1
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Tian Y, Wu X, Luo S, Xiong D, Liu R, Hu L, Yuan Y, Shi G, Yao J, Huang Z, Fu F, Yang X, Tang Z, Zhang J, Hu K. A multi-omic single-cell landscape of cellular diversification in the developing human cerebral cortex. Comput Struct Biotechnol J 2024; 23:2173-2189. [PMID: 38827229 PMCID: PMC11141146 DOI: 10.1016/j.csbj.2024.05.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 05/09/2024] [Accepted: 05/13/2024] [Indexed: 06/04/2024] Open
Abstract
The vast neuronal diversity in the human neocortex is vital for high-order brain functions, necessitating elucidation of the regulatory mechanisms underlying such unparalleled diversity. However, recent studies have yet to comprehensively reveal the diversity of neurons and the molecular logic of neocortical origin in humans at single-cell resolution through profiling transcriptomic or epigenomic landscapes, owing to the application of unimodal data alone to depict exceedingly heterogeneous populations of neurons. In this study, we generated a comprehensive compendium of the developing human neocortex by simultaneously profiling gene expression and open chromatin from the same cell. We computationally reconstructed the differentiation trajectories of excitatory projection neurons of cortical origin and inferred the regulatory logic governing lineage bifurcation decisions for neuronal diversification. We demonstrated that neuronal diversity arises from progenitor cell lineage specificity and postmitotic differentiation at distinct stages. Our data paves the way for understanding the primarily coordinated regulatory logic for neuronal diversification in the neocortex.
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Affiliation(s)
- Yuhan Tian
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510275, China
| | - Xia Wu
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510275, China
| | - Songhao Luo
- School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China
| | - Dan Xiong
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510275, China
| | - Rong Liu
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510275, China
| | - Lanqi Hu
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510275, China
| | - Yuchen Yuan
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510275, China
| | - Guowei Shi
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510275, China
| | - Junjie Yao
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510275, China
| | - Zhiwei Huang
- School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China
| | - Fang Fu
- Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 511436, China
| | - Xin Yang
- Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 511436, China
| | - Zhonghui Tang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510275, China
| | - Jiajun Zhang
- School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China
| | - Kunhua Hu
- Guangdong Provincial Key Laboratory of Brain Function and Disease, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510275, China
- Public Platform Laboratory, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510630, China
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2
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Sashittal P, Zhang RY, Law BK, Strzalkowski A, Schmidt H, Bolondi A, Chan MM, Raphael BJ. Inferring cell differentiation maps from lineage tracing data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.09.611835. [PMID: 39314473 PMCID: PMC11419031 DOI: 10.1101/2024.09.09.611835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/25/2024]
Abstract
During development, mulitpotent cells differentiate through a hierarchy of increasingly restricted progenitor cell types until they realize specialized cell types. A cell differentiation map describes this hierarchy, and inferring these maps is an active area of research spanning traditional single marker lineage studies to data-driven trajectory inference methods on single-cell RNA-seq data. Recent high-throughput lineage tracing technologies profile lineages and cell types at scale, but current methods to infer cell differentiation maps from these data rely on simple models with restrictive assumptions about the developmental process. We introduce a mathematical framework for cell differentiation maps based on the concept of potency, and develop an algorithm, Carta , that infers an optimal cell differentiation map from single-cell lineage tracing data. The key insight in Carta is to balance the trade-off between the complexity of the cell differentiation map and the number of unobserved cell type transitions on the lineage tree. We show that Carta more accurately infers cell differentiation maps on both simulated and real data compared to existing methods. In models of mammalian trunk development and mouse hematopoiesis, Carta identifies important features of development that are not revealed by other methods including convergent differentiation of specialized cell types, progenitor differentiation dynamics, and the refinement of routes of differentiation via new intermediate progenitors. Code availability Carta software is available at https://github.com/raphael-group/CARTA.
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3
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Meng Y, Nerlov C. Epigenetic regulation of hematopoietic stem cell fate. Trends Cell Biol 2024:S0962-8924(24)00162-4. [PMID: 39271425 DOI: 10.1016/j.tcb.2024.08.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Revised: 08/12/2024] [Accepted: 08/14/2024] [Indexed: 09/15/2024]
Abstract
Hematopoietic stem cells (HSCs) sustain blood cell production throughout the mammalian life span. However, it has become clear that at the single cell level a subset of HSCs is stably biased in their lineage output, and that such heterogeneity may play a key role in physiological processes including aging and adaptive immunity. Analysis of chromatin accessibility, DNA methylation, and histone modifications has revealed that HSCs with different lineage bias exhibit distinct epigenetic traits inscribed at poised, lineage-specific enhancers. This allows for lineage priming without initiating lineage-specific gene expression in HSCs, controlling lineage bias while preserving self-renewal and multipotency. Here, we review our current understanding of epigenetic regulation in the establishment and maintenance of HSC fate decisions under different physiological conditions.
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Affiliation(s)
- Yiran Meng
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Headington, Oxford, UK
| | - Claus Nerlov
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Headington, Oxford, UK.
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4
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Safina K, van Galen P. New frameworks for hematopoiesis derived from single-cell genomics. Blood 2024; 144:1039-1047. [PMID: 38985829 DOI: 10.1182/blood.2024024006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Revised: 06/21/2024] [Accepted: 06/22/2024] [Indexed: 07/12/2024] Open
Abstract
ABSTRACT Recent advancements in single-cell genomics have enriched our understanding of hematopoiesis, providing intricate details about hematopoietic stem cell biology, differentiation, and lineage commitment. Technological advancements have highlighted extensive heterogeneity of cell populations and continuity of differentiation routes. Nevertheless, intermediate "attractor" states signify structure in stem and progenitor populations that link state transition dynamics to fate potential. We discuss how innovative model systems quantify lineage bias and how stress accelerates differentiation, thereby reducing fate plasticity compared with native hematopoiesis. We conclude by offering our perspective on the current model of hematopoiesis and discuss how a more precise understanding can translate to strategies that extend healthy hematopoiesis and prevent disease.
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Affiliation(s)
- Ksenia Safina
- Division of Hematology, Brigham and Women's Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- Broad Institute of MIT and Harvard, Cambridge, MA
- Ludwig Center at Harvard, Boston, MA
| | - Peter van Galen
- Division of Hematology, Brigham and Women's Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- Broad Institute of MIT and Harvard, Cambridge, MA
- Ludwig Center at Harvard, Boston, MA
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5
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Candelas A, Vianay B, Gelin M, Faivre L, Larghero J, Blanchoin L, Théry M, Brunet S. Heterotypic interaction promotes asymmetric division of human hematopoietic progenitors. Development 2024; 151:dev203088. [PMID: 39136544 DOI: 10.1242/dev.203088] [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: 05/22/2024] [Accepted: 07/17/2024] [Indexed: 09/04/2024]
Abstract
Hematopoietic stem and progenitor cells (HSPCs) give rise to all cell types of the hematopoietic system through various processes, including asymmetric divisions. However, the contribution of stromal cells of the hematopoietic niches in the control of HSPC asymmetric divisions remains unknown. Using polyacrylamide microwells as minimalist niches, we show that specific heterotypic interactions with osteoblast and endothelial cells promote asymmetric divisions of human HSPCs. Upon interaction, HSPCs polarize in interphase with the centrosome, the Golgi apparatus, and lysosomes positioned close to the site of contact. Subsequently, during mitosis, HSPCs orient their spindle perpendicular to the plane of contact. This division mode gives rise to siblings with unequal amounts of lysosomes and of the differentiation marker CD34. Such asymmetric inheritance generates heterogeneity in the progeny, which is likely to contribute to the plasticity of the early steps of hematopoiesis.
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Affiliation(s)
- Adrian Candelas
- Human Immunology, Pathophysiology, Immunotherapy, INSERM Unit 976, Institut de Recherche St Louis, AP-HP, Hôpital Saint-Louis, Université Paris Cité, F-75010 Paris, France
| | - Benoit Vianay
- Cytomorpholab, University Grenoble-Alpes, CEA, CNRS, INRA, Laboratoire de Phyiologie Cellulaire & Végétale, F-38054 Grenoble, France
| | - Matthieu Gelin
- Human Immunology, Pathophysiology, Immunotherapy, INSERM Unit 976, Institut de Recherche St Louis, AP-HP, Hôpital Saint-Louis, Université Paris Cité, F-75010 Paris, France
| | - Lionel Faivre
- Unité de Thérapie Cellulaire, Human Immunology, Pathophysiology, Immunotherapy, INSERM Unit 976, AP-HP, Hôpital Saint-Louis, Center of Clinical Investigations in Biotherapies of Cancer CBT501, Université Paris Cité, F-75010 Paris, France
| | - Jerome Larghero
- Unité de Thérapie Cellulaire, Human Immunology, Pathophysiology, Immunotherapy, INSERM Unit 976, AP-HP, Hôpital Saint-Louis, Center of Clinical Investigations in Biotherapies of Cancer CBT501, Université Paris Cité, F-75010 Paris, France
| | - Laurent Blanchoin
- Cytomorpholab, University Grenoble-Alpes, CEA, CNRS, INRA, Laboratoire de Phyiologie Cellulaire & Végétale, F-38054 Grenoble, France
| | - Manuel Théry
- Human Immunology, Pathophysiology, Immunotherapy, INSERM Unit 976, Institut de Recherche St Louis, AP-HP, Hôpital Saint-Louis, Université Paris Cité, F-75010 Paris, France
- Cytomorpholab, University Grenoble-Alpes, CEA, CNRS, INRA, Laboratoire de Phyiologie Cellulaire & Végétale, F-38054 Grenoble, France
| | - Stéphane Brunet
- Human Immunology, Pathophysiology, Immunotherapy, INSERM Unit 976, Institut de Recherche St Louis, AP-HP, Hôpital Saint-Louis, Université Paris Cité, F-75010 Paris, France
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6
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Liu C, Hong T, Zhao C, Xue T, Wang S, Ren Z. Single-nucleus transcriptomics and chromatin accessibility analysis of musk gland development in Chinese forest musk deer (Moschus berezovskii). Integr Zool 2024; 19:955-974. [PMID: 38644525 DOI: 10.1111/1749-4877.12823] [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: 07/10/2023] [Revised: 12/28/2023] [Accepted: 02/15/2024] [Indexed: 04/23/2024]
Abstract
Musk secreted by male forest musk deer (Moschus berezovskii) musk glands is an invaluable component of medicine and perfume. Musk secretion depends on musk gland maturation; however, the mechanism of its development remains elusive. Herein, using single cell multiome ATAC + gene expression coupled with several bioinformatic analyses, a dynamic transcriptional cell atlas of musk gland development was revealed, and key genes and transcription factors affecting its development were determined. Twelve cell types, including two different types of acinar cells (Clusters 0 and 10) were identified. Single-nucleus RNA and single-nucleus ATAC sequencing analyses revealed that seven core target genes associated with musk secretion (Hsd17b2, Acacb, Lss, Vapa, Aldh16a1, Aldh7a1, and Sqle) were regulated by 12 core transcription factors (FOXO1, CUX2, RORA, RUNX1, KLF6, MGA, NFIC, FOXO3, ETV5, NR3C1, HSF4, and MITF) during the development of Cluster 0 acinar cells. Kyoto Encyclopedia of Genes and Genomes enrichment showed significant changes in the pathways associated with musk secretion during acinar cell development. Gene set variation analysis also revealed that certain pathways associated with musk secretion were enriched in 6-year-old acinar cells. A gene co-expression network was constructed during acinar cell development to provide a precise understanding of the connections between transcription factors, genes, and pathways. Finally, intercellular communication analysis showed that intercellular communication is involved in musk gland development. This study provides crucial insights into the changes and key factors underlying musk gland development, which serve as valuable resources for studying musk secretion mechanisms and promoting the protection of this endangered species.
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Affiliation(s)
- Chenmiao Liu
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China
| | - Tingting Hong
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China
| | - Chengcheng Zhao
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China
| | - Tao Xue
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China
| | - Shuhui Wang
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China
| | - Zhanjun Ren
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China
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7
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LeRoy N, Smith J, Zheng G, Rymuza J, Gharavi E, Brown D, Zhang A, Sheffield N. Fast clustering and cell-type annotation of scATAC data using pre-trained embeddings. NAR Genom Bioinform 2024; 6:lqae073. [PMID: 38974799 PMCID: PMC11224678 DOI: 10.1093/nargab/lqae073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 04/29/2024] [Accepted: 06/20/2024] [Indexed: 07/09/2024] Open
Abstract
Data from the single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) are now widely available. One major computational challenge is dealing with high dimensionality and inherent sparsity, which is typically addressed by producing lower dimensional representations of single cells for downstream clustering tasks. Current approaches produce such individual cell embeddings directly through a one-step learning process. Here, we propose an alternative approach by building embedding models pre-trained on reference data. We argue that this provides a more flexible analysis workflow that also has computational performance advantages through transfer learning. We implemented our approach in scEmbed, an unsupervised machine-learning framework that learns low-dimensional embeddings of genomic regulatory regions to represent and analyze scATAC-seq data. scEmbed performs well in terms of clustering ability and has the key advantage of learning patterns of region co-occurrence that can be transferred to other, unseen datasets. Moreover, models pre-trained on reference data can be exploited to build fast and accurate cell-type annotation systems without the need for other data modalities. scEmbed is implemented in Python and it is available to download from GitHub. We also make our pre-trained models available on huggingface for public use. scEmbed is open source and available at https://github.com/databio/geniml. Pre-trained models from this work can be obtained on huggingface: https://huggingface.co/databio.
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Affiliation(s)
- Nathan J LeRoy
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
- Department of Biomedical Engineering, School of Medicine, University of Virginia, Charlottesville, VA 22904, USA
| | - Jason P Smith
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
- Child Health Research Center, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Guangtao Zheng
- Department of Computer Science, School of Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Julia Rymuza
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Erfaneh Gharavi
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
- School of Data Science, University of Virginia, Charlottesville, VA 22904, USA
| | - Donald E Brown
- School of Data Science, University of Virginia, Charlottesville, VA 22904, USA
- Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Aidong Zhang
- Department of Biomedical Engineering, School of Medicine, University of Virginia, Charlottesville, VA 22904, USA
- Department of Computer Science, School of Engineering, University of Virginia, Charlottesville, VA 22908, USA
- School of Data Science, University of Virginia, Charlottesville, VA 22904, USA
| | - Nathan C Sheffield
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
- Department of Biomedical Engineering, School of Medicine, University of Virginia, Charlottesville, VA 22904, USA
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
- Child Health Research Center, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
- Department of Computer Science, School of Engineering, University of Virginia, Charlottesville, VA 22908, USA
- School of Data Science, University of Virginia, Charlottesville, VA 22904, USA
- Department of Public Health Sciences, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
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8
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Kodali S, Proietti L, Valcarcel G, López-Rubio AV, Pessina P, Eder T, Shi J, Jen A, Lupión-Garcia N, Starner AC, Bartels MD, Cui Y, Sands CM, Planas-Riverola A, Martínez A, Velasco-Hernandez T, Tomás-Daza L, Alber B, Manhart G, Mayer IM, Kollmann K, Fatica A, Menendez P, Shishkova E, Rau RE, Javierre BM, Coon J, Chen Q, Van Nostrand EL, Sardina JL, Grebien F, Di Stefano B. RNA sequestration in P-bodies sustains myeloid leukaemia. Nat Cell Biol 2024:10.1038/s41556-024-01489-6. [PMID: 39169219 DOI: 10.1038/s41556-024-01489-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 07/18/2024] [Indexed: 08/23/2024]
Abstract
Post-transcriptional mechanisms are fundamental safeguards of progenitor cell identity and are often dysregulated in cancer. Here, we identified regulators of P-bodies as crucial vulnerabilities in acute myeloid leukaemia (AML) through genome-wide CRISPR screens in normal and malignant haematopoietic progenitors. We found that leukaemia cells harbour aberrantly elevated numbers of P-bodies and show that P-body assembly is crucial for initiation and maintenance of AML. Notably, P-body loss had little effect upon homoeostatic haematopoiesis but impacted regenerative haematopoiesis. Molecular characterization of P-bodies purified from human AML cells unveiled their critical role in sequestering messenger RNAs encoding potent tumour suppressors from the translational machinery. P-body dissolution promoted translation of these mRNAs, which in turn rewired gene expression and chromatin architecture in leukaemia cells. Collectively, our findings highlight the contrasting and unique roles of RNA sequestration in P-bodies during tissue homoeostasis and oncogenesis. These insights open potential avenues for understanding myeloid leukaemia and future therapeutic interventions.
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Affiliation(s)
- Srikanth Kodali
- Stem Cells and Regenerative Medicine Center, Baylor College of Medicine, Houston, TX, USA
- Center for Cell and Gene Therapy, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA
- Center for Cancer Epigenetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ludovica Proietti
- Institute for Medical Biochemistry, University of Veterinary Medicine Vienna, Vienna, Austria
| | - Gemma Valcarcel
- Josep Carreras Leukaemia Research Institute, Badalona, Spain
| | | | - Patrizia Pessina
- Stem Cells and Regenerative Medicine Center, Baylor College of Medicine, Houston, TX, USA
- Center for Cell and Gene Therapy, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA
- Center for Cancer Epigenetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Thomas Eder
- Institute for Medical Biochemistry, University of Veterinary Medicine Vienna, Vienna, Austria
| | - Junchao Shi
- Division of Biomedical Sciences, School of Medicine, University of California, Riverside, Riverside, CA, USA
| | - Annie Jen
- Department of Biomolecular Chemistry, University of Wisconsin, Madison, WI, USA
| | - Núria Lupión-Garcia
- Stem Cells and Regenerative Medicine Center, Baylor College of Medicine, Houston, TX, USA
- Center for Cell and Gene Therapy, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA
- Center for Cancer Epigenetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Anne C Starner
- Verna & Marrs McLean Department of Biochemistry & Molecular Biology and Therapeutic Innovation Center, Baylor College of Medicine, Houston, TX, USA
| | - Mason D Bartels
- Verna & Marrs McLean Department of Biochemistry & Molecular Biology and Therapeutic Innovation Center, Baylor College of Medicine, Houston, TX, USA
| | - Yingzhi Cui
- Stem Cells and Regenerative Medicine Center, Baylor College of Medicine, Houston, TX, USA
- Center for Cell and Gene Therapy, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA
- Center for Cancer Epigenetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Caroline M Sands
- Stem Cells and Regenerative Medicine Center, Baylor College of Medicine, Houston, TX, USA
- Center for Cell and Gene Therapy, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA
- Center for Cancer Epigenetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | - Alba Martínez
- Josep Carreras Leukaemia Research Institute, Badalona, Spain
| | | | | | - Bernhard Alber
- Institute for Medical Biochemistry, University of Veterinary Medicine Vienna, Vienna, Austria
| | - Gabriele Manhart
- Institute for Medical Biochemistry, University of Veterinary Medicine Vienna, Vienna, Austria
| | - Isabella Maria Mayer
- Institute of Pharmacology and Toxicology, University of Veterinary Medicine Vienna, Vienna, Austria
| | - Karoline Kollmann
- Institute of Pharmacology and Toxicology, University of Veterinary Medicine Vienna, Vienna, Austria
| | - Alessandro Fatica
- Department of Biology and Biotechnology 'Charles Darwin', Sapienza University of Rome, Rome, Italy
| | - Pablo Menendez
- Josep Carreras Leukaemia Research Institute, Badalona, Spain
| | - Evgenia Shishkova
- Department of Biomolecular Chemistry, University of Wisconsin, Madison, WI, USA
- National Center for Quantitative Biology of Complex Systems, Madison, WI, USA
| | - Rachel E Rau
- Stem Cells and Regenerative Medicine Center, Baylor College of Medicine, Houston, TX, USA
- Center for Cell and Gene Therapy, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
- Department of Pediatrics, Baylor College of Medicine, Texas Children's Hospital, Houston, TX, USA
| | | | - Joshua Coon
- Department of Biomolecular Chemistry, University of Wisconsin, Madison, WI, USA
- National Center for Quantitative Biology of Complex Systems, Madison, WI, USA
- Department of Chemistry, University of Wisconsin, Madison, WI, USA
- Morgridge Institute for Research, Madison, WI, USA
| | - Qi Chen
- Molecular Medicine Program, Division of Urology, Department of Surgery, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Eric L Van Nostrand
- Verna & Marrs McLean Department of Biochemistry & Molecular Biology and Therapeutic Innovation Center, Baylor College of Medicine, Houston, TX, USA
| | - Jose L Sardina
- Josep Carreras Leukaemia Research Institute, Badalona, Spain.
| | - Florian Grebien
- Institute for Medical Biochemistry, University of Veterinary Medicine Vienna, Vienna, Austria.
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria.
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria.
| | - Bruno Di Stefano
- Stem Cells and Regenerative Medicine Center, Baylor College of Medicine, Houston, TX, USA.
- Center for Cell and Gene Therapy, Baylor College of Medicine, Houston, TX, USA.
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA.
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA.
- Center for Cancer Epigenetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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9
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Gulati GS, D'Silva JP, Liu Y, Wang L, Newman AM. Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics. Nat Rev Mol Cell Biol 2024:10.1038/s41580-024-00768-2. [PMID: 39169166 DOI: 10.1038/s41580-024-00768-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/16/2024] [Indexed: 08/23/2024]
Abstract
Single-cell transcriptomics has broadened our understanding of cellular diversity and gene expression dynamics in healthy and diseased tissues. Recently, spatial transcriptomics has emerged as a tool to contextualize single cells in multicellular neighbourhoods and to identify spatially recurrent phenotypes, or ecotypes. These technologies have generated vast datasets with targeted-transcriptome and whole-transcriptome profiles of hundreds to millions of cells. Such data have provided new insights into developmental hierarchies, cellular plasticity and diverse tissue microenvironments, and spurred a burst of innovation in computational methods for single-cell analysis. In this Review, we discuss recent advancements, ongoing challenges and prospects in identifying and characterizing cell states and multicellular neighbourhoods. We discuss recent progress in sample processing, data integration, identification of subtle cell states, trajectory modelling, deconvolution and spatial analysis. Furthermore, we discuss the increasing application of deep learning, including foundation models, in analysing single-cell and spatial transcriptomics data. Finally, we discuss recent applications of these tools in the fields of stem cell biology, immunology, and tumour biology, and the future of single-cell and spatial transcriptomics in biological research and its translation to the clinic.
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Affiliation(s)
- Gunsagar S Gulati
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - Yunhe Liu
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Linghua Wang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA
| | - Aaron M Newman
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA.
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA.
- Chan Zuckerberg Biohub - San Francisco, San Francisco, CA, USA.
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10
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Luo S, Germain PL, Robinson MD, von Meyenn F. Benchmarking computational methods for single-cell chromatin data analysis. Genome Biol 2024; 25:225. [PMID: 39152456 PMCID: PMC11328424 DOI: 10.1186/s13059-024-03356-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 07/29/2024] [Indexed: 08/19/2024] Open
Abstract
BACKGROUND Single-cell chromatin accessibility assays, such as scATAC-seq, are increasingly employed in individual and joint multi-omic profiling of single cells. As the accumulation of scATAC-seq and multi-omics datasets continue, challenges in analyzing such sparse, noisy, and high-dimensional data become pressing. Specifically, one challenge relates to optimizing the processing of chromatin-level measurements and efficiently extracting information to discern cellular heterogeneity. This is of critical importance, since the identification of cell types is a fundamental step in current single-cell data analysis practices. RESULTS We benchmark 8 feature engineering pipelines derived from 5 recent methods to assess their ability to discover and discriminate cell types. By using 10 metrics calculated at the cell embedding, shared nearest neighbor graph, or partition levels, we evaluate the performance of each method at different data processing stages. This comprehensive approach allows us to thoroughly understand the strengths and weaknesses of each method and the influence of parameter selection. CONCLUSIONS Our analysis provides guidelines for choosing analysis methods for different datasets. Overall, feature aggregation, SnapATAC, and SnapATAC2 outperform latent semantic indexing-based methods. For datasets with complex cell-type structures, SnapATAC and SnapATAC2 are preferred. With large datasets, SnapATAC2 and ArchR are most scalable.
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Affiliation(s)
- Siyuan Luo
- Laboratory of Nutrition and Metabolic Epigenetics, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
| | - Pierre-Luc Germain
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland
- Institute for Neuroscience, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Mark D Robinson
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland.
- SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland.
| | - Ferdinand von Meyenn
- Laboratory of Nutrition and Metabolic Epigenetics, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland.
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11
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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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12
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Kim Y, Calderon AA, Favaro P, Glass DR, Tsai AG, Ho D, Borges L, Greenleaf WJ, Bendall SC. Terminal deoxynucleotidyl transferase and CD84 identify human multi-potent lymphoid progenitors. Nat Commun 2024; 15:5910. [PMID: 39003273 PMCID: PMC11246490 DOI: 10.1038/s41467-024-49883-w] [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: 12/02/2022] [Accepted: 06/24/2024] [Indexed: 07/15/2024] Open
Abstract
Lymphoid specification in human hematopoietic progenitors is not fully understood. To better associate lymphoid identity with protein-level cell features, we conduct a highly multiplexed single-cell proteomic screen on human bone marrow progenitors. This screen identifies terminal deoxynucleotidyl transferase (TdT), a specialized DNA polymerase intrinsic to VDJ recombination, broadly expressed within CD34+ progenitors prior to B/T cell emergence. While these TdT+ cells coincide with granulocyte-monocyte progenitor (GMP) immunophenotype, their accessible chromatin regions show enrichment for lymphoid-associated transcription factor (TF) motifs. TdT expression on GMPs is inversely related to the SLAM family member CD84. Prospective isolation of CD84lo GMPs demonstrates robust lymphoid potentials ex vivo, while still retaining significant myeloid differentiation capacity, akin to LMPPs. This multi-omic study identifies human bone marrow lymphoid-primed progenitors, further defining the lympho-myeloid axis in human hematopoiesis.
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Affiliation(s)
- YeEun Kim
- Immunology Graduate Program, Stanford University, Stanford, CA, USA
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Ariel A Calderon
- Immunology Graduate Program, Stanford University, Stanford, CA, USA
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Patricia Favaro
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - David R Glass
- Immunology Graduate Program, Stanford University, Stanford, CA, USA
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Albert G Tsai
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Daniel Ho
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Luciene Borges
- Department of Pathology, Stanford University, Stanford, CA, USA
| | | | - Sean C Bendall
- Department of Pathology, Stanford University, Stanford, CA, USA.
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13
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Mulet-Lazaro R, van Herk S, Nuetzel M, Sijs-Szabo A, Díaz N, Kelly K, Erpelinck-Verschueren C, Schwarzfischer-Pfeilschifter L, Stanewsky H, Ackermann U, Glatz D, Raithel J, Fischer A, Pohl S, Rijneveld A, Vaquerizas JM, Thiede C, Plass C, Wouters BJ, Delwel R, Rehli M, Gebhard C. Epigenetic alterations affecting hematopoietic regulatory networks as drivers of mixed myeloid/lymphoid leukemia. Nat Commun 2024; 15:5693. [PMID: 38972954 PMCID: PMC11228033 DOI: 10.1038/s41467-024-49811-y] [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: 08/11/2023] [Accepted: 06/19/2024] [Indexed: 07/09/2024] Open
Abstract
Leukemias with ambiguous lineage comprise several loosely defined entities, often without a clear mechanistic basis. Here, we extensively profile the epigenome and transcriptome of a subgroup of such leukemias with CpG Island Methylator Phenotype. These leukemias exhibit comparable hybrid myeloid/lymphoid epigenetic landscapes, yet heterogeneous genetic alterations, suggesting they are defined by their shared epigenetic profile rather than common genetic lesions. Gene expression enrichment reveals similarity with early T-cell precursor acute lymphoblastic leukemia and a lymphoid progenitor cell of origin. In line with this, integration of differential DNA methylation and gene expression shows widespread silencing of myeloid transcription factors. Moreover, binding sites for hematopoietic transcription factors, including CEBPA, SPI1 and LEF1, are uniquely inaccessible in these leukemias. Hypermethylation also results in loss of CTCF binding, accompanied by changes in chromatin interactions involving key transcription factors. In conclusion, epigenetic dysregulation, and not genetic lesions, explains the mixed phenotype of this group of leukemias with ambiguous lineage. The data collected here constitute a useful and comprehensive epigenomic reference for subsequent studies of acute myeloid leukemias, T-cell acute lymphoblastic leukemias and mixed-phenotype leukemias.
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Affiliation(s)
- Roger Mulet-Lazaro
- Department of Hematology, Erasmus MC Cancer Institute, Rotterdam, the Netherlands
- Oncode Institute, Utrecht, the Netherlands
| | - Stanley van Herk
- Department of Hematology, Erasmus MC Cancer Institute, Rotterdam, the Netherlands
- Oncode Institute, Utrecht, the Netherlands
| | - Margit Nuetzel
- Department of Internal Medicine III, University Hospital Regensburg, Regensburg, Germany
| | - Aniko Sijs-Szabo
- Department of Hematology, Erasmus MC Cancer Institute, Rotterdam, the Netherlands
| | - Noelia Díaz
- Max Planck Institute for Molecular Biomedicine, Muenster, Germany
- Renewable Marine Resources Department, Institute of Marine Sciences (ICM-CSIC), Barcelona, Spain
| | - Katherine Kelly
- Division of Cancer Epigenomics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Claudia Erpelinck-Verschueren
- Department of Hematology, Erasmus MC Cancer Institute, Rotterdam, the Netherlands
- Oncode Institute, Utrecht, the Netherlands
| | | | - Hanna Stanewsky
- Department of Internal Medicine III, University Hospital Regensburg, Regensburg, Germany
| | - Ute Ackermann
- Department of Internal Medicine III, University Hospital Regensburg, Regensburg, Germany
| | - Dagmar Glatz
- Department of Internal Medicine III, University Hospital Regensburg, Regensburg, Germany
| | - Johanna Raithel
- Department of Internal Medicine III, University Hospital Regensburg, Regensburg, Germany
| | - Alexander Fischer
- Department of Internal Medicine III, University Hospital Regensburg, Regensburg, Germany
| | - Sandra Pohl
- Department of Internal Medicine III, University Hospital Regensburg, Regensburg, Germany
- Department of Conservative Dentistry and Periodontology, University Hospital Regensburg, Regensburg, Germany
| | - Anita Rijneveld
- Department of Hematology, Erasmus MC Cancer Institute, Rotterdam, the Netherlands
| | - Juan M Vaquerizas
- Max Planck Institute for Molecular Biomedicine, Muenster, Germany
- MRC London Institute of Medical Sciences, London, United Kingdom
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, Hammersmith Hospital 8 Campus, London, United Kingdom
| | - Christian Thiede
- Medizinische Klinik und Poliklinik I, Universitätsklinikum Carl Gustav Carus, Dresden, Germany
| | - Christoph Plass
- Division of Cancer Epigenomics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Bas J Wouters
- Department of Hematology, Erasmus MC Cancer Institute, Rotterdam, the Netherlands.
- Oncode Institute, Utrecht, the Netherlands.
| | - Ruud Delwel
- Department of Hematology, Erasmus MC Cancer Institute, Rotterdam, the Netherlands.
- Oncode Institute, Utrecht, the Netherlands.
| | - Michael Rehli
- Department of Internal Medicine III, University Hospital Regensburg, Regensburg, Germany.
- Leibniz Institute for Immunotherapy (LIT), Regensburg, Germany.
| | - Claudia Gebhard
- Department of Internal Medicine III, University Hospital Regensburg, Regensburg, Germany.
- Leibniz Institute for Immunotherapy (LIT), Regensburg, Germany.
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14
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Wang X, Lian Q, Dong H, Xu S, Su Y, Wu X. Benchmarking Algorithms for Gene Set Scoring of Single-cell ATAC-seq Data. GENOMICS, PROTEOMICS & BIOINFORMATICS 2024; 22:qzae014. [PMID: 39049508 DOI: 10.1093/gpbjnl/qzae014] [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: 05/01/2022] [Revised: 06/20/2023] [Accepted: 06/25/2023] [Indexed: 07/27/2024]
Abstract
Gene set scoring (GSS) has been routinely conducted for gene expression analysis of bulk or single-cell RNA sequencing (RNA-seq) data, which helps to decipher single-cell heterogeneity and cell type-specific variability by incorporating prior knowledge from functional gene sets. Single-cell assay for transposase accessible chromatin using sequencing (scATAC-seq) is a powerful technique for interrogating single-cell chromatin-based gene regulation, and genes or gene sets with dynamic regulatory potentials can be regarded as cell type-specific markers as if in single-cell RNA-seq (scRNA-seq). However, there are few GSS tools specifically designed for scATAC-seq, and the applicability and performance of RNA-seq GSS tools on scATAC-seq data remain to be investigated. Here, we systematically benchmarked ten GSS tools, including four bulk RNA-seq tools, five scRNA-seq tools, and one scATAC-seq method. First, using matched scATAC-seq and scRNA-seq datasets, we found that the performance of GSS tools on scATAC-seq data was comparable to that on scRNA-seq, suggesting their applicability to scATAC-seq. Then, the performance of different GSS tools was extensively evaluated using up to ten scATAC-seq datasets. Moreover, we evaluated the impact of gene activity conversion, dropout imputation, and gene set collections on the results of GSS. Results show that dropout imputation can significantly promote the performance of almost all GSS tools, while the impact of gene activity conversion methods or gene set collections on GSS performance is more dependent on GSS tools or datasets. Finally, we provided practical guidelines for choosing appropriate preprocessing methods and GSS tools in different application scenarios.
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Affiliation(s)
- Xi Wang
- Pasteurien College, Suzhou Medical College of Soochow University, Soochow University, Suzhou 215000, China
- Department of Automation, Xiamen University, Xiamen 361005, China
| | - Qiwei Lian
- Pasteurien College, Suzhou Medical College of Soochow University, Soochow University, Suzhou 215000, China
- Department of Automation, Xiamen University, Xiamen 361005, China
| | - Haoyu Dong
- Pasteurien College, Suzhou Medical College of Soochow University, Soochow University, Suzhou 215000, China
| | - Shuo Xu
- Department of Automation, Xiamen University, Xiamen 361005, China
| | - Yaru Su
- College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China
| | - Xiaohui Wu
- Pasteurien College, Suzhou Medical College of Soochow University, Soochow University, Suzhou 215000, China
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15
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Li J, Fu L, Li Y, Sun W, Yi Y, Jia W, Li H, Liu H, Guo P, Wang Y, Shen Y, Zhang X, Lv Y, Qin B, Li W, Liu C, Liu L, Mazid MA, Lai Y, Esteban MA, Jiang Y, Wu L. A single-cell chromatin accessibility dataset of human primed and naïve pluripotent stem cell-derived teratoma. Sci Data 2024; 11:725. [PMID: 38956385 PMCID: PMC11220047 DOI: 10.1038/s41597-024-03558-9] [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: 02/28/2024] [Accepted: 06/20/2024] [Indexed: 07/04/2024] Open
Abstract
Teratoma, due to its remarkable ability to differentiate into multiple cell lineages, is a valuable model for studying human embryonic development. The similarity of the gene expression and chromatin accessibility patterns in these cells to those observed in vivo further underscores its potential as a research tool. Notably, teratomas derived from human naïve (pre-implantation epiblast-like) pluripotent stem cells (PSCs) have larger embryonic cell diversity and contain extraembryonic lineages, making them more suitable to study developmental processes. However, the cell type-specific epigenetic profiles of naïve PSC teratomas have not been yet characterized. Using single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq), we analyzed 66,384 cell profiles from five teratomas derived from human naïve PSCs and their post-implantation epiblast-like (primed) counterparts. We observed 17 distinct cell types from both embryonic and extraembryonic lineages, resembling the corresponding cell types in human fetal tissues. Additionally, we identified key transcription factors specific to different cell types. Our dataset provides a resource for investigating gene regulatory programs in a relevant model of human embryonic development.
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Affiliation(s)
- Jinxiu Li
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
- BGI Research, Shenzhen, 518083, China
- BGI Research, Hangzhou, 310030, China
| | - Lixin Fu
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
- BGI Research, Shenzhen, 518083, China
- BGI Research, Hangzhou, 310030, China
| | - Yunpan Li
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
| | - Wei Sun
- BGI Research, Shenzhen, 518083, China
- BGI Research, Hangzhou, 310030, China
- College of Life Sciences, Nankai University, Tianjin, 300071, China
| | - Yao Yi
- MRC Metabolic Diseases Unit, Wellcome Trust-Medical Research Council Institute of Metabolic Science, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Wenqi Jia
- BGI Research, Shenzhen, 518083, China
- BGI Research, Hangzhou, 310030, China
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
| | - Haiwei Li
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
- Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health and Guangzhou Medical University, Guangzhou, Guangdong, 510530, China
| | - Hao Liu
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
| | - Pengcheng Guo
- BGI Research, Shenzhen, 518083, China
- BGI Research, Hangzhou, 310030, China
| | - Yang Wang
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
- BGI Research, Hangzhou, 310030, China
| | - Yue Shen
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
- BGI Research, Shenzhen, 518083, China
- BGI Research, Changzhou, 213299, China
| | - Xiuqing Zhang
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
- BGI Research, Shenzhen, 518083, China
| | - Yuan Lv
- BGI Research, Shenzhen, 518083, China
- BGI Research, Hangzhou, 310030, China
| | - Baoming Qin
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
| | - Wenjuan Li
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
| | - Chuanyu Liu
- BGI Research, Shenzhen, 518083, China
- BGI Research, Hangzhou, 310030, China
| | - Longqi Liu
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
- BGI Research, Shenzhen, 518083, China
- BGI Research, Hangzhou, 310030, China
| | - Md Abdul Mazid
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
| | - Yiwei Lai
- BGI Research, Shenzhen, 518083, China
- BGI Research, Hangzhou, 310030, China
- 3DCStar lab, BGI, Shenzhen, 518083, China
| | - Miguel A Esteban
- BGI Research, Shenzhen, 518083, China
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
- 3DCStar lab, BGI, Shenzhen, 518083, China
| | - Yu Jiang
- BGI Research, Shenzhen, 518083, China.
- BGI Research, Hangzhou, 310030, China.
- State Key Laboratory for Diagnosis and Treatment of Severe Zoonotic Infectious Diseases, Key Laboratory for Zoonosis Research of the Ministry of Education, Institute of Zoonosis, and College of Veterinary Medicine, Jilin University, Changchun, 130062, China.
| | - Liang Wu
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China.
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16
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Wang X, Zhang W, Zhao S, Yan H, Xin Z, Cui T, Zang R, Zhao L, Wang H, Zhou J, Li X, Yue W, Xi J, Zhang Z, Fang X, Pei X. Decoding human in vitro terminal erythropoiesis originating from umbilical cord blood mononuclear cells and pluripotent stem cells. Cell Prolif 2024; 57:e13614. [PMID: 38499435 PMCID: PMC11216933 DOI: 10.1111/cpr.13614] [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: 07/14/2023] [Revised: 12/18/2023] [Accepted: 01/30/2024] [Indexed: 03/20/2024] Open
Abstract
Ex vivo red blood cell (RBC) production generates unsatisfactory erythroid cells. A deep exploration into terminally differentiated cells is required to understand the impairments for RBC generation and the underlying mechanisms. Here, we mapped an atlas of terminally differentiated cells from umbilical cord blood mononuclear cells (UCBMN) and pluripotent stem cells (PSC) and observed their dynamic regulation of erythropoiesis at single-cell resolution. Interestingly, we detected a few progenitor cells and non-erythroid cells from both origins. In PSC-derived erythropoiesis (PSCE), the expression of haemoglobin switch regulators (BCL11A and ZBTB7A) were significantly absent, which could be the restraint for its adult globin expression. We also found that PSCE were less active in stress erythropoiesis than in UCBMN-derived erythropoiesis (UCBE), and explored an agonist of stress erythropoiesis gene, TRIB3, could enhance the expression of adult globin in PSCE. Compared with UCBE, there was a lower expression of epigenetic-related proteins (e.g., CASPASE 3 and UBE2O) and transcription factors (e.g., FOXO3 and TAL1) in PSCE, which might restrict PSCE's enucleation. Moreover, we characterized a subpopulation with high proliferation capacity marked by CD99high in colony-forming unit-erythroid cells. Inhibition of CD99 reduced the proliferation of PSC-derived cells and facilitated erythroid maturation. Furthermore, CD99-CD99 mediated the interaction between macrophages and erythroid cells, illustrating a mechanism by which macrophages participate in erythropoiesis. This study provided a reference for improving ex vivo RBC generation.
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Affiliation(s)
- Xiaoling Wang
- Stem Cell and Regenerative Medicine LabBeijing Institute of Radiation MedicineBeijingPR China
| | - Wei Zhang
- Beijing Institute of Genomics & China National Center for BioinformationChinese Academy of SciencesBeijingPR China
| | - Siqi Zhao
- Beijing Institute of Genomics & China National Center for BioinformationChinese Academy of SciencesBeijingPR China
| | - Hao Yan
- Stem Cell and Regenerative Medicine LabBeijing Institute of Radiation MedicineBeijingPR China
| | - Zijuan Xin
- Beijing Institute of Genomics & China National Center for BioinformationChinese Academy of SciencesBeijingPR China
| | - Tiantian Cui
- Stem Cell and Regenerative Medicine LabBeijing Institute of Radiation MedicineBeijingPR China
| | - Ruge Zang
- Stem Cell and Regenerative Medicine LabBeijing Institute of Radiation MedicineBeijingPR China
| | - Lingping Zhao
- Stem Cell and Regenerative Medicine LabBeijing Institute of Radiation MedicineBeijingPR China
| | - Haiyang Wang
- Stem Cell and Regenerative Medicine LabBeijing Institute of Radiation MedicineBeijingPR China
| | - Junnian Zhou
- Stem Cell and Regenerative Medicine LabBeijing Institute of Radiation MedicineBeijingPR China
| | - Xuan Li
- Stem Cell and Regenerative Medicine LabBeijing Institute of Radiation MedicineBeijingPR China
| | - Wen Yue
- Stem Cell and Regenerative Medicine LabBeijing Institute of Radiation MedicineBeijingPR China
| | - Jiafei Xi
- Stem Cell and Regenerative Medicine LabBeijing Institute of Radiation MedicineBeijingPR China
| | - Zhaojun Zhang
- Beijing Institute of Genomics & China National Center for BioinformationChinese Academy of SciencesBeijingPR China
- Institute for Stem Cell and RegenerationChinese Academy of SciencesBeijingPR China
- Sino‐Danish CollegeUniversity of Chinese Academy of SciencesBeijingPR China
- Beijing Key Laboratory of Genome and Precision Medicine TechnologiesBeijingPR China
| | - Xiangdong Fang
- Beijing Institute of Genomics & China National Center for BioinformationChinese Academy of SciencesBeijingPR China
- Institute for Stem Cell and RegenerationChinese Academy of SciencesBeijingPR China
- Sino‐Danish CollegeUniversity of Chinese Academy of SciencesBeijingPR China
- Beijing Key Laboratory of Genome and Precision Medicine TechnologiesBeijingPR China
- College of Life SciencesUniversity of Chinese Academy of SciencesBeijingPR China
- Savaid Medical SchoolUniversity of Chinese Academy of SciencesBeijingPR China
- School of Future TechnologyUniversity of Chinese Academy of SciencesBeijingPR China
| | - Xuetao Pei
- Stem Cell and Regenerative Medicine LabBeijing Institute of Radiation MedicineBeijingPR China
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17
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Vincelette ND, Yu X, Kuykendall AT, Moon J, Su S, Cheng CH, Sammut R, Razabdouski TN, Nguyen HV, Eksioglu EA, Chan O, Al Ali N, Patel PC, Lee DH, Nakanishi S, Ferreira RB, Hyjek E, Mo Q, Cory S, Lawrence HR, Zhang L, Murphy DJ, Komrokji RS, Lee D, Kaufmann SH, Cleveland JL, Yun S. Trisomy 8 Defines a Distinct Subtype of Myeloproliferative Neoplasms Driven by the MYC-Alarmin Axis. Blood Cancer Discov 2024; 5:276-297. [PMID: 38713018 PMCID: PMC11215389 DOI: 10.1158/2643-3230.bcd-23-0210] [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: 10/09/2023] [Revised: 03/16/2024] [Accepted: 05/06/2024] [Indexed: 05/08/2024] Open
Abstract
Despite advances in understanding the genetic abnormalities in myeloproliferative neoplasms (MPN) and the development of JAK2 inhibitors, there is an urgent need to devise new treatment strategies, particularly for patients with triple-negative (TN) myelofibrosis (MF) who lack mutations in the JAK2 kinase pathway and have very poor clinical outcomes. Here we report that MYC copy number gain and increased MYC expression frequently occur in TN-MF and that MYC-directed activation of S100A9, an alarmin protein that plays pivotal roles in inflammation and innate immunity, is necessary and sufficient to drive development and progression of MF. Notably, the MYC-S100A9 circuit provokes a complex network of inflammatory signaling that involves numerous hematopoietic cell types in the bone marrow microenvironment. Accordingly, genetic ablation of S100A9 or treatment with small molecules targeting the MYC-S100A9 pathway effectively ameliorates MF phenotypes, highlighting the MYC-alarmin axis as a novel therapeutic vulnerability for this subgroup of MPNs. Significance: This study establishes that MYC expression is increased in TN-MPNs via trisomy 8, that a MYC-S100A9 circuit manifest in these cases is sufficient to provoke myelofibrosis and inflammation in diverse hematopoietic cell types in the BM niche, and that the MYC-S100A9 circuit is targetable in TN-MPNs.
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Affiliation(s)
- Nicole D. Vincelette
- Department of Malignant Hematology, Moffitt Cancer Center & Research Institute, Tampa, Florida.
| | - Xiaoqing Yu
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center & Research Institute, Tampa, Florida.
| | - Andrew T. Kuykendall
- Department of Malignant Hematology, Moffitt Cancer Center & Research Institute, Tampa, Florida.
| | - Jungwon Moon
- Department of Malignant Hematology, Moffitt Cancer Center & Research Institute, Tampa, Florida.
| | - Siyuan Su
- Department of Chemistry, University of Illinois Chicago, Chicago, Illinois.
| | - Chia-Ho Cheng
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center & Research Institute, Tampa, Florida.
| | - Rinzine Sammut
- Department of Malignant Hematology, Moffitt Cancer Center & Research Institute, Tampa, Florida.
- Département d’Hématologie Clinique, Centre Hospitalier Universitaire de Nice, Nice, France.
| | - Tiffany N. Razabdouski
- Department of Malignant Hematology, Moffitt Cancer Center & Research Institute, Tampa, Florida.
| | - Hai V. Nguyen
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia.
| | - Erika A. Eksioglu
- Department of Immunology, Moffitt Cancer Center & Research Institute, Tampa, Florida.
| | - Onyee Chan
- Department of Malignant Hematology, Moffitt Cancer Center & Research Institute, Tampa, Florida.
| | - Najla Al Ali
- Department of Malignant Hematology, Moffitt Cancer Center & Research Institute, Tampa, Florida.
| | - Parth C. Patel
- Department of Malignant Hematology, Moffitt Cancer Center & Research Institute, Tampa, Florida.
- Department of Internal Medicine, University of South Florida, Tampa, Florida.
| | - Dae H. Lee
- Division of Cardiovascular Science, Department of Internal Medicine, University of South Florida, Tampa, Florida
| | - Shima Nakanishi
- Department of Tumor Microenvironment & Metastasis, Moffitt Cancer Center & Research Institute, Tampa, Florida.
| | - Renan B. Ferreira
- Department of Drug Discovery, Moffitt Cancer Center & Research Institute, Tampa, Florida.
| | - Elizabeth Hyjek
- Department of Pathology and Laboratory Medicine, Moffitt Cancer Center & Research Institute, Tampa, Florida.
| | - Qianxing Mo
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center & Research Institute, Tampa, Florida.
| | - Suzanne Cory
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia.
| | - Harshani R. Lawrence
- Department of Drug Discovery, Moffitt Cancer Center & Research Institute, Tampa, Florida.
| | - Ling Zhang
- Department of Pathology and Laboratory Medicine, Moffitt Cancer Center & Research Institute, Tampa, Florida.
| | - Daniel J. Murphy
- School of Cancer Sciences, University of Glasgow, Glasgow, United Kingdom.
- Cancer Research UK Scotland Institute, Glasgow, United Kingdom.
| | - Rami S. Komrokji
- Department of Malignant Hematology, Moffitt Cancer Center & Research Institute, Tampa, Florida.
| | - Daesung Lee
- Department of Chemistry, University of Illinois Chicago, Chicago, Illinois.
| | - Scott H. Kaufmann
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota.
- Department of Oncology, Mayo Clinic, Rochester, Minnesota.
| | - John L. Cleveland
- Department of Tumor Microenvironment & Metastasis, Moffitt Cancer Center & Research Institute, Tampa, Florida.
| | - Seongseok Yun
- Department of Malignant Hematology, Moffitt Cancer Center & Research Institute, Tampa, Florida.
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18
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Iskander D, Karadimitris A, Roberts I. Harnessing Single-Cell Technologies in the Search for New Therapies for Diamond-Blackfan Anemia Syndrome. Exp Hematol 2024; 135:104235. [PMID: 38740323 DOI: 10.1016/j.exphem.2024.104235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 05/07/2024] [Accepted: 05/09/2024] [Indexed: 05/16/2024]
Abstract
The emergence of multiomic single-cell technologies over the last decade has led to improved insights into both normal hematopoiesis and its perturbation in a variety of hematological disorders. Diamond-Blackfan anemia (DBA) syndrome is one such disorder where single-cell assays have helped to delineate the cellular and molecular defects underlying the disease. DBA is caused by heterozygous loss-of-function germline variants in genes encoding ribosomal proteins (RPs). Despite the widespread role of ribosomes in hematopoiesis, the most frequent and severe cytopenia in DBA is anemia. In this review we discussed how single-cell studies, including clonogenic cell culture assays, fluorescence-activated cell sorting (FACS) and single-cell RNA sequencing (scRNA-seq), have led to insights into the pathogenesis of DBA. The main therapies are regular blood transfusions, glucocorticoids, or hematopoietic stem cell transplantation (HSCT) but all are associated with significant morbidity and mortality. We will therefore outline how single-cell studies can inform new therapies for DBA. Furthermore, we discussed how DBA serves as a useful model for understanding normal erythropoiesis in terms of its cellular hierarchy, molecular regulation during homeostasis, and response to "stress."
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Affiliation(s)
- Deena Iskander
- Centre for Haematology, Department of Immunology and Inflammation, Imperial College, London, United Kingdom; Department of Paediatric Haematology, St Mary's Hospital, Imperial College Healthcare Trust, London, United Kingdom.
| | - Anastasios Karadimitris
- Centre for Haematology, Department of Immunology and Inflammation, Imperial College, London, United Kingdom
| | - Irene Roberts
- MRC Molecular Haematology Unit, WIMM, University of Oxford, Oxford, United Kingdom; Department of Paediatrics, Children's Hospital and MHU, WIMM, Oxford University and John Radcliffe Hospital, Oxford, United Kingdom
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19
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Otto DJ, Jordan C, Dury B, Dien C, Setty M. Quantifying cell-state densities in single-cell phenotypic landscapes using Mellon. Nat Methods 2024; 21:1185-1195. [PMID: 38890426 DOI: 10.1038/s41592-024-02302-w] [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: 07/11/2023] [Accepted: 05/08/2024] [Indexed: 06/20/2024]
Abstract
Cell-state density characterizes the distribution of cells along phenotypic landscapes and is crucial for unraveling the mechanisms that drive diverse biological processes. Here, we present Mellon, an algorithm for estimation of cell-state densities from high-dimensional representations of single-cell data. We demonstrate Mellon's efficacy by dissecting the density landscape of differentiating systems, revealing a consistent pattern of high-density regions corresponding to major cell types intertwined with low-density, rare transitory states. We present evidence implicating enhancer priming and the activation of master regulators in emergence of these transitory states. Mellon offers the flexibility to perform temporal interpolation of time-series data, providing a detailed view of cell-state dynamics during developmental processes. Mellon facilitates density estimation across various single-cell data modalities, scaling linearly with the number of cells. Our work underscores the importance of cell-state density in understanding the differentiation processes, and the potential of Mellon to provide insights into mechanisms guiding biological trajectories.
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Affiliation(s)
- Dominik J Otto
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Translational Data Science IRC, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Cailin Jordan
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Translational Data Science IRC, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA
| | - Brennan Dury
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Translational Data Science IRC, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Christine Dien
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Translational Data Science IRC, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | - Manu Setty
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
- Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA.
- Translational Data Science IRC, Fred Hutchinson Cancer Center, Seattle, WA, USA.
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20
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Yokomizo T, Oshima M, Iwama A. Epigenetics of hematopoietic stem cell aging. Curr Opin Hematol 2024; 31:207-216. [PMID: 38640057 DOI: 10.1097/moh.0000000000000818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/21/2024]
Abstract
PURPOSE OF REVIEW The development of new antiaging medicines is of great interest to the current elderly and aging population. Aging of the hematopoietic system is attributed to the aging of hematopoietic stem cells (HSCs), and epigenetic alterations are the key effectors driving HSC aging. Understanding the epigenetics of HSC aging holds promise of providing new insights for combating HSC aging and age-related hematological malignancies. RECENT FINDINGS Aging is characterized by the progressive loss of physiological integrity, leading to impaired function and increased vulnerability to death. During aging, the HSCs undergo both quantitative and qualitative changes. These functional changes in HSCs cause dysregulated hematopoiesis, resulting in anemia, immune dysfunction, and an increased risk of hematological malignancies. Various cell-intrinsic and cell-extrinsic effectors influencing HSC aging have also been identified. Epigenetic alterations are one such mechanism. SUMMARY Cumulative epigenetic alterations in aged HSCs affect their fate, leading to aberrant self-renewal, differentiation, and function of aged HSCs. In turn, these factors provide an opportunity for aged HSCs to expand by modulating their self-renewal and differentiation balance, thereby contributing to the development of hematological malignancies.
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Affiliation(s)
- Takako Yokomizo
- Division of Stem Cell and Molecular Medicine, Center for Stem Cell Biology and Regenerative Medicine, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
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21
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Montalban-Bravo G, Thongon N, Rodriguez-Sevilla JJ, Ma F, Ganan-Gomez I, Yang H, Kim YJ, Adema V, Wildeman B, Tanaka T, Darbaniyan F, Al-Atrash G, Dwyer K, Loghavi S, Kanagal-Shamanna R, Song X, Zhang J, Takahashi K, Kantarjian H, Garcia-Manero G, Colla S. Targeting MCL1-driven anti-apoptotic pathways overcomes blast progression after hypomethylating agent failure in chronic myelomonocytic leukemia. Cell Rep Med 2024; 5:101585. [PMID: 38781960 PMCID: PMC11228590 DOI: 10.1016/j.xcrm.2024.101585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 11/27/2023] [Accepted: 04/30/2024] [Indexed: 05/25/2024]
Abstract
RAS pathway mutations, which are present in 30% of patients with chronic myelomonocytic leukemia (CMML) at diagnosis, confer a high risk of resistance to and progression after hypomethylating agent (HMA) therapy, the current standard of care for the disease. Here, using single-cell, multi-omics technologies, we seek to dissect the biological mechanisms underlying the initiation and progression of RAS pathway-mutated CMML. We identify that RAS pathway mutations induce transcriptional reprogramming of hematopoietic stem and progenitor cells (HSPCs) and downstream monocytic populations in response to cell-intrinsic and -extrinsic inflammatory signaling that also impair the functions of immune cells. HSPCs expand at disease progression after therapy with HMA or the BCL2 inhibitor venetoclax and rely on the NF-κB pathway effector MCL1 to maintain survival. Our study has implications for the development of therapies to improve the survival of patients with RAS pathway-mutated CMML.
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MESH Headings
- Leukemia, Myelomonocytic, Chronic/drug therapy
- Leukemia, Myelomonocytic, Chronic/pathology
- Leukemia, Myelomonocytic, Chronic/genetics
- Leukemia, Myelomonocytic, Chronic/metabolism
- Myeloid Cell Leukemia Sequence 1 Protein/metabolism
- Myeloid Cell Leukemia Sequence 1 Protein/genetics
- Myeloid Cell Leukemia Sequence 1 Protein/antagonists & inhibitors
- Humans
- Apoptosis/drug effects
- Animals
- Mutation/genetics
- Mice
- Signal Transduction/drug effects
- Hematopoietic Stem Cells/metabolism
- Hematopoietic Stem Cells/drug effects
- Disease Progression
- Sulfonamides/pharmacology
- Sulfonamides/therapeutic use
- NF-kappa B/metabolism
- DNA Methylation/drug effects
- DNA Methylation/genetics
- Bridged Bicyclo Compounds, Heterocyclic/pharmacology
- Bridged Bicyclo Compounds, Heterocyclic/therapeutic use
- Blast Crisis/pathology
- Blast Crisis/drug therapy
- Blast Crisis/genetics
- Blast Crisis/metabolism
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Affiliation(s)
| | - Natthakan Thongon
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | - Feiyang Ma
- Department of Molecular, Cell and Developmental Biology, University of California Los Angeles, Los Angeles, CA, USA
| | - Irene Ganan-Gomez
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Hui Yang
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yi June Kim
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Vera Adema
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Bethany Wildeman
- Department of Leukemia, 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
| | - Faezeh Darbaniyan
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Gheath Al-Atrash
- Department of Stem Cell Transplantation and Hematopoietic Biology and Malignancy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Karen Dwyer
- Department of Stem Cell Transplantation and Hematopoietic Biology and Malignancy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Sanam Loghavi
- Department of Hematopathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Rashmi Kanagal-Shamanna
- Department of Hematopathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Xingzhi Song
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jianhua Zhang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Koichi Takahashi
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Hagop Kantarjian
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | - Simona Colla
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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22
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Moiani A, Letort G, Lizot S, Chalumeau A, Foray C, Felix T, Le Clerre D, Temburni-Blake S, Hong P, Leduc S, Pinard N, Marechal A, Seclen E, Boyne A, Mayer L, Hong R, Pulicani S, Galetto R, Gouble A, Cavazzana M, Juillerat A, Miccio A, Duclert A, Duchateau P, Valton J. Non-viral DNA delivery and TALEN editing correct the sickle cell mutation in hematopoietic stem cells. Nat Commun 2024; 15:4965. [PMID: 38862518 PMCID: PMC11166989 DOI: 10.1038/s41467-024-49353-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 06/03/2024] [Indexed: 06/13/2024] Open
Abstract
Sickle cell disease is a devastating blood disorder that originates from a single point mutation in the HBB gene coding for hemoglobin. Here, we develop a GMP-compatible TALEN-mediated gene editing process enabling efficient HBB correction via a DNA repair template while minimizing risks associated with HBB inactivation. Comparing viral versus non-viral DNA repair template delivery in hematopoietic stem and progenitor cells in vitro, both strategies achieve comparable HBB correction and result in over 50% expression of normal adult hemoglobin in red blood cells without inducing β-thalassemic phenotype. In an immunodeficient female mouse model, transplanted cells edited with the non-viral strategy exhibit higher engraftment and gene correction levels compared to those edited with the viral strategy. Transcriptomic analysis reveals that non-viral DNA repair template delivery mitigates P53-mediated toxicity and preserves high levels of long-term hematopoietic stem cells. This work paves the way for TALEN-based autologous gene therapy for sickle cell disease.
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Affiliation(s)
| | - Gil Letort
- Cellectis S.A., 8 Rue de la Croix Jarry, Paris, France
| | - Sabrina Lizot
- Cellectis S.A., 8 Rue de la Croix Jarry, Paris, France
| | - Anne Chalumeau
- Université Paris Cité, Imagine Institute, Laboratory of Chromatin and Gene Regulation During Development, INSERM UMR 1163, Paris, France
| | - Chloe Foray
- Cellectis S.A., 8 Rue de la Croix Jarry, Paris, France
| | - Tristan Felix
- Université Paris Cité, Imagine Institute, Laboratory of Chromatin and Gene Regulation During Development, INSERM UMR 1163, Paris, France
| | | | | | - Patrick Hong
- Cellectis Inc., 430 East 29th Street, New York, NY, USA
| | - Sophie Leduc
- Cellectis S.A., 8 Rue de la Croix Jarry, Paris, France
| | - Noemie Pinard
- Cellectis S.A., 8 Rue de la Croix Jarry, Paris, France
| | - Alan Marechal
- Cellectis S.A., 8 Rue de la Croix Jarry, Paris, France
| | | | - Alex Boyne
- Cellectis Inc., 430 East 29th Street, New York, NY, USA
| | - Louisa Mayer
- Cellectis Inc., 430 East 29th Street, New York, NY, USA
| | - Robert Hong
- Cellectis Inc., 430 East 29th Street, New York, NY, USA
| | | | - Roman Galetto
- Cellectis S.A., 8 Rue de la Croix Jarry, Paris, France
| | - Agnès Gouble
- Cellectis S.A., 8 Rue de la Croix Jarry, Paris, France
| | - Marina Cavazzana
- Biotherapy Clinical Investigation Center, Necker Children's Hospital, Assistance Publique Hopitaux de Paris, Paris, France
- Human Lymphohematopoiesis Laboratory, Imagine Institute, INSERM UMR1163, Paris Cité University, Paris, France
- Biotherapy Department, Necker Children's Hospital, Assistance Publique Hopitaux de Paris, Paris, France
| | | | - Annarita Miccio
- Université Paris Cité, Imagine Institute, Laboratory of Chromatin and Gene Regulation During Development, INSERM UMR 1163, Paris, France
| | | | | | - Julien Valton
- Cellectis S.A., 8 Rue de la Croix Jarry, Paris, France.
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23
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Jindal K, Adil MT, Yamaguchi N, Yang X, Wang HC, Kamimoto K, Rivera-Gonzalez GC, Morris SA. Single-cell lineage capture across genomic modalities with CellTag-multi reveals fate-specific gene regulatory changes. Nat Biotechnol 2024; 42:946-959. [PMID: 37749269 PMCID: PMC11180607 DOI: 10.1038/s41587-023-01931-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 07/31/2023] [Indexed: 09/27/2023]
Abstract
Complex gene regulatory mechanisms underlie differentiation and reprogramming. Contemporary single-cell lineage-tracing (scLT) methods use expressed, heritable DNA barcodes to combine cell lineage readout with single-cell transcriptomics. However, reliance on transcriptional profiling limits adaptation to other single-cell assays. With CellTag-multi, we present an approach that enables direct capture of heritable random barcodes expressed as polyadenylated transcripts, in both single-cell RNA sequencing and single-cell Assay for Transposase Accessible Chromatin using sequencing assays, allowing for independent clonal tracking of transcriptional and epigenomic cell states. We validate CellTag-multi to characterize progenitor cell lineage priming during mouse hematopoiesis. Additionally, in direct reprogramming of fibroblasts to endoderm progenitors, we identify core regulatory programs underlying on-target and off-target fates. Furthermore, we reveal the transcription factor Zfp281 as a regulator of reprogramming outcome, biasing cells toward an off-target mesenchymal fate. Our results establish CellTag-multi as a lineage-tracing method compatible with multiple single-cell modalities and demonstrate its utility in revealing fate-specifying gene regulatory changes across diverse paradigms of differentiation and reprogramming.
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Affiliation(s)
- Kunal Jindal
- Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Mohd Tayyab Adil
- Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Naoto Yamaguchi
- Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Xue Yang
- Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Helen C Wang
- Department of Pediatrics, Division of Hematology and Oncology, Washington University School of Medicine, St. Louis, MO, USA
| | - Kenji Kamimoto
- Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Guillermo C Rivera-Gonzalez
- Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Samantha A Morris
- Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, USA.
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA.
- Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO, USA.
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24
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Chen H, Ryu J, Vinyard ME, Lerer A, Pinello L. SIMBA: single-cell embedding along with features. Nat Methods 2024; 21:1003-1013. [PMID: 37248389 PMCID: PMC11166568 DOI: 10.1038/s41592-023-01899-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 04/26/2023] [Indexed: 05/31/2023]
Abstract
Most current single-cell analysis pipelines are limited to cell embeddings and rely heavily on clustering, while lacking the ability to explicitly model interactions between different feature types. Furthermore, these methods are tailored to specific tasks, as distinct single-cell problems are formulated differently. To address these shortcomings, here we present SIMBA, a graph embedding method that jointly embeds single cells and their defining features, such as genes, chromatin-accessible regions and DNA sequences, into a common latent space. By leveraging the co-embedding of cells and features, SIMBA allows for the study of cellular heterogeneity, clustering-free marker discovery, gene regulation inference, batch effect removal and omics data integration. We show that SIMBA provides a single framework that allows diverse single-cell problems to be formulated in a unified way and thus simplifies the development of new analyses and extension to new single-cell modalities. SIMBA is implemented as a comprehensive Python library ( https://simba-bio.readthedocs.io ).
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Affiliation(s)
- Huidong Chen
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Pathology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Jayoung Ryu
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Pathology, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Michael E Vinyard
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Pathology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
| | - Adam Lerer
- Facebook AI Research, New York, NY, USA.
| | - Luca Pinello
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA.
- Department of Pathology, Harvard Medical School, Boston, MA, USA.
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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25
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Tayyebi Z, Pine AR, Leslie CS. Scalable and unbiased sequence-informed embedding of single-cell ATAC-seq data with CellSpace. Nat Methods 2024; 21:1014-1022. [PMID: 38724693 PMCID: PMC11166566 DOI: 10.1038/s41592-024-02274-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 04/11/2024] [Indexed: 06/13/2024]
Abstract
Standard scATAC sequencing (scATAC-seq) analysis pipelines represent cells as sparse numeric vectors relative to an atlas of peaks or genomic tiles and consequently ignore genomic sequence information at accessible loci. Here we present CellSpace, an efficient and scalable sequence-informed embedding algorithm for scATAC-seq that learns a mapping of DNA k-mers and cells to the same space, to address this limitation. We show that CellSpace captures meaningful latent structure in scATAC-seq datasets, including cell subpopulations and developmental hierarchies, and can score transcription factor activities in single cells based on proximity to binding motifs embedded in the same space. Importantly, CellSpace implicitly mitigates batch effects arising from multiple samples, donors or assays, even when individual datasets are processed relative to different peak atlases. Thus, CellSpace provides a powerful tool for integrating and interpreting large-scale scATAC-seq compendia.
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Affiliation(s)
- Zakieh Tayyebi
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY, USA
| | - Allison R Pine
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY, USA
| | - Christina S Leslie
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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26
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Yuan Y, Chen Q, Brovkina M, Clowney EJ, Yadlapalli S. Clock-dependent chromatin accessibility rhythms regulate circadian transcription. PLoS Genet 2024; 20:e1011278. [PMID: 38805552 PMCID: PMC11161047 DOI: 10.1371/journal.pgen.1011278] [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: 09/22/2023] [Revised: 06/07/2024] [Accepted: 04/29/2024] [Indexed: 05/30/2024] Open
Abstract
Chromatin organization plays a crucial role in gene regulation by controlling the accessibility of DNA to transcription machinery. While significant progress has been made in understanding the regulatory role of clock proteins in circadian rhythms, how chromatin organization affects circadian rhythms remains poorly understood. Here, we employed ATAC-seq (Assay for Transposase-Accessible Chromatin with Sequencing) on FAC-sorted Drosophila clock neurons to assess genome-wide chromatin accessibility at dawn and dusk over the circadian cycle. We observed significant oscillations in chromatin accessibility at promoter and enhancer regions of hundreds of genes, with enhanced accessibility either at dusk or dawn, which correlated with their peak transcriptional activity. Notably, genes with enhanced accessibility at dusk were enriched with E-box motifs, while those more accessible at dawn were enriched with VRI/PDP1-box motifs, indicating that they are regulated by the core circadian feedback loops, PER/CLK and VRI/PDP1, respectively. Further, we observed a complete loss of chromatin accessibility rhythms in per01 null mutants, with chromatin consistently accessible at both dawn and dusk, underscoring the critical role of Period protein in driving chromatin compaction during the repression phase at dawn. Together, this study demonstrates the significant role of chromatin organization in circadian regulation, revealing how the interplay between clock proteins and chromatin structure orchestrates the precise timing of biological processes throughout the day. This work further implies that variations in chromatin accessibility might play a central role in the generation of diverse circadian gene expression patterns in clock neurons.
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Affiliation(s)
- Ye Yuan
- Cellular and Molecular Biology Program, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Qianqian Chen
- Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Margarita Brovkina
- Cellular and Molecular Biology Program, University of Michigan, Ann Arbor, Michigan, United States of America
| | - E Josephine Clowney
- Cellular and Molecular Biology Program, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Molecular, Cellular and Developmental Biology, University of Michigan, Ann Arbor, Michigan, United States of America
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Swathi Yadlapalli
- Cellular and Molecular Biology Program, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, Michigan, United States of America
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, Michigan, United States of America
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27
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Gao Z, Chen X, Li Z, Cui X, Jiang Q, Li K, Chen S, Jiang R. scEpiTools: a database to comprehensively interrogate analytic tools for single-cell epigenomic data. J Genet Genomics 2024; 51:462-465. [PMID: 37769837 DOI: 10.1016/j.jgg.2023.09.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 09/03/2023] [Accepted: 09/19/2023] [Indexed: 10/03/2023]
Affiliation(s)
- Zijing Gao
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Xiaoyang Chen
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Zhen Li
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Xuejian Cui
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Qun Jiang
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Keyi Li
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Shengquan Chen
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China.
| | - Rui Jiang
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China.
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28
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Malekpour SA, Haghverdi L, Sadeghi M. Single-cell multi-omics analysis identifies context-specific gene regulatory gates and mechanisms. Brief Bioinform 2024; 25:bbae180. [PMID: 38653489 PMCID: PMC11036345 DOI: 10.1093/bib/bbae180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 01/29/2024] [Accepted: 04/02/2024] [Indexed: 04/25/2024] Open
Abstract
There is a growing interest in inferring context specific gene regulatory networks from single-cell RNA sequencing (scRNA-seq) data. This involves identifying the regulatory relationships between transcription factors (TFs) and genes in individual cells, and then characterizing these relationships at the level of specific cell types or cell states. In this study, we introduce scGATE (single-cell gene regulatory gate) as a novel computational tool for inferring TF-gene interaction networks and reconstructing Boolean logic gates involving regulatory TFs using scRNA-seq data. In contrast to current Boolean models, scGATE eliminates the need for individual formulations and likelihood calculations for each Boolean rule (e.g. AND, OR, XOR). By employing a Bayesian framework, scGATE infers the Boolean rule after fitting the model to the data, resulting in significant reductions in time-complexities for logic-based studies. We have applied assay for transposase-accessible chromatin with sequencing (scATAC-seq) data and TF DNA binding motifs to filter out non-relevant TFs in gene regulations. By integrating single-cell clustering with these external cues, scGATE is able to infer context specific networks. The performance of scGATE is evaluated using synthetic and real single-cell multi-omics data from mouse tissues and human blood, demonstrating its superiority over existing tools for reconstructing TF-gene networks. Additionally, scGATE provides a flexible framework for understanding the complex combinatorial and cooperative relationships among TFs regulating target genes by inferring Boolean logic gates among them.
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Affiliation(s)
- Seyed Amir Malekpour
- School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), 19395-5746, Tehran, Iran
| | - Laleh Haghverdi
- Berlin Institute for Medical Systems Biology, Max Delbrück Center (BIMSB-MDC) in the Helmholtz Association, Berlin, Germany
| | - Mehdi Sadeghi
- Department of Medical Genetics, National Institute of Genetic Engineering and Biotechnology, 1497716316, Tehran, Iran
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29
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Rodriguez-Sevilla JJ, Ganan-Gomez I, Ma F, Chien K, Del Rey M, Loghavi S, Montalban-Bravo G, Adema V, Wildeman B, Kanagal-Shamanna R, Bazinet A, Chifotides HT, Thongon N, Calvo X, Hernández-Rivas JM, Díez-Campelo M, Garcia-Manero G, Colla S. Hematopoietic stem cells with granulo-monocytic differentiation state overcome venetoclax sensitivity in patients with myelodysplastic syndromes. Nat Commun 2024; 15:2428. [PMID: 38499526 PMCID: PMC10948794 DOI: 10.1038/s41467-024-46424-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 02/09/2024] [Indexed: 03/20/2024] Open
Abstract
The molecular mechanisms of venetoclax-based therapy failure in patients with acute myeloid leukemia were recently clarified, but the mechanisms by which patients with myelodysplastic syndromes (MDS) acquire secondary resistance to venetoclax after an initial response remain to be elucidated. Here, we show an expansion of MDS hematopoietic stem cells (HSCs) with a granulo-monocytic-biased transcriptional differentiation state in MDS patients who initially responded to venetoclax but eventually relapsed. While MDS HSCs in an undifferentiated cellular state are sensitive to venetoclax treatment, differentiation towards a granulo-monocytic-biased transcriptional state, through the acquisition or expansion of clones with STAG2 or RUNX1 mutations, affects HSCs' survival dependence from BCL2-mediated anti-apoptotic pathways to TNFα-induced pro-survival NF-κB signaling and drives resistance to venetoclax-mediated cytotoxicity. Our findings reveal how hematopoietic stem and progenitor cell (HSPC) can eventually overcome therapy-induced depletion and underscore the importance of using close molecular monitoring to prevent HSPC hierarchical change in MDS patients enrolled in clinical trials of venetoclax.
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Affiliation(s)
| | - Irene Ganan-Gomez
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Feiyang Ma
- Department of Molecular, Cell and Developmental Biology, University of California Los Angeles, Los Angeles, CA, USA
| | - Kelly Chien
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Monica Del Rey
- Hematology Department, University Hospital of Salamanca, IBSAL Cancer Center, Salamanca, Spain
| | - Sanam Loghavi
- Department of Hematopathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | - Vera Adema
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Bethany Wildeman
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Rashmi Kanagal-Shamanna
- Department of Hematopathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Alexandre Bazinet
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Helen T Chifotides
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Natthakan Thongon
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Xavier Calvo
- Laboratori de Citologia Hematològica, Servei de Patologia, Grup de Recerca Translacional en Neoplàsies Hematològiques (GRETNHE), Hospital del Mar Research Institute (IMIM), Barcelona, Spain
| | | | - Maria Díez-Campelo
- Hematology Department, University Hospital of Salamanca, IBSAL Cancer Center, Salamanca, Spain
| | | | - Simona Colla
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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30
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Goshisht MK. Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges. ACS OMEGA 2024; 9:9921-9945. [PMID: 38463314 PMCID: PMC10918679 DOI: 10.1021/acsomega.3c05913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 01/19/2024] [Accepted: 01/30/2024] [Indexed: 03/12/2024]
Abstract
Machine learning (ML), particularly deep learning (DL), has made rapid and substantial progress in synthetic biology in recent years. Biotechnological applications of biosystems, including pathways, enzymes, and whole cells, are being probed frequently with time. The intricacy and interconnectedness of biosystems make it challenging to design them with the desired properties. ML and DL have a synergy with synthetic biology. Synthetic biology can be employed to produce large data sets for training models (for instance, by utilizing DNA synthesis), and ML/DL models can be employed to inform design (for example, by generating new parts or advising unrivaled experiments to perform). This potential has recently been brought to light by research at the intersection of engineering biology and ML/DL through achievements like the design of novel biological components, best experimental design, automated analysis of microscopy data, protein structure prediction, and biomolecular implementations of ANNs (Artificial Neural Networks). I have divided this review into three sections. In the first section, I describe predictive potential and basics of ML along with myriad applications in synthetic biology, especially in engineering cells, activity of proteins, and metabolic pathways. In the second section, I describe fundamental DL architectures and their applications in synthetic biology. Finally, I describe different challenges causing hurdles in the progress of ML/DL and synthetic biology along with their solutions.
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Affiliation(s)
- Manoj Kumar Goshisht
- Department of Chemistry, Natural and
Applied Sciences, University of Wisconsin—Green
Bay, Green
Bay, Wisconsin 54311-7001, United States
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31
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Gong M, Yu Y, Wang Z, Zhang J, Wang X, Fu C, Zhang Y, Wang X. scAuto as a comprehensive framework for single-cell chromatin accessibility data analysis. Comput Biol Med 2024; 171:108230. [PMID: 38442554 DOI: 10.1016/j.compbiomed.2024.108230] [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: 10/11/2023] [Revised: 02/06/2024] [Accepted: 02/25/2024] [Indexed: 03/07/2024]
Abstract
Interpreting single-cell chromatin accessibility data is crucial for understanding intercellular heterogeneity regulation. Despite the progress in computational methods for analyzing this data, there is still a lack of a comprehensive analytical framework and a user-friendly online analysis tool. To fill this gap, we developed a pre-trained deep learning-based framework, single-cell auto-correlation transformers (scAuto), to overcome the challenge. Following DNABERT's methodology of pre-training and fine-tuning, scAuto learns a general understanding of DNA sequence's grammar by being pre-trained on unlabeled human genome via self-supervision; it is then transferred to the single-cell chromatin accessibility analysis task of scATAC-seq data for supervised fine-tuning. We extensively validated scAuto on the Buenrostro2018 dataset, demonstrating its superior performance on chromatin accessibility prediction, single-cell clustering, and data denoising. Based on scAuto, we further developed an interactive web server for single-cell chromatin accessibility data analysis. It integrates tutorial-style interfaces for those with limited programming skills. The platform is accessible at http://zhanglab.icaup.cn. To our knowledge, this work is expected to help analyze single-cell chromatin accessibility data and facilitate the development of precision medicine.
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Affiliation(s)
- Meiqin Gong
- Department of Obstetrics and Gynecology, West China Second University Hospital, Sichuan University, Chengdu, 610041, China
| | - Yun Yu
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
| | - Zixuan Wang
- College of Electronics and information Engineering, SiChuan University, Chengdu, 610065, China
| | - Junming Zhang
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
| | - Xiongyi Wang
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
| | - Cheng Fu
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
| | - Yongqing Zhang
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
| | - Xiaodong Wang
- Department of Obstetrics and Gynecology, West China Second University Hospital, Sichuan University, Chengdu, 610041, China.
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32
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Tang S, Cui X, Wang R, Li S, Li S, Huang X, Chen S. scCASE: accurate and interpretable enhancement for single-cell chromatin accessibility sequencing data. Nat Commun 2024; 15:1629. [PMID: 38388573 PMCID: PMC10884038 DOI: 10.1038/s41467-024-46045-w] [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: 09/21/2023] [Accepted: 02/12/2024] [Indexed: 02/24/2024] Open
Abstract
Single-cell chromatin accessibility sequencing (scCAS) has emerged as a valuable tool for interrogating and elucidating epigenomic heterogeneity and gene regulation. However, scCAS data inherently suffers from limitations such as high sparsity and dimensionality, which pose significant challenges for downstream analyses. Although several methods are proposed to enhance scCAS data, there are still challenges and limitations that hinder the effectiveness of these methods. Here, we propose scCASE, a scCAS data enhancement method based on non-negative matrix factorization which incorporates an iteratively updating cell-to-cell similarity matrix. Through comprehensive experiments on multiple datasets, we demonstrate the advantages of scCASE over existing methods for scCAS data enhancement. The interpretable cell type-specific peaks identified by scCASE can provide valuable biological insights into cell subpopulations. Moreover, to leverage the large compendia of available omics data as a reference, we further expand scCASE to scCASER, which enables the incorporation of external reference data to improve enhancement performance.
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Affiliation(s)
- Songming Tang
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, 300071, China
| | - Xuejian Cui
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division of BNRIST, Department of Automation, Tsinghua University, 100084, Beijing, China
| | - Rongxiang Wang
- Department of Computer Science, University of Virginia, Charlottesville, VA, 22903, USA
| | - Sijie Li
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, 300071, China
| | - Siyu Li
- School of Statistics and Data Science, Nankai University, Tianjin, 300071, China
| | - Xin Huang
- Beijing Key Laboratory for Radiobiology, Department of Radiation Biology, Beijing Institute of Radiation Medicine, 100850, Beijing, China
| | - Shengquan Chen
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, 300071, China.
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33
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Martini L, Bardini R, Savino A, Di Carlo S. Cross-Omic Transcription Factor Analysis: An Insight on Transcription Factor Accessibility and Expression Correlation. Genes (Basel) 2024; 15:268. [PMID: 38540327 PMCID: PMC10970009 DOI: 10.3390/genes15030268] [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: 01/16/2024] [Revised: 02/13/2024] [Accepted: 02/17/2024] [Indexed: 06/15/2024] Open
Abstract
It is well known how sequencing technologies propelled cellular biology research in recent years, providing incredible insight into the basic mechanisms of cells. Single-cell RNA sequencing is at the front in this field, with single-cell ATAC sequencing supporting it and becoming more popular. In this regard, multi-modal technologies play a crucial role, allowing the possibility to simultaneously perform the mentioned sequencing modalities on the same cells. Yet, there still needs to be a clear and dedicated way to analyze these multi-modal data. One of the current methods is to calculate the Gene Activity Matrix (GAM), which summarizes the accessibility of the genes at the genomic level, to have a more direct link with the transcriptomic data. However, this concept is not well defined, and it is unclear how various accessible regions impact the expression of the genes. Moreover, the transcription process is highly regulated by the transcription factors that bind to the different DNA regions. Therefore, this work presents a continuation of the meta-analysis of Genomic-Annotated Gene Activity Matrix (GAGAM) contributions, aiming to investigate the correlation between the TF expression and motif information in the different functional genomic regions to understand the different Transcription Factors (TFs) dynamics involved in different cell types.
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Affiliation(s)
| | | | | | - Stefano Di Carlo
- Control and Computer Engineering Department, Politecnico di Torino, 10129 Torino, Italy; (L.M.); (R.B.); (A.S.)
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34
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Glass DS, Bren A, Vaisbourd E, Mayo A, Alon U. A synthetic differentiation circuit in Escherichia coli for suppressing mutant takeover. Cell 2024; 187:931-944.e12. [PMID: 38320549 PMCID: PMC10882425 DOI: 10.1016/j.cell.2024.01.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 11/27/2023] [Accepted: 01/16/2024] [Indexed: 02/08/2024]
Abstract
Differentiation is crucial for multicellularity. However, it is inherently susceptible to mutant cells that fail to differentiate. These mutants outcompete normal cells by excessive self-renewal. It remains unclear what mechanisms can resist such mutant expansion. Here, we demonstrate a solution by engineering a synthetic differentiation circuit in Escherichia coli that selects against these mutants via a biphasic fitness strategy. The circuit provides tunable production of synthetic analogs of stem, progenitor, and differentiated cells. It resists mutations by coupling differentiation to the production of an essential enzyme, thereby disadvantaging non-differentiating mutants. The circuit selected for and maintained a positive differentiation rate in long-term evolution. Surprisingly, this rate remained constant across vast changes in growth conditions. We found that transit-amplifying cells (fast-growing progenitors) underlie this environmental robustness. Our results provide insight into the stability of differentiation and demonstrate a powerful method for engineering evolutionarily stable multicellular consortia.
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Affiliation(s)
- David S Glass
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 76100, Israel.
| | - Anat Bren
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Elizabeth Vaisbourd
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Avi Mayo
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Uri Alon
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 76100, Israel.
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35
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Sun H, Qu H, Duan K, Du W. scMGCN: A Multi-View Graph Convolutional Network for Cell Type Identification in scRNA-seq Data. Int J Mol Sci 2024; 25:2234. [PMID: 38396909 PMCID: PMC10889820 DOI: 10.3390/ijms25042234] [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: 02/07/2024] [Accepted: 02/09/2024] [Indexed: 02/25/2024] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) data reveal the complexity and diversity of cellular ecosystems and molecular interactions in various biomedical research. Hence, identifying cell types from large-scale scRNA-seq data using existing annotations is challenging and requires stable and interpretable methods. However, the current cell type identification methods have limited performance, mainly due to the intrinsic heterogeneity among cell populations and extrinsic differences between datasets. Here, we present a robust graph artificial intelligence model, a multi-view graph convolutional network model (scMGCN) that integrates multiple graph structures from raw scRNA-seq data and applies graph convolutional networks with attention mechanisms to learn cell embeddings and predict cell labels. We evaluate our model on single-dataset, cross-species, and cross-platform experiments and compare it with other state-of-the-art methods. Our results show that scMGCN outperforms the other methods regarding stability, accuracy, and robustness to batch effects. Our main contributions are as follows: Firstly, we introduce multi-view learning and multiple graph construction methods to capture comprehensive cellular information from scRNA-seq data. Secondly, we construct a scMGCN that combines graph convolutional networks with attention mechanisms to extract shared, high-order information from cells. Finally, we demonstrate the effectiveness and superiority of the scMGCN on various datasets.
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Affiliation(s)
| | | | | | - Wei Du
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China; (H.S.); (H.Q.); (K.D.)
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36
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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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37
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Zhang K, Zemke NR, Armand EJ, Ren B. A fast, scalable and versatile tool for analysis of single-cell omics data. Nat Methods 2024; 21:217-227. [PMID: 38191932 PMCID: PMC10864184 DOI: 10.1038/s41592-023-02139-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 11/23/2023] [Indexed: 01/10/2024]
Abstract
Single-cell omics technologies have revolutionized the study of gene regulation in complex tissues. A major computational challenge in analyzing these datasets is to project the large-scale and high-dimensional data into low-dimensional space while retaining the relative relationships between cells. This low dimension embedding is necessary to decompose cellular heterogeneity and reconstruct cell-type-specific gene regulatory programs. Traditional dimensionality reduction techniques, however, face challenges in computational efficiency and in comprehensively addressing cellular diversity across varied molecular modalities. Here we introduce a nonlinear dimensionality reduction algorithm, embodied in the Python package SnapATAC2, which not only achieves a more precise capture of single-cell omics data heterogeneities but also ensures efficient runtime and memory usage, scaling linearly with the number of cells. Our algorithm demonstrates exceptional performance, scalability and versatility across diverse single-cell omics datasets, including single-cell assay for transposase-accessible chromatin using sequencing, single-cell RNA sequencing, single-cell Hi-C and single-cell multi-omics datasets, underscoring its utility in advancing single-cell analysis.
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Affiliation(s)
- Kai Zhang
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Westlake Laboratory of Life Sciences and Biomedicine, School of Life Sciences, Westlake University, Hangzhou, China
| | - Nathan R Zemke
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Center for Epigenomics, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - Ethan J Armand
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA
| | - Bing Ren
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA.
- Center for Epigenomics, University of California, San Diego School of Medicine, La Jolla, CA, USA.
- Ludwig Institute for Cancer Research, La Jolla, CA, USA.
- Institute for Genomic Medicine, University of California, San Diego, La Jolla, CA, USA.
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38
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Rueda AD, Salvador-Martínez I, Sospedra-Arrufat I, Alcaina-Caro A, Fernández-Miñán A, Burgos-Ruiz AM, Cases I, Mohedano A, Tena JJ, Heyn H, Lopez-Rios J, Nusspaumer G. The cellular landscape of the endochondral bone during the transition to extrauterine life. Immunol Cell Biol 2024; 102:131-148. [PMID: 38184783 DOI: 10.1111/imcb.12718] [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: 11/22/2023] [Revised: 12/09/2023] [Accepted: 12/12/2023] [Indexed: 01/08/2024]
Abstract
The cellular complexity of the endochondral bone underlies its essential and pleiotropic roles during organismal life. While the adult bone has received significant attention, we still lack a deep understanding of the perinatal bone cellulome. Here, we have profiled the full composition of the murine endochondral bone at the single-cell level during the transition from fetal to newborn life and in comparison with the adult tissue, with particular emphasis on the mesenchymal compartment. The perinatal bone contains different fibroblastic clusters with blastema-like characteristics in organizing and supporting skeletogenesis, angiogenesis and hematopoiesis. Our data also suggest dynamic inter- and intra-compartment interactions, as well as a bone marrow milieu that seems prone to anti-inflammation, which we hypothesize is necessary to ensure the proper program of lymphopoiesis and the establishment of central and peripheral tolerance in early life. Our study provides an integrative roadmap for the future design of genetic and cellular functional assays to validate cellular interactions and lineage relationships within the perinatal bone.
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Affiliation(s)
- Alejandro Díaz Rueda
- Centro Andaluz de Biología del Desarrollo (CABD), CSIC-Universidad Pablo de Olavide-Junta de Andalucía, Seville, Spain
| | - Irepan Salvador-Martínez
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Ismael Sospedra-Arrufat
- Centro Andaluz de Biología del Desarrollo (CABD), CSIC-Universidad Pablo de Olavide-Junta de Andalucía, Seville, Spain
| | - Ana Alcaina-Caro
- Centro Andaluz de Biología del Desarrollo (CABD), CSIC-Universidad Pablo de Olavide-Junta de Andalucía, Seville, Spain
| | - Ana Fernández-Miñán
- Centro Andaluz de Biología del Desarrollo (CABD), CSIC-Universidad Pablo de Olavide-Junta de Andalucía, Seville, Spain
| | - Ana M Burgos-Ruiz
- Centro Andaluz de Biología del Desarrollo (CABD), CSIC-Universidad Pablo de Olavide-Junta de Andalucía, Seville, Spain
| | - Ildefonso Cases
- Centro Andaluz de Biología del Desarrollo (CABD), CSIC-Universidad Pablo de Olavide-Junta de Andalucía, Seville, Spain
| | - Alberto Mohedano
- Intensive Care Unit, Severo Ochoa University Hospital Leganés, Madrid, Spain
| | - Juan J Tena
- Centro Andaluz de Biología del Desarrollo (CABD), CSIC-Universidad Pablo de Olavide-Junta de Andalucía, Seville, Spain
| | - Holger Heyn
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Javier Lopez-Rios
- Centro Andaluz de Biología del Desarrollo (CABD), CSIC-Universidad Pablo de Olavide-Junta de Andalucía, Seville, Spain
- Universidad Loyola Andalucía, School of Health Sciences, Dos Hermanas, Seville, Spain
| | - Gretel Nusspaumer
- Centro Andaluz de Biología del Desarrollo (CABD), CSIC-Universidad Pablo de Olavide-Junta de Andalucía, Seville, Spain
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Ueda K, Ikeda K. Cellular carcinogenesis in preleukemic conditions:drivers and defenses. Fukushima J Med Sci 2024; 70:11-24. [PMID: 37952978 PMCID: PMC10867434 DOI: 10.5387/fms.2023-17] [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: 04/18/2023] [Accepted: 09/26/2023] [Indexed: 11/14/2023] Open
Abstract
Acute myeloid leukemia (AML) arises from preleukemic conditions. We have investigated the pathogenesis of typical preleukemia, myeloproliferative neoplasms, and clonal hematopoiesis. Hematopoietic stem cells in both preleukemic conditions harbor recurrent driver mutations; additional mutation provokes further malignant transformation, leading to AML onset. Although genetic alterations are defined as the main cause of malignant transformation, non-genetic factors are also involved in disease progression. In this review, we focus on a non-histone chromatin protein, high mobility group AT-hook2 (HMGA2), and a physiological p53 inhibitor, murine double minute X (MDMX). HMGA2 is mainly overexpressed by dysregulation of microRNAs or mutations in polycomb components, and provokes expansion of preleukemic clones through stem cell signature disruption. MDMX is overexpressed by altered splicing balance in myeloid malignancies. MDMX induces leukemic transformation from preleukemia via suppression of p53 and p53-independent activation of WNT/β-catenin signaling. We also discuss how these non-genetic factors can be targeted for leukemia prevention therapy.
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Affiliation(s)
- Koki Ueda
- Department of Blood Transfusion and Transplantation Immunology, Fukushima Medical University
| | - Kazuhiko Ikeda
- Department of Blood Transfusion and Transplantation Immunology, Fukushima Medical University
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40
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Calderon A, Mestvirishvili T, Boccalatte F, Ruggles KV, David G. Chromatin accessibility and cell cycle progression are controlled by the HDAC-associated Sin3B protein in murine hematopoietic stem cells. Epigenetics Chromatin 2024; 17:2. [PMID: 38254205 PMCID: PMC10804615 DOI: 10.1186/s13072-024-00526-w] [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: 11/07/2023] [Accepted: 01/09/2024] [Indexed: 01/24/2024] Open
Abstract
BACKGROUND Blood homeostasis requires the daily production of millions of terminally differentiated effector cells that all originate from hematopoietic stem cells (HSCs). HSCs are rare and exhibit unique self-renewal and multipotent properties, which depend on their ability to maintain quiescence through ill-defined processes. Defective control of cell cycle progression can eventually lead to bone marrow failure or malignancy. In particular, the molecular mechanisms tying cell cycle re-entry to cell fate commitment in HSCs remain elusive. Previous studies have identified chromatin coordination as a key regulator of differentiation in embryonic stem cells. RESULTS Here, we utilized genetic inactivation of the chromatin-associated Sin3B protein to manipulate cell cycle control and found dysregulated chromatin accessibility and cell cycle progression in HSCs. Single cell transcriptional profiling of hematopoietic stem and progenitor cells (HSPCs) inactivated for Sin3B reveals aberrant progression through the G1 phase of the cell cycle, which correlates with the engagement of specific signaling pathways, including aberrant expression of cell adhesion molecules and the interferon signaling program in LT-HSCs. In addition, we uncover the Sin3B-dependent accessibility of genomic elements controlling HSC differentiation, which points to cell cycle progression possibly dictating the priming of HSCs for differentiation. CONCLUSIONS Our findings provide new insights into controlled cell cycle progression as a potential regulator of HSC lineage commitment through the modulation of chromatin features.
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Affiliation(s)
- Alexander Calderon
- Department of Biochemistry and Molecular Pharmacology, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, 10016, USA
- Perlmutter Cancer Center, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, 10016, USA
| | - Tamara Mestvirishvili
- Department of Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, 10016, USA
| | - Francesco Boccalatte
- Department of Pathology, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, 10016, USA
| | - Kelly V Ruggles
- Department of Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, 10016, USA
| | - Gregory David
- Department of Biochemistry and Molecular Pharmacology, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, 10016, USA.
- Perlmutter Cancer Center, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, 10016, USA.
- Department of Urology, New York University Grossman School of Medicine, NYU Langone Health, New York, NY, 10016, USA.
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41
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Yu J, Leng J, Hou Z, Sun D, Wu LY. Incorporating network diffusion and peak location information for better single-cell ATAC-seq data analysis. Brief Bioinform 2024; 25:bbae093. [PMID: 38493346 PMCID: PMC10944575 DOI: 10.1093/bib/bbae093] [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: 09/21/2023] [Revised: 12/22/2023] [Accepted: 02/20/2024] [Indexed: 03/18/2024] Open
Abstract
Single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) data provided new insights into the understanding of epigenetic heterogeneity and transcriptional regulation. With the increasing abundance of dataset resources, there is an urgent need to extract more useful information through high-quality data analysis methods specifically designed for scATAC-seq. However, analyzing scATAC-seq data poses challenges due to its near binarization, high sparsity and ultra-high dimensionality properties. Here, we proposed a novel network diffusion-based computational method to comprehensively analyze scATAC-seq data, named Single-Cell ATAC-seq Analysis via Network Refinement with Peaks Location Information (SCARP). SCARP formulates the Network Refinement diffusion method under the graph theory framework to aggregate information from different network orders, effectively compensating for missing signals in the scATAC-seq data. By incorporating distance information between adjacent peaks on the genome, SCARP also contributes to depicting the co-accessibility of peaks. These two innovations empower SCARP to obtain lower-dimensional representations for both cells and peaks more effectively. We have demonstrated through sufficient experiments that SCARP facilitated superior analyses of scATAC-seq data. Specifically, SCARP exhibited outstanding cell clustering performance, enabling better elucidation of cell heterogeneity and the discovery of new biologically significant cell subpopulations. Additionally, SCARP was also instrumental in portraying co-accessibility relationships of accessible regions and providing new insight into transcriptional regulation. Consequently, SCARP identified genes that were involved in key Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to diseases and predicted reliable cis-regulatory interactions. To sum up, our studies suggested that SCARP is a promising tool to comprehensively analyze the scATAC-seq data.
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Affiliation(s)
- Jiating Yu
- School of Mathematics and Statistics, Nanjing University of Information Science & Technology, Nanjing 210044, China
- IAM, MADIS, NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jiacheng Leng
- IAM, MADIS, NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
- Zhejiang Lab, Hangzhou 311121, China
| | - Zhichao Hou
- IAM, MADIS, NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Duanchen Sun
- School of Mathematics, Shandong University, Jinan 250100, China
| | - Ling-Yun Wu
- IAM, MADIS, NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
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42
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Mancheno-Ferris A, Immarigeon C, Rivero A, Depierre D, Schickele N, Fosseprez O, Chanard N, Aughey G, Lhoumaud P, Anglade J, Southall T, Plaza S, Payre F, Cuvier O, Polesello C. Crosstalk between chromatin and Shavenbaby defines transcriptional output along the Drosophila intestinal stem cell lineage. iScience 2024; 27:108624. [PMID: 38174321 PMCID: PMC10762455 DOI: 10.1016/j.isci.2023.108624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 07/05/2023] [Accepted: 11/30/2023] [Indexed: 01/05/2024] Open
Abstract
The transcription factor Shavenbaby (Svb), the only member of the OvoL family in Drosophila, controls the fate of various epithelial embryonic cells and adult stem cells. Post-translational modification of Svb produces two protein isoforms, Svb-ACT and Svb-REP, which promote adult intestinal stem cell renewal or differentiation, respectively. To define Svb mode of action, we used engineered cell lines and develop an unbiased method to identify Svb target genes across different contexts. Within a given cell type, Svb-ACT and Svb-REP antagonistically regulate the expression of a set of target genes, binding specific enhancers whose accessibility is constrained by chromatin landscape. Reciprocally, Svb-REP can influence local chromatin marks of active enhancers to help repressing target genes. Along the intestinal lineage, the set of Svb target genes progressively changes, together with chromatin accessibility. We propose that Svb-ACT-to-REP transition promotes enterocyte differentiation of intestinal stem cells through direct gene regulation and chromatin remodeling.
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Affiliation(s)
- Alexandra Mancheno-Ferris
- Molecular, Cellular and Developmental biology department (MCD), Centre de Biologie Integrative (CBI), Université de Toulouse, CNRS, UPS, 31062 Toulouse, France
- Control of cell shape remodeling team, CBI, CNRS, UPS, 31062 Toulouse, France
| | - Clément Immarigeon
- Molecular, Cellular and Developmental biology department (MCD), Centre de Biologie Integrative (CBI), Université de Toulouse, CNRS, UPS, 31062 Toulouse, France
- Control of cell shape remodeling team, CBI, CNRS, UPS, 31062 Toulouse, France
| | - Alexia Rivero
- Molecular, Cellular and Developmental biology department (MCD), Centre de Biologie Integrative (CBI), Université de Toulouse, CNRS, UPS, 31062 Toulouse, France
- Control of cell shape remodeling team, CBI, CNRS, UPS, 31062 Toulouse, France
| | - David Depierre
- Molecular, Cellular and Developmental biology department (MCD), Centre de Biologie Integrative (CBI), Université de Toulouse, CNRS, UPS, 31062 Toulouse, France
- Chromatin Dynamics and Cell Proliferation team, CBI, CNRS, UPS, 31062 Toulouse, France
| | - Naomi Schickele
- Molecular, Cellular and Developmental biology department (MCD), Centre de Biologie Integrative (CBI), Université de Toulouse, CNRS, UPS, 31062 Toulouse, France
- Chromatin Dynamics and Cell Proliferation team, CBI, CNRS, UPS, 31062 Toulouse, France
| | - Olivier Fosseprez
- Molecular, Cellular and Developmental biology department (MCD), Centre de Biologie Integrative (CBI), Université de Toulouse, CNRS, UPS, 31062 Toulouse, France
- Chromatin Dynamics and Cell Proliferation team, CBI, CNRS, UPS, 31062 Toulouse, France
| | - Nicolas Chanard
- Molecular, Cellular and Developmental biology department (MCD), Centre de Biologie Integrative (CBI), Université de Toulouse, CNRS, UPS, 31062 Toulouse, France
- Chromatin Dynamics and Cell Proliferation team, CBI, CNRS, UPS, 31062 Toulouse, France
| | - Gabriel Aughey
- Imperial College London, Sir Ernst Chain Building, South Kensington Campus, London SW7 2AZ, UK
| | - Priscilla Lhoumaud
- Molecular, Cellular and Developmental biology department (MCD), Centre de Biologie Integrative (CBI), Université de Toulouse, CNRS, UPS, 31062 Toulouse, France
- Chromatin Dynamics and Cell Proliferation team, CBI, CNRS, UPS, 31062 Toulouse, France
- Institut Jacques Monod, Université Paris Cité/CNRS, 15 rue Hélène Brion, 75205 Paris Cedex 13, France
| | - Julien Anglade
- Molecular, Cellular and Developmental biology department (MCD), Centre de Biologie Integrative (CBI), Université de Toulouse, CNRS, UPS, 31062 Toulouse, France
- Chromatin Dynamics and Cell Proliferation team, CBI, CNRS, UPS, 31062 Toulouse, France
| | - Tony Southall
- Imperial College London, Sir Ernst Chain Building, South Kensington Campus, London SW7 2AZ, UK
| | - Serge Plaza
- Molecular, Cellular and Developmental biology department (MCD), Centre de Biologie Integrative (CBI), Université de Toulouse, CNRS, UPS, 31062 Toulouse, France
- Laboratoire de Recherche en Sciences Végétales, CNRS/UPS/INPT, 31320 Auzeville-Tolosane, France
| | - François Payre
- Molecular, Cellular and Developmental biology department (MCD), Centre de Biologie Integrative (CBI), Université de Toulouse, CNRS, UPS, 31062 Toulouse, France
- Control of cell shape remodeling team, CBI, CNRS, UPS, 31062 Toulouse, France
| | - Olivier Cuvier
- Molecular, Cellular and Developmental biology department (MCD), Centre de Biologie Integrative (CBI), Université de Toulouse, CNRS, UPS, 31062 Toulouse, France
- Chromatin Dynamics and Cell Proliferation team, CBI, CNRS, UPS, 31062 Toulouse, France
| | - Cédric Polesello
- Molecular, Cellular and Developmental biology department (MCD), Centre de Biologie Integrative (CBI), Université de Toulouse, CNRS, UPS, 31062 Toulouse, France
- Control of cell shape remodeling team, CBI, CNRS, UPS, 31062 Toulouse, France
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43
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Beumer J, Clevers H. Hallmarks of stemness in mammalian tissues. Cell Stem Cell 2024; 31:7-24. [PMID: 38181752 PMCID: PMC10769195 DOI: 10.1016/j.stem.2023.12.006] [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: 11/06/2023] [Revised: 12/03/2023] [Accepted: 12/08/2023] [Indexed: 01/07/2024]
Abstract
All adult tissues experience wear and tear. Most tissues can compensate for cell loss through the activity of resident stem cells. Although the cellular maintenance strategies vary greatly between different adult (read: postnatal) tissues, the function of stem cells is best defined by their capacity to replace lost tissue through division. We discuss a set of six complementary hallmarks that are key enabling features of this basic function. These include longevity and self-renewal, multipotency, transplantability, plasticity, dependence on niche signals, and maintenance of genome integrity. We discuss these hallmarks in the context of some of the best-understood adult stem cell niches.
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Affiliation(s)
- Joep Beumer
- Institute of Human Biology (IHB), Roche Pharma Research and Early Development, Basel, Switzerland.
| | - Hans Clevers
- Institute of Human Biology (IHB), Roche Pharma Research and Early Development, Basel, Switzerland.
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44
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Cai S, Li H, Tie R, Shan W, Luo Q, Wang S, Feng C, Chen H, Zhang M, Xu Y, Li X, Chen M, Lu J, Qian P, Huang H. Nlrc3 signaling is indispensable for hematopoietic stem cell emergence via Notch signaling in vertebrates. Nat Commun 2024; 15:226. [PMID: 38172511 PMCID: PMC10764762 DOI: 10.1038/s41467-023-44251-6] [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: 11/12/2022] [Accepted: 12/06/2023] [Indexed: 01/05/2024] Open
Abstract
Hematopoietic stem and progenitor cells generate all the lineages of blood cells throughout the lifespan of vertebrates. The emergence of hematopoietic stem and progenitor cells is finely tuned by a variety of signaling pathways. Previous studies have revealed the roles of pattern-recognition receptors such as Toll-like receptors and RIG-I-like receptors in hematopoiesis. In this study, we find that Nlrc3, a nucleotide-binding domain leucine-rich repeat containing family gene, is highly expressed in hematopoietic differentiation stages in vivo and vitro and is required in hematopoiesis in zebrafish. Mechanistically, nlrc3 activates the Notch pathway and the downstream gene of Notch hey1. Furthermore, NF-kB signaling acts upstream of nlrc3 to enhance its transcriptional activity. Finally, we find that Nlrc3 signaling is conserved in the regulation of murine embryonic hematopoiesis. Taken together, our findings uncover an indispensable role of Nlrc3 signaling in hematopoietic stem and progenitor cell emergence and provide insights into inflammation-related hematopoietic ontogeny and the in vitro expansion of hematopoietic stem and progenitor cells.
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Affiliation(s)
- Shuyang Cai
- Bone Marrow Transplantation Center, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, China
- Institute of Hematology, Zhejiang University, Hangzhou, China
- Zhejiang Engineering Laboratory for Stem Cell and Immunotherapy, Hangzhou, China
- Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Honghu Li
- Bone Marrow Transplantation Center, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, China
- Institute of Hematology, Zhejiang University, Hangzhou, China
- Zhejiang Engineering Laboratory for Stem Cell and Immunotherapy, Hangzhou, China
| | - Ruxiu Tie
- Bone Marrow Transplantation Center, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, China
- Institute of Hematology, Zhejiang University, Hangzhou, China
- Zhejiang Engineering Laboratory for Stem Cell and Immunotherapy, Hangzhou, China
- Department of Hematology, the Second Clinical Medical College, Shanxi Medical University, Taiyuan, China
- Department of Hematology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
| | - Wei Shan
- Bone Marrow Transplantation Center, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, China
- Institute of Hematology, Zhejiang University, Hangzhou, China
- Zhejiang Engineering Laboratory for Stem Cell and Immunotherapy, Hangzhou, China
| | - Qian Luo
- Bone Marrow Transplantation Center, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, China
- Institute of Hematology, Zhejiang University, Hangzhou, China
- Zhejiang Engineering Laboratory for Stem Cell and Immunotherapy, Hangzhou, China
| | - Shufen Wang
- Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Cong Feng
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, China
- Bioinformatics Center, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Huiqiao Chen
- Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Meng Zhang
- Bone Marrow Transplantation Center, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, China
- Institute of Hematology, Zhejiang University, Hangzhou, China
- Zhejiang Engineering Laboratory for Stem Cell and Immunotherapy, Hangzhou, China
| | - Yulin Xu
- Bone Marrow Transplantation Center, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, China
- Institute of Hematology, Zhejiang University, Hangzhou, China
- Zhejiang Engineering Laboratory for Stem Cell and Immunotherapy, Hangzhou, China
| | - Xia Li
- Bone Marrow Transplantation Center, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, China
- Institute of Hematology, Zhejiang University, Hangzhou, China
- Zhejiang Engineering Laboratory for Stem Cell and Immunotherapy, Hangzhou, China
| | - Ming Chen
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, China
- Bioinformatics Center, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Jiahui Lu
- Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
| | - Pengxu Qian
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, China.
- Institute of Hematology, Zhejiang University, Hangzhou, China.
- Zhejiang Engineering Laboratory for Stem Cell and Immunotherapy, Hangzhou, China.
- Center for Stem Cell and Regenerative Medicine and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
| | - He Huang
- Bone Marrow Transplantation Center, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, China.
- Institute of Hematology, Zhejiang University, Hangzhou, China.
- Zhejiang Engineering Laboratory for Stem Cell and Immunotherapy, Hangzhou, China.
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45
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Zhang Y, Liu F. The evolving views of hematopoiesis: from embryo to adulthood and from in vivo to in vitro. J Genet Genomics 2024; 51:3-15. [PMID: 37734711 DOI: 10.1016/j.jgg.2023.09.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 09/13/2023] [Accepted: 09/13/2023] [Indexed: 09/23/2023]
Abstract
The hematopoietic system composed of hematopoietic stem and progenitor cells (HSPCs) and their differentiated lineages serves as an ideal model to uncover generic principles of cell fate transitions. From gastrulation onwards, there successively emerge primitive hematopoiesis (that produces specialized hematopoietic cells), pro-definitive hematopoiesis (that produces lineage-restricted progenitor cells), and definitive hematopoiesis (that produces multipotent HSPCs). These nascent lineages develop in several transient hematopoietic sites and finally colonize into lifelong hematopoietic sites. The development and maintenance of hematopoietic lineages are orchestrated by cell-intrinsic gene regulatory networks and cell-extrinsic microenvironmental cues. Owing to the progressive methodology (e.g., high-throughput lineage tracing and single-cell functional and omics analyses), our understanding of the developmental origin of hematopoietic lineages and functional properties of certain hematopoietic organs has been updated; meanwhile, new paradigms to characterize rare cell types, cell heterogeneity and its causes, and comprehensive regulatory landscapes have been provided. Here, we review the evolving views of HSPC biology during developmental and postnatal hematopoiesis. Moreover, we discuss recent advances in the in vitro induction and expansion of HSPCs, with a focus on the implications for clinical applications.
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Affiliation(s)
- Yifan Zhang
- Shandong Provincial Key Laboratory of Animal Cell and Developmental Biology, School of Life Sciences, Shandong University, Qingdao, Shandong 266237, China
| | - Feng Liu
- Shandong Provincial Key Laboratory of Animal Cell and Developmental Biology, School of Life Sciences, Shandong University, Qingdao, Shandong 266237, China; State Key Laboratory of Membrane Biology, Institute of Zoology, Institute for Stem Cell and Regeneration, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100101, China.
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46
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Gayoso A, Weiler P, Lotfollahi M, Klein D, Hong J, Streets A, Theis FJ, Yosef N. Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells. Nat Methods 2024; 21:50-59. [PMID: 37735568 PMCID: PMC10776389 DOI: 10.1038/s41592-023-01994-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 08/08/2023] [Indexed: 09/23/2023]
Abstract
RNA velocity has been rapidly adopted to guide interpretation of transcriptional dynamics in snapshot single-cell data; however, current approaches for estimating RNA velocity lack effective strategies for quantifying uncertainty and determining the overall applicability to the system of interest. Here, we present veloVI (velocity variational inference), a deep generative modeling framework for estimating RNA velocity. veloVI learns a gene-specific dynamical model of RNA metabolism and provides a transcriptome-wide quantification of velocity uncertainty. We show that veloVI compares favorably to previous approaches with respect to goodness of fit, consistency across transcriptionally similar cells and stability across preprocessing pipelines for quantifying RNA abundance. Further, we demonstrate that veloVI's posterior velocity uncertainty can be used to assess whether velocity analysis is appropriate for a given dataset. Finally, we highlight veloVI as a flexible framework for modeling transcriptional dynamics by adapting the underlying dynamical model to use time-dependent transcription rates.
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Affiliation(s)
- Adam Gayoso
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Philipp Weiler
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- Department of Mathematics, Technical University of Munich, Munich, Germany
| | - Mohammad Lotfollahi
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- Wellcome Sanger Institute, Cambridge, UK
| | - Dominik Klein
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- Department of Mathematics, Technical University of Munich, Munich, Germany
| | - Justin Hong
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
- Department of Computer Science, Columbia University, New York, NY, USA
| | - Aaron Streets
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
- Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany.
- Department of Mathematics, Technical University of Munich, Munich, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
| | - Nir Yosef
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA.
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA.
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47
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Lambo S, Trinh DL, Ries RE, Jin D, Setiadi A, Ng M, Leblanc VG, Loken MR, Brodersen LE, Dai F, Pardo LM, Ma X, Vercauteren SM, Meshinchi S, Marra MA. A longitudinal single-cell atlas of treatment response in pediatric AML. Cancer Cell 2023; 41:2117-2135.e12. [PMID: 37977148 DOI: 10.1016/j.ccell.2023.10.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 09/15/2023] [Accepted: 10/26/2023] [Indexed: 11/19/2023]
Abstract
Pediatric acute myeloid leukemia (pAML) is characterized by heterogeneous cellular composition, driver alterations and prognosis. Characterization of this heterogeneity and how it affects treatment response remains understudied in pediatric patients. We used single-cell RNA sequencing and single-cell ATAC sequencing to profile 28 patients representing different pAML subtypes at diagnosis, remission and relapse. At diagnosis, cellular composition differed between genetic subgroups. Upon relapse, cellular hierarchies transitioned toward a more primitive state regardless of subtype. Primitive cells in the relapsed tumor were distinct compared to cells at diagnosis, with under-representation of myeloid transcriptional programs and over-representation of other lineage programs. In some patients, this was accompanied by the appearance of a B-lymphoid-like hierarchy. Our data thus reveal the emergence of apparent subtype-specific plasticity upon treatment and inform on potentially targetable processes.
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Affiliation(s)
- Sander Lambo
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada
| | - Diane L Trinh
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada
| | - Rhonda E Ries
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Dan Jin
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada
| | - Audi Setiadi
- British Columbia Children's Hospital Research Institute, Vancouver, BC, Canada; Department of Pathology & Laboratory Medicine, Division of Hematopathology, Children's and Women's Health Centre of British Columbia, Vancouver, BC, Canada; Department of Pathology & Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Michelle Ng
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada; Department of Medical Genetics and Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada
| | - Veronique G Leblanc
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada
| | | | | | - Fangyan Dai
- Hematologics, Incorporated, Seattle, WA, USA
| | | | - Xiaotu Ma
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Suzanne M Vercauteren
- British Columbia Children's Hospital Research Institute, Vancouver, BC, Canada; Department of Pathology & Laboratory Medicine, Division of Hematopathology, Children's and Women's Health Centre of British Columbia, Vancouver, BC, Canada; Department of Pathology & Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Soheil Meshinchi
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Marco A Marra
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada; Department of Medical Genetics and Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada.
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Wang K, Zhang X, Cheng H, Ma W, Bao G, Dong L, Gou Y, Yang J, Cai H. SingleScan: a comprehensive resource for single-cell sequencing data processing and mining. BMC Bioinformatics 2023; 24:463. [PMID: 38062357 PMCID: PMC10704760 DOI: 10.1186/s12859-023-05590-9] [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: 07/24/2023] [Accepted: 11/30/2023] [Indexed: 12/18/2023] Open
Abstract
Single-cell sequencing has shed light on previously inaccessible biological questions from different fields of research, including organism development, immune function, and disease progression. The number of single-cell-based studies increased dramatically over the past decade. Several new methods and tools have been continuously developed, making it extremely tricky to navigate this research landscape and develop an up-to-date workflow to analyze single-cell sequencing data, particularly for researchers seeking to enter this field without computational experience. Moreover, choosing appropriate tools and optimal parameters to meet the demands of researchers represents a major challenge in processing single-cell sequencing data. However, a specific resource for easy access to detailed information on single-cell sequencing methods and data processing pipelines is still lacking. In the present study, an online resource called SingleScan was developed to curate all up-to-date single-cell transcriptome/genome analyzing tools and pipelines. All the available tools were categorized according to their main tasks, and several typical workflows for single-cell data analysis were summarized. In addition, spatial transcriptomics, which is a breakthrough molecular analysis method that enables researchers to measure all gene activity in tissue samples and map the site of activity, was included along with a portion of single-cell and spatial analysis solutions. For each processing step, the available tools and specific parameters used in published articles are provided and how these parameters affect the results is shown in the resource. All information used in the resource was manually extracted from related literature. An interactive website was designed for data retrieval, visualization, and download. By analyzing the included tools and literature, users can gain insights into the trends of single-cell studies and easily grasp the specific usage of a specific tool. SingleScan will facilitate the analysis of single-cell sequencing data and promote the development of new tools to meet the growing and diverse needs of the research community. The SingleScan database is publicly accessible via the website at http://cailab.labshare.cn/SingleScan .
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Affiliation(s)
- Kun Wang
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, 610065, China
| | - Xiao Zhang
- Department of Breast Surgery, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 611731, China
- Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu, 610072, China
| | - Hansen Cheng
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, 610065, China
| | - Wenhao Ma
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, 610065, China
| | - Guangchao Bao
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, 610065, China
| | - Liting Dong
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, 610065, China
| | - Yixiong Gou
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, 610065, China
| | - Jian Yang
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, 610065, China.
| | - Haoyang Cai
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, 610065, China.
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Li K, Chen X, Song S, Hou L, Chen S, Jiang R. Cofea: correlation-based feature selection for single-cell chromatin accessibility data. Brief Bioinform 2023; 25:bbad458. [PMID: 38113078 PMCID: PMC10782922 DOI: 10.1093/bib/bbad458] [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: 07/14/2023] [Revised: 11/19/2023] [Accepted: 11/20/2023] [Indexed: 12/21/2023] Open
Abstract
Single-cell chromatin accessibility sequencing (scCAS) technologies have enabled characterizing the epigenomic heterogeneity of individual cells. However, the identification of features of scCAS data that are relevant to underlying biological processes remains a significant gap. Here, we introduce a novel method Cofea, to fill this gap. Through comprehensive experiments on 5 simulated and 54 real datasets, Cofea demonstrates its superiority in capturing cellular heterogeneity and facilitating downstream analysis. Applying this method to identification of cell type-specific peaks and candidate enhancers, as well as pathway enrichment analysis and partitioned heritability analysis, we illustrate the potential of Cofea to uncover functional biological process.
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Affiliation(s)
- Keyi Li
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Xiaoyang Chen
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Shuang Song
- Center for Statistical Science, Department of Industrial Engineering, Tsinghua University, Beijing 100084, China
| | - Lin Hou
- Center for Statistical Science, Department of Industrial Engineering, Tsinghua University, Beijing 100084, China
| | - Shengquan Chen
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China
| | - Rui Jiang
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China
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Akhtyamov P, Shaheen L, Raevskiy M, Stupnikov A, Medvedeva YA. scATAC-seq preprocessing and imputation evaluation system for visualization, clustering and digital footprinting. Brief Bioinform 2023; 25:bbad447. [PMID: 38084919 PMCID: PMC10714317 DOI: 10.1093/bib/bbad447] [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: 08/24/2023] [Revised: 10/29/2023] [Accepted: 11/14/2023] [Indexed: 12/18/2023] Open
Abstract
Single-cell ATAC-seq (scATAC-seq) is a recently developed approach that provides means to investigate open chromatin at single cell level, to assess epigenetic regulation and transcription factors binding landscapes. The sparsity of the scATAC-seq data calls for imputation. Similarly, preprocessing (filtering) may be required to reduce computational load due to the large number of open regions. However, optimal strategies for both imputation and preprocessing have not been yet evaluated together. We present SAPIEnS (scATAC-seq Preprocessing and Imputation Evaluation System), a benchmark for scATAC-seq imputation frameworks, a combination of state-of-the-art imputation methods with commonly used preprocessing techniques. We assess different types of scATAC-seq analysis, i.e. clustering, visualization and digital genomic footprinting, and attain optimal preprocessing-imputation strategies. We discuss the benefits of the imputation framework depending on the task and the number of the dataset features (peaks). We conclude that the preprocessing with the Boruta method is beneficial for the majority of tasks, while imputation is helpful mostly for small datasets. We also implement a SAPIEnS database with pre-computed transcription factor footprints based on imputed data with their activity scores in a specific cell type. SAPIEnS is published at: https://github.com/lab-medvedeva/SAPIEnS. SAPIEnS database is available at: https://sapiensdb.com.
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Affiliation(s)
- Pavel Akhtyamov
- Department of Biomedical Physics, Moscow Institute of Physics and Technology (National Research University), 9 Institutskiy per., 141701, Moscow Region, Russian Federation
- The National Medical Research Center for Endocrinology, Dm. Ulyanova, 11, 117036, Moscow, Russian Federation
| | - Layal Shaheen
- Department of Biomedical Physics, Moscow Institute of Physics and Technology (National Research University), 9 Institutskiy per., 141701, Moscow Region, Russian Federation
- The National Medical Research Center for Endocrinology, Dm. Ulyanova, 11, 117036, Moscow, Russian Federation
| | - Mikhail Raevskiy
- Department, École Polytechnique Fédérale de Lausanne, Rte Cantonale, 1015, Lausanne, Vaud, Switzerland
| | - Alexey Stupnikov
- Department of Biomedical Physics, Moscow Institute of Physics and Technology (National Research University), 9 Institutskiy per., 141701, Moscow Region, Russian Federation
- The National Medical Research Center for Endocrinology, Dm. Ulyanova, 11, 117036, Moscow, Russian Federation
- Institute of Bioengineering, Research Center of Biotechnology, Russian Academy of Science, Leninsky prospect, 33, build. 2, 119071, Moscow, Russian Federation
| | - Yulia A Medvedeva
- Department of Biomedical Physics, Moscow Institute of Physics and Technology (National Research University), 9 Institutskiy per., 141701, Moscow Region, Russian Federation
- The National Medical Research Center for Endocrinology, Dm. Ulyanova, 11, 117036, Moscow, Russian Federation
- Institute of Bioengineering, Research Center of Biotechnology, Russian Academy of Science, Leninsky prospect, 33, build. 2, 119071, Moscow, Russian Federation
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