McCord, R. P. | bioRxiv Science

bioRxiv2018

Iteratively Improving Hi-C Experiments One Step at a Time

The 3D organization of eukaryotic chromosomes affects key processes such as gene expression, DNA replication, cell division, and response to DNA damage. The genome-wide chromosome conformation capture (Hi-C) approach can characterize the landscape of 3D genome organization by measuring interaction frequencies between all genomic regions. Hi-C protocol improvements and rapid advances in DNA sequencing power have made Hi-C useful to diverse biological systems, not only to elucidate the role of 3D genome structure in proper cellular function, but also to characterize genomic rearrangements, assemble new genomes, and consider chromatin interactions as potential biomarkers for diseases. Yet, the Hi-C protocol is still complex and subject to variations at numerous steps that can affect the resulting data. Thus, there is still a need for better understanding and control of factors that contribute to Hi-C experiment success and data quality. Here, we evaluate recently proposed Hi-C protocol modifications as well as often overlooked variables in sample preparation and examine their effects on Hi-C data quality. We examine artifacts that can occur during Hi-C library preparation, including microhomology-based artificial template copying and chimera formation that can add noise to the downstream data. Exploring the mechanisms underlying Hi-C artifacts pinpoints steps that should be further optimized in the future. To improve the utility of Hi-C in characterizing the 3D genome of specialized populations of cells or small samples of primary tissue, we identify steps prone to DNA loss which should be optimized to adapt Hi-C to lower cell numbers.\n\nHighlights3 to 5 bullet points (maximum 85 characters, including spaces, per bullet point)\n\nO_LIVariability in Hi-C libraries can arise from early steps of cell preparation\nC_LIO_LIHi-C 2.0 changes to interaction capture steps also benefit 6-cutter libraries\nC_LIO_LIArtificial molecule fusions can arise during end repair and PCR, increasing noise\nC_LIO_LICommon causes of Hi-C DNA loss identified for future optimization\nC_LI

genomics↗

bioRxiv2018

Characterizing the 3D structure and dynamics of chromosomes and proteins in a common contact matrix framework

Any conformational ensemble of biopolymers, whether they are proteins or chromosomes, can be described using contact matrices derived from experimental or computational studies. One powerful approach to extract meaningful information from these contact matrices is to perform principal component analysis (PCA) on the covariance matrix of the contact data. Indeed, PCA on Hi-C chromosome contact matrices has revealed the spatial segregation of active and inactive chromatin. Separately, PCA on contact matrices from snapshots of protein conformations has characterized correlated fluctuations of protein domains. However, despite the similarities of these data and analyses, there has been little synergy between the PCA approaches and the comparison of resulting biological insights obtained for protein and chromosome structures. We note that, to date, different styles of analyses were applied exclusively to each biomolecule type: explicit contact correlation analysis (E-PCA) for proteins and implicit contact correlation (I-PCA) for chromosomes. In this work, we compare the results of applying both methods to both classes of biopolymers. While I-PCA reveals only average features of 3D chromosome structure, we find that applying E-PCA to an ensemble of chromosome structures from microscopy data reveals the dominant motion (concerted fluctuation) of the chromosome. Applying E-PCA to Hi-C data across the human blood cell lineage isolates the aspects of chromosome structure that most strongly differentiate cell types. Conversely, when we apply I-PCA to simulation snapshots of two protein complexes, the major component reports the consensus features of the structure, while the previously applied E-PCA characterizes correlated deviations from the mean structure.

biophysics↗