Over the past decade, 3C-related methods, complemented by increasingly detailed microscopic views of the nucleus, have provided unprecedented insights into chromosome folding in vivo. Here, to overcome the resolution limits inherent to the majority of genome-wide chromosome architecture mapping studies, we extend a recently-developed Hi-C variant, Micro-C, to map chromosome architecture at nucleosome resolution in human embryonic stem cells and fibroblasts. Micro-C maps robustly capture well-described features of mammalian chromosome folding including .

Chromosome conformation capture (3C) assays are used to map chromatin interactions genome-wide. Chromatin interaction maps provide insights into the spatial organization of chromosomes and the mechanisms by which they fold. Hi-C and Micro-C are widely used 3C protocols that differ in key experimental parameters including cross-linking chemistry and chromatin fragmentation strategy. To understand how the choice of experimental protocol determines the ability to detect and quantify aspects of chromosome folding we have performed a systematic evaluation of 3C experimental parameters. We identified optimal protocol variants for either loop or compartment detection, optimizing fragment size and cross-linking chemistry. We used this knowledge to develop a greatly improved Hi-C protocol (Hi-C 3.0) that can detect both loops and compartments relatively effectively. In addition to providing benchmarked protocols, this work produced ultra-deep chromatin interaction maps using Micro-C, conventional Hi-C and Hi-C 3.0 for key cell lines used by the 4D Nucleome project.

ABSTRACTOver the past decade, 3C-related methods, complemented by increasingly detailed microscopic views of the nucleus, have provided unprecedented insights into chromosome folding in vivo. Here, to overcome the resolution limits inherent to the majority of genome-wide chromosome architecture mapping studies, we extend a recently-developed Hi-C variant, Micro-C, to map chromosome architecture at nucleosome resolution in human embryonic stem cells and fibroblasts. Micro-C maps robustly capture well-described features of mammalian chromosome folding including A/B compartment organization, topologically associating domains (TADs), and cis interaction peaks anchored at CTCF binding sites, while also providing a detailed 1-dimensional map of nucleosome positioning and phasing genome-wide. Compared to high-resolution in situ Hi-C, Micro-C exhibits substantially improved signal-to-noise with an order of magnitude greater dynamic range, enabling not only localization of domain boundaries with single-nucleosome accuracy, but also resolving more than 20,000 additional looping interaction peaks in each cell type. Intriguingly, many of these newly-identified peaks are localized along stripe patterns and form transitive grids, consistent with their anchors being pause sites impeding the process of cohesin-dependent loop extrusion. Together, our analyses provide the highest resolution maps of chromosome folding in human cells to date, and provide a valuable resource for studies of chromosome folding mechanisms.

Chromosome conformation capture (3C) technologies reveal the incredible complexity of genome organization. Maps of increasing size, depth, and resolution are now used to probe genome architecture across cell states, types, and organisms. Larger datasets add challenges at each step of computational analysis, from storage and memory constraints to researchers' time. Analysis tools that meet these increased resource demands have not kept pace. Furthermore, existing tools offer limited support for customizing analysis for specific use cases or new biology. Here we introduce cooltools (https://github.com/open2c/cooltools), a suite of computational tools that enables flexible, scalable, and reproducible analysis of high-resolution contact frequency data. Cooltools leverages the widely-adopted cooler format which handles storage and access for high-resolution datasets. Cooltools provides a paired command line interface (CLI) and Python application programming interface (API), which respectively facilitate workflows on high-performance computing clusters and in interactive analysis environments. In short, cooltools enables the effective use of the latest and largest genome folding datasets.