Ribonucleic acid (RNA) molecules are traditionally known as an intermediate product in gene expression, but they are a diverse group of biomolecules with wide-ranging roles in regulating cell state. High-throughput RNA-sequencing technologies have enabled sensitive measurements of the collection of RNA molecules in cells, also known as the transcriptome. More recently, single-cell transcriptomics methods have been adapted to record the spatial location of each RNA molecule. Spatial transcriptomics gives context to measurements of gene expression and reveals patterns of gene regulation. Single-cell and spatial transcriptomics represent a powerful tool kit to study any biological system. The modularity of transcriptomics measurements enables integration of data from many disjointed experiments. We leveraged this facet of single-cell transcriptomics to jointly analyze single-cell transcriptomics data from more than 100 individual samples, together representing more than 365,000 single-cell transcriptomes. We used this data set to study rare cell types in skeletal muscle regeneration. We then applied low-resolution spatial transcriptomics to understand the spatiotemporal patterns of gene expression in skeletal muscle regeneration and used our harmonized transcriptomic reference to achieve a local deconvolution of cell subtypes. This analysis identified the spatiotemporal variation in cell subtype colocalization during injury recovery. Next, we extended these data with a new molecular strategy to broaden the repertoire of RNAs which can be captured by existing spatial transcriptomics platforms. Spatial total RNA-sequencing (STRS) leverages in situ polyadenylation of non-A-tailed RNAs to capture noncoding and non-host transcripts which are missed by standard methods. We applied STRS to a time course of muscle injury response and identified patterns of noncoding RNAs which localize to the injury. We then used STRS to spatially co-map viral RNAs and the host gene expression response in a mouse model of viral myocarditis. Finally, we review the state of transcriptomic technologies in skeletal muscle research and discuss the potential impacts of emerging technologies. Here we also include an updated resource with more than 2.5 million single-cell and single-nucleus transcriptomes from either mouse or human musculoskeletal tissues. Together, these efforts demonstrate new molecular and computational strategies to extend transcriptomics technologies to new cell types, new analytes, and new applications.