This work was funded by the ETH Grant 1-005733

Fabricating Mouse Tendon Assembloids to Study Cellular Interactions in Disease

The authors have nothing to disclose.

Overall, the assembloid model system presented here has several critical steps to highlight. First, the model system is only as good as the quality of its components. It is vital to check the core explant and the to-be-seeded cell populations under the microscope prior to initiating the assembly process. It is similarly important to verify the phenotype of the isolated cell populations at least once with flow cytometry. Especially when a new batch of collagen-1 is used for the first time, it is advantageous to check the crosslinking speed in a trial run before embedding cells into it. The assembloid assembly requires a lot of manual handling, which increases the risk of infections. To minimize the risk of infections, work in a sterile biosafety hood with laminar air flow, exchange gloves often, and decontaminate the gloves as well as the working space with 80% ethanol. For similar reasons, do not use the 3D-printed clamp holders more than once. Before the embedding process itself, it is important to keep all the hydrogel components (crosslinking solution, collagen-1 solution) on ice to prevent premature crosslinking. Consequently, one must work quickly once the cells are added to the crosslinking solution to limit cell death due to the high pH and low temperature of the crosslinking solution. To prevent drying-related cell death in the core explant, aspirate the medium covering the clamped core explants immediately before mixing the crosslinking solution with the collagen-1 solution. To guarantee the central placement of the core explant within the hydrogel, it is ideal to cast the hydrogel around a clamped core explant that is slightly tensioned. To do so, use the dowel pin and the M3 x 16 mm bolt screw to fixate the clamp holders to a (3D-printed) plate set with holes at the appropriate lengths. After the 50 min polymerization time, the embedded core explant can be detensioned again depending on the desired culture conditions. The amount of tension the assembloid experiences during culture has a profound impact on experimental outcomes and is to be kept uniform across samples and conditions21.
Nevertheless, the large impact of mechanical (un-)loading on experimental outcomes is a main advantage of the assembloid model over most tissue-engineered alternatives, especially since the maintained matrix composition of the core explant should also recreate the complex in vivo loading patterns on the cellular level90. While in practice, only the measurement of the linear elastic modulus, the maximum tensile strain, and the maximum tensile stress of assembloids have been demonstrated so far, protocols for fatigue strength and stress relaxation measurements have been described for tendon core explants elsewhere and should be applicable to the assembloids91,92. In addition to the in vivo-like loading patterns, the assembloid&#39;s multi-level modularity is likely its biggest advantage. Thanks to the individual culture chambers, a controllable set of niche conditions can be set for each sample separately (i.e., temperature, oxygen tension, glucose concentration, supplementation, stimulators, inhibitors, and static stretch with a plate). Next, matrix stiffness and matrix composition of the extrinsic compartment are customizable through the hydrogel composition and would, for example, allow for studying the impact of an increasingly diseased tissue microenvironment by incorporating more collagen-3 and cellular fibronectin93,94,95. The assessed cell populations in the extrinsic compartment are easily adaptable by selecting which cells to seed but can also be modified in the tendon core explant by leveraging established genetically modified cell lines and mouse lines (i.e., ScxLin cell depletion)96. The differing matrix and cell composition of the two compartments further provides a unique compartmentalized 3D structure that is another central tendon hallmark1,30,46.
When using this system, it is important to consider the consequences of the system&#39;s modularity for the granularity of the outcome parameters. While cell proliferation and recruitment can be assessed for each compartment separately, the mechanical properties, secretome components, and degradation products are currently only measurable for the complete assembloid. Regarding throughput, one properly trained person can prepare up to 50 assembloids in a regular workday, with the main bottleneck being the clamping procedure. While some of the readout methods are mutually exclusive, it is possible to assess mechanical properties and secretome components repetitively on the same sample as well as either cell population composition (flow cytometry), cell transcriptome (RT-qPCR, RNA-sequencing), or matrix and cell distribution (immunocytochemistry/fluorescence microscopy) at endpoints. In previous publications, these methods have been deployed to extensively characterize intercellular, cross-compartmental interactions in core // fibroblast and core // macrophage assembloids exposed to a lesion-like niche84,85. In this work, the capability of the assembloid model system to probe the cross-compartmental interplay between the core and extrinsic endothelial cells under different microenvironmental stimuli has been explored.
The modularity of the model system allows for future refinement of the method, which is necessary to overcome the following limitations of the current design iteration. The flow cytometric analysis presented in this work and single-cell RNA-sequencing data published recently revealed that the tendon core-resident tenocytes and Achilles tendon-derived populations are more heterogeneous than previously assumed24,34,59,84,97. In addition, the migratory behavior of initially core- or hydrogel-resident cell populations blur assembloid compartmentalization during culture. Both factors together make it challenging to attribute transcriptomic differences to specific cell types and to separate proliferation- from migration-based processes. This limitation could be overcome by refining the input population with fluorescence-activated cell sorting (FACS) based on the cellular composition of healthy or diseased tendons characterized in recent in vivo studies, improving the readout by implementing single-cell RNA-sequencing, and integrating proliferation markers such as an EdU (5-ethynyl-2&#39;-deoxyuridine) staining during microscopy.
The assembloids presented here also share a weakness with most of the currently available in vitro systems that simulate diseased organs disconnected from the rest of the body98,99. However, the culture chamber-based platform used here positions the model system well for integration into a multi-organ platform where assembloids mimicking different organs are connected and interorgan interactions can be studied.
At its core, the model system is based on positional rodent tendons, which results in its own unique set of drawbacks. First, the translatability of results is hampered by wild-type mice not developing or suffering from tendon diseases8,100,101. Integrating tissues and cells from humans or newly developed mouse strains that exhibit aspects of tendon disease could alleviate this issue102. The switch towards a human-based assembloid is particularly interesting, as it would enable studies with patient-derived tissues from differently diseased tendons (i.e., tendinitis, tendinosis, or peritendinitis) and even treatment-resistant donors that could unlock more personalized treatment programs. Second, murine tail tendon explants do not handle overload-induced microdamage particularly well, which limits the applicability of the model system for the study of acute tendon damage.
For all these reasons, explant // hydrogel assembloids are in a prime position to study tendon core biology, matrix structure-function interactions, and cross-compartmental interactions between specific cell populations in response to niche-induced microdamage. Insights gathered from these rather high-throughput studies could give direction to in vivo research and treatment development.

In their function of transferring muscle forces to the bones to enable movement, tendons face some of the most extreme mechanical stresses occurring in the human body1,2,3. Due to aging societies, increasing obesity prevalence, and the growing popularity of mechanically demanding sports activities, the prevalence of tendon diseases and injuries is projected to climb in developed countries4,5,6. The development of novel evidence-based and disease-modifying treatment regimens to combat this increase has been hindered by limitations of currently available model systems1,7,8.
Ideally, disease and injury repair models would allow studying how the target organ processes a defined set of input parameters (imitating disease triggers, Table 1) into measurable output parameters (representing disease hallmarks, Table 2) while controlling for confounding factors. Studies using such model systems would then be able to identify the (patho-) physiological processes underlying disease and injury repair and gain knowledge that could be exploited to prevent or reduce disease and injury hallmarks in the clinics. Applying this principle to tendons, a useful model system should recapitulate central parts of the in vivo tendon response to disease and injury, which encompass the following hallmarks: microdamage, inflammation, neovascularization, hypercellularity, accelerated matrix turnover, and decompartmentalization9,10,11,12,13,14,15. Using these hallmarks as a base, the following requirements for a successful tendon disease and injury repair model system can be inferred.
Mechanical overloading is hypothesized to be a central factor in tendon injury and disease pathogenesis and is thus a commonly used experimental approach to create microdamage16. Controllable mechanical loadability is, therefore, a prime prerequisite for tendon disease and injury repair models. Ideally, the model system enables three main modes: single stretch-to-damage loading, fatigue loading, and unloading8,17,18. Upon mechanical deformation, tissue-resident cells experience a complex combination of tensional forces, shear forces (due to the sliding of collagen fibers surrounding the cells), and compression forces occurring during unloading or near the enthesis19,20. Model systems should recreate these complex loading patterns as closely as possible.
An alternative way to introduce matrix microdamage is to leverage biochemical stressors that mimic systemic predispositions for tendon disease and injury, such as (pro-)inflammatory cytokines, oxidative stress, or high glucose concentrations21,22,23. Consequently, a controllable niche microenvironment is advantageous for a tendon disease and injury repair model system.
A common prerequisite for model systems to be able to recapitulate inflammation, neovascularization, and hypercellularity is the selective presence of cell populations that drive these processes24. For inflammatory processes, these populations include neutrophils, T-cells, and macrophages, while endothelial cells and pericytes would be needed to study neovascularization25,26,27,28,29. Tendon fibroblasts are not only vital for tendon repair but, as proliferative and migrating cells, also partially responsible for the local hypercellularity observed in tendon disease30,31,32,33,34,35,36.
Besides changes in resident cell populations, the tendon matrix composition is altered in tendon disease and injury as well7,37,38,39,40. To present the right disease-relevant microenvironmental cues, model systems should be able to integrate an extracellular matrix composition matched to the targeted disease or injury stage, for example, by enabling relevant proportional combinations of collagen-1, collagen-3, and cellular fibronectin41.
The compartmentalization of healthy tendons into the tendon core and the extrinsic compartments (i.e., endotenon, epitenon, and paratenon) is central to their function and often disturbed in diseased or injured tendons1,42,43,44,45,46,47. Incorporating 3D tendon compartmentalization into tendon model systems is therefore not only required to simulate the processes underlying de- and re-compartmentalization more closely but also helps to establish the correct spatiotemporal gradients of cytokines and nutrients48,49.
Finally, modularity is another central asset of model systems, allowing researchers to combine the correct relative contribution of and interplay between the previously described stressors during the investigated processes8,17.
Besides selecting the optimal input modalities, an important step is being able to measure, observe, and track changes in the resulting output. The mechanical properties of the model system (i.e., toe region length, linear elastic modulus, maximum tensile strain, maximum tensile stress, fatigue strength, and stress relaxation) are central here, as they characterize the tendon&#39;s main function50,51,52. To link these functional changes to tissue-level changes, it is important to enable methods detecting (collagen) structural matrix damage and tracking proliferation and recruitment of disease- and repair-relevant cell populations30,53,54,55,56,57,58,59,60.
To study emerging cell-cell and cell-matrix crosstalk, one should be able to isolate or mark proteins in adequate amounts for quantification (i.e., ELISA, proteomics, immunohistochemistry, flow cytometry)14,21,61,62. Population- or at least compartment-specific gene expression analysis should be possible as well (i.e., fluorescence-activated cell sorting [FACS], single-cell/bulk RNA- sequencing, and real-time quantitative polymerase chain reaction (RT-qPCR))21,24,27,63. The model system should allow measuring as many of the aforementioned output parameters on the same specimen and on multiple specimens in a manner fast enough to unlock high-throughput studies.
Among model systems currently available to study human tendon disease and injury repair, the human body itself is, of course, the most representative one. It is also the least compatible with experimental intervention. While patients with acute tendon injuries are abundantly available for clinical studies, patients with early tendinopathy (the most common tendon disease) are largely symptom-free and often go clinically undetected until more severe changes manifest14,64,65. This makes it hard to pinpoint the critical moment when tendon homeostasis derails and the mechanisms behind this derailment16,66,67,68,69. In addition, extracting biopsies from healthy tendons is ethically challenging, as it may result in persisting damage. Hamstring tendon remnants from anterior cruciate ligament reconstruction surgery are often used as healthy controls but arguably differ in function, mechanical properties, cell populations, and matrix composition compared to the rotator cuff, Achilles, and patellar tendons commonly affected by tendon disease and injury70,71,72,73.
In vivo animal models are more accessible and tractable, but their usage imposes a significant ethical burden on the animals and economic cost on the researchers. In addition, most of the popular model animals either do not develop tendinopathic lesions spontaneously (i.e., rats, mice, rabbits) or lack the primers and genetically modified strains necessary to track the multicellular communication pathways involved in it (i.e., horses, rabbits).
Simple 2D in vitro model systems are on the other side of the complexity/tractability spectrum and better allow controlled, time-efficient study of specific intercellular communication pathways in response to a more controllable set of triggers8,74. However, these simplified systems commonly fail to recapitulate the multi-dimensional mechanical loading (i.e., tension, compression, and shear) that is central to tendon functionality. In addition, the (too) high stiffnesses of tissue culture plastic tend to override any matrix cues provided by coatings intended to mimic the disease state of interest75,76.
To overcome this drawback, increasingly sophisticated tissue-engineered 3D model systems have been developed to provide a loadable matrix whose composition can at least be partially matched to the desired disease state77,78,79. Still, these systems not only struggle to accurately replicate the complex in vivo extracellular matrix compositions and cellular loading patterns but generally lack long-term loadability and the compartmental interfaces required to study the cross-compartmental communication pathways that coordinate tendon disease and injury repair48,49,80.
Ex vivo tendon explant model systems have the distinct advantage of a built-in in vivo-like matrix composition that comprises pericellular niches, cross-compartmental barriers, as well as spatiotemporal cytokine/nutrient gradients and recapitulates complex loading patterns when stretched8. As a result of size-dependent nutrient diffusion limits, explants from larger animal models (i.e., horses) are difficult to keep alive for the long-term study of tendon disease and injury repair81,82,83. Meanwhile, smaller explants from murine species (i.e., Achilles tendon, patellar tendon) are challenging to reproducibly clamp and mechanically load. Their size also constrains the amount of material that can be gathered for cell-, protein-, and gene-level readouts without pooling samples and decreasing throughput. In this sense, murine tail tendon fascicles offer the potential to unlock high-throughput study of tendon disease and injury repair as they are readily available in large quantities from a single mouse, preserve the complex in vivo pericellular matrix composition, and recapitulate cellular loading patterns. During the extraction process, however, they lose most of their extrinsic compartment and the therein contained vascular, immune, and fibroblast populations that are now considered to drive tendon disease and repair8,18.
To bridge this gap, a model system combining the advantages of murine tail tendon-derived core explants with the advantages of 3D hydrogel-based model systems has been developed. This model system consists of a cell-laden (collagen-1) hydrogel cast around tail tendon explants84,85. In this paper, the necessary manufacturing steps are provided in detail alongside useful readouts that can be obtained by co-culturing core explants (intrinsic compartment) within an endothelial cell-laden type-1 collagen hydrogel (extrinsic compartment).

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>0.4 mm x 25 mm injection needle (G27)</td>
			<td>Sterican</td>
			<td>9186174</td>
			<td></td>
		</tr>
		<tr>
			<td>3D printing filament: Clear polylactic acid prusament</td>
			<td>Prusa</td>
			<td>NA</td>
			<td></td>
		</tr>
		<tr>
			<td>4% formaldehyde</td>
			<td>Roti-Histofix</td>
			<td>P087.4</td>
			<td></td>
		</tr>
		<tr>
			<td>Accutase cell detachment solution</td>
			<td>Sigma-Aldrich</td>
			<td>A6964-100ML</td>
			<td></td>
		</tr>
		<tr>
			<td>Amphotericin</td>
			<td>VWR</td>
			<td>L0009-100</td>
			<td></td>
		</tr>
		<tr>
			<td>Attachable digital C-mount camera: Moticam 2</td>
			<td>Motic</td>
			<td>NA</td>
			<td></td>
		</tr>
		<tr>
			<td>Bolt screw M3 x 16 mm, stainless steel</td>
			<td>RS PRO</td>
			<td>1871235</td>
			<td></td>
		</tr>
		<tr>
			<td>Bolt screw M3 x 6 mm, stainless steel</td>
			<td>RS PRO</td>
			<td>1871207</td>
			<td></td>
		</tr>
		<tr>
			<td>CaCl2</td>
			<td>Sigma-Aldrich</td>
			<td>C5670</td>
			<td></td>
		</tr>
		<tr>
			<td>CD146 antibody: PE anti-mouse</td>
			<td>BioLegend</td>
			<td>134703</td>
			<td></td>
		</tr>
		<tr>
			<td>CD206 antibody: Alexa Fluor 488 anti-mouse</td>
			<td>BioLegend</td>
			<td>141709</td>
			<td></td>
		</tr>
		<tr>
			<td>CD31 antibody: Alexa Fluor 488 anti-mouse</td>
			<td>BioLegend</td>
			<td>102413</td>
			<td></td>
		</tr>
		<tr>
			<td>CD86 antibody: PE anti-mouse</td>
			<td>BioLegend</td>
			<td>105007</td>
			<td></td>
		</tr>
		<tr>
			<td>Collagenase I</td>
			<td>Thermo Fisher Scientific</td>
			<td>17100017</td>
			<td></td>
		</tr>
		<tr>
			<td>Collagenase IV&#160;</td>
			<td>Gibco</td>
			<td>17104-019</td>
			<td></td>
		</tr>
		<tr>
			<td>Dialyzed Fetal Bovine Serum (FBS)</td>
			<td>Sigma-Aldrich</td>
			<td>F0392-100ML</td>
			<td></td>
		</tr>
		<tr>
			<td>Dimethyl sulfoxide (DMSO)</td>
			<td>Sigma-Aldrich</td>
			<td>7000183</td>
			<td></td>
		</tr>
		<tr>
			<td>Dispase II</td>
			<td>Sigma-Aldrich</td>
			<td>D4693-1G</td>
			<td></td>
		</tr>
		<tr>
			<td>DMEM/F12&#160;</td>
			<td>Sigma</td>
			<td>7002211</td>
			<td></td>
		</tr>
		<tr>
			<td>Dowel Pin, 3 mm x 16 mm, stainless steel</td>
			<td>Accu</td>
			<td>HDP-3-16-A1</td>
			<td></td>
		</tr>
		<tr>
			<td>Dragon Skin 10 Slow/1 silicone</td>
			<td>KauPO</td>
			<td>09301-004-000001</td>
			<td></td>
		</tr>
		<tr>
			<td>Endopan 3 Kit</td>
			<td>Pan-Biotech</td>
			<td>P04-0010K</td>
			<td></td>
		</tr>
		<tr>
			<td>Endothelial cell growth supplement&#160;</td>
			<td>Lonza</td>
			<td>CC-3162</td>
			<td></td>
		</tr>
		<tr>
			<td>Eppendorf safe-lock plastic tubes (1.5 mL)</td>
			<td>Eppendorf</td>
			<td>30121023</td>
			<td></td>
		</tr>
		<tr>
			<td>Ethidium homodimer, EthD-1, 2 mM stock in DMSO</td>
			<td>Sigma-Aldrich</td>
			<td>46043-1MG-F</td>
			<td></td>
		</tr>
		<tr>
			<td>F4/80 antibody: Apc/fire 750 anti-mouse</td>
			<td>BioLegend</td>
			<td>123151</td>
			<td></td>
		</tr>
		<tr>
			<td>Falcon plastic tube (15 mL)</td>
			<td>Corning</td>
			<td>352096</td>
			<td></td>
		</tr>
		<tr>
			<td>Falcon plastic tube (50 mL)</td>
			<td>Corning</td>
			<td>352070</td>
			<td></td>
		</tr>
		<tr>
			<td>Flow cytometer: LSR II Fortessa</td>
			<td>BD Bioscience</td>
			<td>23-11617-02</td>
			<td></td>
		</tr>
		<tr>
			<td>Gelatin</td>
			<td>Invitrogen</td>
			<td>D12054</td>
			<td></td>
		</tr>
		<tr>
			<td>Hellmanex III alkaline cleaning concentrate</td>
			<td>Sigma</td>
			<td>Z805939-1EA</td>
			<td></td>
		</tr>
		<tr>
			<td>Heparin</td>
			<td>Sigma-Aldrich</td>
			<td>H3149-10KU</td>
			<td></td>
		</tr>
		<tr>
			<td>Hydroxyproline assay</td>
			<td>Sigma-Aldrich</td>
			<td>MAK008</td>
			<td></td>
		</tr>
		<tr>
			<td>Image analysis software: Motic Images Plus 3.0 ML</td>
			<td>Motic</td>
			<td>NA</td>
			<td></td>
		</tr>
		<tr>
			<td>L-Ascorbic Acid Phosphate Magnesium Salt n-Hydrate</td>
			<td>Wako Chemicals</td>
			<td>013-19641</td>
			<td></td>
		</tr>
		<tr>
			<td>LSE Low Speed Orbital Shaker</td>
			<td>Corning</td>
			<td>6780-FP</td>
			<td></td>
		</tr>
		<tr>
			<td>MEM non-essential amino acids</td>
			<td>Sigma</td>
			<td>7002231</td>
			<td></td>
		</tr>
		<tr>
			<td>Mouse macrophage-stimulating factor (m-CSF)</td>
			<td>PeproTech</td>
			<td>315-02-50ug</td>
			<td></td>
		</tr>
		<tr>
			<td>MSD assay</td>
			<td>Mesoscale Discovery</td>
			<td>various</td>
			<td></td>
		</tr>
		<tr>
			<td>NucBlue</td>
			<td>Thermo Fisher Scientific</td>
			<td>R37605</td>
			<td></td>
		</tr>
		<tr>
			<td>Nylon mesh strainer cap, 100 &#181;m</td>
			<td>Corning</td>
			<td>734-2761</td>
			<td></td>
		</tr>
		<tr>
			<td>Original Prusa i3 MK3S 3D printer</td>
			<td>Prusa</td>
			<td>i3 MK3S</td>
			<td></td>
		</tr>
		<tr>
			<td>Penicillin-Streptomycin</td>
			<td>Sigma-Aldrich</td>
			<td>P4333</td>
			<td></td>
		</tr>
		<tr>
			<td>Phosphate-buffered saline (PBS), ph 7.4, sterile, 10 L</td>
			<td>Gibco</td>
			<td>10010001</td>
			<td></td>
		</tr>
		<tr>
			<td>Puromycin</td>
			<td>Gibco</td>
			<td>A1113803</td>
			<td></td>
		</tr>
		<tr>
			<td>RBC lysis buffer</td>
			<td>VWR</td>
			<td>786-650</td>
			<td></td>
		</tr>
		<tr>
			<td>recombinant m-CSF&#160;</td>
			<td>PeproTech</td>
			<td>315-02</td>
			<td></td>
		</tr>
		<tr>
			<td>RNA extraction kit: Rneasy plus Micro</td>
			<td>Qiagen</td>
			<td>74034</td>
			<td></td>
		</tr>
		<tr>
			<td>Slicing software: PrusaSlicer</td>
			<td>Prusa</td>
			<td>NA</td>
			<td>Version 2.6.0 or higher</td>
		</tr>
		<tr>
			<td>Sterile Cell Strainer 100 &#181;m</td>
			<td>Fisherbrand</td>
			<td>22363549</td>
			<td></td>
		</tr>
		<tr>
			<td>Surgical scalpel blade No. 21</td>
			<td>Swann-Morton</td>
			<td>307</td>
			<td></td>
		</tr>
		<tr>
			<td>Trizol reagent</td>
			<td>Thermo Fisher Scientific</td>
			<td>15596018</td>
			<td></td>
		</tr>
		<tr>
			<td>Trypsin-EDTA (0.5 %)</td>
			<td>Gibco</td>
			<td>15400054</td>
			<td></td>
		</tr>
	</tbody>
</table>

engineering tendon assembloids to probe cellular crosstalk in disease and repair

Tendons enable locomotion by transferring muscle forces to bones. They rely on a tough tendon core comprising collagen fibers and stromal cell populations. This load-bearing core is encompassed, nourished, and repaired by a synovial-like tissue layer comprising the extrinsic tendon compartment. Despite this sophisticated design, tendon injuries are common, and clinical treatment still relies on physiotherapy and surgery. The limitations of available experimental model systems have slowed the development of novel disease-modifying treatments and relapse-preventing clinical regimes. 
In vivo human studies are limited to comparing healthy tendons to end-stage diseased or ruptured tissues sampled during repair surgery and do not allow the longitudinal study of the underlying tendon disease. In vivo animal models also present important limits regarding opaque physiological complexity, the ethical burden on the animals, and large economic costs associated with their use. Further, in vivo animal models are poorly suited to systematic probing of drugs and multicellular, multi-tissue interaction pathways. Simpler in vitro model systems have also fallen short. One major reason is a failure to adequately replicate the three-dimensional mechanical loading necessary to meaningfully study tendon cells and their function. 
The new 3D model system presented here alleviates some of these issues by exploiting murine tail tendon core explants. Importantly, these explants are easily accessible in large numbers from a single mouse, retain 3D in situ loading patterns at the cellular level, and feature an in vivo-like extracellular matrix. In this protocol, step-by-step instructions are given on how to augment tendon core explants with collagen hydrogels laden with muscle-derived endothelial cells, tendon-derived fibroblasts, and bone marrow-derived macrophages to substitute disease- and injury-activated cell populations within the extrinsic tendon compartment. It is demonstrated how the resulting tendon assembloids can be challenged mechanically or through defined microenvironmental stimuli to investigate emerging multicellular crosstalk during disease and injury.

Author Spotlight: Advancing Tendon Research by Developing Mouse Assembloids to Understand Cellular Mechanisms

Engineering Tendon Assembloids to Probe Cellular Crosstalk in Disease and Repair

Watch this Scientific Journal Video about Author Spotlight: Advancing Tendon Research by Developing Mouse Assembloids to Understand Cellular Mechanisms at JoVE.com

Here, we present an assembloid model system to mimic tendon cellular crosstalk between the load-bearing tendon core tissue and an extrinsic compartment containing cell populations activated by disease and injury. As an important use case, we demonstrate how the system can be deployed to probe disease-relevant activation of extrinsic endothelial cells.

Tendons enable locomotion by transferring muscle forces to bones. They rely on a tough tendon core comprising collagen fibers and stromal cell ...

engineering-tendon-assembloids-to-probe-cellular-crosstalk-disease

Research

JoVE Journal

Bioengineering

480 Views.  Balgrist, University Hospital Zurich, University of Zurich. Here, we present an assembloid model system to mimic tendon cellular crosstalk between the load-bearing tendon core tissue and an extrinsic compartment containing cell populations activated by disease and injury. As an important use case, we demonstrate how the system can be deployed to probe disease-relevant activation of extrinsic endothelial cells.