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Xiao Y, Zou X. Mathematical modeling and quantitative analysis of phenotypic plasticity during tumor evolution based on single-cell data. J Math Biol 2024; 89:34. [PMID: 39162836 DOI: 10.1007/s00285-024-02133-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2024] [Revised: 06/24/2024] [Accepted: 08/08/2024] [Indexed: 08/21/2024]
Abstract
Tumor is a complex and aggressive type of disease that poses significant health challenges. Understanding the cellular mechanisms underlying its progression is crucial for developing effective treatments. In this study, we develop a novel mathematical framework to investigate the role of cellular plasticity and heterogeneity in tumor progression. By leveraging temporal single-cell data, we propose a reaction-convection-diffusion model that effectively captures the spatiotemporal dynamics of tumor cells and macrophages within the tumor microenvironment. Through theoretical analysis, we obtain the estimate of the pulse wave speed and analyze the stability of the homogeneous steady state solutions. Notably, we employe the AddModuleScore function to quantify cellular plasticity. One of the highlights of our approach is the introduction of pulse wave speed as a quantitative measure to precisely gauge the rate of cell phenotype transitions, as well as the novel implementation of the high-plasticity cell state/low-plasticity cell state ratio as an indicator of tumor malignancy. Furthermore, the bifurcation analysis reveals the complex dynamics of tumor cell populations. Our extensive analysis demonstrates that an increased rate of phenotype transition is associated with heightened malignancy, attributable to the tumor's ability to explore a wider phenotypic space. The study also investigates how the proliferation rate and the death rate of tumor cells, phenotypic convection velocity, and the midpoint of the phenotype transition stage affect the speed of tumor cell phenotype transitions and the progression to adenocarcinoma. These insights and quantitative measures can help guide the development of targeted therapeutic strategies to regulate cellular plasticity and control tumor progression effectively.
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Affiliation(s)
- Yuyang Xiao
- School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, China
| | - Xiufen Zou
- School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, China.
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2
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Weiner S, Li B, Nabavi S. Improved allele-specific single-cell copy number estimation in low-coverage DNA-sequencing. Bioinformatics 2024; 40:btae506. [PMID: 39133157 PMCID: PMC11346770 DOI: 10.1093/bioinformatics/btae506] [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: 04/03/2024] [Revised: 07/12/2024] [Accepted: 08/09/2024] [Indexed: 08/13/2024] Open
Abstract
MOTIVATION Advances in whole-genome single-cell DNA sequencing (scDNA-seq) have led to the development of numerous methods for detecting copy number aberrations (CNAs), a key driver of genetic heterogeneity in cancer. While most of these methods are limited to the inference of total copy number, some recent approaches now infer allele-specific CNAs using innovative techniques for estimating allele-frequencies in low coverage scDNA-seq data. However, these existing allele-specific methods are limited in their segmentation strategies, a crucial step in the CNA detection pipeline. RESULTS We present SEACON (Single-cell Estimation of Allele-specific COpy Numbers), an allele-specific copy number profiler for scDNA-seq data. SEACON uses a Gaussian Mixture Model to identify latent copy number states and breakpoints between contiguous segments across cells, filters the segments for high-quality breakpoints using an ensemble technique, and adopts several strategies for tolerating noisy read-depth and allele frequency measurements. Using a wide array of both real and simulated datasets, we show that SEACON derives accurate copy numbers and surpasses existing approaches under numerous experimental conditions, and identify its strengths and weaknesses. AVAILABILITY AND IMPLEMENTATION SEACON is implemented in Python and is freely available open-source from https://github.com/NabaviLab/SEACON and https://doi.org/10.5281/zenodo.12727008.
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Affiliation(s)
- Samson Weiner
- School of Computing, University of Connecticut, Storrs, CT 06082, United States
| | - Bingjun Li
- School of Computing, University of Connecticut, Storrs, CT 06082, United States
| | - Sheida Nabavi
- School of Computing, University of Connecticut, Storrs, CT 06082, United States
- Institute for Systems Genomics, University of Connecticut, Storrs, CT 06082, United States
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3
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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 2024; 82:329-342. [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] [MESH Headings] [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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4
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Qiao Z, Teng X, Liu A, Yang W. Novel Isolating Approaches to Circulating Tumor Cell Enrichment Based on Microfluidics: A Review. MICROMACHINES 2024; 15:706. [PMID: 38930676 PMCID: PMC11206030 DOI: 10.3390/mi15060706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Revised: 05/14/2024] [Accepted: 05/24/2024] [Indexed: 06/28/2024]
Abstract
Circulating tumor cells (CTCs), derived from the primary tumor and carrying genetic information, contribute significantly to the process of tumor metastasis. The analysis and detection of CTCs can be used to assess the prognosis and treatment response in patients with tumors, as well as to help study the metastatic mechanisms of tumors and the development of new drugs. Since CTCs are very rare in the blood, it is a challenging problem to enrich CTCs efficiently. In this paper, we provide a comprehensive overview of microfluidics-based enrichment devices for CTCs in recent years. We explore in detail the methods of enrichment based on the physical or biological properties of CTCs; among them, physical properties cover factors such as size, density, and dielectric properties, while biological properties are mainly related to tumor-specific markers on the surface of CTCs. In addition, we provide an in-depth description of the methods for enrichment of single CTCs and illustrate the importance of single CTCs for performing tumor analyses. Future research will focus on aspects such as improving the separation efficiency, reducing costs, and increasing the detection sensitivity and accuracy.
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Affiliation(s)
- Zezheng Qiao
- School of Electromechanical and Automotive Engineering, Yantai University, Yantai 264005, China; (Z.Q.); (X.T.)
| | - Xiangyu Teng
- School of Electromechanical and Automotive Engineering, Yantai University, Yantai 264005, China; (Z.Q.); (X.T.)
| | - Anqin Liu
- School of Mechanical and Electrical Engineering, Yantai Institute of Technology, Yantai 264005, China
| | - Wenguang Yang
- School of Electromechanical and Automotive Engineering, Yantai University, Yantai 264005, China; (Z.Q.); (X.T.)
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Koptagel H, Jun SH, Hård J, Lagergren J. Scuphr: A probabilistic framework for cell lineage tree reconstruction. PLoS Comput Biol 2024; 20:e1012094. [PMID: 38723024 PMCID: PMC11125557 DOI: 10.1371/journal.pcbi.1012094] [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: 12/12/2022] [Revised: 05/24/2024] [Accepted: 04/20/2024] [Indexed: 05/25/2024] Open
Abstract
Cell lineage tree reconstruction methods are developed for various tasks, such as investigating the development, differentiation, and cancer progression. Single-cell sequencing technologies enable more thorough analysis with higher resolution. We present Scuphr, a distance-based cell lineage tree reconstruction method using bulk and single-cell DNA sequencing data from healthy tissues. Common challenges of single-cell DNA sequencing, such as allelic dropouts and amplification errors, are included in Scuphr. Scuphr computes the distance between cell pairs and reconstructs the lineage tree using the neighbor-joining algorithm. With its embarrassingly parallel design, Scuphr can do faster analysis than the state-of-the-art methods while obtaining better accuracy. The method's robustness is investigated using various synthetic datasets and a biological dataset of 18 cells.
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Affiliation(s)
- Hazal Koptagel
- School of EECS, KTH Royal Institute of Technology, Stockholm, Sweden
- Science for Life Laboratory, Stockholm, Sweden
| | - Seong-Hwan Jun
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, New York, United States of America
| | - Joanna Hård
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Jens Lagergren
- School of EECS, KTH Royal Institute of Technology, Stockholm, Sweden
- Science for Life Laboratory, Stockholm, Sweden
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6
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Allen TA. The Role of Circulating Tumor Cells as a Liquid Biopsy for Cancer: Advances, Biology, Technical Challenges, and Clinical Relevance. Cancers (Basel) 2024; 16:1377. [PMID: 38611055 PMCID: PMC11010957 DOI: 10.3390/cancers16071377] [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: 02/27/2024] [Revised: 03/23/2024] [Accepted: 03/26/2024] [Indexed: 04/14/2024] Open
Abstract
Cancer remains a leading cause of mortality worldwide, with metastasis significantly contributing to its lethality. The metastatic spread of tumor cells, primarily through the bloodstream, underscores the importance of circulating tumor cells (CTCs) in oncological research. As a critical component of liquid biopsies, CTCs offer a non-invasive and dynamic window into tumor biology, providing invaluable insights into cancer dissemination, disease progression, and response to treatment. This review article delves into the recent advancements in CTC research, highlighting their emerging role as a biomarker in various cancer types. We explore the latest technologies and methods for CTC isolation and detection, alongside novel approaches to characterizing their biology through genomics, transcriptomics, proteomics, and epigenetic profiling. Additionally, we examine the clinical implementation of these findings, assessing how CTCs are transforming the landscape of cancer diagnosis, prognosis, and management. By offering a comprehensive overview of current developments and potential future directions, this review underscores the significance of CTCs in enhancing our understanding of cancer and in shaping personalized therapeutic strategies, particularly for patients with metastatic disease.
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7
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Liu F, Shi F, Yu Z. Inferring single-cell copy number profiles through cross-cell segmentation of read counts. BMC Genomics 2024; 25:25. [PMID: 38166601 PMCID: PMC10762977 DOI: 10.1186/s12864-023-09901-5] [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: 05/17/2023] [Accepted: 12/12/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Copy number alteration (CNA) is one of the major genomic variations that frequently occur in cancers, and accurate inference of CNAs is essential for unmasking intra-tumor heterogeneity (ITH) and tumor evolutionary history. Single-cell DNA sequencing (scDNA-seq) makes it convenient to profile CNAs at single-cell resolution, and thus aids in better characterization of ITH. Despite that several computational methods have been proposed to decipher single-cell CNAs, their performance is limited in either breakpoint detection or copy number estimation due to the high dimensionality and noisy nature of read counts data. RESULTS By treating breakpoint detection as a process to segment high dimensional read count sequence, we develop a novel method called DeepCNA for cross-cell segmentation of read count sequence and per-cell inference of CNAs. To cope with the difficulty of segmentation, an autoencoder (AE) network is employed in DeepCNA to project the original data into a low-dimensional space, where the breakpoints can be efficiently detected along each latent dimension and further merged to obtain the final breakpoints. Unlike the existing methods that manually calculate certain statistics of read counts to find breakpoints, the AE model makes it convenient to automatically learn the representations. Based on the inferred breakpoints, we employ a mixture model to predict copy numbers of segments for each cell, and leverage expectation-maximization algorithm to efficiently estimate cell ploidy by exploring the most abundant copy number state. Benchmarking results on simulated and real data demonstrate our method is able to accurately infer breakpoints as well as absolute copy numbers and surpasses the existing methods under different test conditions. DeepCNA can be accessed at: https://github.com/zhyu-lab/deepcna . CONCLUSIONS Profiling single-cell CNAs based on deep learning is becoming a new paradigm of scDNA-seq data analysis, and DeepCNA is an enhancement to the current arsenal of computational methods for investigating cancer genomics.
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Affiliation(s)
- Furui Liu
- School of Information Engineering, Ningxia University, Yinchuan, 750021, China
| | - Fangyuan Shi
- School of Information Engineering, Ningxia University, Yinchuan, 750021, China
- Collaborative Innovation Center for Ningxia Big Data and Artificial Intelligence Co-Founded By Ningxia Municipality and Ministry of Education, Ningxia University, Yinchuan, 750021, China
| | - Zhenhua Yu
- School of Information Engineering, Ningxia University, Yinchuan, 750021, China.
- Collaborative Innovation Center for Ningxia Big Data and Artificial Intelligence Co-Founded By Ningxia Municipality and Ministry of Education, Ningxia University, Yinchuan, 750021, China.
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Rossi N, Gigante N, Vitacolonna N, Piazza C. Inferring Markov Chains to Describe Convergent Tumor Evolution With CIMICE. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024; 21:106-119. [PMID: 38015671 DOI: 10.1109/tcbb.2023.3337258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2023]
Abstract
The field of tumor phylogenetics focuses on studying the differences within cancer cell populations. Many efforts are done within the scientific community to build cancer progression models trying to understand the heterogeneity of such diseases. These models are highly dependent on the kind of data used for their construction, therefore, as the experimental technologies evolve, it is of major importance to exploit their peculiarities. In this work we describe a cancer progression model based on Single Cell DNA Sequencing data. When constructing the model, we focus on tailoring the formalism on the specificity of the data. We operate by defining a minimal set of assumptions needed to reconstruct a flexible DAG structured model, capable of identifying progression beyond the limitation of the infinite site assumption. Our proposal is conservative in the sense that we aim to neither discard nor infer knowledge which is not represented in the data. We provide simulations and analytical results to show the features of our model, test it on real data, show how it can be integrated with other approaches to cope with input noise. Moreover, our framework can be exploited to produce simulated data that follows our theoretical assumptions. Finally, we provide an open source R implementation of our approach, called CIMICE, that is publicly available on BioConductor.
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Edrisi M, Huang X, Ogilvie HA, Nakhleh L. Accurate integration of single-cell DNA and RNA for analyzing intratumor heterogeneity using MaCroDNA. Nat Commun 2023; 14:8262. [PMID: 38092737 PMCID: PMC10719311 DOI: 10.1038/s41467-023-44014-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 11/27/2023] [Indexed: 12/17/2023] Open
Abstract
Cancers develop and progress as mutations accumulate, and with the advent of single-cell DNA and RNA sequencing, researchers can observe these mutations and their transcriptomic effects and predict proteomic changes with remarkable temporal and spatial precision. However, to connect genomic mutations with their transcriptomic and proteomic consequences, cells with either only DNA data or only RNA data must be mapped to a common domain. For this purpose, we present MaCroDNA, a method that uses maximum weighted bipartite matching of per-gene read counts from single-cell DNA and RNA-seq data. Using ground truth information from colorectal cancer data, we demonstrate the advantage of MaCroDNA over existing methods in accuracy and speed. Exemplifying the utility of single-cell data integration in cancer research, we suggest, based on results derived using MaCroDNA, that genomic mutations of large effect size increasingly contribute to differential expression between cells as Barrett's esophagus progresses to esophageal cancer, reaffirming the findings of the previous studies.
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Affiliation(s)
| | - Xiru Huang
- Department of Computer Science, Rice University, Houston, Texas, USA
| | - Huw A Ogilvie
- Department of Computer Science, Rice University, Houston, Texas, USA.
| | - Luay Nakhleh
- Department of Computer Science, Rice University, Houston, Texas, USA.
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10
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Pan J, Chang Z, Zhang X, Dong Q, Zhao H, Shi J, Wang G. Research progress of single-cell sequencing in tuberculosis. Front Immunol 2023; 14:1276194. [PMID: 37901241 PMCID: PMC10611525 DOI: 10.3389/fimmu.2023.1276194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 09/29/2023] [Indexed: 10/31/2023] Open
Abstract
Tuberculosis is a major infectious disease caused by Mycobacterium tuberculosis infection. The pathogenesis and immune mechanism of tuberculosis are not clear, and it is urgent to find new drugs, diagnosis, and treatment targets. A useful tool in the quest to reveal the enigmas related to Mycobacterium tuberculosis infection and disease is the single-cell sequencing technique. By clarifying cell heterogeneity, identifying pathogenic cell groups, and finding key gene targets, the map at the single cell level enables people to better understand the cell diversity of complex organisms and the immune state of hosts during infection. Here, we briefly reviewed the development of single-cell sequencing, and emphasized the different applications and limitations of various technologies. Single-cell sequencing has been widely used in the study of the pathogenesis and immune response of tuberculosis. We review these works summarizing the most influential findings. Combined with the multi-molecular level and multi-dimensional analysis, we aim to deeply understand the blank and potential future development of the research on Mycobacterium tuberculosis infection using single-cell sequencing technology.
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Affiliation(s)
| | | | | | | | | | - Jingwei Shi
- Key Laboratory of Pathobiology Ministry of Education, College of Basic Medical Sciences/China-Japan Union Hospital of Jilin University, Jilin University, Changchun, China
| | - Guoqing Wang
- Key Laboratory of Pathobiology Ministry of Education, College of Basic Medical Sciences/China-Japan Union Hospital of Jilin University, Jilin University, Changchun, China
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11
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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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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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13
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Volchkov EV, Khozyainova AA, Gurzhikhanova MK, Larionova IV, Matveev VE, Evseev DA, Ignatova AK, Menyailo ME, Venyov DA, Vorobev RS, Semchenkova AA, Olshanskaya YV, Denisov EV, Maschan MA. Potential value of high-throughput single-cell DNA sequencing of Juvenile myelomonocytic leukemia: report of two cases. NPJ Syst Biol Appl 2023; 9:41. [PMID: 37684264 PMCID: PMC10491583 DOI: 10.1038/s41540-023-00303-7] [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: 02/27/2023] [Accepted: 08/14/2023] [Indexed: 09/10/2023] Open
Abstract
Juvenile myelomonocytic leukemia (JMML) is a rare myeloproliferative disease of early childhood that develops due to mutations in the genes of the RAS-signaling pathway. Next-generation high throughput sequencing (NGS) enables identification of various secondary molecular genetic events that can facilitate JMML progression and transformation into secondary acute myeloid leukemia (sAML). The methods of single-cell DNA sequencing (scDNA-seq) enable overcoming limitations of bulk NGS and exploring genetic heterogeneity at the level of individual cells, which can help in a better understanding of the mechanisms leading to JMML progression and provide an opportunity to evaluate the response of leukemia to therapy. In the present work, we applied a two-step droplet microfluidics approach to detect DNA alterations among thousands of single cells and to analyze clonal dynamics in two JMML patients with sAML transformation before and after hematopoietic stem cell transplantation (HSCT). At the time of diagnosis both of our patients harbored only "canonical" mutations in the RAS signaling pathway genes detected by targeted DNA sequencing. Analysis of samples from the time of transformation JMML to sAML revealed additional genetic events that are potential drivers for disease progression in both patients. ScDNA-seq was able to measure of chimerism level and detect a residual tumor clone in the second patient after HSCT (sensitivity of less than 0.1% tumor cells). The data obtained demonstrate the value of scDNA-seq to assess the clonal evolution of JMML to sAML, response to therapy and engraftment monitoring.
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Affiliation(s)
- E V Volchkov
- Dmitry Rogachev National Medical Research Center of Pediatric Hematology, Oncology and Immunology (D. Rogachev NMRCPHOI) of Ministry of Healthсare of the Russian Federation, 1, Samory Mashela St., Moscow, 117997, Russia.
- Laboratory of Single Cell Biology, Research Institute of Molecular and Cellular Medicine, RUDN University, Moscow, 117198, Russia.
| | - A A Khozyainova
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634009, Russia
| | - M Kh Gurzhikhanova
- Dmitry Rogachev National Medical Research Center of Pediatric Hematology, Oncology and Immunology (D. Rogachev NMRCPHOI) of Ministry of Healthсare of the Russian Federation, 1, Samory Mashela St., Moscow, 117997, Russia
| | - I V Larionova
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634009, Russia
| | - V E Matveev
- Dmitry Rogachev National Medical Research Center of Pediatric Hematology, Oncology and Immunology (D. Rogachev NMRCPHOI) of Ministry of Healthсare of the Russian Federation, 1, Samory Mashela St., Moscow, 117997, Russia
| | - D A Evseev
- Dmitry Rogachev National Medical Research Center of Pediatric Hematology, Oncology and Immunology (D. Rogachev NMRCPHOI) of Ministry of Healthсare of the Russian Federation, 1, Samory Mashela St., Moscow, 117997, Russia
| | - A K Ignatova
- Dmitry Rogachev National Medical Research Center of Pediatric Hematology, Oncology and Immunology (D. Rogachev NMRCPHOI) of Ministry of Healthсare of the Russian Federation, 1, Samory Mashela St., Moscow, 117997, Russia
| | - M E Menyailo
- Laboratory of Single Cell Biology, Research Institute of Molecular and Cellular Medicine, RUDN University, Moscow, 117198, Russia
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634009, Russia
| | - D A Venyov
- Dmitry Rogachev National Medical Research Center of Pediatric Hematology, Oncology and Immunology (D. Rogachev NMRCPHOI) of Ministry of Healthсare of the Russian Federation, 1, Samory Mashela St., Moscow, 117997, Russia
| | - R S Vorobev
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634009, Russia
| | - A A Semchenkova
- Dmitry Rogachev National Medical Research Center of Pediatric Hematology, Oncology and Immunology (D. Rogachev NMRCPHOI) of Ministry of Healthсare of the Russian Federation, 1, Samory Mashela St., Moscow, 117997, Russia
| | - Yu V Olshanskaya
- Dmitry Rogachev National Medical Research Center of Pediatric Hematology, Oncology and Immunology (D. Rogachev NMRCPHOI) of Ministry of Healthсare of the Russian Federation, 1, Samory Mashela St., Moscow, 117997, Russia
| | - E V Denisov
- Laboratory of Single Cell Biology, Research Institute of Molecular and Cellular Medicine, RUDN University, Moscow, 117198, Russia
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634009, Russia
| | - M A Maschan
- Dmitry Rogachev National Medical Research Center of Pediatric Hematology, Oncology and Immunology (D. Rogachev NMRCPHOI) of Ministry of Healthсare of the Russian Federation, 1, Samory Mashela St., Moscow, 117997, Russia.
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14
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Takahashi K, Tanaka T. Clonal evolution and hierarchy in myeloid malignancies. Trends Cancer 2023; 9:707-715. [PMID: 37302922 PMCID: PMC10766088 DOI: 10.1016/j.trecan.2023.05.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 05/15/2023] [Accepted: 05/18/2023] [Indexed: 06/13/2023]
Abstract
Myeloid malignancies, a group of hematopoietic disorders that includes acute myeloid leukemia (AML), myelodysplastic syndromes (MDS), and myeloproliferative neoplasms (MPNs), are caused by the accumulation of genetic and epigenetic changes in hematopoietic stem and progenitor cells (HSPCs) over time. Despite the relatively low number of genomic drivers compared with other forms of cancer, the process by which these changes shape the genomic architecture of myeloid malignancies remains elusive. Recent advancements in clonal hematopoiesis research and the use of cutting-edge single cell technologies have shed new light on the developmental process of myeloid malignancies. In this review, we delve into the intricacies of clonal evolution in myeloid malignancies and its implications for the development of new diagnostic and therapeutic approaches.
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Affiliation(s)
- Koichi Takahashi
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
| | - Tomoyuki Tanaka
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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15
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Moffett AS, Deng Y, Levine H. Modeling the Role of Immune Cell Conversion in the Tumor-Immune Microenvironment. Bull Math Biol 2023; 85:93. [PMID: 37658264 PMCID: PMC10474003 DOI: 10.1007/s11538-023-01201-z] [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: 03/22/2023] [Accepted: 08/17/2023] [Indexed: 09/03/2023]
Abstract
Tumors develop in a complex physical, biochemical, and cellular milieu, referred to as the tumor microenvironment. Of special interest is the set of immune cells that reciprocally interact with the tumor, the tumor-immune microenvironment (TIME). The diversity of cell types and cell-cell interactions in the TIME has led researchers to apply concepts from ecology to describe the dynamics. However, while tumor cells are known to induce immune cells to switch from anti-tumor to pro-tumor phenotypes, this type of ecological interaction has been largely overlooked. To address this gap in cancer modeling, we develop a minimal, ecological model of the TIME with immune cell conversion, to highlight this important interaction and explore its consequences. A key finding is that immune conversion increases the range of parameters supporting a co-existence phase in which the immune system and the tumor reach a stalemate. Our results suggest that further investigation of the consequences of immune cell conversion, using detailed, data-driven models, will be critical for greater understanding of TIME dynamics.
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Affiliation(s)
- Alexander S. Moffett
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA 02115 USA
- Department of Physics, Northeastern University, Boston, MA 02115 USA
| | - Youyuan Deng
- Center for Theoretical Biological Physics, Rice University, Houston, TX 77005 USA
- Applied Physics Graduate Program, Smalley-Curl Institute, Rice University, Houston, TX 77005 USA
| | - Herbert Levine
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA 02115 USA
- Department of Physics, Northeastern University, Boston, MA 02115 USA
- Department of Bioengineering, Northeastern University, Boston, MA 02115 USA
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16
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Wang K, Kumar T, Wang J, Minussi DC, Sei E, Li J, Tran TM, Thennavan A, Hu M, Casasent AK, Xiao Z, Bai S, Yang L, King LM, Shah V, Kristel P, van der Borden CL, Marks JR, Zhao Y, Zurita AJ, Aparicio A, Chapin B, Ye J, Zhang J, Gibbons DL, Sawyer E, Thompson AM, Futreal A, Hwang ES, Wesseling J, Lips EH, Navin NE. Archival single-cell genomics reveals persistent subclones during DCIS progression. Cell 2023; 186:3968-3982.e15. [PMID: 37586362 DOI: 10.1016/j.cell.2023.07.024] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Revised: 05/09/2023] [Accepted: 07/17/2023] [Indexed: 08/18/2023]
Abstract
Ductal carcinoma in situ (DCIS) is a common precursor of invasive breast cancer. Our understanding of its genomic progression to recurrent disease remains poor, partly due to challenges associated with the genomic profiling of formalin-fixed paraffin-embedded (FFPE) materials. Here, we developed Arc-well, a high-throughput single-cell DNA-sequencing method that is compatible with FFPE materials. We validated our method by profiling 40,330 single cells from cell lines, a frozen tissue, and 27 FFPE samples from breast, lung, and prostate tumors stored for 3-31 years. Analysis of 10 patients with matched DCIS and cancers that recurred 2-16 years later show that many primary DCIS had already undergone whole-genome doubling and clonal diversification and that they shared genomic lineages with persistent subclones in the recurrences. Evolutionary analysis suggests that most DCIS cases in our cohort underwent an evolutionary bottleneck, and further identified chromosome aberrations in the persistent subclones that were associated with recurrence.
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Affiliation(s)
- Kaile Wang
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Tapsi Kumar
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA; MD Anderson UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA; Department of Genomic Medicine, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Junke Wang
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA; MD Anderson UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Darlan Conterno Minussi
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA; MD Anderson UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Emi Sei
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jianzhuo Li
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Tuan M Tran
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Aatish Thennavan
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Min Hu
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Anna K Casasent
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Zhenna Xiao
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Shanshan Bai
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Lei Yang
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA; MD Anderson UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Lorraine M King
- Department of Surgery, Duke University School of Medicine, Durham, NC 27707, USA
| | - Vandna Shah
- School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, Guy's Cancer Centre, King's College London, London WC2R 2LS, UK
| | - Petra Kristel
- Division of Molecular Pathology, the Netherlands Cancer Institute, Amsterdam 1066 CX, the Netherlands
| | - Carolien L van der Borden
- Division of Molecular Pathology, the Netherlands Cancer Institute, Amsterdam 1066 CX, the Netherlands
| | - Jeffrey R Marks
- Department of Surgery, Duke University School of Medicine, Durham, NC 27707, USA
| | - Yuehui Zhao
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Amado J Zurita
- Department of Genitourinary Medical Oncology, UT MD Anderson Cancer Center, Houston, TX, USA
| | - Ana Aparicio
- Department of Genitourinary Medical Oncology, UT MD Anderson Cancer Center, Houston, TX, USA
| | - Brian Chapin
- Department of Urology, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jie Ye
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA; MD Anderson UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA; Department of Thoracic/Head and Neck Medical Oncology, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jianjun Zhang
- Department of Genomic Medicine, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Thoracic/Head and Neck Medical Oncology, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Don L Gibbons
- Department of Thoracic/Head and Neck Medical Oncology, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Ellinor Sawyer
- School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, Guy's Cancer Centre, King's College London, London WC2R 2LS, UK
| | - Alastair M Thompson
- Department of Surgery, Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Andrew Futreal
- Department of Genomic Medicine, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - E Shelley Hwang
- Department of Surgery, Duke University School of Medicine, Durham, NC 27707, USA
| | - Jelle Wesseling
- Department of Pathology, the Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam 1066 CX, the Netherlands; Department of Pathology, Leiden University Medical Center, Leiden 2333 ZC, the Netherlands
| | - Esther H Lips
- Department of Pathology, the Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam 1066 CX, the Netherlands; Department of Pathology, Leiden University Medical Center, Leiden 2333 ZC, the Netherlands
| | - Nicholas E Navin
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA; MD Anderson UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA; Department of Bioinformatics, UT MD Anderson Cancer Center, Houston, TX 77030, USA.
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17
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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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18
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Miura S, Dolker T, Sanderford M, Kumar S. Improving cellular phylogenies through the integrated use of mutation order and optimality principles. Comput Struct Biotechnol J 2023; 21:3894-3903. [PMID: 37602230 PMCID: PMC10432911 DOI: 10.1016/j.csbj.2023.07.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Revised: 07/10/2023] [Accepted: 07/19/2023] [Indexed: 08/22/2023] Open
Abstract
The study of tumor evolution is being revolutionalized by single-cell sequencing technologies that survey the somatic variation of cancer cells. In these endeavors, reliable inference of the evolutionary relationship of single cells is a key step. However, single-cell sequences contain many errors and missing bases, which necessitate advancing standard molecular phylogenetics approaches for applications in analyzing these datasets. We have developed a computational approach that integratively applies standard phylogenetic optimality principles and patterns of co-occurrence of sequence variations to produce more expansive and accurate cellular phylogenies from single-cell sequence datasets. We found the new approach to also perform well for CRISPR/Cas9 genome editing datasets, suggesting that it can be useful for various applications. We apply the new approach to some empirical datasets to showcase its use for reconstructing recurrent mutations and mutational reversals as well as for phylodynamics analysis to infer metastatic cell migrations between tumors.
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Affiliation(s)
- Sayaka Miura
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA
- Department of Biology, Temple University, Philadelphia, PA 19122, USA
| | - Tenzin Dolker
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA
- Department of Biology, Temple University, Philadelphia, PA 19122, USA
| | - Maxwell Sanderford
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA
- Department of Biology, Temple University, Philadelphia, PA 19122, USA
| | - Sudhir Kumar
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA
- Department of Biology, Temple University, Philadelphia, PA 19122, USA
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19
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Stanley J, Lohith A, Debiaso L, Wang K, Ton M, Cui W, Gu W, Fu A, Pourmand N. High throughput isolation of RNA from single-cells within an intact tissue for spatial and temporal sequencing a reality. PLoS One 2023; 18:e0289279. [PMID: 37527243 PMCID: PMC10393160 DOI: 10.1371/journal.pone.0289279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 07/16/2023] [Indexed: 08/03/2023] Open
Abstract
Single-cell transcriptomics is essential for understanding biological variability among cells in a heterogenous population. Acquiring high-quality single-cell sequencing data from a tissue sample has multiple challenges including isolation of individual cells as well as amplification of the genetic material. Commercially available techniques require the isolation of individual cells from a tissue through extensive manual manipulation before single cell sequence data can be acquired. However, since cells within a tissue have different dissociation constants, enzymatic and mechanical manipulation do not guarantee the isolation of a homogenous population of cells. To overcome this drawback, in this research we have developed a revolutionary approach that utilizes a fully automated nanopipette technology in combination with magnetic nanoparticles to obtain high quality sequencing reads from individual cells within an intact tissue thereby eliminating the need for manual manipulation and single cell isolation. With the proposed technology, it is possible to sample an individual cell within the tissue multiple times to obtain longitudinal information. Single-cell RNAseq was achieved by aspirating only1-5% of sub-single-cell RNA content from individual cells within fresh frozen tissue samples. As a proof of concept, aspiration was carried out from 22 cells within a breast cancer tissue slice using quartz nanopipettes. The mRNA from the aspirate was then selectively captured using magnetic nanoparticles. The RNAseq data from aspiration of 22 individual cells provided high alignment rates (80%) with 2 control tissue samples. The technology is exceptionally simple, quick and efficient as the entire cell targeting and aspiration process is fully automated.
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Affiliation(s)
- John Stanley
- Department of Biomolecular Engineering, University of California, Santa Cruz, California, United States of America
| | - Akshar Lohith
- Department of Biomolecular Engineering, University of California, Santa Cruz, California, United States of America
| | - Lucca Debiaso
- Department of Biomolecular Engineering, University of California, Santa Cruz, California, United States of America
| | - Kevan Wang
- NVIGEN Inc, Campbell, California, United States of America
| | - Minh Ton
- NVIGEN Inc, Campbell, California, United States of America
| | - Wenwu Cui
- NVIGEN Inc, Campbell, California, United States of America
| | - Weiwei Gu
- NVIGEN Inc, Campbell, California, United States of America
| | - Aihua Fu
- NVIGEN Inc, Campbell, California, United States of America
| | - Nader Pourmand
- Department of Biomolecular Engineering, University of California, Santa Cruz, California, United States of America
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20
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Zhang M, Bouland GA, Holstege H, Reinders MJT. Identifying Aging and Alzheimer Disease-Associated Somatic Variations in Excitatory Neurons From the Human Frontal Cortex. Neurol Genet 2023; 9:e200066. [PMID: 37123987 PMCID: PMC10136684 DOI: 10.1212/nxg.0000000000200066] [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: 10/24/2022] [Accepted: 02/03/2023] [Indexed: 05/02/2023]
Abstract
Background and Objectives With age, somatic mutations accumulated in human brain cells can lead to various neurologic disorders and brain tumors. Because the incidence rate of Alzheimer disease (AD) increases exponentially with age, investigating the association between AD and the accumulation of somatic mutation can help understand the etiology of AD. Methods We designed a somatic mutation detection workflow by contrasting genotypes derived from whole-genome sequencing (WGS) data with genotypes derived from scRNA-seq data and applied this workflow to 76 participants from the Religious Order Study and the Rush Memory and Aging Project (ROSMAP) cohort. We focused only on excitatory neurons, the dominant cell type in the scRNA-seq data. Results We identified 196 sites that harbored at least 1 individual with an excitatory neuron-specific somatic mutation (ENSM), and these 196 sites were mapped to 127 genes. The single base substitution (SBS) pattern of the putative ENSMs was best explained by signature SBS5 from the Catalogue of Somatic Mutations in Cancer (COSMIC) mutational signatures, a clock-like pattern correlating with the age of the individual. The count of ENSMs per individual also showed an increasing trend with age. Among the mutated sites, we found 2 sites tend to have more mutations in older individuals (16:6899517 [RBFOX1], p = 0.04; 4:21788463 [KCNIP4], p < 0.05). In addition, 2 sites were found to have a higher odds ratio to detect a somatic mutation in AD samples (6:73374221 [KCNQ5], p = 0.01 and 13:36667102 [DCLK1], p = 0.02). Thirty-two genes that harbor somatic mutations unique to AD and the KCNQ5 and DCLK1 genes were used for gene ontology (GO)-term enrichment analysis. We found the AD-specific ENSMs enriched in the GO-term "vocalization behavior" and "intraspecies interaction between organisms." Of interest we observed both age-specific and AD-specific ENSMs enriched in the K+ channel-associated genes. Discussion Our results show that combining scRNA-seq and WGS data can successfully detect putative somatic mutations. The putative somatic mutations detected from ROSMAP data set have provided new insights into the association of AD and aging with brain somatic mutagenesis.
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Affiliation(s)
- Meng Zhang
- Delft Bioinformatics Lab (M.Z., G.A.B., H.H., M.J.T.R.), Delft University of Technology; Department of Human Genetics (M.Z., H.H.), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC; and Department of Human Genetics (G.A.B., M.J.T.R.), Leiden University Medical Center, the Netherlands
| | - Gerard A Bouland
- Delft Bioinformatics Lab (M.Z., G.A.B., H.H., M.J.T.R.), Delft University of Technology; Department of Human Genetics (M.Z., H.H.), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC; and Department of Human Genetics (G.A.B., M.J.T.R.), Leiden University Medical Center, the Netherlands
| | - Henne Holstege
- Delft Bioinformatics Lab (M.Z., G.A.B., H.H., M.J.T.R.), Delft University of Technology; Department of Human Genetics (M.Z., H.H.), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC; and Department of Human Genetics (G.A.B., M.J.T.R.), Leiden University Medical Center, the Netherlands
| | - Marcel J T Reinders
- Delft Bioinformatics Lab (M.Z., G.A.B., H.H., M.J.T.R.), Delft University of Technology; Department of Human Genetics (M.Z., H.H.), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC; and Department of Human Genetics (G.A.B., M.J.T.R.), Leiden University Medical Center, the Netherlands
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21
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Ordóñez CD, Redrejo-Rodríguez M. DNA Polymerases for Whole Genome Amplification: Considerations and Future Directions. Int J Mol Sci 2023; 24:9331. [PMID: 37298280 PMCID: PMC10253169 DOI: 10.3390/ijms24119331] [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: 04/13/2023] [Revised: 05/24/2023] [Accepted: 05/25/2023] [Indexed: 06/12/2023] Open
Abstract
In the same way that specialized DNA polymerases (DNAPs) replicate cellular and viral genomes, only a handful of dedicated proteins from various natural origins as well as engineered versions are appropriate for competent exponential amplification of whole genomes and metagenomes (WGA). Different applications have led to the development of diverse protocols, based on various DNAPs. Isothermal WGA is currently widely used due to the high performance of Φ29 DNA polymerase, but PCR-based methods are also available and can provide competent amplification of certain samples. Replication fidelity and processivity must be considered when selecting a suitable enzyme for WGA. However, other properties, such as thermostability, capacity to couple replication, and double helix unwinding, or the ability to maintain DNA replication opposite to damaged bases, are also very relevant for some applications. In this review, we provide an overview of the different properties of DNAPs widely used in WGA and discuss their limitations and future research directions.
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Affiliation(s)
- Carlos D. Ordóñez
- CIC bioGUNE, Bizkaia Science and Technology Park, Building 800, 48160 Derio, Spain
| | - Modesto Redrejo-Rodríguez
- Department of Biochemistry, Universidad Autónoma de Madrid and Instituto de Investigaciones Biomédicas “Alberto Sols”, CSIC-UAM, 28029 Madrid, Spain
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22
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Yildiz S, Kinali M, Wei JJ, Milad M, Yin P, Adli M, Bulun SE. Adenomyosis: single-cell transcriptomic analysis reveals a paracrine mesenchymal-epithelial interaction involving the WNT/SFRP pathway. Fertil Steril 2023; 119:869-882. [PMID: 36736810 PMCID: PMC11257082 DOI: 10.1016/j.fertnstert.2023.01.041] [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: 09/26/2022] [Revised: 01/21/2023] [Accepted: 01/25/2023] [Indexed: 02/04/2023]
Abstract
OBJECTIVE To assess the cellular and molecular landscape of adenomyosis. DESIGN Single-cell analysis of genome-wide messenger RNA (mRNA) expression (single-cell RNA sequencing) of matched tissues of endometrium, adenomyosis, and myometrium using relatively large numbers of viable cells. SETTING Not applicable. PATIENT(S) Patients (n = 3, age range 40-44 years) undergoing hysterectomy for diffuse adenomyosis. MAIN OUTCOME MEASURE(S) Definition of the molecular landscape of matched adenomyotic, endometrial and myometrial tissues from the same uterus using single-cell RNA sequencing and comparison of distinct cell types in these tissues to identify disease-specific cell populations, abnormal gene expression and pathway activation, and mesenchymal-epithelial interactions. RESULT(S) The largest cell population in the endometrium was composed of closely clustered fibroblast groups, which comprise 36% of all cells and seem to originate from pericyte progenitors differentiating to estrogen/progesterone receptor-expressing endometrial stromal- cells. In contrast, the entire fibroblast population in adenomyosis comprised a larger (50%) portion of all cells and was not linked to any pericyte progenitors. Adenomyotic fibroblasts eventually differentiate into extracellular matrix protein-expressing fibroblasts and smooth muscle cells. Hierarchical clustering of mRNA expression revealed a unique adenomyotic fibroblast population that clustered transcriptomically with endometrial fibroblasts, suggestive of an endometrial stromal cell population serving as progenitors of adenomyosis. Four other adenomyotic fibroblast clusters with disease-specific transcriptomes were distinct from those of endometrial or myometrial fibroblasts. The mRNA levels of the natural WNT inhibitors, named, secreted frizzled-related proteins 1, 2, and 4, were higher in these 4 adenomyotic fibroblast clusters than in endometrial fibroblast clusters. Moreover, we found that multiple WNTs, which originate from fibroblasts and target ciliated and unciliated epithelial cells and endothelial cells, constitute a critical paracrine signaling network in adenomyotic tissue. Compared with endometrial tissue, unciliated and ciliated epithelial cells in adenomyosis comprised a significantly smaller portion of this tissue and exhibited molecular evidence of progesterone resistance and diminished regulation of estrogen signaling. CONCLUSION(S) We found a high degree of heterogeneity in fibroblast-like cells in the adenomyotic uterus. The WNT signaling involving differential expression of secreted frizzled-related proteins, which act as decoy receptors for WNTs, in adenomyotic fibroblasts may have a key role in the pathophysiology of this disease.
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Affiliation(s)
- Sule Yildiz
- Department of Obstetrics and Gynecology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Obstetrics and Gynecology, Koc University School of Medicine, Istanbul, Turkey
| | - Meric Kinali
- Department of Obstetrics and Gynecology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Jian Jun Wei
- Department of Pathology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Magdy Milad
- Department of Obstetrics and Gynecology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Ping Yin
- Department of Obstetrics and Gynecology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Mazhar Adli
- Department of Obstetrics and Gynecology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Serdar E Bulun
- Department of Obstetrics and Gynecology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois.
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23
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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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24
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Sandmann S, Richter S, Jiang X, Varghese J. Reconstructing Clonal Evolution-A Systematic Evaluation of Current Bioinformatics Approaches. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:5128. [PMID: 36982036 PMCID: PMC10049679 DOI: 10.3390/ijerph20065128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 03/04/2023] [Accepted: 03/13/2023] [Indexed: 06/18/2023]
Abstract
The accurate reconstruction of clonal evolution, including the identification of newly developing, highly aggressive subclones, is essential for the application of precision medicine in cancer treatment. Reconstruction, aiming for correct variant clustering and clonal evolution tree reconstruction, is commonly performed by tedious manual work. While there is a plethora of tools to automatically generate reconstruction, their reliability, especially reasons for unreliability, are not systematically assessed. We developed clevRsim-an approach to simulate clonal evolution data, including single-nucleotide variants as well as (overlapping) copy number variants. From this, we generated 88 data sets and performed a systematic evaluation of the tools for the reconstruction of clonal evolution. The results indicate a major negative influence of a high number of clones on both clustering and tree reconstruction. Low coverage as well as an extreme number of time points usually leads to poor clustering results. An underlying branched independent evolution hampers correct tree reconstruction. A further major decline in performance could be observed for large deletions and duplications overlapping single-nucleotide variants. In summary, to explore the full potential of reconstructing clonal evolution, improved algorithms that can properly handle the identified limitations are greatly needed.
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Affiliation(s)
- Sarah Sandmann
- Institute of Medical Informatics, University of Münster, 48149 Münster, Germany
| | - Silja Richter
- Institute of Medical Informatics, University of Münster, 48149 Münster, Germany
| | - Xiaoyi Jiang
- Department of Computer Science, University of Münster, 48149 Münster, Germany
| | - Julian Varghese
- Institute of Medical Informatics, University of Münster, 48149 Münster, Germany
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25
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Lu N, Qiao Y, Lu Z, Tu J. Chimera: The spoiler in multiple displacement amplification. Comput Struct Biotechnol J 2023; 21:1688-1696. [PMID: 36879882 PMCID: PMC9984789 DOI: 10.1016/j.csbj.2023.02.034] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 02/18/2023] [Accepted: 02/18/2023] [Indexed: 02/24/2023] Open
Abstract
Multiple displacement amplification (MDA) based on isothermal random priming and high fidelity phi29 DNA polymerase-mediated processive extension has revolutionized the field of whole genome amplification by enabling the amplification of minute amounts of DNA, such as from a single cell, generating vast amounts of DNA with high genome coverage. Despite its advantages, MDA has its own challenges, one of the grandest being the formation of chimeric sequences (chimeras), which presents in all MDA products and seriously disturbs the downstream analysis. In this review, we provide a comprehensive overview of current research on MDA chimeras. We first reviewed the mechanisms of chimera formation and chimera detection methods. We then systematically summarized the characteristics of chimeras, including overlap, chimeric distance, chimeric density, and chimeric rate, as found in independently published sequencing data. Finally, we reviewed the methods used to process chimeric sequences and their impacts on the improvement of data utilization efficiency. The information presented in this review will be useful for those interested in understanding the challenges with MDA and in improving its performance.
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Affiliation(s)
- Na Lu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Yi Qiao
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Zuhong Lu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Jing Tu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
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26
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Leighton J, Hu M, Sei E, Meric-Bernstam F, Navin NE. Reconstructing mutational lineages in breast cancer by multi-patient-targeted single-cell DNA sequencing. CELL GENOMICS 2023; 3:100215. [PMID: 36777188 PMCID: PMC9903705 DOI: 10.1016/j.xgen.2022.100215] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 07/21/2022] [Accepted: 10/18/2022] [Indexed: 11/11/2022]
Abstract
Single-cell DNA sequencing (scDNA-seq) methods are powerful tools for profiling mutations in cancer cells; however, most genomic regions sequenced in single cells are non-informative. To overcome this issue, we developed a multi-patient-targeted (MPT) scDNA-seq method. MPT involves first performing bulk exome sequencing across a cohort of cancer patients to identify somatic mutations, which are then pooled together to develop a single custom targeted panel for high-throughput scDNA-seq using a microfluidics platform. We applied MPT to profile 330 mutations across 23,500 cells from 5 patients with triple negative-breast cancer (TNBC), which showed that 3 tumors were monoclonal and 2 tumors were polyclonal. From these data, we reconstructed mutational lineages and identified early mutational and copy-number events, including early TP53 mutations that occurred in all five patients. Collectively, our data suggest that MPT can overcome a major technical obstacle for studying tumor evolution using scDNA-seq by profiling information-rich mutation sites.
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Affiliation(s)
- Jake Leighton
- Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA
- Graduate School of Biological Sciences, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Min Hu
- Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Emi Sei
- Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Funda Meric-Bernstam
- Graduate School of Biological Sciences, UT MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Precision Oncology, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Nicholas E. Navin
- Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA
- Graduate School of Biological Sciences, UT MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA
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27
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Papetti DM, Spolaor S, Nazari I, Tirelli A, Leonardi T, Caprioli C, Besozzi D, Vlachou T, Pelicci PG, Cazzaniga P, Nobile MS. Barcode demultiplexing of nanopore sequencing raw signals by unsupervised machine learning. FRONTIERS IN BIOINFORMATICS 2023; 3:1067113. [PMID: 37181486 PMCID: PMC10173771 DOI: 10.3389/fbinf.2023.1067113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 04/17/2023] [Indexed: 05/16/2023] Open
Abstract
Introduction: Oxford Nanopore Technologies (ONT) is a third generation sequencing approach that allows the analysis of individual, full-length nucleic acids. ONT records the alterations of an ionic current flowing across a nano-scaled pore while a DNA or RNA strand is threading through the pore. Basecalling methods are then leveraged to translate the recorded signal back to the nucleic acid sequence. However, basecall generally introduces errors that hinder the process of barcode demultiplexing, a pivotal task in single-cell RNA sequencing that allows for separating the sequenced transcripts on the basis of their cell of origin. Methods: To solve this issue, we present a novel framework, called UNPLEX, designed to tackle the barcode demultiplexing problem by operating directly on the recorded signals. UNPLEX combines two unsupervised machine learning methods: autoencoders and self-organizing maps (SOM). The autoencoders extract compact, latent representations of the recorded signals that are then clustered by the SOM. Results and Discussion: Our results, obtained on two datasets composed of in silico generated ONT-like signals, show that UNPLEX represents a promising starting point for the development of effective tools to cluster the signals corresponding to the same cell.
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Affiliation(s)
- Daniele M. Papetti
- Department of Informatics, Systems, and Communication, University of Milano-Bicocca, Milan, Italy
| | - Simone Spolaor
- Microsystems, Eindhoven University of Technology, Eindhoven, Netherlands
- Institute for Complex Molecular Systems (ICMS), Eindhoven University of Technology, Eindhoven, Netherlands
| | - Iman Nazari
- Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy
- European School of Molecular Medicine (SEMM), Milan, Italy
| | - Andrea Tirelli
- Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy
- International School for Advanced Studies (SISSA), Trieste, Italy
| | - Tommaso Leonardi
- Center for Genomic Science of IIT@SEMM, Istituto Italiano di Tecnologia (IIT), Milan, Italy
| | - Chiara Caprioli
- Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Daniela Besozzi
- Department of Informatics, Systems, and Communication, University of Milano-Bicocca, Milan, Italy
- Bicocca Bioinformatics, Biostatistics and Bioimaging (B4) Research Center, Milan, Italy
| | - Thalia Vlachou
- Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Pier Giuseppe Pelicci
- Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Paolo Cazzaniga
- Bicocca Bioinformatics, Biostatistics and Bioimaging (B4) Research Center, Milan, Italy
- Department of Human and Social Sciences, University of Bergamo, Bergamo, Italy
- *Correspondence: Paolo Cazzaniga, ; Marco S. Nobile,
| | - Marco S. Nobile
- Bicocca Bioinformatics, Biostatistics and Bioimaging (B4) Research Center, Milan, Italy
- Department of Environmental Sciences, Informatics, and Statistics, Ca’ Foscari University of Venice, Venice, Italy
- Department of Industrial Engineering and Innovation Sciences, Eindhoven of University of Technology, Eindhoven, Netherlands
- *Correspondence: Paolo Cazzaniga, ; Marco S. Nobile,
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28
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Mitoyan L, Gard C, Nin S, Loriod B, Guasch G. Defining Anorectal Transition Zone Heterogeneity Using Single-Cell RNA Sequencing. Methods Mol Biol 2023; 2650:89-103. [PMID: 37310626 DOI: 10.1007/978-1-0716-3076-1_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Special regions called transition zones (TZs) are found at numerous places in the body. TZs represent the junction between two different types of epithelia and are located between the esophagus and the stomach, in the cervix, in the eye, and between the anal canal and the rectum. TZ is a heterogeneous population, and the detailed characterization of its populations requires an analysis at the single-cell level. In this chapter, we provide a protocol to do single-cell RNA sequencing primary analysis of anal canal, TZ, and rectum epithelia.
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Affiliation(s)
- Louciné Mitoyan
- Aix-Marseille University, CNRS, INSERM, Institut Paoli-Calmettes, CRCM, Epithelial Stem Cells and Cancer Team, Marseille, France
| | - Charlyne Gard
- Aix-Marseille University, INSERM, TAGC, TGML, Marseille, France
| | - Sébastien Nin
- Aix-Marseille University, INSERM, TAGC, TGML, Marseille, France
| | - Béatrice Loriod
- Aix-Marseille University, INSERM, TAGC, TGML, Marseille, France
| | - Géraldine Guasch
- Aix-Marseille University, CNRS, INSERM, Institut Paoli-Calmettes, CRCM, Epithelial Stem Cells and Cancer Team, Marseille, France.
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29
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Mitoyan L, Gard C, Nin S, Loriod B, Guasch G. In Vivo Model for Isolating Epithelial Cells of the Anorectal Transition Zone. Methods Mol Biol 2023; 2650:43-52. [PMID: 37310622 DOI: 10.1007/978-1-0716-3076-1_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Different epithelia line the body and organs and form a continuous lining of cells. The junction of two different types of epithelia represents a special region called transition zone (TZ). TZ are small areas found in numerous places in the body such as between the esophagus and the stomach, in the cervix, in the eye, and between the anal canal and the rectum. These zones are associated with diverse pathologies such as cancers; however, the cellular and molecular mechanisms involved in tumor progression are poorly investigated. We recently characterized the role of anorectal TZ cells during homeostasis and after injury using an in vivo (lineage tracing) approach. To follow TZ cells, we previously developed a mouse model of lineage tracing using cytokeratin 17 (Krt17) as a promoter and GFP as a reporter. Krt17 is expressed by TZ but also by anal glands located below the TZ in the stroma that can interfere with TZ cell population isolation and analysis afterward. In this chapter, we provide a new dissection method to remove specifically anal glands without affecting anorectal TZ cells. This protocol allows the specific dissection and isolation of anal canal, TZ, and rectum epithelia.
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Affiliation(s)
- Louciné Mitoyan
- Aix-Marseille University, CNRS, INSERM, Institute Paoli-Calmettes, CRCM, Epithelial Stem Cells and Cancer Team, Marseille, France
| | - Charlyne Gard
- Aix-Marseille University, INSERM, TAGC, TGML, Marseille, France
| | - Sébastien Nin
- Aix-Marseille University, INSERM, TAGC, TGML, Marseille, France
| | - Béatrice Loriod
- Aix-Marseille University, INSERM, TAGC, TGML, Marseille, France
| | - Géraldine Guasch
- Aix-Marseille University, CNRS, INSERM, Institute Paoli-Calmettes, CRCM, Epithelial Stem Cells and Cancer Team, Marseille, France.
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30
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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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31
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Gao Y, Gaither J, Chifman J, Kubatko L. A phylogenetic approach to inferring the order in which mutations arise during cancer progression. PLoS Comput Biol 2022; 18:e1010560. [PMID: 36459515 DOI: 10.1371/journal.pcbi.1010560] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Revised: 12/14/2022] [Accepted: 09/12/2022] [Indexed: 12/05/2022] Open
Abstract
Although the role of evolutionary process in cancer progression is widely accepted, increasing attention is being given to the evolutionary mechanisms that can lead to differences in clinical outcome. Recent studies suggest that the temporal order in which somatic mutations accumulate during cancer progression is important. Single-cell sequencing (SCS) provides a unique opportunity to examine the effect that the mutation order has on cancer progression and treatment effect. However, the error rates associated with single-cell sequencing are known to be high, which greatly complicates the task. We propose a novel method for inferring the order in which somatic mutations arise within an individual tumor using noisy data from single-cell sequencing. Our method incorporates models at two levels in that the evolutionary process of somatic mutation within the tumor is modeled along with the technical errors that arise from the single-cell sequencing data collection process. Through analyses of simulations across a wide range of realistic scenarios, we show that our method substantially outperforms existing approaches for identifying mutation order. Most importantly, our method provides a unique means to capture and quantify the uncertainty in the inferred mutation order along a given phylogeny. We illustrate our method by analyzing data from colorectal and prostate cancer patients, in which our method strengthens previously reported mutation orders. Our work is an important step towards producing meaningful prediction of mutation order with high accuracy and measuring the uncertainty of predicted mutation order in cancer patients, with the potential to lead to new insights about the evolutionary trajectories of cancer.
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Affiliation(s)
- Yuan Gao
- Division of Biostatistics, The Ohio State University, Columbus, Ohio, United States of America
| | - Jeff Gaither
- Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, Ohio, United States of America
| | - Julia Chifman
- Dept of Mathematics and Statistics, American University, Washington D. C., United States of America
| | - Laura Kubatko
- Dept of Statistics, The Ohio State University, Columbus, Ohio, United States of America
- Dept of Evolution, Ecology, and Organismal Biology, The Ohio State University, Columbus, Ohio, United States of America
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32
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Alves JM, Estévez-Gómez N, Valecha M, Prado-López S, Tomás L, Alvariño P, Piñeiro R, Muinelo-Romay L, Mondelo-Macía P, Salgado M, Iglesias-Gómez A, Codesido-Prada L, Cubiella J, Posada D. Comparative analysis of capture methods for genomic profiling of circulating tumor cells in colorectal cancer. Genomics 2022; 114:110500. [PMID: 36202322 DOI: 10.1016/j.ygeno.2022.110500] [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: 06/10/2022] [Revised: 09/23/2022] [Accepted: 10/02/2022] [Indexed: 01/14/2023]
Abstract
The genomic profiling of circulating tumor cells (CTCs) in the bloodstream should provide clinically relevant information on therapeutic efficacy and help predict cancer survival. Here, we contrasted the genomic profiles of CTC pools recovered from metastatic colorectal cancer (mCRC) patients using different enrichment strategies (CellSearch, Parsortix, and FACS). Mutations inferred in the CTC pools differed depending on the enrichment strategy and, in all cases, represented a subset of the mutations detected in the matched primary tumor samples. However, the CTC pools from Parsortix, and in part, CellSearch, showed diversity estimates, mutational signatures, and drug-suitability scores remarkably close to those found in matching primary tumor samples. In addition, FACS CTC pools were enriched in apparent sequencing artifacts, leading to much higher genomic diversity estimates. Our results highlight the utility of CTCs to assess the genomic heterogeneity of individual tumors and help clinicians prioritize drugs in mCRC.
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Affiliation(s)
- Joao M Alves
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain; Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Spain.
| | - Nuria Estévez-Gómez
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain; Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Spain
| | - Monica Valecha
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain; Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Spain
| | - Sonia Prado-López
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain; Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Spain
| | - Laura Tomás
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain; Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Spain
| | - Pilar Alvariño
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain; Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Spain
| | - Roberto Piñeiro
- Roche-Chus Joint Unit, Translational Medical Oncology Group, Oncomet, Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain; Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain
| | - Laura Muinelo-Romay
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain; Liquid Biopsy Analysis Unit, Translational Medical Oncology Group, Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
| | - Patricia Mondelo-Macía
- Liquid Biopsy Analysis Unit, Translational Medical Oncology Group, Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
| | - Mercedes Salgado
- Department of Oncology, Hospital Universitario de Ourense, Research Group in Gastrointestinal Oncology-Ourense, Ourense, Spain
| | - Agueda Iglesias-Gómez
- Department of Gastroenterology Hospital Universitario de Ourense, Research Group in Gastrointestinal Oncology-Ourense, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Ourense, Spain
| | - Laura Codesido-Prada
- Department of Gastroenterology Hospital Universitario de Ourense, Research Group in Gastrointestinal Oncology-Ourense, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Ourense, Spain
| | - Joaquin Cubiella
- Department of Gastroenterology Hospital Universitario de Ourense, Research Group in Gastrointestinal Oncology-Ourense, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Ourense, Spain
| | - David Posada
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain; Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Spain; Department of Biochemistry, Genetics, and Immunology, Universidade de Vigo, 36310 Vigo, Spain.
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Pellegrina L, Vandin F. Discovering significant evolutionary trajectories in cancer phylogenies. Bioinformatics 2022; 38:ii49-ii55. [PMID: 36124798 DOI: 10.1093/bioinformatics/btac467] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
MOTIVATION Tumors are the result of a somatic evolutionary process leading to substantial intra-tumor heterogeneity. Single-cell and multi-region sequencing enable the detailed characterization of the clonal architecture of tumors and have highlighted its extensive diversity across tumors. While several computational methods have been developed to characterize the clonal composition and the evolutionary history of tumors, the identification of significantly conserved evolutionary trajectories across tumors is still a major challenge. RESULTS We present a new algorithm, MAximal tumor treeS TRajectOries (MASTRO), to discover significantly conserved evolutionary trajectories in cancer. MASTRO discovers all conserved trajectories in a collection of phylogenetic trees describing the evolution of a cohort of tumors, allowing the discovery of conserved complex relations between alterations. MASTRO assesses the significance of the trajectories using a conditional statistical test that captures the coherence in the order in which alterations are observed in different tumors. We apply MASTRO to data from nonsmall-cell lung cancer bulk sequencing and to acute myeloid leukemia data from single-cell panel sequencing, and find significant evolutionary trajectories recapitulating and extending the results reported in the original studies. AVAILABILITY AND IMPLEMENTATION MASTRO is available at https://github.com/VandinLab/MASTRO. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Leonardo Pellegrina
- Department of Information Engineering, University of Padova, Padova, 35129, Italy
| | - Fabio Vandin
- Department of Information Engineering, University of Padova, Padova, 35129, Italy
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34
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Kuipers J, Singer J, Beerenwinkel N. Single-cell mutation calling and phylogenetic tree reconstruction with loss and recurrence. Bioinformatics 2022; 38:4713-4719. [PMID: 36000873 PMCID: PMC9563700 DOI: 10.1093/bioinformatics/btac577] [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: 02/01/2022] [Revised: 07/08/2022] [Accepted: 08/23/2022] [Indexed: 11/13/2022] Open
Abstract
Motivation Tumours evolve as heterogeneous populations of cells, which may be distinguished by different genomic aberrations. The resulting intra-tumour heterogeneity plays an important role in cancer patient relapse and treatment failure, so that obtaining a clear understanding of each patient’s tumour composition and evolutionary history is key for personalized therapies. Single-cell sequencing (SCS) now provides the possibility to resolve tumour heterogeneity at the highest resolution of individual tumour cells, but brings with it challenges related to the particular noise profiles of the sequencing protocols as well as the complexity of the underlying evolutionary process. Results By modelling the noise processes and allowing mutations to be lost or to reoccur during tumour evolution, we present a method to jointly call mutations in each cell, reconstruct the phylogenetic relationship between cells, and determine the locations of mutational losses and recurrences. Our Bayesian approach allows us to accurately call mutations as well as to quantify our certainty in such predictions. We show the advantages of allowing mutational loss or recurrence with simulated data and present its application to tumour SCS data. Availability and implementation SCIΦN is available at https://github.com/cbg-ethz/SCIPhIN. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jack Kuipers
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Jochen Singer
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Basel, Switzerland
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35
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Chen T, Cao C, Zhang J, Streets A, Li T, Huang Y. Histologically resolved multiomics enables precise molecular profiling of human intratumor heterogeneity. PLoS Biol 2022; 20:e3001699. [PMID: 35776767 PMCID: PMC9282480 DOI: 10.1371/journal.pbio.3001699] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 07/14/2022] [Accepted: 06/08/2022] [Indexed: 11/19/2022] Open
Abstract
Both the composition of cell types and their spatial distribution in a tissue play a critical role in cellular function, organ development, and disease progression. For example, intratumor heterogeneity and the distribution of transcriptional and genetic events in single cells drive the genesis and development of cancer. However, it can be challenging to fully characterize the molecular profile of cells in a tissue with high spatial resolution because microscopy has limited ability to extract comprehensive genomic information, and the spatial resolution of genomic techniques tends to be limited by dissection. There is a growing need for tools that can be used to explore the relationship between histological features, gene expression patterns, and spatially correlated genomic alterations in healthy and diseased tissue samples. Here, we present a technique that combines label-free histology with spatially resolved multiomics in unfixed and unstained tissue sections. This approach leverages stimulated Raman scattering microscopy to provide chemical contrast that reveals histological tissue architecture, allowing for high-resolution in situ laser microdissection of regions of interests. These microtissue samples are then processed for DNA and RNA sequencing to identify unique genetic profiles that correspond to distinct anatomical regions. We demonstrate the capabilities of this technique by mapping gene expression and copy number alterations to histologically defined regions in human oral squamous cell carcinoma (OSCC). Our approach provides complementary insights in tumorigenesis and offers an integrative tool for macroscale cancer tissues with spatial multiomics assessments.
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Affiliation(s)
- Tao Chen
- Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University, Beijing, China
- College of Engineering, Peking University, Beijing, China
| | - Chen Cao
- Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University, Beijing, China
| | - Jianyun Zhang
- Department of Oral Pathology, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing, China
- Beijing Key Laboratory of Digital Stomatology
| | - Aaron Streets
- Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University, Beijing, China
| | - Tiejun Li
- Department of Oral Pathology, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing, China
- Beijing Key Laboratory of Digital Stomatology
- Research Unit of Precision Pathologic Diagnosis in Tumors of the Oral and Maxillofacial Regions, Chinese Academy of Medical Sciences (2019RU034), Beijing, China
| | - Yanyi Huang
- Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University, Beijing, China
- College of Engineering, Peking University, Beijing, China
- Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
- Beijing Advanced Innovation Center for Genomics (ICG), Peking University, Beijing, China
- College of Chemistry and Molecular Engineering, Peking University, Beijing, China
- Institute for Cell Analysis, Shenzhen Bay Laboratory, Guangdong, China
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36
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Edrisi M, Valecha MV, Chowdary SBV, Robledo S, Ogilvie HA, Posada D, Zafar H, Nakhleh L. Phylovar: toward scalable phylogeny-aware inference of single-nucleotide variations from single-cell DNA sequencing data. Bioinformatics 2022; 38:i195-i202. [PMID: 35758771 PMCID: PMC9235480 DOI: 10.1093/bioinformatics/btac254] [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] [Indexed: 11/30/2022] Open
Abstract
Motivation Single-nucleotide variants (SNVs) are the most common variations in the human genome. Recently developed methods for SNV detection from single-cell DNA sequencing data, such as SCIΦ and scVILP, leverage the evolutionary history of the cells to overcome the technical errors associated with single-cell sequencing protocols. Despite being accurate, these methods are not scalable to the extensive genomic breadth of single-cell whole-genome (scWGS) and whole-exome sequencing (scWES) data. Results Here, we report on a new scalable method, Phylovar, which extends the phylogeny-guided variant calling approach to sequencing datasets containing millions of loci. Through benchmarking on simulated datasets under different settings, we show that, Phylovar outperforms SCIΦ in terms of running time while being more accurate than Monovar (which is not phylogeny-aware) in terms of SNV detection. Furthermore, we applied Phylovar to two real biological datasets: an scWES triple-negative breast cancer data consisting of 32 cells and 3375 loci as well as an scWGS data of neuron cells from a normal human brain containing 16 cells and approximately 2.5 million loci. For the cancer data, Phylovar detected somatic SNVs with high or moderate functional impact that were also supported by bulk sequencing dataset and for the neuron dataset, Phylovar identified 5745 SNVs with non-synonymous effects some of which were associated with neurodegenerative diseases. Availability and implementation Phylovar is implemented in Python and is publicly available at https://github.com/NakhlehLab/Phylovar.
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Affiliation(s)
| | | | - Sunkara B V Chowdary
- Department of Computer Science & Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, India
| | | | - Huw A Ogilvie
- Department of Computer Science, Rice University, Houston, TX 77005, USA
| | - David Posada
- CINBIO, Universidade de Vigo, Vigo 36310, Spain.,Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain.,Department of Biochemistry, Genetics, and Immunology, Universidade de Vigo, Vigo 36310, Spain
| | - Hamim Zafar
- Department of Computer Science & Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, India.,Department of Biological Sciences & Bioengineering, Institute of Technology Kanpur, Kanpur 208016, India.,Mehta Family Centre for Engineering in Medicine, Indian Institute of Technology Kanpur, Kanpur 208016, India
| | - Luay Nakhleh
- Department of Computer Science, Rice University, Houston, TX 77005, USA
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37
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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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38
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Valecha M, Posada D. Somatic variant calling from single-cell DNA sequencing data. Comput Struct Biotechnol J 2022; 20:2978-2985. [PMID: 35782734 PMCID: PMC9218383 DOI: 10.1016/j.csbj.2022.06.013] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 06/06/2022] [Accepted: 06/06/2022] [Indexed: 11/03/2022] Open
Abstract
Single-cell sequencing has gained popularity in recent years. Despite its numerous applications, single-cell DNA sequencing data is highly error-prone due to technical biases arising from uneven sequencing coverage, allelic dropout, and amplification error. With these artifacts, the identification of somatic genomic variants becomes a challenging task, and over the years, several methods have been developed explicitly for this type of data. Single-cell variant callers implement distinct strategies, make different use of the data, and typically result in many discordant calls when applied to real data. Here, we review current approaches for single-cell variant calling, emphasizing single nucleotide variants. We highlight their potential benefits and shortcomings to help users choose a suitable tool for their data at hand.
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Key Words
- ADO, allelic dropout
- Allele dropout
- Amplification error
- CNV, copy number variant
- Indel, short insertion or deletion
- LDO, locus dropout
- SNV, single nucleotide variant
- SV, structural variant
- Single-cell genomics
- Somatic variants
- VAF, variant allele frequency
- Variant calling
- hSNP, heterozygous single-nucleotide polymorphism
- scATAC-seq, single-cell sequencing assay for transposase-accessible chromatin
- scDNA-seq, single-cell DNA sequencing
- scHi-C, single-cell Hi-C sequencing
- scMethyl-seq, single-cell Methylation sequencing
- scRNA-seq, single-cell RNA sequencing
- scWGA, single-cell whole-genome amplification
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Affiliation(s)
- Monica Valecha
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Spain
| | - David Posada
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Spain
- Department of Biochemistry, Genetics, and Immunology, Universidade de Vigo, 36310 Vigo, Spain
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39
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Tian B, Li Q. Single-Cell Sequencing and Its Applications in Liver Cancer. Front Oncol 2022; 12:857037. [PMID: 35574365 PMCID: PMC9097917 DOI: 10.3389/fonc.2022.857037] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 03/24/2022] [Indexed: 02/06/2023] Open
Abstract
As one of the most lethal cancers, primary liver cancer (PLC) has high tumor heterogeneity, including the heterogeneity between cancer cells. Traditional methods which have been used to identify tumor heterogeneity for a long time are based on large mixed cell samples, and the research results usually show average level of the cell population, ignoring the heterogeneity between cancer cells. In recent years, single-cell sequencing has been increasingly applied to the studies of PLCs. It can detect the heterogeneity between cancer cells, distinguish each cell subgroup in the tumor microenvironment (TME), and also reveal the clonal characteristics of cancer cells, contributing to understand the evolution of tumor. Here, we introduce the process of single-cell sequencing, review the applications of single-cell sequencing in the heterogeneity of cancer cells, TMEs, oncogenesis, and metastatic mechanisms of liver cancer, and discuss some of the current challenges in the field.
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40
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Redavid I, Conserva MR, Anelli L, Zagaria A, Specchia G, Musto P, Albano F. Single-Cell Sequencing: Ariadne’s Thread in the Maze of Acute Myeloid Leukemia. Diagnostics (Basel) 2022; 12:diagnostics12040996. [PMID: 35454044 PMCID: PMC9024495 DOI: 10.3390/diagnostics12040996] [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: 03/07/2022] [Revised: 04/12/2022] [Accepted: 04/14/2022] [Indexed: 02/01/2023] Open
Abstract
Acute myeloid leukemia (AML) is a haematological neoplasm resulting from the accumulation of genetic and epigenetic alterations. Patients’ prognoses vary with AML genetic heterogeneity, which hampers successful treatments. Single-cell approaches have provided new insights of the clonal architecture of AML, revealing the mutational history from diagnosis, during treatment and to relapse. In this review, we imagine single-cell technologies as the Ariadne’s thread that will guide us out of the AML maze, provide a precise identikit of the leukemic cell at single-cell resolution and explore genomic, transcriptomic, epigenetic and proteomic levels.
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Affiliation(s)
- Immacolata Redavid
- Hematology Section, Department of Emergency and Organ Transplantation (D.E.T.O.), University of Bari ‘Aldo Moro’, 70124 Bari, Italy; (I.R.); (M.R.C.); (L.A.); (A.Z.); (P.M.)
| | - Maria Rosa Conserva
- Hematology Section, Department of Emergency and Organ Transplantation (D.E.T.O.), University of Bari ‘Aldo Moro’, 70124 Bari, Italy; (I.R.); (M.R.C.); (L.A.); (A.Z.); (P.M.)
| | - Luisa Anelli
- Hematology Section, Department of Emergency and Organ Transplantation (D.E.T.O.), University of Bari ‘Aldo Moro’, 70124 Bari, Italy; (I.R.); (M.R.C.); (L.A.); (A.Z.); (P.M.)
| | - Antonella Zagaria
- Hematology Section, Department of Emergency and Organ Transplantation (D.E.T.O.), University of Bari ‘Aldo Moro’, 70124 Bari, Italy; (I.R.); (M.R.C.); (L.A.); (A.Z.); (P.M.)
| | - Giorgina Specchia
- School of Medicine, University of Bari ‘Aldo Moro’, 70124 Bari, Italy;
| | - Pellegrino Musto
- Hematology Section, Department of Emergency and Organ Transplantation (D.E.T.O.), University of Bari ‘Aldo Moro’, 70124 Bari, Italy; (I.R.); (M.R.C.); (L.A.); (A.Z.); (P.M.)
| | - Francesco Albano
- Hematology Section, Department of Emergency and Organ Transplantation (D.E.T.O.), University of Bari ‘Aldo Moro’, 70124 Bari, Italy; (I.R.); (M.R.C.); (L.A.); (A.Z.); (P.M.)
- Correspondence:
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41
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Chen Z, Gong F, Wan L, Ma L. BiTSC
2: Bayesian inference of tumor clonal tree by joint analysis of single-cell SNV and CNA data. Brief Bioinform 2022; 23:6562684. [PMID: 35368055 PMCID: PMC9116244 DOI: 10.1093/bib/bbac092] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 01/29/2022] [Accepted: 02/23/2022] [Indexed: 12/14/2022] Open
Abstract
Abstract
The rapid development of single-cell DNA sequencing (scDNA-seq) technology has greatly enhanced the resolution of tumor cell profiling, providing an unprecedented perspective in characterizing intra-tumoral heterogeneity and understanding tumor progression and metastasis. However, prominent algorithms for constructing tumor phylogeny based on scDNA-seq data usually only take single nucleotide variations (SNVs) as markers, failing to consider the effect caused by copy number alterations (CNAs). Here, we propose BiTSC$^2$, Bayesian inference of Tumor clonal Tree by joint analysis of Single-Cell SNV and CNA data. BiTSC$^2$ takes raw reads from scDNA-seq as input, accounts for the overlapping of CNA and SNV, models allelic dropout rate, sequencing errors and missing rate, as well as assigns single cells into subclones. By applying Markov Chain Monte Carlo sampling, BiTSC$^2$ can simultaneously estimate the subclonal scCNA and scSNV genotype matrices, subclonal assignments and tumor subclonal evolutionary tree. In comparison with existing methods on synthetic and real tumor data, BiTSC$^2$ shows high accuracy in genotype recovery, subclonal assignment and tree reconstruction. BiTSC$^2$ also performs robustly in dealing with scDNA-seq data with low sequencing depth and variant missing rate. BiTSC$^2$ software is available at https://github.com/ucasdp/BiTSC2.
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Affiliation(s)
- Ziwei Chen
- Institute of Zoology, Chinese Academy of Sciences, Beichen West Road, 100101, Beijing, Country
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Zhongguancun East Road, 100190, Beijing, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Yuquan Road, 100049, Beijing, China
| | - Fuzhou Gong
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Zhongguancun East Road, 100190, Beijing, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Yuquan Road, 100049, Beijing, China
| | - Lin Wan
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Zhongguancun East Road, 100190, Beijing, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Yuquan Road, 100049, Beijing, China
| | - Liang Ma
- Institute of Zoology, Chinese Academy of Sciences, Beichen West Road, 100101, Beijing, Country
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Yuquan Road, 100049, Beijing, China
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42
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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: 3] [Impact Index Per Article: 1.5] [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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43
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Deciphering Tumour Heterogeneity: From Tissue to Liquid Biopsy. Cancers (Basel) 2022; 14:cancers14061384. [PMID: 35326534 PMCID: PMC8946040 DOI: 10.3390/cancers14061384] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 03/04/2022] [Accepted: 03/05/2022] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Most malignant tumours are highly heterogeneous at molecular and phenotypic levels. Tumour variability poses challenges for the management of patients, as it arises between patients and even evolves in space and time within a single patient. Currently, treatment-decision making usually relies on the molecular characteristics of a limited tumour tissue sample at the time of diagnosis or disease progression but does not take into account the complexity of the bulk tumours and their constant evolution over time. In this review, we explore the extent of tumour heterogeneity and report the mechanisms that promote and sustain this diversity in cancers. We summarise the clinical strikes of tumour diversity in the management of patients with cancer. Finally, we discuss the current material and technological approaches that are relevant to adequately appreciate tumour heterogeneity. Abstract Human solid malignancies harbour a heterogeneous set of cells with distinct genotypes and phenotypes. This heterogeneity is installed at multiple levels. A biological diversity is commonly observed between tumours from different patients (inter-tumour heterogeneity) and cannot be fully captured by the current consensus molecular classifications for specific cancers. To extend the complexity in cancer, there are substantial differences from cell to cell within an individual tumour (intra-tumour heterogeneity, ITH) and the features of cancer cells evolve in space and time. Currently, treatment-decision making usually relies on the molecular characteristics of a limited tumour tissue sample at the time of diagnosis or disease progression but does not take into account the complexity of the bulk tumours and their constant evolution over time. In this review, we explore the extent of tumour heterogeneity with an emphasis on ITH and report the mechanisms that promote and sustain this diversity in cancers. We summarise the clinical strikes of ITH in the management of patients with cancer. Finally, we discuss the current material and technological approaches that are relevant to adequately appreciate ITH.
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44
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Wang X, Liu Y, Liu H, Pan W, Ren J, Zheng X, Tan Y, Chen Z, Deng Y, He N, Chen H, Li S. Recent advances and application of whole genome amplification in molecular diagnosis and medicine. MedComm (Beijing) 2022; 3:e116. [PMID: 35281794 PMCID: PMC8906466 DOI: 10.1002/mco2.116] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 01/11/2022] [Accepted: 01/12/2022] [Indexed: 11/30/2022] Open
Abstract
Whole genome amplification (WGA) is a technology for non-selective amplification of the whole genome sequence, first appearing in 1992. Its primary purpose is to amplify and reflect the whole genome of trace tissues and single cells without sequence bias and to provide sufficient DNA template for subsequent multigene and multilocus analysis, along with comprehensive genome research. WGA provides a method to obtain a large amount of genetic information from a small amount of DNA and provides a valuable tool for preserving limited samples in molecular biology. WGA technology is especially suitable for forensic identification and genetic disease research, along with new technologies such as next-generation sequencing (NGS). In addition, WGA is also widely used in single-cell sequencing. Due to the small amount of DNA in a single cell, it is often unable to meet the amount of samples needed for sequencing, so WGA is generally used to achieve the amplification of trace samples. This paper reviews WGA methods based on different principles, summarizes both amplification principle and amplification quality, and discusses the application prospects and challenges of WGA technology in molecular diagnosis and medicine.
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Affiliation(s)
- Xiaoyu Wang
- Hunan Key Laboratory of Biomedical Nanomaterials and DevicesHunan University of TechnologyZhuzhouChina
| | - Yapeng Liu
- School of Early‐Childhood Education, Nanjing Xiaozhuang UniversityNanjingChina
| | - Hongna Liu
- Hunan Key Laboratory of Biomedical Nanomaterials and DevicesHunan University of TechnologyZhuzhouChina
| | - Wenjing Pan
- Hunan Key Laboratory of Biomedical Nanomaterials and DevicesHunan University of TechnologyZhuzhouChina
| | - Jie Ren
- Hunan Key Laboratory of Biomedical Nanomaterials and DevicesHunan University of TechnologyZhuzhouChina
| | - Xiangming Zheng
- Hunan Key Laboratory of Biomedical Nanomaterials and DevicesHunan University of TechnologyZhuzhouChina
| | - Yimin Tan
- Hunan Key Laboratory of Biomedical Nanomaterials and DevicesHunan University of TechnologyZhuzhouChina
| | - Zhu Chen
- Hunan Key Laboratory of Biomedical Nanomaterials and DevicesHunan University of TechnologyZhuzhouChina
| | - Yan Deng
- Hunan Key Laboratory of Biomedical Nanomaterials and DevicesHunan University of TechnologyZhuzhouChina
| | - Nongyue He
- Hunan Key Laboratory of Biomedical Nanomaterials and DevicesHunan University of TechnologyZhuzhouChina
- State Key Laboratory of BioelectronicsSoutheast UniversityNanjingChina
| | - Hui Chen
- Hunan Key Laboratory of Biomedical Nanomaterials and DevicesHunan University of TechnologyZhuzhouChina
| | - Song Li
- Hunan Key Laboratory of Biomedical Nanomaterials and DevicesHunan University of TechnologyZhuzhouChina
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45
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Sandmann S, Richter S, Jiang X, Varghese J. Exploring Current Challenges and Perspectives for Automatic Reconstruction of Clonal Evolution. Cancer Genomics Proteomics 2022; 19:194-204. [PMID: 35181588 DOI: 10.21873/cgp.20314] [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: 10/29/2021] [Revised: 12/11/2021] [Accepted: 12/17/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND/AIM In the field of cancer research, reconstructing clonal evolution is of major interest. The technique provides new insights for analysis and prediction of tumor development. However, reconstruction based on mutational data is characterized by several challenges. MATERIALS AND METHODS By performing extensive literature research, we identified 51 currently available tools for reconstructing clonal evolution. By analyzing two cancer data sets (n=21), we investigated the applicability and performance of each tool. RESULTS Seventeen out of 51 tools could be applied to our data. Correct clustering of variants can be observed for 4 patients in the presence of ≤3 clusters and ≥5 time points. Correct phylogenetic trees are determined for 10 patients. Accurate visualization is possible, by applying adjustments to the original algorithms. CONCLUSION Despite bearing considerable potential, automatic reconstruction of clonal evolution remains challenging. To replace tedious manual reconstruction, further research including systematic error analyses using simulation tools needs to be conducted.
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Affiliation(s)
- Sarah Sandmann
- Institute of Medical Informatics, University of Münster, Münster, Germany;
| | - Silja Richter
- Institute of Medical Informatics, University of Münster, Münster, Germany
| | - Xiaoyi Jiang
- Department of Computer Science, University of Münster, Münster, Germany
| | - Julian Varghese
- Institute of Medical Informatics, University of Münster, Münster, Germany
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46
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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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47
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Caprioli C, Nazari I, Milovanovic S, Pelicci PG. Single-Cell Technologies to Decipher the Immune Microenvironment in Myeloid Neoplasms: Perspectives and Opportunities. Front Oncol 2022; 11:796477. [PMID: 35186713 PMCID: PMC8847379 DOI: 10.3389/fonc.2021.796477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 12/31/2021] [Indexed: 11/26/2022] Open
Abstract
Myeloid neoplasms (MN) are heterogeneous clonal disorders arising from the expansion of hematopoietic stem and progenitor cells. In parallel with genetic and epigenetic dynamics, the immune system plays a critical role in modulating tumorigenesis, evolution and therapeutic resistance at the various stages of disease progression. Single-cell technologies represent powerful tools to assess the cellular composition of the complex tumor ecosystem and its immune environment, to dissect interactions between neoplastic and non-neoplastic components, and to decipher their functional heterogeneity and plasticity. In addition, recent progress in multi-omics approaches provide an unprecedented opportunity to study multiple molecular layers (DNA, RNA, proteins) at the level of single-cell or single cellular clones during disease evolution or in response to therapy. Applying single-cell technologies to MN holds the promise to uncover novel cell subsets or phenotypic states and highlight the connections between clonal evolution and immune escape, which is crucial to fully understand disease progression and therapeutic resistance. This review provides a perspective on the various opportunities and challenges in the field, focusing on key questions in MN research and discussing their translational value, particularly for the development of more efficient immunotherapies.
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Affiliation(s)
- Chiara Caprioli
- Department of Experimental Oncology, IRCCS Istituto Europeo di Oncologia, Milan, Italy
- Scuola Europea di Medicina Molecolare (SEMM) European School of Molecular Medicine, Milan, Italy
- Hematology and Bone Marrow Transplant Unit, Papa Giovanni XXIII Hospital, Bergamo, Italy
| | - Iman Nazari
- Department of Experimental Oncology, IRCCS Istituto Europeo di Oncologia, Milan, Italy
- Scuola Europea di Medicina Molecolare (SEMM) European School of Molecular Medicine, Milan, Italy
| | - Sara Milovanovic
- Department of Experimental Oncology, IRCCS Istituto Europeo di Oncologia, Milan, Italy
- Scuola Europea di Medicina Molecolare (SEMM) European School of Molecular Medicine, Milan, Italy
| | - Pier Giuseppe Pelicci
- Department of Experimental Oncology, IRCCS Istituto Europeo di Oncologia, Milan, Italy
- Scuola Europea di Medicina Molecolare (SEMM) European School of Molecular Medicine, Milan, Italy
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48
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Kozlov A, Alves JM, Stamatakis A, Posada D. CellPhy: accurate and fast probabilistic inference of single-cell phylogenies from scDNA-seq data. Genome Biol 2022; 23:37. [PMID: 35081992 PMCID: PMC8790911 DOI: 10.1186/s13059-021-02583-w] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 12/20/2021] [Indexed: 01/15/2023] Open
Abstract
We introduce CellPhy, a maximum likelihood framework for inferring phylogenetic trees from somatic single-cell single-nucleotide variants. CellPhy leverages a finite-site Markov genotype model with 16 diploid states and considers amplification error and allelic dropout. We implement CellPhy into RAxML-NG, a widely used phylogenetic inference package that provides statistical confidence measurements and scales well on large datasets with hundreds or thousands of cells. Comprehensive simulations suggest that CellPhy is more robust to single-cell genomics errors and outperforms state-of-the-art methods under realistic scenarios, both in accuracy and speed. CellPhy is freely available at https://github.com/amkozlov/cellphy .
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Affiliation(s)
- Alexey Kozlov
- Computational Molecular Evolution Group, Heidelberg Institute for Theoretical Studies, 69118 Heidelberg, Germany
- Institute for Theoretical Informatics, Karlsruhe Institute of Technology, 76128 Karlsruhe, Germany
| | - Joao M. Alves
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Department of Biochemistry, Genetics, and Immunology, Universidade de Vigo, 36310 Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
| | - Alexandros Stamatakis
- Computational Molecular Evolution Group, Heidelberg Institute for Theoretical Studies, 69118 Heidelberg, Germany
| | - David Posada
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Department of Biochemistry, Genetics, and Immunology, Universidade de Vigo, 36310 Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
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49
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Wang R, Jiang Y. Copy Number Variation Detection by Single-Cell DNA Sequencing with SCOPE. Methods Mol Biol 2022; 2493:279-288. [PMID: 35751822 DOI: 10.1007/978-1-0716-2293-3_18] [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] [Indexed: 06/15/2023]
Abstract
Whole-genome single-cell DNA sequencing (scDNA-seq) enables the characterization of copy number profiles at the cellular level. This circumvents the averaging effects associated with bulk-tissue sequencing and has increased resolution yet decreased ambiguity in deconvolving cancer subclones and elucidating cancer evolutionary history. ScDNA-seq data is, however, sparse, noisy, and highly variable even within a homogeneous cell population, due to the biases and artifacts that are introduced during the library preparation and sequencing procedure. Here, we describe SCOPE, a normalization and copy number estimation method for scDNA-seq data. We give an overview of the methodology and illustrate SCOPE with step-by-step demonstrations.
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Affiliation(s)
- Rujin Wang
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Yuchao Jiang
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.
- Department of Genetics, School of Medicine, University of North Carolina, Chapel Hill, NC, USA.
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA.
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50
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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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