PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
October 9, 20250 citations

Navigating the 3D genome at single-cell resolution: techniques, computation, and mechanistic landscapes.

View Full Paper
FHFeitong HongKHKun HanYHYuduo Hao

Key Points

  • Single-cell 3D genomics reveals chromatin architecture, enhancing understanding of gene regulation and cellular identity.
  • Recent techniques improve genome-wide chromatin contact mapping, linking 3D genome organization to disease progression.
  • Advanced computational frameworks reconstruct dynamic chromatin topologies from high-dimensional data, enabling more precise analyses.
  • These insights support future developments in precision medicine and topology-guided therapeutic strategies.

Abstract

The 3D organization of the genome is critical for gene expression regulation, cellular identity, and disease progression. Traditional methods that analyze bulk genomic data often obscure cell-to-cell heterogeneity, limiting the resolution of intrinsic variability within complex biological systems. To overcome this, single-cell 3D genomics has emerged, revealing chromatin architecture at the individual cell level. Advanced experimental approaches enable genome-wide chromatin contact mapping, while computational frameworks reconstruct dynamic chromatin topologies from high-dimensional data. Building on these breakthroughs, recent advances in single-cell 3D genomics have led to transformative progress in epigenetics, linking 3D genome architecture with gene regulation, cellular identity, and disease phenotypes. This review focuses on the breakthroughs in single-cell 3D genomics, demonstrating how integrated experimental, computational, and mechanistic approaches decode chromatin architecture. These insights have deepened the understanding of genome function at the single-cell level and lay the foundation for future advances in precision medicine and topology-guided therapeutic strategies.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Hong et al. (2025) studied this question.

synapsesocial.com/papers/68e80eb363e2e2f707877bdfhttps://doi.org/10.1093/bib/bbaf520
Ask AI
Helpful
Bookmark
Share
View Full Paper

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A gene regulatory network–aware graph learning method for cell identity annotation in single-cell RNA-seq data2024 · 24 citations
  2. 2Identifying TAD-like domains on single-cell Hi-C data by graph embedding and changepoint detection2024 · 7 citations
  3. 3Inferring gene regulatory network from single-cell transcriptomes with graph autoencoder model2023 · 146 citations
  4. 4Capturing Chromosome Conformation2002 · 3,841 citations
  5. 5Sparse regularized joint projection model for identifying associations of non-coding RNAs and human diseases2022 · 20 citations