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Abstract Histopathology is widely used to analyze clinical biopsy specimens and tissues from pre-clinical models of a variety of musculoskeletal conditions. Histological assessment relies on scoring systems that require expertise, time, and resources, which can lead to an analysis bottleneck. Recent advancements in digital imaging and image processing provide an opportunity to automate histological analyses by implementing advanced statistical models such as machine learning and deep learning, which would greatly benefit the musculoskeletal field. This review provides a high-level overview of machine learning applications, a general pipeline of tissue collection to model selection, and highlights the development of image analysis methods, including some machine learning applications, to solve musculoskeletal problems. We discuss the optimization steps for tissue processing, sectioning, staining, and imaging that are critical for the successful generalizability of an automated image analysis model. We also commenting on the considerations that should be taken into account during model selection and the considerable advances in the field of computer vision outside of histopathology, which can be leveraged for image analysis. Finally, we provide a historic perspective of the previously used histopathological image analysis applications for musculoskeletal diseases, and we contrast it with the advantages of implementing state-of-the-art computational pathology approaches. While some deep learning approaches have been used, there is a significant opportunity to expand the use of such approaches to solve musculoskeletal problems. 

It has been presumed that rheumatoid arthritis (RA) joint pain is related to inflammation in the synovium; however, recent studies reveal that pain scores in patients do not correlate with synovial inflammation. We developed a machine-learning approach (graph-based gene expression module identification or GbGMI) to identify an 815-gene expression module associated with pain in synovial biopsy samples from patients with established RA who had limited synovial inflammation at arthroplasty. We then validated this finding in an independent cohort of synovial biopsy samples from patients who had early untreated RA with little inflammation. Single-cell RNA sequencing analyses indicated that most of these 815 genes were most robustly expressed by lining layer synovial fibroblasts. Receptor-ligand interaction analysis predicted cross-talk between human lining layer fibroblasts and human dorsal root ganglion neurons expressing calcitonin gene–related peptide (CGRP⁺). Both RA synovial fibroblast culture supernatant and netrin-4, which is abundantly expressed by lining fibroblasts and was within the GbGMI-identified pain-associated gene module, increased the branching of pain-sensitive murine CGRP⁺dorsal root ganglion neurons in vitro. Imaging of solvent-cleared synovial tissue with little inflammation from humans with RA revealed CGRP⁺pain-sensing neurons encasing blood vessels growing into synovial hypertrophic papilla. Together, these findings support a model whereby synovial lining fibroblasts express genes associated with pain that enhance the growth of pain-sensing neurons into regions of synovial hypertrophy in RA.