David W. McKellar

TL;DR: In this paper, the development of the chicken heart from the early to late four-chambered heart stage was studied using single-cell RNA sequencing and spatial transcriptomics with algorithms for data integration.

TL;DR: In this paper, a large-scale integration of single-cell and spatial transcriptomic data for skeletal muscle repair is presented, which includes more than 365,000 cells and spans a wide range of ages, injury, and repair conditions.

TL;DR: The spatial total RNA-sequencing (STRS) as mentioned in this paper approach captures coding RNAs, non-coding RNAs and viral RNAs by using enzymatic in situ polyadenylation of RNA.

TL;DR: A densely sampled transcriptomic model of myogenesis, from stem-cell quiescence to myofiber maturation and identified rare, short-lived transitional states of progenitor commitment and fusion that are poorly represented in individual datasets support the utility of large-scale integration of single-cell transcriptomic data as a tool for biological discovery.

TL;DR: In this article, a bioinformatic tool that leverages single-cell data to uncover biologically relevant transcripts beyond the best available genome annotation is presented, which uses singlecell expression analyses as a filter to direct annotation efforts to biologically significant transcripts and thereby uncovers biology to which scRNAseq would otherwise be in the dark.