Biology subjects

Musella, F.

Publications and source records attributed to Musella, F..

5 recordsLinked to original sources

bioRxiv2026

Decoding single-cell replication states from imaging data reveals locus-specific subnuclear position dependencies of replication initiation

Replication timing (RT) is a fundamental feature of genome regulation, tightly linked to chromatin state and three-dimensional (3D) nuclear organization. Yet how the reproducible population-level timing program emerges from stochastic replication decisions in individual cells remains unclear, because it requires measuring RT and 3D genome organization jointly in the same cell. Here, we introduce RepTile, the first systematic framework that infers allele-resolved, single-cell replication states directly from multiplexed FISH and chromosome tracing experiments. By extracting replication information from spot-count distributions while correcting for genomic and spatial detection biases, RepTile infers cell-cycle progression, locus-specific replication timing, cell-to-cell heterogeneity in replication timing, and replication states of individual allele-resolved locus copies within their native three-dimensional nuclear context. Using RepTile on mouse embryonic stem cells, we find that the association between replication timing and subnuclear position observed in population-level genomic experiments conceals fundamentally different locus-specific behaviors. We found two classes of chromatin regions: "position-sensitive" and "position-insensitive". Position-sensitive regions replicate at different times depending on their subnuclear location, typically firing earliest when positioned in their canonical locale. Strikingly, this holds even when the canonical locale is repressive, such as the nuclear envelope. On the other hand, position-insensitive loci replicate at their characteristic times regardless of subnuclear position. These classes map onto distinct regulatory regimes, which are particularly pronounced at replication initiation zones (IZ): position-sensitive IZs are enriched in domains that replicate constitutively early or late across cell types, whereas position-insensitive IZs are enriched in domains that switch RT developmentally and contain early replication control elements (ERCEs), cis-regulatory elements known to govern IZ firing times. Together, these results challenge the view that subnuclear location broadly defines replication timing, indicating that positional dependence is specific to constitutive domains, while developmental domains carry intrinsic cis-acting programs that decouple their replication from subnuclear location.

genomics↗

bioRxiv2025

Specialised super-enhancer networks in stem cells and neurons

Super-enhancers (SEs) are clusters of enhancers with high transcriptional activity that play essential roles in defining cell identity through regulation of nearby genes. SEs preferentially form multiway chromatin interactions with other SEs and highly transcribed regions in embryonic stem cells. However, the properties of the interacting SEs and their specific contributions to complex regulatory interactions in differentiated cell types remain poorly understood. Here, we compare the structural and functional properties of SEs between embryonic stem cells (ESCs) and dopaminergic neurons (DNs) by combining Genome Architecture Mapping (GAM), chromatin accessibility, histone modification, and transcriptome data. Most SEs are cell-type specific and establish extensive pairwise and multiway chromatin interactions with other SEs and genes with cell-type specific expression. SE interactions span megabase genomic distances and frequently connect distant topologically associating domains. By applying network centrality analyses, we detected SEs with different hierarchical importance. Highest network centrality SEs contain binding motifs for cell-type specific transcription factors, and are candidate regulatory hubs. The functional heterogeneity of SEs is also highlighted by their organisation into modular sub-networks that differ in structure and spatial scale between ESCs and DNs, with more specific and strongly connected SE modules in post-mitotic neurons. Our results uncover both the high complexity and specificity of SE-based 3D regulatory networks and provide a resource for prioritizing SEs with potential roles in transcriptional regulation and disease.

genomics↗

bioRxiv2024

Prediction of single-cell chromatin compartments from single-cell chromosome structures by MaxComp

The genome is partitioned into distinct chromatin compartments with at least two main classes, a transcriptionally active A and an inactive B compartment, corresponding mostly to the segregation of euchromatin and heterochromatin. Chromatin within the same compartment has a higher tendency to interact with itself than with regions in opposing compartments. A/B compartments are traditionally derived from ensemble Hi-C contact matrices through principal component analysis of their covariance matrices. However, defining compartments in single cells from single-cell Hi-C maps is non trivial due to sparsity of the data and the fact that homologous copies are typically not resolved. Here we present an unsupervised approach, named MaxComp, to determine single-cell A/B compartments from geometric considerations in 3D chromosome structures, either from multiplexed FISH imaging or from models derived from Hi-C data. By representing each single-cell structure as an undirected graph with edge-weights encoding structural information, the problem of predicting chromosome compartments can be transformed to an alternative form of the Max-cut problem, a semidefinite graph programming method (SPD) to determine an optimal division of a chromosome structure graph into two structural compartments. Our results show that compartment annotations from principal component analysis of ensemble Hi-C data can be perfectly reproduced as population averages of our single-cell compartment predictions. We therefore prove that compartment predictions can be achieved from geometric considerations alone using 3D coordinates of chromatin regions together with information about their nuclear microenvironment. Our results reveal substantial cell-to-cell heterogeneity of compartments in a cell population, which substantially differs between individual genomic regions. Moreover, by applying our approach to multiplexed FISH tracing experiments, our method sheds light on the relationship between single-cell compartment annotations and gene transcriptional activity in single cells. Overall our approach provides new insights into single-cell chromatin condensation, relationship between population and single-cell chromatin compartmentalization, the cell-to-cell variations of chromatin compartments and its impact on gene transcription. Author SummaryChromosome conformation capture and imaging techniques revealed the segregation of genomic chromatin into at least two functional compartments. Hi-C contact frequency matrices show checkerboard-like patterns indicating that chromatin regions are divided into at least two states, possibly a result of phase separation. Chromatin regions in the same state have preferential interactions with each other, often over extended sequence distances, while interactions to regions in the opposing state are minimized. Principal component analysis (PCA) on ensemble Hi-C contact frequency matrices can identify these compartment states. However, because the compartment annotations are derived from a cell population, this method cannot provide information about compartments in single cells. Here in this study, we introduce an unsupervised method to predict single-cell compartments using graph-based programming, which utilizes only structural information in single cells. Our results demonstrate that PCA-based ensemble compartment annotations can be reproduced as population averages of our single-cell compartment predictions. Moreover, our results reveal the cell-to-cell heterogeneity of compartments in a cell population, which shows significant disparities among different chromatin regions. Moreover, by applying our approach to multiplexed FISH tracing experiments, our method reveals the relationship between single-cell compartment annotations and gene transcriptional activity in single cells. Finally, our approach also allows us to relate chromatin structural features in single cells with compartment properties. Comparison with other existing approaches showed that our method produces overall better compartmentalization scores in single cells.

bioinformatics↗

bioRxiv2020

Comparison of the Hi-C, GAM and SPRITE methods by use of polymer models of chromatin

Powerful technologies have been developed to probe chromatin 3D physical interactions genome-wide, such as Hi-C, GAM and SPRITE. Due to their intrinsic differences and without a benchmarking reference, it is currently difficult to assess how well each method represents the genome 3D structure and their relative performance. Here, we develop a computational approach to implement Hi-C, GAM and SPRITE in-silico to compare the three methods in a simplified, yet controlled framework against known polymer 3D structures. We test our approach on models of three 6-Mb genomic regions, around the Sox9 and the HoxD genes in mouse ES cells, and around the Epha4 gene in mouse CHLX-12 cells. The model-derived contact matrices consistently match Hi-C, GAM and SPRITE experiments. We show that in-silico Hi-C, GAM and SPRITE average data are overall faithful to the 3D structures of the polymer models. We find that the inherent variability of model single-molecule 3D conformations and experimental efficiency differently affect the contact data of the different methods. Similarly, the noise-to-signal levels vary with genomic distance differently in in-silico Hi-C, SPRITE and GAM. We benchmark the performance of each technology in bulk and in single-cell experiments, and identify the minimal number of cells required for replicates to return statistically consistent chromatin contact measures. Under the same experimental conditions, SPRITE requires the lowest number of cells, Hi-C is close to SPRITE, while GAM is the most reproducible method to capture interactions at large genomic distances.

biophysics↗

bioRxiv2020

Cell-type specialization in the brain is encoded by specific long-range chromatin topologies

Neurons and oligodendrocytes are terminally differentiated cells that sustain cascades of gene activation and repression to execute highly specialized functions, while retaining homeostatic control. To study long-range chromatin folding without disturbing the native tissue environment, we developed Genome Architecture Mapping in combination with immunoselection (immunoGAM), and applied it to three cell types from the adult murine brain: dopaminergic neurons (DNs) from the midbrain, pyramidal glutamatergic neurons (PGNs) from the hippocampus, and oligodendroglia (OLGs) from the cortex. We find cell-type specific 3D chromatin structures that relate with patterns of gene expression at multiple genomic scales, including extensive reorganization of topological domains (TADs) and chromatin compartments. We discover the loss of TAD insulation, or TAD melting, at long genes (>400 kb) when they are highly transcribed. We find many neuron-specific contacts which contain accessible chromatin regions enriched for putative binding sites for multiple neuronal transcription factors, and which connect cell-type specific genes that are associated with neurodegenerative disorders such as Parkinsons disease, or specialized functions such as synaptic plasticity and memory. Lastly, sensory receptor genes exhibit increased membership in heterochromatic compartments that establish strong contacts in brain cells. However, their silencing is compromised in a subpopulation of PGNs with molecular signatures of long-term potentiation. Overall, our work shows that the 3D organization of the genome is highly cell-type specific, and essential to better understand mechanisms of gene regulation in highly specialized tissues such as the brain.

molecular biology↗