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bioRxiv · 10.1101/2024.01.27.577362

Identification of anisotropy in chromosome dynamics by principal component analysis using integrated spatial genomics

Nara, T.·Takahashi, H.·Awazu, A.·Kikuchi, Y.
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Abstract

Eukaryotic interphase chromosomes maintain a three-dimensional structure within the nucleus and undergo fluctuations. It has been reported that such dynamics are involved in transcription, replication, and DNA repair. However, the analysis of chromosomal dynamics has been limited to high-throughput chromosome conformation capture data, which records the contact frequencies between chromosomal regions and lack direct information about the dynamic. Herein, we investigated chromosome fluctuations as polymers based on experimental data from sequential fluorescence in situ hybridization (seqFISH)+ using a multiomics methodology. To describe the principal modes of chromosome fluctuations, we applied principal component analysis to the three-dimensional structure information of single chromosomes in 446 mouse embryonic stem cells (mESCs) obtained from seqFISH+ data analysis for spatial genomics and signals of nuclear factors (SNFs: histone marks, repeat DNAs, and nuclear compartments). We found that chromosome fluctuations exhibit both isotropic and anisotropic modes. The properties of anisotropy in chromosome fluctuation vary among chromosomes and appear to depend on the interaction between repeat DNAs on the chromosomes and nuclear compartments. Furthermore, our principal component analysis revealed anisotropic chromosome fluctuations before and after the mitotic phase, specifically when chromosomes adopt a spindle-like shape. This result suggests the potential involvement of anisotropic chromosomal fluctuations in the transition of nuclear organization during the cell cycle. Our results represent the first study to elucidate the dynamics of chromosomes as polymers based on real multiomics data.

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Nara, T., Takahashi, H., Awazu, A., Kikuchi, Y.. 2024-01-30. Identification of anisotropy in chromosome dynamics by principal component analysis using integrated spatial genomics. https://doi.org/10.1101/2024.01.27.577362

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