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Wang J, Ye F, Chai H, Jiang Y, Wang T, Ran X, Xia Q, Xu Z, Fu Y, Zhang G, Wu H, Guo G, Guo H, Ruan Y, Wang Y, Xing D, Xu X, Zhang Z. Advances and applications in single-cell and spatial genomics. SCIENCE CHINA. LIFE SCIENCES 2025; 68:1226-1282. [PMID: 39792333 DOI: 10.1007/s11427-024-2770-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2024] [Accepted: 10/10/2024] [Indexed: 01/12/2025]
Abstract
The applications of single-cell and spatial technologies in recent times have revolutionized the present understanding of cellular states and the cellular heterogeneity inherent in complex biological systems. These advancements offer unprecedented resolution in the examination of the functional genomics of individual cells and their spatial context within tissues. In this review, we have comprehensively discussed the historical development and recent progress in the field of single-cell and spatial genomics. We have reviewed the breakthroughs in single-cell multi-omics technologies, spatial genomics methods, and the computational strategies employed toward the analyses of single-cell atlas data. Furthermore, we have highlighted the advances made in constructing cellular atlases and their clinical applications, particularly in the context of disease. Finally, we have discussed the emerging trends, challenges, and opportunities in this rapidly evolving field.
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Affiliation(s)
- Jingjing Wang
- Bone Marrow Transplantation Center of the First Affiliated Hospital & Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou, 310058, China
| | - Fang Ye
- Bone Marrow Transplantation Center of the First Affiliated Hospital & Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou, 310058, China
| | - Haoxi Chai
- Life Sciences Institute and The Second Affiliated Hospital, Zhejiang University, Hangzhou, 310058, China
| | - Yujia Jiang
- BGI Research, Shenzhen, 518083, China
- BGI Research, Hangzhou, 310030, China
| | - Teng Wang
- Biomedical Pioneering Innovation Center (BIOPIC) and School of Life Sciences, Peking University, Beijing, 100871, China
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
| | - Xia Ran
- Bone Marrow Transplantation Center of the First Affiliated Hospital & Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Institute of Hematology, Zhejiang University, Hangzhou, 310000, China
| | - Qimin Xia
- Biomedical Pioneering Innovation Center (BIOPIC) and School of Life Sciences, Peking University, Beijing, 100871, China
| | - Ziye Xu
- Department of Laboratory Medicine of The First Affiliated Hospital & Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou, 310058, China
| | - Yuting Fu
- Bone Marrow Transplantation Center of the First Affiliated Hospital & Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
| | - Guodong Zhang
- Bone Marrow Transplantation Center of the First Affiliated Hospital & Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
| | - Hanyu Wu
- Bone Marrow Transplantation Center of the First Affiliated Hospital & Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
| | - Guoji Guo
- Bone Marrow Transplantation Center of the First Affiliated Hospital & Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou, 310058, China.
- Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China.
- Zhejiang Provincial Key Lab for Tissue Engineering and Regenerative Medicine, Dr. Li Dak Sum & Yip Yio Chin Center for Stem Cell and Regenerative Medicine, Hangzhou, 310058, China.
- Institute of Hematology, Zhejiang University, Hangzhou, 310000, China.
| | - Hongshan Guo
- Bone Marrow Transplantation Center of the First Affiliated Hospital & Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou, 310058, China.
- Institute of Hematology, Zhejiang University, Hangzhou, 310000, China.
| | - Yijun Ruan
- Life Sciences Institute and The Second Affiliated Hospital, Zhejiang University, Hangzhou, 310058, China.
| | - Yongcheng Wang
- Department of Laboratory Medicine of The First Affiliated Hospital & Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou, 310058, China.
| | - Dong Xing
- Biomedical Pioneering Innovation Center (BIOPIC) and School of Life Sciences, Peking University, Beijing, 100871, China.
- Beijing Advanced Innovation Center for Genomics (ICG), Peking University, Beijing, 100871, China.
| | - Xun Xu
- BGI Research, Shenzhen, 518083, China.
- BGI Research, Hangzhou, 310030, China.
- Guangdong Provincial Key Laboratory of Genome Read and Write, BGI Research, Shenzhen, 518083, China.
| | - Zemin Zhang
- Biomedical Pioneering Innovation Center (BIOPIC) and School of Life Sciences, Peking University, Beijing, 100871, China.
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Song M, Ma S, Wang G, Wang Y, Yang Z, Xie B, Guo T, Huang X, Zhang L. Benchmarking copy number aberrations inference tools using single-cell multi-omics datasets. Brief Bioinform 2025; 26:bbaf076. [PMID: 40037644 PMCID: PMC11879432 DOI: 10.1093/bib/bbaf076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2024] [Revised: 12/21/2024] [Accepted: 02/12/2025] [Indexed: 03/06/2025] Open
Abstract
Copy number alterations (CNAs) are an important type of genomic variation which play a crucial role in the initiation and progression of cancer. With the explosion of single-cell RNA sequencing (scRNA-seq), several computational methods have been developed to infer CNAs from scRNA-seq studies. However, to date, no independent studies have comprehensively benchmarked their performance. Herein, we evaluated five state-of-the-art methods based on their performance in tumor versus normal cell classification; CNAs profile accuracy, tumor subclone inference, and aneuploidy identification in non-malignant cells. Our results showed that Numbat outperformed others across most evaluation criteria, while CopyKAT excelled in scenarios when expression matrix alone was used as input. In specific tasks, SCEVAN showed the best performance in clonal breakpoint detection and Numbat showed high sensitivity in copy number neutral LOH (cnLOH) detection. Additionally, we investigated how referencing settings, inclusion of tumor microenvironment cells, tumor type, and tumor purity impact the performance of these tools. This study provides a valuable guideline for researchers in selecting the appropriate methods for their datasets.
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Affiliation(s)
- Minfang Song
- Research Center for Life Sciences Computing, Zhejiang Lab, Kechuang Avenue, Zhongtai Sub-District, Yuhang District, Hangzhou, Zhejiang 311121, China
- School of Life Science and Technology, ShanghaiTech University, Haike Road, Pudong New District, Shanghai 201210, China
- Shanghai Clinical Research and Trial Center, Keyuan Road, Pudong New District, Shanghai, 201210, China
| | - Shuai Ma
- School of Life Science and Technology, ShanghaiTech University, Haike Road, Pudong New District, Shanghai 201210, China
- Shanghai Clinical Research and Trial Center, Keyuan Road, Pudong New District, Shanghai, 201210, China
| | - Gong Wang
- School of Life Science and Technology, ShanghaiTech University, Haike Road, Pudong New District, Shanghai 201210, China
- Shanghai Clinical Research and Trial Center, Keyuan Road, Pudong New District, Shanghai, 201210, China
| | - Yukun Wang
- School of Life Science and Technology, ShanghaiTech University, Haike Road, Pudong New District, Shanghai 201210, China
- Shanghai Clinical Research and Trial Center, Keyuan Road, Pudong New District, Shanghai, 201210, China
| | - Zhenzhen Yang
- Yazhouwan National Laboratory, Yazhou Bay Science and Technology City, Yazhou District, Sanya, Hainan Province 572025, China
| | - Bin Xie
- Research Center for Life Sciences Computing, Zhejiang Lab, Kechuang Avenue, Zhongtai Sub-District, Yuhang District, Hangzhou, Zhejiang 311121, China
| | - Tongkun Guo
- Research Center for Life Sciences Computing, Zhejiang Lab, Kechuang Avenue, Zhongtai Sub-District, Yuhang District, Hangzhou, Zhejiang 311121, China
| | - Xingxu Huang
- Research Center for Life Sciences Computing, Zhejiang Lab, Kechuang Avenue, Zhongtai Sub-District, Yuhang District, Hangzhou, Zhejiang 311121, China
| | - Liye Zhang
- School of Life Science and Technology, ShanghaiTech University, Haike Road, Pudong New District, Shanghai 201210, China
- Shanghai Clinical Research and Trial Center, Keyuan Road, Pudong New District, Shanghai, 201210, China
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Kuipers J, Tuncel MA, Ferreira PF, Jahn K, Beerenwinkel N. Single-cell copy number calling and event history reconstruction. Bioinformatics 2025; 41:btaf072. [PMID: 39946094 PMCID: PMC11897432 DOI: 10.1093/bioinformatics/btaf072] [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: 06/18/2024] [Revised: 01/06/2025] [Accepted: 02/11/2025] [Indexed: 03/14/2025] Open
Abstract
MOTIVATION Copy number alterations are driving forces of tumour development and the emergence of intra-tumour heterogeneity. A comprehensive picture of these genomic aberrations is therefore essential for the development of personalised and precise cancer diagnostics and therapies. Single-cell sequencing offers the highest resolution for copy number profiling down to the level of individual cells. Recent high-throughput protocols allow for the processing of hundreds of cells through shallow whole-genome DNA sequencing. The resulting low read-depth data poses substantial statistical and computational challenges to the identification of copy number alterations. RESULTS We developed SCICoNE, a statistical model and MCMC algorithm tailored to single-cell copy number profiling from shallow whole-genome DNA sequencing data. SCICoNE reconstructs the history of copy number events in the tumour and uses these evolutionary relationships to identify the copy number profiles of the individual cells. We show the accuracy of this approach in evaluations on simulated data and demonstrate its practicability in applications to two breast cancer samples from different sequencing protocols. AVAILABILITY AND IMPLEMENTATION SCICoNE is available at https://github.com/cbg-ethz/SCICoNE.
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Affiliation(s)
- Jack Kuipers
- Department of Biosystems Science and Engineering, ETH Zurich, Basel 4056, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel 4056, Switzerland
| | - Mustafa Anıl Tuncel
- Department of Biosystems Science and Engineering, ETH Zurich, Basel 4056, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel 4056, Switzerland
| | - Pedro F Ferreira
- Department of Biosystems Science and Engineering, ETH Zurich, Basel 4056, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel 4056, Switzerland
| | - Katharina Jahn
- Department of Biosystems Science and Engineering, ETH Zurich, Basel 4056, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel 4056, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Basel 4056, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel 4056, Switzerland
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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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Zhang L, Zhou XM, Mallory X. SCCNAInfer: a robust and accurate tool to infer the absolute copy number on scDNA-seq data. BIOINFORMATICS (OXFORD, ENGLAND) 2024; 40:btae454. [PMID: 39067018 DOI: 10.1093/bioinformatics/btae454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 06/13/2024] [Accepted: 07/26/2024] [Indexed: 07/30/2024]
Abstract
MOTIVATION Copy number alterations (CNAs) play an important role in disease progression, especially in cancer. Single-cell DNA sequencing (scDNA-seq) facilitates the detection of CNAs of each cell that is sequenced at a shallow and uneven coverage. However, the state-of-the-art CNA detection tools based on scDNA-seq are still subject to genome-wide errors due to the wrong estimation of the ploidy. RESULTS We developed SCCNAInfer, a computational tool that utilizes the subclonal signal inside the tumor cells to more accurately infer each cell's ploidy and CNAs. Given the segmentation result of an existing CNA detection method, SCCNAInfer clusters the cells, infers the ploidy of each subclone, refines the read count by bin clustering, and accurately infers the CNAs for each cell. Both simulated and real datasets show that SCCNAInfer consistently improves upon the state-of-the-art CNA detection tools such as Aneufinder, Ginkgo, SCOPE and SeCNV. AVAILABILITY AND IMPLEMENTATION SCCNAInfer is freely available at https://github.com/compbio-mallory/SCCNAInfer. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Liting Zhang
- Department of Computer Science, Florida State University, Florida 32304, USA
| | - Xin Maizie Zhou
- Department of Biomedical Engineering, Vanderbilt University, Tennessee 37235, USA
| | - Xian Mallory
- Department of Computer Science, Florida State University, Florida 32304, USA
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Liu F, Shi F, Du F, Cao X, Yu Z. CoT: a transformer-based method for inferring tumor clonal copy number substructure from scDNA-seq data. Brief Bioinform 2024; 25:bbae187. [PMID: 38670159 PMCID: PMC11052634 DOI: 10.1093/bib/bbae187] [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: 12/06/2023] [Revised: 03/08/2024] [Accepted: 04/16/2024] [Indexed: 04/28/2024] Open
Abstract
Single-cell DNA sequencing (scDNA-seq) has been an effective means to unscramble intra-tumor heterogeneity, while joint inference of tumor clones and their respective copy number profiles remains a challenging task due to the noisy nature of scDNA-seq data. We introduce a new bioinformatics method called CoT for deciphering clonal copy number substructure. The backbone of CoT is a Copy number Transformer autoencoder that leverages multi-head attention mechanism to explore correlations between different genomic regions, and thus capture global features to create latent embeddings for the cells. CoT makes it convenient to first infer cell subpopulations based on the learned embeddings, and then estimate single-cell copy numbers through joint analysis of read counts data for the cells belonging to the same cluster. This exploitation of clonal substructure information in copy number analysis helps to alleviate the effect of read counts non-uniformity, and yield robust estimations of the tumor copy numbers. Performance evaluation on synthetic and real datasets showcases that CoT outperforms the state of the arts, and is highly useful for deciphering clonal copy number substructure.
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Affiliation(s)
- Furui Liu
- School of Information Engineering, Ningxia University, 750021, Ningxia, China
| | - Fangyuan Shi
- School of Information Engineering, Ningxia University, 750021, Ningxia, China
- Collaborative Innovation Center for Ningxia Big Data and Artificial Intelligence Co-founded by Ningxia Municipality and Ministry of Education, Ningxia University, 750021, Ningxia, China
| | - Fang Du
- School of Information Engineering, Ningxia University, 750021, Ningxia, China
- Collaborative Innovation Center for Ningxia Big Data and Artificial Intelligence Co-founded by Ningxia Municipality and Ministry of Education, Ningxia University, 750021, Ningxia, China
| | - Xiangmei Cao
- Basic Medical School, Ningxia Medical University, 750001, Ningxia, China
| | - Zhenhua Yu
- School of Information Engineering, Ningxia University, 750021, Ningxia, China
- Collaborative Innovation Center for Ningxia Big Data and Artificial Intelligence Co-founded by Ningxia Municipality and Ministry of Education, Ningxia University, 750021, Ningxia, China
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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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Zhang L, Bass HW, Irianto J, Mallory X. Integrating SNVs and CNAs on a phylogenetic tree from single-cell DNA sequencing data. Genome Res 2023; 33:2002-2017. [PMID: 37993137 PMCID: PMC10760445 DOI: 10.1101/gr.277249.122] [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: 08/26/2022] [Accepted: 10/25/2023] [Indexed: 11/24/2023]
Abstract
Single-cell DNA sequencing enables the construction of evolutionary trees that can reveal how tumors gain mutations and grow. Different whole-genome amplification procedures render genomic materials of different characteristics, often suitable for the detection of either single-nucleotide variation or copy number aberration, but not ideally for both. Consequently, this hinders the inference of a comprehensive phylogenetic tree and limits opportunities to investigate the interplay of SNVs and CNAs. Existing methods such as SCARLET and COMPASS require that the SNVs and CNAs are detected from the same sets of cells, which is technically challenging. Here we present a novel computational tool, SCsnvcna, that places SNVs on a tree inferred from CNA signals, whereas the sets of cells rendering the SNVs and CNAs are independent, offering a more practical solution in terms of the technical challenges. SCsnvcna is a Bayesian probabilistic model using both the genotype constraints on the tree and the cellular prevalence to search the optimal solution. Comprehensive simulations and comparison with seven state-of-the-art methods show that SCsnvcna is robust and accurate in a variety of circumstances. Particularly, SCsnvcna most frequently produces the lowest error rates, with ability to scale to a wide range of numerical values for leaf nodes in the tree, SNVs, and SNV cells. The application of SCsnvcna to two published colorectal cancer data sets shows highly consistent placement of SNV cells and SNVs with the original study while also supporting a refined placement of ATP7B, illustrating SCsnvcna's value in analyzing complex multitumor samples.
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Affiliation(s)
- Liting Zhang
- Department of Computer Science, Florida State University, Tallahassee, Florida 32306, USA
| | - Hank W Bass
- Department of Biological Science, Florida State University, Tallahassee, Florida 32306, USA
| | - Jerome Irianto
- College of Medicine, Florida State University, Tallahassee, Florida 32306, USA
| | - Xian Mallory
- Department of Computer Science, Florida State University, Tallahassee, Florida 32306, USA;
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Liu Y, Wu G. The utilization of single-cell sequencing technology in investigating the immune microenvironment of ccRCC. Front Immunol 2023; 14:1276658. [PMID: 38090562 PMCID: PMC10715415 DOI: 10.3389/fimmu.2023.1276658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 11/07/2023] [Indexed: 12/18/2023] Open
Abstract
The growth and advancement of ccRCC are strongly associated with the presence of immune infiltration and the tumor microenvironment, comprising tumor cells, immune cells, stromal cells, vascular cells, myeloid-derived cells, and extracellular matrix (ECM). Nevertheless, as a result of the diverse and constantly evolving characteristics of the tumor microenvironment, prior advanced sequencing methods have frequently disregarded specific less prevalent cellular traits at varying intervals, thereby concealing their significance. The advancement and widespread use of single-cell sequencing technology enable us to comprehend the source of individual tumor cells and the characteristics of a greater number of individual cells. This, in turn, minimizes the impact of intercellular heterogeneity and temporal heterogeneity of the same cell on experimental outcomes. This review examines the attributes of the tumor microenvironment in ccRCC and provides an overview of the progress made in single-cell sequencing technology and its particular uses in the current focus of immune infiltration in ccRCC.
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Affiliation(s)
| | - Guangzhen Wu
- Department of Urology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
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