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Zhang H, Zhang W, Zheng X, Li Y. scRDEN: single-cell dynamic gene rank differential expression network and robust trajectory inference. Sci Rep 2025; 15:16963. [PMID: 40374885 DOI: 10.1038/s41598-025-01969-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2024] [Accepted: 05/09/2025] [Indexed: 05/18/2025] Open
Abstract
The remarkable advancement of single-cell RNA sequencing (scRNA-seq) technology has empowered researchers to probe gene expression at the single-cell level with unprecedented precision. To gain a profound understanding of the heterogeneity inherent in cell fate determination, a central challenge lies in the comprehensive analysis of the dynamic regulatory alterations that underlie transcriptional differences and the accurate inference of the differentiation trajectory. Here, we propose the method scRDEN, a robust framework that infers important cell sub-populations and differential expression networks of multiple genes along the differentiation directions of each branch by converting the unstable gene expression values in cells into relatively stable gene-gene interactions (global features) and extracting the order of differential expression (network features), and further integrating the expression features of different dimension reduction methods. When applied to five published scRNA-seq datasets from human and mouse cell differentiation, scRDEN not only successfully captures the stable cell subpopulations with potential marker genes, measures the transcriptional differences of gene pairs to identify the rank differential expression network along the differentiation direction of each branch. In addition, in multiple gene rank differential expression networks, the rank expression directly related to transcription factors/marker genes shows a significant strengthening and weakening trend along with their expression changes, and the distribution of diversity and cluster coefficient show a non-monotonic change trend, including the cases of increasing first and then decreasing or decreasing first and then increasing. This may correspond to the mechanism of cells gradually differentiating into stable functions. It is particularly noteworthy that scRDEN method yielded exceptional results when applied to the large-scale, multi-branched, double-batch mouse dentate gyrus data. This outstanding performance provides novel and valuable insights into large-scale, multi-batch trajectory inference and the study of transcriptional mechanism regulation during the processes of differentiation and development.
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Affiliation(s)
- Han Zhang
- School of Mathematics and Physics, Wuhan Institute of Technology, Wuhan, 430073, China
| | - Wei Zhang
- School of Mathematics and Physics, Wuhan Institute of Technology, Wuhan, 430073, China
| | - Xiaoying Zheng
- School of Mathematics and Physics, Wuhan Institute of Technology, Wuhan, 430073, China.
| | - Yuanyuan Li
- School of Mathematics and Physics, Wuhan Institute of Technology, Wuhan, 430073, China.
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2
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Labaki WW, Ram S, Namvar A, Bell AJ, Hoff BA, Kazerooni EA, Galban S, Martinez FJ, Hatt CR, Murray S, Mirkes EM, Gorban AN, Zinovyev A, Han MK, Galban CJ. Quantitative CT Scoring for Local COPD Severity. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2025:2025.04.09.25324951. [PMID: 40297437 PMCID: PMC12036413 DOI: 10.1101/2025.04.09.25324951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 04/30/2025]
Abstract
Chronic obstructive pulmonary disease (COPD) is complex, and its course is difficult to predict due to its diverse pathophysiology. Small airway disease (SAD), a key component of COPD and potential target for emerging therapeutics, may be reversible in mild COPD, but left unchecked, may worsen, leading to airway loss and emphysema. The dual nature of SAD complicates clinical management of COPD patients, necessitating more accurate monitoring methods. To meet this need, we developed elastic Parametric Response Mapping (ePRM), a tiered scoring system that classifies local lung volumes by the degree of PRM-derived SAD, normal, and emphysematous tissue. In individuals with or at risk for COPD, we demonstrate that chest CT ePRM can categorize local lung tissue into distinct tiers of disease severity that distinguish between tissue characterized by early reversible SAD and progressive destruction. This level of characterization is crucial to developing personalized treatment strategies for COPD.
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Affiliation(s)
- Wassim W. Labaki
- Department of Internal Medicine, Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, MI, United States
| | - Sundaresh Ram
- Department of Radiology and Imaging Sciences, Emory University and Georgia Institute of Technology, Atlanta, GA, United States
| | - Ali Namvar
- Department of Radiology, University of Michigan, Ann Arbor, MI, United States
| | - Alexander J. Bell
- Department of Radiology, University of Michigan, Ann Arbor, MI, United States
| | - Benjamin A. Hoff
- Department of Radiology, University of Michigan, Ann Arbor, MI, United States
| | - Ella A. Kazerooni
- Department of Radiology, University of Michigan, Ann Arbor, MI, United States
| | - Stefanie Galban
- Department of Radiology, University of Michigan, Ann Arbor, MI, United States
| | | | | | - Susan Murray
- School of Public Health, University of Michigan, Ann Arbor, MI, United States
| | - Evgeny M. Mirkes
- School of Computing and Mathematical Sciences, University of Leicester, Leicester, Leicestershire, United Kingdom
| | - Alexander N. Gorban
- School of Computing and Mathematical Sciences, University of Leicester, Leicester, Leicestershire, United Kingdom
| | | | - MeiLan K. Han
- Department of Internal Medicine, Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, MI, United States
| | - Craig J. Galban
- Department of Radiology, University of Michigan, Ann Arbor, MI, United States
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3
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Nesbitt NM, Araldi GL, Pennacchia L, Marchenko N, Assar Z, Muzzarelli KM, Thekke Veedu RR, Medel-Lacruz B, Lee E, Eisenmesser EZ, Kreitler DF, Bahou WF. Small molecule BLVRB redox inhibitor promotes megakaryocytopoiesis and stress thrombopoiesis in vivo. Nat Commun 2025; 16:3480. [PMID: 40216753 PMCID: PMC11992022 DOI: 10.1038/s41467-025-58497-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2024] [Accepted: 03/17/2025] [Indexed: 04/14/2025] Open
Abstract
Biliverdin IXβ reductase (BLVRB) is an NADPH-dependent enzyme previously implicated in a redox-regulated mechanism of thrombopoiesis distinct from the thrombopoietin (TPO)/c-MPL axis. Here, we apply computational modeling to inform molecule design, followed by de novo syntheses and screening of unique small molecules retaining the capacity for selective BLVRB inhibition as a novel platelet-enhancing strategy. Two distinct classes of molecules are identified, and NMR spectroscopy and co-crystallization studies confirm binding modes within the BLVRB active site and ring stacking between the nicotinamide moiety of the NADP+ cofactor. A diazabicyclo derivative displaying minimal off-target promiscuity and excellent bioavailability characteristics promotes megakaryocyte speciation in biphenotypic (erythro/megakaryocyte) cellular models and synergizes with TPO-dependent megakaryocyte formation in hematopoietic stem cells. Upon oral delivery into mice, this inhibitor expands platelet recovery in stress thrombopoietic models with no adverse effects. In this work, we identify and validate a cellular redox inhibitor retaining the potential to selectively promote megakaryocytopoiesis and enhance stress-associated platelet formation in vivo distinct from TPO receptor agonists.
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Affiliation(s)
| | | | | | | | - Zahra Assar
- Department of Structural Biology, Cayman Chemical, Ann Arbor, MI, USA
| | | | | | | | - Eunjeong Lee
- Department of Biochemistry and Molecular Genetics, University of Colorado, Aurora, CO, USA
| | - Elan Z Eisenmesser
- Department of Biochemistry and Molecular Genetics, University of Colorado, Aurora, CO, USA
| | - Dale F Kreitler
- Center for Biomolecular Structure, Brookhaven National Laboratory, Upton, NY, USA
| | - Wadie F Bahou
- Department of Medicine, Stony Brook University, Stony Brook, NY, USA
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4
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Perelli L, Zhang L, Mangiameli S, Giannese F, Mahadevan KK, Peng F, Citron F, Khan H, Le C, Gurreri E, Carbone F, Russell AJC, Soeung M, Lam TNA, Lundgren S, Marisetty S, Zhu C, Catania D, Mohamed AMT, Feng N, Augustine JJ, Sgambato A, Tortora G, Draetta GF, Tonon G, Futreal A, Giuliani V, Carugo A, Viale A, Kim MP, Heffernan TP, Wang L, Kalluri R, Cittaro D, Chen F, Genovese G. Evolutionary fingerprints of epithelial-to-mesenchymal transition. Nature 2025; 640:1083-1092. [PMID: 40044861 DOI: 10.1038/s41586-025-08671-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 01/17/2025] [Indexed: 04/13/2025]
Abstract
Mesenchymal plasticity has been extensively described in advanced epithelial cancers; however, its functional role in malignant progression is controversial1-5. The function of epithelial-to-mesenchymal transition (EMT) and cell plasticity in tumour heterogeneity and clonal evolution is poorly understood. Here we clarify the contribution of EMT to malignant progression in pancreatic cancer. We used somatic mosaic genome engineering technologies to trace and ablate malignant mesenchymal lineages along the EMT continuum. The experimental evidence clarifies the essential contribution of mesenchymal lineages to pancreatic cancer evolution. Spatial genomic analysis, single-cell transcriptomic and epigenomic profiling of EMT clarifies its contribution to the emergence of genomic instability, including events of chromothripsis. Genetic ablation of mesenchymal lineages robustly abolished these mutational processes and evolutionary patterns, as confirmed by cross-species analysis of pancreatic and other human solid tumours. Mechanistically, we identified that malignant cells with mesenchymal features display increased chromatin accessibility, particularly in the pericentromeric and centromeric regions, in turn resulting in delayed mitosis and catastrophic cell division. Thus, EMT favours the emergence of genomic-unstable, highly fit tumour cells, which strongly supports the concept of cell-state-restricted patterns of evolution, whereby cancer cell speciation is propagated to progeny within restricted functional compartments. Restraining the evolutionary routes through ablation of clones capable of mesenchymal plasticity, and extinction of the derived lineages, halts the malignant potential of one of the most aggressive forms of human cancer.
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Affiliation(s)
- Luigi Perelli
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
| | - Li Zhang
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Sarah Mangiameli
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | | | - Krishnan K Mahadevan
- Department of Cancer Biology, Metastasis Research Center, 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
| | - Francesca Citron
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Hania Khan
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Courtney Le
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Enrico Gurreri
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Translational Medicine and Surgery, Università Cattolica del Sacro Cuore, Rome, Italy
- Medical Oncology, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | | | - Andrew J C Russell
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Melinda Soeung
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Truong Nguyen Anh Lam
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Sebastian Lundgren
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Sujay Marisetty
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Cihui Zhu
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Desiree Catania
- TRACTION Platform, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Alaa M T Mohamed
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ningping Feng
- TRACTION Platform, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jithesh Jose Augustine
- Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Alessandro Sgambato
- Department of Translational Medicine and Surgery, Università Cattolica del Sacro Cuore, Rome, Italy
- Multiplex Spatial Imaging Facility, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Giampaolo Tortora
- Department of Translational Medicine and Surgery, Università Cattolica del Sacro Cuore, Rome, Italy
- Medical Oncology, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Giulio F Draetta
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Giovanni Tonon
- Center for Omics Sciences, IRCCS San Raffaele Institute, Milan, Italy
| | - Andrew Futreal
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Virginia Giuliani
- TRACTION Platform, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | - Andrea Viale
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Michael P Kim
- Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Timothy P Heffernan
- TRACTION Platform, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Linghua Wang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- The University of Texas MD Anderson Cancer Center, UT Health Houston Graduate School of Biomedical Sciences (GSBS), Houston, TX, USA
| | - Raghu Kalluri
- Department of Cancer Biology, Metastasis Research Center, University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Bioengineering, Rice University, Houston, TX, USA
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
| | - Davide Cittaro
- Center for Omics Sciences, IRCCS San Raffaele Institute, Milan, Italy.
| | - Fei Chen
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA.
| | - Giannicola Genovese
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
- TRACTION Platform, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
- David H. Koch Center for Applied Research of Genitourinary Cancers, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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5
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Bell AJ, Ram S, Labaki WW, Murray S, Kazerooni EA, Galban S, Martinez FJ, Hatt CR, Wang JM, Ivanov V, McGettigan P, Khokhlovich E, Maiorino E, Suryadevara R, Boueiz A, Castaldi PJ, Mirkes EM, Zinovyev A, Gorban AN, Galban CJ, Han MK. Temporal Exploration of Chronic Obstructive Pulmonary Disease Phenotypes: Insights from the COPDGene and SPIROMICS Cohorts. Am J Respir Crit Care Med 2025; 211:569-576. [PMID: 39269427 PMCID: PMC12005034 DOI: 10.1164/rccm.202401-0127oc] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2024] [Accepted: 09/12/2024] [Indexed: 09/15/2024] Open
Abstract
Rationale: Chronic obstructive pulmonary disease (COPD) exhibits considerable progression heterogeneity. We hypothesized that elastic principal graph analysis (EPGA) would identify distinct clinical phenotypes and their longitudinal relationships. Objectives: Our primary objective was to create a map of COPD phenotypes and their connectivity using EPGA. Secondarily, we used longitudinal and external data sets to test the validity and reproducibility of this map. Methods: Cross-sectional data from 8,972 tobacco-exposed COPDGene participants, with and without COPD, were used to train a model with EPGA, using thirty clinical, physiologic and CT features. 4,585 participants from COPDGene Phase 2 were used to test longitudinal trajectories. 2,652 participants from SPIROMICS tested external reproducibility. Measurements and Main Results: Our analysis used cross-sectional data to create an elastic principal tree, where time is associated with distance on the tree. Six clinically distinct tree segments were identified that differed by lung function, symptoms, and CT features: Subclinical (SC); Parenchymal Abnormality (PA); Chronic Bronchitis (CB); Emphysema Male (EM); Emphysema Female (EF); and Severe Airways (SA) disease. 5-year data from COPDGene mapped longitudinal changes onto the tree, and longitudinal trajectories demonstrated a net flow of patients from SC towards EM and EF, including trajectories through airway disease predominant phenotypes, CB and SA. Cross-sectional SPIROMICS data projected onto the tree showed clinically similar patient groupings. Conclusions: This novel analytic methodology provides an approach to defining longitudinal phenotypic trajectories using cross sectional data. These insights are clinically relevant and could facilitate precision therapy and future trials to modify disease progression. Clinical trial registered with www.clinicaltrials.gov (NCT00608764 and NCT01969344).
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Affiliation(s)
| | | | | | - Susan Murray
- School of Public Health, University of Michigan, Ann Arbor, Michigan
| | | | | | | | | | | | - Vladimir Ivanov
- Novartis Institutes for BioMedical Research, Inc., Cambridge, Massachusetts
| | - Paul McGettigan
- Novartis Institutes for BioMedical Research, Inc., Cambridge, Massachusetts
| | - Edward Khokhlovich
- Novartis Institutes for BioMedical Research, Inc., Cambridge, Massachusetts
| | - Enrico Maiorino
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Rahul Suryadevara
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Adel Boueiz
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Peter J. Castaldi
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Evgeny M. Mirkes
- School of Computing and Mathematical Sciences, University of Leicester, Leicester, United Kingdom
| | | | - Alexander N. Gorban
- School of Computing and Mathematical Sciences, University of Leicester, Leicester, United Kingdom
- Department of Mathematics, King’s College London, London, United Kingdom
| | | | - MeiLan K. Han
- Division of Pulmonary and Critical Care Medicine, and
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Miyazaki K, Horie K, Watanabe H, Hidaka R, Hayashi R, Hayatsu N, Fujiwara K, Kuwata R, Uehata T, Ochi Y, Takenaka M, Kawaguchi RK, Ikuta K, Takeuchi O, Ogawa S, Hozumi K, Holländer GA, Kondoh G, Akiyama T, Miyazaki M. A feedback amplifier circuit with Notch and E2A orchestrates T-cell fate and suppresses the innate lymphoid cell lineages during thymic ontogeny. Genes Dev 2025; 39:384-400. [PMID: 39904558 PMCID: PMC11874989 DOI: 10.1101/gad.352111.124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2024] [Accepted: 01/13/2025] [Indexed: 02/06/2025]
Abstract
External signals from the thymic microenvironment and the activities of lineage-specific transcription factors (TFs) instruct T-cell versus innate lymphoid cell (ILC) fates. However, mechanistic insights into how factors such as Notch1-Delta-like-4 (Dll4) signaling and E-protein TFs collaborate to establish T-cell identity remain rudimentary. Using multiple in vivo approaches and single-cell multiome analysis, we identified a feedback amplifier circuit that specifies fetal and adult T-cell fates. In early T progenitors (ETPs) in the fetal thymus, Notch signaling minimally lowered E-protein antagonist Id2 levels, and high Id2 abundance favored the differentiation of ETPs into ILCs. Conversely, in the adult thymus, Notch signaling markedly decreased Id2 abundance in ETPs, substantially elevating E-protein DNA binding and in turn promoting the activation of a T-cell lineage-specific gene expression program linked with V(D)J gene recombination and T-cell receptor signaling. Our findings indicate that, in the fetal versus the adult thymus, a simple feedback amplifier circuit dictated by Notch-mediated signals and Id2 abundance enforces T-cell identity and suppresses ILC development.
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Affiliation(s)
- Kazuko Miyazaki
- Laboratory of Immunology, Institute for Life and Medical Sciences, Kyoto University, Kyoto 606-8507, Japan
| | - Kenta Horie
- Laboratory for Immune Homeostasis, RIKEN Center for Integrative Medical Sciences, Kanagawa 230-0045, Japan
| | - Hitomi Watanabe
- Laboratory of Integrative Biological Sciences, Institute for Life and Medical Sciences, Kyoto University, Kyoto 606-8507, Japan
| | - Reiko Hidaka
- Laboratory of Immunology, Institute for Life and Medical Sciences, Kyoto University, Kyoto 606-8507, Japan
| | - Rinako Hayashi
- Laboratory of Immunology, Institute for Life and Medical Sciences, Kyoto University, Kyoto 606-8507, Japan
| | - Norihito Hayatsu
- Laboratory for Comprehensive Genomic Analysis, RIKEN Center for Integrative Medical Sciences, Kanagawa 230-0045, Japan
| | - Kentaro Fujiwara
- Laboratory of Immunology, Institute for Life and Medical Sciences, Kyoto University, Kyoto 606-8507, Japan
| | - Rei Kuwata
- Laboratory of Immunology, Institute for Life and Medical Sciences, Kyoto University, Kyoto 606-8507, Japan
| | - Takuya Uehata
- Department of Medical Chemistry, Graduate School of Medicine, Kyoto University, Kyoto 606-8501, Japan
| | - Yotaro Ochi
- Department of Pathology and Tumor Biology, Graduate School of Medicine, Kyoto University, Kyoto 606-8501, Japan
| | - Makoto Takenaka
- Laboratory of Integrative Biological Sciences, Institute for Life and Medical Sciences, Kyoto University, Kyoto 606-8507, Japan
| | | | - Koichi Ikuta
- Laboratory of Immune Regulation, Institute for Life and Medical Sciences, Kyoto University, Kyoto 606-8507, Japan
| | - Osamu Takeuchi
- Department of Medical Chemistry, Graduate School of Medicine, Kyoto University, Kyoto 606-8501, Japan
| | - Seishi Ogawa
- Department of Pathology and Tumor Biology, Graduate School of Medicine, Kyoto University, Kyoto 606-8501, Japan
- Institute for the Advanced Study of Human Biology (WPI ASHBi), Graduate School of Medicine, Kyoto University, Kyoto 606-8501, Japan
- Department of Medicine, Center for Hematology and Regenerative Medicine, Karolinska Institute, Stockholm 171 77, Sweden
| | - Katsuto Hozumi
- Department of Immunology, Tokai University School of Medicine, Kanagawa 259-1193, Japan
| | - Georg A Holländer
- Department of Pediatrics, Institute of Developmental and Regenerative Medicine, University of Oxford, Oxford OX3 7TY, United Kingdom
- Pediatric Immunology, Department of Biomedicine, University of Basel and University Children's Hospital Basel, Basel 4056, Switzerland
- Department of Biosystems Science and Engineering, Eidgenössische Technische Hochschule (ETH) Zurich, Basel 4056, Switzerland
| | - Gen Kondoh
- Laboratory of Integrative Biological Sciences, Institute for Life and Medical Sciences, Kyoto University, Kyoto 606-8507, Japan
| | - Taishin Akiyama
- Laboratory for Immune Homeostasis, RIKEN Center for Integrative Medical Sciences, Kanagawa 230-0045, Japan
| | - Masaki Miyazaki
- Laboratory of Immunology, Institute for Life and Medical Sciences, Kyoto University, Kyoto 606-8507, Japan;
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7
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Chen X, Ma Y, Shi Y, Zhang B, Wu H, Gao J. Fuzzy-Based Identification of Transition Cells to Infer Cell Trajectory for Single-Cell Transcriptomics. J Comput Biol 2025; 32:253-273. [PMID: 39670822 DOI: 10.1089/cmb.2023.0432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2024] Open
Abstract
With the continuous evolution of single-cell RNA sequencing technology, it has become feasible to reconstruct cell development processes using computational methods. Trajectory inference is a crucial downstream analytical task that provides valuable insights into understanding cell cycle and differentiation. During cell development, cells exhibit both stable and transition states, which makes it challenging to accurately identify these cells. To address this challenge, we propose a novel single-cell trajectory inference method using fuzzy clustering, named scFCTI. By introducing fuzzy clustering and quantifying cell uncertainty, scFCTI can identify transition cells within unstable cell states. Moreover, scFCTI can obtain refined cell classification by characterizing different cell stages, which gain more accurate single-cell trajectory reconstruction containing transition paths. To validate the effectiveness of scFCTI, we conduct experiments on five real datasets and four different structure simulation datasets, comparing them with several state-of-the-art trajectory inference methods. The results demonstrate that scFCTI outperforms these methods by successfully identifying unstable cell clusters and obtaining more accurate cell paths with transition states. Especially the experimental results demonstrate that scFCTI can reconstruct the cell trajectory more precisely.
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Affiliation(s)
- Xiang Chen
- School of Science, Jiangnan University, Wuxi, China
| | - Yibing Ma
- School of Science, Jiangnan University, Wuxi, China
| | - Yongle Shi
- School of Science, Jiangnan University, Wuxi, China
| | - Bai Zhang
- School of Science, Jiangnan University, Wuxi, China
| | - Hanwen Wu
- School of Science, Jiangnan University, Wuxi, China
| | - Jie Gao
- School of Science, Jiangnan University, Wuxi, China
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8
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Cortés-Ríos J, Rodriguez-Fernandez M, Sorger PK, Fröhlich F. Dynamic Modeling of Cell Cycle Arrest Through Integrated Single-Cell and Mathematical Modelling Approaches. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2025.02.20.639240. [PMID: 40060624 PMCID: PMC11888159 DOI: 10.1101/2025.02.20.639240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 03/18/2025]
Abstract
Highly multiplexed imaging assays allow simultaneous quantification of multiple protein and phosphorylation markers, providing a static snapshots of cell types and states. Pseudo-time techniques can transform these static snapshots of unsynchronized cells into dynamic trajectories, enabling the study of dynamic processes such as development trajectories and the cell cycle. Such ordering also enables training of mathematical models on these data, but technical challenges have hitherto made it difficult to integrate multiple experimental conditions, limiting the predictive power and insights these models can generate. In this work, we propose data processing and model training approaches for integrating multiplexed, multi-condition immunofluorescence data with mathematical modelling. We devise training strategies that are applicable to datasets where cells exhibit oscillatory as well as arrested dynamics and use them to train a cell cycle model on a dataset of MCF-10A mammary epithelial exposed to cell-cycle arresting small molecules. We validate the model by investigating predicted growth factor sensitivities and responses to inhibitors of cells at different initial conditions. We anticipate that our framework will generalise to other highly multiplexed measurement techniques such as mass-cytometry, rendering larger bodies of data accessible to dynamic modelling and paving the way to deeper biological insights.
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Affiliation(s)
- Javiera Cortés-Ríos
- Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Chile
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, State University of New York, Buffalo, New York, United States of America
| | - Maria Rodriguez-Fernandez
- Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Chile
| | - Peter K Sorger
- Laboratory of Systems Pharmacology and Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Fabian Fröhlich
- Dynamics of Living Systems Laboratory, The Francis Crick Institute, London, United Kingdom
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9
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Ji P, Wang N, Yu Y, Zhu J, Zuo Z, Zhang B, Zhao F. Single-cell delineation of the microbiota-gut-brain axis: Probiotic intervention in Chd8 haploinsufficient mice. CELL GENOMICS 2025; 5:100768. [PMID: 39914389 PMCID: PMC11872533 DOI: 10.1016/j.xgen.2025.100768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2024] [Revised: 12/02/2024] [Accepted: 01/14/2025] [Indexed: 02/16/2025]
Abstract
Emerging research underscores the gut microbiome's impact on the nervous system via the microbiota-gut-brain axis, yet comprehensive insights remain limited. Using a CHD8-haploinsufficient model for autism spectrum disorder (ASD), we explored host-gut microbiota interactions by constructing a single-cell transcriptome atlas of brain and intestinal tissues in wild-type and mutant mice across three developmental stages. CHD8 haploinsufficiency caused delayed development of radial glial precursors and excitatory neural progenitors in the E14.5 brain, inflammation in the adult brain, immunodeficiency, and abnormal intestinal development. Selective CHD8 knockdown in intestinal epithelial cells generated Chd8ΔIEC mice, which exhibited normal sociability but impaired social novelty recognition. Probiotic intervention with Lactobacillus murinus selectively rescued social deficits in Chd8ΔIEC mice, with single-cell transcriptome analysis revealing underlying mechanisms. This study provides a detailed single-cell transcriptomic dataset of ASD-related neural and intestinal changes, advancing our understanding of the gut-brain axis and offering potential therapeutic strategies for ASD.
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Affiliation(s)
- Peifeng Ji
- Institute of Zoology, Chinese Academy of Sciences, Beijing, China
| | - Ning Wang
- Institute of Zoology, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - You Yu
- Institute of Zoology, Chinese Academy of Sciences, Beijing, China
| | - Junjie Zhu
- Institute of Zoology, Chinese Academy of Sciences, Beijing, China; Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, China
| | - Zhenqiang Zuo
- Institute of Zoology, Chinese Academy of Sciences, Beijing, China
| | - Bing Zhang
- Institute of Zoology, Chinese Academy of Sciences, Beijing, China
| | - Fangqing Zhao
- Institute of Zoology, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, China.
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10
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Sun F, Li H, Sun D, Fu S, Gu L, Shao X, Wang Q, Dong X, Duan B, Xing F, Wu J, Xiao M, Zhao F, Han JDJ, Liu Q, Fan X, Li C, Wang C, Shi T. Single-cell omics: experimental workflow, data analyses and applications. SCIENCE CHINA. LIFE SCIENCES 2025; 68:5-102. [PMID: 39060615 DOI: 10.1007/s11427-023-2561-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 04/18/2024] [Indexed: 07/28/2024]
Abstract
Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.
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Affiliation(s)
- Fengying Sun
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China
| | - Haoyan Li
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Dongqing Sun
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Shaliu Fu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Lei Gu
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Shao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China
| | - Qinqin Wang
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Dong
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Bin Duan
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Feiyang Xing
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Jun Wu
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Minmin Xiao
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
| | - Fangqing Zhao
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Jing-Dong J Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China.
| | - Qi Liu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China.
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China.
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China.
- Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China.
| | - Chen Li
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Chenfei Wang
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Tieliu Shi
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China.
- Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics, East China Normal University, Shanghai, 200062, China.
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11
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Paczkowska J, Tang M, Wright KT, Song L, Luu K, Shanmugam V, Welsh EL, Weirather JL, Besson N, Olszewski H, Porter BA, Pfaff KL, Redd RA, Cader FZ, Mandato E, Ouyang J, Calabretta E, Bai G, Lawton LN, Armand P, Rodig SJ, Liu XS, Shipp MA. Cancer-specific innate and adaptive immune rewiring drives resistance to PD-1 blockade in classic Hodgkin lymphoma. Nat Commun 2024; 15:10740. [PMID: 39737927 PMCID: PMC11686379 DOI: 10.1038/s41467-024-54512-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 11/11/2024] [Indexed: 01/01/2025] Open
Abstract
Hodgkin Reed-Sternberg (HRS) cells of classic Hodgkin lymphoma (cHL), like many solid tumors, elicit ineffective immune responses. However, patients with cHL are highly responsive to PD-1 blockade, which largely depends on HRS cell-specific retention of MHC class II and implicates CD4+ T cells and additional MHC class I-independent immune effectors. Here, we utilize single-cell RNA sequencing and spatial analysis to define shared circulating and microenvironmental features of the immune response to PD-1 blockade in cHL. Compared with non-responders, responding patients have more circulating CD4+ naïve and central memory T cells and B cells, as well as more diverse CD4+ T cell and B cell receptor repertoires. Importantly, a population of circulating and tumor-infiltrating IL1β+ monocytes/macrophages is detectable in patients with cHL but not healthy donors, and a proinflammatory, tumor-promoting signature of these circulating IL1β+ monocytes is associated with resistance to PD-1 blockade in cHL. Altogether, our findings reveal extensive immune rewiring and complementary roles of CD4+ T cells, B cells and IL1β+ monocytes in the response to PD-1 blockade and suggest that these features can be captured with a peripheral blood test.
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Affiliation(s)
- Julia Paczkowska
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Ming Tang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
- Astra Zeneca, Waltham, MA, USA
| | - Kyle T Wright
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
- Department of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Li Song
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biomedical Data Science, Dartmouth College, Hanover, NH, USA
| | - Kelsey Luu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
- PathAI, Boston, MA, USA
| | - Vignesh Shanmugam
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Emma L Welsh
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
- Center for Immuno-Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Jason L Weirather
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Naomi Besson
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
- Center for Immuno-Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Harrison Olszewski
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
- Center for Immuno-Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Billie A Porter
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
- Center for Immuno-Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Kathleen L Pfaff
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
- Center for Immuno-Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Robert A Redd
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Fathima Zumla Cader
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- AstraZeneca, City House, Cambridge, UK
| | - Elisa Mandato
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Jing Ouyang
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Mechanisms of Cancer Resistance Thematic Center, Bristol Myers Squibb, Cambridge, MA, USA
| | - Eleonora Calabretta
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Gali Bai
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Lee N Lawton
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Philippe Armand
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Scott J Rodig
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
- Center for Immuno-Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Xiaole Shirley Liu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
- GV20 Therapeutics, LLC, Cambridge, MA, USA
| | - Margaret A Shipp
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
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12
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Zhang J, Peng Q, Fan J, Liu F, Chen H, Bi X, Yuan S, Jiang W, Pan T, Li K, Tan S, Chen P. Single-cell and spatial transcriptomics reveal SPP1-CD44 signaling drives primary resistance to immune checkpoint inhibitors in RCC. J Transl Med 2024; 22:1157. [PMID: 39736762 DOI: 10.1186/s12967-024-06018-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2024] [Accepted: 12/18/2024] [Indexed: 01/01/2025] Open
Abstract
BACKGROUND Immune checkpoint inhibitors (ICIs) are a cornerstone therapy for advanced renal cell carcinoma (RCC). However, significant rates of primary resistance hinder their efficacy, and the underlying mechanisms remain poorly understood. This study aims to unravel the tumor-immune interactions and signaling pathways driving primary resistance to ICIs in RCC. METHODS We integrated single-cell RNA sequencing, spatial transcriptomics, and clinical sample analysis to investigate the tumor microenvironment and intercellular signaling. Advanced computational methods, including cell-cell communication networks, pseudotime trajectories, and gene set enrichment analysis (GSEA), were employed to uncover the underlying resistance mechanisms. RESULTS Compared to the sensitive group, the primary resistance group exhibited a significant increase in SPP1-CD44 signaling-mediated interactions between tumor cells and immune cells. These interactions disrupted antigen presentation in immune effector cells and suppressed key chemokine and cytokine pathways, thereby impairing effective immune responses. In contrast, the sensitive group showed more active antigen presentation and cytokine signaling, which facilitated stronger immune responses. Furthermore, the interaction between SPP1-secreting tumor cells and CD44-expressing exhausted CD8 + T cells activated the MAPK signaling pathway within CD8 + Tex cells, exacerbating T cell exhaustion and driving the development of ICI resistance in RCC. CONCLUSION Our findings reveal a potential mechanism by which SPP1-CD44 signaling mediates tumor-immune cell interactions leading to ICI resistance, providing a theoretical basis for targeting and disrupting this signaling to overcome primary resistance in RCC.
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Affiliation(s)
- Junfeng Zhang
- Department of Urology, Xinjiang Medical University Affiliated Cancer Hospital, Urumqi, China
| | - Qingyan Peng
- School of Nursing, Xinjiang Medical University, Urumqi, China
| | - Jin Fan
- Department of Oncology, The Central Hospital of Enshi Tujia and Miao Autonomous Prefecture, Enshi, Hubei, China
| | - Fuzhong Liu
- Cancer Institute, Xinjiang Medical University Affiliated Cancer Hospital, Urumqi, China
| | - Hongbo Chen
- Department of Urology, The Central Hospital of Enshi Tujia and Miao Autonomous Prefecture, Enshi, Hubei, China
| | - Xing Bi
- Department of Urology, Xinjiang Medical University Affiliated Cancer Hospital, Urumqi, China
| | - Shuai Yuan
- Department of Urology, Xinjiang Medical University Affiliated Cancer Hospital, Urumqi, China
| | - Wei Jiang
- Department of Urology, Xinjiang Medical University Affiliated Cancer Hospital, Urumqi, China
| | - Ting Pan
- Department of Urology, Xinjiang Medical University Affiliated Cancer Hospital, Urumqi, China
| | - Kailing Li
- Department of Urology, The Central Hospital of Enshi Tujia and Miao Autonomous Prefecture, Enshi, Hubei, China
| | - Sihai Tan
- Department of Pediatric, The Central Hospital of Enshi Tujia and Miao Autonomous Prefecture, Enshi, Hubei, China.
| | - Peng Chen
- Department of Urology, Xinjiang Medical University Affiliated Cancer Hospital, Urumqi, China.
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13
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Zhang J, Zhang N, Mai Q, Zhou C. The frontier of precision medicine: application of single-cell multi-omics in preimplantation genetic diagnosis. Brief Funct Genomics 2024; 23:726-732. [PMID: 39486398 DOI: 10.1093/bfgp/elae041] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2024] [Revised: 10/03/2024] [Indexed: 11/04/2024] Open
Abstract
The advent of single-cell multi-omics technologies has revolutionized the landscape of preimplantation genetic diagnosis (PGD), offering unprecedented insights into the genetic, transcriptomic, and proteomic profiles of individual cells in early-stage embryos. This breakthrough holds the promise of enhancing the accuracy, efficiency, and scope of PGD, thereby significantly improving outcomes in assisted reproductive technologies (ARTs) and genetic disease prevention. This review provides a comprehensive overview of the importance of PGD in the context of precision medicine and elucidates how single-cell multi-omics technologies have transformed this field. We begin with a brief history of PGD, highlighting its evolution and application in detecting genetic disorders and facilitating ART. Subsequently, we delve into the principles, methodologies, and applications of single-cell genomics, transcriptomics, and proteomics in PGD, emphasizing their role in improving diagnostic precision and efficiency. Furthermore, we review significant recent advances within this domain, including key experimental designs, findings, and their implications for PGD practices. The advantages and limitations of these studies are analyzed to assess their potential impact on the future development of PGD technologies. Looking forward, we discuss the emerging research directions and challenges, focusing on technological advancements, new application areas, and strategies to overcome existing limitations. In conclusion, this review underscores the pivotal role of single-cell multi-omics in PGD, highlighting its potential to drive the progress of precision medicine and personalized treatment strategies, thereby marking a new era in reproductive genetics and healthcare.
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Affiliation(s)
- Jinglei Zhang
- Reproductive Medical Center, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, Guangdong, China
| | - Nan Zhang
- General Surgery, The First Affiliated Hospital of Henan University of CM, Zhengzhou 450052, China
| | - Qingyun Mai
- Reproductive Medical Center, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, Guangdong, China
| | - Canquan Zhou
- Reproductive Medical Center, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, Guangdong, China
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14
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Do HT, Ono M, Wang Z, Kitagawa W, Dang AT, Yonezawa T, Kuboki T, Oohashi T, Kubota S. Inverse genetics tracing the differentiation pathway of human chondrocytes. Osteoarthritis Cartilage 2024; 32:1419-1432. [PMID: 38925474 DOI: 10.1016/j.joca.2024.06.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 06/16/2024] [Accepted: 06/18/2024] [Indexed: 06/28/2024]
Abstract
OBJECTIVE Mammalian somatic cells can be reprogrammed into induced pluripotent stem cells (iPSCs) via the forced expression of Yamanaka reprogramming factors. However, only a limited population of the cells that pass through a particular pathway can metamorphose into iPSCs, while the others do not. This study aimed to clarify the pathways that chondrocytes follow during the reprogramming process. DESIGN The fate of human articular chondrocytes under reprogramming was investigated through a time-coursed single-cell transcriptomic analysis, which we termed an inverse genetic approach. The iPS interference technique was also employed to verify that chondrocytes inversely return to pluripotency following the proper differentiation pathway. RESULTS We confirmed that human chondrocytes could be converted into cells with an iPSC phenotype. Moreover, it was clarified that a limited population that underwent the silencing of SOX9, a master gene for chondrogenesis, at a specific point during the proper transcriptome transition pathway, could eventually become iPSCs. Interestingly, the other cells, which failed to be reprogrammed, followed a distinct pathway toward cells with a surface zone chondrocyte phenotype. The critical involvement of cellular communication network factors (CCNs) in this process was indicated. The idea that chondrocytes, when reprogrammed into iPSCs, follow the differentiation pathway backward was supported by the successful iPS interference using SOX9. CONCLUSIONS This inverse genetic strategy may be useful for seeking candidates for the master genes for the differentiation of various somatic cells. The utility of CCNs in articular cartilage regeneration is also supported.
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Affiliation(s)
- H T Do
- Department of Molecular Biology and Biochemistry, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan; Department of Oral Rehabilitation and Regenerative Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan.
| | - M Ono
- Department of Molecular Biology and Biochemistry, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan; Department of Oral Rehabilitation and Implantology, Okayama University Hospital, Okayama, Japan.
| | - Z Wang
- Department of Molecular Biology and Biochemistry, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan.
| | - W Kitagawa
- Department of Molecular Biology and Biochemistry, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan; Department of Oral Rehabilitation and Regenerative Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan.
| | - A T Dang
- Department of Molecular Biology and Biochemistry, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan; Department of Oral Rehabilitation and Regenerative Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan.
| | - T Yonezawa
- Department of Molecular Biology and Biochemistry, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan.
| | - T Kuboki
- Department of Oral Rehabilitation and Regenerative Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan; Department of Oral Rehabilitation and Implantology, Okayama University Hospital, Okayama, Japan.
| | - T Oohashi
- Department of Molecular Biology and Biochemistry, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan.
| | - S Kubota
- Department of Biochemistry and Molecular Dentistry, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan.
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15
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Farzad N, Enninful A, Bao S, Zhang D, Deng Y, Fan R. Spatially resolved epigenome sequencing via Tn5 transposition and deterministic DNA barcoding in tissue. Nat Protoc 2024; 19:3389-3425. [PMID: 38943021 DOI: 10.1038/s41596-024-01013-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 04/11/2024] [Indexed: 06/30/2024]
Abstract
Spatial epigenetic mapping of tissues enables the study of gene regulation programs and cellular functions with the dependency on their local tissue environment. Here we outline a complete procedure for two spatial epigenomic profiling methods: spatially resolved genome-wide profiling of histone modifications using in situ cleavage under targets and tagmentation (CUT&Tag) chemistry (spatial-CUT&Tag) and transposase-accessible chromatin sequencing (spatial-ATAC-sequencing) for chromatin accessibility. Both assays utilize in-tissue Tn5 transposition to recognize genomic DNA loci followed by microfluidic deterministic barcoding to incorporate spatial address codes. Furthermore, these two methods do not necessitate prior knowledge of the transcription or epigenetic markers for a given tissue or cell type but permit genome-wide unbiased profiling pixel-by-pixel at the 10 μm pixel size level and single-base resolution. To support the widespread adaptation of these methods, details are provided in five general steps: (1) sample preparation; (2) Tn5 transposition in spatial-ATAC-sequencing or antibody-controlled pA-Tn5 tagmentation in CUT&Tag; (3) library preparation; (4) next-generation sequencing; and (5) data analysis using our customed pipelines available at: https://github.com/dyxmvp/Spatial_ATAC-seq and https://github.com/dyxmvp/spatial-CUT-Tag . The whole procedure can be completed on four samples in 2-3 days. Familiarity with basic molecular biology and bioinformatics skills with access to a high-performance computing environment are required. A rudimentary understanding of pathology and specimen sectioning, as well as deterministic barcoding in tissue-specific skills (e.g., design of a multiparameter barcode panel and creation of microfluidic devices), are also advantageous. In this protocol, we mainly focus on spatial profiling of tissue region-specific epigenetic landscapes in mouse embryos and mouse brains using spatial-ATAC-sequencing and spatial-CUT&Tag, but these methods can be used for other species with no need for species-specific probe design.
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Affiliation(s)
- Negin Farzad
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
- Yale Stem Cell Center and Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA
| | - Archibald Enninful
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
- Yale Stem Cell Center and Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA
| | - Shuozhen Bao
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
- Yale Stem Cell Center and Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA
| | - Di Zhang
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Yanxiang Deng
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Pennsylvania, PA, USA
| | - Rong Fan
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
- Yale Stem Cell Center and Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA.
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA.
- Human and Translational Immunology Program, Yale School of Medicine, New Haven, CT, USA.
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16
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Fischer A, Albert TK, Moreno N, Interlandi M, Mormann J, Glaser S, Patil P, de Faria FW, Richter M, Verma A, Balbach ST, Wagener R, Bens S, Dahlum S, Göbel C, Münter D, Inserte C, Graf M, Kremer E, Melcher V, Di Stefano G, Santi R, Chan A, Dogan A, Bush J, Hasselblatt M, Cheng S, Spetalen S, Fosså A, Hartmann W, Herbrüggen H, Robert S, Oyen F, Dugas M, Walter C, Sandmann S, Varghese J, Rossig C, Schüller U, Tzankov A, Pedersen MB, d'Amore FA, Mellgren K, Kontny U, Kancherla V, Veloza L, Missiaglia E, Fataccioli V, Gaulard P, Burkhardt B, Soehnlein O, Klapper W, de Leval L, Siebert R, Kerl K. Lack of SMARCB1 expression characterizes a subset of human and murine peripheral T-cell lymphomas. Nat Commun 2024; 15:8571. [PMID: 39362842 PMCID: PMC11452211 DOI: 10.1038/s41467-024-52826-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 09/23/2024] [Indexed: 10/05/2024] Open
Abstract
Peripheral T-cell lymphoma, not otherwise specified (PTCL-NOS) is a heterogeneous group of malignancies with poor outcome. Here, we identify a subgroup, PTCL-NOSSMARCB1-, which is characterized by the lack of the SMARCB1 protein and occurs more frequently in young patients. Human and murine PTCL-NOSSMARCB1- show similar DNA methylation profiles, with hypermethylation of T-cell-related genes and hypomethylation of genes involved in myeloid development. Single-cell analyses of human and murine tumors revealed a rich and complex network of interactions between tumor cells and an immunosuppressive and exhausted tumor microenvironment (TME). In a drug screen, we identified histone deacetylase inhibitors (HDACi) as a class of drugs effective against PTCL-NOSSmarcb1-. In vivo treatment of mouse tumors with SAHA, a pan-HDACi, triggered remodeling of the TME, promoting replenishment of lymphoid compartments and reversal of the exhaustion phenotype. These results provide a rationale for further exploration of HDACi combination therapies targeting PTCL-NOSSMARCB1- within the TME.
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MESH Headings
- Animals
- SMARCB1 Protein/genetics
- SMARCB1 Protein/metabolism
- Humans
- Lymphoma, T-Cell, Peripheral/genetics
- Lymphoma, T-Cell, Peripheral/drug therapy
- Lymphoma, T-Cell, Peripheral/metabolism
- Lymphoma, T-Cell, Peripheral/pathology
- Mice
- Histone Deacetylase Inhibitors/pharmacology
- Tumor Microenvironment/genetics
- Tumor Microenvironment/drug effects
- DNA Methylation
- Gene Expression Regulation, Neoplastic
- Female
- Cell Line, Tumor
- Male
- Vorinostat/pharmacology
- Single-Cell Analysis
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Affiliation(s)
- Anja Fischer
- Institute of Human Genetics, Ulm University Medical Center, Ulm, Germany
| | - Thomas K Albert
- Department of Pediatric Hematology and Oncology, University Children's Hospital Münster, Münster, Germany
| | - Natalia Moreno
- Department of Pediatric Hematology and Oncology, University Children's Hospital Münster, Münster, Germany
| | - Marta Interlandi
- Department of Pediatric Hematology and Oncology, University Children's Hospital Münster, Münster, Germany
- Institute of Medical Informatics, University of Münster, 48149, Münster, Germany
| | - Jana Mormann
- Department of Pediatric Hematology and Oncology, University Children's Hospital Münster, Münster, Germany
| | - Selina Glaser
- Institute of Human Genetics, Ulm University Medical Center, Ulm, Germany
| | - Paurnima Patil
- Institute of Human Genetics, Ulm University Medical Center, Ulm, Germany
| | - Flavia W de Faria
- Department of Pediatric Hematology and Oncology, University Children's Hospital Münster, Münster, Germany
| | - Mathis Richter
- Institute for Experimental Pathology, Center for Molecular Biology of Inflammation, University of Münster, Münster, Germany
| | - Archana Verma
- Department of Pediatric Hematology and Oncology, University Children's Hospital Münster, Münster, Germany
| | - Sebastian T Balbach
- Department of Pediatric Hematology and Oncology, University Children's Hospital Münster, Münster, Germany
| | - Rabea Wagener
- Institute of Human Genetics, Ulm University Medical Center, Ulm, Germany
| | - Susanne Bens
- Institute of Human Genetics, Ulm University Medical Center, Ulm, Germany
| | - Sonja Dahlum
- Institute of Human Genetics, Ulm University Medical Center, Ulm, Germany
| | - Carolin Göbel
- Department of Pediatric Hematology and Oncology, University Medical Center Hamburg, Eppendorf (UKE), 20251, Hamburg, Germany
- Research Institute Children's Cancer Center, 20251, Hamburg, Germany
| | - Daniel Münter
- Department of Pediatric Hematology and Oncology, University Children's Hospital Münster, Münster, Germany
| | - Clara Inserte
- Institute of Medical Informatics, University of Münster, 48149, Münster, Germany
| | - Monika Graf
- Department of Pediatric Hematology and Oncology, University Children's Hospital Münster, Münster, Germany
| | - Eva Kremer
- Department of Pediatric Hematology and Oncology, University Children's Hospital Münster, Münster, Germany
| | - Viktoria Melcher
- Department of Pediatric Hematology and Oncology, University Children's Hospital Münster, Münster, Germany
| | - Gioia Di Stefano
- Pathological Anatomy Section, Careggi University Hospital, Florence, Italy
| | - Raffaella Santi
- Pathological Anatomy Section, Careggi University Hospital, Florence, Italy
| | - Alexander Chan
- Department of Pathology, Hematopathology Service, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
| | - Ahmet Dogan
- Department of Pathology, Hematopathology Service, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
| | - Jonathan Bush
- Division of Anatomical Pathology, British Columbia Children's Hospital and Women's Hospital and Health Center, Vancouver, BC, Canada
| | - Martin Hasselblatt
- Institute of Neuropathology, University Hospital Münster, 48149, Münster, Germany
| | - Sylvia Cheng
- Division of Pediatric Hematology/Oncology/BMT, Department of Pediatrics, British Columbia Children's Hospital, University of British Columbia, Vancouver, BC, Canada
| | - Signe Spetalen
- Department of Pathology, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Alexander Fosså
- Department of Oncology, Oslo University Hospital-Norwegian Radium Hospital, Oslo, Norway
| | - Wolfgang Hartmann
- Division of Translational Pathology, Gerhard-Domagk-Institut für Pathologie, Universitätsklinikum Münster, Albert-Schweitzer-Campus 1, Gebäude D17, 48149, Münster, Germany
| | - Heidi Herbrüggen
- Department of Pediatric Hematology and Oncology, University Children's Hospital Münster, Münster, Germany
| | - Stella Robert
- Department of Medicine A, Hematology, Oncology, and Pneumology, University Hospital Münster, Münster, Germany
| | - Florian Oyen
- Department of Pediatric Hematology and Oncology, University Medical Center Hamburg, Eppendorf (UKE), 20251, Hamburg, Germany
| | - Martin Dugas
- Institute of Medical Informatics, University of Münster, 48149, Münster, Germany
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Carolin Walter
- Institute of Medical Informatics, University of Münster, 48149, Münster, Germany
| | - Sarah Sandmann
- Institute of Medical Informatics, University of Münster, 48149, Münster, Germany
| | - Julian Varghese
- Institute of Medical Informatics, University of Münster, 48149, Münster, Germany
| | - Claudia Rossig
- Department of Pediatric Hematology and Oncology, University Children's Hospital Münster, Münster, Germany
| | - Ulrich Schüller
- Department of Pediatric Hematology and Oncology, University Medical Center Hamburg, Eppendorf (UKE), 20251, Hamburg, Germany
- Research Institute Children's Cancer Center, 20251, Hamburg, Germany
- Institute of Neuropathology, University Medical Center Hamburg-Eppendorf (UKE), 20251, Hamburg, Germany
| | - Alexandar Tzankov
- Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
| | - Martin B Pedersen
- Department of Hematology, Aarhus University Hospital, Aarhus, Denmark
| | - Francesco A d'Amore
- Department of Hematology, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Karin Mellgren
- Department of Pediatric Oncology and Hematology, Sahlgrenska University Hospital, The Queen Silvia Children's Hospital, Gothenburg, Sweden
| | - Udo Kontny
- Section of Pediatric Hematology, Oncology, and Stem Cell Transplantation, Department of Pediatric and Adolescent Medicine, RWTH Aachen University Hospital, Aachen, Germany
| | - Venkatesh Kancherla
- Institute of Pathology, Department of Laboratory Medicine and Pathology, Lausanne University Hospital, Lausanne, Switzerland
| | - Luis Veloza
- Institute of Pathology, Department of Laboratory Medicine and Pathology, Lausanne University Hospital, Lausanne, Switzerland
| | - Edoardo Missiaglia
- Institute of Pathology, Department of Laboratory Medicine and Pathology, Lausanne University Hospital, Lausanne, Switzerland
| | - Virginie Fataccioli
- INSERM U955, Université Paris-Est, Créteil, France
- Département de Pathologie, Hôpitaux Universitaires Henri Mondor, AP-HP, INSERM U955, Université Paris Est Créteil, Créteil, France
| | - Philippe Gaulard
- Département de Pathologie, Hôpitaux Universitaires Henri Mondor, AP-HP, INSERM U955, Université Paris Est Créteil, Créteil, France
| | - Birgit Burkhardt
- Department of Pediatric Hematology and Oncology, University Children's Hospital Münster, Münster, Germany
| | - Oliver Soehnlein
- Institute for Experimental Pathology, Center for Molecular Biology of Inflammation, University of Münster, Münster, Germany
| | - Wolfram Klapper
- Department of Pathology, Haematopathology Section and Lymph Node Registry, University Hospital Schleswig-Holstein, Kiel, Germany
| | - Laurence de Leval
- Institute of Pathology, Department of Laboratory Medicine and Pathology, Lausanne University Hospital, Lausanne, Switzerland
| | - Reiner Siebert
- Institute of Human Genetics, Ulm University Medical Center, Ulm, Germany
| | - Kornelius Kerl
- Department of Pediatric Hematology and Oncology, University Children's Hospital Münster, Münster, Germany.
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17
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Protti G, Spreafico R. A primer on single-cell RNA-seq analysis using dendritic cells as a case study. FEBS Lett 2024. [PMID: 39245787 DOI: 10.1002/1873-3468.15009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 07/18/2024] [Accepted: 08/12/2024] [Indexed: 09/10/2024]
Abstract
Recent advances in single-cell (sc) transcriptomics have revolutionized our understanding of dendritic cells (DCs), pivotal players of the immune system. ScRNA-sequencing (scRNA-seq) has unraveled a previously unrecognized complexity and heterogeneity of DC subsets, shedding light on their ontogeny and specialized roles. However, navigating the rapid technological progress and computational methods can be daunting for researchers unfamiliar with the field. This review aims to provide immunologists with a comprehensive introduction to sc transcriptomic analysis, offering insights into recent developments in DC biology. Addressing common analytical queries, we guide readers through popular tools and methodologies, supplemented with references to benchmarks and tutorials for in-depth understanding. By examining findings from pioneering studies, we illustrate how computational techniques have expanded our knowledge of DC biology. Through this synthesis, we aim to equip researchers with the necessary tools and knowledge to navigate and leverage scRNA-seq for unraveling the intricacies of DC biology and advancing immunological research.
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Affiliation(s)
- Giulia Protti
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Milan, Italy
| | - Roberto Spreafico
- Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, CA, USA
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18
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Hendriksen JD, Locallo A, Maarup S, Debnath O, Ishaque N, Hasselbach B, Skjøth-Rasmussen J, Yde CW, Poulsen HS, Lassen U, Weischenfeldt J. Immunotherapy drives mesenchymal tumor cell state shift and TME immune response in glioblastoma patients. Neuro Oncol 2024; 26:1453-1466. [PMID: 38695342 PMCID: PMC11300009 DOI: 10.1093/neuonc/noae085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/07/2024] Open
Abstract
BACKGROUND Glioblastoma is a highly aggressive type of brain tumor for which there is no curative treatment available. Immunotherapies have shown limited responses in unselected patients, and there is an urgent need to identify mechanisms of treatment resistance to design novel therapy strategies. METHODS Here we investigated the phenotypic and transcriptional dynamics at single-cell resolution during nivolumab immune checkpoint treatment of glioblastoma patients. RESULTS We present the integrative paired single-cell RNA-seq analysis of 76 tumor samples from patients in a clinical trial of the PD-1 inhibitor nivolumab and untreated patients. We identify a distinct aggressive phenotypic signature in both tumor cells and the tumor microenvironment in response to nivolumab. Moreover, nivolumab-treatment was associated with an increased transition to mesenchymal stem-like tumor cells, and an increase in TAMs and exhausted and proliferative T cells. We verify and extend our findings in large external glioblastoma dataset (n = 298), develop a latent immune signature and find 18% of primary glioblastoma samples to be latent immune, associated with mesenchymal tumor cell state and TME immune response. Finally, we show that latent immune glioblastoma patients are associated with shorter overall survival following immune checkpoint treatment (P = .0041). CONCLUSIONS We find a resistance mechanism signature in one fifth of glioblastoma patients associated with a tumor-cell transition to a more aggressive mesenchymal-like state, increase in TAMs and proliferative and exhausted T cells in response to immunotherapy. These patients may instead benefit from neuro-oncology therapies targeting mesenchymal tumor cells.
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Affiliation(s)
- Josephine D Hendriksen
- The Finsen Laboratory, Copenhagen University Hospital—Rigshospitalet, Copenhagen, Denmark
- Biotech Research and Innovation Centre (BRIC), University of Copenhagen, Copenhagen, Denmark
- The DCCC Brain Tumor Center, Danish Comprehensive Cancer Center, Denmark
| | - Alessio Locallo
- The Finsen Laboratory, Copenhagen University Hospital—Rigshospitalet, Copenhagen, Denmark
- Biotech Research and Innovation Centre (BRIC), University of Copenhagen, Copenhagen, Denmark
- The DCCC Brain Tumor Center, Danish Comprehensive Cancer Center, Denmark
| | - Simone Maarup
- The DCCC Brain Tumor Center, Danish Comprehensive Cancer Center, Denmark
- Department of Radiation Biology, Copenhagen University Hospital—Rigshospitalet, Copenhagen, Denmark
| | - Olivia Debnath
- Berlin Institute of Health at Charité—Universitätsmedizin Berlin, Digital Health Center, Berlin, Germany
| | - Naveed Ishaque
- Berlin Institute of Health at Charité—Universitätsmedizin Berlin, Digital Health Center, Berlin, Germany
| | - Benedikte Hasselbach
- The DCCC Brain Tumor Center, Danish Comprehensive Cancer Center, Denmark
- Department of Oncology, Copenhagen University Hospital—Rigshospitalet, Copenhagen, Denmark
| | - Jane Skjøth-Rasmussen
- The DCCC Brain Tumor Center, Danish Comprehensive Cancer Center, Denmark
- Department of Neurosurgery, Copenhagen University Hospital—Rigshospitalet, Copenhagen, Denmark
| | - Christina Westmose Yde
- Department of Genomic Medicine, Copenhagen University Hospital—Rigshospitalet, Copenhagen, Denmark
| | - Hans S Poulsen
- The DCCC Brain Tumor Center, Danish Comprehensive Cancer Center, Denmark
- Department of Radiation Biology, Copenhagen University Hospital—Rigshospitalet, Copenhagen, Denmark
| | - Ulrik Lassen
- The DCCC Brain Tumor Center, Danish Comprehensive Cancer Center, Denmark
- Department of Oncology, Copenhagen University Hospital—Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Copenhagen University Hospital—Rigshospitalet, Copenhagen, Denmark
| | - Joachim Weischenfeldt
- The Finsen Laboratory, Copenhagen University Hospital—Rigshospitalet, Copenhagen, Denmark
- Biotech Research and Innovation Centre (BRIC), University of Copenhagen, Copenhagen, Denmark
- The DCCC Brain Tumor Center, Danish Comprehensive Cancer Center, Denmark
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19
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Bonham-Carter B, Schiebinger G. Cellular proliferation biases clonal lineage tracing and trajectory inference. Bioinformatics 2024; 40:btae483. [PMID: 39102821 PMCID: PMC11316616 DOI: 10.1093/bioinformatics/btae483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Revised: 03/28/2024] [Accepted: 07/30/2024] [Indexed: 08/07/2024] Open
Abstract
MOTIVATION Lineage tracing and trajectory inference from single-cell RNA-sequencing data hold tremendous potential for uncovering the genetic programs driving development and disease. Single cell datasets are thought to provide an unbiased view on the diverse cellular architecture of tissues. Sampling bias, however, can skew single cell datasets away from the cellular composition they are meant to represent. RESULTS We demonstrate a novel form of sampling bias, caused by a statistical phenomenon related to repeated sampling from a growing, heterogeneous population. Relative growth rates of cells influence the probability that they will be sampled in clones observed across multiple time points. We support our probabilistic derivations with a simulation study and an analysis of a real time-course of T-cell development. We find that this bias can impact fate probability predictions, and we explore how to develop trajectory inference methods which are robust to this bias. AVAILABILITY AND IMPLEMENTATION Source code for the simulated datasets and to create the figures in this manuscript is freely available in python at https://github.com/rbonhamcarter/simulate-clones. A python implementation of the extension of the LineageOT method is freely available at https://github.com/rbonhamcarter/LineageOT/tree/multi-time-clones.
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Affiliation(s)
- Becca Bonham-Carter
- Department of Mathematics, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Geoffrey Schiebinger
- Department of Mathematics, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
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20
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Liu J, Ma J, Wen J, Zhou X. A Cell Cycle-Aware Network for Data Integration and Label Transferring of Single-Cell RNA-Seq and ATAC-Seq. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2401815. [PMID: 38887194 PMCID: PMC11336957 DOI: 10.1002/advs.202401815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 04/22/2024] [Indexed: 06/20/2024]
Abstract
In recent years, the integration of single-cell multi-omics data has provided a more comprehensive understanding of cell functions and internal regulatory mechanisms from a non-single omics perspective, but it still suffers many challenges, such as omics-variance, sparsity, cell heterogeneity, and confounding factors. As it is known, the cell cycle is regarded as a confounder when analyzing other factors in single-cell RNA-seq data, but it is not clear how it will work on the integrated single-cell multi-omics data. Here, a cell cycle-aware network (CCAN) is developed to remove cell cycle effects from the integrated single-cell multi-omics data while keeping the cell type-specific variations. This is the first computational model to study the cell-cycle effects in the integration of single-cell multi-omics data. Validations on several benchmark datasets show the outstanding performance of CCAN in a variety of downstream analyses and applications, including removing cell cycle effects and batch effects of scRNA-seq datasets from different protocols, integrating paired and unpaired scRNA-seq and scATAC-seq data, accurately transferring cell type labels from scRNA-seq to scATAC-seq data, and characterizing the differentiation process from hematopoietic stem cells to different lineages in the integration of differentiation data.
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Affiliation(s)
- Jiajia Liu
- Center for Computational Systems MedicineMcWilliams School of Biomedical InformaticsThe University of Texas Health Science Center at HoustonHoustonTX77030USA
| | - Jian Ma
- Department of Electronic Information and Computer EngineeringThe Engineering & Technical College of Chengdu University of TechnologyLeshanSichuan614000China
| | - Jianguo Wen
- Center for Computational Systems MedicineMcWilliams School of Biomedical InformaticsThe University of Texas Health Science Center at HoustonHoustonTX77030USA
| | - Xiaobo Zhou
- Center for Computational Systems MedicineMcWilliams School of Biomedical InformaticsThe University of Texas Health Science Center at HoustonHoustonTX77030USA
- McGovern Medical SchoolThe University of Texas Health Science Center at HoustonHoustonTX77030USA
- School of DentistryThe University of Texas Health Science Center at HoustonHoustonTX77030USA
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21
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Hirsinger E, Blavet C, Bonnin MA, Bellenger L, Gharsalli T, Duprez D. Limb connective tissue is organized in a continuum of promiscuous fibroblast identities during development. iScience 2024; 27:110305. [PMID: 39050702 PMCID: PMC11267076 DOI: 10.1016/j.isci.2024.110305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 05/21/2024] [Accepted: 06/17/2024] [Indexed: 07/27/2024] Open
Abstract
Connective tissue (CT), which includes tendon and muscle CT, plays critical roles in development, in particular as positional cue provider. Nonetheless, our understanding of fibroblast developmental programs is hampered because fibroblasts are highly heterogeneous and poorly characterized. Combining single-cell RNA-sequencing-based strategies including trajectory inference and in situ hybridization analyses, we address the diversity of fibroblasts and their developmental trajectories during chicken limb fetal development. We show that fibroblasts switch from a positional information to a lineage diversification program at the fetal period onset. Muscle CT and tendon are composed of several fibroblast populations that emerge asynchronously. Once the final muscle pattern is set, transcriptionally close populations are found in neighboring locations in limbs, prefiguring the adult fibroblast layers. We propose that the limb CT is organized in a continuum of promiscuous fibroblast identities, allowing for the robust and efficient connection of muscle to bone and skin.
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Affiliation(s)
- Estelle Hirsinger
- Sorbonne Université, Institut Biologie Paris Seine, CNRS UMR7622, Developmental Biology Laboratory, Inserm U1156, 75005 Paris, France
| | - Cédrine Blavet
- Sorbonne Université, Institut Biologie Paris Seine, CNRS UMR7622, Developmental Biology Laboratory, Inserm U1156, 75005 Paris, France
| | - Marie-Ange Bonnin
- Sorbonne Université, Institut Biologie Paris Seine, CNRS UMR7622, Developmental Biology Laboratory, Inserm U1156, 75005 Paris, France
| | - Léa Bellenger
- Sorbonne Université, CNRS FR3631, Inserm U1156, Institut de Biologie Paris Seine (IBPS), ARTbio Bioinformatics Analysis Facility, Paris, Institut Français de Bioinformatique (IFB), 75005 Paris, France
| | - Tarek Gharsalli
- Sorbonne Université, Institut Biologie Paris Seine, CNRS UMR7622, Developmental Biology Laboratory, Inserm U1156, 75005 Paris, France
- Inovarion, 75005 Paris, France
| | - Delphine Duprez
- Sorbonne Université, Institut Biologie Paris Seine, CNRS UMR7622, Developmental Biology Laboratory, Inserm U1156, 75005 Paris, France
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22
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Palmer JW, Kumar N, An L, White AC, Mukhtar MS, Harris ML. Molecular heterogeneity of quiescent melanocyte stem cells revealed by single-cell RNA-sequencing. Pigment Cell Melanoma Res 2024; 37:480-495. [PMID: 38613320 PMCID: PMC11178447 DOI: 10.1111/pcmr.13169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 03/04/2024] [Accepted: 03/31/2024] [Indexed: 04/14/2024]
Abstract
Melanocyte stem cells (McSCs) of the hair follicle are a rare cell population within the skin and are notably underrepresented in whole-skin, single-cell RNA sequencing (scRNA-seq) datasets. Using a cell enrichment strategy to isolate KIT+/CD45- cells from the telogen skin of adult female C57BL/6J mice, we evaluated the transcriptional landscape of quiescent McSCs (qMcSCs) at high resolution. Through this evaluation, we confirmed existing molecular signatures for qMcCS subpopulations (e.g., Kit+, Cd34+/-, Plp1+, Cd274+/-, Thy1+, Cdh3+/-) and identified novel qMcSC subpopulations, including two that differentially regulate their immune privilege status. Within qMcSC subpopulations, we also predicted melanocyte differentiation potential, neural crest potential, and quiescence depth. Taken together, the results demonstrate that the qMcSC population is heterogeneous and future studies focused on investigating changes in qMcSCs should consider changes in subpopulation composition.
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Affiliation(s)
- Joseph W. Palmer
- Department of Biology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Nilesh Kumar
- Department of Biology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Luye An
- Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA
| | - Andrew C. White
- Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA
| | - M. Shahid Mukhtar
- Department of Biology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Melissa L. Harris
- Department of Biology, University of Alabama at Birmingham, Birmingham, Alabama
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23
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Hong Y, Li H, Long C, Liang P, Zhou J, Zuo Y. An increment of diversity method for cell state trajectory inference of time-series scRNA-seq data. FUNDAMENTAL RESEARCH 2024; 4:770-776. [PMID: 39156571 PMCID: PMC11330101 DOI: 10.1016/j.fmre.2024.01.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 08/29/2023] [Accepted: 01/03/2024] [Indexed: 08/20/2024] Open
Abstract
The increasing emergence of the time-series single-cell RNA sequencing (scRNA-seq) data, inferring developmental trajectory by connecting transcriptome similar cell states (i.e., cell types or clusters) has become a major challenge. Most existing computational methods are designed for individual cells and do not take into account the available time series information. We present IDTI based on the Increment of Diversity for Trajectory Inference, which combines time series information and the minimum increment of diversity method to infer cell state trajectory of time-series scRNA-seq data. We apply IDTI to simulated and three real diverse tissue development datasets, and compare it with six other commonly used trajectory inference methods in terms of topology similarity and branching accuracy. The results have shown that the IDTI method accurately constructs the cell state trajectory without the requirement of starting cells. In the performance test, we further demonstrate that IDTI has the advantages of high accuracy and strong robustness.
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Affiliation(s)
| | | | - Chunshen Long
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, College of Life Sciences, Inner Mongolia University, Hohhot 010020, China
| | - Pengfei Liang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, College of Life Sciences, Inner Mongolia University, Hohhot 010020, China
| | - Jian Zhou
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, College of Life Sciences, Inner Mongolia University, Hohhot 010020, China
| | - Yongchun Zuo
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, College of Life Sciences, Inner Mongolia University, Hohhot 010020, China
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24
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Dao L, You Z, Lu L, Xu T, Sarkar AK, Zhu H, Liu M, Calandrelli R, Yoshida G, Lin P, Miao Y, Mierke S, Kalva S, Zhu H, Gu M, Vadivelu S, Zhong S, Huang LF, Guo Z. Modeling blood-brain barrier formation and cerebral cavernous malformations in human PSC-derived organoids. Cell Stem Cell 2024; 31:818-833.e11. [PMID: 38754427 PMCID: PMC11162335 DOI: 10.1016/j.stem.2024.04.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 02/24/2024] [Accepted: 04/22/2024] [Indexed: 05/18/2024]
Abstract
The human blood-brain barrier (hBBB) is a highly specialized structure that regulates passage across blood and central nervous system (CNS) compartments. Despite its critical physiological role, there are no reliable in vitro models that can mimic hBBB development and function. Here, we constructed hBBB assembloids from brain and blood vessel organoids derived from human pluripotent stem cells. We validated the acquisition of blood-brain barrier (BBB)-specific molecular, cellular, transcriptomic, and functional characteristics and uncovered an extensive neuro-vascular crosstalk with a spatial pattern within hBBB assembloids. When we used patient-derived hBBB assembloids to model cerebral cavernous malformations (CCMs), we found that these assembloids recapitulated the cavernoma anatomy and BBB breakdown observed in patients. Upon comparison of phenotypes and transcriptome between patient-derived hBBB assembloids and primary human cavernoma tissues, we uncovered CCM-related molecular and cellular alterations. Taken together, we report hBBB assembloids that mimic the core properties of the hBBB and identify a potentially underlying cause of CCMs.
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Affiliation(s)
- Lan Dao
- Center for Stem Cell and Organoid Medicine, Division of Developmental Biology, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Zhen You
- Department of Pediatric and Adolescent Medicine, Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
| | - Lu Lu
- Center for Stem Cell and Organoid Medicine, Division of Developmental Biology, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Tianyang Xu
- Shu Chien-Gene Lay Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Avijite Kumer Sarkar
- Center for Stem Cell and Organoid Medicine, Division of Developmental Biology, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Hui Zhu
- Center for Stem Cell and Organoid Medicine, Division of Developmental Biology, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Miao Liu
- Department of Pediatric and Adolescent Medicine, Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
| | - Riccardo Calandrelli
- Shu Chien-Gene Lay Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - George Yoshida
- Center for Stem Cell and Organoid Medicine, Division of Developmental Biology, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Pei Lin
- Shu Chien-Gene Lay Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Yifei Miao
- Center for Stem Cell and Organoid Medicine, Division of Pulmonary Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Sarah Mierke
- Divisions of Pediatric Neurosurgery and Interventional Neuroradiology, Cincinnati Children's Hospital, Cincinnati, OH 45229, USA
| | - Srijan Kalva
- Center for Stem Cell and Organoid Medicine, Division of Developmental Biology, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Haining Zhu
- Department of Pharmacology and Toxicology, R. Ken Coit College of Pharmacy, University of Arizona, Tucson, AZ 85721, USA
| | - Mingxia Gu
- Center for Stem Cell and Organoid Medicine, Division of Pulmonary Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Sudhakar Vadivelu
- Divisions of Pediatric Neurosurgery and Interventional Neuroradiology, Cincinnati Children's Hospital, Cincinnati, OH 45229, USA
| | - Sheng Zhong
- Shu Chien-Gene Lay Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA.
| | - L Frank Huang
- Department of Pediatric and Adolescent Medicine, Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA.
| | - Ziyuan Guo
- Center for Stem Cell and Organoid Medicine, Division of Developmental Biology, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA.
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Gaudet A, Zheng X, Kambham N, Bhalla V. Esm-1 mediates transcriptional polarization associated with diabetic kidney disease. Am J Physiol Renal Physiol 2024; 326:F1016-F1031. [PMID: 38601985 PMCID: PMC11386982 DOI: 10.1152/ajprenal.00419.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 03/20/2024] [Accepted: 04/04/2024] [Indexed: 04/12/2024] Open
Abstract
Esm-1, endothelial cell-specific molecule-1, is a susceptibility gene for diabetic kidney disease (DKD) and is a secreted proteoglycan, with notable expression in kidney, which attenuates inflammation and albuminuria. However, little is known about Esm1 expression in mature tissues in the presence or absence of diabetes. We utilized publicly available single-cell RNA sequencing data to characterize Esm1 expression in 27,786 renal endothelial cells (RECs) obtained from three mouse and four human databases. We validated our findings using bulk transcriptome data from 20 healthy subjects and 41 patients with DKD and using RNAscope. In both mice and humans, Esm1 is expressed in a subset of all REC types and represents a minority of glomerular RECs. In patients, Esm1(+) cells exhibit conserved enrichment for blood vessel development genes. With diabetes, these cells are fewer in number and shift expression toward chemotaxis pathways. Esm1 correlates with a majority of genes within these pathways, delineating a glomerular transcriptional polarization reflected by the magnitude of Esm1 deficiency. Diabetes correlates with lower Esm1 expression and with changes in the functional characterization of Esm1(+) cells. Thus, Esm1 appears to be a marker for glomerular transcriptional polarization in DKD.NEW & NOTEWORTHY Esm-1 is primarily expressed in glomerular endothelium in humans. Cells expressing Esm1 exhibit a high degree of conservation in the enrichment of genes related to blood vessel development. In the context of diabetes, these cells are reduced in number and show a significant transcriptional shift toward the chemotaxis pathway. In diabetes, there is a transcriptional polarization in the glomerulus that is reflected by the degree of Esm1 deficiency.
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Affiliation(s)
- Alexandre Gaudet
- Division of Nephrology, Department of Medicine, Stanford University School of Medicine, Stanford, California, United States
- Univ. Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, U1019-UMR9017-CIIL-Centre d'Infection et d'Immunité de Lille, Lille, France
| | - Xiaoyi Zheng
- Division of Nephrology, Department of Medicine, Stanford University School of Medicine, Stanford, California, United States
| | - Neeraja Kambham
- Department of Pathology, Stanford University School of Medicine, Stanford, California, United States
| | - Vivek Bhalla
- Division of Nephrology, Department of Medicine, Stanford University School of Medicine, Stanford, California, United States
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Radley A, Boeing S, Smith A. Branching topology of the human embryo transcriptome revealed by Entropy Sort Feature Weighting. Development 2024; 151:dev202832. [PMID: 38691188 PMCID: PMC11213519 DOI: 10.1242/dev.202832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 04/24/2024] [Indexed: 05/03/2024]
Abstract
Analysis of single cell transcriptomics (scRNA-seq) data is typically performed after subsetting to highly variable genes (HVGs). Here, we show that Entropy Sorting provides an alternative mathematical framework for feature selection. On synthetic datasets, continuous Entropy Sort Feature Weighting (cESFW) outperforms HVG selection in distinguishing cell-state-specific genes. We apply cESFW to six merged scRNA-seq datasets spanning human early embryo development. Without smoothing or augmenting the raw counts matrices, cESFW generates a high-resolution embedding displaying coherent developmental progression from eight-cell to post-implantation stages and delineating 15 distinct cell states. The embedding highlights sequential lineage decisions during blastocyst development, while unsupervised clustering identifies branch point populations obscured in previous analyses. The first branching region, where morula cells become specified for inner cell mass or trophectoderm, includes cells previously asserted to lack a developmental trajectory. We quantify the relatedness of different pluripotent stem cell cultures to distinct embryo cell types and identify marker genes of naïve and primed pluripotency. Finally, by revealing genes with dynamic lineage-specific expression, we provide markers for staging progression from morula to blastocyst.
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Affiliation(s)
- Arthur Radley
- Living Systems Institute, University of Exeter, Stocker Road, Exeter EX4 4QD, UK
| | - Stefan Boeing
- Bioinformatics and Biostatistics Science Technology Platform, The Francis Crick Institute, London NW1 1AT, UK
| | - Austin Smith
- Living Systems Institute, University of Exeter, Stocker Road, Exeter EX4 4QD, UK
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Kuraz Abebe B, Wang J, Guo J, Wang H, Li A, Zan L. A review of the role of epigenetic studies for intramuscular fat deposition in beef cattle. Gene 2024; 908:148295. [PMID: 38387707 DOI: 10.1016/j.gene.2024.148295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 01/23/2024] [Accepted: 02/15/2024] [Indexed: 02/24/2024]
Abstract
Intramuscular fat (IMF) deposition profoundly influences meat quality and economic value in beef cattle production. Meanwhile, contemporary developments in epigenetics have opened new outlooks for understanding the molecular basics of IMF regulation, and it has become a key area of research for world scholars. Therefore, the aim of this paper was to provide insight and synthesis into the intricate relationship between epigenetic mechanisms and IMF deposition in beef cattle. The methodology involves a thorough analysis of existing literature, including pertinent books, academic journals, and online resources, to provide a comprehensive overview of the role of epigenetic studies in IMF deposition in beef cattle. This review summarizes the contemporary studies in epigenetic mechanisms in IMF regulation, high-resolution epigenomic mapping, single-cell epigenomics, multi-omics integration, epigenome editing approaches, longitudinal studies in cattle growth, environmental epigenetics, machine learning in epigenetics, ethical and regulatory considerations, and translation to industry practices from perspectives of IMF deposition in beef cattle. Moreover, this paper highlights DNA methylation, histone modifications, acetylation, phosphorylation, ubiquitylation, non-coding RNAs, DNA hydroxymethylation, epigenetic readers, writers, and erasers, chromatin immunoprecipitation followed by sequencing, whole genome bisulfite sequencing, epigenome-wide association studies, and their profound impact on the expression of crucial genes governing adipogenesis and lipid metabolism. Nutrition and stress also have significant influences on epigenetic modifications and IMF deposition. The key findings underscore the pivotal role of epigenetic studies in understanding and enhancing IMF deposition in beef cattle, with implications for precision livestock farming and ethical livestock management. In conclusion, this review highlights the crucial significance of epigenetic pathways and environmental factors in affecting IMF deposition in beef cattle, providing insightful information for improving the economics and meat quality of cattle production.
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Affiliation(s)
- Belete Kuraz Abebe
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, People's Republic of China; Department of Animal Science, Werabe University, P.O. Box 46, Werabe, Ethiopia
| | - Jianfang Wang
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, People's Republic of China
| | - Juntao Guo
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, People's Republic of China
| | - Hongbao Wang
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, People's Republic of China
| | - Anning Li
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, People's Republic of China
| | - Linsen Zan
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, People's Republic of China; National Beef Cattle Improvement Center, Northwest A&F University, Yangling, Shaanxi 712100, People's Republic of China.
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28
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Su Y, Yu Z, Yang Y, Wong K, Li X. Distribution-Agnostic Deep Learning Enables Accurate Single-Cell Data Recovery and Transcriptional Regulation Interpretation. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2307280. [PMID: 38380499 PMCID: PMC11040354 DOI: 10.1002/advs.202307280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 01/16/2024] [Indexed: 02/22/2024]
Abstract
Single-cell RNA sequencing (scRNA-seq) is a robust method for studying gene expression at the single-cell level, but accurately quantifying genetic material is often hindered by limited mRNA capture, resulting in many missing expression values. Existing imputation methods rely on strict data assumptions, limiting their broader application, and lack reliable supervision, leading to biased signal recovery. To address these challenges, authors developed Bis, a distribution-agnostic deep learning model for accurately recovering missing sing-cell gene expression from multiple platforms. Bis is an optimal transport-based autoencoder model that can capture the intricate distribution of scRNA-seq data while addressing the characteristic sparsity by regularizing the cellular embedding space. Additionally, they propose a module using bulk RNA-seq data to guide reconstruction and ensure expression consistency. Experimental results show Bis outperforms other models across simulated and real datasets, showcasing superiority in various downstream analyses including batch effect removal, clustering, differential expression analysis, and trajectory inference. Moreover, Bis successfully restores gene expression levels in rare cell subsets in a tumor-matched peripheral blood dataset, revealing developmental characteristics of cytokine-induced natural killer cells within a head and neck squamous cell carcinoma microenvironment.
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Affiliation(s)
- Yanchi Su
- School of Artificial IntelligenceJilin UniversityChangchun130012China
| | - Zhuohan Yu
- School of Artificial IntelligenceJilin UniversityChangchun130012China
| | - Yuning Yang
- Donnelly Centre for Cellular and Biomolecular ResearchUniversity of TorontoTorontoONM5S 3E1Canada
| | - Ka‐Chun Wong
- Department of Computer ScienceCity University of Hong KongHong Kong SAR999077China
| | - Xiangtao Li
- School of Artificial IntelligenceJilin UniversityChangchun130012China
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29
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Zeng Y, Luo M, Shangguan N, Shi P, Feng J, Xu J, Chen K, Lu Y, Yu W, Yang Y. Deciphering cell types by integrating scATAC-seq data with genome sequences. NATURE COMPUTATIONAL SCIENCE 2024; 4:285-298. [PMID: 38600256 DOI: 10.1038/s43588-024-00622-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 03/18/2024] [Indexed: 04/12/2024]
Abstract
The single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) technology provides insight into gene regulation and epigenetic heterogeneity at single-cell resolution, but cell annotation from scATAC-seq remains challenging due to high dimensionality and extreme sparsity within the data. Existing cell annotation methods mostly focus on the cell peak matrix without fully utilizing the underlying genomic sequence. Here we propose a method, SANGO, for accurate single-cell annotation by integrating genome sequences around the accessibility peaks within scATAC data. The genome sequences of peaks are encoded into low-dimensional embeddings, and then iteratively used to reconstruct the peak statistics of cells through a fully connected network. The learned weights are considered as regulatory modes to represent cells, and utilized to align the query cells and the annotated cells in the reference data through a graph transformer network for cell annotations. SANGO was demonstrated to consistently outperform competing methods on 55 paired scATAC-seq datasets across samples, platforms and tissues. SANGO was also shown to be able to detect unknown tumor cells through attention edge weights learned by the graph transformer. Moreover, from the annotated cells, we found cell-type-specific peaks that provide functional insights/biological signals through expression enrichment analysis, cis-regulatory chromatin interaction analysis and motif enrichment analysis.
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Affiliation(s)
- Yuansong Zeng
- School of Big Data and Software Engineering, Chongqing University, Chongqing, China
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Mai Luo
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Ningyuan Shangguan
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Peiyu Shi
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Junxi Feng
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Jin Xu
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Ken Chen
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Yutong Lu
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Weijiang Yu
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Yuedong Yang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China.
- Key Laboratory of Machine Intelligence and Advanced Computing (MOE), Guangzhou, China.
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30
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Croizer H, Mhaidly R, Kieffer Y, Gentric G, Djerroudi L, Leclere R, Pelon F, Robley C, Bohec M, Meng A, Meseure D, Romano E, Baulande S, Peltier A, Vincent-Salomon A, Mechta-Grigoriou F. Deciphering the spatial landscape and plasticity of immunosuppressive fibroblasts in breast cancer. Nat Commun 2024; 15:2806. [PMID: 38561380 PMCID: PMC10984943 DOI: 10.1038/s41467-024-47068-z] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 03/19/2024] [Indexed: 04/04/2024] Open
Abstract
Although heterogeneity of FAP+ Cancer-Associated Fibroblasts (CAF) has been described in breast cancer, their plasticity and spatial distribution remain poorly understood. Here, we analyze trajectory inference, deconvolute spatial transcriptomics at single-cell level and perform functional assays to generate a high-resolution integrated map of breast cancer (BC), with a focus on inflammatory and myofibroblastic (iCAF/myCAF) FAP+ CAF clusters. We identify 10 spatially-organized FAP+ CAF-related cellular niches, called EcoCellTypes, which are differentially localized within tumors. Consistent with their spatial organization, cancer cells drive the transition of detoxification-associated iCAF (Detox-iCAF) towards immunosuppressive extracellular matrix (ECM)-producing myCAF (ECM-myCAF) via a DPP4- and YAP-dependent mechanism. In turn, ECM-myCAF polarize TREM2+ macrophages, regulatory NK and T cells to induce immunosuppressive EcoCellTypes, while Detox-iCAF are associated with FOLR2+ macrophages in an immuno-protective EcoCellType. FAP+ CAF subpopulations accumulate differently according to the invasive BC status and predict invasive recurrence of ductal carcinoma in situ (DCIS), which could help in identifying low-risk DCIS patients eligible for therapeutic de-escalation.
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Affiliation(s)
- Hugo Croizer
- Institut Curie, Stress and Cancer Laboratory, Equipe Labélisée par la Ligue Nationale Contre le Cancer, PSL Research University, 26, Rue d'Ulm, F-75248, Paris, France
- Inserm, U830, 26, Rue d'Ulm, F-75005, Paris, France
| | - Rana Mhaidly
- Institut Curie, Stress and Cancer Laboratory, Equipe Labélisée par la Ligue Nationale Contre le Cancer, PSL Research University, 26, Rue d'Ulm, F-75248, Paris, France
- Inserm, U830, 26, Rue d'Ulm, F-75005, Paris, France
| | - Yann Kieffer
- Institut Curie, Stress and Cancer Laboratory, Equipe Labélisée par la Ligue Nationale Contre le Cancer, PSL Research University, 26, Rue d'Ulm, F-75248, Paris, France
- Inserm, U830, 26, Rue d'Ulm, F-75005, Paris, France
| | - Geraldine Gentric
- Institut Curie, Stress and Cancer Laboratory, Equipe Labélisée par la Ligue Nationale Contre le Cancer, PSL Research University, 26, Rue d'Ulm, F-75248, Paris, France
- Inserm, U830, 26, Rue d'Ulm, F-75005, Paris, France
| | - Lounes Djerroudi
- Institut Curie, Stress and Cancer Laboratory, Equipe Labélisée par la Ligue Nationale Contre le Cancer, PSL Research University, 26, Rue d'Ulm, F-75248, Paris, France
- Inserm, U830, 26, Rue d'Ulm, F-75005, Paris, France
- Department of Diagnostic and Theragnostic Medicine, Institut Curie Hospital Group, 26, Rue d'Ulm, F-75248, Paris, France
| | - Renaud Leclere
- Department of Diagnostic and Theragnostic Medicine, Institut Curie Hospital Group, 26, Rue d'Ulm, F-75248, Paris, France
| | - Floriane Pelon
- Institut Curie, Stress and Cancer Laboratory, Equipe Labélisée par la Ligue Nationale Contre le Cancer, PSL Research University, 26, Rue d'Ulm, F-75248, Paris, France
- Inserm, U830, 26, Rue d'Ulm, F-75005, Paris, France
| | - Catherine Robley
- Institut Curie, Stress and Cancer Laboratory, Equipe Labélisée par la Ligue Nationale Contre le Cancer, PSL Research University, 26, Rue d'Ulm, F-75248, Paris, France
- Inserm, U830, 26, Rue d'Ulm, F-75005, Paris, France
| | - Mylene Bohec
- Institut Curie, PSL Research University, ICGex Next-Generation Sequencing Platform, 75005, Paris, France
- Institut Curie, PSL Research University, Single Cell Initiative, 75005, Paris, France
| | - Arnaud Meng
- Institut Curie, Stress and Cancer Laboratory, Equipe Labélisée par la Ligue Nationale Contre le Cancer, PSL Research University, 26, Rue d'Ulm, F-75248, Paris, France
- Inserm, U830, 26, Rue d'Ulm, F-75005, Paris, France
| | - Didier Meseure
- Department of Diagnostic and Theragnostic Medicine, Institut Curie Hospital Group, 26, Rue d'Ulm, F-75248, Paris, France
| | - Emanuela Romano
- Department of Medical Oncology, Center for Cancer Immunotherapy, Institut Curie, 26, Rue d'Ulm, F-75248, Paris, France
| | - Sylvain Baulande
- Institut Curie, PSL Research University, ICGex Next-Generation Sequencing Platform, 75005, Paris, France
- Institut Curie, PSL Research University, Single Cell Initiative, 75005, Paris, France
| | - Agathe Peltier
- Institut Curie, Stress and Cancer Laboratory, Equipe Labélisée par la Ligue Nationale Contre le Cancer, PSL Research University, 26, Rue d'Ulm, F-75248, Paris, France
- Inserm, U830, 26, Rue d'Ulm, F-75005, Paris, France
| | - Anne Vincent-Salomon
- Department of Diagnostic and Theragnostic Medicine, Institut Curie Hospital Group, 26, Rue d'Ulm, F-75248, Paris, France
| | - Fatima Mechta-Grigoriou
- Institut Curie, Stress and Cancer Laboratory, Equipe Labélisée par la Ligue Nationale Contre le Cancer, PSL Research University, 26, Rue d'Ulm, F-75248, Paris, France.
- Inserm, U830, 26, Rue d'Ulm, F-75005, Paris, France.
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31
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Maroni G, Krishnan I, Alfieri R, Maymi VA, Pandell N, Csizmadia E, Zhang J, Weetall M, Branstrom A, Braccini G, Cabrera San Millán E, Storti B, Bizzarri R, Kocher O, Bassères DS, Welner RS, Magli MC, Merelli I, Clohessy JG, Ali A, Tenen DG, Levantini E. Tumor Microenvironment Landscapes Supporting EGFR-mutant NSCLC Are Modulated at the Single-cell Interaction Level by Unesbulin Treatment. CANCER RESEARCH COMMUNICATIONS 2024; 4:919-937. [PMID: 38546390 PMCID: PMC10964845 DOI: 10.1158/2767-9764.crc-23-0161] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 01/26/2024] [Accepted: 03/12/2024] [Indexed: 11/03/2024]
Abstract
Lung cancer is the leading cause of cancer deaths. Lethal pulmonary adenocarcinomas (ADC) present with frequent mutations in the EGFR. Genetically engineered murine models of lung cancer expedited comprehension of the molecular mechanisms driving tumorigenesis and drug response. Here, we systematically analyzed the evolution of tumor heterogeneity in the context of dynamic interactions occurring with the intermingled tumor microenvironment (TME) by high-resolution transcriptomics. Our effort identified vulnerable tumor-specific epithelial cells, as well as their cross-talk with niche components (endothelial cells, fibroblasts, and tumor-infiltrating immune cells), whose symbiotic interface shapes tumor aggressiveness and is almost completely abolished by treatment with Unesbulin, a tubulin binding agent that reduces B cell-specific Moloney murine leukemia virus integration site 1 (BMI-1) activity. Simultaneous magnetic resonance imaging (MRI) analysis demonstrated decreased tumor growth, setting the stage for future investigations into the potential of novel therapeutic strategies for EGFR-mutant ADCs. SIGNIFICANCE Targeting the TME is an attractive strategy for treatment of solid tumors. Here we revealed how EGFR-mutant landscapes are affected at the single-cell resolution level during Unesbulin treatment. This novel drug, by targeting cancer cells and their interactions with crucial TME components, could be envisioned for future therapeutic advancements.
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Affiliation(s)
- Giorgia Maroni
- Cancer Science Institute of Singapore, National University of Singapore, Singapore
- Harvard Medical School, Boston, Massachusetts
- Institute of Biomedical Technologies, National Research Council (CNR), Pisa, Italy
| | | | - Roberta Alfieri
- Institute of Biomedical Technologies, National Research Council (CNR), Pisa, Italy
| | - Valerie A. Maymi
- Beth Israel Deaconess Medical Center, Boston, Massachusetts
- Preclinical Murine Pharmacogenetics Core, Beth Israel Deaconess Cancer Center, Dana-Farber/Harvard Cancer Center, Boston, Massachusetts
| | - Nicole Pandell
- Beth Israel Deaconess Medical Center, Boston, Massachusetts
- Preclinical Murine Pharmacogenetics Core, Beth Israel Deaconess Cancer Center, Dana-Farber/Harvard Cancer Center, Boston, Massachusetts
| | - Eva Csizmadia
- Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Junyan Zhang
- Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | | | | | - Giulia Braccini
- Institute of Biomedical Technologies, National Research Council (CNR), Pisa, Italy
| | | | - Barbara Storti
- NEST, Scuola Normale Superiore and Istituto Nanoscienze-CNR, Pisa, Italy
| | - Ranieri Bizzarri
- NEST, Scuola Normale Superiore and Istituto Nanoscienze-CNR, Pisa, Italy
- Department of Surgical, Medical and Molecular Pathology, and Critical Care Medicine, University of Pisa, Pisa, Italy
| | - Olivier Kocher
- Harvard Medical School, Boston, Massachusetts
- Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Daniela S. Bassères
- Biochemistry Department, Chemistry Institute, University of Sao Paulo, Sao Paulo, Brazil
| | - Robert S. Welner
- Department of Medicine, Hemathology/Oncology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Maria Cristina Magli
- Institute of Biomedical Technologies, National Research Council (CNR), Pisa, Italy
| | - Ivan Merelli
- Institute of Biomedical Technologies, National Research Council (CNR), Pisa, Italy
| | - John G. Clohessy
- Harvard Medical School, Boston, Massachusetts
- Beth Israel Deaconess Medical Center, Boston, Massachusetts
- Preclinical Murine Pharmacogenetics Core, Beth Israel Deaconess Cancer Center, Dana-Farber/Harvard Cancer Center, Boston, Massachusetts
| | - Azhar Ali
- Cancer Science Institute of Singapore, National University of Singapore, Singapore
| | - Daniel G. Tenen
- Cancer Science Institute of Singapore, National University of Singapore, Singapore
- Harvard Medical School, Boston, Massachusetts
- Harvard Stem Cell Institute, Cambridge, Massachusetts
| | - Elena Levantini
- Harvard Medical School, Boston, Massachusetts
- Institute of Biomedical Technologies, National Research Council (CNR), Pisa, Italy
- Harvard Stem Cell Institute, Cambridge, Massachusetts
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Xia L, Lee C, Li JJ. Statistical method scDEED for detecting dubious 2D single-cell embeddings and optimizing t-SNE and UMAP hyperparameters. Nat Commun 2024; 15:1753. [PMID: 38409103 PMCID: PMC10897166 DOI: 10.1038/s41467-024-45891-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 02/06/2024] [Indexed: 02/28/2024] Open
Abstract
Two-dimensional (2D) embedding methods are crucial for single-cell data visualization. Popular methods such as t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP) are commonly used for visualizing cell clusters; however, it is well known that t-SNE and UMAP's 2D embeddings might not reliably inform the similarities among cell clusters. Motivated by this challenge, we present a statistical method, scDEED, for detecting dubious cell embeddings output by a 2D-embedding method. By calculating a reliability score for every cell embedding based on the similarity between the cell's 2D-embedding neighbors and pre-embedding neighbors, scDEED identifies the cell embeddings with low reliability scores as dubious and those with high reliability scores as trustworthy. Moreover, by minimizing the number of dubious cell embeddings, scDEED provides intuitive guidance for optimizing the hyperparameters of an embedding method. We show the effectiveness of scDEED on multiple datasets for detecting dubious cell embeddings and optimizing the hyperparameters of t-SNE and UMAP.
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Affiliation(s)
- Lucy Xia
- Department of ISOM, School of Business and Management, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
| | - Christy Lee
- Department of Statistics and Data Science, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jingyi Jessica Li
- Department of Statistics and Data Science, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA.
- Radcliffe Institute of Advanced Study, Harvard University, Cambridge, MA, USA.
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33
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Tietze E, Barbosa AR, Araujo B, Euclydes V, Spiegelberg B, Cho HJ, Lee YK, Wang Y, McCord A, Lorenzetti A, Feltrin A, van de Leemput J, Di Carlo P, Ursini G, Benjamin KJ, Brentani H, Kleinman JE, Hyde TM, Weinberger DR, McKay R, Shin JH, Sawada T, Paquola ACM, Erwin JA. Human archetypal pluripotent stem cells differentiate into trophoblast stem cells via endogenous BMP5/7 induction without transitioning through naive state. Sci Rep 2024; 14:3291. [PMID: 38332235 PMCID: PMC10853519 DOI: 10.1038/s41598-024-53381-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 01/31/2024] [Indexed: 02/10/2024] Open
Abstract
Primary human trophoblast stem cells (TSCs) and TSCs derived from human pluripotent stem cells (hPSCs) can potentially model placental processes in vitro. Yet, the pluripotent states and factors involved in the differentiation of hPSCs to TSCs remain poorly understood. In this study, we demonstrate that the primed pluripotent state can generate TSCs by activating pathways such as Epidermal Growth Factor (EGF) and Wingless-related integration site (WNT), and by suppressing tumor growth factor beta (TGFβ), histone deacetylases (HDAC), and Rho-associated protein kinase (ROCK) signaling pathways, all without the addition of exogenous Bone morphogenetic protein 4 (BMP4)-a condition we refer to as the TS condition. We characterized this process using temporal single-cell RNA sequencing to compare TS conditions with differentiation protocols involving BMP4 activation alone or BMP4 activation in conjunction with WNT inhibition. The TS condition consistently produced a stable, proliferative cell type that closely mimics first-trimester placental cytotrophoblasts, marked by the activation of endogenous retroviral genes and the absence of amnion expression. This was observed across multiple cell lines, including various primed induced pluripotent stem cell (iPSC) and embryonic stem cell (ESC) lines. Primed-derived TSCs can proliferate for over 30 passages and further specify into multinucleated syncytiotrophoblasts and extravillous trophoblast cells. Our research establishes that the differentiation of primed hPSCs to TSC under TS conditions triggers the induction of TMSB4X, BMP5/7, GATA3, and TFAP2A without progressing through a naive state. These findings propose that the primed hPSC state is part of a continuum of potency with the capacity to differentiate into TSCs through multiple routes.
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Affiliation(s)
- Ethan Tietze
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Andre Rocha Barbosa
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Inter-Institutional Graduate Program on Bioinformatics, University of São Paulo, São Paulo, SP, Brazil
| | - Bruno Araujo
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Veronica Euclydes
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Psychiatry, University of Sao Paulo, Medical School, São Paulo, Brazil
| | - Bailey Spiegelberg
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Hyeon Jin Cho
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Yong Kyu Lee
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Yanhong Wang
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | | | | | - Arthur Feltrin
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Center for Mathematics, Computation and Cognition, Federal University of ABC, Santo André, SP, Brazil
| | - Joyce van de Leemput
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Center for Precision Disease Modeling and Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Pasquale Di Carlo
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Basic Medical Science, Neuroscience, and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Gianluca Ursini
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Kynon J Benjamin
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Helena Brentani
- Inter-Institutional Graduate Program on Bioinformatics, University of São Paulo, São Paulo, SP, Brazil
- Department of Psychiatry, University of Sao Paulo, Medical School, São Paulo, Brazil
| | - Joel E Kleinman
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Thomas M Hyde
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ronald McKay
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Joo Heon Shin
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Tomoyo Sawada
- Lieber Institute for Brain Development, Baltimore, MD, USA.
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA.
| | - Apua C M Paquola
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Jennifer A Erwin
- Lieber Institute for Brain Development, Baltimore, MD, USA.
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA.
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA.
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Liu J, Ma J, Wen J, Zhou X. A Cell Cycle-aware Network for Data Integration and Label Transferring of Single-cell RNA-seq and ATAC-seq. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.31.578213. [PMID: 38352302 PMCID: PMC10862874 DOI: 10.1101/2024.01.31.578213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
In recent years, the integration of single-cell multi-omics data has provided a more comprehensive understanding of cell functions and internal regulatory mechanisms from a non-single omics perspective, but it still suffers many challenges, such as omics-variance, sparsity, cell heterogeneity and confounding factors. As we know, cell cycle is regarded as a confounder when analyzing other factors in single-cell RNA-seq data, but it's not clear how it will work on the integrated single-cell multi-omics data. Here, we developed a Cell Cycle-Aware Network (CCAN) to remove cell cycle effects from the integrated single-cell multi-omics data while keeping the cell type-specific variations. This is the first computational model to study the cell-cycle effects in the integration of single-cell multi-omics data. Validations on several benchmark datasets show the out-standing performance of CCAN in a variety of downstream analyses and applications, including removing cell cycle effects and batch effects of scRNA-seq datasets from different protocols, integrating paired and unpaired scRNA-seq and scATAC-seq data, accurately transferring cell type labels from scRNA-seq to scATAC-seq data, and characterizing the differentiation process from hematopoietic stem cells to different lineages in the integration of differentiation data.
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Murakami M, Hara K, Ikeda K, Horino M, Okazaki R, Niitsu Y, Takeuchi A, Aoki J, Shiba K, Tsujimoto K, Komiya C, Nakamura Y, Kurata M, Akashi T, Fujii Y, Yamada T. Single-Nucleus Analysis Reveals Tumor Heterogeneity of Aldosterone-Producing Adenoma. Hypertension 2024; 81:361-371. [PMID: 38095094 DOI: 10.1161/hypertensionaha.123.21446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 12/03/2023] [Indexed: 01/19/2024]
Abstract
BACKGROUND Recent advances in omics techniques have allowed detailed genetic characterization of aldosterone-producing adenoma (APA). The pathogenesis of APA is characterized by tumorigenesis-associated aldosterone synthesis. The pathophysiological intricacies of APAs have not yet been elucidated at the level of individual cells. Therefore, a single-cell level analysis is speculated to be valuable in studying the differentiation process of APA. METHODS We conducted single-nucleus RNA sequencing of APAs with KCNJ5 mutation and nonfunctional adenomas obtained from 3 and 2 patients, respectively. RESULTS The single-nucleus RNA sequencing revealed the intratumoral heterogeneity of APA and identified cell populations consisting of a shared cluster of nonfunctional adenoma and APA. In addition, we extracted 2 cell fates in APA and obtained a cell population specialized in aldosterone synthesis. Genes related to ribosomes and neurodegenerative diseases were upregulated in 1 of these fates, whereas those related to the regulation of glycolysis were upregulated in the other fate. Furthermore, the total RNA reads in the nucleus were higher in hormonally activated clusters, indicating a marked activation of transcription per cell. CONCLUSIONS The single-nucleus RNA sequencing revealed intratumoral heterogeneity of APA with KCNJ5 mutation. The observation of 2 cell fates in KCNJ5-mutated APAs provides the postulation that a heterogeneous process of cellular differentiation was implicated in the pathophysiological mechanisms underlying APA tumors.
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Affiliation(s)
- Masanori Murakami
- Department of Molecular Endocrinology and Metabolism, Graduate School of Medical and Dental Sciences (M.M., K.H., K.I., M.H., R.O., Y.N., A.T., J.A., K.S., K.T., C.K., T.Y.), Tokyo Medical and Dental University, Japan
| | - Kazunari Hara
- Department of Molecular Endocrinology and Metabolism, Graduate School of Medical and Dental Sciences (M.M., K.H., K.I., M.H., R.O., Y.N., A.T., J.A., K.S., K.T., C.K., T.Y.), Tokyo Medical and Dental University, Japan
| | - Kenji Ikeda
- Department of Molecular Endocrinology and Metabolism, Graduate School of Medical and Dental Sciences (M.M., K.H., K.I., M.H., R.O., Y.N., A.T., J.A., K.S., K.T., C.K., T.Y.), Tokyo Medical and Dental University, Japan
| | - Masato Horino
- Department of Molecular Endocrinology and Metabolism, Graduate School of Medical and Dental Sciences (M.M., K.H., K.I., M.H., R.O., Y.N., A.T., J.A., K.S., K.T., C.K., T.Y.), Tokyo Medical and Dental University, Japan
| | - Rei Okazaki
- Department of Molecular Endocrinology and Metabolism, Graduate School of Medical and Dental Sciences (M.M., K.H., K.I., M.H., R.O., Y.N., A.T., J.A., K.S., K.T., C.K., T.Y.), Tokyo Medical and Dental University, Japan
| | - Yoshihiro Niitsu
- Department of Molecular Endocrinology and Metabolism, Graduate School of Medical and Dental Sciences (M.M., K.H., K.I., M.H., R.O., Y.N., A.T., J.A., K.S., K.T., C.K., T.Y.), Tokyo Medical and Dental University, Japan
| | - Akira Takeuchi
- Department of Molecular Endocrinology and Metabolism, Graduate School of Medical and Dental Sciences (M.M., K.H., K.I., M.H., R.O., Y.N., A.T., J.A., K.S., K.T., C.K., T.Y.), Tokyo Medical and Dental University, Japan
| | - Jun Aoki
- Department of Molecular Endocrinology and Metabolism, Graduate School of Medical and Dental Sciences (M.M., K.H., K.I., M.H., R.O., Y.N., A.T., J.A., K.S., K.T., C.K., T.Y.), Tokyo Medical and Dental University, Japan
| | - Kumiko Shiba
- Department of Molecular Endocrinology and Metabolism, Graduate School of Medical and Dental Sciences (M.M., K.H., K.I., M.H., R.O., Y.N., A.T., J.A., K.S., K.T., C.K., T.Y.), Tokyo Medical and Dental University, Japan
- Center for Personalized Medicine for Healthy Aging (K.S.), Tokyo Medical and Dental University, Japan
| | - Kazutaka Tsujimoto
- Department of Molecular Endocrinology and Metabolism, Graduate School of Medical and Dental Sciences (M.M., K.H., K.I., M.H., R.O., Y.N., A.T., J.A., K.S., K.T., C.K., T.Y.), Tokyo Medical and Dental University, Japan
| | - Chikara Komiya
- Department of Molecular Endocrinology and Metabolism, Graduate School of Medical and Dental Sciences (M.M., K.H., K.I., M.H., R.O., Y.N., A.T., J.A., K.S., K.T., C.K., T.Y.), Tokyo Medical and Dental University, Japan
| | - Yuki Nakamura
- Department of Urology, Graduate School of Medical and Dental Sciences (Y.N., Y.F.), Tokyo Medical and Dental University, Japan
| | - Morito Kurata
- Department of Comprehensive Pathology, Graduate School of Medical and Dental Sciences (M.K.), Tokyo Medical and Dental University, Japan
| | - Takumi Akashi
- Department of Diagnostic Pathology, Graduate School of Medical and Dental Sciences (T.A.), Tokyo Medical and Dental University, Japan
- Division of Surgical Pathology, Tokyo Medical and Dental University Hospital, Japan (T.A.)
| | - Yasuhisa Fujii
- Department of Urology, Graduate School of Medical and Dental Sciences (Y.N., Y.F.), Tokyo Medical and Dental University, Japan
| | - Tetsuya Yamada
- Department of Molecular Endocrinology and Metabolism, Graduate School of Medical and Dental Sciences (M.M., K.H., K.I., M.H., R.O., Y.N., A.T., J.A., K.S., K.T., C.K., T.Y.), Tokyo Medical and Dental University, Japan
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Guise AJ, Misal SA, Carson R, Chu JH, Boekweg H, Van Der Watt D, Welsh NC, Truong T, Liang Y, Xu S, Benedetto G, Gagnon J, Payne SH, Plowey ED, Kelly RT. TDP-43-stratified single-cell proteomics of postmortem human spinal motor neurons reveals protein dynamics in amyotrophic lateral sclerosis. Cell Rep 2024; 43:113636. [PMID: 38183652 PMCID: PMC10926001 DOI: 10.1016/j.celrep.2023.113636] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 11/02/2023] [Accepted: 12/14/2023] [Indexed: 01/08/2024] Open
Abstract
A limitation of conventional bulk-tissue proteome studies in amyotrophic lateral sclerosis (ALS) is the confounding of motor neuron (MN) signals by admixed non-MN proteins. Here, we leverage laser capture microdissection and nanoPOTS single-cell mass spectrometry-based proteomics to query changes in protein expression in single MNs from postmortem ALS and control tissues. In a follow-up analysis, we examine the impact of stratification of MNs based on cytoplasmic transactive response DNA-binding protein 43 (TDP-43)+ inclusion pathology on the profiles of 2,238 proteins. We report extensive overlap in differentially abundant proteins identified in ALS MNs with or without overt TDP-43 pathology, suggesting early and sustained dysregulation of cellular respiration, mRNA splicing, translation, and vesicular transport in ALS. Together, these data provide insights into proteome-level changes associated with TDP-43 proteinopathy and begin to demonstrate the utility of pathology-stratified trace sample proteomics for understanding single-cell protein dynamics in human neurologic diseases.
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Affiliation(s)
| | - Santosh A Misal
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT 84602, USA
| | - Richard Carson
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT 84602, USA
| | | | - Hannah Boekweg
- Biology Department, Brigham Young University, Provo, UT 84602, USA
| | | | | | - Thy Truong
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT 84602, USA
| | - Yiran Liang
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT 84602, USA
| | | | | | | | - Samuel H Payne
- Biology Department, Brigham Young University, Provo, UT 84602, USA
| | | | - Ryan T Kelly
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT 84602, USA.
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Sprooten J, Vanmeerbeek I, Datsi A, Govaerts J, Naulaerts S, Laureano RS, Borràs DM, Calvet A, Malviya V, Kuballa M, Felsberg J, Sabel MC, Rapp M, Knobbe-Thomsen C, Liu P, Zhao L, Kepp O, Boon L, Tejpar S, Borst J, Kroemer G, Schlenner S, De Vleeschouwer S, Sorg RV, Garg AD. Lymph node and tumor-associated PD-L1 + macrophages antagonize dendritic cell vaccines by suppressing CD8 + T cells. Cell Rep Med 2024; 5:101377. [PMID: 38232703 PMCID: PMC10829875 DOI: 10.1016/j.xcrm.2023.101377] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 08/23/2023] [Accepted: 12/18/2023] [Indexed: 01/19/2024]
Abstract
Current immunotherapies provide limited benefits against T cell-depleted tumors, calling for therapeutic innovation. Using multi-omics integration of cancer patient data, we predict a type I interferon (IFN) responseHIGH state of dendritic cell (DC) vaccines, with efficacious clinical impact. However, preclinical DC vaccines recapitulating this state by combining immunogenic cancer cell death with induction of type I IFN responses fail to regress mouse tumors lacking T cell infiltrates. Here, in lymph nodes (LNs), instead of activating CD4+/CD8+ T cells, DCs stimulate immunosuppressive programmed death-ligand 1-positive (PD-L1+) LN-associated macrophages (LAMs). Moreover, DC vaccines also stimulate PD-L1+ tumor-associated macrophages (TAMs). This creates two anatomically distinct niches of PD-L1+ macrophages that suppress CD8+ T cells. Accordingly, a combination of PD-L1 blockade with DC vaccines achieves significant tumor regression by depleting PD-L1+ macrophages, suppressing myeloid inflammation, and de-inhibiting effector/stem-like memory T cells. Importantly, clinical DC vaccines also potentiate T cell-suppressive PD-L1+ TAMs in glioblastoma patients. We propose that a multimodal immunotherapy and vaccination regimen is mandatory to overcome T cell-depleted tumors.
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Affiliation(s)
- Jenny Sprooten
- Laboratory of Cell Stress & Immunity, Department of Cellular & Molecular Medicine, KU Leuven, Leuven, Belgium
| | - Isaure Vanmeerbeek
- Laboratory of Cell Stress & Immunity, Department of Cellular & Molecular Medicine, KU Leuven, Leuven, Belgium
| | - Angeliki Datsi
- Institute for Transplantation Diagnostics and Cell Therapeutics, Medical Faculty, Heinrich Heine University Hospital, Düsseldorf, Germany
| | - Jannes Govaerts
- Laboratory of Cell Stress & Immunity, Department of Cellular & Molecular Medicine, KU Leuven, Leuven, Belgium
| | - Stefan Naulaerts
- Laboratory of Cell Stress & Immunity, Department of Cellular & Molecular Medicine, KU Leuven, Leuven, Belgium
| | - Raquel S Laureano
- Laboratory of Cell Stress & Immunity, Department of Cellular & Molecular Medicine, KU Leuven, Leuven, Belgium
| | - Daniel M Borràs
- Laboratory of Cell Stress & Immunity, Department of Cellular & Molecular Medicine, KU Leuven, Leuven, Belgium
| | - Anna Calvet
- Laboratory of Cell Stress & Immunity, Department of Cellular & Molecular Medicine, KU Leuven, Leuven, Belgium
| | - Vanshika Malviya
- Department of Microbiology, Immunology and Transplantation, KU Leuven-University of Leuven, Leuven, Belgium
| | - Marc Kuballa
- Institute for Transplantation Diagnostics and Cell Therapeutics, Medical Faculty, Heinrich Heine University Hospital, Düsseldorf, Germany
| | - Jörg Felsberg
- Department of Neurosurgery, Medical Faculty, Heinrich Heine University Hospital, Düsseldorf, Germany
| | - Michael C Sabel
- Department of Neurosurgery, Medical Faculty, Heinrich Heine University Hospital, Düsseldorf, Germany
| | - Marion Rapp
- Department of Neurosurgery, Medical Faculty, Heinrich Heine University Hospital, Düsseldorf, Germany
| | - Christiane Knobbe-Thomsen
- Department of Neurosurgery, Medical Faculty, Heinrich Heine University Hospital, Düsseldorf, Germany
| | - Peng Liu
- Metabolomics and Cell Biology Platforms, Gustave Roussy Cancer Center, Université Paris Saclay, Villejuif, France; Centre de Recherche des Cordeliers, Equipe labellisée par la Ligue contre le cancer, Université de Paris, Sorbonne Université, Inserm U1138, Institut Universitaire de France, Paris, France
| | - Liwei Zhao
- Metabolomics and Cell Biology Platforms, Gustave Roussy Cancer Center, Université Paris Saclay, Villejuif, France; Centre de Recherche des Cordeliers, Equipe labellisée par la Ligue contre le cancer, Université de Paris, Sorbonne Université, Inserm U1138, Institut Universitaire de France, Paris, France
| | - Oliver Kepp
- Metabolomics and Cell Biology Platforms, Gustave Roussy Cancer Center, Université Paris Saclay, Villejuif, France; Centre de Recherche des Cordeliers, Equipe labellisée par la Ligue contre le cancer, Université de Paris, Sorbonne Université, Inserm U1138, Institut Universitaire de France, Paris, France
| | | | - Sabine Tejpar
- Laboratory for Molecular Digestive Oncology, Department of Oncology, KU Leuven, Leuven, Belgium
| | - Jannie Borst
- Department of Immunology and Oncode Institute, Leiden University Medical Center, Leiden, the Netherlands
| | - Guido Kroemer
- Metabolomics and Cell Biology Platforms, Gustave Roussy Cancer Center, Université Paris Saclay, Villejuif, France; Centre de Recherche des Cordeliers, Equipe labellisée par la Ligue contre le cancer, Université de Paris, Sorbonne Université, Inserm U1138, Institut Universitaire de France, Paris, France; Institut du Cancer Paris CARPEM, Department of Biology, Hôpital Européen Georges Pompidou, AP-HP, Paris, France
| | - Susan Schlenner
- Department of Microbiology, Immunology and Transplantation, KU Leuven-University of Leuven, Leuven, Belgium
| | - Steven De Vleeschouwer
- Department of Neurosurgery, University Hospitals Leuven, Leuven, Belgium; Laboratory of Experimental Neurosurgery and Neuroanatomy, Department of Neurosciences, KU Leuven, Leuven, Belgium; Leuven Brain Institute (LBI), Leuven, Belgium
| | - Rüdiger V Sorg
- Institute for Transplantation Diagnostics and Cell Therapeutics, Medical Faculty, Heinrich Heine University Hospital, Düsseldorf, Germany
| | - Abhishek D Garg
- Laboratory of Cell Stress & Immunity, Department of Cellular & Molecular Medicine, KU Leuven, Leuven, Belgium.
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Yan M, Liu M, Davis AG, Stoner SA, Zhang DE. Single-cell RNA sequencing of a new transgenic t(8;21) preleukemia mouse model reveals regulatory networks promoting leukemic transformation. Leukemia 2024; 38:31-44. [PMID: 37838757 PMCID: PMC10776403 DOI: 10.1038/s41375-023-02063-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 09/22/2023] [Accepted: 10/05/2023] [Indexed: 10/16/2023]
Abstract
T(8;21)(q22;q22), which generates the AML1-ETO fusion oncoprotein, is a common chromosomal abnormality in acute myeloid leukemia (AML) patients. Despite having favorable prognosis, 40% of patients will relapse, highlighting the need for innovative models and application of the newest technologies to study t(8;21) leukemogenesis. Currently, available AML1-ETO mouse models have limited utility for studying the pre-leukemic stage because AML1-ETO produces mild hematopoietic phenotypes and no leukemic transformation. Conversely, overexpression of a truncated variant, AML1-ETO9a (AE9a), promotes fully penetrant leukemia and is too potent for studying pre-leukemic changes. To overcome these limitations, we devised a germline-transmitted Rosa26 locus AE9a knock-in mouse model that moderately overexpressed AE9a and developed leukemia with long latency and low penetrance. We observed pre-leukemic alterations in AE9a mice, including skewing of progenitors towards granulocyte/monocyte lineages and replating of stem and progenitor cells. Next, we performed single-cell RNA sequencing to identify specific cell populations that contribute to these pre-leukemic phenotypes. We discovered a subset of common myeloid progenitors that have heightened granulocyte/monocyte bias in AE9a mice. We also observed dysregulation of key hematopoietic transcription factor target gene networks, blocking cellular differentiation. Finally, we identified Sox4 activation as a potential contributor to stem cell self-renewal during the pre-leukemic stage.
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Affiliation(s)
- Ming Yan
- Moores Cancer Center, University of California San Diego, La Jolla, CA, USA
- Department of Pathology, University of California San Diego, La Jolla, CA, USA
| | - Mengdan Liu
- Moores Cancer Center, University of California San Diego, La Jolla, CA, USA
- Department of Pathology, University of California San Diego, La Jolla, CA, USA
- School of Biological Sciences, University of California San Diego, La Jolla, CA, USA
| | - Amanda G Davis
- Moores Cancer Center, University of California San Diego, La Jolla, CA, USA
- Department of Pathology, University of California San Diego, La Jolla, CA, USA
| | - Samuel A Stoner
- Moores Cancer Center, University of California San Diego, La Jolla, CA, USA
| | - Dong-Er Zhang
- Moores Cancer Center, University of California San Diego, La Jolla, CA, USA.
- Department of Pathology, University of California San Diego, La Jolla, CA, USA.
- School of Biological Sciences, University of California San Diego, La Jolla, CA, USA.
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Craig AJ, Silveira MAD, Ma L, Revsine M, Wang L, Heinrich S, Rae Z, Ruchinskas A, Dadkhah K, Do W, Behrens S, Mehrabadi FR, Dominguez DA, Forgues M, Budhu A, Chaisaingmongkol J, Hernandez JM, Davis JL, Tran B, Marquardt JU, Ruchirawat M, Kelly M, Greten TF, Wang XW. Genome-wide profiling of transcription factor activity in primary liver cancer using single-cell ATAC sequencing. Cell Rep 2023; 42:113446. [PMID: 37980571 PMCID: PMC10750269 DOI: 10.1016/j.celrep.2023.113446] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 09/24/2023] [Accepted: 10/31/2023] [Indexed: 11/21/2023] Open
Abstract
Primary liver cancer (PLC) consists of two main histological subtypes; hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA). The role of transcription factors (TFs) in malignant hepatobiliary lineage commitment between HCC and iCCA remains underexplored. Here, we present genome-wide profiling of transcription regulatory elements of 16 PLC patients using single-cell assay for transposase accessible chromatin sequencing. Single-cell open chromatin profiles reflect the compositional diversity of liver cancer, identifying both malignant and microenvironment component cells. TF motif enrichment levels of 31 TFs strongly discriminate HCC from iCCA tumors. These TFs are members of the nuclear/retinoid receptor, POU, or ETS motif families. POU factors are associated with prognostic features in iCCA. Overall, nuclear receptors, ETS and POU TF motif families delineate transcription regulation between HCC and iCCA tumors, which may be relevant to development and selection of PLC subtype-specific therapeutics.
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Affiliation(s)
- Amanda J Craig
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Maruhen A Datsch Silveira
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Lichun Ma
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA; Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA; Liver Cancer Program, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Mahler Revsine
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Limin Wang
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Sophia Heinrich
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA; Department of Gastroenterology, Hepatology, Infectious Diseases and Endocrinology, Hanover Medical School, 30159 Hanover, Germany
| | - Zachary Rae
- Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, MD 20701, USA
| | - Allison Ruchinskas
- Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, MD 20701, USA
| | - Kimia Dadkhah
- Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, MD 20701, USA
| | - Whitney Do
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Shay Behrens
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA; Surgical Oncology Program, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Farid R Mehrabadi
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Dana A Dominguez
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA; Department of Surgical Oncology, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Marshonna Forgues
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Anuradha Budhu
- Liver Cancer Program, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Jittiporn Chaisaingmongkol
- Laboratory of Chemical Carcinogenesis, Chulabhorn Research Institute, Bangkok 10210, Thailand; Center of Excellence on Environmental Health and Toxicology, Office of Higher Education Commission, Ministry of Higher Education, Science, Research and Innovation, Bangkok 10400, Thailand
| | - Jonathan M Hernandez
- Liver Cancer Program, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA; Surgical Oncology Program, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Jeremy L Davis
- Surgical Oncology Program, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Bao Tran
- Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, MD 20701, USA
| | - Jens U Marquardt
- Department of Medicine I, University of Lübeck, 23552 Lübeck, Germany
| | - Mathuros Ruchirawat
- Laboratory of Chemical Carcinogenesis, Chulabhorn Research Institute, Bangkok 10210, Thailand; Center of Excellence on Environmental Health and Toxicology, Office of Higher Education Commission, Ministry of Higher Education, Science, Research and Innovation, Bangkok 10400, Thailand
| | - Michael Kelly
- Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, MD 20701, USA
| | - Tim F Greten
- Liver Cancer Program, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA; Thoracic and GI Malignancies Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Xin W Wang
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA; Liver Cancer Program, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA.
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Li R, Guan J, Wang Z, Zhou S. A new and effective two-step clustering approach for single cell RNA sequencing data. BMC Genomics 2023; 23:864. [PMID: 37946133 PMCID: PMC10636845 DOI: 10.1186/s12864-023-09577-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 08/10/2023] [Indexed: 11/12/2023] Open
Abstract
BACKGROUND The rapid devolvement of single cell RNA sequencing (scRNA-seq) technology leads to huge amounts of scRNA-seq data, which greatly advance the research of many biomedical fields involving tissue heterogeneity, pathogenesis of disease and drug resistance etc. One major task in scRNA-seq data analysis is to cluster cells in terms of their expression characteristics. Up to now, a number of methods have been proposed to infer cell clusters, yet there is still much space to improve their performance. RESULTS In this paper, we develop a new two-step clustering approach to effectively cluster scRNA-seq data, which is called TSC - the abbreviation of Two-Step Clustering. Particularly, by dividing all cells into two types: core cells (those possibly lying around the centers of clusters) and non-core cells (those locating in the boundary areas of clusters), we first clusters the core cells by hierarchical clustering (the first step) and then assigns the non-core cells to the corresponding nearest clusters (the second step). Extensive experiments on 12 real scRNA-seq datasets show that TSC outperforms the state of the art methods. CONCLUSION TSC is an effective clustering method due to its two-steps clustering strategy, and it is a useful tool for scRNA-seq data analysis.
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Affiliation(s)
- Ruiyi Li
- Translational Medical Center for Stem Cell Therapy, Shanghai East Hospital, and School of Medicine, Tongji University, 1239 Siping Road, 200092, Shanghai, China
- Department of Computer Science and Technology, Tongji University, 4800 Caoan Road, 201804, Shanghai, China
| | - Jihong Guan
- Department of Computer Science and Technology, Tongji University, 4800 Caoan Road, 201804, Shanghai, China.
| | - Zhiye Wang
- Department of Computer Science and Technology, Tongji University, 4800 Caoan Road, 201804, Shanghai, China
| | - Shuigeng Zhou
- Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University, 2005 Songhu Road, 200438, Shanghai, China.
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Rao J, Djeffal Y, Chal J, Marchianò F, Wang CH, Al Tanoury Z, Gapon S, Mayeuf-Louchart A, Glass I, Sefton EM, Habermann B, Kardon G, Watt FM, Tseng YH, Pourquié O. Reconstructing human brown fat developmental trajectory in vitro. Dev Cell 2023; 58:2359-2375.e8. [PMID: 37647896 PMCID: PMC10873093 DOI: 10.1016/j.devcel.2023.08.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 08/23/2022] [Accepted: 08/01/2023] [Indexed: 09/01/2023]
Abstract
Brown adipocytes (BAs) represent a specialized cell type that is able to uncouple nutrient catabolism from ATP generation to dissipate energy as heat. In humans, the brown fat tissue is composed of discrete depots found throughout the neck and trunk region. BAs originate from a precursor common to skeletal muscle, but their developmental trajectory remains poorly understood. Here, we used single-cell RNA sequencing to characterize the development of interscapular brown fat in mice. Our analysis identified a transient stage of BA differentiation characterized by the expression of the transcription factor GATA6. We show that recapitulating the sequence of signaling cues identified in mice can lead to efficient differentiation of BAs in vitro from human pluripotent stem cells. These precursors can in turn be efficiently converted into functional BAs that can respond to signals mimicking adrenergic stimuli by increasing their metabolism, resulting in heat production.
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Affiliation(s)
- Jyoti Rao
- Department of Pathology, Brigham and Women's Hospital, 60 Fenwood Road, Boston, MA 02115, USA; Department of Genetics, Harvard Medical School, 60 Fenwood Road, Boston, MA 02115, USA; Harvard Stem Cell Institute, Harvard University, Cambridge, MA 02138, USA
| | - Yannis Djeffal
- Department of Pathology, Brigham and Women's Hospital, 60 Fenwood Road, Boston, MA 02115, USA; Department of Genetics, Harvard Medical School, 60 Fenwood Road, Boston, MA 02115, USA; Harvard Stem Cell Institute, Harvard University, Cambridge, MA 02138, USA
| | - Jerome Chal
- Department of Pathology, Brigham and Women's Hospital, 60 Fenwood Road, Boston, MA 02115, USA; Department of Genetics, Harvard Medical School, 60 Fenwood Road, Boston, MA 02115, USA; Harvard Stem Cell Institute, Harvard University, Cambridge, MA 02138, USA
| | - Fabio Marchianò
- Aix-Marseille University, CNRS, IBDM, The Turing Center for Living Systems, 13009 Marseille, France
| | - Chih-Hao Wang
- Section on Integrative Physiology and Metabolism, Joslin Diabetes Center, Harvard Medical School, Boston, MA 02215, USA
| | - Ziad Al Tanoury
- Department of Pathology, Brigham and Women's Hospital, 60 Fenwood Road, Boston, MA 02115, USA; Department of Genetics, Harvard Medical School, 60 Fenwood Road, Boston, MA 02115, USA; Harvard Stem Cell Institute, Harvard University, Cambridge, MA 02138, USA
| | - Svetlana Gapon
- Department of Pathology, Brigham and Women's Hospital, 60 Fenwood Road, Boston, MA 02115, USA; Department of Genetics, Harvard Medical School, 60 Fenwood Road, Boston, MA 02115, USA; Harvard Stem Cell Institute, Harvard University, Cambridge, MA 02138, USA
| | | | - Ian Glass
- Department of Pediatrics, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Elizabeth M Sefton
- Department of Human Genetics, University of Utah, Salt Lake City, UT 84112, USA
| | - Bianca Habermann
- Aix-Marseille University, CNRS, IBDM, The Turing Center for Living Systems, 13009 Marseille, France
| | - Gabrielle Kardon
- Department of Human Genetics, University of Utah, Salt Lake City, UT 84112, USA
| | - Fiona M Watt
- King's College London Centre for Stem Cells and Regenerative Medicine, Great Maze Pond, London SE1 9RT, UK
| | - Yu-Hua Tseng
- Section on Integrative Physiology and Metabolism, Joslin Diabetes Center, Harvard Medical School, Boston, MA 02215, USA
| | - Olivier Pourquié
- Department of Pathology, Brigham and Women's Hospital, 60 Fenwood Road, Boston, MA 02115, USA; Department of Genetics, Harvard Medical School, 60 Fenwood Road, Boston, MA 02115, USA; Harvard Stem Cell Institute, Harvard University, Cambridge, MA 02138, USA.
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42
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Tian W, Zhou J, Bartlett A, Zeng Q, Liu H, Castanon RG, Kenworthy M, Altshul J, Valadon C, Aldridge A, Nery JR, Chen H, Xu J, Johnson ND, Lucero J, Osteen JK, Emerson N, Rink J, Lee J, Li Y, Siletti K, Liem M, Claffey N, O’Connor C, Yanny AM, Nyhus J, Dee N, Casper T, Shapovalova N, Hirschstein D, Ding SL, Hodge R, Levi BP, Keene CD, Linnarsson S, Lein E, Ren B, Behrens MM, Ecker JR. Single-cell DNA methylation and 3D genome architecture in the human brain. Science 2023; 382:eadf5357. [PMID: 37824674 PMCID: PMC10572106 DOI: 10.1126/science.adf5357] [Citation(s) in RCA: 61] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 09/05/2023] [Indexed: 10/14/2023]
Abstract
Delineating the gene-regulatory programs underlying complex cell types is fundamental for understanding brain function in health and disease. Here, we comprehensively examined human brain cell epigenomes by probing DNA methylation and chromatin conformation at single-cell resolution in 517 thousand cells (399 thousand neurons and 118 thousand non-neurons) from 46 regions of three adult male brains. We identified 188 cell types and characterized their molecular signatures. Integrative analyses revealed concordant changes in DNA methylation, chromatin accessibility, chromatin organization, and gene expression across cell types, cortical areas, and basal ganglia structures. We further developed single-cell methylation barcodes that reliably predict brain cell types using the methylation status of select genomic sites. This multimodal epigenomic brain cell atlas provides new insights into the complexity of cell-type-specific gene regulation in adult human brains.
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Affiliation(s)
- Wei Tian
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Jingtian Zhou
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA 92037, USA
| | - Anna Bartlett
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Qiurui Zeng
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
- Division of Biological Sciences, University of California, San Diego, La Jolla, CA 92037, USA
| | - Hanqing Liu
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Rosa G. Castanon
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Mia Kenworthy
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Jordan Altshul
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Cynthia Valadon
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Andrew Aldridge
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Joseph R. Nery
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Huaming Chen
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Jiaying Xu
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Nicholas D. Johnson
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Jacinta Lucero
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Julia K. Osteen
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Nora Emerson
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Jon Rink
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Jasper Lee
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Yang Li
- Ludwig Institute for Cancer Research, La Jolla, CA 92037, USA
| | - Kimberly Siletti
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet; 171 77 Stockholm, Sweden
| | - Michelle Liem
- Flow Cytometry Core Facility, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Naomi Claffey
- Flow Cytometry Core Facility, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Caz O’Connor
- Flow Cytometry Core Facility, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | | | - Julie Nyhus
- Allen Institute for Brain Science; Seattle, WA 98109, USA
| | - Nick Dee
- Allen Institute for Brain Science; Seattle, WA 98109, USA
| | - Tamara Casper
- Allen Institute for Brain Science; Seattle, WA 98109, USA
| | | | | | - Song-Lin Ding
- Allen Institute for Brain Science; Seattle, WA 98109, USA
| | - Rebecca Hodge
- Allen Institute for Brain Science; Seattle, WA 98109, USA
| | - Boaz P. Levi
- Allen Institute for Brain Science; Seattle, WA 98109, USA
| | - C. Dirk Keene
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98195, USA
| | - Sten Linnarsson
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet; 171 77 Stockholm, Sweden
| | - Ed Lein
- Allen Institute for Brain Science; Seattle, WA 98109, USA
| | - Bing Ren
- Ludwig Institute for Cancer Research, La Jolla, CA 92037, USA
- Center for Epigenomics, University of California, San Diego School of Medicine, La Jolla, CA 92037, USA
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA 92037, USA
- Institute of Genomic Medicine, University of California, San Diego School of Medicine, La Jolla, CA 92037, USA
- Moores Cancer Center, University of California, San Diego School of Medicine, La Jolla, CA 92037, USA
| | - M. Margarita Behrens
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Joseph R. Ecker
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
- Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
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Yi D, Nam JW, Jeong H. Toward the functional interpretation of somatic structural variations: bulk- and single-cell approaches. Brief Bioinform 2023; 24:bbad297. [PMID: 37587831 PMCID: PMC10516374 DOI: 10.1093/bib/bbad297] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 07/05/2023] [Accepted: 07/23/2023] [Indexed: 08/18/2023] Open
Abstract
Structural variants (SVs) are genomic rearrangements that can take many different forms such as copy number alterations, inversions and translocations. During cell development and aging, somatic SVs accumulate in the genome with potentially neutral, deleterious or pathological effects. Generation of somatic SVs is a key mutational process in cancer development and progression. Despite their importance, the detection of somatic SVs is challenging, making them less studied than somatic single-nucleotide variants. In this review, we summarize recent advances in whole-genome sequencing (WGS)-based approaches for detecting somatic SVs at the tissue and single-cell levels and discuss their advantages and limitations. First, we describe the state-of-the-art computational algorithms for somatic SV calling using bulk WGS data and compare the performance of somatic SV detectors in the presence or absence of a matched-normal control. We then discuss the unique features of cutting-edge single-cell-based techniques for analyzing somatic SVs. The advantages and disadvantages of bulk and single-cell approaches are highlighted, along with a discussion of their sensitivity to copy-neutral SVs, usefulness for functional inferences and experimental and computational costs. Finally, computational approaches for linking somatic SVs to their functional readouts, such as those obtained from single-cell transcriptome and epigenome analyses, are illustrated, with a discussion of the promise of these approaches in health and diseases.
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Affiliation(s)
- Dohun Yi
- Department of Life Science, College of Natural Sciences, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
| | - Jin-Wu Nam
- Department of Life Science, College of Natural Sciences, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
- Research Institute for Convergence of Basic Sciences, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
- Bio-BigData Center, Hanyang Institute of Bioscience and Biotechnology, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
- Hanyang Institute of Advanced BioConvergence, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
| | - Hyobin Jeong
- Department of Life Science, College of Natural Sciences, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
- Bio-BigData Center, Hanyang Institute of Bioscience and Biotechnology, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
- Hanyang Institute of Advanced BioConvergence, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
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Xia L, Lee C, Li JJ. scDEED: a statistical method for detecting dubious 2D single-cell embeddings and optimizing t-SNE and UMAP hyperparameters. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.21.537839. [PMID: 37163087 PMCID: PMC10168265 DOI: 10.1101/2023.04.21.537839] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Two-dimensional (2D) embedding methods are crucial for single-cell data visualization. Popular methods such as t-SNE and UMAP are commonly used for visualizing cell clusters; however, it is well known that t-SNE and UMAP's 2D embedding might not reliably inform the similarities among cell clusters. Motivated by this challenge, we developed a statistical method, scDEED, for detecting dubious cell embeddings output by any 2D-embedding method. By calculating a reliability score for every cell embedding, scDEED identifies the cell embeddings with low reliability scores as dubious and those with high reliability scores as trustworthy. Moreover, by minimizing the number of dubious cell embeddings, scDEED provides intuitive guidance for optimizing the hyperparameters of an embedding method. Applied to multiple scRNA-seq datasets, scDEED demonstrates its effectiveness for detecting dubious cell embeddings and optimizing the hyperparameters of t-SNE and UMAP.
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Wang L, Trasanidis N, Wu T, Dong G, Hu M, Bauer DE, Pinello L. Dictys: dynamic gene regulatory network dissects developmental continuum with single-cell multiomics. Nat Methods 2023; 20:1368-1378. [PMID: 37537351 DOI: 10.1038/s41592-023-01971-3] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 07/05/2023] [Indexed: 08/05/2023]
Abstract
Gene regulatory networks (GRNs) are key determinants of cell function and identity and are dynamically rewired during development and disease. Despite decades of advancement, challenges remain in GRN inference, including dynamic rewiring, causal inference, feedback loop modeling and context specificity. To address these challenges, we develop Dictys, a dynamic GRN inference and analysis method that leverages multiomic single-cell assays of chromatin accessibility and gene expression, context-specific transcription factor footprinting, stochastic process network and efficient probabilistic modeling of single-cell RNA-sequencing read counts. Dictys improves GRN reconstruction accuracy and reproducibility and enables the inference and comparative analysis of context-specific and dynamic GRNs across developmental contexts. Dictys' network analyses recover unique insights in human blood and mouse skin development with cell-type-specific and dynamic GRNs. Its dynamic network visualizations enable time-resolved discovery and investigation of developmental driver transcription factors and their regulated targets. Dictys is available as a free, open-source and user-friendly Python package.
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Affiliation(s)
- Lingfei Wang
- Molecular Pathology Unit and Center for Cancer Research, Massachusetts General Hospital Research Institute, Department of Pathology, Harvard Medical School, Boston, MA, USA
- Gene Regulation Observatory, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nikolaos Trasanidis
- Molecular Pathology Unit and Center for Cancer Research, Massachusetts General Hospital Research Institute, Department of Pathology, Harvard Medical School, Boston, MA, USA
- Hugh and Josseline Langmuir Centre for Myeloma Research, Centre for Haematology, Department of Immunology and Inflammation, Imperial College London, London, UK
| | - Ting Wu
- Division of Hematology/Oncology, Boston Children's Hospital, Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Stem Cell Institute, Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Guanlan Dong
- Molecular Pathology Unit and Center for Cancer Research, Massachusetts General Hospital Research Institute, Department of Pathology, Harvard Medical School, Boston, MA, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Department of Pediatrics, Harvard Medical School, Bioinformatics and Integrative Genomics PhD Program, Harvard Medical School, Boston, MA, USA
| | - Michael Hu
- Molecular Pathology Unit and Center for Cancer Research, Massachusetts General Hospital Research Institute, Department of Pathology, Harvard Medical School, Boston, MA, USA
| | - Daniel E Bauer
- Gene Regulation Observatory, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Hematology/Oncology, Boston Children's Hospital, Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Stem Cell Institute, Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Luca Pinello
- Molecular Pathology Unit and Center for Cancer Research, Massachusetts General Hospital Research Institute, Department of Pathology, Harvard Medical School, Boston, MA, USA.
- Gene Regulation Observatory, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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Mao S, Liu J, Zhao W, Zhou X. LVPT: Lazy Velocity Pseudotime Inference Method. Biomolecules 2023; 13:1242. [PMID: 37627306 PMCID: PMC10452358 DOI: 10.3390/biom13081242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 08/09/2023] [Accepted: 08/10/2023] [Indexed: 08/27/2023] Open
Abstract
The emergence of RNA velocity has enriched our understanding of the dynamic transcriptional landscape within individual cells. In light of this breakthrough, we embarked on integrating RNA velocity with cellular pseudotime inference, aiming to improve the prediction of cell orders along biological trajectories beyond existing methods. Here, we developed LVPT, a novel method for pseudotime and trajectory inference. LVPT introduces a lazy probability to indicate the probability that the cell stays in the original state and calculates the transition matrix based on RNA velocity to provide the probability and direction of cell differentiation. LVPT shows better and comparable performance of pseudotime inference compared with other existing methods on both simulated datasets with different structures and real datasets. The validation results were consistent with prior knowledge, indicating that LVPT is an accurate and efficient method for pseudotime inference.
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Affiliation(s)
- Shuainan Mao
- The Department of Biotherapy and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu 610041, China
- Med-X Center for Informatics, Sichuan University, Chengdu 610041, China
- Center for Computational Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Jiajia Liu
- Center for Computational Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Weiling Zhao
- Center for Computational Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Xiaobo Zhou
- Center for Computational Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX 77054, USA
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Erbe R, Stein-O’Brien G, Fertig EJ. Transcriptomic forecasting with neural ordinary differential equations. PATTERNS (NEW YORK, N.Y.) 2023; 4:100793. [PMID: 37602211 PMCID: PMC10435954 DOI: 10.1016/j.patter.2023.100793] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 04/03/2023] [Accepted: 06/13/2023] [Indexed: 08/22/2023]
Abstract
Single-cell transcriptomics technologies can uncover changes in the molecular states that underlie cellular phenotypes. However, understanding the dynamic cellular processes requires extending from inferring trajectories from snapshots of cellular states to estimating temporal changes in cellular gene expression. To address this challenge, we have developed a neural ordinary differential-equation-based method, RNAForecaster, for predicting gene expression states in single cells for multiple future time steps in an embedding-independent manner. We demonstrate that RNAForecaster can accurately predict future expression states in simulated single-cell transcriptomic data with cellular tracking over time. We then show that by using metabolic labeling single-cell RNA sequencing (scRNA-seq) data from constitutively dividing cells, RNAForecaster accurately recapitulates many of the expected changes in gene expression during progression through the cell cycle over a 3-day period. Thus, RNAForecaster enables short-term estimation of future expression states in biological systems from high-throughput datasets with temporal information.
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Affiliation(s)
- Rossin Erbe
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Johns Hopkins Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA
| | - Genevieve Stein-O’Brien
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Johns Hopkins Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Kavli Neurodiscovery Institute, Baltimore, MD, USA
- Single Cell Training and Analysis Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Elana J. Fertig
- Johns Hopkins Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA
- Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA
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Fouché A, Zinovyev A. Omics data integration in computational biology viewed through the prism of machine learning paradigms. FRONTIERS IN BIOINFORMATICS 2023; 3:1191961. [PMID: 37600970 PMCID: PMC10436311 DOI: 10.3389/fbinf.2023.1191961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 07/26/2023] [Indexed: 08/22/2023] Open
Abstract
Important quantities of biological data can today be acquired to characterize cell types and states, from various sources and using a wide diversity of methods, providing scientists with more and more information to answer challenging biological questions. Unfortunately, working with this amount of data comes at the price of ever-increasing data complexity. This is caused by the multiplication of data types and batch effects, which hinders the joint usage of all available data within common analyses. Data integration describes a set of tasks geared towards embedding several datasets of different origins or modalities into a joint representation that can then be used to carry out downstream analyses. In the last decade, dozens of methods have been proposed to tackle the different facets of the data integration problem, relying on various paradigms. This review introduces the most common data types encountered in computational biology and provides systematic definitions of the data integration problems. We then present how machine learning innovations were leveraged to build effective data integration algorithms, that are widely used today by computational biologists. We discuss the current state of data integration and important pitfalls to consider when working with data integration tools. We eventually detail a set of challenges the field will have to overcome in the coming years.
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Affiliation(s)
- Aziz Fouché
- Institut Curie, PSL Research University, Paris, France
- Institut National de la Santé et de la Recherche Médicale, Paris, France
- CBIO-Centre for Computational Biology, ParisTech, PSL Research University, Paris, France
- Ecole Normale Supérieure Paris-Saclay, Cachan, France
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Orrapin S, Thongkumkoon P, Udomruk S, Moonmuang S, Sutthitthasakul S, Yongpitakwattana P, Pruksakorn D, Chaiyawat P. Deciphering the Biology of Circulating Tumor Cells through Single-Cell RNA Sequencing: Implications for Precision Medicine in Cancer. Int J Mol Sci 2023; 24:12337. [PMID: 37569711 PMCID: PMC10418766 DOI: 10.3390/ijms241512337] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 07/25/2023] [Accepted: 07/27/2023] [Indexed: 08/13/2023] Open
Abstract
Circulating tumor cells (CTCs) hold unique biological characteristics that directly involve them in hematogenous dissemination. Studying CTCs systematically is technically challenging due to their extreme rarity and heterogeneity and the lack of specific markers to specify metastasis-initiating CTCs. With cutting-edge technology, single-cell RNA sequencing (scRNA-seq) provides insights into the biology of metastatic processes driven by CTCs. Transcriptomics analysis of single CTCs can decipher tumor heterogeneity and phenotypic plasticity for exploring promising novel therapeutic targets. The integrated approach provides a perspective on the mechanisms underlying tumor development and interrogates CTCs interactions with other blood cell types, particularly those of the immune system. This review aims to comprehensively describe the current study on CTC transcriptomic analysis through scRNA-seq technology. We emphasize the workflow for scRNA-seq analysis of CTCs, including enrichment, single cell isolation, and bioinformatic tools applied for this purpose. Furthermore, we elucidated the translational knowledge from the transcriptomic profile of individual CTCs and the biology of cancer metastasis for developing effective therapeutics through targeting key pathways in CTCs.
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Affiliation(s)
- Santhasiri Orrapin
- Center of Multidisciplinary Technology for Advanced Medicine (CMUTEAM), Faculty of Medicine, Chiang Mai University, Muang, Chiang Mai 50200, Thailand; (S.O.); (P.T.); (S.U.); (S.M.); (S.S.); (P.Y.); (D.P.)
| | - Patcharawadee Thongkumkoon
- Center of Multidisciplinary Technology for Advanced Medicine (CMUTEAM), Faculty of Medicine, Chiang Mai University, Muang, Chiang Mai 50200, Thailand; (S.O.); (P.T.); (S.U.); (S.M.); (S.S.); (P.Y.); (D.P.)
| | - Sasimol Udomruk
- Center of Multidisciplinary Technology for Advanced Medicine (CMUTEAM), Faculty of Medicine, Chiang Mai University, Muang, Chiang Mai 50200, Thailand; (S.O.); (P.T.); (S.U.); (S.M.); (S.S.); (P.Y.); (D.P.)
- Musculoskeletal Science and Translational Research (MSTR) Center, Faculty of Medicine, Chiang Mai University, Muang, Chiang Mai 50200, Thailand
| | - Sutpirat Moonmuang
- Center of Multidisciplinary Technology for Advanced Medicine (CMUTEAM), Faculty of Medicine, Chiang Mai University, Muang, Chiang Mai 50200, Thailand; (S.O.); (P.T.); (S.U.); (S.M.); (S.S.); (P.Y.); (D.P.)
| | - Songphon Sutthitthasakul
- Center of Multidisciplinary Technology for Advanced Medicine (CMUTEAM), Faculty of Medicine, Chiang Mai University, Muang, Chiang Mai 50200, Thailand; (S.O.); (P.T.); (S.U.); (S.M.); (S.S.); (P.Y.); (D.P.)
| | - Petlada Yongpitakwattana
- Center of Multidisciplinary Technology for Advanced Medicine (CMUTEAM), Faculty of Medicine, Chiang Mai University, Muang, Chiang Mai 50200, Thailand; (S.O.); (P.T.); (S.U.); (S.M.); (S.S.); (P.Y.); (D.P.)
| | - Dumnoensun Pruksakorn
- Center of Multidisciplinary Technology for Advanced Medicine (CMUTEAM), Faculty of Medicine, Chiang Mai University, Muang, Chiang Mai 50200, Thailand; (S.O.); (P.T.); (S.U.); (S.M.); (S.S.); (P.Y.); (D.P.)
- Musculoskeletal Science and Translational Research (MSTR) Center, Faculty of Medicine, Chiang Mai University, Muang, Chiang Mai 50200, Thailand
- Department of Orthopedics, Faculty of Medicine, Chiang Mai University, Muang, Chiang Mai 50200, Thailand
| | - Parunya Chaiyawat
- Center of Multidisciplinary Technology for Advanced Medicine (CMUTEAM), Faculty of Medicine, Chiang Mai University, Muang, Chiang Mai 50200, Thailand; (S.O.); (P.T.); (S.U.); (S.M.); (S.S.); (P.Y.); (D.P.)
- Musculoskeletal Science and Translational Research (MSTR) Center, Faculty of Medicine, Chiang Mai University, Muang, Chiang Mai 50200, Thailand
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Li C, Chen X, Chen S, Jiang R, Zhang X. simCAS: an embedding-based method for simulating single-cell chromatin accessibility sequencing data. Bioinformatics 2023; 39:btad453. [PMID: 37494428 PMCID: PMC10394124 DOI: 10.1093/bioinformatics/btad453] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 06/25/2023] [Accepted: 07/25/2023] [Indexed: 07/28/2023] Open
Abstract
MOTIVATION Single-cell chromatin accessibility sequencing (scCAS) technology provides an epigenomic perspective to characterize gene regulatory mechanisms at single-cell resolution. With an increasing number of computational methods proposed for analyzing scCAS data, a powerful simulation framework is desirable for evaluation and validation of these methods. However, existing simulators generate synthetic data by sampling reads from real data or mimicking existing cell states, which is inadequate to provide credible ground-truth labels for method evaluation. RESULTS We present simCAS, an embedding-based simulator, for generating high-fidelity scCAS data from both cell- and peak-wise embeddings. We demonstrate simCAS outperforms existing simulators in resembling real data and show that simCAS can generate cells of different states with user-defined cell populations and differentiation trajectories. Additionally, simCAS can simulate data from different batches and encode user-specified interactions of chromatin regions in the synthetic data, which provides ground-truth labels more than cell states. We systematically demonstrate that simCAS facilitates the benchmarking of four core tasks in downstream analysis: cell clustering, trajectory inference, data integration, and cis-regulatory interaction inference. We anticipate simCAS will be a reliable and flexible simulator for evaluating the ongoing computational methods applied on scCAS data. AVAILABILITY AND IMPLEMENTATION simCAS is freely available at https://github.com/Chen-Li-17/simCAS.
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Affiliation(s)
- Chen Li
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division of BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Xiaoyang Chen
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division of BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Shengquan Chen
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China
| | - Rui Jiang
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division of BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Xuegong Zhang
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division of BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
- Center for Synthetic and Systems Biology, School of Life Sciences and School of Medicine, Tsinghua University, Beijing 100084, China
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