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Lin H, Hu H, Feng Z, Xu F, Lyu J, Li X, Liu L, Yang G, Shuai J. SCTC: inference of developmental potential from single-cell transcriptional complexity. Nucleic Acids Res 2024:gkae340. [PMID: 38709881 DOI: 10.1093/nar/gkae340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 03/09/2024] [Accepted: 04/18/2024] [Indexed: 05/08/2024] Open
Abstract
Inferring the developmental potential of single cells from scRNA-Seq data and reconstructing the pseudo-temporal path of cell development are fundamental but challenging tasks in single-cell analysis. Although single-cell transcriptional diversity (SCTD) measured by the number of expressed genes per cell has been widely used as a hallmark of developmental potential, it may lead to incorrect estimation of differentiation states in some cases where gene expression does not decrease monotonously during the development process. In this study, we propose a novel metric called single-cell transcriptional complexity (SCTC), which draws on insights from the economic complexity theory and takes into account the sophisticated structure information of scRNA-Seq count matrix. We show that SCTC characterizes developmental potential more accurately than SCTD, especially in the early stages of development where cells typically have lower diversity but higher complexity than those in the later stages. Based on the SCTC, we provide an unsupervised method for accurate, robust, and transferable inference of single-cell pseudotime. Our findings suggest that the complexity emerging from the interplay between cells and genes determines the developmental potential, providing new insights into the understanding of biological development from the perspective of complexity theory.
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Affiliation(s)
- Hai Lin
- Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang 325001, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou, Zhejiang 325001, China
| | - Huan Hu
- Institute of Applied Genomics, Fuzhou University, Fuzhou 350108, China
| | - Zhen Feng
- First Affiliated Hospital of Wenzhou Medical University, Wenzhou Medical University, Wenzhou 325000, China
| | - Fei Xu
- Department of Physics, Anhui Normal University, Wuhu, Anhui 241002, China
| | - Jie Lyu
- Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang 325001, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou, Zhejiang 325001, China
| | - Xiang Li
- Department of Physics, College of Physical Science and Technology, Xiamen University, Xiamen 361005, China
| | - Liyu Liu
- Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang 325001, China
- Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials, College of Physics, Chongqing University, Chongqing 401331, China
| | - Gen Yang
- Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang 325001, China
- State Key Laboratory of Nuclear Physics and Technology, School of Physics, Peking University, Beijing 100871, China
| | - Jianwei Shuai
- Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang 325001, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou, Zhejiang 325001, China
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2
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Ng JCF, Montamat Garcia G, Stewart AT, Blair P, Mauri C, Dunn-Walters DK, Fraternali F. sciCSR infers B cell state transition and predicts class-switch recombination dynamics using single-cell transcriptomic data. Nat Methods 2024; 21:823-834. [PMID: 37932398 PMCID: PMC11093741 DOI: 10.1038/s41592-023-02060-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 10/02/2023] [Indexed: 11/08/2023]
Abstract
Class-switch recombination (CSR) is an integral part of B cell maturation. Here we present sciCSR (pronounced 'scissor', single-cell inference of class-switch recombination), a computational pipeline that analyzes CSR events and dynamics of B cells from single-cell RNA sequencing (scRNA-seq) experiments. Validated on both simulated and real data, sciCSR re-analyzes scRNA-seq alignments to differentiate productive heavy-chain immunoglobulin transcripts from germline 'sterile' transcripts. From a snapshot of B cell scRNA-seq data, a Markov state model is built to infer the dynamics and direction of CSR. Applying sciCSR on severe acute respiratory syndrome coronavirus 2 vaccination time-course scRNA-seq data, we observe that sciCSR predicts, using data from an earlier time point in the collected time-course, the isotype distribution of B cell receptor repertoires of subsequent time points with high accuracy (cosine similarity ~0.9). Using processes specific to B cells, sciCSR identifies transitions that are often missed by conventional RNA velocity analyses and can reveal insights into the dynamics of B cell CSR during immune response.
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Affiliation(s)
- Joseph C F Ng
- Department of Structural and Molecular Biology, Division of Biosciences and Institute of Structural and Molecular Biology, University College London, London, UK.
| | - Guillem Montamat Garcia
- Division of Infection and Immunity and Institute of Immunity and Transplantation, Royal Free Hospital, University College London, London, UK
| | | | - Paul Blair
- Division of Infection and Immunity and Institute of Immunity and Transplantation, Royal Free Hospital, University College London, London, UK
| | - Claudia Mauri
- Division of Infection and Immunity and Institute of Immunity and Transplantation, Royal Free Hospital, University College London, London, UK
| | | | - Franca Fraternali
- Department of Structural and Molecular Biology, Division of Biosciences and Institute of Structural and Molecular Biology, University College London, London, UK.
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3
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Wang K, Hou L, Wang X, Zhai X, Lu Z, Zi Z, Zhai W, He X, Curtis C, Zhou D, Hu Z. PhyloVelo enhances transcriptomic velocity field mapping using monotonically expressed genes. Nat Biotechnol 2024; 42:778-789. [PMID: 37524958 DOI: 10.1038/s41587-023-01887-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 06/28/2023] [Indexed: 08/02/2023]
Abstract
Single-cell RNA sequencing (scRNA-seq) is a powerful approach for studying cellular differentiation, but accurately tracking cell fate transitions can be challenging, especially in disease conditions. Here we introduce PhyloVelo, a computational framework that estimates the velocity of transcriptomic dynamics by using monotonically expressed genes (MEGs) or genes with expression patterns that either increase or decrease, but do not cycle, through phylogenetic time. Through integration of scRNA-seq data with lineage information, PhyloVelo identifies MEGs and reconstructs a transcriptomic velocity field. We validate PhyloVelo using simulated data and Caenorhabditis elegans ground truth data, successfully recovering linear, bifurcated and convergent differentiations. Applying PhyloVelo to seven lineage-traced scRNA-seq datasets, generated using CRISPR-Cas9 editing, lentiviral barcoding or immune repertoire profiling, demonstrates its high accuracy and robustness in inferring complex lineage trajectories while outperforming RNA velocity. Additionally, we discovered that MEGs across tissues and organisms share similar functions in translation and ribosome biogenesis.
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Affiliation(s)
- Kun Wang
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- School of Mathematical Sciences, Xiamen University, Xiamen, China
| | - Liangzhen Hou
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Faculty of Health Sciences, University of Macau, Taipa, Macau, China
| | - Xin Wang
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Xiangwei Zhai
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Zhaolian Lu
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zhike Zi
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Weiwei Zhai
- CAS Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China
| | - Xionglei He
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Christina Curtis
- Department of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Da Zhou
- School of Mathematical Sciences, Xiamen University, Xiamen, China.
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China.
| | - Zheng Hu
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
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4
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Irac SE, Soon MSF, Borcherding N, Tuong ZK. Single-cell immune repertoire analysis. Nat Methods 2024; 21:777-792. [PMID: 38637691 DOI: 10.1038/s41592-024-02243-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 03/12/2024] [Indexed: 04/20/2024]
Abstract
Single-cell T cell and B cell antigen receptor-sequencing data analysis can potentially perform in-depth assessments of adaptive immune cells that inform on understanding immune cell development to tracking clonal expansion in disease and therapy. However, it has been extremely challenging to analyze and interpret T cells and B cells and their adaptive immune receptor repertoires at the single-cell level due to not only the complexity of the data but also the underlying biology. In this Review, we delve into the computational breakthroughs that have transformed the analysis of single-cell T cell and B cell antigen receptor-sequencing data.
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Affiliation(s)
- Sergio E Irac
- Cancer Immunoregulation and Immunotherapy, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Megan Sioe Fei Soon
- Ian Frazer Centre for Children's Immunotherapy Research, Child Health Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia
| | - Nicholas Borcherding
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA
- Omniscope, Palo Alto, CA, USA
| | - Zewen Kelvin Tuong
- Ian Frazer Centre for Children's Immunotherapy Research, Child Health Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia.
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia.
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5
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'Snipping' the transcriptome unravels the dynamics of antibody response. Nat Methods 2024; 21:756-7. [PMID: 37932400 DOI: 10.1038/s41592-023-02061-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2023]
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6
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Gao CF, Vaikuntanathan S, Riesenfeld SJ. Dissection and integration of bursty transcriptional dynamics for complex systems. Proc Natl Acad Sci U S A 2024; 121:e2306901121. [PMID: 38669186 PMCID: PMC11067469 DOI: 10.1073/pnas.2306901121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 03/06/2024] [Indexed: 04/28/2024] Open
Abstract
RNA velocity estimation is a potentially powerful tool to reveal the directionality of transcriptional changes in single-cell RNA-sequencing data, but it lacks accuracy, absent advanced metabolic labeling techniques. We developed an approach, TopicVelo, that disentangles simultaneous, yet distinct, dynamics by using a probabilistic topic model, a highly interpretable form of latent space factorization, to infer cells and genes associated with individual processes, thereby capturing cellular pluripotency or multifaceted functionality. Focusing on process-associated cells and genes enables accurate estimation of process-specific velocities via a master equation for a transcriptional burst model accounting for intrinsic stochasticity. The method obtains a global transition matrix by leveraging cell topic weights to integrate process-specific signals. In challenging systems, this method accurately recovers complex transitions and terminal states, while our use of first-passage time analysis provides insights into transient transitions. These results expand the limits of RNA velocity, empowering future studies of cell fate and functional responses.
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Affiliation(s)
- Cheng Frank Gao
- Department of Chemistry, University of Chicago, Chicago, IL60637
| | - Suriyanarayanan Vaikuntanathan
- Department of Chemistry, University of Chicago, Chicago, IL60637
- Institute for Biophysical Dynamics, University of Chicago, Chicago, IL60637
| | - Samantha J. Riesenfeld
- Institute for Biophysical Dynamics, University of Chicago, Chicago, IL60637
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL60637
- Department of Medicine, University of Chicago, Chicago, IL60637
- Committee on Immunology, Biological Sciences Division, University of Chicago, Chicago, IL60637
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7
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Licón-Muñoz Y, Avalos V, Subramanian S, Granger B, Martinez F, Varela S, Moore D, Perkins E, Kogan M, Berto S, Chohan M, Bowers C, Piccirillo S. Single-nucleus and spatial landscape of the sub-ventricular zone in human glioblastoma. bioRxiv 2024:2024.04.24.590852. [PMID: 38712234 PMCID: PMC11071523 DOI: 10.1101/2024.04.24.590852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
The sub-ventricular zone (SVZ) is the most well-characterized neurogenic area in the mammalian brain. We previously showed that in 65% of patients with glioblastoma (GBM), the SVZ is a reservoir of cancer stem-like cells that contribute to treatment resistance and emergence of recurrence. Here, we built a single-nucleus RNA-sequencing-based microenvironment landscape of the tumor mass (T_Mass) and the SVZ (T_SVZ) of 15 GBM patients and 2 histologically normal SVZ (N_SVZ) samples as controls. We identified a mesenchymal signature in the T_SVZ of GBM patients: tumor cells from the T_SVZ relied on the ZEB1 regulatory network, whereas tumor cells in the T_Mass relied on the TEAD1 regulatory network. Moreover, the T_SVZ microenvironment was predominantly characterized by tumor-supportive microglia, which spatially co-exist and establish heterotypic interactions with tumor cells. Lastly, differential gene expression analyses, predictions of ligand-receptor and incoming/outgoing interactions, and functional assays revealed that the IL-1β/IL-1RAcP and Wnt-5a/Frizzled-3 pathways are therapeutic targets in the T_SVZ microenvironment. Our data provide insights into the biology of the SVZ in GBM patients and identify specific targets of this microenvironment.
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Affiliation(s)
- Y. Licón-Muñoz
- The Brain Tumor Translational Laboratory, Department of Cell Biology and Physiology, University of New Mexico Health Sciences Center, Albuquerque, NM
- University of New Mexico Comprehensive Cancer Center, Albuquerque, NM
| | - V. Avalos
- The Brain Tumor Translational Laboratory, Department of Cell Biology and Physiology, University of New Mexico Health Sciences Center, Albuquerque, NM
- University of New Mexico Comprehensive Cancer Center, Albuquerque, NM
| | - S. Subramanian
- Bioinformatics Core, Department of Neuroscience, Medical University of South Carolina, Charleston, SC
- Neurogenomics Laboratory, Department of Neuroscience, Medical University of South Carolina, Charleston, SC
| | - B. Granger
- Bioinformatics Core, Department of Neuroscience, Medical University of South Carolina, Charleston, SC
- Neurogenomics Laboratory, Department of Neuroscience, Medical University of South Carolina, Charleston, SC
| | - F. Martinez
- The Brain Tumor Translational Laboratory, Department of Cell Biology and Physiology, University of New Mexico Health Sciences Center, Albuquerque, NM
- University of New Mexico Comprehensive Cancer Center, Albuquerque, NM
| | - S. Varela
- University of New Mexico School of Medicine, Albuquerque, NM
| | - D. Moore
- Bioinformatics Core, Department of Neuroscience, Medical University of South Carolina, Charleston, SC
- Neurogenomics Laboratory, Department of Neuroscience, Medical University of South Carolina, Charleston, SC
| | - E. Perkins
- Department of Neurosurgery, University of Mississippi Medical Center, Jackson, MS
| | - M. Kogan
- Department of Neurosurgery, University of New Mexico Hospital, Albuquerque, NM
| | - S. Berto
- Bioinformatics Core, Department of Neuroscience, Medical University of South Carolina, Charleston, SC
- Neurogenomics Laboratory, Department of Neuroscience, Medical University of South Carolina, Charleston, SC
| | - M.O. Chohan
- Department of Neurosurgery, University of Mississippi Medical Center, Jackson, MS
| | - C.A. Bowers
- Department of Neurosurgery, University of New Mexico Hospital, Albuquerque, NM
| | - S.G.M. Piccirillo
- The Brain Tumor Translational Laboratory, Department of Cell Biology and Physiology, University of New Mexico Health Sciences Center, Albuquerque, NM
- University of New Mexico Comprehensive Cancer Center, Albuquerque, NM
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8
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Barrios EL, Leary JR, Darden DB, Rincon JC, Willis M, Polcz VE, Gillies GS, Munley JA, Dirain ML, Ungaro R, Nacionales DC, Gauthier MPL, Larson SD, Morel L, Loftus TJ, Mohr AM, Maile R, Kladde MP, Mathews CE, Brusko MA, Brusko TM, Moldawer LL, Bacher R, Efron PA. The post-septic peripheral myeloid compartment reveals unexpected diversity in myeloid-derived suppressor cells. Front Immunol 2024; 15:1355405. [PMID: 38720891 PMCID: PMC11076668 DOI: 10.3389/fimmu.2024.1355405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 04/09/2024] [Indexed: 05/12/2024] Open
Abstract
Introduction Sepsis engenders distinct host immunologic changes that include the expansion of myeloid-derived suppressor cells (MDSCs). These cells play a physiologic role in tempering acute inflammatory responses but can persist in patients who develop chronic critical illness. Methods Cellular Indexing of Transcriptomes and Epitopes by Sequencing and transcriptomic analysis are used to describe MDSC subpopulations based on differential gene expression, RNA velocities, and biologic process clustering. Results We identify a unique lineage and differentiation pathway for MDSCs after sepsis and describe a novel MDSC subpopulation. Additionally, we report that the heterogeneous response of the myeloid compartment of blood to sepsis is dependent on clinical outcome. Discussion The origins and lineage of these MDSC subpopulations were previously assumed to be discrete and unidirectional; however, these cells exhibit a dynamic phenotype with considerable plasticity.
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Affiliation(s)
- Evan L. Barrios
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville, FL, United States
| | - Jack R. Leary
- Department of Biostatistics, University of Florida College of Medicine and Public Health and Health Sciences, Gainesville, FL, United States
| | - Dijoia B. Darden
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville, FL, United States
| | - Jaimar C. Rincon
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville, FL, United States
| | - Micah Willis
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville, FL, United States
| | - Valerie E. Polcz
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville, FL, United States
| | - Gwendolyn S. Gillies
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville, FL, United States
| | - Jennifer A. Munley
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville, FL, United States
| | - Marvin L. Dirain
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville, FL, United States
| | - Ricardo Ungaro
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville, FL, United States
| | - Dina C. Nacionales
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville, FL, United States
| | - Marie-Pierre L. Gauthier
- Department of Biochemistry and Molecular Biology, University of Florida College of Medicine, Gainesville, FL, United States
| | - Shawn D. Larson
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville, FL, United States
| | - Laurence Morel
- Department of Microbiology and Immunology, University of Texas San Antonio School of Medicine, San Antonio, TX, United States
| | - Tyler J. Loftus
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville, FL, United States
| | - Alicia M. Mohr
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville, FL, United States
| | - Robert Maile
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville, FL, United States
| | - Michael P. Kladde
- Department of Biochemistry and Molecular Biology, University of Florida College of Medicine, Gainesville, FL, United States
| | - Clayton E. Mathews
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida College of Medicine, Gainesville, FL, United States
| | - Maigan A. Brusko
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida College of Medicine, Gainesville, FL, United States
| | - Todd M. Brusko
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida College of Medicine, Gainesville, FL, United States
| | - Lyle L. Moldawer
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville, FL, United States
| | - Rhonda Bacher
- Department of Biostatistics, University of Florida College of Medicine and Public Health and Health Sciences, Gainesville, FL, United States
| | - Philip A. Efron
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville, FL, United States
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9
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Su Z, Tong Y, Wei GW. Hodge Decomposition of Single-Cell RNA Velocity. J Chem Inf Model 2024; 64:3558-3568. [PMID: 38572676 PMCID: PMC11035094 DOI: 10.1021/acs.jcim.4c00132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 03/21/2024] [Accepted: 03/22/2024] [Indexed: 04/05/2024]
Abstract
RNA velocity has the ability to capture the cell dynamic information in the biological processes; yet, a comprehensive analysis of the cell state transitions and their associated chemical and biological processes remains a gap. In this work, we provide the Hodge decomposition, coupled with discrete exterior calculus (DEC), to unveil cell dynamics by examining the decomposed curl-free, divergence-free, and harmonic components of the RNA velocity field in a low dimensional representation, such as a UMAP or a t-SNE representation. Decomposition results show that the decomposed components distinctly reveal key cell dynamic features such as cell cycle, bifurcation, and cell lineage differentiation, regardless of the choice of the low-dimensional representations. The consistency across different representations demonstrates that the Hodge decomposition is a reliable and robust way to extract these cell dynamic features, offering unique analysis and insightful visualization of single-cell RNA velocity fields.
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Affiliation(s)
- Zhe Su
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Yiying Tong
- Department
of Computer Science and Engineering, Michigan
State University, East Lansing, Michigan 48824, United States
| | - Guo-Wei Wei
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
- Department
of Electrical and Computer Engineering, Michigan State University, East
Lansing, Michigan 48824, United States
- Department
of Biochemistry and Molecular Biology, Michigan
State University, East Lansing, Michigan 48824, United States
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10
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Rosebrock D, Vingron M, Arndt PF. Modeling gene expression cascades during cell state transitions. iScience 2024; 27:109386. [PMID: 38500834 PMCID: PMC10946328 DOI: 10.1016/j.isci.2024.109386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 12/14/2023] [Accepted: 02/27/2024] [Indexed: 03/20/2024] Open
Abstract
During cellular processes such as differentiation or response to external stimuli, cells exhibit dynamic changes in their gene expression profiles. Single-cell RNA sequencing (scRNA-seq) can be used to investigate these dynamic changes. To this end, cells are typically ordered along a pseudotemporal trajectory which recapitulates the progression of cells as they transition from one cell state to another. We infer transcriptional dynamics by modeling the gene expression profiles in pseudotemporally ordered cells using a Bayesian inference approach. This enables ordering genes along transcriptional cascades, estimating differences in the timing of gene expression dynamics, and deducing regulatory gene interactions. Here, we apply this approach to scRNA-seq datasets derived from mouse embryonic forebrain and pancreas samples. This analysis demonstrates the utility of the method to derive the ordering of gene dynamics and regulatory relationships critical for proper cellular differentiation and maturation across a variety of developmental contexts.
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Affiliation(s)
- Daniel Rosebrock
- Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany
| | - Martin Vingron
- Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany
| | - Peter F. Arndt
- Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany
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11
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Xie C, Yang Y, Yu H, He Q, Yuan M, Dong B, Zhang L, Yang M. RNA velocity prediction via neural ordinary differential equation. iScience 2024; 27:109635. [PMID: 38623336 PMCID: PMC11016905 DOI: 10.1016/j.isci.2024.109635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 12/04/2023] [Accepted: 03/26/2024] [Indexed: 04/17/2024] Open
Abstract
RNA velocity is a crucial tool for unraveling the trajectory of cellular responses. Several approaches, including ordinary differential equations and machine learning models, have been proposed to interpret velocity. However, the practicality of these methods is constrained by underlying assumptions. In this study, we introduce SymVelo, a dual-path framework that effectively integrates high- and low-dimensional information. Rigorous benchmarking and extensive studies demonstrate that SymVelo is capable of inferring differentiation trajectories in developing organs, analyzing gene responses to stimulation, and uncovering transcription dynamics. Moreover, the adaptable architecture of SymVelo enables customization to accommodate intricate data and diverse modalities in forthcoming research, thereby providing a promising avenue for advancing our understanding of cellular behavior.
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Affiliation(s)
- Chenxi Xie
- MGI, BGI-Shenzhen, Shenzhen 518083, China
| | | | - Hao Yu
- Peking University, Beijing 100871, China
| | - Qiushun He
- MGI, BGI-Shenzhen, Shenzhen 518083, China
| | | | - Bin Dong
- Peking University, Beijing 100871, China
| | - Li Zhang
- Peking University, Beijing 100871, China
| | - Meng Yang
- MGI, BGI-Shenzhen, Shenzhen 518083, China
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12
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Liu X, Wang X, Yang Q, Luo L, Liu Z, Ren X, Lei K, Li S, Xie Z, Zheng G, Zhang Y, Hao Y, Zhou Q, Hou Y, Fang F, Song W, Cui J, Ma J, Xie W, Shen S, Tang C, Peng S, Yu J, Kuang M, Song X, Wang F, Xu L. Th17 Cells Secrete TWEAK to Trigger Epithelial-Mesenchymal Transition and Promote Colorectal Cancer Liver Metastasis. Cancer Res 2024; 84:1352-1371. [PMID: 38335276 DOI: 10.1158/0008-5472.can-23-2123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 11/28/2023] [Accepted: 02/07/2024] [Indexed: 02/12/2024]
Abstract
Liver metastasis is the leading cause of mortality in patients with colorectal cancer. Given the significance of both epithelial-mesenchymal transition (EMT) of tumor cells and the immune microenvironment in colorectal cancer liver metastasis (CRLM), the interplay between them could hold the key for developing improved treatment options. We employed multiomics analysis of 130 samples from 18 patients with synchronous CRLM integrated with external datasets to comprehensively evaluate the interaction between immune cells and EMT of tumor cells in liver metastasis. Single-cell RNA sequencing analysis revealed distinct distributions of nonmalignant cells between primary tumors from patients with metastatic colorectal cancer (mCRC) and non-metastatic colorectal cancer, showing that Th17 cells were predominantly enriched in the primary lesion of mCRC. TWEAK, a cytokine secreted by Th17 cells, promoted EMT by binding to receptor Fn14 on tumor cells, and the TWEAK-Fn14 interaction enhanced tumor migration and invasion. In mouse models, targeting Fn14 using CRISPR-induced knockout or lipid nanoparticle-encapsulated siRNA alleviated metastasis and prolonged survival. Mice lacking Il17a or Tnfsf12 (encoding TWEAK) exhibited fewer metastases compared with wild-type mice, while cotransfer of Th17 with tumor cells promoted liver metastasis. Higher TWEAK expression was associated with a worse prognosis in patients with colorectal cancer. In addition, CD163L1+ macrophages interacted with Th17 cells, recruiting Th17 via the CCL4-CCR5 axis. Collectively, this study unveils the role of immune cells in the EMT process and identifies TWEAK secreted by Th17 as a driver of CRLM. SIGNIFICANCE TWEAK secreted by Th17 cells promotes EMT by binding to Fn14 on colorectal cancer cells, suggesting that blocking the TWEAK-Fn14 interaction may be a promising therapeutic approach to inhibit liver metastasis.
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Affiliation(s)
- Xin Liu
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, P.R. China
| | - Xin Wang
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, P.R. China
| | - Qingxia Yang
- Department of Oncology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, P.R. China
| | - Li Luo
- Department of Oncology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, P.R. China
| | - Ziqin Liu
- Department of Oncology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, P.R. China
| | - Xiaoxue Ren
- Department of Oncology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, P.R. China
| | - Kai Lei
- Center of Gastrointestinal Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, P.R. China
| | - Shangru Li
- Department of Oncology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, P.R. China
| | - Zonglin Xie
- Department of Oncology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, P.R. China
| | - Gaomin Zheng
- Department of Oncology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, P.R. China
| | - Yifan Zhang
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, P.R. China
| | - Yijie Hao
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, P.R. China
| | - Qianying Zhou
- Department of Oncology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, P.R. China
| | - Yingdong Hou
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, P.R. China
| | - Fei Fang
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, P.R. China
| | - Wu Song
- Center of Gastrointestinal Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, P.R. China
| | - Ji Cui
- Center of Gastrointestinal Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, P.R. China
| | - Jinping Ma
- Center of Gastrointestinal Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, P.R. China
| | - Wenxuan Xie
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, P.R. China
| | - Shunli Shen
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, P.R. China
| | - Ce Tang
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, P.R. China
| | - Sui Peng
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, P.R. China
- Department of Gastroenterology and Hepatology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, P.R. China
- Clinical Trial Unit, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, P.R. China
| | - Jun Yu
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, P.R. China
- Department of Medicine and Therapeutics, State Key Laboratory of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR, P.R. China
| | - Ming Kuang
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, P.R. China
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, P.R. China
| | - Xinming Song
- Center of Gastrointestinal Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, P.R. China
| | - Fang Wang
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, P.R. China
| | - Lixia Xu
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, P.R. China
- Department of Oncology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, P.R. China
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13
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Fan Y, Li L, Sun S. Powerful and accurate detection of temporal gene expression patterns from multi-sample multi-stage single-cell transcriptomics data with TDEseq. Genome Biol 2024; 25:96. [PMID: 38622747 PMCID: PMC11020788 DOI: 10.1186/s13059-024-03237-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 04/03/2024] [Indexed: 04/17/2024] Open
Abstract
We present a non-parametric statistical method called TDEseq that takes full advantage of smoothing splines basis functions to account for the dependence of multiple time points in scRNA-seq studies, and uses hierarchical structure linear additive mixed models to model the correlated cells within an individual. As a result, TDEseq demonstrates powerful performance in identifying four potential temporal expression patterns within a specific cell type. Extensive simulation studies and the analysis of four published scRNA-seq datasets show that TDEseq can produce well-calibrated p-values and up to 20% power gain over the existing methods for detecting temporal gene expression patterns.
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Affiliation(s)
- Yue Fan
- Center for Single-Cell Omics and Health, School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi, 710061, People's Republic of China
- Collaborative Innovation Center of Endemic Diseases and Health Promotion in Silk Road Region; NHC Key Laboratory of Environment and Endemic Diseases, Xi'an Jiaotong University, Xi'an, Shaanxi, 710061, People's Republic of China
- Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education, Xi'an, Shaanxi, 710061, People's Republic of China
| | - Lei Li
- Center for Single-Cell Omics and Health, School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi, 710061, People's Republic of China
- Collaborative Innovation Center of Endemic Diseases and Health Promotion in Silk Road Region; NHC Key Laboratory of Environment and Endemic Diseases, Xi'an Jiaotong University, Xi'an, Shaanxi, 710061, People's Republic of China
| | - Shiquan Sun
- Center for Single-Cell Omics and Health, School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi, 710061, People's Republic of China.
- Collaborative Innovation Center of Endemic Diseases and Health Promotion in Silk Road Region; NHC Key Laboratory of Environment and Endemic Diseases, Xi'an Jiaotong University, Xi'an, Shaanxi, 710061, People's Republic of China.
- Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education, Xi'an, Shaanxi, 710061, People's Republic of China.
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an, Shaanxi, 710061, People's Republic of China.
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14
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Qu R, Cheng X, Sefik E, Stanley Iii JS, Landa B, Strino F, Platt S, Garritano J, Odell ID, Coifman R, Flavell RA, Myung P, Kluger Y. Gene trajectory inference for single-cell data by optimal transport metrics. Nat Biotechnol 2024:10.1038/s41587-024-02186-3. [PMID: 38580861 DOI: 10.1038/s41587-024-02186-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 02/26/2024] [Indexed: 04/07/2024]
Abstract
Single-cell RNA sequencing has been widely used to investigate cell state transitions and gene dynamics of biological processes. Current strategies to infer the sequential dynamics of genes in a process typically rely on constructing cell pseudotime through cell trajectory inference. However, the presence of concurrent gene processes in the same group of cells and technical noise can obscure the true progression of the processes studied. To address this challenge, we present GeneTrajectory, an approach that identifies trajectories of genes rather than trajectories of cells. Specifically, optimal transport distances are calculated between gene distributions across the cell-cell graph to extract gene programs and define their gene pseudotemporal order. Here we demonstrate that GeneTrajectory accurately extracts progressive gene dynamics in myeloid lineage maturation. Moreover, we show that GeneTrajectory deconvolves key gene programs underlying mouse skin hair follicle dermal condensate differentiation that could not be resolved by cell trajectory approaches. GeneTrajectory facilitates the discovery of gene programs that control the changes and activities of biological processes.
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Affiliation(s)
- Rihao Qu
- Computational Biology & Bioinformatics Program, Yale University, New Haven, CT, USA
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
| | - Xiuyuan Cheng
- Department of Mathematics, Duke University, Durham, NC, USA
| | - Esen Sefik
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
| | | | - Boris Landa
- Program in Applied Mathematics, Yale University, New Haven, CT, USA
| | | | - Sarah Platt
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
- Department of Dermatology, Yale University School of Medicine, New Haven, CT, USA
| | - James Garritano
- Program in Applied Mathematics, Yale University, New Haven, CT, USA
| | - Ian D Odell
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
- Department of Dermatology, Yale University School of Medicine, New Haven, CT, USA
| | - Ronald Coifman
- Program in Applied Mathematics, Yale University, New Haven, CT, USA
- Department of Mathematics, Yale University, New Haven, CT, USA
- Department of Electrical Engineering, Yale University, New Haven, CT, USA
| | - Richard A Flavell
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
- Howard Hughes Medical Institute, Yale University School of Medicine, New Haven, CT, USA
| | - Peggy Myung
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
- Department of Dermatology, Yale University School of Medicine, New Haven, CT, USA
| | - Yuval Kluger
- Computational Biology & Bioinformatics Program, Yale University, New Haven, CT, USA.
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA.
- Program in Applied Mathematics, Yale University, New Haven, CT, USA.
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15
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Schlenker R, Schwalie PC, Dettling S, Huesser T, Irmisch A, Mariani M, Martínez Gómez JM, Ribeiro A, Limani F, Herter S, Yángüez E, Hoves S, Somandin J, Siebourg-Polster J, Kam-Thong T, de Matos IG, Umana P, Dummer R, Levesque MP, Bacac M. Myeloid-T cell interplay and cell state transitions associated with checkpoint inhibitor response in melanoma. Med 2024:S2666-6340(24)00127-2. [PMID: 38593812 DOI: 10.1016/j.medj.2024.03.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 11/23/2023] [Accepted: 03/17/2024] [Indexed: 04/11/2024]
Abstract
BACKGROUND The treatment of melanoma, the deadliest form of skin cancer, has greatly benefited from immunotherapy. However, many patients do not show a durable response, which is only partially explained by known resistance mechanisms. METHODS We performed single-cell RNA sequencing of tumor immune infiltrates and matched peripheral blood mononuclear cells of 22 checkpoint inhibitor (CPI)-naive stage III-IV metastatic melanoma patients. After sample collection, the same patients received CPI treatment, and their response was assessed. FINDINGS CPI responders showed high levels of classical monocytes in peripheral blood, which preferentially transitioned toward CXCL9-expressing macrophages in tumors. Trajectories of tumor-infiltrating CD8+ T cells diverged at the level of effector memory/stem-like T cells, with non-responder cells progressing into a state characterized by cellular stress and apoptosis-related gene expression. Consistently, predicted non-responder-enriched myeloid-T/natural killer cell interactions were primarily immunosuppressive, while responder-enriched interactions were supportive of T cell priming and effector function. CONCLUSIONS Our study illustrates that the tumor immune microenvironment prior to CPI treatment can be indicative of response. In perspective, modulating the myeloid and/or effector cell compartment by altering the described cell interactions and transitions could improve immunotherapy response. FUNDING This research was funded by Roche Pharma Research and Early Development.
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Affiliation(s)
- Ramona Schlenker
- Roche Innovation Center Munich, Roche Pharma Research and Early Development (pRED), Penzberg, Germany.
| | | | - Steffen Dettling
- Roche Innovation Center Munich, Roche Pharma Research and Early Development (pRED), Penzberg, Germany
| | - Tamara Huesser
- Roche Innovation Center Zurich, pRED, Schlieren, Switzerland
| | - Anja Irmisch
- Department of Dermatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Marisa Mariani
- Roche Innovation Center Zurich, pRED, Schlieren, Switzerland
| | - Julia M Martínez Gómez
- Department of Dermatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Alison Ribeiro
- Roche Innovation Center Zurich, pRED, Schlieren, Switzerland
| | - Florian Limani
- Roche Innovation Center Zurich, pRED, Schlieren, Switzerland
| | - Sylvia Herter
- Roche Innovation Center Zurich, pRED, Schlieren, Switzerland
| | - Emilio Yángüez
- Roche Innovation Center Zurich, pRED, Schlieren, Switzerland
| | - Sabine Hoves
- Roche Innovation Center Munich, Roche Pharma Research and Early Development (pRED), Penzberg, Germany
| | - Jitka Somandin
- Roche Innovation Center Zurich, pRED, Schlieren, Switzerland
| | | | | | | | - Pablo Umana
- Roche Innovation Center Zurich, pRED, Schlieren, Switzerland
| | - Reinhard Dummer
- Department of Dermatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Mitchell P Levesque
- Department of Dermatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Marina Bacac
- Roche Innovation Center Zurich, pRED, Schlieren, Switzerland
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16
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Mitic N, Neuschulz A, Spanjaard B, Schneider J, Fresmann N, Novoselc KT, Strunk T, Münster L, Olivares-Chauvet P, Ninkovic J, Junker JP. Dissecting the spatiotemporal diversity of adult neural stem cells. Mol Syst Biol 2024; 20:321-337. [PMID: 38365956 PMCID: PMC10987636 DOI: 10.1038/s44320-024-00022-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 01/31/2024] [Accepted: 02/01/2024] [Indexed: 02/18/2024] Open
Abstract
Adult stem cells are important for tissue turnover and regeneration. However, in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here, we dissected the diversity of neural stem cells in the adult zebrafish brain, an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single-cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze the regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells, with some subtypes being restricted to a single brain region, while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation, with different states being involved in proliferative and non-proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for the functional manipulation of neural stem cell subtypes.
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Affiliation(s)
- Nina Mitic
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
| | - Anika Neuschulz
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
- Humboldt Universität zu Berlin, Institute for Biology, Berlin, Germany
| | - Bastiaan Spanjaard
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
| | - Julia Schneider
- Helmholtz Center Munich - German Research Center for Environmental Health, Institute of Stem Cell Research, Munich, Germany
- Biomedical Center Munich (BMC), Department of Cell Biology and Anatomy, Medical Faculty, LMU, Munich, Germany
- Graduate School of Systemic Neurosciences, LMU, Munich, Germany
| | - Nora Fresmann
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Klara Tereza Novoselc
- Helmholtz Center Munich - German Research Center for Environmental Health, Institute of Stem Cell Research, Munich, Germany
- Biomedical Center Munich (BMC), Department of Cell Biology and Anatomy, Medical Faculty, LMU, Munich, Germany
- Graduate School of Systemic Neurosciences, LMU, Munich, Germany
| | - Taraneh Strunk
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
| | - Lisa Münster
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
| | - Pedro Olivares-Chauvet
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
| | - Jovica Ninkovic
- Helmholtz Center Munich - German Research Center for Environmental Health, Institute of Stem Cell Research, Munich, Germany
- Biomedical Center Munich (BMC), Department of Cell Biology and Anatomy, Medical Faculty, LMU, Munich, Germany
| | - Jan Philipp Junker
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany.
- Charité - Universitätsmedizin Berlin, Berlin, Germany.
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17
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Rauber S, Mohammadian H, Schmidkonz C, Atzinger A, Soare A, Treutlein C, Kemble S, Mahony CB, Geisthoff M, Angeli MR, Raimondo MG, Xu C, Yang KT, Lu L, Labinsky H, Saad MSA, Gwellem CA, Chang J, Huang K, Kampylafka E, Knitza J, Bilyy R, Distler JHW, Hanlon MM, Fearon U, Veale DJ, Roemer FW, Bäuerle T, Maric HM, Maschauer S, Ekici AB, Buckley CD, Croft AP, Kuwert T, Prante O, Cañete JD, Schett G, Ramming A. CD200 + fibroblasts form a pro-resolving mesenchymal network in arthritis. Nat Immunol 2024; 25:682-692. [PMID: 38396288 DOI: 10.1038/s41590-024-01774-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 01/30/2024] [Indexed: 02/25/2024]
Abstract
Fibroblasts are important regulators of inflammation, but whether fibroblasts change phenotype during resolution of inflammation is not clear. Here we use positron emission tomography to detect fibroblast activation protein (FAP) as a means to visualize fibroblast activation in vivo during inflammation in humans. While tracer accumulation is high in active arthritis, it decreases after tumor necrosis factor and interleukin-17A inhibition. Biopsy-based single-cell RNA-sequencing analyses in experimental arthritis show that FAP signal reduction reflects a phenotypic switch from pro-inflammatory MMP3+/IL6+ fibroblasts (high FAP internalization) to pro-resolving CD200+DKK3+ fibroblasts (low FAP internalization). Spatial transcriptomics of human joints indicates that pro-resolving niches of CD200+DKK3+ fibroblasts cluster with type 2 innate lymphoid cells, whereas MMP3+/IL6+ fibroblasts colocalize with inflammatory immune cells. CD200+DKK3+ fibroblasts stabilized the type 2 innate lymphoid cell phenotype and induced resolution of arthritis via CD200-CD200R1 signaling. Taken together, these data suggest a dynamic molecular regulation of the mesenchymal compartment during resolution of inflammation.
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Affiliation(s)
- Simon Rauber
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Hashem Mohammadian
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Christian Schmidkonz
- Department of Nuclear Medicine, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Department of Industrial Engineering and Health, Technical University Amberg-Weiden, Institute of Medical Engineering, Weiden, Germany
| | - Armin Atzinger
- Department of Nuclear Medicine, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Alina Soare
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Christoph Treutlein
- Institute of Radiology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Samuel Kemble
- Rheumatology Research Group, Institute for Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Queen Elizabeth Hospital, Birmingham, UK
- NIHR Birmingham Biomedical Research Center and Clinical Research Facility, University of Birmingham, Queen Elizabeth Hospital, Birmingham, UK
| | - Christopher B Mahony
- Rheumatology Research Group, Institute for Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Queen Elizabeth Hospital, Birmingham, UK
- NIHR Birmingham Biomedical Research Center and Clinical Research Facility, University of Birmingham, Queen Elizabeth Hospital, Birmingham, UK
| | - Manuel Geisthoff
- Department of Nuclear Medicine, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Mario R Angeli
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Maria G Raimondo
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Cong Xu
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Kai-Ting Yang
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Le Lu
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Hannah Labinsky
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Mina S A Saad
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Charles A Gwellem
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Jiyang Chang
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Kaiyue Huang
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Eleni Kampylafka
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Johannes Knitza
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Rostyslav Bilyy
- Department of Histology, Cytology, Embryology, Danylo Halytsky Lviv National Medical University, Lviv, Ukraine
- Institute of Cellular Biology and Pathology 'Nicolae Simionescu', Bucharest, Romania
| | - Jörg H W Distler
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Clinic for Rheumatology, University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
- Hiller Research Center, University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Megan M Hanlon
- Molecular Rheumatology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Ursula Fearon
- Molecular Rheumatology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Douglas J Veale
- EULAR Centre for Arthritis & Rheumatic Diseases, St. Vincent's University Hospital, University College Dublin, Dublin, Ireland
| | - Frank W Roemer
- Institute of Radiology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Tobias Bäuerle
- Institute of Radiology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Hans M Maric
- Rudolf-Virchow-Center for Integrative and Translational Imaging, University of Würzburg, Würzburg, Germany
| | - Simone Maschauer
- Department of Nuclear Medicine, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Arif B Ekici
- Institute of Human Genetics, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | | | - Adam P Croft
- Rheumatology Research Group, Institute for Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Queen Elizabeth Hospital, Birmingham, UK
- NIHR Birmingham Biomedical Research Center and Clinical Research Facility, University of Birmingham, Queen Elizabeth Hospital, Birmingham, UK
| | - Torsten Kuwert
- Department of Nuclear Medicine, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Olaf Prante
- Department of Nuclear Medicine, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | | | - Georg Schett
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Andreas Ramming
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany.
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany.
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18
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Chea S, Kreger J, Lopez-Burks ME, MacLean AL, Lander AD, Calof AL. Gastrulation-stage gene expression in Nipbl+/- mouse embryos foreshadows the development of syndromic birth defects. Sci Adv 2024; 10:eadl4239. [PMID: 38507484 PMCID: PMC10954218 DOI: 10.1126/sciadv.adl4239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 02/15/2024] [Indexed: 03/22/2024]
Abstract
In animal models, Nipbl deficiency phenocopies gene expression changes and birth defects seen in Cornelia de Lange syndrome, the most common cause of which is Nipbl haploinsufficiency. Previous studies in Nipbl+/- mice suggested that heart development is abnormal as soon as cardiogenic tissue is formed. To investigate this, we performed single-cell RNA sequencing on wild-type and Nipbl+/- mouse embryos at gastrulation and early cardiac crescent stages. Nipbl+/- embryos had fewer mesoderm cells than wild-type and altered proportions of mesodermal cell subpopulations. These findings were associated with underexpression of genes implicated in driving specific mesodermal lineages. In addition, Nanog was found to be overexpressed in all germ layers, and many gene expression changes observed in Nipbl+/- embryos could be attributed to Nanog overexpression. These findings establish a link between Nipbl deficiency, Nanog overexpression, and gene expression dysregulation/lineage misallocation, which ultimately manifest as birth defects in Nipbl+/- animals and Cornelia de Lange syndrome.
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Affiliation(s)
- Stephenson Chea
- Department of Developmental and Cell Biology, School of Biological Sciences, University of California Irvine, Irvine, CA 92697, USA
- Center for Complex Biological Systems, University of California Irvine, Irvine, CA 92697, USA
| | - Jesse Kreger
- Department of Quantitative and Computational Biology, Dornsife College of Letters, Arts, and Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Martha E. Lopez-Burks
- Department of Developmental and Cell Biology, School of Biological Sciences, University of California Irvine, Irvine, CA 92697, USA
- Center for Complex Biological Systems, University of California Irvine, Irvine, CA 92697, USA
| | - Adam L. MacLean
- Department of Quantitative and Computational Biology, Dornsife College of Letters, Arts, and Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Arthur D. Lander
- Department of Developmental and Cell Biology, School of Biological Sciences, University of California Irvine, Irvine, CA 92697, USA
- Center for Complex Biological Systems, University of California Irvine, Irvine, CA 92697, USA
| | - Anne L. Calof
- Department of Developmental and Cell Biology, School of Biological Sciences, University of California Irvine, Irvine, CA 92697, USA
- Center for Complex Biological Systems, University of California Irvine, Irvine, CA 92697, USA
- Department of Anatomy and Neurobiology, School of Medicine, University of California Irvine, Irvine, CA 92697, USA
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19
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Kang M, Armenteros JJA, Gulati GS, Gleyzer R, Avagyan S, Brown EL, Zhang W, Usmani A, Earland N, Wu Z, Zou J, Fields RC, Chen DY, Chaudhuri AA, Newman AM. Mapping single-cell developmental potential in health and disease with interpretable deep learning. bioRxiv 2024:2024.03.19.585637. [PMID: 38562882 PMCID: PMC10983880 DOI: 10.1101/2024.03.19.585637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of cell fate in developmental systems. However, identifying the molecular hallmarks of potency - the capacity of a cell to differentiate into other cell types - has remained challenging. Here, we introduce CytoTRACE 2, an interpretable deep learning framework for characterizing potency and differentiation states on an absolute scale from scRNA-seq data. Across 31 human and mouse scRNA-seq datasets encompassing 28 tissue types, CytoTRACE 2 outperformed existing methods for recovering experimentally determined potency levels and differentiation states covering the entire range of cellular ontogeny. Moreover, it reconstructed the temporal hierarchy of mouse embryogenesis across 62 timepoints; identified pan-tissue expression programs that discriminate major potency levels; and facilitated discovery of cellular phenotypes in cancer linked to survival and immunotherapy resistance. Our results illuminate a fundamental feature of cell biology and provide a broadly applicable platform for delineating single-cell differentiation landscapes in health and disease.
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20
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Wang D, Ling J, Tan R, Wang H, Qu Y, Li X, Lin J, Zhang Q, Hu Q, Liu Z, Lu Z, Lin Y, Sun L, Wang D, Zhou M, Shi Z, Gao W, Ye H, Lin X. CD169 + classical monocyte as an important participant in Graves' ophthalmopathy through CXCL12-CXCR4 axis. iScience 2024; 27:109213. [PMID: 38439953 PMCID: PMC10910260 DOI: 10.1016/j.isci.2024.109213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 09/11/2023] [Accepted: 02/07/2024] [Indexed: 03/06/2024] Open
Abstract
Patients with Graves' disease (GD) can develop Graves' ophthalmopathy (GO), but the underlying pathological mechanisms driving this development remain unclear. In our study, which included patients with GD and GO, we utilized single-cell RNA sequencing (scRNA-seq) and multiplatform analyses to investigate CD169+ classical monocytes, which secrete proinflammatory cytokines and are expanded through activated interferon signaling. We found that CD169+ clas_mono was clinically significant in predicting GO progression and prognosis, and differentiated into CD169+ macrophages that promote inflammation, adipogenesis, and fibrosis. Our murine model of early-stage GO showed that CD169+ classical monocytes accumulated in orbital tissue via the Cxcl12-Cxcr4 axis. Further studies are needed to investigate whether targeting circulating monocytes and the Cxcl12-Cxcr4 axis could alleviate GO progression.
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Affiliation(s)
- Dongliang Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China
| | - Jie Ling
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China
| | - RongQiang Tan
- The First People’s Hospital of Zhaoqing, Zhaoqing 526000, China
| | - Huishi Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China
| | - Yixin Qu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China
| | - Xingyi Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China
| | - Jinshan Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China
| | - Qikai Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China
| | - Qiuling Hu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China
| | - Zhong Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China
| | - Zhaojing Lu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China
| | - Yuheng Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China
| | - Li Sun
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China
| | - Dingqiao Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China
| | - Ming Zhou
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China
| | - Zhuoxing Shi
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China
| | - Wuyou Gao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China
| | - Huijing Ye
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China
| | - Xianchai Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China
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21
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Franken A, Bila M, Mechels A, Kint S, Van Dessel J, Pomella V, Vanuytven S, Philips G, Bricard O, Xiong J, Boeckx B, Hatse S, Van Brussel T, Schepers R, Van Aerde C, Geurs S, Vandecaveye V, Hauben E, Vander Poorten V, Verbandt S, Vandereyken K, Qian J, Tejpar S, Voet T, Clement PM, Lambrechts D. CD4 + T cell activation distinguishes response to anti-PD-L1+anti-CTLA4 therapy from anti-PD-L1 monotherapy. Immunity 2024; 57:541-558.e7. [PMID: 38442708 DOI: 10.1016/j.immuni.2024.02.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 11/30/2023] [Accepted: 02/08/2024] [Indexed: 03/07/2024]
Abstract
Cancer patients often receive a combination of antibodies targeting programmed death-ligand 1 (PD-L1) and cytotoxic T lymphocyte antigen-4 (CTLA4). We conducted a window-of-opportunity study in head and neck squamous cell carcinoma (HNSCC) to examine the contribution of anti-CTLA4 to anti-PD-L1 therapy. Single-cell profiling of on- versus pre-treatment biopsies identified T cell expansion as an early response marker. In tumors, anti-PD-L1 triggered the expansion of mostly CD8+ T cells, whereas combination therapy expanded both CD4+ and CD8+ T cells. Such CD4+ T cells exhibited an activated T helper 1 (Th1) phenotype. CD4+ and CD8+ T cells co-localized with and were surrounded by dendritic cells expressing T cell homing factors or antibody-producing plasma cells. T cell receptor tracing suggests that anti-CTLA4, but not anti-PD-L1, triggers the trafficking of CD4+ naive/central-memory T cells from tumor-draining lymph nodes (tdLNs), via blood, to the tumor wherein T cells acquire a Th1 phenotype. Thus, CD4+ T cell activation and recruitment from tdLNs are hallmarks of early response to anti-PD-L1 plus anti-CTLA4 in HNSCC.
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Affiliation(s)
- Amelie Franken
- Laboratory for Translational Genetics, Department of Human Genetics, KU Leuven, Leuven 3000, Belgium; VIB Center for Cancer Biology, Leuven 3000, Belgium
| | - Michel Bila
- Laboratory of Experimental Oncology (LEO), Department of Oncology, KU Leuven, 3000 Leuven, Belgium; Department of General Medical Oncology, UZ Leuven, 3000 Leuven, Belgium; Department of Oral and Maxillofacial Surgery, UZ Leuven, Leuven 3000, Belgium
| | - Aurelie Mechels
- Laboratory for Translational Genetics, Department of Human Genetics, KU Leuven, Leuven 3000, Belgium; VIB Center for Cancer Biology, Leuven 3000, Belgium
| | - Sam Kint
- Laboratory of Reproductive Genomics, Department of Human Genetics, KU Leuven, Leuven 3000, Belgium; KU Leuven Institute for Single Cell Omics (LISCO), Leuven 3000, Belgium
| | - Jeroen Van Dessel
- Department of Oral and Maxillofacial Surgery, UZ Leuven, Leuven 3000, Belgium
| | | | - Sebastiaan Vanuytven
- Laboratory of Reproductive Genomics, Department of Human Genetics, KU Leuven, Leuven 3000, Belgium; KU Leuven Institute for Single Cell Omics (LISCO), Leuven 3000, Belgium
| | - Gino Philips
- Laboratory for Translational Genetics, Department of Human Genetics, KU Leuven, Leuven 3000, Belgium; VIB Center for Cancer Biology, Leuven 3000, Belgium
| | - Orian Bricard
- Laboratory for Translational Genetics, Department of Human Genetics, KU Leuven, Leuven 3000, Belgium; VIB Center for Cancer Biology, Leuven 3000, Belgium
| | - Jieyi Xiong
- Laboratory for Translational Genetics, Department of Human Genetics, KU Leuven, Leuven 3000, Belgium; VIB Center for Cancer Biology, Leuven 3000, Belgium
| | - Bram Boeckx
- Laboratory for Translational Genetics, Department of Human Genetics, KU Leuven, Leuven 3000, Belgium; VIB Center for Cancer Biology, Leuven 3000, Belgium
| | - Sigrid Hatse
- Laboratory of Experimental Oncology (LEO), Department of Oncology, KU Leuven, 3000 Leuven, Belgium; Department of General Medical Oncology, UZ Leuven, 3000 Leuven, Belgium
| | - Thomas Van Brussel
- Laboratory for Translational Genetics, Department of Human Genetics, KU Leuven, Leuven 3000, Belgium; VIB Center for Cancer Biology, Leuven 3000, Belgium
| | - Rogier Schepers
- Laboratory for Translational Genetics, Department of Human Genetics, KU Leuven, Leuven 3000, Belgium; VIB Center for Cancer Biology, Leuven 3000, Belgium
| | - Cedric Van Aerde
- Department of Imaging and Pathology, KU Leuven, UZ Leuven, Leuven 3000, Belgium
| | - Sarah Geurs
- Laboratory of Reproductive Genomics, Department of Human Genetics, KU Leuven, Leuven 3000, Belgium; KU Leuven Institute for Single Cell Omics (LISCO), Leuven 3000, Belgium; Department of Biomolecular Medicine, UZ Ghent, Ghent 9052, Belgium
| | | | - Esther Hauben
- Otorhinolaryngology, Head and Neck Surgery, Leuven 3000, Belgium
| | - Vincent Vander Poorten
- Otorhinolaryngology, Head and Neck Surgery, Leuven 3000, Belgium; Department of Oncology, Section Head and Neck Oncology, Leuven 3000, Belgium
| | - Sara Verbandt
- Digestive Oncology, KU Leuven, UZ Leuven, Leuven 3000, Belgium
| | - Katy Vandereyken
- Laboratory of Reproductive Genomics, Department of Human Genetics, KU Leuven, Leuven 3000, Belgium; KU Leuven Institute for Single Cell Omics (LISCO), Leuven 3000, Belgium
| | - Junbin Qian
- Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China; Institute of Genetics, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Sabine Tejpar
- Digestive Oncology, KU Leuven, UZ Leuven, Leuven 3000, Belgium
| | - Thierry Voet
- Laboratory of Reproductive Genomics, Department of Human Genetics, KU Leuven, Leuven 3000, Belgium; KU Leuven Institute for Single Cell Omics (LISCO), Leuven 3000, Belgium
| | - Paul M Clement
- Laboratory of Experimental Oncology (LEO), Department of Oncology, KU Leuven, 3000 Leuven, Belgium; Department of General Medical Oncology, UZ Leuven, 3000 Leuven, Belgium.
| | - Diether Lambrechts
- Laboratory for Translational Genetics, Department of Human Genetics, KU Leuven, Leuven 3000, Belgium; VIB Center for Cancer Biology, Leuven 3000, Belgium; KU Leuven Institute for Single Cell Omics (LISCO), Leuven 3000, Belgium.
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22
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Martins LR, Sieverling L, Michelhans M, Schiller C, Erkut C, Grünewald TGP, Triana S, Fröhling S, Velten L, Glimm H, Scholl C. Single-cell division tracing and transcriptomics reveal cell types and differentiation paths in the regenerating lung. Nat Commun 2024; 15:2246. [PMID: 38472236 DOI: 10.1038/s41467-024-46469-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 02/28/2024] [Indexed: 03/14/2024] Open
Abstract
Understanding the molecular and cellular processes involved in lung epithelial regeneration may fuel the development of therapeutic approaches for lung diseases. We combine mouse models allowing diphtheria toxin-mediated damage of specific epithelial cell types and parallel GFP-labeling of functionally dividing cells with single-cell transcriptomics to characterize the regeneration of the distal lung. We uncover cell types, including Krt13+ basal and Krt15+ club cells, detect an intermediate cell state between basal and goblet cells, reveal goblet cells as actively dividing progenitor cells, and provide evidence that adventitial fibroblasts act as supporting cells in epithelial regeneration. We also show that diphtheria toxin-expressing cells can persist in the lung, express specific inflammatory factors, and transcriptionally resemble a previously undescribed population in the lungs of COVID-19 patients. Our study provides a comprehensive single-cell atlas of the distal lung that characterizes early transcriptional and cellular responses to concise epithelial injury, encompassing proliferation, differentiation, and cell-to-cell interactions.
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Affiliation(s)
- Leila R Martins
- Division of Applied Functional Genomics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and Heidelberg University Hospital, Heidelberg, Germany.
| | - Lina Sieverling
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and Heidelberg University Hospital, Heidelberg, Germany
- Division of Translational Medical Oncology, DKFZ, Heidelberg, Germany
| | - Michelle Michelhans
- Division of Applied Functional Genomics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and Heidelberg University Hospital, Heidelberg, Germany
- Division of Translational Medical Oncology, DKFZ, Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | - Chiara Schiller
- Division of Applied Functional Genomics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and Heidelberg University Hospital, Heidelberg, Germany
- Institute for Computational Biomedicine, Faculty of Medicine, Heidelberg University Hospital and Heidelberg University, Heidelberg, Germany
| | - Cihan Erkut
- Division of Applied Functional Genomics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and Heidelberg University Hospital, Heidelberg, Germany
| | - Thomas G P Grünewald
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and Heidelberg University Hospital, Heidelberg, Germany
- Hopp-Children's Cancer Center (KiTZ), Heidelberg, Germany
- Division of Translational Pediatric Sarcoma Research, DKFZ, Heidelberg, Germany
- Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
- German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Sergio Triana
- Structural and Computational Biology, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
- Broad Institute of Harvard and MIT, Cambridge, USA
- Department of Chemistry, Institute for Medical Engineering and Sciences (IMES), and Koch Institute for Integrative Cancer Research, MIT, Cambridge, USA
| | - Stefan Fröhling
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and Heidelberg University Hospital, Heidelberg, Germany
- Division of Translational Medical Oncology, DKFZ, Heidelberg, Germany
- German Cancer Consortium (DKTK), Heidelberg, Germany
- Institute of Human Genetics, Heidelberg University, Heidelberg, Germany
| | - Lars Velten
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Hanno Glimm
- Department for Translational Medical Oncology, National Center for Tumor Diseases Dresden (NCT/UCC), a partnership between DKFZ, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, and Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany
- Translational Medical Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Translational Functional Cancer Genomics, DKFZ, Heidelberg, Germany
- DKTK, partner site Dresden, Dresden, Germany
| | - Claudia Scholl
- Division of Applied Functional Genomics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and Heidelberg University Hospital, Heidelberg, Germany.
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23
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Yen A, Chen X, Skinner DD, Leti F, Crosby M, Hoisington-Lopez J, Wu Y, Chen J, Mitra RD, Dougherty JD. MYT1L deficiency impairs excitatory neuron trajectory during cortical development. bioRxiv 2024:2024.03.06.583632. [PMID: 38496654 PMCID: PMC10942489 DOI: 10.1101/2024.03.06.583632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Mutations that reduce the function of MYT1L, a neuron-specific transcription factor, are associated with a syndromic neurodevelopmental disorder. Furthermore, MYT1L is routinely used as a proneural factor in fibroblast-to-neuron transdifferentiation. MYT1L has been hypothesized to play a role in the trajectory of neuronal specification and subtype specific maturation, but this hypothesis has not been directly tested, nor is it clear which neuron types are most impacted by MYT1L loss. In this study, we profiled 313,335 nuclei from the forebrains of wild-type and MYT1L-deficient mice at two developmental stages: E14 at the peak of neurogenesis and P21, when neurogenesis is complete, to examine the role of MYT1L levels in the trajectory of neuronal development. We found that MYT1L deficiency significantly disrupted the relative proportion of cortical excitatory neurons at E14 and P21. Significant changes in gene expression were largely concentrated in excitatory neurons, suggesting that transcriptional effects of MYT1L deficiency are largely due to disruption of neuronal maturation programs. Most effects on gene expression were cell autonomous and persistent through development. In addition, while MYT1L can both activate and repress gene expression, the repressive effects were most sensitive to haploinsufficiency, and thus more likely mediate MYT1L syndrome. These findings illuminate the intricate role of MYT1L in orchestrating gene expression dynamics during neuronal development, providing insights into the molecular underpinnings of MYT1L syndrome.
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Affiliation(s)
- Allen Yen
- Department of Genetics, Washington University School of Medicine, Saint Louis, MO, USA
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Xuhua Chen
- Department of Genetics, Washington University School of Medicine, Saint Louis, MO, USA
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, Saint Louis, MO, USA
| | | | | | - MariaLynn Crosby
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, Saint Louis, MO, USA
- DNA Sequencing and Innovation Lab, Washington University School of Medicine, Saint Louis, MO
| | - Jessica Hoisington-Lopez
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, Saint Louis, MO, USA
- DNA Sequencing and Innovation Lab, Washington University School of Medicine, Saint Louis, MO
| | - Yizhe Wu
- Department of Genetics, Washington University School of Medicine, Saint Louis, MO, USA
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Jiayang Chen
- Department of Genetics, Washington University School of Medicine, Saint Louis, MO, USA
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Robi D. Mitra
- Department of Genetics, Washington University School of Medicine, Saint Louis, MO, USA
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Joseph D. Dougherty
- Department of Genetics, Washington University School of Medicine, Saint Louis, MO, USA
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
- Intellectual and Developmental Disabilities Research Center, Washington University School of Medicine, Saint Louis, MO, USA
- Lead contact
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24
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Zhang R, Qiu C, Filippova G, Li G, Shendure J, Vert JP, Deng X, Disteche C, Noble WS. Multi-condition and multi-modal temporal profile inference during mouse embryonic development. bioRxiv 2024:2024.03.03.583179. [PMID: 38496477 PMCID: PMC10942306 DOI: 10.1101/2024.03.03.583179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
The emergence of single-cell time-series datasets enables modeling of changes in various types of cellular profiles over time. However, due to the disruptive nature of single-cell measurements, it is impossible to capture the full temporal trajectory of a particular cell. Furthermore, single-cell profiles can be collected at mismatched time points across different conditions (e.g., sex, batch, disease) and data modalities (e.g., scRNA-seq, scATAC-seq), which makes modeling challenging. Here we propose a joint modeling framework, Sunbear, for integrating multi-condition and multi-modal single-cell profiles across time. Sunbear can be used to impute single-cell temporal profile changes, align multi-dataset and multi-modal profiles across time, and extrapolate single-cell profiles in a missing modality. We applied Sunbear to reveal sex-biased transcription during mouse embryonic development and predict dynamic relationships between epigenetic priming and transcription for cells in which multi-modal profiles are unavailable. Sunbear thus enables the projection of single-cell time-series snapshots to multi-modal and multi-condition views of cellular trajectories.
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Affiliation(s)
- Ran Zhang
- Department of Genome Sciences, University of Washington
- eScience Institute, University of Washington
| | | | | | - Gang Li
- Department of Genome Sciences, University of Washington
- eScience Institute, University of Washington
| | - Jay Shendure
- Brotman Baty Institute for Precision Medicine, University of Washington
- Howard Hughes Medical Institute
- Allen Center for Cell Lineage Tracing
| | | | - Xinxian Deng
- Department of Pathology, University of Washington
| | | | - William Stafford Noble
- Department of Genome Sciences, University of Washington
- Paul G. Allen School of Computer Science and Engineering, University of Washington
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25
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Han G, Sinjab A, Rahal Z, Lynch AM, Treekitkarnmongkol W, Liu Y, Serrano AG, Feng J, Liang K, Khan K, Lu W, Hernandez SD, Liu Y, Cao X, Dai E, Pei G, Hu J, Abaya C, Gomez-Bolanos LI, Peng F, Chen M, Parra ER, Cascone T, Sepesi B, Moghaddam SJ, Scheet P, Negrao MV, Heymach JV, Li M, Dubinett SM, Stevenson CS, Spira AE, Fujimoto J, Solis LM, Wistuba II, Chen J, Wang L, Kadara H. An atlas of epithelial cell states and plasticity in lung adenocarcinoma. Nature 2024; 627:656-663. [PMID: 38418883 PMCID: PMC10954546 DOI: 10.1038/s41586-024-07113-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 01/24/2024] [Indexed: 03/02/2024]
Abstract
Understanding the cellular processes that underlie early lung adenocarcinoma (LUAD) development is needed to devise intervention strategies1. Here we studied 246,102 single epithelial cells from 16 early-stage LUADs and 47 matched normal lung samples. Epithelial cells comprised diverse normal and cancer cell states, and diversity among cancer cells was strongly linked to LUAD-specific oncogenic drivers. KRAS mutant cancer cells showed distinct transcriptional features, reduced differentiation and low levels of aneuploidy. Non-malignant areas surrounding human LUAD samples were enriched with alveolar intermediate cells that displayed elevated KRT8 expression (termed KRT8+ alveolar intermediate cells (KACs) here), reduced differentiation, increased plasticity and driver KRAS mutations. Expression profiles of KACs were enriched in lung precancer cells and in LUAD cells and signified poor survival. In mice exposed to tobacco carcinogen, KACs emerged before lung tumours and persisted for months after cessation of carcinogen exposure. Moreover, they acquired Kras mutations and conveyed sensitivity to targeted KRAS inhibition in KAC-enriched organoids derived from alveolar type 2 (AT2) cells. Last, lineage-labelling of AT2 cells or KRT8+ cells following carcinogen exposure showed that KACs are possible intermediates in AT2-to-tumour cell transformation. This study provides new insights into epithelial cell states at the root of LUAD development, and such states could harbour potential targets for prevention or intervention.
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Affiliation(s)
- Guangchun Han
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ansam Sinjab
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Zahraa Rahal
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Anne M Lynch
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Graduate Program in Developmental Biology, Baylor College of Medicine, Houston, TX, USA
| | - Warapen Treekitkarnmongkol
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yuejiang Liu
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- The University of Texas Health Houston Graduate School of Biomedical Sciences, Houston, TX, USA
| | - Alejandra G Serrano
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jiping Feng
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ke Liang
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Khaja Khan
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Wei Lu
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Sharia D Hernandez
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yunhe Liu
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Xuanye Cao
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Enyu Dai
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Guangsheng Pei
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jian Hu
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Camille Abaya
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Lorena I Gomez-Bolanos
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Fuduan Peng
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Minyue Chen
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- The University of Texas Health Houston Graduate School of Biomedical Sciences, Houston, TX, USA
| | - Edwin R Parra
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Tina Cascone
- Department of Thoracic, Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Boris Sepesi
- Department of Cardiovascular and Thoracic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Seyed Javad Moghaddam
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Paul Scheet
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Marcelo V Negrao
- Department of Thoracic, Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - John V Heymach
- Department of Thoracic, Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Mingyao Li
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Steven M Dubinett
- Department of Medicine, The University of California Los Angeles, Los Angeles, CA, USA
| | | | - Avrum E Spira
- Lung Cancer Initiative at Johnson & Johnson, Boston, MA, USA
- Section of Computational Biomedicine, School of Medicine, Boston University, Boston, MA, USA
| | - Junya Fujimoto
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Luisa M Solis
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ignacio I Wistuba
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jichao Chen
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
- The University of Texas Health Houston Graduate School of Biomedical Sciences, Houston, TX, USA.
| | - Linghua Wang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
- The University of Texas Health Houston Graduate School of Biomedical Sciences, Houston, TX, USA.
| | - Humam Kadara
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
- The University of Texas Health Houston Graduate School of Biomedical Sciences, Houston, TX, USA.
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26
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Malla SB, Byrne RM, Lafarge MW, Corry SM, Fisher NC, Tsantoulis PK, Mills ML, Ridgway RA, Lannagan TRM, Najumudeen AK, Gilroy KL, Amirkhah R, Maguire SL, Mulholland EJ, Belnoue-Davis HL, Grassi E, Viviani M, Rogan E, Redmond KL, Sakhnevych S, McCooey AJ, Bull C, Hoey E, Sinevici N, Hall H, Ahmaderaghi B, Domingo E, Blake A, Richman SD, Isella C, Miller C, Bertotti A, Trusolino L, Loughrey MB, Kerr EM, Tejpar S, Maughan TS, Lawler M, Campbell AD, Leedham SJ, Koelzer VH, Sansom OJ, Dunne PD. Pathway level subtyping identifies a slow-cycling biological phenotype associated with poor clinical outcomes in colorectal cancer. Nat Genet 2024; 56:458-472. [PMID: 38351382 PMCID: PMC10937375 DOI: 10.1038/s41588-024-01654-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 01/03/2024] [Indexed: 02/29/2024]
Abstract
Molecular stratification using gene-level transcriptional data has identified subtypes with distinctive genotypic and phenotypic traits, as exemplified by the consensus molecular subtypes (CMS) in colorectal cancer (CRC). Here, rather than gene-level data, we make use of gene ontology and biological activation state information for initial molecular class discovery. In doing so, we defined three pathway-derived subtypes (PDS) in CRC: PDS1 tumors, which are canonical/LGR5+ stem-rich, highly proliferative and display good prognosis; PDS2 tumors, which are regenerative/ANXA1+ stem-rich, with elevated stromal and immune tumor microenvironmental lineages; and PDS3 tumors, which represent a previously overlooked slow-cycling subset of tumors within CMS2 with reduced stem populations and increased differentiated lineages, particularly enterocytes and enteroendocrine cells, yet display the worst prognosis in locally advanced disease. These PDS3 phenotypic traits are evident across numerous bulk and single-cell datasets, and demark a series of subtle biological states that are currently under-represented in pre-clinical models and are not identified using existing subtyping classifiers.
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Affiliation(s)
- Sudhir B Malla
- The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
| | - Ryan M Byrne
- The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
| | - Maxime W Lafarge
- Department of Pathology and Molecular Pathology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Shania M Corry
- The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
| | - Natalie C Fisher
- The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
| | | | | | | | | | | | | | - Raheleh Amirkhah
- The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
| | - Sarah L Maguire
- The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
| | | | | | - Elena Grassi
- Candiolo Cancer Institute, FPO IRCCS, Candiolo, Torino, Italy
- Department of Oncology, University of Torino, Candiolo, Torino, Italy
| | - Marco Viviani
- Candiolo Cancer Institute, FPO IRCCS, Candiolo, Torino, Italy
- Department of Oncology, University of Torino, Candiolo, Torino, Italy
| | - Emily Rogan
- The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
| | - Keara L Redmond
- The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
| | - Svetlana Sakhnevych
- The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
| | - Aoife J McCooey
- The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
| | - Courtney Bull
- The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
| | - Emily Hoey
- The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
| | - Nicoleta Sinevici
- The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
| | - Holly Hall
- Cancer Research UK Scotland Institute, Glasgow, UK
| | - Baharak Ahmaderaghi
- School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, UK
| | - Enric Domingo
- Department of Oncology, University of Oxford, Oxford, Oxfordshire, UK
| | - Andrew Blake
- Department of Oncology, University of Oxford, Oxford, Oxfordshire, UK
| | - Susan D Richman
- Leeds Institute of Medical Research, University of Leeds, Leeds, UK
| | - Claudio Isella
- Candiolo Cancer Institute, FPO IRCCS, Candiolo, Torino, Italy
- Department of Oncology, University of Torino, Candiolo, Torino, Italy
| | - Crispin Miller
- Cancer Research UK Scotland Institute, Glasgow, UK
- School of Cancer Sciences, University of Glasgow, Glasgow, UK
| | - Andrea Bertotti
- Candiolo Cancer Institute, FPO IRCCS, Candiolo, Torino, Italy
- Department of Oncology, University of Torino, Candiolo, Torino, Italy
| | - Livio Trusolino
- Candiolo Cancer Institute, FPO IRCCS, Candiolo, Torino, Italy
- Department of Oncology, University of Torino, Candiolo, Torino, Italy
| | - Maurice B Loughrey
- The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
- Department of Cellular Pathology, Royal Victoria Hospital, Belfast Health and Social Care Trust, Belfast, UK
| | - Emma M Kerr
- The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
| | - Sabine Tejpar
- Department of Oncology, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Timothy S Maughan
- Department of Oncology, University of Oxford, Oxford, Oxfordshire, UK
| | - Mark Lawler
- The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
| | | | | | - Viktor H Koelzer
- Department of Pathology and Molecular Pathology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Department of Oncology, University of Oxford, Oxford, Oxfordshire, UK
| | - Owen J Sansom
- Cancer Research UK Scotland Institute, Glasgow, UK
- School of Cancer Sciences, University of Glasgow, Glasgow, UK
| | - Philip D Dunne
- The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK.
- Cancer Research UK Scotland Institute, Glasgow, UK.
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27
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Patta I, Zand M, Lee L, Mishra S, Bortnick A, Lu H, Prusty A, McArdle S, Mikulski Z, Wang HY, Cheng CS, Fisch KM, Hu M, Murre C. Nuclear morphology is shaped by loop-extrusion programs. Nature 2024; 627:196-203. [PMID: 38355805 PMCID: PMC11052650 DOI: 10.1038/s41586-024-07086-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 01/17/2024] [Indexed: 02/16/2024]
Abstract
It is well established that neutrophils adopt malleable polymorphonuclear shapes to migrate through narrow interstitial tissue spaces1-3. However, how polymorphonuclear structures are assembled remains unknown4. Here we show that in neutrophil progenitors, halting loop extrusion-a motor-powered process that generates DNA loops by pulling in chromatin5-leads to the assembly of polymorphonuclear genomes. Specifically, we found that in mononuclear neutrophil progenitors, acute depletion of the loop-extrusion loading factor nipped-B-like protein (NIPBL) induced the assembly of horseshoe, banded, ringed and hypersegmented nuclear structures and led to a reduction in nuclear volume, mirroring what is observed during the differentiation of neutrophils. Depletion of NIPBL also induced cell-cycle arrest, activated a neutrophil-specific gene program and conditioned a loss of interactions across topologically associating domains to generate a chromatin architecture that resembled that of differentiated neutrophils. Removing NIPBL resulted in enrichment for mega-loops and interchromosomal hubs that contain genes associated with neutrophil-specific enhancer repertoires and an inflammatory gene program. On the basis of these observations, we propose that in neutrophil progenitors, loop-extrusion programs produce lineage-specific chromatin architectures that permit the packing of chromosomes into geometrically confined lobular structures. Our data also provide a blueprint for the assembly of polymorphonuclear structures, and point to the possibility of engineering de novo nuclear shapes to facilitate the migration of effector cells in densely populated tumorigenic environments.
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Affiliation(s)
- Indumathi Patta
- Department of Molecular Biology, University of California, San Diego, La Jolla, CA, USA
| | - Maryam Zand
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Lindsay Lee
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Shreya Mishra
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Alexandra Bortnick
- Department of Molecular Biology, University of California, San Diego, La Jolla, CA, USA
| | - Hanbin Lu
- Department of Molecular Biology, University of California, San Diego, La Jolla, CA, USA
| | - Arpita Prusty
- Department of Molecular Biology, University of California, San Diego, La Jolla, CA, USA
| | - Sara McArdle
- Microscopy and Histology Core Facility, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Zbigniew Mikulski
- Microscopy and Histology Core Facility, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Huan-You Wang
- Department of Pathology, University of California, San Diego, La Jolla, CA, USA
| | - Christine S Cheng
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Kathleen M Fisch
- Department of Obstetrics, Gynecology and Reproductive Sciences, University of California, San Diego, La Jolla, CA, USA.
| | - Ming Hu
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, USA.
| | - Cornelis Murre
- Department of Molecular Biology, University of California, San Diego, La Jolla, CA, USA.
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28
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Huang C, Wang X, Wang Y, Feng Y, Wang X, Chen S, Yan P, Liao J, Zhang Q, Mao C, Li Y, Wang L, Wang X, Yi W, Cai W, Chen S, Hong N, He W, Chen J, Jin W. Sirpα on tumor-associated myeloid cells restrains antitumor immunity in colorectal cancer independent of its interaction with CD47. Nat Cancer 2024; 5:500-516. [PMID: 38200243 DOI: 10.1038/s43018-023-00691-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 11/15/2023] [Indexed: 01/12/2024]
Abstract
Immunosuppressive myeloid cells hinder immunotherapeutic efficacy in tumors, but the precise mechanisms remain undefined. Here, by performing single-cell RNA sequencing in colorectal cancer tissues, we found tumor-associated macrophages and granulocytic myeloid-derived suppressor cells increased most compared to their counterparts in normal tissue and displayed the highest immune-inhibitory signatures among all immunocytes. These cells exhibited significantly increased expression of immunoreceptor tyrosine-based inhibitory motif-bearing receptors, including SIRPA. Notably, Sirpa-/- mice were more resistant to tumor progression than wild-type mice. Moreover, Sirpα deficiency reprogramed the tumor microenvironment through expansion of TAM_Ccl8hi and gMDSC_H2-Q10hi subsets showing strong antitumor activity. Sirpa-/- macrophages presented strong phagocytosis and antigen presentation to enhance T cell activation and proliferation. Furthermore, Sirpa-/- macrophages facilitated T cell recruitment via Syk/Btk-dependent Ccl8 secretion. Therefore, Sirpα deficiency enhances innate and adaptive immune activation independent of expression of CD47 and Sirpα blockade could be a promising strategy to improve cancer immunotherapy efficacy.
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Affiliation(s)
- Chunliu Huang
- Molecular Imaging Center, Guangdong Provincial Key Laboratory of Biomedical Imaging, The Fifth Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Zhuhai, China
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Xuefei Wang
- School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Yingzhao Wang
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Yongyi Feng
- Department of Immunology and Microbiology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Xiumei Wang
- Department of Immunology and Microbiology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Shan Chen
- School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Peidong Yan
- School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Jing Liao
- GMU-GIBH Joint School of Life Sciences, Guangzhou Medical University, Guangzhou, China
| | - Qi Zhang
- School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Chengzhou Mao
- Department of Anatomy and Histology, Shenzhen University Health Science Center, Shenzhen, China
| | - Yang Li
- Shenzhen People's Hospital, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China
| | - Lixiang Wang
- Department of Immunology and Microbiology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Xinyu Wang
- Department of Immunology and Microbiology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Wei Yi
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, China
| | - Weibin Cai
- Guangdong Engineering & Technology Research Center for Disease-Model Animals, Laboratory Animal Center, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
- Department of Biochemistry, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Shoudeng Chen
- Molecular Imaging Center, Guangdong Provincial Key Laboratory of Biomedical Imaging, The Fifth Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Zhuhai, China
| | - Ni Hong
- School of Life Sciences, Southern University of Science and Technology, Shenzhen, China.
| | - Weiling He
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.
- Department of Gastrointestinal Surgery, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
| | - Jun Chen
- Department of Immunology and Microbiology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.
- Guangdong Engineering & Technology Research Center for Disease-Model Animals, Laboratory Animal Center, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.
- Key Laboratory of Tropical Disease Control of the Ministry of Education, Sun Yat-sen University, Guangzhou, China.
- Jinfeng Laboratory, Chongqing, China.
| | - Wenfei Jin
- School of Life Sciences, Southern University of Science and Technology, Shenzhen, China.
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
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29
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Tur S, Palii CG, Brand M. Cell fate decision in erythropoiesis: Insights from multiomics studies. Exp Hematol 2024; 131:104167. [PMID: 38262486 PMCID: PMC10939800 DOI: 10.1016/j.exphem.2024.104167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 01/10/2024] [Accepted: 01/13/2024] [Indexed: 01/25/2024]
Abstract
Every second, the body produces 2 million red blood cells through a process called erythropoiesis. Erythropoiesis is hierarchical in that it results from a series of cell fate decisions whereby hematopoietic stem cells progress toward the erythroid lineage. Single-cell transcriptomic and proteomic approaches have revolutionized the way we understand erythropoiesis, revealing it to be a gradual process that underlies a progressive restriction of fate potential driven by quantitative changes in lineage-specifying transcription factors. Despite these major advances, we still know very little about what cell fate decision entails at the molecular level. Novel approaches that simultaneously measure additional properties in single cells, including chromatin accessibility, transcription factor binding, and/or cell surface proteins are being developed at a fast pace, providing the means to exciting new advances in the near future. In this review, we briefly summarize the main findings obtained from single-cell studies of erythropoiesis, highlight outstanding questions, and suggest recent technological advances to address them.
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Affiliation(s)
- Steven Tur
- Department of Cell and Regenerative Biology, Wisconsin Blood Cancer Research Institute, Wisconsin Institutes for Medical Research, University of Wisconsin School of Medicine and Public Health, Carbone Cancer Center, Madison, WI; Cellular and Molecular Biology Graduate Program, University of Wisconsin School of Medicine and Public Health, Madison, WI
| | - Carmen G Palii
- Department of Cell and Regenerative Biology, Wisconsin Blood Cancer Research Institute, Wisconsin Institutes for Medical Research, University of Wisconsin School of Medicine and Public Health, Carbone Cancer Center, Madison, WI
| | - Marjorie Brand
- Department of Cell and Regenerative Biology, Wisconsin Blood Cancer Research Institute, Wisconsin Institutes for Medical Research, University of Wisconsin School of Medicine and Public Health, Carbone Cancer Center, Madison, WI.
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30
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Nakanoh S, Sham K, Ghimire S, Mohorianu I, Rayon T, Vallier L. Human surface ectoderm and amniotic ectoderm are sequentially specified according to cellular density. Sci Adv 2024; 10:eadh7748. [PMID: 38427729 PMCID: PMC10906920 DOI: 10.1126/sciadv.adh7748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 01/29/2024] [Indexed: 03/03/2024]
Abstract
Mechanisms specifying amniotic ectoderm and surface ectoderm are unresolved in humans due to their close similarities in expression patterns and signal requirements. This lack of knowledge hinders the development of protocols to accurately model human embryogenesis. Here, we developed a human pluripotent stem cell model to investigate the divergence between amniotic and surface ectoderms. In the established culture system, cells differentiated into functional amnioblast-like cells. Single-cell RNA sequencing analyses of amnioblast differentiation revealed an intermediate cell state with enhanced surface ectoderm gene expression. Furthermore, when the differentiation started at the confluent condition, cells retained the expression profile of surface ectoderm. Collectively, we propose that human amniotic ectoderm and surface ectoderm are specified along a common nonneural ectoderm trajectory based on cell density. Our culture system also generated extraembryonic mesoderm-like cells from the primed pluripotent state. Together, this study provides an integrative understanding of the human nonneural ectoderm development and a model for embryonic and extraembryonic human development around gastrulation.
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Affiliation(s)
- Shota Nakanoh
- Wellcome-MRC Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge CB2 0AW, UK
- Epigenetics & Signalling Programmes, Babraham Institute, Cambridge CB22 3AT, UK
| | - Kendig Sham
- Wellcome-MRC Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge CB2 0AW, UK
| | - Sabitri Ghimire
- Wellcome-MRC Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge CB2 0AW, UK
| | - Irina Mohorianu
- Wellcome-MRC Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge CB2 0AW, UK
| | - Teresa Rayon
- Epigenetics & Signalling Programmes, Babraham Institute, Cambridge CB22 3AT, UK
| | - Ludovic Vallier
- Wellcome-MRC Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge CB2 0AW, UK
- Berlin Institute of Health Centre for Regenerative Therapies, Charité - Universitätsmedizin Berlin, Berlin 13353, Germany
- Max Planck Institute for Molecular Genetics, Berlin 14195, Germany
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Kaur H, Jha P, Ochatt SJ, Kumar V. Single-cell transcriptomics is revolutionizing the improvement of plant biotechnology research: recent advances and future opportunities. Crit Rev Biotechnol 2024; 44:202-217. [PMID: 36775666 DOI: 10.1080/07388551.2023.2165900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Revised: 11/04/2022] [Accepted: 12/08/2022] [Indexed: 02/14/2023]
Abstract
Single-cell approaches are a promising way to obtain high-resolution transcriptomics data and have the potential to revolutionize the study of plant growth and development. Recent years have seen the advent of unprecedented technological advances in the field of plant biology to study the transcriptional information of individual cells by single-cell RNA sequencing (scRNA-seq). This review focuses on the modern advancements of single-cell transcriptomics in plants over the past few years. In addition, it also offers a new insight of how these emerging methods will expedite advance research in plant biotechnology in the near future. Lastly, the various technological hurdles and inherent limitations of single-cell technology that need to be conquered to develop such outstanding possible knowledge gain is critically analyzed and discussed.
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Affiliation(s)
- Harmeet Kaur
- Division of Research and Development, Plant Biotechnology Lab, Lovely Professional University, Phagwara, Punjab, India
- Department of Biotechnology, Lovely Faculty of Technology and Sciences, Lovely Professional University, Phagwara, Punjab, India
| | - Priyanka Jha
- Department of Biotechnology, Lovely Faculty of Technology and Sciences, Lovely Professional University, Phagwara, Punjab, India
- Department of Research Facilitation, Division of Research and Development, Lovely Professional University, Phagwara, Punjab, India
| | - Sergio J Ochatt
- Agroécologie, InstitutAgro Dijon, INRAE, Univ. Bourgogne Franche-Comté, Dijon, France
| | - Vijay Kumar
- Division of Research and Development, Plant Biotechnology Lab, Lovely Professional University, Phagwara, Punjab, India
- Department of Biotechnology, Lovely Faculty of Technology and Sciences, Lovely Professional University, Phagwara, Punjab, India
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Chea S, Kreger J, Lopez-Burks ME, MacLean AL, Lander AD, Calof AL. Gastrulation-stage gene expression in Nipbl+/- mouse embryos foreshadows the development of syndromic birth defects. bioRxiv 2024:2023.10.16.558465. [PMID: 37905011 PMCID: PMC10614802 DOI: 10.1101/2023.10.16.558465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
In animal models, Nipbl -deficiency phenocopies gene expression changes and birth defects seen in Cornelia de Lange Syndrome (CdLS), the most common cause of which is Nipbl -haploinsufficiency. Previous studies in Nipbl +/- mice suggested that heart development is abnormal as soon as cardiogenic tissue is formed. To investigate this, we performed single-cell RNA-sequencing on wildtype (WT) and Nipbl +/- mouse embryos at gastrulation and early cardiac crescent stages. Nipbl +/- embryos had fewer mesoderm cells than WT and altered proportions of mesodermal cell subpopulations. These findings were associated with underexpression of genes implicated in driving specific mesodermal lineages. In addition, Nanog was found to be overexpressed in all germ layers, and many gene expression changes observed in Nipbl +/- embryos could be attributed to Nanog overexpression. These findings establish a link between Nipbl -deficiency, Nanog overexpression, and gene expression dysregulation/lineage misallocation, which ultimately manifest as birth defects in Nipbl +/- animals and CdLS. Teaser Gene expression changes during gastrulation of Nipbl -deficient mice shed light on early origins of structural birth defects.
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33
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Harkany T, Tretiakov E, Varela L, Jarc J, Rebernik P, Newbold S, Keimpema E, Verkhratsky A, Horvath T, Romanov R. Molecularly stratified hypothalamic astrocytes are cellular foci for obesity. Res Sq 2024:rs.3.rs-3748581. [PMID: 38405925 PMCID: PMC10889077 DOI: 10.21203/rs.3.rs-3748581/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Astrocytes safeguard the homeostasis of the central nervous system1,2. Despite their prominent morphological plasticity under conditions that challenge the brain's adaptive capacity3-5, the classification of astrocytes, and relating their molecular make-up to spatially devolved neuronal operations that specify behavior or metabolism, remained mostly futile6,7. Although it seems unexpected in the era of single-cell biology, the lack of a major advance in stratifying astrocytes under physiological conditions rests on the incompatibility of 'neurocentric' algorithms that rely on stable developmental endpoints, lifelong transcriptional, neurotransmitter, and neuropeptide signatures for classification6-8 with the dynamic functional states, anatomic allocation, and allostatic plasticity of astrocytes1. Simplistically, therefore, astrocytes are still grouped as 'resting' vs. 'reactive', the latter referring to pathological states marked by various inducible genes3,9,10. Here, we introduced a machine learning-based feature recognition algorithm that benefits from the cumulative power of published single-cell RNA-seq data on astrocytes as a reference map to stepwise eliminate pleiotropic and inducible cellular features. For the healthy hypothalamus, this walk-back approach revealed gene regulatory networks (GRNs) that specified subsets of astrocytes, and could be used as landmarking tools for their anatomical assignment. The core molecular censuses retained by astrocyte subsets were sufficient to stratify them by allostatic competence, chiefly their signaling and metabolic interplay with neurons. Particularly, we found differentially expressed mitochondrial genes in insulin-sensing astrocytes and demonstrated their reciprocal signaling with neurons that work antagonistically within the food intake circuitry. As a proof-of-concept, we showed that disrupting Mfn2 expression in astrocytes reduced their ability to support dynamic circuit reorganization, a time-locked feature of satiety in the hypothalamus, thus leading to obesity in mice. Overall, our results suggest that astrocytes in the healthy brain are fundamentally more heterogeneous than previously thought and topologically mirror the specificity of local neurocircuits.
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Affiliation(s)
- Tibor Harkany
- Center for Brain Research, Medical University of Vienna
| | | | | | - Jasna Jarc
- Center for Brain Research, Medical University of Vienna
| | | | | | - Erik Keimpema
- Medical University of Vienna, Center for Brain Research
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Gardner EE, Earlie EM, Li K, Thomas J, Hubisz MJ, Stein BD, Zhang C, Cantley LC, Laughney AM, Varmus H. Lineage-specific intolerance to oncogenic drivers restricts histological transformation. Science 2024; 383:eadj1415. [PMID: 38330136 DOI: 10.1126/science.adj1415] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 12/08/2023] [Indexed: 02/10/2024]
Abstract
Lung adenocarcinoma (LUAD) and small cell lung cancer (SCLC) are thought to originate from different epithelial cell types in the lung. Intriguingly, LUAD can histologically transform into SCLC after treatment with targeted therapies. In this study, we designed models to follow the conversion of LUAD to SCLC and found that the barrier to histological transformation converges on tolerance to Myc, which we implicate as a lineage-specific driver of the pulmonary neuroendocrine cell. Histological transformations are frequently accompanied by activation of the Akt pathway. Manipulating this pathway permitted tolerance to Myc as an oncogenic driver, producing rare, stem-like cells that transcriptionally resemble the pulmonary basal lineage. These findings suggest that histological transformation may require the plasticity inherent to the basal stem cell, enabling tolerance to previously incompatible oncogenic driver programs.
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Affiliation(s)
- Eric E Gardner
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY
| | - Ethan M Earlie
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY
- Department of Physiology, Biophysics, and Systems Biology, Weill Cornell Medicine, New York, NY
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY
| | - Kate Li
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY
| | - Jerin Thomas
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY
| | - Melissa J Hubisz
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY
- Department of Physiology, Biophysics, and Systems Biology, Weill Cornell Medicine, New York, NY
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY
- Bioinformatics Facility, Institute of Biotechnology, Cornell University, Ithaca, NY
| | - Benjamin D Stein
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY
- Department of Medicine, Weill Cornell Medicine, New York, NY
| | - Chen Zhang
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY
| | - Lewis C Cantley
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY
- Department of Medicine, Weill Cornell Medicine, New York, NY
| | - Ashley M Laughney
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY
- Department of Physiology, Biophysics, and Systems Biology, Weill Cornell Medicine, New York, NY
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY
| | - Harold Varmus
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY
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Liu Y, Huang K, Chen W. Resolving cellular dynamics using single-cell temporal transcriptomics. Curr Opin Biotechnol 2024; 85:103060. [PMID: 38194753 DOI: 10.1016/j.copbio.2023.103060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 12/04/2023] [Accepted: 12/10/2023] [Indexed: 01/11/2024]
Abstract
Cellular dynamics, the transition of a cell from one state to another, is central to understanding developmental processes and disease progression. Single-cell transcriptomics has been pushing the frontiers of cellular dynamics studies into a genome-wide and single-cell level. While most single-cell RNA sequencing approaches are disruptive and only provide a snapshot of cell states, the dynamics of a cell could be reconstructed by either exploiting temporal information hiding in the transcriptomics data or integrating additional information. In this review, we describe these approaches, highlighting their underlying principles, key assumptions, and the rationality to interpret the results as models. We also discuss the recently emerging nondisruptive live-cell transcriptomics methods, which are highly complementary to the computational models for their assumption-free nature.
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Affiliation(s)
- Yifei Liu
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Kai Huang
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Wanze Chen
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
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36
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Pan X, Zhang X. Studying temporal dynamics of single cells: expression, lineage and regulatory networks. Biophys Rev 2024; 16:57-67. [PMID: 38495440 PMCID: PMC10937865 DOI: 10.1007/s12551-023-01090-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 06/27/2023] [Indexed: 03/19/2024] Open
Abstract
Learning how multicellular organs are developed from single cells to different cell types is a fundamental problem in biology. With the high-throughput scRNA-seq technology, computational methods have been developed to reveal the temporal dynamics of single cells from transcriptomic data, from phenomena on cell trajectories to the underlying mechanism that formed the trajectory. There are several distinct families of computational methods including Trajectory Inference (TI), Lineage Tracing (LT), and Gene Regulatory Network (GRN) Inference which are involved in such studies. This review summarizes these computational approaches which use scRNA-seq data to study cell differentiation and cell fate specification as well as the advantages and limitations of different methods. We further discuss how GRNs can potentially affect cell fate decisions and trajectory structures. Supplementary Information The online version contains supplementary material available at 10.1007/s12551-023-01090-5.
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Affiliation(s)
- Xinhai Pan
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA
| | - Xiuwei Zhang
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA
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Kucinski I, Campos J, Barile M, Severi F, Bohin N, Moreira PN, Allen L, Lawson H, Haltalli MLR, Kinston SJ, O'Carroll D, Kranc KR, Göttgens B. A time- and single-cell-resolved model of murine bone marrow hematopoiesis. Cell Stem Cell 2024; 31:244-259.e10. [PMID: 38183977 PMCID: PMC7615671 DOI: 10.1016/j.stem.2023.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 09/25/2023] [Accepted: 12/04/2023] [Indexed: 01/08/2024]
Abstract
The paradigmatic hematopoietic tree model is increasingly recognized to be limited, as it is based on heterogeneous populations largely defined by non-homeostatic assays testing cell fate potentials. Here, we combine persistent labeling with time-series single-cell RNA sequencing to build a real-time, quantitative model of in vivo tissue dynamics for murine bone marrow hematopoiesis. We couple cascading single-cell expression patterns with dynamic changes in differentiation and growth speeds. The resulting explicit linkage between molecular states and cellular behavior reveals widely varying self-renewal and differentiation properties across distinct lineages. Transplanted stem cells show strong acceleration of differentiation at specific stages of erythroid and neutrophil production, illustrating how the model can quantify the impact of perturbations. Our reconstruction of dynamic behavior from snapshot measurements is akin to how a kinetoscope allows sequential images to merge into a movie. We posit that this approach is generally applicable to understanding tissue-scale dynamics at high resolution.
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Affiliation(s)
- Iwo Kucinski
- Wellcome-MRC Cambridge Stem Cell Institute, Department of Haematology, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK
| | - Joana Campos
- Centre for Haemato-Oncology, Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK; Institute of Cancer Research, London SM2 5NG, UK
| | - Melania Barile
- Wellcome-MRC Cambridge Stem Cell Institute, Department of Haematology, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK; Centre for Translational Stem Cell Biology, Hong Kong SAR, China
| | - Francesco Severi
- Centre for Regenerative Medicine, University of Edinburgh, Edinburgh EH16 4UU, UK; Wellcome Centre for Cell Biology, University of Edinburgh, Edinburgh EH9 3BF, UK
| | - Natacha Bohin
- Centre for Haemato-Oncology, Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK
| | - Pedro N Moreira
- Centre for Regenerative Medicine, University of Edinburgh, Edinburgh EH16 4UU, UK; Wellcome Centre for Cell Biology, University of Edinburgh, Edinburgh EH9 3BF, UK
| | - Lewis Allen
- Centre for Haemato-Oncology, Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK; Institute of Cancer Research, London SM2 5NG, UK
| | - Hannah Lawson
- Centre for Haemato-Oncology, Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK; Institute of Cancer Research, London SM2 5NG, UK
| | - Myriam L R Haltalli
- Wellcome-MRC Cambridge Stem Cell Institute, Department of Haematology, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK
| | - Sarah J Kinston
- Wellcome-MRC Cambridge Stem Cell Institute, Department of Haematology, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK
| | - Dónal O'Carroll
- Centre for Regenerative Medicine, University of Edinburgh, Edinburgh EH16 4UU, UK; Wellcome Centre for Cell Biology, University of Edinburgh, Edinburgh EH9 3BF, UK.
| | - Kamil R Kranc
- Centre for Haemato-Oncology, Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK; Institute of Cancer Research, London SM2 5NG, UK.
| | - Berthold Göttgens
- Wellcome-MRC Cambridge Stem Cell Institute, Department of Haematology, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK.
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38
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Abstract
Single-cell analysis is currently one of the most high-resolution techniques to study biology. The large complex datasets that have been generated have spurred numerous developments in computational biology, in particular the use of advanced statistics and machine learning. This review attempts to explain the deeper theoretical concepts that underpin current state-of-the-art analysis methods. Single-cell analysis is covered from cell, through instruments, to current and upcoming models. The aim of this review is to spread concepts which are not yet in common use, especially from topology and generative processes, and how new statistical models can be developed to capture more of biology. This opens epistemological questions regarding our ontology and models, and some pointers will be given to how natural language processing (NLP) may help overcome our cognitive limitations for understanding single-cell data.
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Affiliation(s)
- Ionut Sebastian Mihai
- The Laboratory for Molecular Infection Medicine Sweden (MIMS), Umeå, Sweden
- Umeå Centre for Microbial Research (UCMR), Department of Molecular Biology, Umeå University, Umeå, Sweden
- Industrial Doctoral School, Umeå University, Umeå, Sweden
| | - Sarang Chafle
- The Laboratory for Molecular Infection Medicine Sweden (MIMS), Umeå, Sweden
- Umeå Centre for Microbial Research (UCMR), Department of Molecular Biology, Umeå University, Umeå, Sweden
| | - Johan Henriksson
- The Laboratory for Molecular Infection Medicine Sweden (MIMS), Umeå, Sweden
- Umeå Centre for Microbial Research (UCMR), Department of Molecular Biology, Umeå University, Umeå, Sweden
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39
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Mayr CH, Sengupta A, Asgharpour S, Ansari M, Pestoni JC, Ogar P, Angelidis I, Liontos A, Rodriguez-Castillo JA, Lang NJ, Strunz M, Porras-Gonzalez D, Gerckens M, De Sadeleer LJ, Oehrle B, Viteri-Alvarez V, Fernandez IE, Tallquist M, Irmler M, Beckers J, Eickelberg O, Stoleriu GM, Behr J, Kneidinger N, Wuyts WA, Wasnick RM, Yildirim AÖ, Ahlbrecht K, Morty RE, Samakovlis C, Theis FJ, Burgstaller G, Schiller HB. Sfrp1 inhibits lung fibroblast invasion during transition to injury-induced myofibroblasts. Eur Respir J 2024; 63:2301326. [PMID: 38212077 PMCID: PMC10850614 DOI: 10.1183/13993003.01326-2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 11/13/2023] [Indexed: 01/13/2024]
Abstract
BACKGROUND Fibroblast-to-myofibroblast conversion is a major driver of tissue remodelling in organ fibrosis. Distinct lineages of fibroblasts support homeostatic tissue niche functions, yet their specific activation states and phenotypic trajectories during injury and repair have remained unclear. METHODS We combined spatial transcriptomics, multiplexed immunostainings, longitudinal single-cell RNA-sequencing and genetic lineage tracing to study fibroblast fates during mouse lung regeneration. Our findings were validated in idiopathic pulmonary fibrosis patient tissues in situ as well as in cell differentiation and invasion assays using patient lung fibroblasts. Cell differentiation and invasion assays established a function of SFRP1 in regulating human lung fibroblast invasion in response to transforming growth factor (TGF)β1. MEASUREMENTS AND MAIN RESULTS We discovered a transitional fibroblast state characterised by high Sfrp1 expression, derived from both Tcf21-Cre lineage positive and negative cells. Sfrp1 + cells appeared early after injury in peribronchiolar, adventitial and alveolar locations and preceded the emergence of myofibroblasts. We identified lineage-specific paracrine signals and inferred converging transcriptional trajectories towards Sfrp1 + transitional fibroblasts and Cthrc1 + myofibroblasts. TGFβ1 downregulated SFRP1 in noninvasive transitional cells and induced their switch to an invasive CTHRC1+ myofibroblast identity. Finally, using loss-of-function studies we showed that SFRP1 modulates TGFβ1-induced fibroblast invasion and RHOA pathway activity. CONCLUSIONS Our study reveals the convergence of spatially and transcriptionally distinct fibroblast lineages into transcriptionally uniform myofibroblasts and identifies SFRP1 as a modulator of TGFβ1-driven fibroblast phenotypes in fibrogenesis. These findings are relevant in the context of therapeutic interventions that aim at limiting or reversing fibroblast foci formation.
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Affiliation(s)
- Christoph H Mayr
- Comprehensive Pneumology Center (CPC)/Institute of Lung Health and Immunity (LHI), Helmholtz Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
- C.H. Mayr and A. Sengupta contributed equally to this work
| | - Arunima Sengupta
- Comprehensive Pneumology Center (CPC)/Institute of Lung Health and Immunity (LHI), Helmholtz Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
- C.H. Mayr and A. Sengupta contributed equally to this work
| | - Sara Asgharpour
- Comprehensive Pneumology Center (CPC)/Institute of Lung Health and Immunity (LHI), Helmholtz Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Meshal Ansari
- Comprehensive Pneumology Center (CPC)/Institute of Lung Health and Immunity (LHI), Helmholtz Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
- Institute of Computational Biology, Helmholtz Munich, Munich, Germany
| | - Jeanine C Pestoni
- Comprehensive Pneumology Center (CPC)/Institute of Lung Health and Immunity (LHI), Helmholtz Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Paulina Ogar
- Comprehensive Pneumology Center (CPC)/Institute of Lung Health and Immunity (LHI), Helmholtz Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Ilias Angelidis
- Comprehensive Pneumology Center (CPC)/Institute of Lung Health and Immunity (LHI), Helmholtz Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Andreas Liontos
- Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, Stockholm, Sweden
- SciLifeLab, Stockholm, Sweden
| | | | - Niklas J Lang
- Comprehensive Pneumology Center (CPC)/Institute of Lung Health and Immunity (LHI), Helmholtz Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Maximilian Strunz
- Comprehensive Pneumology Center (CPC)/Institute of Lung Health and Immunity (LHI), Helmholtz Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Diana Porras-Gonzalez
- Comprehensive Pneumology Center (CPC)/Institute of Lung Health and Immunity (LHI), Helmholtz Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Michael Gerckens
- Comprehensive Pneumology Center (CPC)/Institute of Lung Health and Immunity (LHI), Helmholtz Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
- Department of Internal Medicine V, Ludwig-Maximilians University (LMU) Munich, Member of the German Center for Lung Research (DZL), CPC-M bioArchive, Munich, Germany
| | - Laurens J De Sadeleer
- Comprehensive Pneumology Center (CPC)/Institute of Lung Health and Immunity (LHI), Helmholtz Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
- Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), Department CHROMETA, KU Leuven, Leuven, Belgium
| | - Bettina Oehrle
- Comprehensive Pneumology Center (CPC)/Institute of Lung Health and Immunity (LHI), Helmholtz Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Valeria Viteri-Alvarez
- Comprehensive Pneumology Center (CPC)/Institute of Lung Health and Immunity (LHI), Helmholtz Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Isis E Fernandez
- Comprehensive Pneumology Center (CPC)/Institute of Lung Health and Immunity (LHI), Helmholtz Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Michelle Tallquist
- Center for Cardiovascular Research, John A. Burns School of Medicine, University of Hawaii, Honolulu, HI, USA
| | - Martin Irmler
- Institute of Experimental Genetics, Helmholtz Zentrum München, Neuherberg, Germany
| | - Johannes Beckers
- Institute of Experimental Genetics, Helmholtz Zentrum München, Neuherberg, Germany
- German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany
- Chair of Experimental Genetics, Technical University of Munich, Freising, Germany
| | - Oliver Eickelberg
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Gabriel Mircea Stoleriu
- Department of Internal Medicine V, Ludwig-Maximilians University (LMU) Munich, Member of the German Center for Lung Research (DZL), CPC-M bioArchive, Munich, Germany
| | - Jürgen Behr
- Department of Internal Medicine V, Ludwig-Maximilians University (LMU) Munich, Member of the German Center for Lung Research (DZL), CPC-M bioArchive, Munich, Germany
| | - Nikolaus Kneidinger
- Department of Internal Medicine V, Ludwig-Maximilians University (LMU) Munich, Member of the German Center for Lung Research (DZL), CPC-M bioArchive, Munich, Germany
| | - Wim A Wuyts
- Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), Department CHROMETA, KU Leuven, Leuven, Belgium
| | - Roxana Maria Wasnick
- Comprehensive Pneumology Center (CPC)/Institute of Lung Health and Immunity (LHI), Helmholtz Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Ali Önder Yildirim
- Comprehensive Pneumology Center (CPC)/Institute of Lung Health and Immunity (LHI), Helmholtz Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
- Institute of Experimental Pneumology, LMU University Hospital, Ludwig-Maximilians University, Munich, Germany
| | - Katrin Ahlbrecht
- Max Planck Institute for Heart and Lung Research, Member of the German Center for Lung Research (DZL), Bad Nauheim, Germany
| | - Rory E Morty
- Department of Translational Pulmonology, University Hospital Heidelberg, and Translational Lung Research Center (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany
| | - Christos Samakovlis
- Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, Stockholm, Sweden
- SciLifeLab, Stockholm, Sweden
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Munich, Munich, Germany
- Department of Mathematics, Technische Universität München, Munich, Germany
| | - Gerald Burgstaller
- Comprehensive Pneumology Center (CPC)/Institute of Lung Health and Immunity (LHI), Helmholtz Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
- G. Burgstaller and H.B. Schiller contributed equally to this article as lead authors and supervised the work
| | - Herbert B Schiller
- Comprehensive Pneumology Center (CPC)/Institute of Lung Health and Immunity (LHI), Helmholtz Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
- Institute of Experimental Pneumology, LMU University Hospital, Ludwig-Maximilians University, Munich, Germany
- G. Burgstaller and H.B. Schiller contributed equally to this article as lead authors and supervised the work
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40
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Yan F, Suzuki A, Iwaya C, Pei G, Chen X, Yoshioka H, Yu M, Simon LM, Iwata J, Zhao Z. Single-cell multiomics decodes regulatory programs for mouse secondary palate development. Nat Commun 2024; 15:821. [PMID: 38280850 PMCID: PMC10821874 DOI: 10.1038/s41467-024-45199-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 01/17/2024] [Indexed: 01/29/2024] Open
Abstract
Perturbations in gene regulation during palatogenesis can lead to cleft palate, which is among the most common congenital birth defects. Here, we perform single-cell multiome sequencing and profile chromatin accessibility and gene expression simultaneously within the same cells (n = 36,154) isolated from mouse secondary palate across embryonic days (E) 12.5, E13.5, E14.0, and E14.5. We construct five trajectories representing continuous differentiation of cranial neural crest-derived multipotent cells into distinct lineages. By linking open chromatin signals to gene expression changes, we characterize the underlying lineage-determining transcription factors. In silico perturbation analysis identifies transcription factors SHOX2 and MEOX2 as important regulators of the development of the anterior and posterior palate, respectively. In conclusion, our study charts epigenetic and transcriptional dynamics in palatogenesis, serving as a valuable resource for further cleft palate research.
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Affiliation(s)
- Fangfang Yan
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Akiko Suzuki
- Department of Diagnostic and Biomedical Sciences, School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX, 77054, USA
- Center for Craniofacial Research, The University of Texas Health Science Center at Houston, Houston, TX, 77054, USA
- Department of Oral and Craniofacial Sciences, School of Dentistry, University of Missouri - Kansas City, Kansas City, Missouri, 64108, USA
| | - Chihiro Iwaya
- Department of Diagnostic and Biomedical Sciences, School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX, 77054, USA
- Center for Craniofacial Research, The University of Texas Health Science Center at Houston, Houston, TX, 77054, USA
| | - Guangsheng Pei
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Xian Chen
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Hiroki Yoshioka
- Department of Diagnostic and Biomedical Sciences, School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX, 77054, USA
- Center for Craniofacial Research, The University of Texas Health Science Center at Houston, Houston, TX, 77054, USA
| | - Meifang Yu
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Lukas M Simon
- Therapeutic Innovation Center, Baylor College of Medicine, Houston, TX, 77030, USA.
| | - Junichi Iwata
- Department of Diagnostic and Biomedical Sciences, School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX, 77054, USA.
- Center for Craniofacial Research, The University of Texas Health Science Center at Houston, Houston, TX, 77054, USA.
- MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, 77030, USA.
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
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41
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Xiang G, Guo Y, Bumcrot D, Sigova A. JMnorm: a novel joint multi-feature normalization method for integrative and comparative epigenomics. Nucleic Acids Res 2024; 52:e11. [PMID: 38055833 PMCID: PMC10810286 DOI: 10.1093/nar/gkad1146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 10/25/2023] [Accepted: 11/14/2023] [Indexed: 12/08/2023] Open
Abstract
Combinatorial patterns of epigenetic features reflect transcriptional states and functions of genomic regions. While many epigenetic features have correlated relationships, most existing data normalization approaches analyze each feature independently. Such strategies may distort relationships between functionally correlated epigenetic features and hinder biological interpretation. We present a novel approach named JMnorm that simultaneously normalizes multiple epigenetic features across cell types, species, and experimental conditions by leveraging information from partially correlated epigenetic features. We demonstrate that JMnorm-normalized data can better preserve cross-epigenetic-feature correlations across different cell types and enhance consistency between biological replicates than data normalized by other methods. Additionally, we show that JMnorm-normalized data can consistently improve the performance of various downstream analyses, which include candidate cis-regulatory element clustering, cross-cell-type gene expression prediction, detection of transcription factor binding and changes upon perturbations. These findings suggest that JMnorm effectively minimizes technical noise while preserving true biologically significant relationships between epigenetic datasets. We anticipate that JMnorm will enhance integrative and comparative epigenomics.
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Affiliation(s)
- Guanjue Xiang
- CAMP4 Therapeutics Corp., One Kendall Square, Building 1400 West, Cambridge, MA 02139, USA
| | - Yuchun Guo
- CAMP4 Therapeutics Corp., One Kendall Square, Building 1400 West, Cambridge, MA 02139, USA
| | - David Bumcrot
- CAMP4 Therapeutics Corp., One Kendall Square, Building 1400 West, Cambridge, MA 02139, USA
| | - Alla Sigova
- CAMP4 Therapeutics Corp., One Kendall Square, Building 1400 West, Cambridge, MA 02139, USA
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42
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Muñoz-Rojas AR, Wang G, Benoist C, Mathis D. Adipose-tissue regulatory T cells are a consortium of subtypes that evolves with age and diet. Proc Natl Acad Sci U S A 2024; 121:e2320602121. [PMID: 38227656 PMCID: PMC10823167 DOI: 10.1073/pnas.2320602121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 12/08/2023] [Indexed: 01/18/2024] Open
Abstract
Foxp3+CD4+ regulatory T (Treg) cells found within tissues regulate local immunity, inflammation, and homeostasis. Tregs in epididymal visceral adipose tissue (eVAT) are critical regulators of local and systemic inflammation and metabolism. During aging and under obesogenic conditions, eVAT Tregs undergo transcriptional and phenotypic changes and are important for containing inflammation and normalizing metabolic indices. We have employed single-cell RNA sequencing, single-cell Tra and Trb sequencing, adoptive transfers, photoconvertible mice, cellular interaction analyses, and in vitro cultures to dissect the evolving heterogeneity of eVAT Tregs with aging and obesity. Distinct Treg subtypes with distinguishable gene expression profiles and functional roles were enriched at differing ages and with differing diets. Like those in lean mice, eVAT Tregs in obese mice were not primarily recruited from the circulation but instead underwent local expansion and had a distinct and diversified T cell receptor repertoire. The different eVAT-Treg subtypes were specialized in different functions; for example, the subtypes enriched in lean, but not obese, mice suppressed adipogenesis. The existence of functionally divergent eVAT-Treg subtypes in response to obesogenic conditions presents possibilities for precision therapeutics in the context of obesity.
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Affiliation(s)
| | - Gang Wang
- Department of Immunology, Harvard Medical School, Boston, MA02115
| | | | - Diane Mathis
- Department of Immunology, Harvard Medical School, Boston, MA02115
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43
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Xu F, Hu H, Lin H, Lu J, Cheng F, Zhang J, Li X, Shuai J. scGIR: deciphering cellular heterogeneity via gene ranking in single-cell weighted gene correlation networks. Brief Bioinform 2024; 25:bbae091. [PMID: 38487851 PMCID: PMC10940817 DOI: 10.1093/bib/bbae091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 02/08/2024] [Accepted: 02/15/2024] [Indexed: 03/18/2024] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating cellular heterogeneity through high-throughput analysis of individual cells. Nevertheless, challenges arise from prevalent sequencing dropout events and noise effects, impacting subsequent analyses. Here, we introduce a novel algorithm, Single-cell Gene Importance Ranking (scGIR), which utilizes a single-cell gene correlation network to evaluate gene importance. The algorithm transforms single-cell sequencing data into a robust gene correlation network through statistical independence, with correlation edges weighted by gene expression levels. We then constructed a random walk model on the resulting weighted gene correlation network to rank the importance of genes. Our analysis of gene importance using PageRank algorithm across nine authentic scRNA-seq datasets indicates that scGIR can effectively surmount technical noise, enabling the identification of cell types and inference of developmental trajectories. We demonstrated that the edges of gene correlation, weighted by expression, play a critical role in enhancing the algorithm's performance. Our findings emphasize that scGIR outperforms in enhancing the clustering of cell subtypes, reverse identifying differentially expressed marker genes, and uncovering genes with potential differential importance. Overall, we proposed a promising method capable of extracting more information from single-cell RNA sequencing datasets, potentially shedding new lights on cellular processes and disease mechanisms.
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Affiliation(s)
- Fei Xu
- Department of Physics, Anhui Normal University, Wuhu 241002, China
- Wenzhou Institute and Wenzhou Key Laboratory of Biophysics, University of Chinese Academy of Sciences, Wenzhou 325001, China
| | - Huan Hu
- Institute of Applied Genomics, Fuzhou University, Fuzhou 350108, China
| | - Hai Lin
- Wenzhou Institute and Wenzhou Key Laboratory of Biophysics, University of Chinese Academy of Sciences, Wenzhou 325001, China
| | - Jun Lu
- Department of Physics, Anhui Normal University, Wuhu 241002, China
- School of Medical Imageology, Wannan Medical College, Wuhu 241002, China
| | - Feng Cheng
- Department of Physics, and Fujian Provincial Key Lab for Soft Functional Materials Research, Xiamen University, Xiamen 361005, China
| | - Jiqian Zhang
- Department of Physics, Anhui Normal University, Wuhu 241002, China
| | - Xiang Li
- Department of Physics, and Fujian Provincial Key Lab for Soft Functional Materials Research, Xiamen University, Xiamen 361005, China
| | - Jianwei Shuai
- Wenzhou Institute and Wenzhou Key Laboratory of Biophysics, University of Chinese Academy of Sciences, Wenzhou 325001, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou 325001, China
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44
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Lederer AR, Leonardi M, Talamanca L, Herrera A, Droin C, Khven I, Carvalho HJF, Valente A, Mantes AD, Arabí PM, Pinello L, Naef F, Manno GL. Statistical inference with a manifold-constrained RNA velocity model uncovers cell cycle speed modulations. bioRxiv 2024:2024.01.18.576093. [PMID: 38328127 PMCID: PMC10849531 DOI: 10.1101/2024.01.18.576093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Across a range of biological processes, cells undergo coordinated changes in gene expression, resulting in transcriptome dynamics that unfold within a low-dimensional manifold. Single-cell RNA-sequencing (scRNA-seq) only measures temporal snapshots of gene expression. However, information on the underlying low-dimensional dynamics can be extracted using RNA velocity, which models unspliced and spliced RNA abundances to estimate the rate of change of gene expression. Available RNA velocity algorithms can be fragile and rely on heuristics that lack statistical control. Moreover, the estimated vector field is not dynamically consistent with the traversed gene expression manifold. Here, we develop a generative model of RNA velocity and a Bayesian inference approach that solves these problems. Our model couples velocity field and manifold estimation in a reformulated, unified framework, so as to coherently identify the parameters of an autonomous dynamical system. Focusing on the cell cycle, we implemented VeloCycle to study gene regulation dynamics on one-dimensional periodic manifolds and validated using live-imaging its ability to infer actual cell cycle periods. We benchmarked RNA velocity inference with sensitivity analyses and demonstrated one- and multiple-sample testing. We also conducted Markov chain Monte Carlo inference on the model, uncovering key relationships between gene-specific kinetics and our gene-independent velocity estimate. Finally, we applied VeloCycle to in vivo samples and in vitro genome-wide Perturb-seq, revealing regionally-defined proliferation modes in neural progenitors and the effect of gene knockdowns on cell cycle speed. Ultimately, VeloCycle expands the scRNA-seq analysis toolkit with a modular and statistically rigorous RNA velocity inference framework.
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45
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Cui H, Maan H, Vladoiu MC, Zhang J, Taylor MD, Wang B. DeepVelo: deep learning extends RNA velocity to multi-lineage systems with cell-specific kinetics. Genome Biol 2024; 25:27. [PMID: 38243313 PMCID: PMC10799431 DOI: 10.1186/s13059-023-03148-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 12/18/2023] [Indexed: 01/21/2024] Open
Abstract
Existing RNA velocity estimation methods strongly rely on predefined dynamics and cell-agnostic constant transcriptional kinetic rates, assumptions often violated in complex and heterogeneous single-cell RNA sequencing (scRNA-seq) data. Using a graph convolution network, DeepVelo overcomes these limitations by generalizing RNA velocity to cell populations containing time-dependent kinetics and multiple lineages. DeepVelo infers time-varying cellular rates of transcription, splicing, and degradation, recovers each cell's stage in the differentiation process, and detects functionally relevant driver genes regulating these processes. Application to various developmental and pathogenic processes demonstrates DeepVelo's capacity to study complex differentiation and lineage decision events in heterogeneous scRNA-seq data.
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Affiliation(s)
- Haotian Cui
- Peter Munk Cardiac Center, University Health Network, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
| | - Hassaan Maan
- Peter Munk Cardiac Center, University Health Network, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Maria C Vladoiu
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Jiao Zhang
- The Arthur and Sonia Labatt Brain Tumor Research Centre, The Hospital for Sick Children, Toronto, Ontario, Canada
- Developmental and Stem Cell Biology Program, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Michael D Taylor
- The Arthur and Sonia Labatt Brain Tumor Research Centre, The Hospital for Sick Children, Toronto, Ontario, Canada
- Developmental and Stem Cell Biology Program, The Hospital for Sick Children, Toronto, Ontario, Canada
- Baylor College of Medicine, Houston, TX, USA
- Texas Children's Hospital, Houston, TX, USA
| | - Bo Wang
- Peter Munk Cardiac Center, University Health Network, Toronto, Ontario, Canada.
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.
- Vector Institute, Toronto, Ontario, Canada.
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada.
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46
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Kobayashi-Kirschvink KJ, Comiter CS, Gaddam S, Joren T, Grody EI, Ounadjela JR, Zhang K, Ge B, Kang JW, Xavier RJ, So PTC, Biancalani T, Shu J, Regev A. Prediction of single-cell RNA expression profiles in live cells by Raman microscopy with Raman2RNA. Nat Biotechnol 2024:10.1038/s41587-023-02082-2. [PMID: 38200118 DOI: 10.1038/s41587-023-02082-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 12/01/2023] [Indexed: 01/12/2024]
Abstract
Single-cell RNA sequencing and other profiling assays have helped interrogate cells at unprecedented resolution and scale, but are inherently destructive. Raman microscopy reports on the vibrational energy levels of proteins and metabolites in a label-free and nondestructive manner at subcellular spatial resolution, but it lacks genetic and molecular interpretability. Here we present Raman2RNA (R2R), a method to infer single-cell expression profiles in live cells through label-free hyperspectral Raman microscopy images and domain translation. We predict single-cell RNA sequencing profiles nondestructively from Raman images using either anchor-based integration with single molecule fluorescence in situ hybridization, or anchor-free generation with adversarial autoencoders. R2R outperformed inference from brightfield images (cosine similarities: R2R >0.85 and brightfield <0.15). In reprogramming of mouse fibroblasts into induced pluripotent stem cells, R2R inferred the expression profiles of various cell states. With live-cell tracking of mouse embryonic stem cell differentiation, R2R traced the early emergence of lineage divergence and differentiation trajectories, overcoming discontinuities in expression space. R2R lays a foundation for future exploration of live genomic dynamics.
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Affiliation(s)
- Koseki J Kobayashi-Kirschvink
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Charles S Comiter
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cutaneous Biology Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Shreya Gaddam
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Genentech, South San Francisco, CA, USA
| | - Taylor Joren
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Emanuelle I Grody
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Johain R Ounadjela
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ke Zhang
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cutaneous Biology Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Baoliang Ge
- Department of Mechanical and Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jeon Woong Kang
- Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ramnik J Xavier
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Computational and Integrative Biology and Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA
| | - Peter T C So
- Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Mechanical and Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Tommaso Biancalani
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Genentech, South San Francisco, CA, USA.
| | - Jian Shu
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Cutaneous Biology Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Genentech, South San Francisco, CA, USA.
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47
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Millet A, Ledo JH, Tavazoie SF. An exhausted-like microglial population accumulates in aged and APOE4 genotype Alzheimer's brains. Immunity 2024; 57:153-170.e6. [PMID: 38159571 PMCID: PMC10805152 DOI: 10.1016/j.immuni.2023.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 10/04/2023] [Accepted: 12/04/2023] [Indexed: 01/03/2024]
Abstract
The dominant risk factors for late-onset Alzheimer's disease (AD) are advanced age and the APOE4 genetic variant. To examine how these factors alter neuroimmune function, we generated an integrative, longitudinal single-cell atlas of brain immune cells in AD model mice bearing the three common human APOE alleles. Transcriptomic and chromatin accessibility analyses identified a reactive microglial population defined by the concomitant expression of inflammatory signals and cell-intrinsic stress markers whose frequency increased with age and APOE4 burden. An analogous population was detectable in the brains of human AD patients, including in the cortical tissue, using multiplexed spatial transcriptomics. This population, which we designate as terminally inflammatory microglia (TIM), exhibited defects in amyloid-β clearance and altered cell-cell communication during aducanumab treatment. TIM may represent an exhausted-like state for inflammatory microglia in the AD milieu that contributes to AD risk and pathology in APOE4 carriers and the elderly, thus presenting a potential therapeutic target for AD.
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Affiliation(s)
- Alon Millet
- Laboratory of Systems Cancer Biology, The Rockefeller University, New York, NY 10065, USA; Tri-Institutional Program in Computational Biology and Medicine, The Rockefeller University, New York, NY 10065, USA
| | - Jose Henrique Ledo
- Laboratory of Systems Cancer Biology, The Rockefeller University, New York, NY 10065, USA; Department of Pathology and Laboratory of Medicine, Department of Neuroscience, South Carolina Alzheimer's Disease Research Center, Medical University of South Carolina, Charleston, SC 29425, USA.
| | - Sohail F Tavazoie
- Laboratory of Systems Cancer Biology, The Rockefeller University, New York, NY 10065, USA; Tri-Institutional Program in Computational Biology and Medicine, The Rockefeller University, New York, NY 10065, USA.
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48
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Kirschenbaum D, Xie K, Ingelfinger F, Katzenelenbogen Y, Abadie K, Look T, Sheban F, Phan TS, Li B, Zwicky P, Yofe I, David E, Mazuz K, Hou J, Chen Y, Shaim H, Shanley M, Becker S, Qian J, Colonna M, Ginhoux F, Rezvani K, Theis FJ, Yosef N, Weiss T, Weiner A, Amit I. Time-resolved single-cell transcriptomics defines immune trajectories in glioblastoma. Cell 2024; 187:149-165.e23. [PMID: 38134933 DOI: 10.1016/j.cell.2023.11.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 09/15/2023] [Accepted: 11/27/2023] [Indexed: 12/24/2023]
Abstract
Deciphering the cell-state transitions underlying immune adaptation across time is fundamental for advancing biology. Empirical in vivo genomic technologies that capture cellular dynamics are currently lacking. We present Zman-seq, a single-cell technology recording transcriptomic dynamics across time by introducing time stamps into circulating immune cells, tracking them in tissues for days. Applying Zman-seq resolved cell-state and molecular trajectories of the dysfunctional immune microenvironment in glioblastoma. Within 24 hours of tumor infiltration, cytotoxic natural killer cells transitioned to a dysfunctional program regulated by TGFB1 signaling. Infiltrating monocytes differentiated into immunosuppressive macrophages, characterized by the upregulation of suppressive myeloid checkpoints Trem2, Il18bp, and Arg1, over 36 to 48 hours. Treatment with an antagonistic anti-TREM2 antibody reshaped the tumor microenvironment by redirecting the monocyte trajectory toward pro-inflammatory macrophages. Zman-seq is a broadly applicable technology, enabling empirical measurements of differentiation trajectories, which can enhance the development of more efficacious immunotherapies.
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Affiliation(s)
- Daniel Kirschenbaum
- Department of Systems Immunology, Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - Ken Xie
- Department of Systems Immunology, Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - Florian Ingelfinger
- Department of Systems Immunology, Weizmann Institute of Science, 7610001 Rehovot, Israel
| | | | - Kathleen Abadie
- Department of Systems Immunology, Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - Thomas Look
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Fadi Sheban
- Department of Systems Immunology, Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - Truong San Phan
- Department of Systems Immunology, Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - Baoguo Li
- Department of Systems Immunology, Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - Pascale Zwicky
- Department of Systems Immunology, Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - Ido Yofe
- Department of Systems Immunology, Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - Eyal David
- Department of Systems Immunology, Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - Kfir Mazuz
- Department of Systems Immunology, Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - Jinchao Hou
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Yun Chen
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Hila Shaim
- Department of Stem Cell Transplantation and Cellular Therapy, the University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Mayra Shanley
- Department of Stem Cell Transplantation and Cellular Therapy, the University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Soeren Becker
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Jiawen Qian
- Shanghai Institute of Immunology, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Marco Colonna
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Florent Ginhoux
- Shanghai Institute of Immunology, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; Singapore Immunology Network (SIgN), Agency for Science, Technology and Research, Singapore 138648, Singapore; Singapore Immunology Network (SIgN), Agency for Science, Technology and Research, Singapore 138648, Singapore
| | - Katayoun Rezvani
- Department of Stem Cell Transplantation and Cellular Therapy, the University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Nir Yosef
- Department of Systems Immunology, Weizmann Institute of Science, 7610001 Rehovot, Israel; Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA; Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Tobias Weiss
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Assaf Weiner
- Department of Systems Immunology, Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - Ido Amit
- Department of Systems Immunology, Weizmann Institute of Science, 7610001 Rehovot, Israel.
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49
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Li M, Guo H, Wang B, Han Z, Wu S, Liu J, Huang H, Zhu J, An F, Lin Z, Mo K, Tan J, Liu C, Wang L, Deng X, Li G, Ji J, Ouyang H. The single-cell transcriptomic atlas and RORA-mediated 3D epigenomic remodeling in driving corneal epithelial differentiation. Nat Commun 2024; 15:256. [PMID: 38177186 PMCID: PMC10766623 DOI: 10.1038/s41467-023-44471-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 12/13/2023] [Indexed: 01/06/2024] Open
Abstract
Proper differentiation of corneal epithelial cells (CECs) from limbal stem/progenitor cells (LSCs) is required for maintenance of ocular homeostasis and clear vision. Here, using a single-cell transcriptomic atlas, we delineate the comprehensive and refined molecular regulatory dynamics during human CEC development and differentiation. We find that RORA is a CEC-specific molecular switch that initiates and drives LSCs to differentiate into mature CECs by activating PITX1. RORA dictates CEC differentiation by establishing CEC-specific enhancers and chromatin interactions between CEC gene promoters and distal regulatory elements. Conversely, RORA silences LSC-specific promoters and disrupts promoter-anchored chromatin loops to turn off LSC genes. Collectively, our work provides detailed and comprehensive insights into the transcriptional dynamics and RORA-mediated epigenetic remodeling underlying human corneal epithelial differentiation.
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Affiliation(s)
- Mingsen Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, 510060, China.
| | - Huizhen Guo
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, 510060, China
| | - Bofeng Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, 510060, China
| | - Zhuo Han
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, 510060, China
| | - Siqi Wu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, 510060, China
| | - Jiafeng Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, 510060, China
| | - Huaxing Huang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, 510060, China
| | - Jin Zhu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, 510060, China
| | - Fengjiao An
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, 510060, China
| | - Zesong Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, 510060, China
| | - Kunlun Mo
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, 510060, China
| | - Jieying Tan
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, 510060, China
| | - Chunqiao Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, 510060, China
| | - Li Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, 510060, China
| | - Xin Deng
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, 999077, China
| | - Guigang Li
- Department of Ophthalmology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
| | - Jianping Ji
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, 510060, China.
| | - Hong Ouyang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, 510060, China.
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Sun N, Teyssier N, Wang B, Drake S, Seyler M, Zaltsman Y, Everitt A, Teerikorpi N, Willsey HR, Goodarzi H, Tian R, Kampmann M, Willsey AJ. Autism genes converge on microtubule biology and RNA-binding proteins during excitatory neurogenesis. bioRxiv 2024:2023.12.22.573108. [PMID: 38187634 PMCID: PMC10769323 DOI: 10.1101/2023.12.22.573108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Recent studies have identified over one hundred high-confidence (hc) autism spectrum disorder (ASD) genes. Systems biological and functional analyses on smaller subsets of these genes have consistently implicated excitatory neurogenesis. However, the extent to which the broader set of hcASD genes are involved in this process has not been explored systematically nor have the biological pathways underlying this convergence been identified. Here, we leveraged CROP-Seq to repress 87 hcASD genes in a human in vitro model of cortical neurogenesis. We identified 17 hcASD genes whose repression significantly alters developmental trajectory and results in a common cellular state characterized by disruptions in proliferation, differentiation, cell cycle, microtubule biology, and RNA-binding proteins (RBPs). We also characterized over 3,000 differentially expressed genes, 286 of which had expression profiles correlated with changes in developmental trajectory. Overall, we uncovered transcriptional disruptions downstream of hcASD gene perturbations, correlated these disruptions with distinct differentiation phenotypes, and reinforced neurogenesis, microtubule biology, and RBPs as convergent points of disruption in ASD.
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