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Summary: Biomolecular condensates have emerged as major drivers of cellular organization. It remains largely unexplored, however, whether these condensates can impart mechanical function(s) to the cell. The heterochromatin protein HP1α (Swi6 in Schizosaccharomyces pombe) crosslinks histone H3K9 methylated nucleosomes and has been proposed to undergo condensation to drive the liquid-like clustering of heterochromatin domains. Here, we leverage the genetically tractable S. pombe model and a separation-of-function allele to elucidate a mechanical function imparted by Swi6 condensation. Using single-molecule imaging, force spectroscopy, and high-resolution live-cell imaging, we show that Swi6 is critical for nuclear resistance to external force. Strikingly, it is the condensed yet dynamic pool of Swi6, rather than the chromatin-bound molecules, that is essential to imparting mechanical stiffness. Our findings suggest that Swi6 condensates embedded in the chromatin meshwork establish the emergent mechanical behavior of the nucleus as a whole, revealing that biomolecular condensation can influence organelle and cell mechanics.

Chromatin is a polymer complex of DNA and proteins that regulates gene expression. The three-dimensional (3D) structure and organization of chromatin controls DNA transcription and replication. High-throughput chromatin conformation capture techniques generate Hi-C maps that can provide insight into the 3D structure of chromatin. Hi-C maps can be represented as a symmetric matrix [Formula: see text], where each element represents the average contact probability or number of contacts between chromatin loci i and j. Previous studies have detected topologically associating domains (TADs), or self-interacting regions in [Formula: see text] within which the contact probability is greater than that outside the region. Many algorithms have been developed to identify TADs within Hi-C maps. However, most TAD identification algorithms are unable to identify nested or overlapping TADs and for a given Hi-C map there is significant variation in the location and number of TADs identified by different methods. We develop a novel method to identify TADs, KerTAD, using a kernel-based technique from computer vision and image processing that is able to accurately identify nested and overlapping TADs. We benchmark this method against state-of-the-art TAD identification methods on both synthetic and experimental data sets. We find that the new method consistently has higher true positive rates (TPR) and lower false discovery rates (FDR) than all tested methods for both synthetic and manually annotated experimental Hi-C maps. The TPR for KerTAD is also largely insensitive to increasing noise and sparsity, in contrast to the other methods. We also find that KerTAD is consistent in the number and size of TADs identified across replicate experimental Hi-C maps for several organisms. Thus, KerTAD will improve automated TAD identification and enable researchers to better correlate changes in TADs to biological phenomena, such as enhancer-promoter interactions and disease states.