Browsing by Author "Blackshaw, Seth"

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    Epigenomic profiling of retinal progenitors reveals LHX2 is required for developmental regulation of open chromatin
    (Springer Nature, 2019-04-25) Zibetti, Cristina; Liu, Sheng; Wan, Jun; Qian, Jiang; Blackshaw, Seth; Medical and Molecular Genetics, School of Medicine
    Retinal neurogenesis occurs through partially overlapping temporal windows, driven by concerted actions of transcription factors which, in turn, may contribute to the establishment of divergent genetic programs in the developing retina by coordinating variations in chromatin landscapes. Here we comprehensively profile murine retinal progenitors by integrating next generation sequencing methods and interrogate changes in chromatin accessibility at embryonic and post-natal stages. An unbiased search for motifs in open chromatin regions identifies putative factors involved in the developmental progression of the epigenome in retinal progenitor cells. Among these factors, the transcription factor LHX2 exhibits a developmentally regulated cis-regulatory repertoire and stage-dependent motif instances. Using loss-of-function assays, we determine LHX2 coordinates variations in chromatin accessibility, by competition for nucleosome occupancy and secondary regulation of candidate pioneer factors.
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    PanoView: An iterative clustering method for single-cell RNA sequencing data
    (PLOS, 2019-08-30) Hu, Ming-Wen; Kim, Dong Won; Liu, Sheng; Zack, Donald J.; Blackshaw, Seth; Qian, Jiang; Medical and Molecular Genetics, School of Medicine
    Single-cell RNA-sequencing (scRNA-seq) provides new opportunities to gain a mechanistic understanding of many biological processes. Current approaches for single cell clustering are often sensitive to the input parameters and have difficulty dealing with cell types with different densities. Here, we present Panoramic View (PanoView), an iterative method integrated with a novel density-based clustering, Ordering Local Maximum by Convex hull (OLMC), that uses a heuristic approach to estimate the required parameters based on the input data structures. In each iteration, PanoView will identify the most confident cell clusters and repeat the clustering with the remaining cells in a new PCA space. Without adjusting any parameter in PanoView, we demonstrated that PanoView was able to detect major and rare cell types simultaneously and outperformed other existing methods in both simulated datasets and published single-cell RNA-sequencing datasets. Finally, we conducted scRNA-Seq analysis of embryonic mouse hypothalamus, and PanoView was able to reveal known cell types and several rare cell subpopulations.