Associate Director Center for Epigenomics, UC San Diego, California, United States
Genomes are more than abstract sequences of nucleotides. Epigenomic technologies have revealed how they exist in situ bound by proteins, modified by enzymes, and folded in complex 3-dimensional structures within a cell's nucleus. This epigenetic information regulates gene expression and is critical for understanding how cells function and malfunction in health and disease. However, conventional technologies to profile the epigenome mask the cellular heterogeneity that exists in biological samples by assaying samples in aggregate. Thus, tissue heterogeneity poses a significant challenge to reading this epigenetic information. Therefore, we have implemented optimized workflows for single-cell omics - including single nucleus RNA-seq and single nucleus ATAC-seq – to profile cells/nuclei from normal and diseased tissue samples across more than 50 sample types - including primary brain, heart, and lung tissue. Here, we present our streamlined workflows for sample preparation, experimentation, library construction, as well as data processing and QC analysis. We employ standardized protocols, semi-automation via liquid handling robotics, rigorous quality control, and uniform data processing to enable all aspects of large-scale data generation including, but not limited to, 1) the rapid optimization of protocols to maximize data quality 2) data quality consistency from sample-to-sample during data production, and 3) the minimization of failure rate. To date, we have deployed our workflows across multiple small- and large-scale efforts, having generated >1,800 datasets and assayed >50 unique sample types spanning >16 species in the process. We will discuss this process and several case studies of generating large-scale datasets via these approaches – including our efforts to generate a cell-type resolved gene regulatory atlas of 30 primary tissues spanning the human body. All together, we will present our efforts to standardize, scale, and combine -omics such as single-nucleus RNA-seq and single-nucleus ATAC-seq, as well as discuss the application of such methods to deconvolute the cellular composition of tissues, identify rare cell populations, and study epigenetic gene regulation in the context of human disease.
