Cong, T. | Search bioRxiv

bioRxiv2024

Tendon Inversion Improves Tendon-to-Bone Healing in a Rat Bicep Tenodesis Model

PurposeTendon- or ligament-to-bone repair remains a surgical challenge. While bone tunnel fixation is a common surgical technique whereby soft tissue is expected to heal against a bone tunnel interface, contemporary methods have yet to recapitulate biomechanical similarity to the native enthesis. In this study, we aim to demonstrate that inside-out longitudinal tendon inversion may improve bone tunnel healing with the hypothesis that inversion removes the gliding epitenon surface from the healing interface thereby improving tunnel interface healing. Methods40 male Sprague-Dawley rats underwent either native tendon tenodesis (control group) or tendon inversion tenodesis (experimental group). Interface tissue was harvested 8 weeks post-operatively. Biomechanical testing was performed to assess tensile strength and modes of failure. Histology was performed to assess tissue architecture, and immunohistochemistry was used confirmed abrogation of epitendinous lubricin from interface tissue. ResultsNeither surgical intervention led to discernible adverse effects on animal health. Maximum tensile strength increased after tendon inversion compared to control surgery. The extracellular matrix protein lubricin was reduced with tendon inversion, and specimens with tendon inversion had greater healing scores and collagen fibril alignment at the healing interface. ConclusionsTendon inversion improves bone tunnel healing in rats. Clinical RelevanceOur findings suggest that longitudinal tendon inversion, or inverse tubularization, in a rat biceps tenodesis model improves tendon-to-bone healing in part due to disruption of the epitendinous surface at the bone healing interface. This work provides molecular insight into future improvements for tendon-to-bone repair surgical techniques.

physiology↗

bioRxiv2023

Reconstructing cellular differentiation networks and identifying cell fate-determining features with CIBER

Trajectory inference methods are frequently used for cell fate analysis, however, most of them are similarity-based and lack an understanding of the causality underlying differentiation processes. Here, we present CIBER, a Causal Inference-Based framework for the Evaluation of feature effects and the Reconstruction of cellular differentiation networks. CIBER provides a novel paradigm for dissecting cell state transitions other than trajectory inference and differential analysis. It is a versatile framework that can be applied to various types of data, including transcriptomic, epigenomic and microarray data. It can identify both known and potential cell-lineage structures with minimal prior knowledge. By integrating the CIBER-learned network with structural causal model and applying in silico perturbation as inventions, we generated an effect matrix that quantifies the impact of different features on each differentiation branch. Using this effect matrix, CIBER can identify crucial features involved in haematopoiesis, even if these features show no significant difference in expression between lineages. Moreover, CIBER can predict novel regulation associations and provide insight into the potential mechanism underlying the influence of transcription factors on biological processes. To validate CIBERs capabilities, we conducted in vivo experiments on Bcl11b, a non-differentially expressed transcription factor identified by CIBER. Our results demonstrate that dysfunction of Bcl11b leads to a bias towards myeloid lineage differentiation at the expense of lymphoid lineage, which is consistent with our predictions.

systems biology↗