SPIN reveals genome-wide landscape of nuclear compartmentalization

We report SPIN, an integrative computational method to reveal genome-wide intranuclear chromosome positioning and nuclear compartmentalization relative to multiple nuclear structures, which are pivotal for modulating genome function. As a proof-of-principle, we use SPIN to integrate nuclear compartment mapping (TSA-seq and DamID) and chromatin interaction data (Hi-C) from K562 cells to identify 10 spatial compartmentalization states genome-wide relative to nuclear speckles, lamina, and putative associations with nucleoli. These SPIN states show novel patterns of genome spatial organization and their relation to other 3D genome features and genome function (transcription and replication timing). SPIN provides critical insights into nuclear spatial and functional compartmentalization.

Principal curve approaches for inferring 3D chromatin architecture

Summary Three-dimensional (3D) genome spatial organization is critical for numerous cellular processes, including transcription, while certain conformation-driven structural alterations are frequently oncogenic. Genome architecture had been notoriously difficult to elucidate, but the advent of the suite of chromatin conformation capture assays, notably Hi-C, has transformed understanding of chromatin structure and provided downstream biological insights. Although many findings have flowed from direct analysis of the pairwise proximity data produced by these assays, there is added value in generating corresponding 3D reconstructions deriving from superposing genomic features on the reconstruction. Accordingly, many methods for inferring 3D architecture from proximity data have been advanced. However, none of these approaches exploit the fact that single chromosome solutions constitute a one-dimensional (1D) curve in 3D. Rather, this aspect has either been addressed by imposition of constraints, which is both computationally burdensome and cell type specific, or ignored with contiguity imposed after the fact. Here, we target finding a 1D curve by extending principal curve methodology to the metric scaling problem. We illustrate how this approach yields a sequence of candidate solutions, indexed by an underlying smoothness or degrees-of-freedom parameter, and propose methods for selection from this sequence. We apply the methodology to Hi-C data obtained on IMR90 cells and so are positioned to evaluate reconstruction accuracy by referencing orthogonal imaging data. The results indicate the utility and reproducibility of our principal curve approach in the face of underlying structural variation.

Principal curve approaches for inferring 3D chromatin architecture

Three dimensional (3D) genome spatial organization is critical for numerous cellular processes, including transcription, while certain conformation-driven structural alterations are frequently oncogenic. Genome architecture had been notoriously difficult to elucidate, but the advent of the suite of chromatin conformation capture assays, notably Hi-C, has transformed understanding of chromatin structure and provided downstream biological insights. Although many findings have flowed from direct analysis of the pairwise proximity data produced by these assays, there is added value in generating corresponding 3D reconstructions deriving from superposing genomic features on the reconstruction. Accordingly, many methods for inferring 3D architecture from proximity d hyperrefata have been advanced. However, none of these approaches exploit the fact that single chromosome solutions constitute a one dimensional (1D) curve in 3D. Rather, this aspect has either been addressed by imposition of constraints, which is both computationally burdensome and cell type specific, or ignored with contiguity imposed after the fact. Here we target finding a 1D curve by extending principal curve methodology to the metric scaling problem. We illustrate how this approach yields a sequence of candidate solutions, indexed by an underlying smoothness or degrees-of-freedom parameter, and propose methods for selection from this sequence. We apply the methodology to Hi-C data obtained on IMR90 cells and so are positioned to evaluate reconstruction accuracy by referencing orthogonal imaging data. The results indicate the utility and reproducibility of our principal curve approach in the face of underlying structural variation.

SPIN reveals genome-wide landscape of nuclear compartmentalization

Chromosomes segregate differentially relative to distinct subnuclear structures, but this genome-wide compartmentalization, pivotal for modulating genome function, remains poorly understood. New genomic mapping methods can reveal chromosome positioning relative to specific nuclear structures. However, computational methods that integrate their results to identify overall intranuclear chromo-some positioning have not yet been developed. We report SPIN, a new method to identify genome-wide nuclear spatial localization patterns. As a proof-of-principle, we use SPIN to integrate nuclear compartment mapping (TSA-seq and DamID) and chromatin interaction data (Hi-C) from K562 cells to identify 10 spatial compartmentalization states genome-wide relative to nuclear speckles, lamina, and nucleoli. These SPIN states show novel patterns of genome spatial organization and their relation to genome function (transcription and replication timing). Comparisons of SPIN states with Hi-C sub-compartments and lamina-associated domains (LADs) from multiple cell types suggest constitutive compartmentalization patterns. By integrating different readouts of higher-order genome organization, SPIN provides critical insights into nuclear spatial and functional compartmentalization.