NSF Award
2120131
A fundamental question in plant biology is how plant cells are patterned to develop the specific forms and functions that comprise plant organs. This collaborative project will investigate the cellular and developmental genetic mechanisms whereby leaf boundary cells acquire their specialized cell fates, an agronomically important process that contributes to light capture and crop yield. This project will train a graduate student and two postdoctoral scientists in state-of-the-art techniques in microscopy, gene expression and genome structure. Machine learning approaches will be used to analyze these complementary data. The project will also train undergraduate summer interns in these techniques and will sponsor yearly, hands-on, educational webinars and workshops on computational analyses of big biological data. In addition, the data generated during this project will seed team-learning projects in a new course developed as part of a separate NSF-funded graduate student training grant, which will also sponsor a free, online training course on machine learning for plant scientists. <br/><br/>How cell-fate acquisition is genetically regulated is the fundamental question in plant organogenesis. A common feature of multicellular organisms is the formation of distinct boundaries between developmentally distinct tissues of an organ. The ligule/auricle comprises the developmental boundary within maize leaves, forming a specialized hinge-like structure between the proximal, supportive sheath tissue and the distal, photosynthetic blade. Ligule/auricle morphology shows natural variation and is a genetically tractable trait; the resultant difference in leaf blade angle are relevant to maize improvement. The cellular parameters of ligule/auricle outgrowth will be characterized during three distinct ontogenetic stages. Single-cell RNA-seq will identify cell-specific genetic networks during ligule/auricle outgrowth, and single-cell ATAC-seq will identify key transcription factors and cis-regulatory features during these same three stages. Machine learning strategies will synthesize and correlate these cell-biological, transcriptomic, and chromatin data, to identify genomic networks and candidate genes involved in ligule/auricle differentiation.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
