In situ epigenomics across length scales
Chiang, Zachary Dylan
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CitationChiang, Zachary Dylan. 2022. In situ epigenomics across length scales. Doctoral dissertation, Harvard University Graduate School of Arts and Sciences.
AbstractChromatin is spatially organized across length scales, from DNA base pairs to chromosomes to tissues. This organization, which is encompassed in the term epigenomics, is thought to regulate gene expression and control cellular function, and varies across cells within organisms. During my dissertation, my collaborators and I have developed methods to interrogate epigenomics across length scales, ranging from primary tumors (slide-DNA-seq) to the 3D organization of chromosomes (In situ genome sequencing) to accessible regulatory regions within single cells (AtacWorks).
First, we describe slide-DNA-seq, a method for capturing spatially resolved DNA sequences from intact tissue sections. We apply this method to a mouse model of metastasis and a primary human cancer, revealing the spatial organization of clonal tumor populations and their associated copy number alterations. Through integration with spatial transcriptomics, we uncover distinct sets of genes associated with clone-specific genetic aberrations or the local tumor microenvironment.
Second, we describe in situ genome sequencing (IGS), a method for simultaneously sequencing and imaging genomes within intact biological samples. We apply IGS to cultured human fibroblasts and intact early mouse embryos at the zygote, two-cell, and four-cell stages of development, spatially localizing thousands of DNA sequences in individual cells. In embryos,
we integrate genotype information and immunostaining to identify and characterize parent-specific changes in genome structure between embryonic stages, including parental genome mixing, chromosome polarization, and nuclear lamina association. We further uncover heterogenous single-cell chromatin domains in paternal zygotic pronuclei and demonstrate epigenetic memory of global chromosome positioning within clonal cell lineages of individual embryos.
Finally, we describe AtacWorks, a deep learning toolkit to denoise sequencing coverage and identify regulatory peaks at base-pair resolution from low cell count or low-coverage ATAC-seq data. We demonstrate that AtacWorks enhances the sensitivity of single-cell experiments by producing results on par with those of conventional methods using ~10 times as many cells, and further show that this framework can be adapted to enable cross-modality inference of protein-DNA interactions. Finally, we show that AtacWorks can identify active regulatory regions associated with lineage priming in rare subpopulations of hematopoietic stem cells.
Citable link to this pagehttps://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37373692
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