Perspective: Challenges Presented for Regeneration of Heterogeneous Musculoskeletal Tissues that Normally Develop in Unique Biomechanical Environments

Perspective: Musculoskeletal (MSK) tissues such as articular cartilage, menisci, tendons, and ligaments are often injured throughout life as a consequence of accidents. Joints can also become compromised due to the presence of inflammatory diseases such as rheumatoid arthritis. Thus, there is a need to develop regenerative approaches to address such injuries to heterogeneous tissues and ones that occur in heterogeneous environments. Such injuries can compromise both the biomechanical integrity and functional capability of these tissues. Thus, there are several challenges to overcome in order to enhance success of efforts to repair and regenerate damaged MSK tissues.Challenges: 1. MSK tissues arise during development in very different biological and biomechanical environments. These early tissues serve as a template to address the biomechanical requirements evolving during growth and maturation towards skeletal maturity. Many of these tissues are heterogeneous and have transition points in their matrix. The heterogeneity of environments thus presents a challenge to replicate with regard to both the cells and the ECM. 2. Growth and maturation of musculoskeletal tissues occurs in the presence of anabolic mediators such as growth hormone and the IGF-1 family of proteins which decline with age and are low when there is a greater need for the repair and regeneration of injured or damaged tissues with advancing age. Thus, there is the challenge of re-creating an anabolic environment to enhance incorporation of implanted constructs. 3. The environments associated with injury or chronic degeneration of tissues are often catabolic or inflammatory. Thus, there is the challenge of creating a more favorable in vivo environment to facilitate the successful implantation of in vitro engineered constructs to regenerate damaged tissues.Conclusions: The goal of regenerating MSK tissues has to be to meet not only the biological requirements (components and structure) but also the heterogeneity of function (biomechanics) in vivo. Furthermore, for many of these tissues, the regenerative approach has to overcome the site of injury being influenced by catabolism/inflammation. Attempts to date using both endogenous cells, exogenous cells and scaffolds of various types have been limited in achieving long term outcomes, but progress is being made.

A neural network-based method for exhaustive cell label assignment using single cell RNA-seq data

The fast-advancing single cell RNA sequencing (scRNA-seq) technology enables researchers to study the transcriptome of heterogeneous tissues at a single cell level. The initial important step of analyzing scRNA-seq data is usually to accurately annotate cells. The traditional approach of annotating cell types based on unsupervised clustering and marker genes is time-consuming and laborious. Taking advantage of the numerous existing scRNA-seq databases, many supervised label assignment methods have been developed. One feature that many label assignment methods shares is to label cells with low confidence as "unassigned." These unassigned cells can be the result of assignment difficulties due to highly similar cell types or caused by the presence of unknown cell types. However, when unknown cell types are not expected, existing methods still label a considerable number of cells as unassigned, which is not desirable. In this work, we develop a neural network-based cell annotation method called NeuCA (Neural network-based Cell Annotation) for scRNA-seq data obtained from well-studied tissues. NeuCA can utilize the hierarchical structure information of the cell types to improve the annotation accuracy, which is especially helpful when data contain closely correlated cell types. We show that NeuCA can achieve more accurate cell annotation results compared with existing methods. Additionally, the applications on eight real datasets show that NeuCA has stable performance for intra- and inter-study annotation, as well as cross-condition annotation. NeuCA is freely available as an R/Bioconductor package at https://bioconductor.org/packages/NeuCA.

Tissue-engineered Collagenous Fibrous Cap Models to Systematically Elucidate Atherosclerotic Plaque Rupture.

A significant amount of vascular thrombotic events is associated with rupture of the fibrous cap that overlie atherosclerotic plaques. Cap rupture is however difficult to predict due to the heterogenous composition of the plaque, unknown material properties, and the stochastic nature of the event. Here, we aim to create tissue engineered human fibrous cap models with a variable but controllable collagen composition, suitable for mechanical testing, to scrutinize the reciprocal relationships between composition and mechanical properties. Myofibroblasts were cultured in 1 x 1.5 cm-sized fibrin-based constrained gels for 21 days according to established (dynamic) culture protocols (i.e. static, intermittent or continuous loading) to vary collagen composition (e.g. amount, type and organization). At day 7, a soft 2 mm ∅ fibrin inclusion was introduced in the centre of each tissue to mimic the soft lipid core, simulating the heterogeneity of a plaque. Results demonstrate reproducible collagenous tissues, that mimic the bulk mechanical properties of human caps and vary in collagen composition due to the presence of an successfully integrated soft inclusion and the culture protocol applied. The models can be deployed to assess tissue mechanics, evolution and failure of fibrous caps or complex heterogeneous tissues in general.

Leveraging the cell lineage to predict cell type specificity of regulatory variation from bulk genomics

Recent computational methods have enabled the inference of the cell-type-specificity of eQTLs based on bulk transcriptomes from highly heterogeneous tissues. However, these methods are limited in their scalability to highly heterogeneous tissues and limited in their broad applicability to any cell-type specificity of eQTLs. Here we present and demonstrate Cell Lineage Genetics (CeL-Gen), a novel computational approach that allows inference of eQTLs together with the subsets of cell types in which they have an effect, from bulk transcriptome data. To obtain improved scalability and broader applicability, CeL-Gen takes as input the known cell lineage tree and relies on the observation that dynamic changes in genetic effects occur relatively infrequently during cell differentiation. CeL-Gen can therefore be used not only to tease apart genetic effects derived from different cell types but also to infer the particular differentiation steps in which genetic effects are altered.

TEM, SEM, and STEM-based immuno-CLEM workflows offer complementary advantages

Identifying endogenous tissue stem cells remains a key challenge in developmental and regenerative biology. To distinguish and molecularly characterise stem cell populations in large heterogeneous tissues, the combination of cytochemical cell markers with ultrastructural morphology is highly beneficial. Here, we realise this through workflows of multi-resolution immuno-correlative light and electron microscopy (iCLEM) methodologies. Taking advantage of the antigenicity preservation of the Tokuyasu technique, we have established robust protocols and workflows and provide a side-by-side comparison of iCLEM used in combination with scanning EM (SEM), scanning TEM (STEM), or transmission EM (TEM). Evaluation of the applications and advantages of each method highlights their practicality for the identification, quantification, and characterization of heterogeneous cell populations in small organisms, organs, or tissues in healthy and diseased states. The iCLEM techniques are broadly applicable and can use either genetically encoded or cytochemical markers on plant, animal and human tissues. We demonstrate how these protocols are particularly suited for investigating neural stem and progenitor cell populations of the vertebrate nervous system.

Challenges of Post-measurement Histology for the Dielectric Characterisation of Heterogeneous Biological Tissues

The dielectric properties of biological tissues are typically measured using the open-ended coaxial probe technique, which is based on the assumption that the tissue sample is homogeneous. Therefore, for heterogeneous tissue samples, additional post-measurement sample processing is conducted. Specifically, post-measurement histological analysis may be performed in order to associate the measured dielectric properties with the tissue types present in a heterogeneous sample. Accurate post-measurement histological analysis enables identification of the constituent tissue types that contributed to the measured dielectric properties, and their relative distributions. There is no standard protocol for conducting post-measurement histological analysis, which leads to high numbers of excluded tissue samples and inconsistencies in the resulting reported data for heterogeneous tissues. To this extent, this study examines the post-measurement histological process and the challenges in associating the acquired dielectric properties with the different tissue types present in heterogeneous samples. The results demonstrate that the histological process inevitably alters the morphology of samples, thus introducing errors in the interpretation of the dielectric properties acquired from heterogeneous biological samples. Notably, sample size was seen to shrink by up to 90% through the histological process, meaning that sensing volume determined from fresh tissues is not directly applicable to histology images.