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Biology subjects

Kariti, H.

Publications and source records attributed to Kariti, H..

3 recordsLinked to original sources

Fast Low-Input Efficient Hi-C

We developed FLIE (Fast Low-Input Efficient) Hi-C, a highly optimized and simplified version of Hi-C. Our method shortens the duration of Hi-C from five days to two days and decreases key reagent amounts 5-20 fold, while maintaining experimental controls and data quality. Importantly, the method also reduces the required input material by 100-1000 fold, as we demonstrate by generating detailed high complexity interaction maps from low-input sorted mouse frozen neuron nuclei and from 5,000-10,000 human Hap1 cells.

genomics↗

Large-scale manipulation of radial positioning does not affect most aspects of genome organization

Although the spatial organization of the genome is closely linked to biological function, genome structure is highly stochastic. Within this heterogeneity, specific function-associated structural elements must be maintained, even when the nucleus is deformed due to physiological physical constraints. The radial positioning of genomic loci - their distance from the nuclear periphery - has long been considered an important feature of genome organization which is correlated with both structural elements and genomic activity. In the current study, we developed an experimental system for manipulating the radial positioning of the genome, by expressing the sperm-specific protein Prm1 in somatic cells. By microscopy, we observe that initial Prm1 nuclear foci develop within 72 hours into a large Prm1 focus occupying most of the nuclear interior, while the entire genome is driven towards the nuclear periphery, resulting in a 3-5 fold reduction in the volume that the genome occupies. Noting that this system enables isolation of a pure population of cells with reorganized nuclei, we then used Hi-C to study the effects of this perturbation. Remarkably, we find that interaction patterns are largely robust to this major nuclear reorganization, with minor changes which mostly reflect a strengthening of heterochromatin self-interactions. Our experimental system provides means for manipulating nuclear organization in a reproducible manner, potentially allowing to examine radial positioning decoupled from other features of genome organization. Highlighting the complementary nature of microscopic and genomic methods, our work further suggests a remarkable resilience of genome structure such that large-scale nuclear changes, including chromosome compression and changes in radial positioning, can occur without extensive alteration of functional genome organization.

genomics↗

Hypothesis-driven probabilistic modelling enables a principled perspective of genomic compartments

The Hi-C method has revolutionized the study of genome organization, yet interpretation of Hi-C interaction frequency maps remains a major challenge. Genomic compartments are a checkered Hi-C interaction pattern suggested to represent the partitioning of the genome into two self-interacting states associated with active and inactive chromatin. Based on a few elementary mechanistic assumptions, we derive a generative probabilistic model of genomic compartments, called deGeco. Testing our model, we find it can explain observed Hi-C interaction maps in a highly robust manner, allowing accurate inference of interaction probability maps from extremely sparse data without any training of parameters. Taking advantage of the interpretability of the model parameters, we then test hypotheses regarding the nature of genomic compartments. We find clear evidence of multiple states, and that these states self-interact with different affinities. We also find that the interaction rules of chromatin states differ considerably within and between chromosomes. Inspecting the molecular underpinnings of a four-state model, we show that a simple classifier can use histone marks to predict the underlying states with 87% accuracy. Finally, we observe instances of mixed-state loci and analyze these loci in single-cell Hi-C maps, finding that mixing of states occurs mainly at the population level.

genomics↗