# In Vitro Synovial Membrane 3D Model Developed by Volumetric Extrusion Bioprinting
## 1 Introduction  
The synovial joint consists of contiguous bony surfaces covered by hyaline cartilage, connected by ligaments, and lined by a synovial membrane (SM); the capsule surrounds it. The SM includes two layers: the intima and the subintima [1]. The intima represents the SM inner part and comprises one or two sheets of macrophages (Type A synovioocytes) and fibroblast-like synovioocytes (FLS) (Type B synovioocytes). Cells from the intima secrete the synovial fluid (SF), which provides articular cartilage lubrication, chondrocyte activity, and nutrition. In particular, Type B synovioocytes produce a glycosaminoglycan termed hyaluron, which strongly contributes to the SF thick mucoid consistency [2, 3]. The subintima is the SM outer part and comprises two-to-three layers of synovioocytes lying over loose connective tissue rich in fibroblasts. Studies on SM have increased in previous years, showing progress in understanding the etiopathogenesis of common rheumatic diseases, such as osteoarthritis (OA) or rheumatoid arthritis (RA). In those pathologies, synovitis is directly responsible for several clinical symptoms and reflects the pathological progression [4]. However, suitable in vitro models are still fundamental to fully investigate the factors contributing to inflammation [5]. In vitro models facilitate the investigation and understanding of specific biological structures and processes. Conventional models rely on experimental cell cultures on monolayer or two-dimensional (2D) substrates. Generally, such culture models are used to perform preclinical analysis, but their predictive accuracy is poor since they cannot recapitulate the complexity of human tissues. Studies in an appropriate animal model can help extrapolate in vitro data to clinical outcomes. However, animal sacrifice presents ethical issues and should be limited. Moreover, the differences between the animal model and the human body in terms of physiology and pathology may adversely impact the translation of the results achieved and the predictive power of drug efficiency [6]. Novel biology and tissue engineering technologies have promoted the development of more sophisticated and robust in vitro models such as three-dimensional (3D) scaffolds, hydrogel-based formats, organoids, and organs-on-chips. Those models can better recapitulate the in vivo microenvironment, thus representing a promising alternative to the current state-of-the-art. So far, the attempts at recapitulating the synovial tissue within in vitro models to study rheumatic diseases are limited and based on the inclusion of synovial fluid or synovial fibroblasts within 2D and 3D monoculture or co-culture models or the study of tissue explants [6; 7]. However, poor physiological relevance and predictiveness of drug efficiency characterize these conventional models. In recent years, more advanced solutions, like 3D microsystems, organoids, or microfluidic organ-on-chip approaches, were developed to recapitulate the synovium itself [6; 7; 8; 9]. Though the use of multicompartment chips represents a noticeable improvement in the synovial joint in vitro modeling, these approaches still do not provide the possibility to replicate the synovovium within a relevant-size three-dimensional engineered tissue, which would provide an interesting alternative to model the complexity of the in vivo environment in a representative fashion. To this purpose, novel biofabrication methods like 3D printing could be combined with microscale technologies (i.e., soft lithography) and advanced biological systems, resulting in more complex culture systems tailored for specific applications [10]. Those engineered platforms can provide additional degrees of flexibility and control over cell function and fate and, thus, build tissues that better emulate the dynamics of the in vivo conditions. Additionally, the models mentioned above allow for the monitoring of the pathophysiological conditions to study disease onset and progression and identify pathogenic factors, biomarkers, and potential therapies [11].