A SINGLE CELL CHROMATIN ACCESSIBILITY ATLAS OF THE DEVELOPING BOVINE PLACENTA

<b>Project Methods</b><br> Methods:Objective 1: Nuclei will be isolated from snap frozen tissues using the Chromium Nuclei Isolation Kit following the Nuclei Isolation Protocol for Single Cell ATAC (PN-1000493) approved by the 10X Genomics platform. Final nuclei concentration will be determined with a Countess II FL (Invitrogen), and a minimum of 2,000 nuclei per sample (target 5,000 nuclei as described in the human single-nuclei ATAC-seq atlas) will be submitted for library preparation and sequencing. Library preparation from nuclei and sequencing will be performed at the University of Missouri Genomics Technology Core by scientists skilled in preparing these libraries from a variety of tissue types and that have extensive experience with troubleshooting. Sequencing will be performed on an Illumina NovaSeq 6000 for 100 cycles (50 bp paired end reads), to achieve 25,000 reads per nucleus. Data analysis: All computational analyses will be performed on the Washington State University High Performance Computing Cluster. Raw BCL files output from the 10X Genomics platform will be demultiplexed and converted to fastq files, mapped to the cattle reference genome ARS-UCD1.2, and processed into a feature matrix using ENSEMBL annotation in the Cell Ranger software (cellranger-2.0.0, 10X Genomics). Count matrices will be processed as previously described by removing doublets with the python package Scrublet, and dimension reduction with SnapATAC. Integration with single-nuclei RNA-seq and differences in open chromatin across cell populations and developmental time points will be investigated with Seurat. Enriched transcription factor binding sites in open chromatin will be identified with JASPAR and candidate driver TFs will be identified as previously described.61,89 Cell-cell communication by ligand-receptor interactions will be predicted between cell populations at each developmental time point using CellPhoneDB. Cis-regulatory elements will be profiled between expressed genes and open chromatin regions using linkage disequilibrium score regression with a Benjamini-Hochberg correction for multiple testing as previously described. Cell trajectory will be predicted with Monocle3 to identify regulatory regions related to binucleate cell formation. All methodologies described are within the skillsets of the PD and collaborating mentors.Objective 2: Data mining: Genetic variants and QTLs associated with fertility traits will be obtained from Animal QTLdb and FAANGMine databases. Literature searches for additional variants not curated within these databases will be performed. Data analysis: Enrichment of variants in open chromatin regions will be performed as previously described across different cell types and developmental time points using a hypergeometric test to evaluate significant QTL enrichment in regions with linkage disequilibrium (r2 > 0.1), as many QTLs have not been fine-mapped. The effects of genetic variation on transcription factor binding sites and regulatory regions will be predicted using deltaSVM models that calculate binding scores as previously described. Biological significance will be investigated with pathway analyses with GO and KEGG enrichment and literature searches. The methods described are within the skillsets of the PD and collaborating mentors.Outcome measures:Objective 1: Identification of open chromatin regions consistent with previously analyzed single-nuclei RNA-seq as described in the preliminary data, as these two datasets have been found to directly relate.Objective 2: The locations of QTLs related to fertility are correctly overlayed from curated databases and located in regions of open chromatin as others have described.