Advances in methods and applications of single-cell Hi-C data analysis_Journal of Biomedical Engineering

Authors：

GONG Haiyan ^1,2 , MA Fuqiang ³ ,  ZHANG Xiaotong ^2,4

1. Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, P. R. China;
2. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, P. R. China;
3. Inspur Group Co. Ltd., Jinan 250101, P. R. China;
4. Shunde Innovation School, University of Science and Technology Beijing, Foshan, Guangdong 528399, P. R. China;

Corresponding author：

Keywords：

Single-cell Hi-C; Data analysis; Imputation; Cell classification; Multi-scale structure

DOI：

10.7507/1001-5515.202303046

Video：

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Chromatin three-dimensional genome structure plays a key role in cell function and gene regulation. Single-cell Hi-C techniques can capture genomic structure information at the cellular level, which provides an opportunity to study changes in genomic structure between different cell types. Recently, some excellent computational methods have been developed for single-cell Hi-C data analysis. In this paper, the available methods for single-cell Hi-C data analysis were first reviewed, including preprocessing of single-cell Hi-C data, multi-scale structure recognition based on single-cell Hi-C data, bulk-like Hi-C contact matrix generation based on single-cell Hi-C data sets, pseudo-time series analysis, and cell classification. Then the application of single-cell Hi-C data in cell differentiation and structural variation was described. Finally, the future development direction of single-cell Hi-C data analysis was also prospected.

Citation： GONG Haiyan, MA Fuqiang, ZHANG Xiaotong. Advances in methods and applications of single-cell Hi-C data analysis. Journal of Biomedical Engineering, 2023, 40(5): 1033-1039. doi: 10.7507/1001-5515.202303046 Copy

1.	Liang Z, Li G, Wang Z, et al. BL-Hi-C is an efficient and sensitive approach for capturing structural and regulatory chromatin interactions. Nat Commun, 2017, 8(1): 1622.
2.	Dixon J R, Selvaraj S, Yue F, et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature, 2012, 485(7398): 376.
3.	Tan L, Xing D, Chang C-H, et al. Three-dimensional genome structures of single diploid human cells. Science, 2018, 361(6405): 924-928.
4.	Gridina M, Taskina A, Lagunov T, et al. Comparison and critical assessment of single-cell Hi-C protocols. Heliyon, 2022, 8(10): e11023.
5.	Han C G, Xie Q, Lin S L. Are dropout imputation methods for scRNA-seq effective for scHi-C data?. Brief Bioinform, 2021, 22(4): bbaa289.
6.	Zhen C W, Wang Y X, Geng J Q, et al. A review and performance evaluation of clustering frameworks for single-cell Hi-C data. Brief Bioinform, 2022, 23(6): bbac385.
7.	Chi Y, Shi J Y, Xing D, et al. Every gene everywhere all at once: High-precision measurement of 3D chromosome architecture with single-cell Hi-C. Front Mol Biosci, 2022, 9: 959688.
8.	Ding T Y, Zhang H. Novel biological insights revealed from the investigation of multiscale genome architecture. Comput Struct Biotechnol J, 2023, 21: 312-325.
9.	Kim K, Kim M, Kim Y, et al. Hi-C as a molecular rangefinder to examine genomic rearrangements. Semin Cell Dev Biol, 2022, 121: 161-170.
10.	Ulianov S V, Razin S V. The two waves in single-cell 3D genomics. Semin Cell Dev Biol, 2022, 121: 143-152.
11.	潘多, 李华梅, 刘宏德, 等. 单细胞数据的整合方法综述. 生物医学工程学杂志, 2021, 38(5): 1010-1017.
12.	Horton C A, Alver B H, Park P J. GiniQC: a measure for quantifying noise in single-cell Hi-C data. Bioinformatics, 2020, 36(9): 2902-2904.
13.	Wolff J, Rabbani L, Gilsbach R, et al. Galaxy HiCExplorer 3: a web server for reproducible Hi-C, capture Hi-C and single-cell Hi-C data analysis, quality control and visualization. Nucleic Acids Res, 2020, 48(W1): W177-W184.
14.	Zheng Y, Shen S Q, Keles S. Normalization and de-noising of single-cell Hi-C data with BandNorm and scVI-3D. Genome Biol, 2022, 23(1): 222.
15.	Wolff J, Abdennur N, Backofen R, et al. Scool: a new data storage format for single-cell Hi-C data. Bioinformatics, 2021, 37(14): 2053-2054.
16.	Gong H, Yang Y, Zhang X, et al. NeRV-3D-DC: A nonlinear dimensionality reduction visualization method for 3D chromosome structure reconstruction with high resolution Hi-C data// 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Las Vegas: IEEE, 2022: 422-429.
17.	Lieberman-Aiden E, Van Berkum N L, Williams L, et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science, 2009, 326(5950): 289-293.
18.	Gong H, Yang Y, Zhang X, et al. CASPIAN: A method to identify chromatin topological associated domains based on spatial density cluster. Comput Struct Biotechnol J, 2022, 20: 4816-4824.
19.	Gong H, Li M, Ji M, et al. MINE is a method for detecting spatial density of regulatory chromatin interactions based on a multi-modal network. Cell Reps Methods, 2023, 3(1): 100386.
20.	Kos P I, Galitsyna A A, Ulianov S V, et al. Perspectives for the reconstruction of 3D chromatin conformation using single cell Hi-C data. Plos Comput Biol, 2021, 17(11): e1009546.
21.	Meng L M, Wang C X, Shi Y, et al. Si-C is a method for inferring super-resolution intact genome structure from single-cell Hi-C data. Nat Commun, 2021, 12(1): 4369.
22.	Messelink J J B, Van Teeseling M C F, Janssen J, et al. Learning the distribution of single-cell chromosome conformations in bacteria reveals emergent order across genomic scales. Nat Commun, 2021, 12(1): 1963.
23.	Zha M S, Wang N, Zhang C Y, et al. Inferring single-cell 3D chromosomal structures based on the Lennard-Jones potential. Int J Mol Sci, 2021, 22(11): 5914.
24.	Galitsyna A A, Gelfand M S. Single-cell Hi-C data analysis: safety in numbers. Brief Bioinform, 2021, 22(6): bbab316.
25.	Polovnikov K, Gorsky A, Nechaev S, et al. Non-backtracking walks reveal compartments in sparse chromatin interaction networks. Sci Rep-UK, 2020, 10(1): 11398.
26.	Li X, Zeng G J, Li A S, et al. DeTOKI identifies and characterizes the dynamics of chromatin TAD-like domains in a single cell. Genome Biol, 2021, 22(1): 1-26.
27.	Ye Y S, Zhang S H, Gao L, et al. Deciphering hierarchical chromatin domains and preference of genomic position forming boundaries in single mouse embryonic stem cells. Adv Sci, 2023, 10(8): 2205162.
28.	Zhang S S, Plummer D, Lu L N, et al. DeepLoop robustly maps chromatin interactions from sparse allele-resolved or single-cell Hi-C data at kilobase resolution. Nat Genet, 2022, 54(7): 1013-1025.
29.	Zhou J, Ma J, Chen Y, et al. Robust single-cell Hi-C clustering by convolution-and random-walk–based imputation. Proc Natl Acad Sci U S A, 2019, 116(28): 14011-14018.
30.	Liu T, Wang Z. scHiCEmbed: Bin-specific embeddings of single-cell Hi-C data using graph auto-encoders. Genes, 2022, 13(6): 1048.
31.	Xie Q, Han C G, Jin V, et al. HiCImpute: A Bayesian hierarchical model for identifying structural zeros and enhancing single cell Hi-C data. Plos Comput Biol, 2022, 18(6): e1010129.
32.	Liu Q, Zeng W W, Zhang W, et al. Deep generative modeling and clustering of single cell Hi-C data. Brief Bioinform, 2023, 24(1): bbac494.
33.	Ye Y S, Gao L, Zhang S H. Circular trajectory reconstruction uncovers cell-cycle progression and regulatory dynamics from single-cell Hi-C maps. Adv Sci, 2019, 6(23): 1900986.
34.	Bonora G, Ramani V, Singh R, et al. Single-cell landscape of nuclear configuration and gene expression during stem cell differentiation and X inactivation. Genome Biol, 2021, 22(1): 279.
35.	Lyu H Q, Liu E H, Wu Z F, et al. scHiCPTR: unsupervised pseudotime inference through dual graph refinement for single-cell Hi-C data. Bioinformatics, 2022, 38(23): 5151-5159.
36.	Kim H J, Ioshikhes I, Bonora G, et al. Capturing cell type-specific chromatin compartment patterns by applying topic modeling to single-cell Hi-C data. Plos Comput Biol, 2020, 16(9): e1008173.
37.	Wolff J, Backofen R, Gruning B. Robust and efficient single-cell Hi-C clustering with approximate k-nearest neighbor graphs. Bioinformatics, 2021, 37(22): 4006-4013.
38.	Zhang R C, Zhou T M, Ma J. Multiscale and integrative single-cell Hi-C analysis with Higashi. Nat Biotechnol, 2022, 40(2): 254-261.
39.	Zhang R C, Zhou T M, Ma J. Ultrafast and interpretable single-cell 3D genome analysis with Fast-Higashi. Cell Syst, 2022, 13(10): 798-807.
40.	Wu H, Wu Y F, Jiang Y H, et al. scHiCStackL: a stacking ensemble learning-based method for single-cell Hi-C classification using cell embedding. Brief Bioinform, 2022, 23(1): bbab396.
41.	Zhou X, Shi Z, Wu Y, et al. scHiCSC: A novel single-cell Hi-C clustering framework by contact-weight-based smoothing and feature fusion// 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Las Vegas: IEEE, 2022: 44-50.
42.	Wagner D E, Klein A M. Lineage tracing meets single-cell omics: opportunities and challenges. Nat Rev Genet, 2020, 21(7): 410-427.
43.	Liu J, Qu S, Zhang T, et al. Applications of single-cell omics in tumor immunology. Front Immunol, 2021, 12: 697412.
44.	Nam A S, Chaligne R, Landau D A. Integrating genetic and non-genetic determinants of cancer evolution by single-cell multi-omics. Nat Rev Genet, 2021, 22(1): 3-18.
45.	Khateb M, Perovanovic J, Ko K D, et al. Transcriptomics, regulatory syntax, and enhancer identification in mesoderm-induced ESCs at single-cell resolution. Cell Rep, 2022, 40(7): 111219.
46.	Wang X, Luan Y, Yue F. EagleC: A deep-learning framework for detecting a full range of structural variations from bulk and single-cell contact maps. Sci Adv, 2022, 8(24): eabn9215.
47.	Lin D, Xu W Z, Hong P, et al. Decoding the spatial chromatin organization and dynamic epigenetic landscapes of macrophage cells during differentiation and immune activation. Nat Commun, 2022, 13(1): 5857.

1. Liang Z, Li G, Wang Z, et al. BL-Hi-C is an efficient and sensitive approach for capturing structural and regulatory chromatin interactions. Nat Commun, 2017, 8(1): 1622.
2. Dixon J R, Selvaraj S, Yue F, et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature, 2012, 485(7398): 376.
3. Tan L, Xing D, Chang C-H, et al. Three-dimensional genome structures of single diploid human cells. Science, 2018, 361(6405): 924-928.
4. Gridina M, Taskina A, Lagunov T, et al. Comparison and critical assessment of single-cell Hi-C protocols. Heliyon, 2022, 8(10): e11023.
5. Han C G, Xie Q, Lin S L. Are dropout imputation methods for scRNA-seq effective for scHi-C data?. Brief Bioinform, 2021, 22(4): bbaa289.
6. Zhen C W, Wang Y X, Geng J Q, et al. A review and performance evaluation of clustering frameworks for single-cell Hi-C data. Brief Bioinform, 2022, 23(6): bbac385.
7. Chi Y, Shi J Y, Xing D, et al. Every gene everywhere all at once: High-precision measurement of 3D chromosome architecture with single-cell Hi-C. Front Mol Biosci, 2022, 9: 959688.
8. Ding T Y, Zhang H. Novel biological insights revealed from the investigation of multiscale genome architecture. Comput Struct Biotechnol J, 2023, 21: 312-325.
9. Kim K, Kim M, Kim Y, et al. Hi-C as a molecular rangefinder to examine genomic rearrangements. Semin Cell Dev Biol, 2022, 121: 161-170.
10. Ulianov S V, Razin S V. The two waves in single-cell 3D genomics. Semin Cell Dev Biol, 2022, 121: 143-152.
11. 潘多, 李华梅, 刘宏德, 等. 单细胞数据的整合方法综述. 生物医学工程学杂志, 2021, 38(5): 1010-1017.
12. Horton C A, Alver B H, Park P J. GiniQC: a measure for quantifying noise in single-cell Hi-C data. Bioinformatics, 2020, 36(9): 2902-2904.
13. Wolff J, Rabbani L, Gilsbach R, et al. Galaxy HiCExplorer 3: a web server for reproducible Hi-C, capture Hi-C and single-cell Hi-C data analysis, quality control and visualization. Nucleic Acids Res, 2020, 48(W1): W177-W184.
14. Zheng Y, Shen S Q, Keles S. Normalization and de-noising of single-cell Hi-C data with BandNorm and scVI-3D. Genome Biol, 2022, 23(1): 222.
15. Wolff J, Abdennur N, Backofen R, et al. Scool: a new data storage format for single-cell Hi-C data. Bioinformatics, 2021, 37(14): 2053-2054.
16. Gong H, Yang Y, Zhang X, et al. NeRV-3D-DC: A nonlinear dimensionality reduction visualization method for 3D chromosome structure reconstruction with high resolution Hi-C data// 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Las Vegas: IEEE, 2022: 422-429.
17. Lieberman-Aiden E, Van Berkum N L, Williams L, et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science, 2009, 326(5950): 289-293.
18. Gong H, Yang Y, Zhang X, et al. CASPIAN: A method to identify chromatin topological associated domains based on spatial density cluster. Comput Struct Biotechnol J, 2022, 20: 4816-4824.
19. Gong H, Li M, Ji M, et al. MINE is a method for detecting spatial density of regulatory chromatin interactions based on a multi-modal network. Cell Reps Methods, 2023, 3(1): 100386.
20. Kos P I, Galitsyna A A, Ulianov S V, et al. Perspectives for the reconstruction of 3D chromatin conformation using single cell Hi-C data. Plos Comput Biol, 2021, 17(11): e1009546.
21. Meng L M, Wang C X, Shi Y, et al. Si-C is a method for inferring super-resolution intact genome structure from single-cell Hi-C data. Nat Commun, 2021, 12(1): 4369.
22. Messelink J J B, Van Teeseling M C F, Janssen J, et al. Learning the distribution of single-cell chromosome conformations in bacteria reveals emergent order across genomic scales. Nat Commun, 2021, 12(1): 1963.
23. Zha M S, Wang N, Zhang C Y, et al. Inferring single-cell 3D chromosomal structures based on the Lennard-Jones potential. Int J Mol Sci, 2021, 22(11): 5914.
24. Galitsyna A A, Gelfand M S. Single-cell Hi-C data analysis: safety in numbers. Brief Bioinform, 2021, 22(6): bbab316.
25. Polovnikov K, Gorsky A, Nechaev S, et al. Non-backtracking walks reveal compartments in sparse chromatin interaction networks. Sci Rep-UK, 2020, 10(1): 11398.
26. Li X, Zeng G J, Li A S, et al. DeTOKI identifies and characterizes the dynamics of chromatin TAD-like domains in a single cell. Genome Biol, 2021, 22(1): 1-26.
27. Ye Y S, Zhang S H, Gao L, et al. Deciphering hierarchical chromatin domains and preference of genomic position forming boundaries in single mouse embryonic stem cells. Adv Sci, 2023, 10(8): 2205162.
28. Zhang S S, Plummer D, Lu L N, et al. DeepLoop robustly maps chromatin interactions from sparse allele-resolved or single-cell Hi-C data at kilobase resolution. Nat Genet, 2022, 54(7): 1013-1025.
29. Zhou J, Ma J, Chen Y, et al. Robust single-cell Hi-C clustering by convolution-and random-walk–based imputation. Proc Natl Acad Sci U S A, 2019, 116(28): 14011-14018.
30. Liu T, Wang Z. scHiCEmbed: Bin-specific embeddings of single-cell Hi-C data using graph auto-encoders. Genes, 2022, 13(6): 1048.
31. Xie Q, Han C G, Jin V, et al. HiCImpute: A Bayesian hierarchical model for identifying structural zeros and enhancing single cell Hi-C data. Plos Comput Biol, 2022, 18(6): e1010129.
32. Liu Q, Zeng W W, Zhang W, et al. Deep generative modeling and clustering of single cell Hi-C data. Brief Bioinform, 2023, 24(1): bbac494.
33. Ye Y S, Gao L, Zhang S H. Circular trajectory reconstruction uncovers cell-cycle progression and regulatory dynamics from single-cell Hi-C maps. Adv Sci, 2019, 6(23): 1900986.
34. Bonora G, Ramani V, Singh R, et al. Single-cell landscape of nuclear configuration and gene expression during stem cell differentiation and X inactivation. Genome Biol, 2021, 22(1): 279.
35. Lyu H Q, Liu E H, Wu Z F, et al. scHiCPTR: unsupervised pseudotime inference through dual graph refinement for single-cell Hi-C data. Bioinformatics, 2022, 38(23): 5151-5159.
36. Kim H J, Ioshikhes I, Bonora G, et al. Capturing cell type-specific chromatin compartment patterns by applying topic modeling to single-cell Hi-C data. Plos Comput Biol, 2020, 16(9): e1008173.
37. Wolff J, Backofen R, Gruning B. Robust and efficient single-cell Hi-C clustering with approximate k-nearest neighbor graphs. Bioinformatics, 2021, 37(22): 4006-4013.
38. Zhang R C, Zhou T M, Ma J. Multiscale and integrative single-cell Hi-C analysis with Higashi. Nat Biotechnol, 2022, 40(2): 254-261.
39. Zhang R C, Zhou T M, Ma J. Ultrafast and interpretable single-cell 3D genome analysis with Fast-Higashi. Cell Syst, 2022, 13(10): 798-807.
40. Wu H, Wu Y F, Jiang Y H, et al. scHiCStackL: a stacking ensemble learning-based method for single-cell Hi-C classification using cell embedding. Brief Bioinform, 2022, 23(1): bbab396.
41. Zhou X, Shi Z, Wu Y, et al. scHiCSC: A novel single-cell Hi-C clustering framework by contact-weight-based smoothing and feature fusion// 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Las Vegas: IEEE, 2022: 44-50.
42. Wagner D E, Klein A M. Lineage tracing meets single-cell omics: opportunities and challenges. Nat Rev Genet, 2020, 21(7): 410-427.
43. Liu J, Qu S, Zhang T, et al. Applications of single-cell omics in tumor immunology. Front Immunol, 2021, 12: 697412.
44. Nam A S, Chaligne R, Landau D A. Integrating genetic and non-genetic determinants of cancer evolution by single-cell multi-omics. Nat Rev Genet, 2021, 22(1): 3-18.
45. Khateb M, Perovanovic J, Ko K D, et al. Transcriptomics, regulatory syntax, and enhancer identification in mesoderm-induced ESCs at single-cell resolution. Cell Rep, 2022, 40(7): 111219.
46. Wang X, Luan Y, Yue F. EagleC: A deep-learning framework for detecting a full range of structural variations from bulk and single-cell contact maps. Sci Adv, 2022, 8(24): eabn9215.
47. Lin D, Xu W Z, Hong P, et al. Decoding the spatial chromatin organization and dynamic epigenetic landscapes of macrophage cells during differentiation and immune activation. Nat Commun, 2022, 13(1): 5857.

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