Sci-Seq of Human Fetal Salivary Tissue Introduces Human Transcriptional Paradigms and a Novel Cell Population.
Devon Duron EhnesAmmar AlghadeerSesha Hanson-DruryYan Ting ZhaoGwen TilmesJulie MathieuHannele Ruohola-BakerPublished in: Frontiers in dental medicine (2022)
Multiple pathologies and non-pathological factors can disrupt the function of the non-regenerative human salivary gland including cancer and cancer therapeutics, autoimmune diseases, infections, pharmaceutical side effects, and traumatic injury. Despite the wide range of pathologies, no therapeutic or regenerative approaches exist to address salivary gland loss, likely due to significant gaps in our understanding of salivary gland development. Moreover, identifying the tissue of origin when diagnosing salivary carcinomas requires an understanding of human fetal development. Using computational tools, we identify developmental branchpoints, a novel stem cell-like population, and key signaling pathways in the human developing salivary glands by analyzing our human fetal single-cell sequencing data. Trajectory and transcriptional analysis suggest that the earliest progenitors yield excretory duct and myoepithelial cells and a transitional population that will yield later ductal cell types. Importantly, this single-cell analysis revealed a previously undescribed population of stem cell-like cells that are derived from SD and expresses high levels of genes associated with stem cell-like function. We have observed these rare cells, not in a single niche location but dispersed within the developing duct at later developmental stages. Our studies introduce new human-specific developmental paradigms for the salivary gland and lay the groundwork for the development of translational human therapeutics.
Keyphrases
- endothelial cells
- stem cells
- single cell
- induced pluripotent stem cells
- rna seq
- mesenchymal stem cells
- spinal cord injury
- induced apoptosis
- squamous cell carcinoma
- high throughput
- transcription factor
- cell proliferation
- machine learning
- dna methylation
- young adults
- cell death
- oxidative stress
- artificial intelligence
- heat shock
- case control
- big data