Abstract

Conditions affecting skeletal muscle, such as chronic rotator cuff tears, low back pain, dystrophies, and many others, often share changes in muscle phenotype: intramuscular adipose and fibrotic tissue increase while contractile tissue is lost. The underlying changes in cell populations and cell ratios observed with these phenotypic changes complicate the interpretation of tissue-level transcriptional data. Novel single-cell transcriptomics has limited capacity to address this problem because muscle fibers are too long to be engulfed in single-cell droplets and single nuclei transcriptomics are complicated by muscle fibers' multinucleation. Therefore, the goal of this project was to evaluate the potential and challenges of a spatial transcriptomics technology to add dimensionality to transcriptional data in an attempt to better understand regional cellular activity in heterogeneous skeletal muscle tissue. The 3' Visium spatial transcriptomics technology was applied to muscle tissue of a rabbit model of rotator cuff tear. Healthy control and tissue collected at 2 and 16 weeks after tenotomy was utilized and freshly snap frozen tissue was compared with tissue stored for over 6 years to evaluate whether this technology is retrospectively useful in previously acquired tissues. Transcriptional information was overlayed with standard hematoxylin and eosin (H&E) stains of the exact same histological sections. Sequencing saturation and number of genes detected was not affected by sample storage duration. Unbiased clustering matched the underlying tissue type-based on H&E assessment. Connective-tissue-rich areas presented with lower unique molecular identifier counts are compared with muscle fibers even though tissue permeabilization was standardized across the section. A qualitative analysis of resulting datasets revealed heterogeneous fiber degeneration-regeneration after tenotomy based on (neonatal) myosin heavy chain 8 detection and associated differentially expressed gene analysis. This protocol can be used in skeletal muscle to explore spatial transcriptional patterns and confidently relate them to the underlying histology, even for tissues that have been stored for up to 6 years. Using this protocol, there is potential for novel transcriptional pathway discovery in longitudinal studies since the transcriptional information is unbiased by muscle composition and cell type changes.

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