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Lai, M.

Publications and source records attributed to Lai, M..

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Higher-order inter-chromosomal hubs shape 3-dimensional genome organization in the nucleus

Eukaryotic genomes are packaged into a 3-dimensional structure in the nucleus of each cell. There are currently two distinct views of genome organization that are derived from different technologies. The first view, derived from genome-wide proximity ligation methods (e.g. Hi-C), suggests that genome organization is largely organized around chromosomes. The second view, derived from in situ imaging, suggests a central role for nuclear bodies. Yet, because microscopy and proximity-ligation methods measure different aspects of genome organization, these two views remain poorly reconciled and our overall understanding of how genomic DNA is organized within the nucleus remains incomplete. Here, we develop Split-Pool Recognition of Interactions by Tag Extension (SPRITE), which moves away from proximity-ligation and enables genome-wide detection of higher-order DNA interactions within the nucleus. Using SPRITE, we recapitulate known genome structures identified by Hi-C and show that the contact frequencies measured by SPRITE strongly correlate with the 3-dimensional distances measured by microscopy. In addition to known structures, SPRITE identifies two major hubs of inter-chromosomal interactions that are spatially arranged around the nucleolus and nuclear speckles, respectively. We find that the majority of genomic regions exhibit preferential spatial association relative to one of these nuclear bodies, with regions that are highly transcribed by RNA Polymerase II organizing around nuclear speckles and transcriptionally inactive and centromere-proximal regions organizing around the nucleolus. Together, our results reconcile the two distinct pictures of nuclear structure and demonstrate that nuclear bodies act as inter-chromosomal hubs that shape the overall 3-dimensional packaging of genomic DNA in the nucleus.

genomics

A Comparison Between Scalp- And Source-Reconstructed EEG Networks

EEG can be used to characterise functional networks using a variety of connectivity (FC) metrics. Unlike EEG source reconstruction, scalp analysis does not allow to make inferences about interacting regions, yet this latter approach has not been abandoned. Although the two approaches use different assumptions, conclusions drawn regarding the topology of the underlying networks should, ideally, not depend on the approach. The aim of the present work was to find an answer to the following questions: does scalp analysis provide a correct estimate of the network topology? how big are the distortions when using various pipelines in different experimental conditions? EEG recordings were analysed with amplitude- and phase-based metrics, founding a strong correlation for the global connectivity between scalp- and source-level. In contrast, network topology was only weakly correlated. The strongest correlations were obtained for MST leaf fraction, but only for FC metrics that limit the effects of volume conduction/signal leakage. These findings suggest that these effects alter the estimated EEG network organization, limiting the interpretation of results of scalp analysis. Finally, this study also suggests that the use of metrics that address the problem of zero lag correlations may give more reliable estimates of the underlying network topology.

neuroscience