1
|
Gridina MM, Stepanchuk YK, Nurridinov MA, Lagunov TA, Torgunakov NY, Shadsky AA, Ryabova AI, Vasiliev NV, Vtorushin SV, Gerashchenko TS, Denisov EV, Travin MA, Korolev MA, Fishman VS. Modification of the Hi-C Technology for Molecular Genetic Analysis of Formalin-Fixed Paraffin-Embedded Sections of Tumor Tissues. BIOCHEMISTRY. BIOKHIMIIA 2024; 89:637-652. [PMID: 38831501 DOI: 10.1134/s0006297924040047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 10/31/2023] [Accepted: 10/31/2023] [Indexed: 06/05/2024]
Abstract
Molecular genetic analysis of tumor tissues is the most important step towards understanding the mechanisms of cancer development; it is also necessary for the choice of targeted therapy. The Hi-C (high-throughput chromatin conformation capture) technology can be used to detect various types of genomic variants, including balanced chromosomal rearrangements, such as inversions and translocations. We propose a modification of the Hi-C method for the analysis of chromatin contacts in formalin-fixed paraffin-embedded (FFPE) sections of tumor tissues. The developed protocol allows to generate high-quality Hi-C data and detect all types of chromosomal rearrangements. We have analyzed various databases to compile a comprehensive list of translocations that hold clinical importance for the targeted therapy selection. The practical value of molecular genetic testing is its ability to influence the treatment strategies and to provide prognostic insights. Detecting specific chromosomal rearrangements can guide the choice of the targeted therapies, which is a critical aspect of personalized medicine in oncology.
Collapse
Affiliation(s)
- Maria M Gridina
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, 630090, Russia.
- Novosibirsk State University, Novosibirsk, 630090, Russia
| | - Yana K Stepanchuk
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, 630090, Russia
- Novosibirsk State University, Novosibirsk, 630090, Russia
| | - Miroslav A Nurridinov
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, 630090, Russia
- Novosibirsk State University, Novosibirsk, 630090, Russia
| | - Timofey A Lagunov
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, 630090, Russia
- Novosibirsk State University, Novosibirsk, 630090, Russia
| | - Nikita Yu Torgunakov
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, 630090, Russia
- Novosibirsk State University, Novosibirsk, 630090, Russia
| | - Artem A Shadsky
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, 630090, Russia
- Novosibirsk State University, Novosibirsk, 630090, Russia
| | - Anastasia I Ryabova
- Research Institute of Oncology, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634009, Russia
| | - Nikolay V Vasiliev
- Research Institute of Oncology, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634009, Russia
| | - Sergey V Vtorushin
- Research Institute of Oncology, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634009, Russia
- Siberian State Medical University, Ministry of Health of Russia, Tomsk, 634050, Russia
| | - Tatyana S Gerashchenko
- Research Institute of Oncology, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634009, Russia
| | - Evgeny V Denisov
- Research Institute of Oncology, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634009, Russia
| | - Mikhail A Travin
- Research Institute of Clinical and Experimental Lymphology, Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, 630117, Russia
| | - Maxim A Korolev
- Research Institute of Clinical and Experimental Lymphology, Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, 630117, Russia
| | - Veniamin S Fishman
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, 630090, Russia
- Novosibirsk State University, Novosibirsk, 630090, Russia
| |
Collapse
|
2
|
Lim J, Park C, Kim M, Kim H, Kim J, Lee DS. Advances in single-cell omics and multiomics for high-resolution molecular profiling. Exp Mol Med 2024; 56:515-526. [PMID: 38443594 PMCID: PMC10984936 DOI: 10.1038/s12276-024-01186-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 12/05/2023] [Accepted: 12/13/2023] [Indexed: 03/07/2024] Open
Abstract
Single-cell omics technologies have revolutionized molecular profiling by providing high-resolution insights into cellular heterogeneity and complexity. Traditional bulk omics approaches average signals from heterogeneous cell populations, thereby obscuring important cellular nuances. Single-cell omics studies enable the analysis of individual cells and reveal diverse cell types, dynamic cellular states, and rare cell populations. These techniques offer unprecedented resolution and sensitivity, enabling researchers to unravel the molecular landscape of individual cells. Furthermore, the integration of multimodal omics data within a single cell provides a comprehensive and holistic view of cellular processes. By combining multiple omics dimensions, multimodal omics approaches can facilitate the elucidation of complex cellular interactions, regulatory networks, and molecular mechanisms. This integrative approach enhances our understanding of cellular systems, from development to disease. This review provides an overview of the recent advances in single-cell and multimodal omics for high-resolution molecular profiling. We discuss the principles and methodologies for representatives of each omics method, highlighting the strengths and limitations of the different techniques. In addition, we present case studies demonstrating the applications of single-cell and multimodal omics in various fields, including developmental biology, neurobiology, cancer research, immunology, and precision medicine.
Collapse
Affiliation(s)
- Jongsu Lim
- Department of Life Science, University of Seoul, Seoul, 02504, Republic of Korea
| | - Chanho Park
- Department of Life Science, University of Seoul, Seoul, 02504, Republic of Korea
| | - Minjae Kim
- Department of Life Science, University of Seoul, Seoul, 02504, Republic of Korea
| | - Hyukhee Kim
- Department of Life Science, University of Seoul, Seoul, 02504, Republic of Korea
| | - Junil Kim
- School of Systems Biomedical Science, Soongsil University, Seoul, 06978, Republic of Korea
| | - Dong-Sung Lee
- Department of Life Science, University of Seoul, Seoul, 02504, Republic of Korea.
| |
Collapse
|
3
|
Lestringant V, Guermouche-Flament H, Jimenez-Pocquet M, Gaillard JB, Penther D. Cytogenetics in the management of hematological malignancies: An overview of alternative technologies for cytogenetic characterization. Curr Res Transl Med 2024; 72:103440. [PMID: 38447270 DOI: 10.1016/j.retram.2024.103440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 12/22/2023] [Accepted: 01/11/2024] [Indexed: 03/08/2024]
Abstract
Genomic characterization is an essential part of the clinical management of hematological malignancies for diagnostic, prognostic and therapeutic purposes. Although CBA and FISH are still the gold standard in hematology for the detection of CNA and SV, some alternative technologies are intended to complement their deficiencies or even replace them in the more or less near future. In this article, we provide a technological overview of these alternatives. CMA is the historical and well established technique for the high-resolution detection of CNA. For SV detection, there are emerging techniques based on the study of chromatin conformation and more established ones such as RTMLPA for the detection of fusion transcripts and RNA-seq to reveal the molecular consequences of SV. Comprehensive techniques that detect both CNA and SV are the most interesting because they provide all the information in a single examination. Among these, OGM is a promising emerging higher-solution technique that offers a complete solution at a contained cost, at the expense of a relatively low throughput per machine. WGS remains the most adaptable solution, with long-read approaches enabling very high-resolution detection of CAs, but requiring a heavy bioinformatics installation and at a still high cost. However, the development of high-resolution genome-wide detection techniques for CAs allows for a much better description of chromoanagenesis. Therefore, we have included in this review an update on the various existing mechanisms and their consequences and implications, especially prognostic, in hematological malignancies.
Collapse
Affiliation(s)
| | | | | | - Jean-Baptiste Gaillard
- Unité de Génétique Chromosomique, Service de Génétique moléculaire et cytogénomique, CHU Montpellier, Montpellier, France
| | | |
Collapse
|
4
|
Lin D, Zou Y, Li X, Wang J, Xiao Q, Gao X, Lin F, Zhang N, Jiao M, Guo Y, Teng Z, Li S, Wei Y, Zhou F, Yin R, Zhang S, Xing L, Xu W, Wu X, Yang B, Xiao K, Wu C, Tao Y, Yang X, Zhang J, Hu S, Dong S, Li X, Ye S, Hong Z, Pan Y, Yang Y, Sun H, Cao G. MGA-seq: robust identification of extrachromosomal DNA and genetic variants using multiple genetic abnormality sequencing. Genome Biol 2023; 24:247. [PMID: 37904244 PMCID: PMC10614391 DOI: 10.1186/s13059-023-03081-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Accepted: 10/04/2023] [Indexed: 11/01/2023] Open
Abstract
Genomic abnormalities are strongly associated with cancer and infertility. In this study, we develop a simple and efficient method - multiple genetic abnormality sequencing (MGA-Seq) - to simultaneously detect structural variation, copy number variation, single-nucleotide polymorphism, homogeneously staining regions, and extrachromosomal DNA (ecDNA) from a single tube. MGA-Seq directly sequences proximity-ligated genomic fragments, yielding a dataset with concurrent genome three-dimensional and whole-genome sequencing information, enabling approximate localization of genomic structural variations and facilitating breakpoint identification. Additionally, by utilizing MGA-Seq, we map focal amplification and oncogene coamplification, thus facilitating the exploration of ecDNA's transcriptional regulatory function.
Collapse
Affiliation(s)
- Da Lin
- Precision Research Center for Refractory Diseases, Institute for Clinical Research, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Yanyan Zou
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Xinyu Li
- Precision Research Center for Refractory Diseases, Institute for Clinical Research, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jinyue Wang
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Bio-Medicine and Health, Huazhong Agricultural University, Wuhan, China
- College of Life Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Qin Xiao
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Bio-Medicine and Health, Huazhong Agricultural University, Wuhan, China
- College of Life Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Xiaochen Gao
- Precision Research Center for Refractory Diseases, Institute for Clinical Research, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fei Lin
- Reproductive Medical Center, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Ningyuan Zhang
- Reproductive Medical Center, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Ming Jiao
- Department of Laboratory Animal Center, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yu Guo
- Department of Laboratory Animal Center, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhaowei Teng
- The First People's Hospital of Yunnan Province, Affiliated Hospital of Kunming University of Science and Technology, Kunming, China
| | - Shiyi Li
- Baylor College of Medicine, Houston, TX, USA
- Department of Radiation & Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Yongchang Wei
- Department of Radiation & Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China
- Hubei Key Laboratory of Tumor Biological Behaviors, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Fuling Zhou
- Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Rong Yin
- Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Siheng Zhang
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Lingyu Xing
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Weize Xu
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Xiaofeng Wu
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Bing Yang
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Ke Xiao
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Chengchao Wu
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Yingfeng Tao
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Xiaoqing Yang
- Hospital of Huazhong Agricultural University, Wuhan, China
| | - Jing Zhang
- Department of Medical Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Sheng Hu
- Department of Medical Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shuang Dong
- Department of Medical Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaoyu Li
- Department of Medical Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shengwei Ye
- Department of Gastrointestinal Surgery, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhidan Hong
- Dapartment of Reproductive Medicine Center, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Yihang Pan
- Precision Medicine Center, Scientific Research Center, School of Medicine, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China
| | - Yuqin Yang
- Department of Laboratory Animal Center, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Haixiang Sun
- Reproductive Medical Center, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China.
| | - Gang Cao
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China.
- College of Bio-Medicine and Health, Huazhong Agricultural University, Wuhan, China.
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China.
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
| |
Collapse
|
6
|
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. SCIENCE ADVANCES 2022; 8:eabn9215. [PMID: 35704579 PMCID: PMC9200291 DOI: 10.1126/sciadv.abn9215] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 04/28/2022] [Indexed: 05/11/2023]
Abstract
The Hi-C technique has been shown to be a promising method to detect structural variations (SVs) in human genomes. However, algorithms that can use Hi-C data for a full-range SV detection have been severely lacking. Current methods can only identify interchromosomal translocations and long-range intrachromosomal SVs (>1 Mb) at less-than-optimal resolution. Therefore, we develop EagleC, a framework that combines deep-learning and ensemble-learning strategies to predict a full range of SVs at high resolution. We show that EagleC can uniquely capture a set of fusion genes that are missed by whole-genome sequencing or nanopore. Furthermore, EagleC also effectively captures SVs in other chromatin interaction platforms, such as HiChIP, Chromatin interaction analysis with paired-end tag sequencing (ChIA-PET), and capture Hi-C. We apply EagleC in more than 100 cancer cell lines and primary tumors and identify a valuable set of high-quality SVs. Last, we demonstrate that EagleC can be applied to single-cell Hi-C and used to study the SV heterogeneity in primary tumors.
Collapse
Affiliation(s)
- Xiaotao Wang
- Department of Biochemistry and Molecular Genetics, Feinberg School of Medicine Northwestern University, Chicago, IL, USA
| | - Yu Luan
- Department of Biochemistry and Molecular Genetics, Feinberg School of Medicine Northwestern University, Chicago, IL, USA
| | - Feng Yue
- Department of Biochemistry and Molecular Genetics, Feinberg School of Medicine Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center of Northwestern University, Chicago, IL, USA
| |
Collapse
|