies in identifying the presence of adenocarcinoma, using leave-one-patient-out cross validation. Among the m/z ratios exhibiting the greatest differential abundance in adenocarcinoma were a series of eight long-chain or very-long-chain fatty acids, consistent with molecular and targeted metabolomics indicators of de novo lipogenesis in CRC tissue. Sample stratification based on the presence of lympovascular invasion (LVI), a poor CRC prognostic indicator, revealed the abundance of oxidized phospholipids, suggestive of pro-apoptotic mechanisms, was increased in LVI-negative compared to LVI-positive patients. This study provides evidence of the potential clinical utility of spatially-resolved DESI profiles to enhance the information available to clinicians for CRC diagnosis and prognosis.

Infantile hypercalcemia type 1 (HCINF1), previously known as idiopathic infantile hypercalcemia, is caused by mutations in the 25-hydroxyvitamin D 24-hydroxylase gene, CYP24A1. The R396W loss-of-function mutation in CYP24A1 is the second most frequent mutated allele observed in affected HCINF1 patients. We have introduced the site-specific R396W mutation within the murine Cyp24a1 gene in knock-in mice to generate a humanized model of HCINF1. On the C57Bl6 inbred background, homozygous mutant mice exhibited high perinatal lethality with 17% survival past weaning. This was corrected by crossbreeding to the CD1 outbred background. Mutant animals had hypercalcemia in the first week of life, developed nephrolithiasis, and had a very high 25(OH)D₃ to 24,25(OH)₂D₃ ratio which is a diagnostic hallmark of the HCINF1 condition. Expression of the mutant Cyp24a1 allele was highly elevated while Cyp27b1 expression was abrogated. Impaired bone fracture healing was detected in CD1-R396^w/w mutant animals. The augmented lethality of the C57Bl6-R396W strain suggests an influence of distinct genetic backgrounds. Our data point to the utility of unique knock-in mice to probe the physiological ramifications of CYP24A1 variants in isolation from other biological and environmental factors.