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Wang J, Li B, Luo M, Huang J, Zhang K, Zheng S, Zhang S, Zhou J. Progression from ductal carcinoma in situ to invasive breast cancer: molecular features and clinical significance. Signal Transduct Target Ther 2024; 9:83. [PMID: 38570490 PMCID: PMC10991592 DOI: 10.1038/s41392-024-01779-3] [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: 06/16/2023] [Revised: 02/14/2024] [Accepted: 02/26/2024] [Indexed: 04/05/2024] Open
Abstract
Ductal carcinoma in situ (DCIS) represents pre-invasive breast carcinoma. In untreated cases, 25-60% DCIS progress to invasive ductal carcinoma (IDC). The challenge lies in distinguishing between non-progressive and progressive DCIS, often resulting in over- or under-treatment in many cases. With increasing screen-detected DCIS in these years, the nature of DCIS has aroused worldwide attention. A deeper understanding of the biological nature of DCIS and the molecular journey of the DCIS-IDC transition is crucial for more effective clinical management. Here, we reviewed the key signaling pathways in breast cancer that may contribute to DCIS initiation and progression. We also explored the molecular features of DCIS and IDC, shedding light on the progression of DCIS through both inherent changes within tumor cells and alterations in the tumor microenvironment. In addition, valuable research tools utilized in studying DCIS including preclinical models and newer advanced technologies such as single-cell sequencing, spatial transcriptomics and artificial intelligence, have been systematically summarized. Further, we thoroughly discussed the clinical advancements in DCIS and IDC, including prognostic biomarkers and clinical managements, with the aim of facilitating more personalized treatment strategies in the future. Research on DCIS has already yielded significant insights into breast carcinogenesis and will continue to pave the way for practical clinical applications.
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Affiliation(s)
- Jing Wang
- The Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Breast Surgery and Oncology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, China
| | - Baizhou Li
- Department of Pathology, the Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China
| | - Meng Luo
- The Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, China
- Department of Plastic Surgery, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jia Huang
- The Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, China
| | - Kun Zhang
- Department of Breast Surgery and Oncology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shu Zheng
- The Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, China
| | - Suzhan Zhang
- The Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
- Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, China.
| | - Jiaojiao Zhou
- The Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
- Department of Breast Surgery and Oncology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
- Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, China.
- Cancer Center, Zhejiang University, Hangzhou, China.
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Fang X, Zhang Y, Miao R, Zhang Y, Yin R, Guan H, Huang X, Tian J. Single-cell sequencing: A promising approach for uncovering the characteristic of pancreatic islet cells in type 2 diabetes. Biomed Pharmacother 2024; 173:116292. [PMID: 38394848 DOI: 10.1016/j.biopha.2024.116292] [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: 12/07/2023] [Revised: 02/03/2024] [Accepted: 02/17/2024] [Indexed: 02/25/2024] Open
Abstract
Single-cell sequencing is a novel and rapidly advancing high-throughput technique that can be used to investigating genomics, transcriptomics, and epigenetics at a single-cell level. Currently, single-cell sequencing can not only be used to draw the pancreatic islet cells map and uncover the characteristics of cellular heterogeneity in type 2 diabetes, but can also be used to label and purify functional beta cells in pancreatic stem cells, improving stem cells and islet organoids therapies. In addition, this technology helps to analyze islet cell dedifferentiation and can be applied to the treatment of type 2 diabetes. In this review, we summarize the development and process of single-cell sequencing, describe the potential applications of single-cell sequencing in the field of type 2 diabetes, and discuss the prospects and limitations of single-cell sequencing to provide a new direction for exploring the pathogenesis of type 2 diabetes and finding therapeutic targets.
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Affiliation(s)
- Xinyi Fang
- Institute of Metabolic Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China; Graduate College, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Yanjiao Zhang
- Institute of Metabolic Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
| | - Runyu Miao
- Institute of Metabolic Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China; Graduate College, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Yuxin Zhang
- Institute of Metabolic Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
| | - Ruiyang Yin
- Institute of Metabolic Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
| | - Huifang Guan
- College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Jilin 130117, China
| | - Xinyue Huang
- First Clinical Medical College, Changzhi Medical College, Shanxi 046013, China
| | - Jiaxing Tian
- Institute of Metabolic Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China.
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Xu C, Shao J. High-throughput omics technologies in inflammatory bowel disease. Clin Chim Acta 2024; 555:117828. [PMID: 38355001 DOI: 10.1016/j.cca.2024.117828] [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: 07/23/2023] [Revised: 02/06/2024] [Accepted: 02/10/2024] [Indexed: 02/16/2024]
Abstract
Inflammatory bowel disease (IBD) is a chronic, relapsing intestinal disease. Elucidation of the pathogenic mechanisms of IBD requires high-throughput technologies (HTTs) to effectively obtain and analyze large amounts of data. Recently, HTTs have been widely used in IBD, including genomics, transcriptomics, proteomics, microbiomics, metabolomics and single-cell sequencing. When combined with endoscopy, the application of these technologies can provide an in-depth understanding on the alterations of intestinal microbe diversity and abundance, the abnormalities of signaling pathway-mediated immune responses and functionality, and the evaluation of therapeutic effects, improving the accuracy of early diagnosis and treatment of IBD. This review comprehensively summarizes the development and advancement of HTTs, and also highlights the challenges and future directions of these technologies in IBD research. Although HTTs have made striking breakthrough in IBD, more standardized methods and large-scale dataset processing are still needed to achieve the goal of personalized medicine.
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Affiliation(s)
- Chen Xu
- Laboratory of Anti-infection and Immunity, College of Integrated Chinese and Western Medicine (College of Life Science), Anhui University of Chinese Medicine, Zhijing Building, 350 Longzihu Road, Xinzhan District, Hefei 230012, Anhui, PR China
| | - Jing Shao
- Laboratory of Anti-infection and Immunity, College of Integrated Chinese and Western Medicine (College of Life Science), Anhui University of Chinese Medicine, Zhijing Building, 350 Longzihu Road, Xinzhan District, Hefei 230012, Anhui, PR China; Institute of Integrated Traditional Chinese and Western Medicine, Anhui Academy of Chinese Medicine, Zhijing Building, 350 Longzihu Road, Xinzhan District, Hefei 230012, Anhui, PR China.
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4
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Li Y, Li C, Wang Q, Ye YJ, Jiang KW. Transcriptomic and genomic profiling of multiple primary colorectal cancers reveals intratumor heterogeneity and a distinct immune microenvironment. Int Immunopharmacol 2024; 126:111276. [PMID: 38016348 DOI: 10.1016/j.intimp.2023.111276] [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: 09/09/2023] [Revised: 11/14/2023] [Accepted: 11/20/2023] [Indexed: 11/30/2023]
Abstract
This study reported on the intratumor genomic and immunological heterogeneity of different tumor lesions from a single patient with multiple primary colorectal cancer (MPCC). The goal of this study was to explore the molecular and microenvironment characteristics of tumor lesions from different primary sites in a patient with MPCC. A total of three tumor lesions located in the hepatic flexure of the transverse colon, sigmoid colon, and rectum were collected from a 72-year-old male patient with MPCC. All three tumor samples were examined by using whole-exome sequencing (WES) and single-cell RNA sequencing (scRNA-seq). The transcriptome data of The Cancer Genome Atlas (TCGA) colon cancer (COAD) dataset were explored to characterize the biological impacts of certain immune cells. Only three nonsynonymous mutations were shared by all of the tumor lesions, whereas a number of single nucleotide variant (SNV) and copy number variation (CNV) mutations were shared by tumor samples from the sigmoid colon and rectum. Transcriptomic analysis showed that tumor lesions derived from the transverse colon had decreased levels of RTK, ERK, and AKT pathway activity, thus suggesting lower oncogenic properties in the transverse lesion compared to the other two samples. Further immune landscape evaluation by using single-cell transcriptomic analysis displayed significant intratumor heterogeneity in MPCC. Specifically, more abundant mucosal-associated invariant T (MAIT) cell infiltration was found in transverse colon tumor lesions. Afterwards, we found that higher MAIT cell infiltration may correlate with a better prognosis of patients with colon cancer (immunohistochemical status was MSI-L/pMMR) by using a publicly available TCGA dataset.
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Affiliation(s)
- Yang Li
- Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Diseases, Beijing 100050, China; Department of Gastroenterological Surgery, Laboratory of Surgical Oncology, Beijing Key Laboratory of Colorectal Cancer Diagnosis and Treatment Research, Peking University People's Hospital, Beijing 100044, China
| | - Chen Li
- Department of Gastroenterological Surgery, Laboratory of Surgical Oncology, Beijing Key Laboratory of Colorectal Cancer Diagnosis and Treatment Research, Peking University People's Hospital, Beijing 100044, China
| | - Quan Wang
- Ambulatory Surgery Center, Xijing Hospital, Air Force Military Medical University, Xi'an 710032, China
| | - Ying-Jiang Ye
- Department of Gastroenterological Surgery, Laboratory of Surgical Oncology, Beijing Key Laboratory of Colorectal Cancer Diagnosis and Treatment Research, Peking University People's Hospital, Beijing 100044, China
| | - Ke-Wei Jiang
- Department of Gastroenterological Surgery, Laboratory of Surgical Oncology, Beijing Key Laboratory of Colorectal Cancer Diagnosis and Treatment Research, Peking University People's Hospital, Beijing 100044, China.
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Yoshida H. Dissecting the Immune System through Gene Regulation. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2024; 1444:219-235. [PMID: 38467983 DOI: 10.1007/978-981-99-9781-7_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
The immune system plays a dual role in human health, functioning both as a protector against pathogens and, at times, as a contributor to disease. This feature emphasizes the importance to uncover the underlying causes of its malfunctions, necessitating an in-depth analysis in both pathological and physiological conditions to better understand the immune system and immune disorders. Recent advances in scientific technology have enabled extensive investigations into gene regulation, a crucial mechanism governing cellular functionality. Studying gene regulatory mechanisms within the immune system is a promising avenue for enhancing our understanding of immune cells and the immune system as a whole. The gene regulatory mechanisms, revealed through various methodologies, and their implications in the field of immunology are discussed in this chapter.
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Affiliation(s)
- Hideyuki Yoshida
- YCI Laboratory for Immunological Transcriptomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
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6
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Ding Y, Peng YY, Li S, Tang C, Gao J, Wang HY, Long ZY, Lu XM, Wang YT. Single-Cell Sequencing Technology and Its Application in the Study of Central Nervous System Diseases. Cell Biochem Biophys 2023:10.1007/s12013-023-01207-3. [PMID: 38133792 DOI: 10.1007/s12013-023-01207-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 11/30/2023] [Indexed: 12/23/2023]
Abstract
The mammalian central nervous system consists of a large number of cells, which contain not only different types of neurons, but also a large number of glial cells, such as astrocytes, oligodendrocytes, and microglia. These cells are capable of performing highly refined electrophysiological activities and providing the brain with functions such as nutritional support, information transmission and pathogen defense. The diversity of cell types and individual differences between cells have brought inspiration to the study of the mechanism of central nervous system diseases. In order to explore the role of different cells, a new technology, single-cell sequencing technology has emerged to perform specific analysis of high-throughput cell populations, and has been continuously developed. Single-cell sequencing technology can accurately analyze single-cell expression in mixed-cell populations and collect cells from different spatial locations, time stages and types. By using single-cell sequencing technology to compare gene expression profiles of normal and diseased cells, it is possible to discover cell subsets associated with specific diseases and their associated genes. Therefore, scientists can understand the development process, related functions and disease state of the nervous system from an unprecedented depth. In conclusion, single-cell sequencing technology provides a powerful technology for the discovery of novel therapeutic targets for central nervous system diseases.
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Affiliation(s)
- Yang Ding
- College of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, 400054, China
- State Key Laboratory of Trauma and Chemical Poisoning, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - Yu-Yuan Peng
- College of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, 400054, China
| | - Sen Li
- State Key Laboratory of Trauma and Chemical Poisoning, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - Can Tang
- College of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, 400054, China
| | - Jie Gao
- College of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, 400054, China
| | - Hai-Yan Wang
- State Key Laboratory of Trauma and Chemical Poisoning, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - Zai-Yun Long
- State Key Laboratory of Trauma and Chemical Poisoning, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - Xiu-Min Lu
- College of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, 400054, China.
| | - Yong-Tang Wang
- State Key Laboratory of Trauma and Chemical Poisoning, Daping Hospital, Army Medical University, Chongqing, 400042, China.
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Zhang L, Bass HW, Irianto J, Mallory X. Integrating SNVs and CNAs on a phylogenetic tree from single-cell DNA sequencing data. Genome Res 2023; 33:gr.277249.122. [PMID: 37993137 PMCID: PMC10760445 DOI: 10.1101/gr.277249.122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 10/25/2023] [Indexed: 11/24/2023]
Abstract
Single-cell DNA sequencing enables the construction of evolutionary trees that can reveal how tumors gain mutations and grow. Different whole-genome amplification procedures render genomic materials of different characteristics, often suitable for the detection of either single-nucleotide variation or copy number aberration, but not ideally for both. Consequently, this hinders the inference of a comprehensive phylogenetic tree and limits opportunities to investigate the interplay of SNVs and CNAs. Existing methods such as SCARLET and COMPASS require that the SNVs and CNAs are detected from the same sets of cells, which is technically challenging. Here we present a novel computational tool, SCsnvcna, that places SNVs on a tree inferred from CNA signals, whereas the sets of cells rendering the SNVs and CNAs are independent, offering a more practical solution in terms of the technical challenges. SCsnvcna is a Bayesian probabilistic model using both the genotype constraints on the tree and the cellular prevalence to search the optimal solution. Comprehensive simulations and comparison with seven state-of-the-art methods show that SCsnvcna is robust and accurate in a variety of circumstances. Particularly, SCsnvcna most frequently produces the lowest error rates, with ability to scale to a wide range of numerical values for leaf nodes in the tree, SNVs, and SNV cells. The application of SCsnvcna to two published colorectal cancer data sets shows highly consistent placement of SNV cells and SNVs with the original study while also supporting a refined placement of ATP7B, illustrating SCsnvcna's value in analyzing complex multitumor samples.
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Affiliation(s)
- Liting Zhang
- Department of Computer Science, Florida State University, Tallahassee, Florida 32306, USA
| | - Hank W Bass
- Department of Biological Science, Florida State University, Tallahassee, Florida 32306, USA
| | - Jerome Irianto
- College of Medicine, Florida State University, Tallahassee, Florida 32306, USA
| | - Xian Mallory
- Department of Computer Science, Florida State University, Tallahassee, Florida 32306, USA;
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Al-Shuhaib MBS, Hashim HO. Mastering DNA chromatogram analysis in Sanger sequencing for reliable clinical analysis. J Genet Eng Biotechnol 2023; 21:115. [PMID: 37955813 PMCID: PMC10643650 DOI: 10.1186/s43141-023-00587-6] [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: 10/04/2023] [Accepted: 11/06/2023] [Indexed: 11/14/2023]
Abstract
BACKGROUND Sanger dideoxy sequencing is vital in clinical analysis due to its accuracy, ability to analyze genetic markers like SNPs and STRs, capability to generate reliable DNA profiles, and its role in resolving complex clinical cases. The precision and robustness of Sanger sequencing contribute significantly to the scientific basis of clinical investigations. Though the reading of chromatograms seems to be a routine step, many errors conducted in PCR may lead to consequent limitations in the readings of AGCT peaks. These errors are possibly associated with improper DNA amplification and its subsequent interpretation of DNA sequencing files, such as noisy peaks, artifacts, and confusion between double-peak technical errors, heterozygosity, and double infection potentials. Thus, it is not feasible to read nucleic acid sequences without giving serious attention to these technical problems. To ensure the accuracy of DNA sequencing outcomes, it is also imperative to detect and rectify technical challenges that may lead to misinterpretation of the DNA sequence, resulting in errors and incongruities in subsequent analyses. SHORT CONCLUSION This overview sheds light on prominent technical concerns that can emerge prior to and during the interpretation of DNA chromatograms in Sanger sequencing, along with offering strategies to address them effectively. The significance of identifying and tackling these technical limitations during the chromatogram analysis is underscored in this review. Recognizing these concerns can aid in enhancing the quality of downstream analyses for Sanger sequencing results, which holds notable improvement in accuracy, reliability, and ability to provide crucial genetic information in clinical analysis.
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Affiliation(s)
- Mohammed Baqur S Al-Shuhaib
- Department of Animal Production, College of Agriculture, Al-Qasim Green University, Al-Qasim 8, Babil, 51001, Iraq.
| | - Hayder O Hashim
- Department of Clinical Laboratory Sciences, College of Pharmacy, University of Babylon, Babil, 51001, Iraq
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Derks LLM, van Boxtel R. Stem cell mutations, associated cancer risk, and consequences for regenerative medicine. Cell Stem Cell 2023; 30:1421-1433. [PMID: 37832550 PMCID: PMC10624213 DOI: 10.1016/j.stem.2023.09.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 09/05/2023] [Accepted: 09/20/2023] [Indexed: 10/15/2023]
Abstract
Mutation accumulation in stem cells has been associated with cancer risk. However, the presence of numerous mutant clones in healthy tissues has raised the question of what limits cancer initiation. Here, we review recent developments in characterizing mutation accumulation in healthy tissues and compare mutation rates in stem cells during development and adult life with corresponding cancer risk. A certain level of mutagenesis within the stem cell pool might be beneficial to limit the size of malignant clones through competition. This knowledge impacts our understanding of carcinogenesis with potential consequences for the use of stem cells in regenerative medicine.
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Affiliation(s)
- Lucca L M Derks
- Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584 CS Utrecht, the Netherlands; Oncode Institute, Jaarbeursplein 6, 3521 AL Utrecht, the Netherlands
| | - Ruben van Boxtel
- Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584 CS Utrecht, the Netherlands; Oncode Institute, Jaarbeursplein 6, 3521 AL Utrecht, the Netherlands.
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Chen S, Zhou Z, Li Y, Du Y, Chen G. Application of single-cell sequencing to the research of tumor microenvironment. Front Immunol 2023; 14:1285540. [PMID: 37965341 PMCID: PMC10641410 DOI: 10.3389/fimmu.2023.1285540] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 10/17/2023] [Indexed: 11/16/2023] Open
Abstract
Single-cell sequencing is a technique for detecting and analyzing genomes, transcriptomes, and epigenomes at the single-cell level, which can detect cellular heterogeneity lost in conventional sequencing hybrid samples, and it has revolutionized our understanding of the genetic heterogeneity and complexity of tumor progression. Moreover, the tumor microenvironment (TME) plays a crucial role in the formation, development and response to treatment of tumors. The application of single-cell sequencing has ushered in a new age for the TME analysis, revealing not only the blueprint of the pan-cancer immune microenvironment, but also the heterogeneity and differentiation routes of immune cells, as well as predicting tumor prognosis. Thus, the combination of single-cell sequencing and the TME analysis provides a unique opportunity to unravel the molecular mechanisms underlying tumor development and progression. In this review, we summarize the recent advances in single-cell sequencing and the TME analysis, highlighting their potential applications in cancer research and clinical translation.
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Affiliation(s)
| | | | | | | | - Guoan Chen
- Department of Human Cell Biology and Genetics, Joint Laboratory of Guangdong-Hong Kong Universities for Vascular Homeostasis and Diseases, School of Medicine, Southern University of Science and Technology, Shenzhen, China
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Bao L, Kong H, Ja Y, Wang C, Qin L, Sun H, Dai S. The relationship between cancer and biomechanics. Front Oncol 2023; 13:1273154. [PMID: 37901315 PMCID: PMC10602664 DOI: 10.3389/fonc.2023.1273154] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 09/27/2023] [Indexed: 10/31/2023] Open
Abstract
The onset, development, diagnosis, and treatment of cancer involve intricate interactions among various factors, spanning the realms of mechanics, physics, chemistry, and biology. Within our bodies, cells are subject to a variety of forces such as gravity, magnetism, tension, compression, shear stress, and biological static force/hydrostatic pressure. These forces are perceived by mechanoreceptors as mechanical signals, which are then transmitted to cells through a process known as mechanical transduction. During tumor development, invasion and metastasis, there are significant biomechanical influences on various aspects such as tumor angiogenesis, interactions between tumor cells and the extracellular matrix (ECM), interactions between tumor cells and other cells, and interactions between tumor cells and the circulatory system and vasculature. The tumor microenvironment comprises a complex interplay of cells, ECM and vasculature, with the ECM, comprising collagen, fibronectins, integrins, laminins and matrix metalloproteinases, acting as a critical mediator of mechanical properties and a key component within the mechanical signaling pathway. The vasculature exerts appropriate shear forces on tumor cells, enabling their escape from immune surveillance, facilitating their dissemination in the bloodstream, dictating the trajectory of circulating tumor cells (CTCs) and playing a pivotal role in regulating adhesion to the vessel wall. Tumor biomechanics plays a critical role in tumor progression and metastasis, as alterations in biomechanical properties throughout the malignant transformation process trigger a cascade of changes in cellular behavior and the tumor microenvironment, ultimately culminating in the malignant biological behavior of the tumor.
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Affiliation(s)
- Liqi Bao
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
- Renji College, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Hongru Kong
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Yang Ja
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
- The First Clinical Medical College, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Chengchao Wang
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
- The First Clinical Medical College, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Lei Qin
- Department of General Surgery, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Hongwei Sun
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Shengjie Dai
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
- Department of General Surgery, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
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Khan R, Mallory X. Assessing the performance of methods for cell clustering from single-cell DNA sequencing data. PLoS Comput Biol 2023; 19:e1010480. [PMID: 37824596 PMCID: PMC10597505 DOI: 10.1371/journal.pcbi.1010480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 10/24/2023] [Accepted: 09/20/2023] [Indexed: 10/14/2023] Open
Abstract
BACKGROUND Many cancer genomes have been known to contain more than one subclone inside one tumor, the phenomenon of which is called intra-tumor heterogeneity (ITH). Characterizing ITH is essential in designing treatment plans, prognosis as well as the study of cancer progression. Single-cell DNA sequencing (scDNAseq) has been proven effective in deciphering ITH. Cells corresponding to each subclone are supposed to carry a unique set of mutations such as single nucleotide variations (SNV). While there have been many studies on the cancer evolutionary tree reconstruction, not many have been proposed that simply characterize the subclonality without tree reconstruction. While tree reconstruction is important in the study of cancer evolutionary history, typically they are computationally expensive in terms of running time and memory consumption due to the huge search space of the tree structure. On the other hand, subclonality characterization of single cells can be converted into a cell clustering problem, the dimension of which is much smaller, and the turnaround time is much shorter. Despite the existence of a few state-of-the-art cell clustering computational tools for scDNAseq, there lacks a comprehensive and objective comparison under different settings. RESULTS In this paper, we evaluated six state-of-the-art cell clustering tools-SCG, BnpC, SCClone, RobustClone, SCITE and SBMClone-on simulated data sets given a variety of parameter settings and a real data set. We designed a simulator specifically for cell clustering, and compared these methods' performances in terms of their clustering accuracy, specificity and sensitivity and running time. For SBMClone, we specifically designed an ultra-low coverage large data set to evaluate its performance in the face of an extremely high missing rate. CONCLUSION From the benchmark study, we conclude that BnpC and SCG's clustering accuracy are the highest and comparable to each other. However, BnpC is more advantageous in terms of running time when cell number is high (> 1500). It also has a higher clustering accuracy than SCG when cluster number is high (> 16). SCClone's accuracy in estimating the number of clusters is the highest. RobustClone and SCITE's clustering accuracy are the lowest for all experiments. SCITE tends to over-estimate the cluster number and has a low specificity, whereas RobustClone tends to under-estimate the cluster number and has a much lower sensitivity than other methods. SBMClone produced reasonably good clustering (V-measure > 0.9) when coverage is > = 0.03 and thus is highly recommended for ultra-low coverage large scDNAseq data sets.
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Affiliation(s)
- Rituparna Khan
- Department of Computer Science, Florida State University, Tallahassee, Florida, United States of America
| | - Xian Mallory
- Department of Computer Science, Florida State University, Tallahassee, Florida, United States of America
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13
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Han X, Xu X, Yang C, Liu G. Microfluidic design in single-cell sequencing and application to cancer precision medicine. CELL REPORTS METHODS 2023; 3:100591. [PMID: 37725985 PMCID: PMC10545941 DOI: 10.1016/j.crmeth.2023.100591] [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: 03/31/2023] [Revised: 07/01/2023] [Accepted: 08/24/2023] [Indexed: 09/21/2023]
Abstract
Single-cell sequencing (SCS) is a crucial tool to reveal the genetic and functional heterogeneity of tumors, providing unique insights into the clonal evolution, microenvironment, drug resistance, and metastatic progression of cancers. Microfluidics is a critical component of many SCS technologies and workflows, conferring advantages in throughput, economy, and automation. Here, we review the current landscape of microfluidic architectures and sequencing techniques for single-cell omics analysis and highlight how these have enabled recent applications in oncology research. We also discuss the challenges and the promise of microfluidics-based single-cell analysis in the future of precision oncology.
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Affiliation(s)
- Xin Han
- CUHK(SZ)-Boyalife Joint Laboratory of Regenerative Medicine Engineering, Biomedical Engineering Programme, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China; Ciechanover Institute of Precision and Regenerative Medicine, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Xing Xu
- State Key Laboratory of Physical Chemistry of Solid Surfaces, MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, Key Laboratory for Chemical Biology of Fujian Province, Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P.R. China; Institute of Molecular Medicine, State Key Laboratory of Oncogenes and Related 12 Genes, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200240, P.R. China
| | - Chaoyang Yang
- State Key Laboratory of Physical Chemistry of Solid Surfaces, MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, Key Laboratory for Chemical Biology of Fujian Province, Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P.R. China; Institute of Molecular Medicine, State Key Laboratory of Oncogenes and Related 12 Genes, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200240, P.R. China.
| | - Guozhen Liu
- CUHK(SZ)-Boyalife Joint Laboratory of Regenerative Medicine Engineering, Biomedical Engineering Programme, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China; Ciechanover Institute of Precision and Regenerative Medicine, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China.
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14
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Borgsmüller N, Valecha M, Kuipers J, Beerenwinkel N, Posada D. Single-cell phylogenies reveal changes in the evolutionary rate within cancer and healthy tissues. CELL GENOMICS 2023; 3:100380. [PMID: 37719146 PMCID: PMC10504633 DOI: 10.1016/j.xgen.2023.100380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 05/03/2023] [Accepted: 07/18/2023] [Indexed: 09/19/2023]
Abstract
Cell lineages accumulate somatic mutations during organismal development, potentially leading to pathological states. The rate of somatic evolution within a cell population can vary due to multiple factors, including selection, a change in the mutation rate, or differences in the microenvironment. Here, we developed a statistical test called the Poisson Tree (PT) test to detect varying evolutionary rates among cell lineages, leveraging the phylogenetic signal of single-cell DNA sequencing (scDNA-seq) data. We applied the PT test to 24 healthy and cancer samples, rejecting a constant evolutionary rate in 11 out of 15 cancer and five out of nine healthy scDNA-seq datasets. In six cancer datasets, we identified subclonal mutations in known driver genes that could explain the rate accelerations of particular cancer lineages. Our findings demonstrate the efficacy of scDNA-seq for studying somatic evolution and suggest that cell lineages often evolve at different rates within cancer and healthy tissues.
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Affiliation(s)
- Nico Borgsmüller
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, 4058 Basel, Switzerland
| | - Monica Valecha
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
| | - Jack Kuipers
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, 4058 Basel, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, 4058 Basel, Switzerland
| | - David Posada
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
- Department of Biochemistry, Genetics, and Immunology, Universidade de Vigo, 36310 Vigo, Spain
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15
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Kim J, Kim S, Yeom H, Song SW, Shin K, Bae S, Ryu HS, Kim JY, Choi A, Lee S, Ryu T, Choi Y, Kim H, Kim O, Jung Y, Kim N, Han W, Lee HB, Lee AC, Kwon S. Barcoded multiple displacement amplification for high coverage sequencing in spatial genomics. Nat Commun 2023; 14:5261. [PMID: 37644058 PMCID: PMC10465490 DOI: 10.1038/s41467-023-41019-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 08/21/2023] [Indexed: 08/31/2023] Open
Abstract
Determining mutational landscapes in a spatial context is essential for understanding genetically heterogeneous cell microniches. Current approaches, such as Multiple Displacement Amplification (MDA), offer high genome coverage but limited multiplexing, which hinders large-scale spatial genomic studies. Here, we introduce barcoded MDA (bMDA), a technique that achieves high-coverage genomic analysis of low-input DNA while enhancing the multiplexing capabilities. By incorporating cell barcodes during MDA, bMDA streamlines library preparation in one pot, thereby overcoming a key bottleneck in spatial genomics. We apply bMDA to the integrative spatial analysis of triple-negative breast cancer tissues by examining copy number alterations, single nucleotide variations, structural variations, and kataegis signatures for each spatial microniche. This enables the assessment of subclonal evolutionary relationships within a spatial context. Therefore, bMDA has emerged as a scalable technology with the potential to advance the field of spatial genomics significantly.
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Affiliation(s)
- Jinhyun Kim
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Sungsik Kim
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Huiran Yeom
- Division of Data Science, College of Information and Communication Technology, The University of Suwon, Hwaseong, 18323, Republic of Korea
| | - Seo Woo Song
- Basic Science and Engineering Initiative, Children's Heart Center, Stanford University, Stanford, CA, USA
| | - Kyoungseob Shin
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Sangwook Bae
- Renal Division and Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Han Suk Ryu
- Cancer Research Institute, Seoul National University, Seoul, 03080, Republic of Korea
- Department of Pathology, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea
| | - Ji Young Kim
- Biomedical Research Institute, Seoul National University Hospital, Seoul, 03080, Republic of Korea
| | - Ahyoun Choi
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Sumin Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea
- Meteor Biotech, Co. Ltd., Seoul, 08826, Republic of Korea
| | - Taehoon Ryu
- ATG Lifetech Inc., Seoul, 08507, Republic of Korea
| | - Yeongjae Choi
- School of Materials Science and Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005, Republic of Korea
| | - Hamin Kim
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Okju Kim
- ATG Lifetech Inc., Seoul, 08507, Republic of Korea
| | - Yushin Jung
- ATG Lifetech Inc., Seoul, 08507, Republic of Korea
| | - Namphil Kim
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Wonshik Han
- Cancer Research Institute, Seoul National University, Seoul, 03080, Republic of Korea
- Biomedical Research Institute, Seoul National University Hospital, Seoul, 03080, Republic of Korea
- Department of Surgery, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea
| | - Han-Byoel Lee
- Cancer Research Institute, Seoul National University, Seoul, 03080, Republic of Korea.
- Biomedical Research Institute, Seoul National University Hospital, Seoul, 03080, Republic of Korea.
- Department of Surgery, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea.
| | - Amos C Lee
- Bio-MAX Institute, Seoul National University, Seoul, 08826, Republic of Korea.
- Meteor Biotech, Co. Ltd., Seoul, 08826, Republic of Korea.
| | - Sunghoon Kwon
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea.
- Biomedical Research Institute, Seoul National University Hospital, Seoul, 03080, Republic of Korea.
- Bio-MAX Institute, Seoul National University, Seoul, 08826, Republic of Korea.
- Inter-University Semiconductor Research Center, Seoul National University, Seoul, 08826, Republic of Korea.
- Institutes of Entrepreneurial BioConvergence, Seoul National University, Seoul, 08826, Republic of Korea.
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16
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Zhu Z, Jiang L, Ding X. Advancing Breast Cancer Heterogeneity Analysis: Insights from Genomics, Transcriptomics and Proteomics at Bulk and Single-Cell Levels. Cancers (Basel) 2023; 15:4164. [PMID: 37627192 PMCID: PMC10452610 DOI: 10.3390/cancers15164164] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 07/23/2023] [Accepted: 08/16/2023] [Indexed: 08/27/2023] Open
Abstract
Breast cancer continues to pose a significant healthcare challenge worldwide for its inherent molecular heterogeneity. This review offers an in-depth assessment of the molecular profiling undertaken to understand this heterogeneity, focusing on multi-omics strategies applied both in traditional bulk and single-cell levels. Genomic investigations have profoundly informed our comprehension of breast cancer, enabling its categorization into six intrinsic molecular subtypes. Beyond genomics, transcriptomics has rendered deeper insights into the gene expression landscape of breast cancer cells. It has also facilitated the formulation of more precise predictive and prognostic models, thereby enriching the field of personalized medicine in breast cancer. The comparison between traditional and single-cell transcriptomics has identified unique gene expression patterns and facilitated the understanding of cell-to-cell variability. Proteomics provides further insights into breast cancer subtypes by illuminating intricate protein expression patterns and their post-translational modifications. The adoption of single-cell proteomics has been instrumental in this regard, revealing the complex dynamics of protein regulation and interaction. Despite these advancements, this review underscores the need for a holistic integration of multiple 'omics' strategies to fully decipher breast cancer heterogeneity. Such integration not only ensures a comprehensive understanding of breast cancer's molecular complexities, but also promotes the development of personalized treatment strategies.
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Affiliation(s)
- Zijian Zhu
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai 200030, China;
| | - Lai Jiang
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200025, China;
| | - Xianting Ding
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai 200030, China;
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200025, China;
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17
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Massimino M, Martorana F, Stella S, Vitale SR, Tomarchio C, Manzella L, Vigneri P. Single-Cell Analysis in the Omics Era: Technologies and Applications in Cancer. Genes (Basel) 2023; 14:1330. [PMID: 37510235 PMCID: PMC10380065 DOI: 10.3390/genes14071330] [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: 05/22/2023] [Revised: 06/16/2023] [Accepted: 06/20/2023] [Indexed: 07/30/2023] Open
Abstract
Cancer molecular profiling obtained with conventional bulk sequencing describes average alterations obtained from the entire cellular population analyzed. In the era of precision medicine, this approach is unable to track tumor heterogeneity and cannot be exploited to unravel the biological processes behind clonal evolution. In the last few years, functional single-cell omics has improved our understanding of cancer heterogeneity. This approach requires isolation and identification of single cells starting from an entire population. A cell suspension obtained by tumor tissue dissociation or hematological material can be manipulated using different techniques to separate individual cells, employed for single-cell downstream analysis. Single-cell data can then be used to analyze cell-cell diversity, thus mapping evolving cancer biological processes. Despite its unquestionable advantages, single-cell analysis produces massive amounts of data with several potential biases, stemming from cell manipulation and pre-amplification steps. To overcome these limitations, several bioinformatic approaches have been developed and explored. In this work, we provide an overview of this entire process while discussing the most recent advances in the field of functional omics at single-cell resolution.
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Affiliation(s)
- Michele Massimino
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
| | - Federica Martorana
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
| | - Stefania Stella
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
| | - Silvia Rita Vitale
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
| | - Cristina Tomarchio
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
| | - Livia Manzella
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
| | - Paolo Vigneri
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
- Humanitas Istituto Clinico Catanese, University Oncology Department, 95045 Catania, Italy
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18
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Chen M, Jiang J, Hou J. Single-cell technologies in multiple myeloma: new insights into disease pathogenesis and translational implications. Biomark Res 2023; 11:55. [PMID: 37259170 DOI: 10.1186/s40364-023-00502-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Accepted: 05/12/2023] [Indexed: 06/02/2023] Open
Abstract
Multiple myeloma (MM) is a hematological malignancy characterized by clonal proliferation of plasma cells. Although therapeutic advances have been made to improve clinical outcomes and to prolong patients' survival in the past two decades, MM remains largely incurable. Single-cell sequencing (SCS) is a powerful method to dissect the cellular and molecular landscape at single-cell resolution, instead of providing averaged results. The application of single-cell technologies promises to address outstanding questions in myeloma biology and has revolutionized our understanding of the inter- and intra-tumor heterogeneity, tumor microenvironment, and mechanisms of therapeutic resistance in MM. In this review, we summarize the recently developed SCS methodologies and latest MM research progress achieved by single-cell profiling, including information regarding the cancer and immune cell landscapes, tumor heterogeneities, underlying mechanisms and biomarkers associated with therapeutic response and resistance. We also discuss future directions of applying transformative SCS approaches with contribution to clinical translation.
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Affiliation(s)
- Mengping Chen
- Department of Hematology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Jinxing Jiang
- Department of Hematology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Jian Hou
- Department of Hematology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China.
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19
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Chen W, Xu D, Liu Q, Wu Y, Wang Y, Yang J. Unraveling the heterogeneity of cholangiocarcinoma and identifying biomarkers and therapeutic strategies with single-cell sequencing technology. Biomed Pharmacother 2023; 162:114697. [PMID: 37060660 DOI: 10.1016/j.biopha.2023.114697] [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: 02/21/2023] [Revised: 04/06/2023] [Accepted: 04/10/2023] [Indexed: 04/17/2023] Open
Abstract
Cholangiocarcinoma (CCA) is a common malignant tumor of the biliary tract that carries a high burden of morbidity and a poor prognosis. Due to the lack of precise diagnostic methods, many patients are often diagnosed at advanced stages of the disease. The current treatment options available are of varying efficacy, underscoring the urgency for the discovery of more effective biomarkers for early diagnosis and improved treatment. Recently, single-cell sequencing (SCS) technology has gained popularity in cancer research. This technology has the ability to analyze tumor tissues at the single-cell level, thus providing insights into the genomics and epigenetics of tumor cells. It also serves as a practical approach to study the mechanisms of cancer progression and to explore therapeutic strategies. In this review, we aim to assess the heterogeneity of CCA using single-cell sequencing technology, with the ultimate goal of identifying possible biomarkers and potential treatment targets.
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Affiliation(s)
- Wangyang Chen
- Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province 310003, China; Department of Gastroenterology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province 310003, China; Key Laboratory of Integrated Traditional Chinese and Western Medicine for Biliary and Pancreatic Diseases of Zhejiang Province, Hangzhou, Zhejiang Province 310003, China; Hangzhou Institute of Digestive Diseases, Hangzhou, Zhejiang Province 310003, China
| | - Dongchao Xu
- Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province 310003, China; Department of Gastroenterology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province 310003, China; Key Laboratory of Integrated Traditional Chinese and Western Medicine for Biliary and Pancreatic Diseases of Zhejiang Province, Hangzhou, Zhejiang Province 310003, China; Hangzhou Institute of Digestive Diseases, Hangzhou, Zhejiang Province 310003, China
| | - Qiang Liu
- Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province 310003, China; Department of Gastroenterology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province 310003, China; Key Laboratory of Integrated Traditional Chinese and Western Medicine for Biliary and Pancreatic Diseases of Zhejiang Province, Hangzhou, Zhejiang Province 310003, China; Hangzhou Institute of Digestive Diseases, Hangzhou, Zhejiang Province 310003, China
| | - Yirong Wu
- Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province 310003, China
| | - Yu Wang
- Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province 310003, China; Department of Gastroenterology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province 310003, China; Key Laboratory of Integrated Traditional Chinese and Western Medicine for Biliary and Pancreatic Diseases of Zhejiang Province, Hangzhou, Zhejiang Province 310003, China; Hangzhou Institute of Digestive Diseases, Hangzhou, Zhejiang Province 310003, China.
| | - Jianfeng Yang
- Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province 310003, China; Department of Gastroenterology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province 310003, China; Key Laboratory of Integrated Traditional Chinese and Western Medicine for Biliary and Pancreatic Diseases of Zhejiang Province, Hangzhou, Zhejiang Province 310003, China; Hangzhou Institute of Digestive Diseases, Hangzhou, Zhejiang Province 310003, China; Key Laboratory of Integrated Oncology and Intelligent Medicine of Zhejiang Province, Hangzhou, Zhejiang Province 310003, China; Zhejiang Provincial Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research, Hangzhou, Zhejiang Province 310003, China.
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20
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Zhang N, Kandalai S, Zhou X, Hossain F, Zheng Q. Applying multi-omics toward tumor microbiome research. IMETA 2023; 2:e73. [PMID: 38868335 PMCID: PMC10989946 DOI: 10.1002/imt2.73] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 10/30/2022] [Accepted: 11/28/2022] [Indexed: 06/14/2024]
Abstract
Rather than a "short-term tenant," the tumor microbiome has been shown to play a vital role as a "permanent resident," affecting carcinogenesis, cancer development, metastasis, and cancer therapies. As the tumor microbiome has great potential to become a target for the early diagnosis and treatment of cancer, recent research on the relevance of the tumor microbiota has attracted a wide range of attention from various scientific fields, resulting in remarkable progress that benefits from the development of interdisciplinary technologies. However, there are still a great variety of challenges in this emerging area, such as the low biomass of intratumoral bacteria and unculturable character of some microbial species. Due to the complexity of tumor microbiome research (e.g., the heterogeneity of tumor microenvironment), new methods with high spatial and temporal resolution are urgently needed. Among these developing methods, multi-omics technologies (combinations of genomics, transcriptomics, proteomics, and metabolomics) are powerful approaches that can facilitate the understanding of the tumor microbiome on different levels of the central dogma. Therefore, multi-omics (especially single-cell omics) will make enormous impacts on the future studies of the interplay between microbes and tumor microenvironment. In this review, we have systematically summarized the advances in multi-omics and their existing and potential applications in tumor microbiome research, thus providing an omics toolbox for investigators to reference in the future.
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Affiliation(s)
- Nan Zhang
- Department of Radiation Oncology, College of MedicineThe Ohio State UniversityColumbusOhioUSA
- Center for Cancer Metabolism, Ohio State University Comprehensive Cancer Center ‐ James Cancer Hospital and Solove Research InstituteThe Ohio State UniversityOhioColumbusUSA
| | - Shruthi Kandalai
- Department of Radiation Oncology, College of MedicineThe Ohio State UniversityColumbusOhioUSA
- Center for Cancer Metabolism, Ohio State University Comprehensive Cancer Center ‐ James Cancer Hospital and Solove Research InstituteThe Ohio State UniversityOhioColumbusUSA
| | - Xiaozhuang Zhou
- Department of Radiation Oncology, College of MedicineThe Ohio State UniversityColumbusOhioUSA
- Center for Cancer Metabolism, Ohio State University Comprehensive Cancer Center ‐ James Cancer Hospital and Solove Research InstituteThe Ohio State UniversityOhioColumbusUSA
| | - Farzana Hossain
- Department of Radiation Oncology, College of MedicineThe Ohio State UniversityColumbusOhioUSA
- Center for Cancer Metabolism, Ohio State University Comprehensive Cancer Center ‐ James Cancer Hospital and Solove Research InstituteThe Ohio State UniversityOhioColumbusUSA
| | - Qingfei Zheng
- Department of Radiation Oncology, College of MedicineThe Ohio State UniversityColumbusOhioUSA
- Center for Cancer Metabolism, Ohio State University Comprehensive Cancer Center ‐ James Cancer Hospital and Solove Research InstituteThe Ohio State UniversityOhioColumbusUSA
- Department of Biological Chemistry and Pharmacology, College of MedicineThe Ohio State UniversityColumbusOhioUSA
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21
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O'Sullivan JM, Mead AJ, Psaila B. Single-cell methods in myeloproliferative neoplasms: old questions, new technologies. Blood 2023; 141:380-390. [PMID: 36322938 DOI: 10.1182/blood.2021014668] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 10/14/2022] [Accepted: 10/14/2022] [Indexed: 11/05/2022] Open
Abstract
Myeloproliferative neoplasms (MPN) are a group of clonal stem cell-derived hematopoietic malignancies driven by aberrant Janus kinase-signal transducer and activator of transcription proteins (JAK/STAT) signaling. Although these are genetically simple diseases, MPNs are phenotypically heterogeneous, reflecting underlying intratumoral heterogeneity driven by the interplay of genetic and nongenetic factors. Their evolution is determined by factors that enable certain cellular subsets to outcompete others. Therefore, techniques that resolve cellular heterogeneity at the single-cell level are ideally placed to provide new insights into MPN biology. With these insights comes the potential to uncover new approaches to predict the clinical course and treat these cancers, ultimately improving outcomes for patients. MPNs present a particularly tractable model of cancer evolution, because most patients present in an early disease phase and only a small proportion progress to aggressive disease. Therefore, it is not surprising that many groundbreaking technological advances in single-cell omics have been pioneered by their application in MPNs. In this review article, we explore how single-cell approaches have provided transformative insights into MPN disease biology, which are broadly applicable across human cancers, and discuss how these studies might be swiftly translated into clinical pathways and may eventually underpin precision medicine.
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Affiliation(s)
- Jennifer Mary O'Sullivan
- Medical Research Council Molecular Haematology Unit, Medical Research Council Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, United Kingdom
- NIHR Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
| | - Adam J Mead
- Medical Research Council Molecular Haematology Unit, Medical Research Council Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, United Kingdom
- NIHR Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
| | - Bethan Psaila
- Medical Research Council Molecular Haematology Unit, Medical Research Council Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, United Kingdom
- NIHR Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
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22
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Yan J, Ma M, Yu Z. bmVAE: a variational autoencoder method for clustering single-cell mutation data. Bioinformatics 2022; 39:6881080. [PMID: 36478203 PMCID: PMC9825778 DOI: 10.1093/bioinformatics/btac790] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 10/26/2022] [Accepted: 12/06/2022] [Indexed: 12/13/2022] Open
Abstract
MOTIVATION Genetic intra-tumor heterogeneity (ITH) characterizes the differences in genomic variations between tumor clones, and accurately unmasking ITH is important for personalized cancer therapy. Single-cell DNA sequencing now emerges as a powerful means for deciphering underlying ITH based on point mutations of single cells. However, detecting tumor clones from single-cell mutation data remains challenging due to the error-prone and discrete nature of the data. RESULTS We introduce bmVAE, a bioinformatics tool for learning low-dimensional latent representation of single cell based on a variational autoencoder and then clustering cells into subpopulations in the latent space. bmVAE takes single-cell binary mutation data as inputs, and outputs inferred cell subpopulations as well as their genotypes. To achieve this, the bmVAE framework is designed to consist of three modules including dimensionality reduction, cell clustering and genotype estimation. We assess the method on various synthetic datasets where different factors including false negative rate, data size and data heterogeneity are considered in simulation, and further demonstrate its effectiveness on two real datasets. The results suggest bmVAE is highly effective in reasoning ITH, and performs competitive to existing methods. AVAILABILITY AND IMPLEMENTATION bmVAE is freely available at https://github.com/zhyu-lab/bmvae. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jiaqian Yan
- School of Information Engineering, Ningxia University, Yinchuan 750021, China
| | - Ming Ma
- School of Information Engineering, Ningxia University, Yinchuan 750021, China
| | - Zhenhua Yu
- To whom correspondence should be addressed.
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Zhang K, Fu Y, Ma C, Wang Y, Zhang J, Lang J, Han K, Chen Y, Yu F, Zhang L. First detection of coccidia species in blue peafowl (Pavo cristatus) in China via both microscopic and molecular methods. Vet Parasitol Reg Stud Reports 2022; 36:100807. [PMID: 36436894 DOI: 10.1016/j.vprsr.2022.100807] [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: 03/30/2022] [Revised: 10/24/2022] [Accepted: 10/30/2022] [Indexed: 11/09/2022]
Abstract
Coccidia are protozoan parasites in the class Conoidasida. To determine the prevalence and molecular characteristics of coccidia species in blue peafowl (Pavo cristatus) in Henan, China, 240 fecal specimens were collected and screened for the presence of Eimeria spp. and Isospora spp. The overall prevalence was 65.0% (156/240), and seven different coccidia species were identified: E. pavonis (51.3%, 123/240), E. arabic (40.0%, 96/240), E. riyadhae (37.1%, 89/240), E. mandali (22.9%, 55/240), E. mayurai (14.2%, 34/240), I. mayuir (10.9%, 26/240), and I. lacazei (8.5%, 21/240). E. arabic and E. riyadhae were detected for the first time in China. Additionally, we provide molecular data of the seven different coccidia species at the 18S and 28S ribosomal RNA and the COI loci. Sequence homology percentages among the five species of Eimeria at the 18S rRNA, 28S rRNA, and COI loci were 96.0%-98.6%, 90.7%-98.2%, and 85.0%-94.9%, respectively, whereas for two species of Isospora the sequence homology percentages were 98.8%, 99.1%, and 95.4% at three corresponding loci. This is the first report of the molecular data of the seven coccidia species in blue peafowl in China.
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Affiliation(s)
- Kaihui Zhang
- College of Veterinary Medicine, Henan Agricultural University, Zhengzhou 450002, Henan Province, China; International Joint Research Laboratory for Zoonotic Diseases of Henan, Zhengzhou 450002, Henan Province, China
| | - Yin Fu
- College of Veterinary Medicine, Henan Agricultural University, Zhengzhou 450002, Henan Province, China; International Joint Research Laboratory for Zoonotic Diseases of Henan, Zhengzhou 450002, Henan Province, China
| | - Chaofeng Ma
- Animal Disease Prevention and Control Center of Xinyang City, Henan Province, China
| | - Yuexin Wang
- College of Veterinary Medicine, Henan Agricultural University, Zhengzhou 450002, Henan Province, China; International Joint Research Laboratory for Zoonotic Diseases of Henan, Zhengzhou 450002, Henan Province, China
| | - Junchen Zhang
- College of Veterinary Medicine, Henan Agricultural University, Zhengzhou 450002, Henan Province, China; International Joint Research Laboratory for Zoonotic Diseases of Henan, Zhengzhou 450002, Henan Province, China
| | - Jiashu Lang
- College of Veterinary Medicine, Henan Agricultural University, Zhengzhou 450002, Henan Province, China; International Joint Research Laboratory for Zoonotic Diseases of Henan, Zhengzhou 450002, Henan Province, China
| | - Kelei Han
- College of Veterinary Medicine, Henan Agricultural University, Zhengzhou 450002, Henan Province, China; International Joint Research Laboratory for Zoonotic Diseases of Henan, Zhengzhou 450002, Henan Province, China
| | - Yuancai Chen
- College of Veterinary Medicine, Henan Agricultural University, Zhengzhou 450002, Henan Province, China; International Joint Research Laboratory for Zoonotic Diseases of Henan, Zhengzhou 450002, Henan Province, China
| | - Fuchang Yu
- College of Veterinary Medicine, Henan Agricultural University, Zhengzhou 450002, Henan Province, China; International Joint Research Laboratory for Zoonotic Diseases of Henan, Zhengzhou 450002, Henan Province, China
| | - Longxian Zhang
- College of Veterinary Medicine, Henan Agricultural University, Zhengzhou 450002, Henan Province, China; International Joint Research Laboratory for Zoonotic Diseases of Henan, Zhengzhou 450002, Henan Province, China.
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Qin R, Zhao H, He Q, Li F, Li Y, Zhao H. Advances in single-cell sequencing technology in the field of hepatocellular carcinoma. Front Genet 2022; 13:996890. [PMID: 36303541 PMCID: PMC9592975 DOI: 10.3389/fgene.2022.996890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 09/28/2022] [Indexed: 11/13/2022] Open
Abstract
Tumors are a class of diseases characterized by altered genetic information and uncontrolled growth. Sequencing technology provide researchers with a better way to explore specific tumor pathogenesis. In recent years, single-cell sequencing technology has shone in tumor research, especially in the study of liver cancer, revealing phenomena that were unexplored by previous studies. Single-cell sequencing (SCS) is a technique for sequencing the cellular genome, transcriptome, epigenome, proteomics, or metabolomics after dissociation of tissues into single cells. Compared with traditional bulk sequencing, single-cell sequencing can dissect human tumors at single-cell resolution, finely delineate different cell types, and reveal the heterogeneity of tumor cells. In view of the diverse pathological types and complex pathogenesis of hepatocellular carcinoma (HCC), the study of the heterogeneity among tumor cells can help improve its clinical diagnosis, treatment and prognostic judgment. On this basis, SCS has revolutionized our understanding of tumor heterogeneity, tumor immune microenvironment, and clonal evolution of tumor cells. This review summarizes the basic process and development of single-cell sequencing technology and its increasing role in the field of hepatocellular carcinoma.
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Affiliation(s)
- Rongyi Qin
- Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, China
| | - Haichao Zhao
- Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, China
- Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China
| | - Qizu He
- Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, China
| | - Feng Li
- Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, China
| | - Yanjun Li
- Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, China
- Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China
- *Correspondence: Yanjun Li, ; Haoliang Zhao,
| | - Haoliang Zhao
- Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, China
- Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China
- *Correspondence: Yanjun Li, ; Haoliang Zhao,
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Su X, Bai S, Xie G, Shi Y, Zhao L, Yang G, Tian F, He KY, Wang L, Li X, Long Q, Han ZG. Accurate tumor clonal structures require single-cell analysis. Ann N Y Acad Sci 2022; 1517:213-224. [PMID: 36081327 DOI: 10.1111/nyas.14897] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Tumor clonal structure is closely related to future progression, which has been mainly investigated as mutation abundance clustering in bulk samples. With relatively limited studies at single-cell resolution, a systematic comparison of the two approaches is still lacking. Here, using bulk and single-cell mutational data from the liver and colorectal cancers, we checked whether co-mutations determined by single-cell analysis had corresponding bulk variant allele frequency (VAF) peaks. While bulk analysis suggested the absence of subclonal peaks and, possibly, neutral evolution in some cases, the single-cell analysis identified coexisting subclones. The overlaps of bulk VAF ranges for co-mutations from different subclones made it difficult to separate them. Complex subclonal structures and dynamic evolution could be hidden under the seemingly clonal neutral pattern at the bulk level, suggesting single-cell analysis is necessary to avoid underestimation of tumor heterogeneity.
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Affiliation(s)
- Xianbin Su
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
| | - Shihao Bai
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
| | - Gangcai Xie
- Institute of Reproductive Medicine, Medical School of Nantong University, Nantong, China
| | - Yi Shi
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, Shanghai, China
| | - Linan Zhao
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
| | - Guoliang Yang
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Futong Tian
- Department of Design, Politecnico di Milano, Milan, Italy
| | - Kun-Yan He
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
| | - Lan Wang
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaolin Li
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
| | - Qi Long
- Joint School of Life Sciences, Guangzhou Medical University & Guangzhou Institutes of Biomedicine and Health-Chinese Academy of Sciences, Guangzhou, China
| | - Ze-Guang Han
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
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Liu Y, Chen L, Yu J, Ye L, Hu H, Wang J, Wu B. Advances in Single-Cell Toxicogenomics in Environmental Toxicology. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:11132-11145. [PMID: 35881918 DOI: 10.1021/acs.est.2c01098] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The toxicity evaluation system of environmental pollutants has undergone numerous changes due to the application of new technologies. Single-cell toxicogenomics is rapidly changing our view on environmental toxicology by increasing the resolution of our analysis to the level of a single cell. Applications of this technology in environmental toxicology have begun to emerge and are rapidly expanding the portfolio of existing technologies and applications. Here, we first summarized different methods involved in single-cell isolation and amplification in single-cell sequencing process, compared the advantages and disadvantages of different methods, and analyzed their development trends. Then, we reviewed the main advances of single-cell toxicogenomics in environmental toxicology, emphatically analyzed the application prospects of this technology in identifying the target cells of pollutants in early embryos, clarifying the heterogeneous response of cell subtypes to pollutants, and finding pathogenic bacteria in unknown microbes, and highlighted the unique characteristics of this approach with high resolution, high throughput, and high specificity by examples. We also offered a prediction of the further application of this technology and the revolution it brings in environmental toxicology. Overall, these advances will provide practical solutions for controlling or mitigating exogenous toxicological effects that threaten human and ecosystem health, contribute to improving our understanding of the physiological processes affected by pollutants, and lead to the emergence of new methods of pollution control.
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Affiliation(s)
- Yuxuan Liu
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, PR China
| | - Ling Chen
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, PR China
| | - Jing Yu
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, PR China
| | - Lin Ye
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, PR China
| | - Haidong Hu
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, PR China
| | - Jinfeng Wang
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, PR China
| | - Bing Wu
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, PR China
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Zheng Y, Yang X. Application and prospect of single-cell sequencing in cancer metastasis. Future Oncol 2022; 18:2723-2736. [PMID: 35686493 DOI: 10.2217/fon-2022-0156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Cancer metastasis is a complicated process driven by internal genetic variations and developed through interactions with the external environment. This process usually causes therapeutic resistance and results in a low survival rate. In recent years, single-cell sequencing has become a popular method for revealing the tumor evolutionary genetic lineage, intra-tumoral heterogeneity and tumor microenvironment of the metastasis process. So as to find more therapeutic targets for clinical application, the spatial transcriptomics method has become a new rising field of cancer studies, which promotes the combination between clinical medicine and molecular biology. In future prospects, more accurate and personalized treatment models will come into reality.
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Affiliation(s)
- Yue Zheng
- Department of Biochemistry & Molecular Biology, Basic Medical College, Shanxi Medical University, Taiyuan City, Shanxi Province, 030000, China
| | - Xiaofeng Yang
- Department of Urology, First Hospital of Shanxi Medical University,Taiyuan City, Shanxi Province, 030000, China
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28
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Dai X, Cai L, He F. Single-cell sequencing: expansion, integration and translation. Brief Funct Genomics 2022; 21:280-295. [PMID: 35753690 DOI: 10.1093/bfgp/elac011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 05/16/2022] [Accepted: 05/24/2022] [Indexed: 12/11/2022] Open
Abstract
With the rapid advancement in sequencing technologies, the concept of omics has revolutionized our understanding of cellular behaviors. Conventional omics investigation approaches measure the averaged behaviors of multiple cells, which may easily hide signals represented by a small-cell cohort, urging for the development of techniques with enhanced resolution. Single-cell RNA sequencing, investigating cell transcriptomics at the resolution of a single cell, has been rapidly expanded to investigate other omics such as genomics, proteomics and metabolomics since its invention. The requirement for comprehensive understanding of complex cellular behavior has led to the integration of multi-omics and single-cell sequencing data with other layers of information such as spatial data and the CRISPR screening technique towards gained knowledge or innovative functionalities. The development of single-cell sequencing in both dimensions has rendered it a unique field that offers us a versatile toolbox to delineate complex diseases, including cancers.
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Fanalli SL, da Silva BPM, Gomes JD, de Almeida VV, Freitas FAO, Moreira GCM, Silva-Vignato B, Afonso J, Reecy J, Koltes J, Koltes D, de Almeida Regitano LC, Garrick DJ, de Carvalho Balieiro JC, Meira AN, Freitas L, Coutinho LL, Fukumasu H, Mourão GB, de Alencar SM, Luchiari Filho A, Cesar ASM. Differential Gene Expression Associated with Soybean Oil Level in the Diet of Pigs. Animals (Basel) 2022; 12:ani12131632. [PMID: 35804531 PMCID: PMC9265114 DOI: 10.3390/ani12131632] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 06/15/2022] [Accepted: 06/20/2022] [Indexed: 12/21/2022] Open
Abstract
Simple Summary Findings from the analysis of the pig transcriptome may help to better understand the biological mechanisms that can be modulated by the diet. Thus, the aim of this study was to identify the differentially expressed genes from the skeletal muscle and liver samples of pigs fed diets with two different levels of soybean oil (1.5 or 3%). The FA profile in the tissues was modified by the diet mainly related to monounsaturated (MUFA) and polyunsaturated (PUFA). This nutrigenomics study verified the effect of different levels of soybean oil in the pig diet on the transcriptome profile of skeletal muscle and liver, where the higher level of soybean oil added to the diet led to a higher expression of genes targeting biological processes related to lipid oxidation and consequently to metabolic diseases and inflammation. Abstract The aim of this study was to identify the differentially expressed genes (DEG) from the skeletal muscle and liver samples of animal models for metabolic diseases in humans. To perform the study, the fatty acid (FA) profile and RNA sequencing (RNA-Seq) data of 35 samples of liver tissue (SOY1.5, n = 17 and SOY3.0, n = 18) and 36 samples of skeletal muscle (SOY1.5, n = 18 and SOY3.0, n = 18) of Large White pigs were analyzed. The FA profile of the tissues was modified by the diet, mainly those related to monounsaturated (MUFA) and polyunsaturated (PUFA) FA. The skeletal muscle transcriptome analysis revealed 45 DEG (FDR 10%), and the functional enrichment analysis identified network maps related to inflammation, immune processes, and pathways associated with oxidative stress, type 2 diabetes, and metabolic dysfunction. For the liver tissue, the transcriptome profile analysis revealed 281 DEG, which participate in network maps related to neurodegenerative diseases. With this nutrigenomics study, we verified that different levels of soybean oil in the pig diet, an animal model for metabolic diseases in humans, affected the transcriptome profile of skeletal muscle and liver tissue. These findings may help to better understand the biological mechanisms that can be modulated by the diet.
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Affiliation(s)
- Simara Larissa Fanalli
- Faculty of Animal Science and Food Engineering, University of São Paulo, Pirassununga 13635-900, SP, Brazil; (S.L.F.); (B.P.M.d.S.); (H.F.)
| | - Bruna Pereira Martins da Silva
- Faculty of Animal Science and Food Engineering, University of São Paulo, Pirassununga 13635-900, SP, Brazil; (S.L.F.); (B.P.M.d.S.); (H.F.)
| | - Julia Dezen Gomes
- Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba 13418-900, SP, Brazil; (J.D.G.); (F.A.O.F.); (B.S.-V.); (A.N.M.); (L.L.C.); (G.B.M.); (S.M.d.A.); (A.L.F.)
| | - Vivian Vezzoni de Almeida
- College of Veterinary Medicine and Animal Science, Federal University of Goiás, Goiânia 74690-900, GO, Brazil;
| | - Felipe André Oliveira Freitas
- Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba 13418-900, SP, Brazil; (J.D.G.); (F.A.O.F.); (B.S.-V.); (A.N.M.); (L.L.C.); (G.B.M.); (S.M.d.A.); (A.L.F.)
| | | | - Bárbara Silva-Vignato
- Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba 13418-900, SP, Brazil; (J.D.G.); (F.A.O.F.); (B.S.-V.); (A.N.M.); (L.L.C.); (G.B.M.); (S.M.d.A.); (A.L.F.)
| | - Juliana Afonso
- Embrapa Pecuária Sudeste, São Carlos 70770-901, SP, Brazil; (J.A.); (L.C.d.A.R.)
| | - James Reecy
- College of Agriculture and Life Sciences, Iowa State University, Ames, IA 50011, USA; (J.R.); (J.K.); (D.K.)
| | - James Koltes
- College of Agriculture and Life Sciences, Iowa State University, Ames, IA 50011, USA; (J.R.); (J.K.); (D.K.)
| | - Dawn Koltes
- College of Agriculture and Life Sciences, Iowa State University, Ames, IA 50011, USA; (J.R.); (J.K.); (D.K.)
| | | | - Dorian John Garrick
- AL Rae Centre for Genetics and Breeding, Massey University, Hamilton 3214, New Zealand;
| | | | - Ariana Nascimento Meira
- Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba 13418-900, SP, Brazil; (J.D.G.); (F.A.O.F.); (B.S.-V.); (A.N.M.); (L.L.C.); (G.B.M.); (S.M.d.A.); (A.L.F.)
| | - Luciana Freitas
- DB Genética de Suínos, Patos de Minas 38706-000, MG, Brazil;
| | - Luiz Lehmann Coutinho
- Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba 13418-900, SP, Brazil; (J.D.G.); (F.A.O.F.); (B.S.-V.); (A.N.M.); (L.L.C.); (G.B.M.); (S.M.d.A.); (A.L.F.)
| | - Heidge Fukumasu
- Faculty of Animal Science and Food Engineering, University of São Paulo, Pirassununga 13635-900, SP, Brazil; (S.L.F.); (B.P.M.d.S.); (H.F.)
| | - Gerson Barreto Mourão
- Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba 13418-900, SP, Brazil; (J.D.G.); (F.A.O.F.); (B.S.-V.); (A.N.M.); (L.L.C.); (G.B.M.); (S.M.d.A.); (A.L.F.)
| | - Severino Matias de Alencar
- Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba 13418-900, SP, Brazil; (J.D.G.); (F.A.O.F.); (B.S.-V.); (A.N.M.); (L.L.C.); (G.B.M.); (S.M.d.A.); (A.L.F.)
| | - Albino Luchiari Filho
- Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba 13418-900, SP, Brazil; (J.D.G.); (F.A.O.F.); (B.S.-V.); (A.N.M.); (L.L.C.); (G.B.M.); (S.M.d.A.); (A.L.F.)
| | - Aline Silva Mello Cesar
- Faculty of Animal Science and Food Engineering, University of São Paulo, Pirassununga 13635-900, SP, Brazil; (S.L.F.); (B.P.M.d.S.); (H.F.)
- Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba 13418-900, SP, Brazil; (J.D.G.); (F.A.O.F.); (B.S.-V.); (A.N.M.); (L.L.C.); (G.B.M.); (S.M.d.A.); (A.L.F.)
- Correspondence:
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Wang W, Chen Y, Wu L, Zhang Y, Yoo S, Chen Q, Liu S, Hou Y, Chen XP, Chen Q, Zhu J. HBV genome-enriched single cell sequencing revealed heterogeneity in HBV-driven hepatocellular carcinoma (HCC). BMC Med Genomics 2022; 15:134. [PMID: 35710421 PMCID: PMC9205089 DOI: 10.1186/s12920-022-01264-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 05/05/2022] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND Hepatitis B virus (HBV) related hepatocellular carcinoma (HCC) is heterogeneous and frequently contains multifocal tumors, but how the multifocal tumors relate to each other in terms of HBV integration and other genomic patterns is not clear. METHODS To interrogate heterogeneity of HBV-HCC, we developed a HBV genome enriched single cell sequencing (HGE-scSeq) procedure and a computational method to identify HBV integration sites and infer DNA copy number variations (CNVs). RESULTS We performed HGE-scSeq on 269 cells from four tumor sites and two tumor thrombi of a HBV-HCC patient. HBV integrations were identified in 142 out of 269 (53%) cells sequenced, and were enriched in two HBV integration hotspots chr1:34,397,059 (CSMD2) and chr8:118,557,327 (MED30/EXT1). There were also 162 rare integration sites. HBV integration sites were enriched in DNA fragile sites and sequences around HBV integration sites were enriched for microhomologous sequences between human and HBV genomes. CNVs were inferred for each individual cell and cells were grouped into four clonal groups based on their CNVs. Cells in different clonal groups had different degrees of HBV integration heterogeneity. All of 269 cells carried chromosome 1q amplification, a recurrent feature of HCC tumors, suggesting that 1q amplification occurred before HBV integration events in this case study. Further, we performed simulation studies to demonstrate that the sequential events (HBV infecting transformed cells) could result in the observed phenotype with biologically reasonable parameters. CONCLUSION Our HGE-scSeq data reveals high heterogeneity of HCC tumor cells in terms of both HBV integrations and CNVs. There were two HBV integration hotspots across cells, and cells from multiple tumor sites shared some HBV integration and CNV patterns.
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Affiliation(s)
- Wenhui Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Ave., New York, NY, 10029, USA
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Sema4, Stamford, CT, USA
| | - Yan Chen
- The Hepatic Surgery Centre at Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology (HUST), Wuhan, China
| | | | - Yi Zhang
- Department of Mathematics, Hebei University of Science and Technology, Shijiazhuang, Hebei, China
| | - Seungyeul Yoo
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Ave., New York, NY, 10029, USA
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Sema4, Stamford, CT, USA
| | - Quan Chen
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Ave., New York, NY, 10029, USA
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Sema4, Stamford, CT, USA
| | | | | | - Xiao-Ping Chen
- The Hepatic Surgery Centre at Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology (HUST), Wuhan, China
| | - Qian Chen
- The Division of Gastroenterology, Department of Internal Medicine at Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology (HUST), Wuhan, China.
| | - Jun Zhu
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Ave., New York, NY, 10029, USA.
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Sema4, Stamford, CT, USA.
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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Clement Dobbins G, Kimberlin D, Ross S. Cytomegalovirus variation among newborns treated with valganciclovir. Antiviral Res 2022; 203:105326. [DOI: 10.1016/j.antiviral.2022.105326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 04/19/2022] [Accepted: 04/20/2022] [Indexed: 11/02/2022]
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Farswan A, Gupta R, Gupta A. ARCANE-ROG: Algorithm for Reconstruction of Cancer Evolution from single-cell data using Robust Graph Learning. J Biomed Inform 2022; 129:104055. [PMID: 35337943 DOI: 10.1016/j.jbi.2022.104055] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 02/17/2022] [Accepted: 03/12/2022] [Indexed: 11/27/2022]
Abstract
Tumor heterogeneity, marked by the presence of divergent clonal subpopulations of tumor cells, impedes the treatment response in cancer patients. Single-cell sequencing technology provides substantial prospects to gain an in-depth understanding of the cellular phenotypic variability driving tumor progression. A comprehensive insight into the intra-tumor heterogeneity may further assist in dealing with the treatment-resistant clones in cancer patients, thereby improving their overall survival. However, this task is hampered due to the challenges associated with the single-cell data, such as false positives, false negatives and missing bases, and the increase in their size. As a result, the computational cost of the existing methods increases, thereby limiting their usage. In this work, we propose a robust graph learning-based method, ARCANE-ROG (Algorithm for Reconstruction of CANcer Evolution via RObust Graph learning), for inferring clonal evolution from single-cell datasets. The first step of the proposed method is a joint framework of denoising with data imputation for the noisy and incomplete matrix while simultaneously learning an adjacency graph. Both the operations in the joint framework boost each other such that the overall performance of the denoising algorithm is improved. In the second step, an optimal number of clusters are identified via the Leiden method. In the last step, clonal evolution trees are inferred via a minimum spanning tree algorithm. The method has been benchmarked against a state-of-the-art method, RobustClone, using simulated datasets of varying sizes and five real datasets. The performance of our proposed method is found to be significantly superior (p-value < 0.05) in terms of reconstruction error, False Positive to False Negative (FPFN) ratio, tree distance error and V-measure compared to the other method. Overall, the proposed method is an improvement over the existing methods as it enhances cluster assignment and inference on clonal hierarchies.
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Affiliation(s)
- Akanksha Farswan
- SBILab, Department of ECE, Indraprastha Institute of Information Technology, New Delhi, India
| | - Ritu Gupta
- Laboratory Oncology Unit, Dr. B.R.A. IRCH, AIIMS, New Delhi, India.
| | - Anubha Gupta
- SBILab, Department of ECE, Indraprastha Institute of Information Technology, New Delhi, India.
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Yu Z, Du F, Song L. SCClone: Accurate Clustering of Tumor Single-Cell DNA Sequencing Data. Front Genet 2022; 13:823941. [PMID: 35154282 PMCID: PMC8830741 DOI: 10.3389/fgene.2022.823941] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Accepted: 01/04/2022] [Indexed: 12/11/2022] Open
Abstract
Single-cell DNA sequencing (scDNA-seq) enables high-resolution profiling of genetic diversity among single cells and is especially useful for deciphering the intra-tumor heterogeneity and evolutionary history of tumor. Specific technical issues such as allele dropout, false-positive errors, and doublets make scDNA-seq data incomplete and error-prone, giving rise to a severe challenge of accurately inferring clonal architecture of tumor. To effectively address these issues, we introduce a new computational method called SCClone for reasoning subclones from single nucleotide variation (SNV) data of single cells. Specifically, SCClone leverages a probability mixture model for binary data to cluster single cells into distinct subclones. To accurately decipher underlying clonal composition, a novel model selection scheme based on inter-cluster variance is employed to find the optimal number of subclones. Extensive evaluations on various simulated datasets suggest SCClone has strong robustness against different technical noises in scDNA-seq data and achieves better performance than the state-of-the-art methods in reasoning clonal composition. Further evaluations of SCClone on three real scDNA-seq datasets show that it can effectively find the underlying subclones from severely disturbed data. The SCClone software is freely available at https://github.com/qasimyu/scclone.
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Affiliation(s)
- Zhenhua Yu
- School of Information Engineering, Ningxia University, Yinchuan, China.,Collaborative Innovation Center for Ningxia Big Data and Artificial Intelligence Co-founded by Ningxia Municipality and Ministry of Education, Ningxia University, Yinchuan, China
| | - Fang Du
- School of Information Engineering, Ningxia University, Yinchuan, China.,Collaborative Innovation Center for Ningxia Big Data and Artificial Intelligence Co-founded by Ningxia Municipality and Ministry of Education, Ningxia University, Yinchuan, China
| | - Lijuan Song
- School of Information Engineering, Ningxia University, Yinchuan, China.,Collaborative Innovation Center for Ningxia Big Data and Artificial Intelligence Co-founded by Ningxia Municipality and Ministry of Education, Ningxia University, Yinchuan, China
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Tu J, Yang Z, Lu N, Lu Z. Improving the efficiency of single-cell genome sequencing based on overlapping pooling strategy and CNV analysis. ROYAL SOCIETY OPEN SCIENCE 2022; 9:211330. [PMID: 35116153 PMCID: PMC8790377 DOI: 10.1098/rsos.211330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/21/2021] [Accepted: 01/04/2022] [Indexed: 06/14/2023]
Abstract
Single-cell genome sequencing has become a useful tool in medicine and biology studies. However, an independent library is required for each cell in single-cell genome sequencing, so that the cost grows with the number of cells. In this study, we report a study which efficiently analyses single-cell copy number variation (CNV) using overlapping pooling strategy and branch and bound (B&B) algorithm. Single cells were overlapped pooled before sequencing, and later were assorted into specific types by estimating their CNV patterns by B&B algorithm. Instead of constructing libraries for each cell, a library is required only for each pool. As the number of pools is smaller than the cells, fewer libraries are required, which means lower cost. Through computer simulations, we overlapped pooled 80 cells into 40 or 27 pools and classified them into cell types based on CNV pattern. The results showed that 84% cells in 40 pools and 76.5% cells in 27 pools were correctly classified on average, while only half or one-third of the sequencing libraries were required. Combining with traditional approaches, our method is expected to significantly improve the efficiency of single-cell genome sequencing.
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Affiliation(s)
- Jing Tu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, People's Republic of China
| | - Zengyan Yang
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, People's Republic of China
| | - Na Lu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, People's Republic of China
| | - Zuhong Lu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, People's Republic of China
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RDAClone: Deciphering Tumor Heterozygosity through Single-Cell Genomics Data Analysis with Robust Deep Autoencoder. Genes (Basel) 2021; 12:genes12121847. [PMID: 34946794 PMCID: PMC8701080 DOI: 10.3390/genes12121847] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 11/19/2021] [Accepted: 11/22/2021] [Indexed: 12/27/2022] Open
Abstract
Rapid advances in single-cell genomics sequencing (SCGS) have allowed researchers to characterize tumor heterozygosity with unprecedented resolution and reveal the phylogenetic relationships between tumor cells or clones. However, high sequencing error rates of current SCGS data, i.e., false positives, false negatives, and missing bases, severely limit its application. Here, we present a deep learning framework, RDAClone, to recover genotype matrices from noisy data with an extended robust deep autoencoder, cluster cells into subclones by the Louvain-Jaccard method, and further infer evolutionary relationships between subclones by the minimum spanning tree. Studies on both simulated and real datasets demonstrate its robustness and superiority in data denoising, cell clustering, and evolutionary tree reconstruction, particularly for large datasets.
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Lähnemann D, Köster J, Fischer U, Borkhardt A, McHardy AC, Schönhuth A. Accurate and scalable variant calling from single cell DNA sequencing data with ProSolo. Nat Commun 2021; 12:6744. [PMID: 34795237 PMCID: PMC8602313 DOI: 10.1038/s41467-021-26938-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Accepted: 10/22/2021] [Indexed: 01/14/2023] Open
Abstract
Accurate single cell mutational profiles can reveal genomic cell-to-cell heterogeneity. However, sequencing libraries suitable for genotyping require whole genome amplification, which introduces allelic bias and copy errors. The resulting data violates assumptions of variant callers developed for bulk sequencing. Thus, only dedicated models accounting for amplification bias and errors can provide accurate calls. We present ProSolo for calling single nucleotide variants from multiple displacement amplified (MDA) single cell DNA sequencing data. ProSolo probabilistically models a single cell jointly with a bulk sequencing sample and integrates all relevant MDA biases in a site-specific and scalable-because computationally efficient-manner. This achieves a higher accuracy in calling and genotyping single nucleotide variants in single cells in comparison to state-of-the-art tools and supports imputation of insufficiently covered genotypes, when downstream tools cannot handle missing data. Moreover, ProSolo implements the first approach to control the false discovery rate reliably and flexibly. ProSolo is implemented in an extendable framework, with code and usage at: https://github.com/prosolo/prosolo.
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Affiliation(s)
- David Lähnemann
- grid.7490.a0000 0001 2238 295XDepartment for Computational Biology of Infection Research, Helmholtz Centre for Infection Research, 38124 Braunschweig, Germany ,grid.6738.a0000 0001 1090 0254Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, 38106 Braunschweig, Germany ,grid.411327.20000 0001 2176 9917Algorithmic Bioinformatics, Faculty of Mathematics and Natural Sciences, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany ,grid.14778.3d0000 0000 8922 7789Department of Paediatric Oncology, Haematology and Immunology, University Hospital, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany ,grid.5718.b0000 0001 2187 5445Algorithms for Reproducible Bioinformatics, Institute of Human Genetics, University of Duisburg-Essen, 45147 Essen, Germany
| | - Johannes Köster
- grid.5718.b0000 0001 2187 5445Algorithms for Reproducible Bioinformatics, Institute of Human Genetics, University of Duisburg-Essen, 45147 Essen, Germany ,grid.6054.70000 0004 0369 4183Genome Data Science, Life Sciences Group, Centrum Wiskunde & Informatica, 1098 XG Amsterdam, The Netherlands
| | - Ute Fischer
- grid.14778.3d0000 0000 8922 7789Department of Paediatric Oncology, Haematology and Immunology, University Hospital, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Arndt Borkhardt
- grid.14778.3d0000 0000 8922 7789Department of Paediatric Oncology, Haematology and Immunology, University Hospital, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Alice C. McHardy
- grid.7490.a0000 0001 2238 295XDepartment for Computational Biology of Infection Research, Helmholtz Centre for Infection Research, 38124 Braunschweig, Germany ,grid.6738.a0000 0001 1090 0254Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, 38106 Braunschweig, Germany ,grid.411327.20000 0001 2176 9917Algorithmic Bioinformatics, Faculty of Mathematics and Natural Sciences, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Alexander Schönhuth
- Genome Data Science, Life Sciences Group, Centrum Wiskunde & Informatica, 1098 XG, Amsterdam, The Netherlands. .,Genome Data Science, Faculty of Technology, Bielefeld University, 33615, Bielefeld, Germany.
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Alpár D, Egyed B, Bödör C, Kovács GT. Single-Cell Sequencing: Biological Insight and Potential Clinical Implications in Pediatric Leukemia. Cancers (Basel) 2021; 13:5658. [PMID: 34830811 PMCID: PMC8616124 DOI: 10.3390/cancers13225658] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 11/05/2021] [Accepted: 11/09/2021] [Indexed: 01/15/2023] Open
Abstract
Single-cell sequencing (SCS) provides high-resolution insight into the genomic, epigenomic, and transcriptomic landscape of oncohematological malignancies including pediatric leukemia, the most common type of childhood cancer. Besides broadening our biological understanding of cellular heterogeneity, sub-clonal architecture, and regulatory network of tumor cell populations, SCS can offer clinically relevant, detailed characterization of distinct compartments affected by leukemia and identify therapeutically exploitable vulnerabilities. In this review, we provide an overview of SCS studies focused on the high-resolution genomic and transcriptomic scrutiny of pediatric leukemia. Our aim is to investigate and summarize how different layers of single-cell omics approaches can expectedly support clinical decision making in the future. Although the clinical management of pediatric leukemia underwent a spectacular improvement during the past decades, resistant disease is a major cause of therapy failure. Currently, only a small proportion of childhood leukemia patients benefit from genomics-driven therapy, as 15-20% of them meet the indication criteria of on-label targeted agents, and their overall response rate falls in a relatively wide range (40-85%). The in-depth scrutiny of various cell populations influencing the development, progression, and treatment resistance of different disease subtypes can potentially uncover a wider range of driver mechanisms for innovative therapeutic interventions.
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Affiliation(s)
- Donát Alpár
- HCEMM-SE Molecular Oncohematology Research Group, 1st Department of Pathology and Experimental Cancer Research, Semmelweis University, H-1085 Budapest, Hungary; (D.A.); (B.E.); (C.B.)
| | - Bálint Egyed
- HCEMM-SE Molecular Oncohematology Research Group, 1st Department of Pathology and Experimental Cancer Research, Semmelweis University, H-1085 Budapest, Hungary; (D.A.); (B.E.); (C.B.)
- 2nd Department of Pediatrics, Semmelweis University, H-1094 Budapest, Hungary
| | - Csaba Bödör
- HCEMM-SE Molecular Oncohematology Research Group, 1st Department of Pathology and Experimental Cancer Research, Semmelweis University, H-1085 Budapest, Hungary; (D.A.); (B.E.); (C.B.)
| | - Gábor T. Kovács
- 2nd Department of Pediatrics, Semmelweis University, H-1094 Budapest, Hungary
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Pan D, Jia D. Application of Single-Cell Multi-Omics in Dissecting Cancer Cell Plasticity and Tumor Heterogeneity. Front Mol Biosci 2021; 8:757024. [PMID: 34722635 PMCID: PMC8554142 DOI: 10.3389/fmolb.2021.757024] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 09/29/2021] [Indexed: 12/20/2022] Open
Abstract
Tumor heterogeneity, a hallmark of cancer, impairs the efficacy of cancer therapy and drives tumor progression. Exploring inter- and intra-tumoral heterogeneity not only provides insights into tumor development and progression, but also guides the design of personalized therapies. Previously, high-throughput sequencing techniques have been used to investigate the heterogeneity of tumor ecosystems. However, they could not provide a high-resolution landscape of cellular components in tumor ecosystem. Recently, advance in single-cell technologies has provided an unprecedented resolution to uncover the intra-tumoral heterogeneity by profiling the transcriptomes, genomes, proteomes and epigenomes of the cellular components and also their spatial distribution, which greatly accelerated the process of basic and translational cancer research. Importantly, it has been demonstrated that some cancer cells are able to transit between different states in order to adapt to the changing tumor microenvironment, which led to increased cellular plasticity and tumor heterogeneity. Understanding the molecular mechanisms driving cancer cell plasticity is critical for developing precision therapies. In this review, we summarize the recent progress in dissecting the cancer cell plasticity and tumor heterogeneity by use of single-cell multi-omics techniques.
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Affiliation(s)
- Deshen Pan
- Laboratory of Cancer Genomics and Biology, Department of Urology, and Institute of Translational Medicine, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Deshui Jia
- Laboratory of Cancer Genomics and Biology, Department of Urology, and Institute of Translational Medicine, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Ogundijo OE, Zhu K, Wang X, Anastassiou D. Characterizing Intra-Tumor Heterogeneity From Somatic Mutations Without Copy-Neutral Assumption. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2271-2280. [PMID: 32070995 DOI: 10.1109/tcbb.2020.2973635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Bulk samples of the same patient are heterogeneous in nature, comprising of different subpopulations (subclones) of cancer cells. Cells in a tumor subclone are characterized by unique mutational genotype profile. Resolving tumor heterogeneity by estimating the genotypes, cellular proportions and the number of subclones present in the tumor can help in understanding cancer progression and treatment. We present a novel method, ChaClone2, to efficiently deconvolve the observed variant allele fractions (VAFs), with consideration for possible effects from copy number aberrations at the mutation loci. Our method describes a state-space formulation of the feature allocation model, deconvolving the observed VAFs from samples of the same patient into three matrices: subclonal total and variant copy numbers for mutated genes, and proportions of subclones in each sample. We describe an efficient sequential Monte Carlo (SMC) algorithm to estimate these matrices. Extensive simulation shows that the ChaClone2 yields better accuracy when compared with other state-of-the-art methods for addressing similar problem and it offers scalability to large datasets. Also, ChaClone2 features that the model parameter estimates can be refined whenever new mutation data of freshly sequenced genomic locations are available. MATLAB code and datasets are available to download at: https://github.com/moyanre/method2.
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40
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Rosendahl Huber A, Van Hoeck A, Van Boxtel R. The Mutagenic Impact of Environmental Exposures in Human Cells and Cancer: Imprints Through Time. Front Genet 2021; 12:760039. [PMID: 34745228 PMCID: PMC8565797 DOI: 10.3389/fgene.2021.760039] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 10/05/2021] [Indexed: 12/25/2022] Open
Abstract
During life, the DNA of our cells is continuously exposed to external damaging processes. Despite the activity of various repair mechanisms, DNA damage eventually results in the accumulation of mutations in the genomes of our cells. Oncogenic mutations are at the root of carcinogenesis, and carcinogenic agents are often highly mutagenic. Over the past decade, whole genome sequencing data of healthy and tumor tissues have revealed how cells in our body gradually accumulate mutations because of exposure to various mutagenic processes. Dissection of mutation profiles based on the type and context specificities of the altered bases has revealed a variety of signatures that reflect past exposure to environmental mutagens, ranging from chemotherapeutic drugs to genotoxic gut bacteria. In this review, we discuss the latest knowledge on somatic mutation accumulation in human cells, and how environmental mutagenic factors further shape the mutation landscapes of tissues. In addition, not all carcinogenic agents induce mutations, which may point to alternative tumor-promoting mechanisms, such as altered clonal selection dynamics. In short, we provide an overview of how environmental factors induce mutations in the DNA of our healthy cells and how this contributes to carcinogenesis. A better understanding of how environmental mutagens shape the genomes of our cells can help to identify potential preventable causes of cancer.
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Affiliation(s)
- Axel Rosendahl Huber
- Princess Máxima Center for Pediatric Oncology, Utrecht, Netherlands
- Oncode Institute, Utrecht, Netherlands
| | - Arne Van Hoeck
- Oncode Institute, Utrecht, Netherlands
- Center for Molecular Medicine, University Medical Centre Utrecht, Utrecht, Netherlands
| | - Ruben Van Boxtel
- Princess Máxima Center for Pediatric Oncology, Utrecht, Netherlands
- Oncode Institute, Utrecht, Netherlands
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Xu Q, Wang Z, Kong Q, Wang X, Huang A, Li C, Liu X. Evaluating the effects of whole genome amplification strategies for amplifying trace DNA using capillary electrophoresis and massive parallel sequencing. Forensic Sci Int Genet 2021; 56:102599. [PMID: 34656831 DOI: 10.1016/j.fsigen.2021.102599] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 09/20/2021] [Accepted: 09/24/2021] [Indexed: 11/04/2022]
Abstract
To draw robust conclusions when trace DNA samples are detected in complex cases, it is essential to successfully recover and genotype short tandem repeats (STRs) from trace DNA. However, obtaining complete STR profiles by the conventional polymerase chain reaction-capillary electrophoresis (PCR-CE) method is generally difficult as trace DNA is often less than 100 pg. Previous studies have proven that through whole-genome amplification (WGA), the yield of DNA from trace DNA samples could be improved. In this study, we used two WGA kits, namely, REPLI-g® Single Cell kit and MALBAC® Single Cell DNA Quick-Amp Kit (hereafter referred to as REPLI and MALBAC), to amplify DNA samples with a series of dilutions (from 5.00 ng/μL to 0.391 pg/μL). Typing of STR markers in samples with and without WGA were then performed on a CE platform by the application of Goldeneye® DNA ID System 20 A kit, as well as directly calling sequences from massive parallel sequencing (MPS) for WGA samples with 1.00 ng, 125 pg and 25.0 pg as DNA inputs. Quantification results demonstrated that the yield of samples with WGA could reach the microgram level. The amplification fold was at least > 2000 and > 200 for REPLI and MALBAC, respectively. CE results showed that the number of correctly called loci was improved for trace DNA after WGA when the DNA inputs were lower than 25.0 pg for REPLI and 6.25 pg for MALBAC, respectively. WGA remarkably improved the percentage of called loci with DNA inputs lower than 50.0 pg, although poor performance in repeatability was observed. MPS results suggested that the correctly called loci calculated by MPS reads were mostly more than those calculated by CE, particularly for those of short length, implying MPS of samples after WGA is worth testing in the future. In conclusion, WGA has the potential usability for forensic trace DNA analysis at the single-cell level with good fidelity, although its repeatability requires further improvement.
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Affiliation(s)
- Qiannan Xu
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, PR China; Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai 200063, PR China
| | - Ziwei Wang
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai 200063, PR China; Department of Forensic Science, Medical School of Soochow University, Suzhou 215123, PR China
| | - Qianqian Kong
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai 200063, PR China; School of Basic Medicine, Inner Mongolia Medical University, Hohhot 010030, PR China
| | - Xiaoxiao Wang
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai 200063, PR China; School of Forensic Medicine, Shanxi Medical University, Taiyuan 030001, PR China
| | - Ao Huang
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai 200063, PR China; Department of Forensic Science, Medical School of Soochow University, Suzhou 215123, PR China
| | - Chengtao Li
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, PR China; Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai 200063, PR China.
| | - Xiling Liu
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai 200063, PR China.
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Ono H, Arai Y, Furukawa E, Narushima D, Matsuura T, Nakamura H, Shiokawa D, Nagai M, Imai T, Mimori K, Okamoto K, Hippo Y, Shibata T, Kato M. Single-cell DNA and RNA sequencing reveals the dynamics of intra-tumor heterogeneity in a colorectal cancer model. BMC Biol 2021; 19:207. [PMID: 34548081 PMCID: PMC8456589 DOI: 10.1186/s12915-021-01147-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 09/06/2021] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Intra-tumor heterogeneity (ITH) encompasses cellular differences in tumors and is related to clinical outcomes such as drug resistance. However, little is known about the dynamics of ITH, owing to the lack of time-series analysis at the single-cell level. Mouse models that recapitulate cancer development are useful for controlled serial time sampling. RESULTS We performed single-cell exome and transcriptome sequencing of 200 cells to investigate how ITH is generated in a mouse colorectal cancer model. In the model, a single normal intestinal cell is grown into organoids that mimic the intestinal crypt structure. Upon RNAi-mediated downregulation of a tumor suppressor gene APC, the transduced organoids were serially transplanted into mice to allow exposure to in vivo microenvironments, which play relevant roles in cancer development. The ITH of the transcriptome increased after the transplantation, while that of the exome decreased. Mutations generated during organoid culture did not greatly change at the bulk-cell level upon the transplantation. The RNA ITH increase was due to the emergence of new transcriptional subpopulations. In contrast to the initial cells expressing mesenchymal-marker genes, new subpopulations repressed these genes after the transplantation. Analyses of colorectal cancer data from The Cancer Genome Atlas revealed a high proportion of metastatic cases in human subjects with expression patterns similar to the new cell subpopulations in mouse. These results suggest that the birth of transcriptional subpopulations may be a key for adaptation to drastic micro-environmental changes when cancer cells have sufficient genetic alterations at later tumor stages. CONCLUSIONS This study revealed an evolutionary dynamics of single-cell RNA and DNA heterogeneity in tumor progression, giving insights into the mesenchymal-epithelial transformation of tumor cells at metastasis in colorectal cancer.
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Affiliation(s)
- Hanako Ono
- Division of Bioinformatics, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Yasuhito Arai
- Division of Cancer Genomics, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Eisaku Furukawa
- Division of Bioinformatics, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Daichi Narushima
- Division of Bioinformatics, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Tetsuya Matsuura
- Department of Animal Experimentation, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Hiromi Nakamura
- Division of Cancer Genomics, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Daisuke Shiokawa
- Division of Cancer Differentiation, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Momoko Nagai
- Division of Bioinformatics, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Toshio Imai
- Department of Animal Experimentation, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Koshi Mimori
- Department of Surgery, Kyushu University Beppu Hospital, 101 Hasamamachiidaigaoka, Yufu, Oita, 879-5593, Japan
| | - Koji Okamoto
- Division of Cancer Differentiation, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Yoshitaka Hippo
- Department of Animal Experimentation, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
- Division of Biochemistry and Molecular Carcinogenesis, Chiba Cancer Center Research Institute, 666-2 Nitona-cho, Chiba Chuo-ku, Chiba, 260-8717, Japan
| | - Tatsuhiro Shibata
- Division of Cancer Genomics, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
- Laboratory of Molecular Medicine, Human Genome Center, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shiroganedai, Minato-ku, Tokyo, 108-8639, Japan
| | - Mamoru Kato
- Division of Bioinformatics, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
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Abstract
Over the past decade, genomic analyses of single cells-the fundamental units of life-have become possible. Single-cell DNA sequencing has shed light on biological questions that were previously inaccessible across diverse fields of research, including somatic mutagenesis, organismal development, genome function, and microbiology. Single-cell DNA sequencing also promises significant future biomedical and clinical impact, spanning oncology, fertility, and beyond. While single-cell approaches that profile RNA and protein have greatly expanded our understanding of cellular diversity, many fundamental questions in biology and important biomedical applications require analysis of the DNA of single cells. Here, we review the applications and biological questions for which single-cell DNA sequencing is uniquely suited or required. We include a discussion of the fields that will be impacted by single-cell DNA sequencing as the technology continues to advance.
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Affiliation(s)
- Gilad D Evrony
- Center for Human Genetics and Genomics, Grossman School of Medicine, New York University, New York, NY 10016, USA;
| | - Anjali Gupta Hinch
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, United Kingdom;
| | - Chongyuan Luo
- Department of Human Genetics, University of California, Los Angeles, California 90095, USA;
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44
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Rothgangl T, Dennis MK, Lin PJC, Oka R, Witzigmann D, Villiger L, Qi W, Hruzova M, Kissling L, Lenggenhager D, Borrelli C, Egli S, Frey N, Bakker N, Walker JA, Kadina AP, Victorov DV, Pacesa M, Kreutzer S, Kontarakis Z, Moor A, Jinek M, Weissman D, Stoffel M, van Boxtel R, Holden K, Pardi N, Thöny B, Häberle J, Tam YK, Semple SC, Schwank G. In vivo adenine base editing of PCSK9 in macaques reduces LDL cholesterol levels. Nat Biotechnol 2021; 39:949-957. [PMID: 34012094 PMCID: PMC8352781 DOI: 10.1038/s41587-021-00933-4] [Citation(s) in RCA: 177] [Impact Index Per Article: 59.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 04/23/2021] [Indexed: 02/02/2023]
Abstract
Most known pathogenic point mutations in humans are C•G to T•A substitutions, which can be directly repaired by adenine base editors (ABEs). In this study, we investigated the efficacy and safety of ABEs in the livers of mice and cynomolgus macaques for the reduction of blood low-density lipoprotein (LDL) levels. Lipid nanoparticle-based delivery of mRNA encoding an ABE and a single-guide RNA targeting PCSK9, a negative regulator of LDL, induced up to 67% editing (on average, 61%) in mice and up to 34% editing (on average, 26%) in macaques. Plasma PCSK9 and LDL levels were stably reduced by 95% and 58% in mice and by 32% and 14% in macaques, respectively. ABE mRNA was cleared rapidly, and no off-target mutations in genomic DNA were found. Re-dosing in macaques did not increase editing, possibly owing to the detected humoral immune response to ABE upon treatment. These findings support further investigation of ABEs to treat patients with monogenic liver diseases.
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Affiliation(s)
- Tanja Rothgangl
- University of Zurich, Institute for Pharmacology and Toxicology, Zurich, Switzerland
| | | | | | - Rurika Oka
- Oncode Institute, Princess Máxima Center for Pediatric Oncology, Utrecht, Netherlands
| | - Dominik Witzigmann
- University of Zurich, Institute for Pharmacology and Toxicology, Zurich, Switzerland
| | - Lukas Villiger
- University of Zurich, Institute for Pharmacology and Toxicology, Zurich, Switzerland
| | - Weihong Qi
- Functional Genomics Center Zurich, ETH Zurich/University of Zurich, Zurich, Switzerland
| | - Martina Hruzova
- Department of Biology, Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
| | - Lucas Kissling
- University of Zurich, Institute for Pharmacology and Toxicology, Zurich, Switzerland
| | - Daniela Lenggenhager
- Department of Pathology and Molecular Pathology, University Hospital and University of Zurich, Zurich, Switzerland
| | - Costanza Borrelli
- Department of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland
| | - Sabina Egli
- University of Zurich, Institute for Pharmacology and Toxicology, Zurich, Switzerland
| | - Nina Frey
- Department of Biology, Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
| | - Noëlle Bakker
- University of Zurich, Institute for Pharmacology and Toxicology, Zurich, Switzerland
| | | | | | | | - Martin Pacesa
- Department of Biochemistry, University of Zurich, Zurich, Switzerland
| | - Susanne Kreutzer
- Functional Genomics Center Zurich, ETH Zurich/University of Zurich, Zurich, Switzerland
- Genome Engineering and Measurement Laboratory, ETH Zurich, Zurich, Switzerland
| | - Zacharias Kontarakis
- Functional Genomics Center Zurich, ETH Zurich/University of Zurich, Zurich, Switzerland
- Genome Engineering and Measurement Laboratory, ETH Zurich, Zurich, Switzerland
| | - Andreas Moor
- Department of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland
| | - Martin Jinek
- Department of Biochemistry, University of Zurich, Zurich, Switzerland
| | - Drew Weissman
- Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Markus Stoffel
- Department of Biology, Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
| | - Ruben van Boxtel
- Oncode Institute, Princess Máxima Center for Pediatric Oncology, Utrecht, Netherlands
| | | | - Norbert Pardi
- Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Beat Thöny
- Division of Metabolism and Children's Research Centre, University Children's Hospital Zurich, Zurich, Switzerland
- Zurich Center for Integrative Human Physiology, Zurich, Switzerland
- Neuroscience Center Zurich, Zurich, Switzerland
| | - Johannes Häberle
- Division of Metabolism and Children's Research Centre, University Children's Hospital Zurich, Zurich, Switzerland
- Zurich Center for Integrative Human Physiology, Zurich, Switzerland
- Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
| | - Ying K Tam
- Acuitas Therapeutics Inc., Vancouver, BC, Canada
| | | | - Gerald Schwank
- University of Zurich, Institute for Pharmacology and Toxicology, Zurich, Switzerland.
- Department of Biology, Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland.
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45
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Malikić S, Mehrabadi FR, Azer ES, Ebrahimabadi MH, Sahinalp SC. Studying the History of Tumor Evolution from Single-Cell Sequencing Data by Exploring the Space of Binary Matrices. J Comput Biol 2021; 28:857-879. [PMID: 34297621 DOI: 10.1089/cmb.2020.0595] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Single-cell sequencing (SCS) data have great potential in reconstructing the evolutionary history of tumors. Rapid advances in SCS technology in the past decade were followed by the design of various computational methods for inferring trees of tumor evolution. Some of the earliest methods were based on the direct search in the space of trees with the goal of finding the maximum likelihood tree. However, it can be shown that instead of searching directly in the tree space, we can perform a search in the space of binary matrices and obtain maximum likelihood tree directly from the maximum likelihood matrix. The potential of the latter tree search strategy has recently been recognized by different research groups and several related methods were published in the past 2 years. Here we provide a review of the theoretical background of these methods and a detailed discussion, which are largely missing in the available publications, of the correlation between the two tree search strategies. We also discuss each of the existing methods based on the search in the space of binary matrices and summarize the best-known single-cell DNA sequencing data sets, which can be used in the future for assessing performance on real data of newly developed methods.
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Affiliation(s)
- Salem Malikić
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Farid Rashidi Mehrabadi
- Department of Computer Science, Indiana University, Bloomington, Indiana, USA.,Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Erfan Sadeqi Azer
- Department of Computer Science, Indiana University, Bloomington, Indiana, USA
| | - Mohammad Haghir Ebrahimabadi
- Department of Computer Science, Indiana University, Bloomington, Indiana, USA.,Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Suleyman Cenk Sahinalp
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
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46
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scDPN for High-throughput Single-cell CNV Detection to Uncover Clonal Evolution During HCC Recurrence. GENOMICS PROTEOMICS & BIOINFORMATICS 2021; 19:346-357. [PMID: 34280548 PMCID: PMC8864190 DOI: 10.1016/j.gpb.2021.03.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Revised: 12/10/2020] [Accepted: 03/06/2021] [Indexed: 11/28/2022]
Abstract
Single-cell genomics provides substantial resources for dissecting cellular heterogeneity and cancer evolution. Unfortunately, classical DNA amplification-based methods have low throughput and introduce coverage bias during sample preamplification. We developed a single-cell DNA library preparation method without preamplification in nanolitre scale (scDPN) to address these issues. The method achieved a throughput of up to 1800 cells per run for copy number variation (CNV) detection. Also, our approach demonstrated a lower level of amplification bias and noise than the multiple displacement amplification (MDA) method and showed high sensitivity and accuracy for cell line and tumor tissue evaluation. We used this approach to profile the tumor clones in paired primary and relapsed tumor samples of hepatocellular carcinoma (HCC). We identified three clonal subpopulations with a multitude of aneuploid alterations across the genome. Furthermore, we observed that a minor clone of the primary tumor containing additional alterations in chromosomes 1q, 10q, and 14q developed into the dominant clone in the recurrent tumor, indicating clonal selection during recurrence in HCC. Overall, this approach provides a comprehensive and scalable solution to understand genome heterogeneity and evolution
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47
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Accurate genomic variant detection in single cells with primary template-directed amplification. Proc Natl Acad Sci U S A 2021; 118:2024176118. [PMID: 34099548 PMCID: PMC8214697 DOI: 10.1073/pnas.2024176118] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Improvements in whole genome amplification (WGA) would enable new types of basic and applied biomedical research, including studies of intratissue genetic diversity that require more accurate single-cell genotyping. Here, we present primary template-directed amplification (PTA), an isothermal WGA method that reproducibly captures >95% of the genomes of single cells in a more uniform and accurate manner than existing approaches, resulting in significantly improved variant calling sensitivity and precision. To illustrate the types of studies that are enabled by PTA, we developed direct measurement of environmental mutagenicity (DMEM), a tool for mapping genome-wide interactions of mutagens with single living human cells at base-pair resolution. In addition, we utilized PTA for genome-wide off-target indel and structural variant detection in cells that had undergone CRISPR-mediated genome editing, establishing the feasibility for performing single-cell evaluations of biopsies from edited tissues. The improved precision and accuracy of variant detection with PTA overcomes the current limitations of accurate WGA, which is the major obstacle to studying genetic diversity and evolution at cellular resolution.
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48
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Yu Z, Liu H, Du F, Tang X. GRMT: Generative Reconstruction of Mutation Tree From Scratch Using Single-Cell Sequencing Data. Front Genet 2021; 12:692964. [PMID: 34149820 PMCID: PMC8212059 DOI: 10.3389/fgene.2021.692964] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 05/17/2021] [Indexed: 12/11/2022] Open
Abstract
Single-cell sequencing (SCS) now promises the landscape of genetic diversity at single cell level, and is particularly useful to reconstruct the evolutionary history of tumor. There are multiple types of noise that make the SCS data notoriously error-prone, and significantly complicate tumor tree reconstruction. Existing methods for tumor phylogeny estimation suffer from either high computational intensity or low-resolution indication of clonal architecture, giving a necessity of developing new methods for efficient and accurate reconstruction of tumor trees. We introduce GRMT (Generative Reconstruction of Mutation Tree from scratch), a method for inferring tumor mutation tree from SCS data. GRMT exploits the k-Dollo parsimony model to allow each mutation to be gained once and lost at most k times. Under this constraint on mutation evolution, GRMT searches for mutation tree structures from a perspective of tree generation from scratch, and implements it to an iterative process that gradually increases the tree size by introducing a new mutation per time until a complete tree structure that contains all mutations is obtained. This enables GRMT to efficiently recover the chronological order of mutations and scale well to large datasets. Extensive evaluations on simulated and real datasets suggest GRMT outperforms the state-of-the-arts in multiple performance metrics. The GRMT software is freely available at https://github.com/qasimyu/grmt.
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Affiliation(s)
- Zhenhua Yu
- School of Information Engineering, Ningxia University, Yinchuan, China.,Collaborative Innovation Center for Ningxia Big Data and Artificial Intelligence Co-founded by Ningxia Municipality and Ministry of Education, Ningxia University, Yinchuan, China
| | - Huidong Liu
- School of Information Engineering, Ningxia University, Yinchuan, China
| | - Fang Du
- School of Information Engineering, Ningxia University, Yinchuan, China.,Collaborative Innovation Center for Ningxia Big Data and Artificial Intelligence Co-founded by Ningxia Municipality and Ministry of Education, Ningxia University, Yinchuan, China
| | - Xiaofen Tang
- School of Information Engineering, Ningxia University, Yinchuan, China.,Collaborative Innovation Center for Ningxia Big Data and Artificial Intelligence Co-founded by Ningxia Municipality and Ministry of Education, Ningxia University, Yinchuan, China
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49
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Abstract
Tumour evolution is a complex interplay between the acquisition of somatic (epi)genomic changes in tumour cells and the phenotypic consequences they cause, all in the context of a changing microenvironment. Single-cell sequencing offers a window into this dynamic process at the ultimate resolution of individual cells. In this review, we discuss the transformative insight offered by single-cell sequencing technologies for understanding tumour evolution.
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Affiliation(s)
- Maximilian Mossner
- Evolution and Cancer Laboratory, Centre for Genomics and Computational Biology, Barts Cancer Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
| | - Ann-Marie C Baker
- Evolution and Cancer Laboratory, Centre for Genomics and Computational Biology, Barts Cancer Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
| | - Trevor A Graham
- Evolution and Cancer Laboratory, Centre for Genomics and Computational Biology, Barts Cancer Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
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50
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Royston D, Mead AJ, Psaila B. Application of Single-Cell Approaches to Study Myeloproliferative Neoplasm Biology. Hematol Oncol Clin North Am 2021; 35:279-293. [PMID: 33641869 PMCID: PMC7935666 DOI: 10.1016/j.hoc.2021.01.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Philadelphia-negative myeloproliferative neoplasms (MPNs) are an excellent tractable disease model of a number of aspects of human cancer biology, including genetic evolution, tissue-associated fibrosis, and cancer stem cells. In this review, we discuss recent insights into MPN biology gained from the application of a number of new single-cell technologies to study human disease, with a specific focus on single-cell genomics, single-cell transcriptomics, and digital pathology.
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Affiliation(s)
- Daniel Royston
- Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine and NIHR Biomedical Research Centre, University of Oxford, Headley Way, Oxford OX39DS, UK
| | - Adam J Mead
- Medical Research Council (MRC) Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, NIHR Biomedical Research Centre, University of Oxford, Headley Way, Oxford OX3 9DS, UK.
| | - Bethan Psaila
- Medical Research Council (MRC) Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, NIHR Biomedical Research Centre, University of Oxford, Headley Way, Oxford OX3 9DS, UK
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