1
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Liu J, Ma J, Wen J, Zhou X. A Cell Cycle-Aware Network for Data Integration and Label Transferring of Single-Cell RNA-Seq and ATAC-Seq. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2401815. [PMID: 38887194 PMCID: PMC11336957 DOI: 10.1002/advs.202401815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 04/22/2024] [Indexed: 06/20/2024]
Abstract
In recent years, the integration of single-cell multi-omics data has provided a more comprehensive understanding of cell functions and internal regulatory mechanisms from a non-single omics perspective, but it still suffers many challenges, such as omics-variance, sparsity, cell heterogeneity, and confounding factors. As it is known, the cell cycle is regarded as a confounder when analyzing other factors in single-cell RNA-seq data, but it is not clear how it will work on the integrated single-cell multi-omics data. Here, a cell cycle-aware network (CCAN) is developed to remove cell cycle effects from the integrated single-cell multi-omics data while keeping the cell type-specific variations. This is the first computational model to study the cell-cycle effects in the integration of single-cell multi-omics data. Validations on several benchmark datasets show the outstanding performance of CCAN in a variety of downstream analyses and applications, including removing cell cycle effects and batch effects of scRNA-seq datasets from different protocols, integrating paired and unpaired scRNA-seq and scATAC-seq data, accurately transferring cell type labels from scRNA-seq to scATAC-seq data, and characterizing the differentiation process from hematopoietic stem cells to different lineages in the integration of differentiation data.
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Affiliation(s)
- Jiajia Liu
- Center for Computational Systems MedicineMcWilliams School of Biomedical InformaticsThe University of Texas Health Science Center at HoustonHoustonTX77030USA
| | - Jian Ma
- Department of Electronic Information and Computer EngineeringThe Engineering & Technical College of Chengdu University of TechnologyLeshanSichuan614000China
| | - Jianguo Wen
- Center for Computational Systems MedicineMcWilliams School of Biomedical InformaticsThe University of Texas Health Science Center at HoustonHoustonTX77030USA
| | - Xiaobo Zhou
- Center for Computational Systems MedicineMcWilliams School of Biomedical InformaticsThe University of Texas Health Science Center at HoustonHoustonTX77030USA
- McGovern Medical SchoolThe University of Texas Health Science Center at HoustonHoustonTX77030USA
- School of DentistryThe University of Texas Health Science Center at HoustonHoustonTX77030USA
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2
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Zhao H, Zhao H, Ji S. A Mesenchymal stem cell Aging Framework, from Mechanisms to Strategies. Stem Cell Rev Rep 2024; 20:1420-1440. [PMID: 38727878 DOI: 10.1007/s12015-024-10732-4] [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] [Accepted: 05/02/2024] [Indexed: 08/13/2024]
Abstract
Mesenchymal stem cells (MSCs) are extensively researched for therapeutic applications in tissue engineering and show significant potential for clinical use. Intrinsic or extrinsic factors causing senescence may lead to reduced proliferation, aberrant differentiation, weakened immunoregulation, and increased inflammation, ultimately limiting the potential of MSCs. It is crucial to comprehend the molecular pathways and internal processes responsible for the decline in MSC function due to senescence in order to devise innovative approaches for rejuvenating senescent MSCs and enhancing MSC treatment. We investigate the main molecular processes involved in senescence, aiming to provide a thorough understanding of senescence-related issues in MSCs. Additionally, we analyze the most recent advancements in cutting-edge approaches to combat MSC senescence based on current research. We are curious whether the aging process of stem cells results in a permanent "memory" and if cellular reprogramming may potentially revert the aging epigenome to a more youthful state.
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Affiliation(s)
- Hongqing Zhao
- Nanbu County People's Hospital, Nanchong City, 637300, Sichuan Province, China
- Jinzhou Medical University, No.82 Songpo Road, Guta District, Jinzhou, 121001, Liaoning Province, China
| | - Houming Zhao
- Graduate School of PLA Medical College, Chinese PLA General Hospital, Beijing, 100083, China
| | - Shuaifei Ji
- Graduate School of PLA Medical College, Chinese PLA General Hospital, Beijing, 100083, China.
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3
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McPherson A, Vázquez-García I, Myers MA, Zatzman M, Al-Rawi D, Weiner A, Freeman S, Mohibullah N, Satas G, Williams MJ, Ceglia N, Zhang AW, Li J, Lim JLP, Wu M, Choi S, Havasov E, Grewal D, Shi H, Kim M, Schwarz R, Kaufmann T, Dinh KN, Uhlitz F, Tran J, Wu Y, Patel R, Ramakrishnan S, Kim D, Clarke J, Green H, Ali E, DiBona M, Varice N, Kundra R, Broach V, Gardner GJ, Roche KL, Sonoda Y, Zivanovic O, Kim SH, Grisham RN, Liu YL, Viale A, Rusk N, Lakhman Y, Ellenson LH, Tavaré S, Aparicio S, Chi DS, Aghajanian C, Abu-Rustum NR, Friedman CF, Zamarin D, Weigelt B, Bakhoum SF, Shah SP. Ongoing genome doubling promotes evolvability and immune dysregulation in ovarian cancer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.11.602772. [PMID: 39071261 PMCID: PMC11275742 DOI: 10.1101/2024.07.11.602772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Whole-genome doubling (WGD) is a critical driver of tumor development and is linked to drug resistance and metastasis in solid malignancies. Here, we demonstrate that WGD is an ongoing mutational process in tumor evolution. Using single-cell whole-genome sequencing, we measured and modeled how WGD events are distributed across cellular populations within tumors and associated WGD dynamics with properties of genome diversification and phenotypic consequences of innate immunity. We studied WGD evolution in 65 high-grade serous ovarian cancer (HGSOC) tissue samples from 40 patients, yielding 29,481 tumor cell genomes. We found near-ubiquitous evidence of WGD as an ongoing mutational process promoting cell-cell diversity, high rates of chromosomal missegregation, and consequent micronucleation. Using a novel mutation-based WGD timing method, doubleTime , we delineated specific modes by which WGD can drive tumor evolution: (i) unitary evolutionary origin followed by significant diversification, (ii) independent WGD events on a pre-existing background of copy number diversity, and (iii) evolutionarily late clonal expansions of WGD populations. Additionally, through integrated single-cell RNA sequencing and high-resolution immunofluorescence microscopy, we found that inflammatory signaling and cGAS-STING pathway activation result from ongoing chromosomal instability and are restricted to tumors that remain predominantly diploid. This contrasted with predominantly WGD tumors, which exhibited significant quiescent and immunosuppressive phenotypic states. Together, these findings establish WGD as an evolutionarily 'active' mutational process that promotes evolvability and dysregulated immunity in late stage ovarian cancer.
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4
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Liu J, Ma J, Wen J, Zhou X. A Cell Cycle-aware Network for Data Integration and Label Transferring of Single-cell RNA-seq and ATAC-seq. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.31.578213. [PMID: 38352302 PMCID: PMC10862874 DOI: 10.1101/2024.01.31.578213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
In recent years, the integration of single-cell multi-omics data has provided a more comprehensive understanding of cell functions and internal regulatory mechanisms from a non-single omics perspective, but it still suffers many challenges, such as omics-variance, sparsity, cell heterogeneity and confounding factors. As we know, cell cycle is regarded as a confounder when analyzing other factors in single-cell RNA-seq data, but it's not clear how it will work on the integrated single-cell multi-omics data. Here, we developed a Cell Cycle-Aware Network (CCAN) to remove cell cycle effects from the integrated single-cell multi-omics data while keeping the cell type-specific variations. This is the first computational model to study the cell-cycle effects in the integration of single-cell multi-omics data. Validations on several benchmark datasets show the out-standing performance of CCAN in a variety of downstream analyses and applications, including removing cell cycle effects and batch effects of scRNA-seq datasets from different protocols, integrating paired and unpaired scRNA-seq and scATAC-seq data, accurately transferring cell type labels from scRNA-seq to scATAC-seq data, and characterizing the differentiation process from hematopoietic stem cells to different lineages in the integration of differentiation data.
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5
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Piran Z, Nitzan M. SiFT: uncovering hidden biological processes by probabilistic filtering of single-cell data. Nat Commun 2024; 15:760. [PMID: 38278815 PMCID: PMC10817921 DOI: 10.1038/s41467-024-44757-7] [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: 10/23/2023] [Accepted: 01/03/2024] [Indexed: 01/28/2024] Open
Abstract
Cellular populations simultaneously encode multiple biological attributes, including spatial configuration, temporal trajectories, and cell-cell interactions. Some of these signals may be overshadowed by others and harder to recover, despite the great progress made to computationally reconstruct biological processes from single-cell data. To address this, we present SiFT, a kernel-based projection method for filtering biological signals in single-cell data, thus uncovering underlying biological processes. SiFT applies to a wide range of tasks, from the removal of unwanted variation in the data to revealing hidden biological structures. We demonstrate how SiFT enhances the liver circadian signal by filtering spatial zonation, recovers regenerative cell subpopulations in spatially-resolved liver data, and exposes COVID-19 disease-related cells, pathways, and dynamics by filtering healthy reference signals. SiFT performs the correction at the gene expression level, can scale to large datasets, and compares favorably to state-of-the-art methods.
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Affiliation(s)
- Zoe Piran
- School of Computer Science and Engineering, The Hebrew University, Jerusalem, Israel
| | - Mor Nitzan
- School of Computer Science and Engineering, The Hebrew University, Jerusalem, Israel.
- Racah Institute of Physics, The Hebrew University, Jerusalem, Israel.
- Faculty of Medicine, The Hebrew University, Jerusalem, Israel.
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6
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Guo X, Chen L. From G1 to M: a comparative study of methods for identifying cell cycle phases. Brief Bioinform 2024; 25:bbad517. [PMID: 38261342 PMCID: PMC10805071 DOI: 10.1093/bib/bbad517] [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: 08/04/2023] [Revised: 11/08/2023] [Accepted: 12/13/2023] [Indexed: 01/24/2024] Open
Abstract
Accurate identification of cell cycle phases in single-cell RNA-sequencing (scRNA-seq) data is crucial for biomedical research. Many methods have been developed to tackle this challenge, employing diverse approaches to predict cell cycle phases. In this review article, we delve into the standard processes in identifying cell cycle phases within scRNA-seq data and present several representative methods for comparison. To rigorously assess the accuracy of these methods, we propose an error function and employ multiple benchmarking datasets encompassing human and mouse data. Our evaluation results reveal a key finding: the fit between the reference data and the dataset being analyzed profoundly impacts the effectiveness of cell cycle phase identification methods. Therefore, researchers must carefully consider the compatibility between the reference data and their dataset to achieve optimal results. Furthermore, we explore the potential benefits of incorporating benchmarking data with multiple known cell cycle phases into the analysis. Merging such data with the target dataset shows promise in enhancing prediction accuracy. By shedding light on the accuracy and performance of cell cycle phase prediction methods across diverse datasets, this review aims to motivate and guide future methodological advancements. Our findings offer valuable insights for researchers seeking to improve their understanding of cellular dynamics through scRNA-seq analysis, ultimately fostering the development of more robust and widely applicable cell cycle identification methods.
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Affiliation(s)
- Xinyu Guo
- Department of Quantitative and Computational Biology, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089, United States
| | - Liang Chen
- Department of Quantitative and Computational Biology, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089, United States
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7
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Wang Z, Xie X, Liu S, Ji Z. scFseCluster: a feature selection-enhanced clustering for single-cell RNA-seq data. Life Sci Alliance 2023; 6:e202302103. [PMID: 37788907 PMCID: PMC10547911 DOI: 10.26508/lsa.202302103] [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: 04/20/2023] [Revised: 09/21/2023] [Accepted: 09/22/2023] [Indexed: 10/05/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) enables researchers to reveal previously unknown cell heterogeneity and functional diversity, which is impossible with bulk RNA sequencing. Clustering approaches are widely used for analyzing scRNA-seq data and identifying cell types and states. In the past few years, various advanced computational strategies emerged. However, the low generalization and high computational cost are the main bottlenecks of existing methods. In this study, we established a novel computational framework, scFseCluster, for scRNA-seq clustering analysis. scFseCluster incorporates a metaheuristic algorithm (Feature Selection based on Quantum Squirrel Search Algorithm) to extract the optimal gene set, which largely guarantees the performance of cell clustering. We conducted simulation experiments in several aspects to verify the performance of the proposed approach. scFseCluster performed very well on eight benchmark scRNA-seq datasets because of the optimal gene sets obtained using the Feature Selection based on Quantum Squirrel Search Algorithm. The comparative study demonstrated the significant advantages of scFseCluster over seven State-of-the-Art algorithms. In addition, our analysis shows that feature selection on high-variable genes can significantly improve clustering performance. In conclusion, our study demonstrates that scFseCluster is a highly versatile tool for enhancing scRNA-seq data clustering analysis.
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Affiliation(s)
- Zongqin Wang
- https://ror.org/05td3s095 College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China
| | - Xiaojun Xie
- https://ror.org/05td3s095 College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China
- https://ror.org/05td3s095 Center for Data Science and Intelligent Computing, Nanjing Agricultural University, Nanjing, China
| | - Shouyang Liu
- https://ror.org/05td3s095 Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing, China
| | - Zhiwei Ji
- https://ror.org/05td3s095 College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China
- https://ror.org/05td3s095 Center for Data Science and Intelligent Computing, Nanjing Agricultural University, Nanjing, China
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8
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Karin J, Bornfeld Y, Nitzan M. scPrisma infers, filters and enhances topological signals in single-cell data using spectral template matching. Nat Biotechnol 2023; 41:1645-1654. [PMID: 36849830 PMCID: PMC10635821 DOI: 10.1038/s41587-023-01663-5] [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: 04/07/2022] [Accepted: 01/06/2023] [Indexed: 03/01/2023]
Abstract
Single-cell RNA sequencing has been instrumental in uncovering cellular spatiotemporal context. This task is challenging as cells simultaneously encode multiple, potentially cross-interfering, biological signals. Here we propose scPrisma, a spectral computational method that uses topological priors to decouple, enhance and filter different classes of biological processes in single-cell data, such as periodic and linear signals. We apply scPrisma to the analysis of the cell cycle in HeLa cells, circadian rhythm and spatial zonation in liver lobules, diurnal cycle in Chlamydomonas and circadian rhythm in the suprachiasmatic nucleus in the brain. scPrisma can be used to distinguish mixed cellular populations by specific characteristics such as cell type and uncover regulatory networks and cell-cell interactions specific to predefined biological signals, such as the circadian rhythm. We show scPrisma's flexibility in incorporating prior knowledge, inference of topologically informative genes and generalization to additional diverse templates and systems. scPrisma can be used as a stand-alone workflow for signal analysis and as a prior step for downstream single-cell analysis.
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Affiliation(s)
- Jonathan Karin
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Yonathan Bornfeld
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Mor Nitzan
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel.
- Racah Institute of Physics, The Hebrew University of Jerusalem, Jerusalem, Israel.
- Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel.
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9
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Briggs EM, Marques CA, Oldrieve GR, Hu J, Otto TD, Matthews KR. Profiling the bloodstream form and procyclic form Trypanosoma brucei cell cycle using single-cell transcriptomics. eLife 2023; 12:e86325. [PMID: 37166108 PMCID: PMC10212563 DOI: 10.7554/elife.86325] [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: 01/20/2023] [Accepted: 05/10/2023] [Indexed: 05/12/2023] Open
Abstract
African trypanosomes proliferate as bloodstream forms (BSFs) and procyclic forms in the mammal and tsetse fly midgut, respectively. This allows them to colonise the host environment upon infection and ensure life cycle progression. Yet, understanding of the mechanisms that regulate and drive the cell replication cycle of these forms is limited. Using single-cell transcriptomics on unsynchronised cell populations, we have obtained high resolution cell cycle regulated (CCR) transcriptomes of both procyclic and slender BSF Trypanosoma brucei without prior cell sorting or synchronisation. Additionally, we describe an efficient freeze-thawing protocol that allows single-cell transcriptomic analysis of cryopreserved T. brucei. Computational reconstruction of the cell cycle using periodic pseudotime inference allowed the dynamic expression patterns of cycling genes to be profiled for both life cycle forms. Comparative analyses identify a core cycling transcriptome highly conserved between forms, as well as several genes where transcript levels dynamics are form specific. Comparing transcript expression patterns with protein abundance revealed that the majority of genes with periodic cycling transcript and protein levels exhibit a relative delay between peak transcript and protein expression. This work reveals novel detail of the CCR transcriptomes of both forms, which are available for further interrogation via an interactive webtool.
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Affiliation(s)
- Emma M Briggs
- Institute for Immunology and Infection Research, School of Biological Sciences, University of EdinburghEdinburghUnited Kingdom
- Wellcome Centre for Integrative Parasitology, School of Infection & Immunity, University of GlasgowGlasgowUnited Kingdom
| | - Catarina A Marques
- Wellcome Centre for Integrative Parasitology, School of Infection & Immunity, University of GlasgowGlasgowUnited Kingdom
| | - Guy R Oldrieve
- Institute for Immunology and Infection Research, School of Biological Sciences, University of EdinburghEdinburghUnited Kingdom
| | - Jihua Hu
- Institute for Immunology and Infection Research, School of Biological Sciences, University of EdinburghEdinburghUnited Kingdom
| | - Thomas D Otto
- Wellcome Centre for Integrative Parasitology, School of Infection & Immunity, University of GlasgowGlasgowUnited Kingdom
| | - Keith R Matthews
- Institute for Immunology and Infection Research, School of Biological Sciences, University of EdinburghEdinburghUnited Kingdom
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10
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Khozyainova AA, Valyaeva AA, Arbatsky MS, Isaev SV, Iamshchikov PS, Volchkov EV, Sabirov MS, Zainullina VR, Chechekhin VI, Vorobev RS, Menyailo ME, Tyurin-Kuzmin PA, Denisov EV. Complex Analysis of Single-Cell RNA Sequencing Data. BIOCHEMISTRY. BIOKHIMIIA 2023; 88:231-252. [PMID: 37072324 PMCID: PMC10000364 DOI: 10.1134/s0006297923020074] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 12/13/2022] [Accepted: 12/13/2022] [Indexed: 03/12/2023]
Abstract
Single-cell RNA sequencing (scRNA-seq) is a revolutionary tool for studying the physiology of normal and pathologically altered tissues. This approach provides information about molecular features (gene expression, mutations, chromatin accessibility, etc.) of cells, opens up the possibility to analyze the trajectories/phylogeny of cell differentiation and cell-cell interactions, and helps in discovery of new cell types and previously unexplored processes. From a clinical point of view, scRNA-seq facilitates deeper and more detailed analysis of molecular mechanisms of diseases and serves as a basis for the development of new preventive, diagnostic, and therapeutic strategies. The review describes different approaches to the analysis of scRNA-seq data, discusses the advantages and disadvantages of bioinformatics tools, provides recommendations and examples of their successful use, and suggests potential directions for improvement. We also emphasize the need for creating new protocols, including multiomics ones, for the preparation of DNA/RNA libraries of single cells with the purpose of more complete understanding of individual cells.
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Affiliation(s)
- Anna A Khozyainova
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050, Russia.
| | - Anna A Valyaeva
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, 119991, Russia
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Mikhail S Arbatsky
- Laboratory of Artificial Intelligence and Bioinformatics, The Russian Clinical Research Center for Gerontology, Pirogov Russian National Medical University, Moscow, 129226, Russia
- School of Public Administration, Lomonosov Moscow State University, Moscow, 119991, Russia
- Faculty of Fundamental Medicine, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Sergey V Isaev
- Research Institute of Personalized Medicine, National Center for Personalized Medicine of Endocrine Diseases, National Medical Research Center for Endocrinology, Moscow, 117036, Russia
| | - Pavel S Iamshchikov
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050, Russia
- Laboratory of Complex Analysis of Big Bioimage Data, National Research Tomsk State University, Tomsk, 634050, Russia
| | - Egor V Volchkov
- Department of Oncohematology, Dmitry Rogachev National Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, 117198, Russia
| | - Marat S Sabirov
- Laboratory of Bioinformatics and Molecular Genetics, Koltzov Institute of Developmental Biology of the Russian Academy of Sciences, Moscow, 119334, Russia
| | - Viktoria R Zainullina
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050, Russia
| | - Vadim I Chechekhin
- Faculty of Fundamental Medicine, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Rostislav S Vorobev
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050, Russia
| | - Maxim E Menyailo
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050, Russia
| | - Pyotr A Tyurin-Kuzmin
- Faculty of Fundamental Medicine, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Evgeny V Denisov
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050, Russia
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11
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Chen L, Li S. Incorporating cell hierarchy to decipher the functional diversity of single cells. Nucleic Acids Res 2023; 51:e9. [PMID: 36373664 PMCID: PMC9881154 DOI: 10.1093/nar/gkac1044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 10/13/2022] [Accepted: 10/21/2022] [Indexed: 11/16/2022] Open
Abstract
Cells possess functional diversity hierarchically. However, most single-cell analyses neglect the nested structures while detecting and visualizing the functional diversity. Here, we incorporate cell hierarchy to study functional diversity at subpopulation, club (i.e., sub-subpopulation), and cell layers. Accordingly, we implement a package, SEAT, to construct cell hierarchies utilizing structure entropy by minimizing the global uncertainty in cell-cell graphs. With cell hierarchies, SEAT deciphers functional diversity in 36 datasets covering scRNA, scDNA, scATAC, and scRNA-scATAC multiome. First, SEAT finds optimal cell subpopulations with high clustering accuracy. It identifies cell types or fates from omics profiles and boosts accuracy from 0.34 to 1. Second, SEAT detects insightful functional diversity among cell clubs. The hierarchy of breast cancer cells reveals that the specific tumor cell club drives AREG-EGFT signaling. We identify a dense co-accessibility network of cis-regulatory elements specified by one cell club in GM12878. Third, the cell order from the hierarchy infers periodic pseudo-time of cells, improving accuracy from 0.79 to 0.89. Moreover, we incorporate cell hierarchy layers as prior knowledge to refine nonlinear dimension reduction, enabling us to visualize hierarchical cell layouts in low-dimensional space.
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Affiliation(s)
- Lingxi Chen
- Department of Computer Science, City University of Hong Kong, 83 Tat Chee Ave, Kowloon Tong, Hong Kong, China
- City University of Hong Kong Shenzhen Research Institute, Shenzhen, 518057, Guangdong, China
| | - Shuai Cheng Li
- Department of Computer Science, City University of Hong Kong, 83 Tat Chee Ave, Kowloon Tong, Hong Kong, China
- City University of Hong Kong Shenzhen Research Institute, Shenzhen, 518057, Guangdong, China
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12
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Auerbach BJ, FitzGerald GA, Li M. Tempo: an unsupervised Bayesian algorithm for circadian phase inference in single-cell transcriptomics. Nat Commun 2022; 13:6580. [PMID: 36323668 PMCID: PMC9630322 DOI: 10.1038/s41467-022-34185-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Accepted: 10/17/2022] [Indexed: 11/05/2022] Open
Abstract
The circadian clock is a 24 h cellular timekeeping mechanism that regulates human physiology. Answering several fundamental questions in circadian biology will require joint measures of single-cell circadian phases and transcriptomes. However, no widespread experimental approaches exist for this purpose. While computational approaches exist to infer cell phase directly from single-cell RNA-sequencing data, existing methods yield poor circadian phase estimates, and do not quantify estimation uncertainty, which is essential for interpretation of results from very sparse single-cell RNA-sequencing data. To address these unmet needs, we introduce Tempo, a Bayesian variational inference approach that incorporates domain knowledge of the clock and quantifies phase estimation uncertainty. Through simulations and analyses of real data, we demonstrate that Tempo yields more accurate estimates of circadian phase than existing methods and provides well-calibrated uncertainty quantifications. Tempo will facilitate large-scale studies of single-cell circadian transcription.
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Affiliation(s)
- Benjamin J Auerbach
- Graduate Group in Genomics and Computational Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA.
| | - Garret A FitzGerald
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Mingyao Li
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA.
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13
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Identification of Human Cell Cycle Phase Markers Based on Single-Cell RNA-Seq Data by Using Machine Learning Methods. BIOMED RESEARCH INTERNATIONAL 2022; 2022:2516653. [PMID: 36004205 PMCID: PMC9393965 DOI: 10.1155/2022/2516653] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 07/25/2022] [Accepted: 07/29/2022] [Indexed: 12/17/2022]
Abstract
The cell cycle is composed of a series of ordered, highly regulated processes through which a cell grows and duplicates its genome and eventually divides into two daughter cells. According to the complex changes in cell structure and biosynthesis, the cell cycle is divided into four phases: gap 1 (G1), DNA synthesis (S), gap 2 (G2), and mitosis (M). Determining which cell cycle phases a cell is in is critical to the research of cancer development and pharmacy for targeting cell cycle. However, current detection methods have the following problems: (1) they are complicated and time consuming to perform, and (2) they cannot detect the cell cycle on a large scale. Rapid developments in single-cell technology have made dissecting cells on a large scale possible with unprecedented resolution. In the present research, we construct efficient classifiers and identify essential gene biomarkers based on single-cell RNA sequencing data through Boruta and three feature ranking algorithms (e.g., mRMR, MCFS, and SHAP by LightGBM) by utilizing four advanced classification algorithms. Meanwhile, we mine a series of classification rules that can distinguish different cell cycle phases. Collectively, we have provided a novel method for determining the cell cycle and identified new potential cell cycle-related genes, thereby contributing to the understanding of the processes that regulate the cell cycle.
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14
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Gaglia G, Kabraji S, Rammos D, Dai Y, Verma A, Wang S, Mills CE, Chung M, Bergholz JS, Coy S, Lin JR, Jeselsohn R, Metzger O, Winer EP, Dillon DA, Zhao JJ, Sorger PK, Santagata S. Temporal and spatial topography of cell proliferation in cancer. Nat Cell Biol 2022; 24:316-326. [PMID: 35292783 PMCID: PMC8959396 DOI: 10.1038/s41556-022-00860-9] [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: 06/02/2021] [Accepted: 01/31/2022] [Indexed: 02/06/2023]
Abstract
Proliferation is a fundamental trait of cancer cells, but its properties and spatial organization in tumours are poorly characterized. Here we use highly multiplexed tissue imaging to perform single-cell quantification of cell cycle regulators and then develop robust, multivariate, proliferation metrics. Across diverse cancers, proliferative architecture is organized at two spatial scales: large domains, and smaller niches enriched for specific immune lineages. Some tumour cells express cell cycle regulators in the (canonical) patterns expected of freely growing cells, a phenomenon we refer to as 'cell cycle coherence'. By contrast, the cell cycles of other tumour cell populations are skewed towards specific phases or exhibit non-canonical (incoherent) marker combinations. Coherence varies across space, with changes in oncogene activity and therapeutic intervention, and is associated with aggressive tumour behaviour. Thus, multivariate measures from high-plex tissue images capture clinically significant features of cancer proliferation, a fundamental step in enabling more precise use of anti-cancer therapies.
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Affiliation(s)
- Giorgio Gaglia
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Sheheryar Kabraji
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA.
- Department of Medical Oncology, Dana Farber Cancer Institute, Boston, MA, USA.
- Department of Cancer Biology, Dana Farber Cancer Institute, Boston, MA, USA.
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Danae Rammos
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Yang Dai
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ana Verma
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Shu Wang
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Harvard Graduate Program in Biophysics, Harvard University, Cambridge, MA, USA
| | - Caitlin E Mills
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Mirra Chung
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Johann S Bergholz
- Department of Cancer Biology, Dana Farber Cancer Institute, Boston, MA, USA
| | - Shannon Coy
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jia-Ren Lin
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
| | - Rinath Jeselsohn
- Department of Medical Oncology, Dana Farber Cancer Institute, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Otto Metzger
- Department of Medical Oncology, Dana Farber Cancer Institute, Boston, MA, USA
| | - Eric P Winer
- Department of Medical Oncology, Dana Farber Cancer Institute, Boston, MA, USA
| | - Deborah A Dillon
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jean J Zhao
- Department of Cancer Biology, Dana Farber Cancer Institute, Boston, MA, USA
| | - Peter K Sorger
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
| | - Sandro Santagata
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA.
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Pathology, Dana Farber Cancer Institute, Boston, MA, USA.
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15
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Zinovyev A, Sadovsky M, Calzone L, Fouché A, Groeneveld CS, Chervov A, Barillot E, Gorban AN. Modeling Progression of Single Cell Populations Through the Cell Cycle as a Sequence of Switches. Front Mol Biosci 2022; 8:793912. [PMID: 35178429 PMCID: PMC8846220 DOI: 10.3389/fmolb.2021.793912] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 12/15/2021] [Indexed: 11/13/2022] Open
Abstract
Cell cycle is a biological process underlying the existence and propagation of life in time and space. It has been an object for mathematical modeling for long, with several alternative mechanistic modeling principles suggested, describing in more or less details the known molecular mechanisms. Recently, cell cycle has been investigated at single cell level in snapshots of unsynchronized cell populations, exploiting the new methods for transcriptomic and proteomic molecular profiling. This raises a need for simplified semi-phenomenological cell cycle models, in order to formalize the processes underlying the cell cycle, at a higher abstracted level. Here we suggest a modeling framework, recapitulating the most important properties of the cell cycle as a limit trajectory of a dynamical process characterized by several internal states with switches between them. In the simplest form, this leads to a limit cycle trajectory, composed by linear segments in logarithmic coordinates describing some extensive (depending on system size) cell properties. We prove a theorem connecting the effective embedding dimensionality of the cell cycle trajectory with the number of its linear segments. We also develop a simplified kinetic model with piecewise-constant kinetic rates describing the dynamics of lumps of genes involved in S-phase and G2/M phases. We show how the developed cell cycle models can be applied to analyze the available single cell datasets and simulate certain properties of the observed cell cycle trajectories. Based on our model, we can predict with good accuracy the cell line doubling time from the length of cell cycle trajectory.
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Affiliation(s)
- Andrei Zinovyev
- Institut Curie, PSL Research University, Paris, France
- INSERM, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
- *Correspondence: Andrei Zinovyev,
| | - Michail Sadovsky
- Institute of Computational Modeling (RAS), Krasnoyarsk, Russia
- Laboratory of Medical Cybernetics, V.F.Voino-Yasenetsky Krasnoyarsk State Medical University, Krasnoyarsk, Russia
- Federal Research and Clinic Center of FMBA of Russia, Krasnoyarsk, Russia
- Laboratory of Advanced Methods for High-Dimensional Data Analysis, Lobachevsky University, Nizhniy Novgorod, Russia
| | - Laurence Calzone
- Institut Curie, PSL Research University, Paris, France
- INSERM, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
| | - Aziz Fouché
- Institut Curie, PSL Research University, Paris, France
- INSERM, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
| | - Clarice S. Groeneveld
- Cartes d’Identité des Tumeurs (CIT) Program, Ligue Nationale Contre le Cancer, Paris, France
- Oncologie Moleculaire, UMR144, Institut Curie, Paris, France
| | - Alexander Chervov
- Institut Curie, PSL Research University, Paris, France
- INSERM, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
| | - Emmanuel Barillot
- Institut Curie, PSL Research University, Paris, France
- INSERM, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
| | - Alexander N. Gorban
- Laboratory of Advanced Methods for High-Dimensional Data Analysis, Lobachevsky University, Nizhniy Novgorod, Russia
- Department of Mathematics, University of Leicester, Leicester, United Kingdom
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16
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Liu J, Yang M, Zhao W, Zhou X. CCPE: cell cycle pseudotime estimation for single cell RNA-seq data. Nucleic Acids Res 2022; 50:704-716. [PMID: 34931240 PMCID: PMC8789092 DOI: 10.1093/nar/gkab1236] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 11/22/2021] [Accepted: 12/15/2021] [Indexed: 11/25/2022] Open
Abstract
Pseudotime analysis from scRNA-seq data enables to characterize the continuous progression of various biological processes, such as the cell cycle. Cell cycle plays an important role in cell fate decisions and differentiation and is often regarded as a confounder in scRNA-seq data analysis when analyzing the role of other factors. Therefore, accurate prediction of cell cycle pseudotime and identification of cell cycle stages are important steps for characterizing the development-related biological processes. Here, we develop CCPE, a novel cell cycle pseudotime estimation method to characterize cell cycle timing and identify cell cycle phases from scRNA-seq data. CCPE uses a discriminative helix to characterize the circular process of the cell cycle and estimates each cell's pseudotime along the cell cycle. We evaluated the performance of CCPE based on a variety of simulated and real scRNA-seq datasets. Our results indicate that CCPE is an effective method for cell cycle estimation and competitive in various applications compared with other existing methods. CCPE successfully identified cell cycle marker genes and is robust to dropout events in scRNA-seq data. Accurate prediction of the cell cycle using CCPE can also effectively facilitate the removal of cell cycle effects across cell types or conditions.
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Affiliation(s)
- Jiajia Liu
- College of Electronic and Information Engineering, Tongji University, Shanghai, Shanghai 201804, China
- Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Mengyuan Yang
- Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Weiling Zhao
- Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Xiaobo Zhou
- Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
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17
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Liu J, Fan Z, Zhao W, Zhou X. Machine Intelligence in Single-Cell Data Analysis: Advances and New Challenges. Front Genet 2021; 12:655536. [PMID: 34135939 PMCID: PMC8203333 DOI: 10.3389/fgene.2021.655536] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 04/26/2021] [Indexed: 12/18/2022] Open
Abstract
The rapid development of single-cell technologies allows for dissecting cellular heterogeneity at different omics layers with an unprecedented resolution. In-dep analysis of cellular heterogeneity will boost our understanding of complex biological systems or processes, including cancer, immune system and chronic diseases, thereby providing valuable insights for clinical and translational research. In this review, we will focus on the application of machine learning methods in single-cell multi-omics data analysis. We will start with the pre-processing of single-cell RNA sequencing (scRNA-seq) data, including data imputation, cross-platform batch effect removal, and cell cycle and cell-type identification. Next, we will introduce advanced data analysis tools and methods used for copy number variance estimate, single-cell pseudo-time trajectory analysis, phylogenetic tree inference, cell-cell interaction, regulatory network inference, and integrated analysis of scRNA-seq and spatial transcriptome data. Finally, we will present the latest analyzing challenges, such as multi-omics integration and integrated analysis of scRNA-seq data.
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Affiliation(s)
- Jiajia Liu
- College of Electronic and Information Engineering, Tongji University, Shanghai, China
- School of Biomedical Informatics, The University of Texas Health Science Centre at Houston, Houston, TX, United States
| | - Zhiwei Fan
- School of Biomedical Informatics, The University of Texas Health Science Centre at Houston, Houston, TX, United States
- West China School of Public Health, West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Weiling Zhao
- School of Biomedical Informatics, The University of Texas Health Science Centre at Houston, Houston, TX, United States
| | - Xiaobo Zhou
- School of Biomedical Informatics, The University of Texas Health Science Centre at Houston, Houston, TX, United States
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18
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Binder H, Schmidt M, Loeffler-Wirth H, Mortensen LS, Kunz M. Melanoma Single-Cell Biology in Experimental and Clinical Settings. J Clin Med 2021; 10:506. [PMID: 33535416 PMCID: PMC7867095 DOI: 10.3390/jcm10030506] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 01/20/2021] [Accepted: 01/25/2021] [Indexed: 01/05/2023] Open
Abstract
Cellular heterogeneity is regarded as a major factor for treatment response and resistance in a variety of malignant tumors, including malignant melanoma. More recent developments of single-cell sequencing technology provided deeper insights into this phenomenon. Single-cell data were used to identify prognostic subtypes of melanoma tumors, with a special emphasis on immune cells and fibroblasts in the tumor microenvironment. Moreover, treatment resistance to checkpoint inhibitor therapy has been shown to be associated with a set of differentially expressed immune cell signatures unraveling new targetable intracellular signaling pathways. Characterization of T cell states under checkpoint inhibitor treatment showed that exhausted CD8+ T cell types in melanoma lesions still have a high proliferative index. Other studies identified treatment resistance mechanisms to targeted treatment against the mutated BRAF serine/threonine protein kinase including repression of the melanoma differentiation gene microphthalmia-associated transcription factor (MITF) and induction of AXL receptor tyrosine kinase. Interestingly, treatment resistance mechanisms not only included selection processes of pre-existing subclones but also transition between different states of gene expression. Taken together, single-cell technology has provided deeper insights into melanoma biology and has put forward our understanding of the role of tumor heterogeneity and transcriptional plasticity, which may impact on innovative clinical trial designs and experimental approaches.
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Affiliation(s)
- Hans Binder
- Interdisciplinary Center for Bioinformatics, University of Leipzig, 04107 Leipzig, Germany; (H.B.); (M.S.); (H.L.-W.); (L.S.M.)
| | - Maria Schmidt
- Interdisciplinary Center for Bioinformatics, University of Leipzig, 04107 Leipzig, Germany; (H.B.); (M.S.); (H.L.-W.); (L.S.M.)
| | - Henry Loeffler-Wirth
- Interdisciplinary Center for Bioinformatics, University of Leipzig, 04107 Leipzig, Germany; (H.B.); (M.S.); (H.L.-W.); (L.S.M.)
| | - Lena Suenke Mortensen
- Interdisciplinary Center for Bioinformatics, University of Leipzig, 04107 Leipzig, Germany; (H.B.); (M.S.); (H.L.-W.); (L.S.M.)
| | - Manfred Kunz
- Department of Dermatology, Venereology and Allergology, University of Leipzig Medical Center, Philipp-Rosenthal-Str. 23-25, 04103 Leipzig, Germany
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