1
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Wagner E, Larijani B, Kirane AR. Predictive Biomarkers for Immune Checkpoint Inhibitor Therapy in Advanced Melanomas. Surg Oncol Clin N Am 2025; 34:437-451. [PMID: 40413009 DOI: 10.1016/j.soc.2025.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/27/2025]
Abstract
Biomarkers capable of predicting adverse melanoma patient responses to immune checkpoint inhibitor (ICI) therapies are an unmet need. Clinical biomarkers are largely prognostic and current response guidelines do not reflect the complex tumor-immune cell interaction dynamics attributed to ICI therapies. Validation of enhanced predictive biomarkers is dependent upon adoption of novel spatial imaging platforms capable of quantifying immune checkpoint receptor-ligand interactions within the tumor microenvironment. Assessments of these interactions at multiple points during neoadjuvant ICI regimens would inform biomarker selection based on changes in receptor-ligand interactions that best correlate with patient survival.
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Affiliation(s)
- Emma Wagner
- Division of General Surgery, Department of Surgery, Section of Surgical Oncology, Stanford University School of Medicine, 1201 Welch Road, Stanford, CA 94305, USA
| | - Banafshé Larijani
- Department of Life Science, Cell Biophysics Laboratory, Centre for Therapeutic Innovation, University of Bath, Claverton Down, Bath BA2 7AY, UK
| | - Amanda Robinson Kirane
- Division of General Surgery, Department of Surgery, Section of Surgical Oncology, Stanford University School of Medicine, 1201 Welch Road, Stanford, CA 94305, USA.
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2
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Dupuis L, Debeaupuis O, Simon F, Isambert H. CausalCCC: a web server to explore intracellular causal pathways enabling cell-cell communication. Nucleic Acids Res 2025:gkaf404. [PMID: 40366019 DOI: 10.1093/nar/gkaf404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2025] [Revised: 04/26/2025] [Accepted: 05/05/2025] [Indexed: 05/15/2025] Open
Abstract
Understanding cell-cell communication (CCC) pathways from single-cell or spatial transcriptomic data is key to unraveling biological processes. Recently, multiple CCC methods have been developed but primarily focus on refining ligand-receptor (L-R) interaction scores. A critical gap for a more comprehensive picture of cellular crosstalks lies in the integration of upstream and downstream intracellular pathways in the sender and receiver cells. We report here CausalCCC, https://miic.curie.fr/causalCCC.php, an interactive web server, which addresses this need by reconstructing gene-gene interaction pathways across two or more interacting cell types from single-cell or spatial transcriptomic data. CausalCCC includes a graphical introduction and a demo dataset within the workbench page as well as a comprehensive tutorial. CausalCCC methodology integrates a robust and scalable causal network reconstruction method, multivariate information-based inductive causation, with internally computed L-R pairs using LIANA+ (including CellphoneDBv5, SingleCellSignalR, Connectome, NATMI, and Log2FC). Alternatively, user-defined L-R pairs from any CCC methods can also be uploaded. We showcase here CausalCCC on different single-cell and spatial transcriptomic datasets from three original CCC methods (NicheNet, CellChat, and Misty). CausalCCC web server offers unique interactive visualization tools dedicated to single-cell data practitioners seeking to go beyond L-R scores and explore extended CCC pathways across multiple interacting cell types.
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Affiliation(s)
| | - Orianne Debeaupuis
- CNRS UMR168, Institut Curie, 75005 Paris, France
- Inserm U1163, Institut Imagine, 75005 Paris, France
| | - Franck Simon
- CNRS UMR168, Institut Curie, 75005 Paris, France
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3
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Duan L, Hashemi M, Ngom A, Rueda L. Ligand-receptor dynamics in heterophily-aware graph neural networks for enhanced cell type prediction from single-cell RNA-seq data. Front Mol Biosci 2025; 12:1547231. [PMID: 40421418 PMCID: PMC12104675 DOI: 10.3389/fmolb.2025.1547231] [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: 12/18/2024] [Accepted: 04/07/2025] [Indexed: 05/28/2025] Open
Abstract
Graph Neural Networks (GNNs) have emerged as powerful tools for analyzing structured data, particularly in domains where relationships and interactions between entities are key. By leveraging the inherent graph structure in datasets, GNNs excel in capturing complex dependencies and patterns that traditional neural networks might miss. This advantage is especially pronounced in the field of computational biology, where the intricate connections between biological entities play a crucial role. In this context, Our work explores the application of GNNs to single-cell RNA sequencing (scRNA-seq) data, a domain characterized by complex and heterogeneous relationships. By extracting ligand-receptor (L-R) associations from LIANA and constructing Cell-Cell association networks with varying edge homophily ratios, based on L-R information, we enhance the biological relevance and accuracy of depicting cellular communication pathways. While standard GNN models like Graph Convolutional Networks (GCN), GraphSAGE, Graph Attention Networks (GAT), and MixHop often assume homophily (similar nodes are more likely to be connected), this assumption does not always hold in biological networks. To address this, we explore advanced graph neural network methods, such asH 2 Graph Convolutional Networks and Gated Bi-Kernel GNNs (GBK-GNN), that are specifically designed to handle heterophilic data. Our study spans across six diverse datasets, enabling a thorough comparison between heterophily-aware GNNs and traditional homophily-assuming models, including Multi-Layer Perceptrons, which disregards graph structure entirely. Our findings highlight the importance of considering data-specific characteristics in GNN applications, demonstrating that heterophily-focused methods can effectively decipher the complex patterns within scRNA-seq data. By integrating multi-omics data, including gene expression profiles and L-R interactions, we pave the way for more accurate and insightful analyses in computational biology, offering a more comprehensive understanding of cellular environments and interactions.
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4
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Liu Z, Yang Y, Fang H, Cen B, Fan Y, Li J, Wang L, He S. Single-cell and spatial analyses reveal the effect of VSIG4 +S100A10 +TAMs on the immunosuppression of glioblastoma and anti-PD-1 immunotherapy. Int J Biol Macromol 2025; 308:142415. [PMID: 40127797 DOI: 10.1016/j.ijbiomac.2025.142415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2024] [Revised: 03/04/2025] [Accepted: 03/21/2025] [Indexed: 03/26/2025]
Abstract
Therapeutic strategies aiming at the tumor immune microenvironment (TIME) hold promise for glioblastoma (GBM) treatment. However, adjuvant immunotherapies targeting checkpoint inhibitors just prove effective for a selected group of GBM patients. The extensive involvement of GBM-associated macrophages highlights their potential role in tumor behavior. In-depth exploration of the impact of macrophages on the efficacy of immunotherapy is crucial for enhancing treatment outcomes. In this study, we conducted a comprehensive analysis using bulk RNA-seq, single-cell RNA sequencing (scRNA-seq), and spatial transcriptomics to explore the heterogeneity of tumor-associated macrophages (TAMs) in GBM. Flow cytometry was employed to investigate the effects of VSIG4 on TAM phenotypes, and co-culture cellular assays were performed to evaluate its contribution to GBM malignancy. Integrating 16 patient samples, we examined the immunological significance of VSIG4+S100A10+TAMs. VSIG4 expression on macrophages is significantly upregulated and correlated with the TIME, promoting the polarization of macrophages towards M2 and facilitating GBM progression. Spatial transcriptomics and human samples multiplex immunofluorescence (mIF) confirmed the co-localization of VSIG4+S100A10+TAMs with various T cells, resulting in the inhibition of T cell immune responses and a reduction in anti-tumor immunity. Our findings demonstrate for the first time that VSIG4+S100A10+TAM is an independent prognostic indicator of poor outcome for GBM and markedly accumulates in patients exhibiting non-responsiveness to anti-PD-1 immunotherapy. Targeting this specific bifunctional subgroup can potentially open up new avenues for the immunotherapy of GBM.
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Affiliation(s)
- Ziyuan Liu
- Department of Pharmacy, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, Guangdong, China; National Medical Products Administration Key Laboratory for Research and Evaluation of Drug Metabolism & Guangdong Provincial Key Laboratory of New Drug Screening & Guangdong-Hongkong-Macao Joint Laboratory for New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Yufan Yang
- National Medical Products Administration Key Laboratory for Research and Evaluation of Drug Metabolism & Guangdong Provincial Key Laboratory of New Drug Screening & Guangdong-Hongkong-Macao Joint Laboratory for New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou, 510515, Guangdong, China; Clinical Pharmacy Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Haiting Fang
- Department of Pharmacy, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, Guangdong, China; National Medical Products Administration Key Laboratory for Research and Evaluation of Drug Metabolism & Guangdong Provincial Key Laboratory of New Drug Screening & Guangdong-Hongkong-Macao Joint Laboratory for New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Bohong Cen
- National Medical Products Administration Key Laboratory for Research and Evaluation of Drug Metabolism & Guangdong Provincial Key Laboratory of New Drug Screening & Guangdong-Hongkong-Macao Joint Laboratory for New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou, 510515, Guangdong, China; Clinical Pharmacy Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Yiqi Fan
- Department of Pharmacy, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, Guangdong, China; National Medical Products Administration Key Laboratory for Research and Evaluation of Drug Metabolism & Guangdong Provincial Key Laboratory of New Drug Screening & Guangdong-Hongkong-Macao Joint Laboratory for New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Jianlong Li
- Department of Pediatrics, Weill Cornell Medicine, New York, NY, USA; Department of Orthopedic Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China.
| | - Lijie Wang
- Department of Pharmacy, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, Guangdong, China.
| | - Shuai He
- Department of Pharmacy, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, Guangdong, China; National Medical Products Administration Key Laboratory for Research and Evaluation of Drug Metabolism & Guangdong Provincial Key Laboratory of New Drug Screening & Guangdong-Hongkong-Macao Joint Laboratory for New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou, 510515, Guangdong, China.
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5
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Jia C, Wang H, Zhao J, Xia J, Zheng C. scSDNE: A semi-supervised method for inferring cell-cell interactions based on graph embedding. PLoS Comput Biol 2025; 21:e1013027. [PMID: 40333631 PMCID: PMC12072665 DOI: 10.1371/journal.pcbi.1013027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2024] [Accepted: 04/04/2025] [Indexed: 05/09/2025] Open
Abstract
As a fundamental characteristic of multicellular organisms, cell-cell communication is achieved through ligand-receptor (L-R) interactions, enabling the exchange of information and revealing the diversity of biological processes and cellular functions. To gain a comprehensive understanding of these complex interaction mechanisms, we constructed a manually curated L-R interaction database and developed a semi-supervised graph embedding model called scSDNE for inferring cell-cell interactions mediated by L-R interactions. scSDNE model utilizes the power of deep learning to map genes from interacting cells into a shared latent space, allowing for a nuanced representation of their relationships. Leveraging the prior information provided by database, scSDNE can infer significant L-R pairs involved in intercellular communication. Experiments on real single-cell RNA sequencing (scRNA-seq) datasets demonstrate that our method detects interactions with a high degree of reliability compared with other methods. More importantly, the model integrates gene regulation information within cells to enhance the accuracy and biological interpretability of the inferences. Our method provides a more comprehensive view of cell-cell interactions, offering new insights into complex intercellular communication.
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Affiliation(s)
- Chenchen Jia
- College of Mathematics and System Sciences, Xinjiang University, Urumqi, China
| | - Haiyun Wang
- School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China
| | - Jianping Zhao
- College of Mathematics and System Sciences, Xinjiang University, Urumqi, China
| | - Junfeng Xia
- College of Mathematics and System Sciences, Xinjiang University, Urumqi, China
- School of Physical Science and Information Technology, Anhui University, Hefei, China
| | - Chunhou Zheng
- School of Physical Science and Information Technology, Anhui University, Hefei, China
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6
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Wu G, Liang Y, Xi Q, Zuo Y. New Insights and Implications of Cell-Cell Interactions in Developmental Biology. Int J Mol Sci 2025; 26:3997. [PMID: 40362237 PMCID: PMC12072105 DOI: 10.3390/ijms26093997] [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: 03/11/2025] [Revised: 04/17/2025] [Accepted: 04/22/2025] [Indexed: 05/15/2025] Open
Abstract
The dynamic and meticulously regulated networks established the foundation for embryonic development, where the intercellular interactions and signal transduction assumed a pivotal role. In recent years, high-throughput technologies such as single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) have advanced dramatically, empowering the systematic dissection of cell-to-cell regulatory networks. The emergence of comprehensive databases and analytical frameworks has further provided unprecedented insights into embryonic development and cell-cell interactions (CCIs). This paper reviewed the exponential increased CCIs works related to developmental biology from 2008 to 2023, comprehensively collected and categorized 93 analytical tools and 39 databases, and demonstrated its practical utility through illustrative case studies. In parallel, the article critically scrutinized the persistent challenges within this field, such as the intricacies of spatial localization and transmembrane state validation at single-cell resolution, and underscored the interpretative limitations inherent in current analytical frameworks. The development of CCIs' analysis tools with harmonizing multi-omics data and the construction of cross-species dynamically updated CCIs databases will be the main direction of future research. Future investigations into CCIs are poised to expeditiously drive the application and clinical translation within developmental biology, unlocking novel dimensions for exploration and progress.
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Affiliation(s)
| | | | | | - Yongchun Zuo
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, School of Life Sciences, Inner Mongolia University, Hohhot 010021, China; (G.W.); (Y.L.); (Q.X.)
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7
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Guo H, Zhang L, Tang H, Liu P, Hu B, Gong Y, Hou R, Wu Z. Exploring the Role of T-Cell Metabolism in Modulating Immunotherapy Efficacy for Non-Small Cell Lung Cancer Based on Clustering. J Clin Lab Anal 2025:e25020. [PMID: 40244859 DOI: 10.1002/jcla.25020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 01/26/2024] [Accepted: 02/08/2024] [Indexed: 04/19/2025] Open
Abstract
BACKGROUND Immunotherapy, especially immune checkpoint blockade (ICB) therapy, has demonstrated noteworthy advancements in the realm of non-small cell lung cancer (NSCLC). However, the efficacy of ICB therapy is limited to a small subset of patients with NSCLC, and the underlying mechanisms remain poorly understood. STUDY DESIGN AND DISCOVERIES In this study, we conducted a comprehensive investigation of the metabolic profiles of infiltrating T cells in NSCLC tumors and revealed the metabolic heterogeneity, which associated with the prognosis of ICB therapy, in three T-cell subtypes. After metabolic clustering, we split these metabolic clusters into two groups: Nonresponse-associated (NR) clusters that enriched with cells from nonresponders, and response-associated (R) clusters that not belonging to NR clusters. Then, we elucidated their metabolic differences and specific functions. Notably, we discovered HSPA1A was significantly downregulated in NR clusters of all three T-cell subtypes. In addition, leveraging single-cell T-cell receptor sequencing data and pseudotime series analysis, we revealed the reciprocal interconversion between R and NR metabolic clusters within the same T-cell clone. This suggests a potential metabolic reprogramming capability of T cells. Furthermore, through the analysis of intercellular communication, we identified the specific intercellular signaling in the R clusters, which might promote the activation and regulation of signal transduction pathways that affect the prognosis of ICB therapy. CONCLUSION In conclusion, our study offers substantial insights into the mechanisms of relationships between T-cell metabolisms and ICB therapy outcomes, shedding light on the mechanism of immunotherapy efficacy in patients with NSCLC. Such investigations will contribute to overcoming treatment resistance.
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Affiliation(s)
- Hongzhe Guo
- School of Electrical and Information Engineering, Anhui University of Technology, Maanshan, China
| | - Liangyu Zhang
- Department of Medical Oncology, The General Hospital of Daqing Oil Field, Daqing, China
| | - Hu Tang
- The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Peiwen Liu
- School of Science, Zhejiang Sci-Tech University, Hangzhou, China
| | - Bin Hu
- Department of Thoracic Surgery, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Yue Gong
- Geneis Beijing Co., Ltd., Beijing, China
| | - Rui Hou
- Geneis Beijing Co., Ltd., Beijing, China
| | - Ziheng Wu
- School of Electrical and Information Engineering, Anhui University of Technology, Maanshan, China
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8
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Li X, Pan L, Li W, Liu B, Xiao C, Chew V, Zhang X, Long W, Ginhoux F, Loscalzo J, Buggert M, Zhang X, Sheng R, Wang Z. Deciphering immune predictors of immunotherapy response: A multiomics approach at the pan-cancer level. Cell Rep Med 2025; 6:101992. [PMID: 40054456 PMCID: PMC12047473 DOI: 10.1016/j.xcrm.2025.101992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2024] [Revised: 01/15/2025] [Accepted: 02/05/2025] [Indexed: 04/18/2025]
Abstract
Immune checkpoint blockade (ICB) therapy has transformed cancer treatment, yet many patients fail to respond. Employing single-cell multiomics, we unveil T cell dynamics influencing ICB response across 480 pan-cancer and 27 normal tissue samples. We identify four immunotherapy response-associated T cells (IRATs) linked to responsiveness or resistance and analyze their pseudotemporal patterns, regulatory mechanisms, and T cell receptor clonal expansion profiles specific to each response. Notably, transforming growth factor β1 (TGF-β1)+ CD4+ and Temra CD8+ T cells negatively correlate with therapy response, in stark contrast to the positive response associated with CXCL13+ CD4+ and CD8+ T cells. Validation with a cohort of 23 colorectal cancer (CRC) samples confirms the significant impact of TGF-β1+ CD4+ and CXCL13+ CD4+ and CD8+ T cells on ICB efficacy. Our study highlights the effectiveness of single-cell multiomics in pinpointing immune markers predictive of immunotherapy outcomes, providing an important resource for crafting targeted immunotherapies for successful ICB treatment across cancers.
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Affiliation(s)
- Xuexin Li
- Department of General Surgery, The Fourth Affiliated Hospital, China Medical University, Shenyang, Liaoning 110032, China; Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Ministry of Education, China Medical University, Shenyang, Liaoning 110122, China; Institute of Health Sciences, China Medical University, Shenyang, Liaoning 110122, China; Department of Physiology and Pharmacology, Karolinska Institutet, 171 65 Solna, Sweden.
| | - Lu Pan
- Institute of Environmental Medicine, Karolinska Institutet, 171 65 Solna, Sweden
| | - Weiyuan Li
- School of Medicine, Yunnan University, Kunming, Yunnan 650091, China; Department of Reproductive Medicine, The First People's Hospital of Yunnan Province, Kunming, Yunnan 650021, China
| | - Bingyang Liu
- Department of Endocrinology, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, China
| | - Chunjie Xiao
- School of Medicine, Yunnan University, Kunming, Yunnan 650091, China
| | - Valerie Chew
- Translational Immunology Institute (TII), SingHealth-Duke NUS Academic Medical Centre, Singapore 169856, Singapore
| | - Xuan Zhang
- Department of Colorectal Surgery, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, Yunnan 650032, China
| | - Wang Long
- Department of Pathology, Nihon University, Tokyo 102-0074, Japan
| | - Florent Ginhoux
- Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A∗STAR), Singapore 138648, Singapore; Institut Gustave Roussy, INSERM U1015, Bâtiment de Médecine Moléculaire 114 rue Edouard Vaillant, 94800 Villejuif, France; Shanghai Institute of Immunology, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Joseph Loscalzo
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Marcus Buggert
- Center for Infectious Medicine, Department of Medicine Huddinge, Karolinska Institutet, 141 52 Huddinge, Sweden
| | - Xiaolu Zhang
- Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China; Shenzhen Research Institute of Shandong University, Shenzhen, Guangdong 518057, China.
| | - Ren Sheng
- College of Life and Health Sciences, Northeastern University, Shenyang, Liaoning 110819, China; School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou, Guangdong 510000, China.
| | - Zhenning Wang
- Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Ministry of Education, China Medical University, Shenyang, Liaoning 110122, China; Institute of Health Sciences, China Medical University, Shenyang, Liaoning 110122, China; The First Affiliated Hospital of China Medical University, Shenyang, Liaoning 110001, China.
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9
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Troulé K, Petryszak R, Cakir B, Cranley J, Harasty A, Prete M, Tuong ZK, Teichmann SA, Garcia-Alonso L, Vento-Tormo R. CellPhoneDB v5: inferring cell-cell communication from single-cell multiomics data. Nat Protoc 2025:10.1038/s41596-024-01137-1. [PMID: 40133495 DOI: 10.1038/s41596-024-01137-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 12/20/2024] [Indexed: 03/27/2025]
Abstract
Cell-cell communication is essential for tissue development, function and regeneration. The revolution of single-cell genomics technologies offers an unprecedented opportunity to uncover how cells communicate in vivo within their tissue niches and how disruption of these niches can lead to diseases and developmental abnormalities. CellPhoneDB is a bioinformatics toolkit designed to infer cell-cell communication by combining a curated repository of bona fide ligand-receptor interactions with methods to integrate these interactions with single-cell genomics data. Here we present a protocol for the latest version of CellPhoneDB (v5), offering several new features. First, the repository has been expanded by one-third with the addition of new interactions, including ~1,000 interactions mediated by nonpeptidic ligands such as steroidogenic hormones, neurotransmitters and small G-protein-coupled receptor (GPCR)-binding ligands. Second, we outline a new way of using the database that allows users to tailor queries to their experimental designs. Third, the update incorporates novel strategies to prioritize specific cell-cell interactions, leveraging information from other modalities such as tissue microenvironments derived from spatial transcriptomics technologies or transcription factor activities derived from a single-cell assay for transposase accessible chromatin assays. Finally, we describe the new CellPhoneDBViz module to interactively visualize and share results. Altogether, CellPhoneDB v5 enhances the precision of cell-cell communication inference, offering new insights into tissue biology in physiological microenvironments. This protocol typically takes ~15 min and requires basic knowledge of python.
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Affiliation(s)
| | | | | | | | - Alicia Harasty
- Ian Frazer Centre for Children's Immunotherapy Research, Child Health Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia
| | | | - Zewen Kelvin Tuong
- Wellcome Sanger Institute, Cambridge, UK
- Ian Frazer Centre for Children's Immunotherapy Research, Child Health Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia
| | - Sarah A Teichmann
- Wellcome Sanger Institute, Cambridge, UK
- Department of Physics, Cavendish Laboratory, University of Cambridge, Cambridge, UK
- Department of Medicine and Cambridge Stem Cell Institute Clinical School, University of Cambridge, Cambridge, UK
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10
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Xin Y, Amanullah M, Qian C, Zhou C, Qian J. Lignature: A Comprehensive Database of Ligand Signatures to Predict Cell-Cell Communication. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2025.03.22.644770. [PMID: 40196598 PMCID: PMC11974740 DOI: 10.1101/2025.03.22.644770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2025]
Abstract
Ligand-receptor interactions mediate intercellular communication, inducing transcriptional changes that regulate physiological and pathological processes. Ligand-induced transcriptomic signatures can be used to predict active ligands; however, the absence of a comprehensive set of ligand-response signatures has limited their practical application in predicting ligand-receptor interactions. To bridge this gap, we developed Lignature, a curated database encompassing intracellular transcriptomic signatures for 362 human ligands, significantly expanding the repertoire of ligands with available intracellular response signatures. Lignature compiles signatures from published transcriptomic datasets and established resources such as CytoSig and ImmuneDictionary, generating both gene- and pathway-based signatures for each ligand. We applied Lignature to predict active ligands driving transcriptomic changes in controlled in vitro experiments and real-world single-cell sequencing datasets. Lignature outperformed existing methods such as NicheNet, achieving higher accuracy in identifying active ligands at both the gene and pathway levels. These results establish Lignature as a robust platform for ligand signaling inference, providing a powerful tool to explore ligand-receptor interactions across diverse experimental and physiological contexts.
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11
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Xue Z, Xuan H, Lau K, Su Y, Wegener M, Li K, Turner L, Adams M, Shi X, Wen H. Expression of ENL YEATS domain tumor mutations in nephrogenic or stromal lineage impairs kidney development. Nat Commun 2025; 16:2531. [PMID: 40087269 PMCID: PMC11909213 DOI: 10.1038/s41467-025-57926-z] [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: 05/10/2024] [Accepted: 03/05/2025] [Indexed: 03/17/2025] Open
Abstract
Recurrent gain-of-function mutations in the histone reader protein ENL have been identified in Wilms tumor, the most prevalent pediatric kidney cancer. However, their pathological significance in kidney development and tumorigenesis in vivo remains elusive. Here, we generate mouse models mimicking ENL tumor (ENLT) mutations and show that heterozygous mutant expression in Six2+ nephrogenic or Foxd1+ stromal lineages leads to severe, lineage-specific kidney defects, both resulting in neonatal lethality. Six2-ENLT mutant kidneys display compromised cap mesenchyme, scant nephron tubules, and cystic glomeruli, indicative of premature progenitor commitment and blocked differentiation. Bulk and spatial transcriptomic analyses reveal aberrant activation of Hox and Wnt signaling genes in mutant nephrogenic cells. In contrast, Foxd1-ENLT mutant kidneys exhibit expansion in renal capsule and cap mesenchyme, with dysregulated stromal gene expression affecting stroma-epithelium crosstalk. Our findings uncover distinct pathways through which ENL mutations disrupt nephrogenesis, providing a foundation for further investigations into their role in tumorigenesis.
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Affiliation(s)
- Zhaoyu Xue
- Department of Epigenetics, Van Andel Institute, Grand Rapids, MI, 49503, USA
| | - Hongwen Xuan
- Department of Epigenetics, Van Andel Institute, Grand Rapids, MI, 49503, USA
| | - Kin Lau
- Bioinformatics and Biostatistics Core, Van Andel Institute, Grand Rapids, MI, 49503, USA
| | - Yangzhou Su
- Department of Epigenetics, Van Andel Institute, Grand Rapids, MI, 49503, USA
| | - Marc Wegener
- Genomics Core, Van Andel Institute, Grand Rapids, MI, 49503, USA
| | - Kuai Li
- Department of Epigenetics, Van Andel Institute, Grand Rapids, MI, 49503, USA
| | - Lisa Turner
- Pathology Core, Van Andel Institute, Grand Rapids, MI, 49503, USA
| | - Marie Adams
- Genomics Core, Van Andel Institute, Grand Rapids, MI, 49503, USA
| | - Xiaobing Shi
- Department of Epigenetics, Van Andel Institute, Grand Rapids, MI, 49503, USA
| | - Hong Wen
- Department of Epigenetics, Van Andel Institute, Grand Rapids, MI, 49503, USA.
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12
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Ge S, Sun S, Xu H, Cheng Q, Ren Z. Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective. Brief Bioinform 2025; 26:bbaf136. [PMID: 40185158 PMCID: PMC11970898 DOI: 10.1093/bib/bbaf136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2024] [Revised: 02/17/2025] [Accepted: 03/05/2025] [Indexed: 04/07/2025] Open
Abstract
The development of single-cell and spatial transcriptomics has revolutionized our capacity to investigate cellular properties, functions, and interactions in both cellular and spatial contexts. Despite this progress, the analysis of single-cell and spatial omics data remains challenging. First, single-cell sequencing data are high-dimensional and sparse, and are often contaminated by noise and uncertainty, obscuring the underlying biological signal. Second, these data often encompass multiple modalities, including gene expression, epigenetic modifications, metabolite levels, and spatial locations. Integrating these diverse data modalities is crucial for enhancing prediction accuracy and biological interpretability. Third, while the scale of single-cell sequencing has expanded to millions of cells, high-quality annotated datasets are still limited. Fourth, the complex correlations of biological tissues make it difficult to accurately reconstruct cellular states and spatial contexts. Traditional feature engineering approaches struggle with the complexity of biological networks, while deep learning, with its ability to handle high-dimensional data and automatically identify meaningful patterns, has shown great promise in overcoming these challenges. Besides systematically reviewing the strengths and weaknesses of advanced deep learning methods, we have curated 21 datasets from nine benchmarks to evaluate the performance of 58 computational methods. Our analysis reveals that model performance can vary significantly across different benchmark datasets and evaluation metrics, providing a useful perspective for selecting the most appropriate approach based on a specific application scenario. We highlight three key areas for future development, offering valuable insights into how deep learning can be effectively applied to transcriptomic data analysis in biological, medical, and clinical settings.
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Affiliation(s)
- Shuang Ge
- Shenzhen International Graduate School, Tsinghua University, 2279 Lishui Road, Nanshan District, Shenzhen 518055, Guangdong, China
- Pengcheng Laboratory, 6001 Shahe West Road, Nanshan District, Shenzhen 518055, Guangdong, China
| | - Shuqing Sun
- Shenzhen International Graduate School, Tsinghua University, 2279 Lishui Road, Nanshan District, Shenzhen 518055, Guangdong, China
| | - Huan Xu
- School of Public Health, Anhui University of Science and Technology, 15 Fengxia Road, Changfeng County, Hefei 231131, Anhui, China
| | - Qiang Cheng
- Department of Computer Science, University of Kentucky, 329 Rose Street, Lexington 40506, Kentucky, USA
- Institute for Biomedical Informatics, University of Kentucky, 800 Rose Street, Lexington 40506, Kentucky, USA
| | - Zhixiang Ren
- Pengcheng Laboratory, 6001 Shahe West Road, Nanshan District, Shenzhen 518055, Guangdong, China
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13
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Griffiths JI, Cosgrove PA, Medina EF, Nath A, Chen J, Adler FR, Chang JT, Khan QJ, Bild AH. Cellular interactions within the immune microenvironment underpins resistance to cell cycle inhibition in breast cancers. Nat Commun 2025; 16:2132. [PMID: 40032842 PMCID: PMC11876604 DOI: 10.1038/s41467-025-56279-x] [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: 04/04/2024] [Accepted: 01/13/2025] [Indexed: 03/05/2025] Open
Abstract
Immune evasion by cancer cells involves reshaping the tumor microenvironment (TME) via communication with non-malignant cells. However, resistance-promoting interactions during treatment remain lesser known. Here we examine the composition, communication, and phenotypes of tumor-associated cells in serial biopsies from stage II and III high-risk estrogen receptor positive (ER+ ) breast cancers of patients receiving endocrine therapy (letrozole) as single agent or in combination with ribociclib, a CDK4/6-targeting cell cycle inhibitor. Single-cell RNA sequencing analyses on longitudinally collected samples show that in tumors overcoming the growth suppressive effects of ribociclib, first cancer cells upregulate cytokines and growth factors that stimulate immune-suppressive myeloid differentiation, resulting in reduced myeloid cell- CD8 + T-cell crosstalk via IL-15/18 signaling. Subsequently, tumors growing during treatment show diminished T-cell activation and recruitment. In vitro, ribociclib does not only inhibit cancer cell growth but also T cell proliferation and activation upon co-culturing. Exogenous IL-15 improves CDK4/6 inhibitor efficacy by augmenting T-cell proliferation and cancer cell killing by T cells. In summary, response to ribociclib in stage II and III high-risk ER + breast cancer depends on the composition, activation phenotypes and communication network of immune cells.
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Affiliation(s)
- Jason I Griffiths
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA, USA.
- Department of Mathematics, University of Utah 155 South 1400 East, Salt Lake City, UT, USA.
| | - Patrick A Cosgrove
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA, USA
| | - Eric F Medina
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA, USA
| | - Aritro Nath
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA, USA
| | - Jinfeng Chen
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA, USA
| | - Frederick R Adler
- Department of Mathematics, University of Utah 155 South 1400 East, Salt Lake City, UT, USA
- School of Biological Sciences, University of Utah 257 South 1400 East, Salt Lake City, UT, USA
| | - Jeffrey T Chang
- Department of Integrative Biology and Pharmacology, School of Medicine, School of Biomedical Informatics, UT Health Sciences Center at Houston, Houston, TX, USA
| | - Qamar J Khan
- Division of Medical Oncology, Department of Internal Medicine, The University of Kansas Medical Center, Kansas City, KS, USA
| | - Andrea H Bild
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA, USA.
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14
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van Santvoort M, Lapuente-Santana Ó, Zopoglou M, Zackl C, Finotello F, van der Hoorn P, Eduati F. Mathematically mapping the network of cells in the tumor microenvironment. CELL REPORTS METHODS 2025; 5:100985. [PMID: 39954673 PMCID: PMC11955271 DOI: 10.1016/j.crmeth.2025.100985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 05/04/2024] [Accepted: 01/24/2025] [Indexed: 02/17/2025]
Abstract
Cell-cell interaction (CCI) networks are key to understanding disease progression and treatment response. However, existing methods for inferring these networks often aggregate data across patients or focus on cell-type level interactions, providing a generalized overview but overlooking patient heterogeneity and local network structures. To address this, we introduce "random cell-cell interaction generator" (RaCInG), a model based on random graphs to derive personalized networks leveraging prior knowledge on ligand-receptor interactions and bulk RNA sequencing data. We applied RaCInG to 8,683 cancer patients to extract 643 network features related to the tumor microenvironment and unveiled associations with immune response and subtypes, enabling prediction and explanation of immunotherapy responses. RaCInG demonstrated robustness and showed consistencies with state-of-the-art methods. Our findings highlight RaCInG's potential to elucidate patient-specific network dynamics, offering insights into cancer biology and treatment responses. RaCInG is poised to advance our understanding of complex CCI s in cancer and other biomedical domains.
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Affiliation(s)
- Mike van Santvoort
- Department of Mathematics and Computer Science, Eindhoven University of Technology, PO Box 513, Eindhoven 5600MB, the Netherlands; Institute for Complex Molecular Systems, Eindhoven University of Technology, PO Box 513, Eindhoven 5600MB, the Netherlands
| | - Óscar Lapuente-Santana
- Institute for Complex Molecular Systems, Eindhoven University of Technology, PO Box 513, Eindhoven 5600MB, the Netherlands; Department of Biomedical Engineering, Eindhoven University of Technology, PO Box 513, Eindhoven 5600MB, the Netherlands; Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain
| | - Maria Zopoglou
- Department of Molecular Biology, Digital Science Center (DiSC), University of Innsbruck, 6020 Innsbruck, Austria
| | - Constantin Zackl
- Department of Molecular Biology, Digital Science Center (DiSC), University of Innsbruck, 6020 Innsbruck, Austria
| | - Francesca Finotello
- Department of Molecular Biology, Digital Science Center (DiSC), University of Innsbruck, 6020 Innsbruck, Austria
| | - Pim van der Hoorn
- Department of Mathematics and Computer Science, Eindhoven University of Technology, PO Box 513, Eindhoven 5600MB, the Netherlands; Institute for Complex Molecular Systems, Eindhoven University of Technology, PO Box 513, Eindhoven 5600MB, the Netherlands.
| | - Federica Eduati
- Institute for Complex Molecular Systems, Eindhoven University of Technology, PO Box 513, Eindhoven 5600MB, the Netherlands; Department of Biomedical Engineering, Eindhoven University of Technology, PO Box 513, Eindhoven 5600MB, the Netherlands.
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15
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Liu J, Ma L, Ju F, Zhao C, Yu L. SpaCcLink: exploring downstream signaling regulations with graph attention network for systematic inference of spatial cell-cell communication. BMC Biol 2025; 23:44. [PMID: 39939849 PMCID: PMC11823213 DOI: 10.1186/s12915-025-02141-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Accepted: 01/23/2025] [Indexed: 02/14/2025] Open
Abstract
BACKGROUND Cellular communication is vital for the proper functioning of multicellular organisms. A comprehensive analysis of cellular communication demands the consideration not only of the binding between ligands and receptors but also of a series of downstream signal transduction reactions within cells. Thanks to the advancements in spatial transcriptomics technology, we are now able to better decipher the process of cellular communication within the cellular microenvironment. Nevertheless, the majority of existing spatial cell-cell communication algorithms fail to take into account the downstream signals within cells. RESULTS In this study, we put forward SpaCcLink, a cell-cell communication analysis method that takes into account the downstream influence of individual receptors within cells and systematically investigates the spatial patterns of communication as well as downstream signal networks. Analyses conducted on real datasets derived from humans and mice have demonstrated that SpaCcLink can help in identifying more relevant ligands and receptors, thereby enabling us to systematically decode the downstream genes and signaling pathways that are influenced by cell-cell communication. Comparisons with other methods suggest that SpaCcLink can identify downstream genes that are more closely associated with biological processes and can also discover reliable ligand-receptor relationships. CONCLUSIONS By means of SpaCcLink, a more profound and all-encompassing comprehension of the mechanisms underlying cellular communication can be achieved, which in turn promotes and deepens our understanding of the intricate complexity within organisms.
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Affiliation(s)
- Jingtao Liu
- School of Computer Science and Technology, Xidian University, Xi'an, 710071, Shaanxi, China
| | - Litian Ma
- School of Computer Science and Technology, Xidian University, Xi'an, 710071, Shaanxi, China
| | - Fen Ju
- Department of Rehabilitation Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, 710032, China
| | - Chenguang Zhao
- Department of Rehabilitation Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, 710032, China.
| | - Liang Yu
- School of Computer Science and Technology, Xidian University, Xi'an, 710071, Shaanxi, China.
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16
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Shanmugam V, Tokcan N, Chafamo D, Sullivan S, Borji M, Martin H, Newton G, Nadaf N, Hanbury S, Barrera I, Cable D, Weir J, Ashenberg O, Pinkus G, Rodig S, Uhler C, Macosko E, Shipp M, Louissaint A, Chen F, Golub T. Genome-scale spatial mapping of the Hodgkin lymphoma microenvironment identifies tumor cell survival factors. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2025.01.24.631210. [PMID: 39896575 PMCID: PMC11785141 DOI: 10.1101/2025.01.24.631210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 02/04/2025]
Abstract
A key challenge in cancer research is to identify the secreted factors that contribute to tumor cell survival. Nowhere is this more evident than in Hodgkin lymphoma, where malignant Hodgkin Reed Sternberg (HRS) cells comprise only 1-5% of the tumor mass, the remainder being infiltrating immune cells that presumably are required for the survival of the HRS cells. Until now, there has been no way to characterize the complex Hodgkin lymphoma tumor microenvironment at genome scale. Here, we performed genome-wide transcriptional profiling with spatial and single-cell resolution. We show that the neighborhood surrounding HRS cells forms a distinct niche involving 31 immune and stromal cell types and is enriched in CD4+ T cells, myeloid and follicular dendritic cells, while being depleted of plasma cells. Moreover, we used machine learning to nominate ligand-receptor pairs enriched in the HRS cell niche. Specifically, we identified IL13 as a candidate survival factor. In support of this hypothesis, recombinant IL13 augmented the proliferation of HRS cells in vitro. In addition, genome-wide CRISPR/Cas9 loss-of-function studies across more than 1,000 human cancer cell lines showed that IL4R and IL13RA1, the heterodimeric partners that constitute the IL13 receptor, were uniquely required for the survival of HRS cells. Moreover, monoclonal antibodies targeting either IL4R or IL13R phenocopied the genetic loss of function studies. IL13-targeting antibodies are already FDA-approved for atopic dermatitis, suggesting that clinical trials testing such agents should be explored in patients with Hodgkin lymphoma.
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Affiliation(s)
- Vignesh Shanmugam
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Neriman Tokcan
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Mathematics, University of Massachusetts Boston, Boston, MA, USA
| | - Daniel Chafamo
- Klarman Cell Observatory, Broad Institute, Cambridge, MA, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sean Sullivan
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Microbiology, University of Chicago, Chicago, IL, USA
| | - Mehdi Borji
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Haley Martin
- Department of Pathology, Massachusetts General Hospital, Boston, MA, USA
| | - Gail Newton
- Department of Pathology, Massachusetts General Hospital, Boston, MA, USA
| | - Naeem Nadaf
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | - Dylan Cable
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Jackson Weir
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Biological and Biomedical Sciences Program, Harvard University, Cambridge, MA, USA
| | - Orr Ashenberg
- Klarman Cell Observatory, Broad Institute, Cambridge, MA, USA
| | - Geraldine Pinkus
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Scott Rodig
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Caroline Uhler
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Laboratory for Information & Decision Systems, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Evan Macosko
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Margaret Shipp
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard Medical School, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Abner Louissaint
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Pathology, Massachusetts General Hospital, Boston, MA, USA
| | - Fei Chen
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Todd Golub
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard Medical School, Boston, MA, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Division of Pediatric Hematology/Oncology, Boston Children's Hospital, Boston, MA, USA
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17
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Xin Y, Jin Y, Qian C, Blackshaw S, Qian J. MetaLigand: A database for predicting non-peptide ligand mediated cell-cell communication. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2025.01.14.633094. [PMID: 39868215 PMCID: PMC11761624 DOI: 10.1101/2025.01.14.633094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2025]
Abstract
Non-peptide ligands (NPLs), including lipids, amino acids, carbohydrates, and non-peptide neurotransmitters and hormones, play a critical role in ligand-receptor-mediated cell-cell communication, driving diverse physiological and pathological processes. To facilitate the study of NPL-dependent intercellular interactions, we introduce MetaLigand, an R-based and web-accessible tool designed to infer NPL production and predict NPL-receptor interactions using transcriptomic data. MetaLigand compiles data for 233 NPLs, including their biosynthetic enzymes, transporter genes, and receptor genes, through a combination of automated pipelines and manual curation from comprehensive databases. The tool integrates both de novo and salvage synthesis pathways, incorporating multiple biosynthetic steps and transport mechanisms to improve prediction accuracy. Comparisons with existing tools demonstrate MetaLigand's superior ability to account for complex biogenesis pathways and model NPL abundance across diverse tissues and cell types. Furthermore, analysis of single-nucleus RNA-seq datasets from age-related macular degeneration samples revealed that distinct retinal cell types exhibit unique NPL profiles and participate in specific NPL-mediated pathological cell-cell interactions. Finally, MetaLigand supports single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics data, enabling the visualization of predicted NPL production levels and heterogeneity at single-cell resolution.
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18
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Gong Z, Wu T, Zhao Y, Guo J, Zhang Y, Li B, Li Y. Intercellular Tunneling Nanotubes as Natural Biophotonic Conveyors. ACS NANO 2025; 19:1036-1043. [PMID: 39630614 DOI: 10.1021/acsnano.4c12681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2024]
Abstract
Tunneling nanotubes (TNTs), submicrometer membranous channels that bridge and connect distant cells, play a pivotal role in intercellular communication. Organelle transfer within TNTs is crucial in regulating cell growth, signal transmission, and disease progression. However, precise control over individual organelle transport within TNTs remains elusive. In this study, we introduce an optical technique that harnesses TNTs as biophotonic conveyors for the directional transport of individual organelles between cells. By utilizing near-infrared light propagating along the TNTs, optical forces were exerted on the organelles, enabling their active transport in a predetermined direction and at a controlled velocity. As a potential application, TNT conveyors were employed to inhibit mitochondrial hijacking from immune cells to cancer cells, thereby activating immune cells and suppressing cancer cell growth. Furthermore, neural modulation was achieved by transporting mitochondria and neurotransmitter-containing vesicles between neurons via TNT conveyors and axonal conveyors, respectively. This study presents a robust and precise approach to immune activation and neural regulation through the manipulation of organelle transfer at the subcellular level.
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Affiliation(s)
- Zhiyong Gong
- Guangdong Provincial Key Laboratory of Nanophotonic Manipulation, Institute of Nanophotonics, College of Physics & Optoelectronic Engineering, Jinan University, Guangzhou 511443, China
- School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Tianli Wu
- Guangdong Provincial Key Laboratory of Nanophotonic Manipulation, Institute of Nanophotonics, College of Physics & Optoelectronic Engineering, Jinan University, Guangzhou 511443, China
| | - Yanan Zhao
- Guangdong Provincial Key Laboratory of Nanophotonic Manipulation, Institute of Nanophotonics, College of Physics & Optoelectronic Engineering, Jinan University, Guangzhou 511443, China
| | - Jinghui Guo
- School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Yao Zhang
- Guangdong Provincial Key Laboratory of Nanophotonic Manipulation, Institute of Nanophotonics, College of Physics & Optoelectronic Engineering, Jinan University, Guangzhou 511443, China
- Key Laboratory of Optoelectronic Information and Sensing Technologies of Guangdong Higher Education Institutes, Jinan University, Guangzhou 510632, China
| | - Baojun Li
- Guangdong Provincial Key Laboratory of Nanophotonic Manipulation, Institute of Nanophotonics, College of Physics & Optoelectronic Engineering, Jinan University, Guangzhou 511443, China
| | - Yuchao Li
- Guangdong Provincial Key Laboratory of Nanophotonic Manipulation, Institute of Nanophotonics, College of Physics & Optoelectronic Engineering, Jinan University, Guangzhou 511443, China
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19
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Jin S, Plikus MV, Nie Q. CellChat for systematic analysis of cell-cell communication from single-cell transcriptomics. Nat Protoc 2025; 20:180-219. [PMID: 39289562 DOI: 10.1038/s41596-024-01045-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 06/27/2024] [Indexed: 09/19/2024]
Abstract
Recent advances in single-cell sequencing technologies offer an opportunity to explore cell-cell communication in tissues systematically and with reduced bias. A key challenge is integrating known molecular interactions and measurements into a framework to identify and analyze complex cell-cell communication networks. Previously, we developed a computational tool, named CellChat, that infers and analyzes cell-cell communication networks from single-cell transcriptomic data within an easily interpretable framework. CellChat quantifies the signaling communication probability between two cell groups using a simplified mass-action-based model, which incorporates the core interaction between ligands and receptors with multisubunit structure along with modulation by cofactors. Importantly, CellChat performs a systematic and comparative analysis of cell-cell communication using a variety of quantitative metrics and machine-learning approaches. CellChat v2 is an updated version that includes additional comparison functionalities, an expanded database of ligand-receptor pairs along with rich functional annotations, and an Interactive CellChat Explorer. Here we provide a step-by-step protocol for using CellChat v2 on single-cell transcriptomic data, including inference and analysis of cell-cell communication from one dataset and identification of altered intercellular communication, signals and cell populations from different datasets across biological conditions. The R implementation of CellChat v2 toolkit and its tutorials together with the graphic outputs are available at https://github.com/jinworks/CellChat . This protocol typically takes ~5 min depending on dataset size and requires a basic understanding of R and single-cell data analysis but no specialized bioinformatics training for its implementation.
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Affiliation(s)
- Suoqin Jin
- School of Mathematics and Statistics, Wuhan University, Wuhan, China.
- Hubei Key Laboratory of Computational Science, Wuhan University, Wuhan, China.
| | - Maksim V Plikus
- NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, Irvine, CA, USA
- Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, USA
| | - Qing Nie
- NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, Irvine, CA, USA.
- Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, USA.
- Department of Mathematics, University of California, Irvine, Irvine, CA, USA.
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20
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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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21
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Qiu X, Zhu DY, Lu Y, Yao J, Jing Z, Min KH, Cheng M, Pan H, Zuo L, King S, Fang Q, Zheng H, Wang M, Wang S, Zhang Q, Yu S, Liao S, Liu C, Wu X, Lai Y, Hao S, Zhang Z, Wu L, Zhang Y, Li M, Tu Z, Lin J, Yang Z, Li Y, Gu Y, Ellison D, Chen A, Liu L, Weissman JS, Ma J, Xu X, Liu S, Bai Y. Spatiotemporal modeling of molecular holograms. Cell 2024; 187:7351-7373.e61. [PMID: 39532097 DOI: 10.1016/j.cell.2024.10.011] [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: 12/09/2022] [Revised: 05/29/2024] [Accepted: 10/08/2024] [Indexed: 11/16/2024]
Abstract
Quantifying spatiotemporal dynamics during embryogenesis is crucial for understanding congenital diseases. We developed Spateo (https://github.com/aristoteleo/spateo-release), a 3D spatiotemporal modeling framework, and applied it to a 3D mouse embryogenesis atlas at E9.5 and E11.5, capturing eight million cells. Spateo enables scalable, partial, non-rigid alignment, multi-slice refinement, and mesh correction to create molecular holograms of whole embryos. It introduces digitization methods to uncover multi-level biology from subcellular to whole organ, identifying expression gradients along orthogonal axes of emergent 3D structures, e.g., secondary organizers such as midbrain-hindbrain boundary (MHB). Spateo further jointly models intercellular and intracellular interaction to dissect signaling landscapes in 3D structures, including the zona limitans intrathalamica (ZLI). Lastly, Spateo introduces "morphometric vector fields" of cell migration and integrates spatial differential geometry to unveil molecular programs underlying asymmetrical murine heart organogenesis and others, bridging macroscopic changes with molecular dynamics. Thus, Spateo enables the study of organ ecology at a molecular level in 3D space over time.
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Affiliation(s)
- Xiaojie Qiu
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA; Basic Sciences and Engineering Initiative, Betty Irene Moore Children's Heart Center, Lucile Packard Children's Hospital, Stanford, CA, USA; Department of Computer Science, Stanford University, Stanford, CA 94305, USA; Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA.
| | - Daniel Y Zhu
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Yifan Lu
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA; Basic Sciences and Engineering Initiative, Betty Irene Moore Children's Heart Center, Lucile Packard Children's Hospital, Stanford, CA, USA; Department of Computer Science, Stanford University, Stanford, CA 94305, USA; Electronic Information School, Wuhan University, Wuhan 430072, China
| | - Jiajun Yao
- BGI Research, Hangzhou 310030, China; BGI Research, Sanya 572025, China; College of Life Sciences, Northwest University, Xi'an 710069, China
| | - Zehua Jing
- BGI Research, Hangzhou 310030, China; BGI Research, Sanya 572025, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Kyung Hoi Min
- Ginkgo Bioworks, The Innovation and Design Building, Boston, MA 02210, USA
| | - Mengnan Cheng
- BGI Research, Hangzhou 310030, China; BGI Research, Shenzhen 518083, China
| | | | - Lulu Zuo
- BGI Research, Shenzhen 518083, China
| | - Samuel King
- Department of Bioengineering, Stanford University School of Medicine, Stanford, CA, USA
| | - Qi Fang
- BGI Research, Hangzhou 310030, China; BGI Research, Shenzhen 518083, China
| | - Huiwen Zheng
- BGI Research, Hangzhou 310030, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Mingyue Wang
- BGI Research, Hangzhou 310030, China; Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Shuai Wang
- BGI Research, Hangzhou 310030, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qingquan Zhang
- Department of Medicine, Division of Cardiology, University of California, San Diego, La Jolla, CA, USA
| | - Sichao Yu
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
| | - Sha Liao
- BGI Research, Shenzhen 518083, China; STOmics Tech Co., Ltd, Shenzhen 518083, China; BGI Research, Chongqing 401329, China
| | - Chao Liu
- BGI Research, Wuhan 430074, China
| | - Xinchao Wu
- BGI Research, Hangzhou 310030, China; BGI Research, Sanya 572025, China; School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Yiwei Lai
- BGI Research, Shenzhen 518083, China
| | | | - Zhewei Zhang
- BGI Research, Hangzhou 310030, China; BGI Research, Sanya 572025, China; School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Liang Wu
- BGI Research, Chongqing 401329, China
| | | | - Mei Li
- STOmics Tech Co., Ltd, Shenzhen 518083, China
| | - Zhencheng Tu
- BGI Research, Hangzhou 310030, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jinpei Lin
- BGI Research, Hangzhou 310030, China; BGI Research, Sanya 572025, China
| | - Zhuoxuan Yang
- BGI Research, Hangzhou 310030, China; School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | | | - Ying Gu
- BGI Research, Hangzhou 310030, China; BGI Research, Shenzhen 518083, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | | | - Ao Chen
- BGI Research, Shenzhen 518083, China; STOmics Tech Co., Ltd, Shenzhen 518083, China; BGI Research, Chongqing 401329, China
| | - Longqi Liu
- BGI Research, Hangzhou 310030, China; Shenzhen Bay Laboratory, Shenzhen 518132, China; Shenzhen Key Laboratory of Single-Cell Omics, BGI-Shenzhen, Shenzhen 518120, China
| | - Jonathan S Weissman
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA; Department of Biology and Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA; Koch Institute for Integrative Cancer Research at MIT, MIT, Cambridge, MA, USA
| | - Jiayi Ma
- Electronic Information School, Wuhan University, Wuhan 430072, China.
| | - Xun Xu
- BGI Research, Hangzhou 310030, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; Guangdong Provincial Key Laboratory of Genome Read and Write, BGI-Shenzhen, Shenzhen 518120, China.
| | - Shiping Liu
- BGI Research, Hangzhou 310030, China; Shenzhen Bay Laboratory, Shenzhen 518132, China; Shenzhen Key Laboratory of Single-Cell Omics, BGI-Shenzhen, Shenzhen 518120, China; The Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangzhou, Guangdong, China.
| | - Yinqi Bai
- BGI Research, Sanya 572025, China; Hainan Technology Innovation Center for Marine Biological Resources Utilization (Preparatory Period), BGI Research, Sanya 572025, China.
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22
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Lee D, Vicari JM, Porras C, Spencer C, Pjanic M, Wang X, Kinrot S, Weiler P, Kosoy R, Bendl J, Prashant NM, Psychogyiou K, Malakates P, Hennigan E, Monteiro Fortes J, Zheng S, Therrien K, Mathur D, Kleopoulos SP, Shao Z, Argyriou S, Alvia M, Casey C, Hong A, Beaumont KG, Sebra R, Kellner CP, Bennett DA, Yuan GC, Voloudakis G, Theis FJ, Haroutunian V, Hoffman GE, Fullard JF, Roussos P. Plasticity of Human Microglia and Brain Perivascular Macrophages in Aging and Alzheimer's Disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.10.25.23297558. [PMID: 39677435 PMCID: PMC11643149 DOI: 10.1101/2023.10.25.23297558] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 12/17/2024]
Abstract
The complex roles of myeloid cells, including microglia and perivascular macrophages, are central to the neurobiology of Alzheimer's disease (AD), yet they remain incompletely understood. Here, we profiled 832,505 human myeloid cells from the prefrontal cortex of 1,607 unique donors covering the human lifespan and varying degrees of AD neuropathology. We delineated 13 transcriptionally distinct myeloid subtypes organized into 6 subclasses and identified AD-associated adaptive changes in myeloid cells over aging and disease progression. The GPNMB subtype, linked to phagocytosis, increased significantly with AD burden and correlated with polygenic AD risk scores. By organizing AD-risk genes into a regulatory hierarchy, we identified and validated MITF as an upstream transcriptional activator of GPNMB, critical for maintaining phagocytosis. Through cell-to-cell interaction networks, we prioritized APOE-SORL1 and APOE-TREM2 ligand-receptor pairs, associated with AD progression. In both human and mouse models, TREM2 deficiency disrupted GPNMB expansion and reduced phagocytic function, suggesting that GPNMB's role in neuroprotection was TREM2-dependent. Our findings clarify myeloid subtypes implicated in aging and AD, advancing the mechanistic understanding of their role in AD and aiding therapeutic discovery.
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Affiliation(s)
- Donghoon Lee
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - James M. Vicari
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Christian Porras
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Collin Spencer
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Milos Pjanic
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Xinyi Wang
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Seon Kinrot
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Philipp Weiler
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- Department of Mathematics, Technical University of Munich, Munich, Germany
| | - Roman Kosoy
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jaroslav Bendl
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - N M Prashant
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Konstantina Psychogyiou
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Periklis Malakates
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Evelyn Hennigan
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jennifer Monteiro Fortes
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Shiwei Zheng
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Karen Therrien
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Deepika Mathur
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Steven P. Kleopoulos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Zhiping Shao
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Stathis Argyriou
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Marcela Alvia
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Clara Casey
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Aram Hong
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kristin G. Beaumont
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Robert Sebra
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - David A. Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Guo-Cheng Yuan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - George Voloudakis
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Fabian J. Theis
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- Department of Mathematics, Technical University of Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Vahram Haroutunian
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research, Education and Clinical Centers, James J. Peters VA Medical Center, Bronx, New York
| | - Gabriel E. Hoffman
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research, Education and Clinical Centers, James J. Peters VA Medical Center, Bronx, New York
| | - John F. Fullard
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Panos Roussos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research, Education and Clinical Centers, James J. Peters VA Medical Center, Bronx, New York
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23
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Agrawal A, Thomann S, Basu S, Grün D. NiCo identifies extrinsic drivers of cell state modulation by niche covariation analysis. Nat Commun 2024; 15:10628. [PMID: 39639035 PMCID: PMC11621405 DOI: 10.1038/s41467-024-54973-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2024] [Accepted: 11/22/2024] [Indexed: 12/07/2024] Open
Abstract
Cell states are modulated by intrinsic driving forces such as gene expression noise and extrinsic signals from the tissue microenvironment. The distinction between intrinsic and extrinsic cell state determinants is essential for understanding the regulation of cell fate in tissues during development, homeostasis and disease. The rapidly growing availability of single-cell resolution spatial transcriptomics makes it possible to meet this challenge. However, available computational methods to infer topological tissue domains, spatially variable genes, or ligand-receptor interactions are limited in their capacity to capture cell state changes driven by crosstalk between individual cell types within the same niche. We present NiCo, a computational framework for integrating single-cell resolution spatial transcriptomics with matched single-cell RNA-sequencing reference data to infer the influence of the spatial niche on the cell state. By applying NiCo to mouse embryogenesis, adult small intestine and liver data, we demonstrate the ability to predict novel niche interactions that govern cell state variation underlying tissue development and homeostasis. In particular, NiCo predicts a feedback mechanism between Kupffer cells and neighboring stellate cells dampening stellate cell activation in the normal liver. NiCo provides a powerful tool to elucidate tissue architecture and to identify drivers of cellular states in local niches.
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Affiliation(s)
- Ankit Agrawal
- Würzburg Institute of Systems Immunology, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Stefan Thomann
- Würzburg Institute of Systems Immunology, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Sukanya Basu
- Würzburg Institute of Systems Immunology, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Dominic Grün
- Würzburg Institute of Systems Immunology, Julius-Maximilians-Universität Würzburg, Würzburg, Germany.
- CAIDAS - Center for Artificial Intelligence and Data Science, Würzburg, Germany.
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24
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Wang X, Almet AA, Nie Q. Detecting global and local hierarchical structures in cell-cell communication using CrossChat. Nat Commun 2024; 15:10542. [PMID: 39627184 PMCID: PMC11615294 DOI: 10.1038/s41467-024-54821-x] [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: 05/01/2024] [Accepted: 11/20/2024] [Indexed: 12/06/2024] Open
Abstract
Cell-cell communication (CCC) occurs across different biological scales, ranging from interactions between large groups of cells to interactions between individual cells, forming a hierarchical structure. Globally, CCC may exist between clusters or only subgroups of a cluster with varying size, while locally, a group of cells as sender or receiver may exhibit distinct signaling properties. Current existing methods infer CCC from single-cell RNA-seq or Spatial Transcriptomics only between predefined cell groups, neglecting the existing hierarchical structure within CCC that are determined by signaling molecules, in particular, ligands and receptors. Here, we develop CrossChat, a novel computational framework designed to infer and analyze the hierarchical cell-cell communication structures using two complementary approaches: a global hierarchical structure using a multi-resolution clustering method, and multiple local hierarchical structures using a tree detection method. This framework provides a comprehensive approach to understand the hierarchical relationships within CCC that govern complex tissue functions. By applying our method to two nonspatial scRNA-seq datasets sampled from COVID-19 patients and mouse embryonic skin, and two spatial transcriptomics datasets generated from Stereo-seq of mouse embryo and 10x Visium of mouse wounded skin, we showcase CrossChat's functionalities for analyzing both global and local hierarchical structures within cell-cell communication.
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Affiliation(s)
- Xinyi Wang
- Department of Mathematics, University of California, Irvine, CA, USA
| | - Axel A Almet
- Department of Mathematics, University of California, Irvine, CA, USA.
- The NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, CA, USA.
| | - Qing Nie
- Department of Mathematics, University of California, Irvine, CA, USA.
- The NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, CA, USA.
- Department of Developmental and Cell Biology, University of California, Irvine, CA, USA.
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25
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Gu Y, Feng Z, Xu X, Jin L. Identification of a novel immune-related gene signature by single-cell and bulk sequencing for the prediction of the immune landscape and prognosis of breast cancer. Cancer Cell Int 2024; 24:393. [PMID: 39627792 PMCID: PMC11613745 DOI: 10.1186/s12935-024-03589-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2024] [Accepted: 11/26/2024] [Indexed: 12/08/2024] Open
Abstract
BACKGROUND As a common cause of cancer-related deaths in women, BRCA (breast cancer) shows complexity and requires precise biomarkers and treatment methods. This study delves into the molecular makeup of BRCA, focusing on immune profiles, molecular subtypes, gene expression and single-cell analysis. METHODS XCell was used to assess immune infiltration based on TCGA (the Cancer Genome Atlas) data and the clustering analysis was made. Differentially expressed genes were examined in distinct clusters, and the WGCNA (weighted correlation network analysis) was made to establish co-expression networks. The prognostic models were developed by Cox and LASSO-Cox regression. The clustering analysis, GSEA (Gene set enrichment analysis), GSVA (gene set variation analysis) and communication analysis of the single-cell dataset GSE161529 were performed to investigate the functional relevance. Real-time polymerase chain reaction (RT-PCR) was employed for evaluating gene expression. RESULTS The results revealed significant differences in immune cell infiltration between two clusters (C1 and C2). C2 had poorer survival outcomes, which was associated with higher expression of immune checkpoints PD1 and PD-L1. The gene modules identified via WGCNA were correlated with the immune-based subtypes. Then, a prognostic model comprising seven genes (ACSL1, ABCB5, XG, ADH4, OPN4, NPR3, NLGN1) was used to divide patients into high- and low-risk subgroups. The high-risk group had worse prognosis and higher scores of TIDE (Tumor Immune Dysfunction and Exclusion). The single-cell analysis depicted the immune landscape. Macrophages and endothelial cells exhibited higher AUCell scores. In cellular communication analysis, notably significant ligand-receptor interactions of HLA-DRA-> CD4 and TNFSF13B-> HLA-DPB1 were observed. The proportion of endothelial cells was correlated with risk scores. Finally, RT-PCR results illustrated the expression of seven genes in BRCA specimens. CONCLUSION The integrative analysis provides new insights into molecular complexities of BRCA. Immune profiles and gene signatures hold potential for improving stratification of BRCA patients and guiding the development of personalized immunotherapy strategies.
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Affiliation(s)
- Yanlin Gu
- Department of Thyroid and Breast Surgery, The Second Affiliated Hospital of Soochow University, Jiangsu, China
| | - Zhengyang Feng
- Department of Oncology, The Second Affiliated Hospital of Soochow University, Jiangsu, China
| | - Xiaoyan Xu
- Department of Operating Room, Traditional Chinese Medicine Hospital of Kunshan, Jiangsu, China
| | - Liyan Jin
- Department of Thyroid and Breast Surgery, The Second Affiliated Hospital of Soochow University, Jiangsu, China.
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26
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Zhao D, Song Z, Shen L, Xia T, Ouyang Q, Zhang H, He X, Kang K. Single-cell transcriptomics and tissue metabolomics uncover mechanisms underlying wooden breast disease in broilers. Poult Sci 2024; 103:104433. [PMID: 39489032 PMCID: PMC11566330 DOI: 10.1016/j.psj.2024.104433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2024] [Revised: 09/26/2024] [Accepted: 10/12/2024] [Indexed: 11/05/2024] Open
Abstract
Accompanied by the accelerated growth rate of chickens, the quality of chicken meat has deteriorated in recent years. Wooden breast (WB) is a severe myopathy affecting meat quality, and its pathophysiology depends on gene expression and intercellular interactions of various cell types, which are not yet fully understood. We have performed a comprehensive transcriptomic and metabolomic atlas of chicken WB muscle. Our data showed a significant increase in the number of immune cells, WB muscle displayed a unique cluster of macrophages (cluster 11), distinct from the M1 and M2 macrophages. Regarding the myocytes, the most significant differences were the decrease in cell number and the intensification of fatty deposits. Satellite cells were involved in muscle repair and regeneration producing more collagen. Interestingly, the interaction network in the WB group was weaker compared to that in normal breast muscle. Additionally, we found six key differential metabolites across 22 pathways. When WB occurs, myocytes and endothelial cells undergo apoptosis, macrophages are activated and exert immune functions, satellite cells participate in muscle rebuilding and repair, and the content of metabolites undergoes significant changes. This cell transcriptome profile provides an essential reference for future studies on the development and remodeling of WB.
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Affiliation(s)
- Di Zhao
- College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China; Hunan Engineering Research Center of Poultry Production Safety, Changsha 410128, China; Yuelushan Laboratory, Changsha 410128, China
| | - Zehe Song
- College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China; Hunan Engineering Research Center of Poultry Production Safety, Changsha 410128, China; Yuelushan Laboratory, Changsha 410128, China
| | - Li Shen
- Shanghai Personal Biotechnology Co., Ltd, Shanghai 200030, China
| | - Tian Xia
- Shanghai Personal Biotechnology Co., Ltd, Shanghai 200030, China
| | - Qingyuan Ouyang
- College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China; Hunan Engineering Research Center of Poultry Production Safety, Changsha 410128, China; Yuelushan Laboratory, Changsha 410128, China
| | - Haihan Zhang
- College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China; Hunan Engineering Research Center of Poultry Production Safety, Changsha 410128, China; Yuelushan Laboratory, Changsha 410128, China
| | - Xi He
- College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China; Hunan Engineering Research Center of Poultry Production Safety, Changsha 410128, China; Yuelushan Laboratory, Changsha 410128, China
| | - Kelang Kang
- College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China; Hunan Engineering Research Center of Poultry Production Safety, Changsha 410128, China; Hunan Academy of Agricultural Sciences, Changsha 410128, China.
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27
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Zhou L, Song J, Li Z, Hu Y, Guo W. THGB: predicting ligand-receptor interactions by combining tree boosting and histogram-based gradient boosting. Sci Rep 2024; 14:29604. [PMID: 39609487 PMCID: PMC11604971 DOI: 10.1038/s41598-024-78954-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2024] [Accepted: 11/05/2024] [Indexed: 11/30/2024] Open
Abstract
Ligand-receptor interaction (LRI) prediction has great significance in biological and medical research and facilitates to infer and analyze cell-to-cell communication. However, wet experiments for new LRI discovery are costly and time-consuming. Here, we propose a computational model called THGB to uncover new LRIs. THGB first extracts feature information of Ligand-Receptor (LR) pairs using iFeature. Next, it adopts a tree boosting model to obtain representative LR features. Finally, it devises the histogram-based gradient boosting model to capture high-quality LRIs. To assess the THGB performance, we compared it with three new LRI prediction models (i.e., CellEnBoost, CellGiQ, and CellComNet) and one classical protein-protein interaction inference model PIPR. The results demonstrated that THGB achieved the best overall predictions in terms of six evaluation indictors (i.e., precision, recall, accuracy, F1-score, AUC, and AUPR). To measure the effect of LR feature selection on the prediction, THGB was compared with four feature selection methods (i.e., PCA, NMF, LLE, and TSVD). The results showed that the tree boosting model was more appropriate to select representative LR features and improve LRI prediction. We also conducted ablation study and found that THGB with feature selection outperformed THGB without feature selection. We hope that THGB is a useful tool to find new LRIs and further infer cell-to-cell communication.
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Affiliation(s)
- Liqian Zhou
- School of Computer Science, Hunan University of Technology, Zhuzhou, 412007, Hunan, China
| | - Jiao Song
- School of Computer Science, Hunan University of Technology, Zhuzhou, 412007, Hunan, China
| | - Zejun Li
- School of Computer Science and Engineering, Hunan Institute of Technology, Hengyang, 421002, Hunan, China.
| | - Yingxi Hu
- School of Science, Hunan University of Technology, Zhuzhou, 412007, Hunan, China
| | - Wenyan Guo
- College of Life Science and Chemistry, Hunan University of Technology, Zhuzhou, 412007, Hunan, China
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28
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Zhang T, Zhang X, Wu Z, Ren J, Zhao Z, Zhang H, Wang G, Wang T. VGAE-CCI: variational graph autoencoder-based construction of 3D spatial cell-cell communication network. Brief Bioinform 2024; 26:bbae619. [PMID: 39581873 PMCID: PMC11586124 DOI: 10.1093/bib/bbae619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2024] [Revised: 10/04/2024] [Accepted: 11/12/2024] [Indexed: 11/26/2024] Open
Abstract
Cell-cell communication plays a critical role in maintaining normal biological functions, regulating development and differentiation, and controlling immune responses. The rapid development of single-cell RNA sequencing and spatial transcriptomics sequencing (ST-seq) technologies provides essential data support for in-depth and comprehensive analysis of cell-cell communication. However, ST-seq data often contain incomplete data and systematic biases, which may reduce the accuracy and reliability of predicting cell-cell communication. Furthermore, other methods for analyzing cell-cell communication mainly focus on individual tissue sections, neglecting cell-cell communication across multiple tissue layers, and fail to comprehensively elucidate cell-cell communication networks within three-dimensional tissues. To address the aforementioned issues, we propose VGAE-CCI, a deep learning framework based on the Variational Graph Autoencoder, capable of identifying cell-cell communication across multiple tissue layers. Additionally, this model can be applied to spatial transcriptomics data with missing or partially incomplete data and can clustered cells at single-cell resolution based on spatial encoding information within complex tissues, thereby enabling more accurate inference of cell-cell communication. Finally, we tested our method on six datasets and compared it with other state of art methods for predicting cell-cell communication. Our method outperformed other methods across multiple metrics, demonstrating its efficiency and reliability in predicting cell-cell communication.
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Affiliation(s)
- Tianjiao Zhang
- College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
| | - Xiang Zhang
- College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
| | - Zhenao Wu
- College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
| | - Jixiang Ren
- College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
| | - Zhongqian Zhao
- College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
| | - Hongfei Zhang
- College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
| | - Guohua Wang
- College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
- Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China
| | - Tao Wang
- School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China
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29
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Xia H, Ji B, Qiao D, Peng S. CellMsg: graph convolutional networks for ligand-receptor-mediated cell-cell communication analysis. Brief Bioinform 2024; 26:bbae716. [PMID: 39800874 PMCID: PMC11725396 DOI: 10.1093/bib/bbae716] [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: 09/09/2024] [Revised: 12/04/2024] [Accepted: 12/27/2024] [Indexed: 01/16/2025] Open
Abstract
The role of cell-cell communications (CCCs) is increasingly recognized as being important to differentiation, invasion, metastasis, and drug resistance in tumoral tissues. Developing CCC inference methods using traditional experimental methods are time-consuming, labor-intensive, cannot handle large amounts of data. To facilitate inference of CCCs, we proposed a computational framework, called CellMsg, which involves two primary steps: identifying ligand-receptor interactions (LRIs) and measuring the strength of LRIs-mediated CCCs. Specifically, CellMsg first identifies high-confident LRIs based on multimodal features of ligands and receptors and graph convolutional networks. Then, CellMsg measures the strength of intercellular communication by combining the identified LRIs and single-cell RNA-seq data using a three-point estimation method. Performance evaluation on four benchmark LRI datasets by five-fold cross validation demonstrated that CellMsg accurately captured the relationships between ligands and receptors, resulting in the identification of high-confident LRIs. Compared with other methods of identifying LRIs, CellMsg has better prediction performance and robustness. Furthermore, the LRIs identified by CellMsg were successfully validated through molecular docking. Finally, we examined the overlap of LRIs between CellMsg and five other classical CCC databases, as well as the intercellular crosstalk among seven cell types within a human melanoma tissue. In summary, CellMsg establishes a complete, reliable, and well-organized LRI database and an effective CCC strength evaluation method for each single-cell RNA-seq data. It provides a computational tool allowing researchers to decipher intercellular communications. CellMsg is freely available at https://github.com/pengsl-lab/CellMsg.
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Affiliation(s)
- Hong Xia
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
| | - Boya Ji
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
| | - Debin Qiao
- School of Computer and Artificial Intelligence, ZhengZhou University, Zhengzhou 450001, China
| | - Shaoliang Peng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
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30
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Ji B, Wang X, Wang X, Xu L, Peng S. scDCA: deciphering the dominant cell communication assembly of downstream functional events from single-cell RNA-seq data. Brief Bioinform 2024; 26:bbae663. [PMID: 39694816 DOI: 10.1093/bib/bbae663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2024] [Revised: 11/24/2024] [Accepted: 12/04/2024] [Indexed: 12/20/2024] Open
Abstract
Cell-cell communications (CCCs) involve signaling from multiple sender cells that collectively impact downstream functional processes in receiver cells. Currently, computational methods are lacking for quantifying the contribution of pairwise combinations of cell types to specific functional processes in receiver cells (e.g. target gene expression or cell states). This limitation has impeded understanding the underlying mechanisms of cancer progression and identifying potential therapeutic targets. Here, we proposed a deep learning-based method, scDCA, to decipher the dominant cell communication assembly (DCA) that have a higher impact on a particular functional event in receiver cells from single-cell RNA-seq data. Specifically, scDCA employed a multi-view graph convolution network to reconstruct the CCCs landscape at single-cell resolution, and then identified DCA by interpreting the model with the attention mechanism. Taking the samples from advanced renal cell carcinoma as a case study, the scDCA was successfully applied and validated in revealing the DCA affecting the crucial gene expression in immune cells. The scDCA was also applied and validated in revealing the DCA responsible for the variation of 14 typical functional states of malignant cells. Furthermore, the scDCA was applied and validated to explore the alteration of CCCs under clinical intervention by comparing the DCA for certain cytotoxic factors between patients with and without immunotherapy. In summary, scDCA provides a valuable and practical tool for deciphering the cell type combinations with the most dominant impact on a specific functional process of receiver cells, which is of great significance for precise cancer treatment. Our data and code are free available at a public GitHub repository: https://github.com/pengsl-lab/scDCA.git.
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Affiliation(s)
- Boya Ji
- College of Computer Science and Electronic Engineering, Hunan University, Yuelu, 410006 Changsha, China
| | - Xiaoqi Wang
- College of Computer Science and Electronic Engineering, Hunan University, Yuelu, 410006 Changsha, China
| | - Xiang Wang
- The Second Xiangya Hospital, Central South University, Yuelu, 410006 Changsha, China
| | - Liwen Xu
- College of Computer Science and Electronic Engineering, Hunan University, Yuelu, 410006 Changsha, China
| | - Shaoliang Peng
- College of Computer Science and Electronic Engineering, Hunan University, Yuelu, 410006 Changsha, China
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31
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Dalla E, Papanicolaou M, Park MD, Barth N, Hou R, Segura-Villalobos D, Valencia Salazar L, Sun D, Forrest ARR, Casanova-Acebes M, Entenberg D, Merad M, Aguirre-Ghiso JA. Lung-resident alveolar macrophages regulate the timing of breast cancer metastasis. Cell 2024; 187:6631-6648.e20. [PMID: 39378878 PMCID: PMC11568918 DOI: 10.1016/j.cell.2024.09.016] [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: 05/09/2023] [Revised: 06/13/2024] [Accepted: 09/11/2024] [Indexed: 10/10/2024]
Abstract
Breast disseminated cancer cells (DCCs) can remain dormant in the lungs for extended periods, but the mechanisms limiting their expansion are not well understood. Research indicates that tissue-resident alveolar macrophages suppress breast cancer metastasis in lung alveoli by inducing dormancy. Through ligand-receptor mapping and intravital imaging, it was found that alveolar macrophages express transforming growth factor (TGF)-β2. This expression, along with persistent macrophage-cancer cell interactions via the TGF-βRIII receptor, maintains cancer cells in a dormant state. Depleting alveolar macrophages or losing the TGF-β2 receptor in cancer cells triggers metastatic awakening. Aggressive breast cancer cells are either suppressed by alveolar macrophages or evade this suppression by avoiding interaction and downregulating the TGF-β2 receptor. Restoring TGF-βRIII in aggressive cells reinstates TGF-β2-mediated macrophage growth suppression. Thus, alveolar macrophages act as a metastasis immune barrier, and downregulation of TGF-β2 signaling allows cancer cells to overcome macrophage-mediated growth suppression.
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Affiliation(s)
- Erica Dalla
- Division of Hematology and Oncology, Department of Medicine and Department of Otolaryngology, Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Oncological Sciences, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael Papanicolaou
- Department of Cell Biology, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA; Cancer Dormancy Institute, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA; Montefiore Einstein Comprehensive Cancer Center, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA; Gruss-Lipper Biophotonics Center, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA; Ruth L. and David S. Gottesman Institute for Stem Cell Research and Regenerative Medicine, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA; Institute for Aging Research, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA
| | - Matthew D Park
- Department of Oncological Sciences, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA; The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nicole Barth
- Cancer Dormancy Institute, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA; Montefiore Einstein Comprehensive Cancer Center, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA; Gruss-Lipper Biophotonics Center, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA; Department of Pathology, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA; Cancer Research UK Edinburgh Centre, University of Edinburgh, Edinburgh, UK
| | - Rui Hou
- Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, The University of Western Australia, Nedlands, WA 6009, Australia
| | - Deisy Segura-Villalobos
- Department of Cell Biology, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA; Cancer Dormancy Institute, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA; Montefiore Einstein Comprehensive Cancer Center, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA; Gruss-Lipper Biophotonics Center, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA; Ruth L. and David S. Gottesman Institute for Stem Cell Research and Regenerative Medicine, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA; Institute for Aging Research, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA
| | - Luis Valencia Salazar
- Department of Cell Biology, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA; Cancer Dormancy Institute, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA; Montefiore Einstein Comprehensive Cancer Center, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA; Gruss-Lipper Biophotonics Center, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA; Ruth L. and David S. Gottesman Institute for Stem Cell Research and Regenerative Medicine, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA; Institute for Aging Research, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA
| | - Dan Sun
- Department of Cell Biology, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA; Cancer Dormancy Institute, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA; Montefiore Einstein Comprehensive Cancer Center, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA; Gruss-Lipper Biophotonics Center, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA
| | - Alistair R R Forrest
- Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, The University of Western Australia, Nedlands, WA 6009, Australia
| | - Maria Casanova-Acebes
- Cancer Immunity Laboratory, Molecular Oncology Program, Spanish National Cancer Centre, Madrid, Spain
| | - David Entenberg
- Cancer Dormancy Institute, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA; Montefiore Einstein Comprehensive Cancer Center, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA; Gruss-Lipper Biophotonics Center, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA; Department of Pathology, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA
| | - Miriam Merad
- Department of Oncological Sciences, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA; The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Julio A Aguirre-Ghiso
- Department of Cell Biology, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA; Cancer Dormancy Institute, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA; Montefiore Einstein Comprehensive Cancer Center, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA; Gruss-Lipper Biophotonics Center, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA; Ruth L. and David S. Gottesman Institute for Stem Cell Research and Regenerative Medicine, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA; Institute for Aging Research, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA.
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Henderson DJ, Alqahtani A, Chaudhry B, Cook A, Eley L, Houyel L, Hughes M, Keavney B, de la Pompa JL, Sled J, Spielmann N, Teboul L, Zaffran S, Mill P, Liu KJ. Beyond genomic studies of congenital heart defects through systematic modelling and phenotyping. Dis Model Mech 2024; 17:dmm050913. [PMID: 39575509 PMCID: PMC11603121 DOI: 10.1242/dmm.050913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2024] [Accepted: 10/29/2024] [Indexed: 12/01/2024] Open
Abstract
Congenital heart defects (CHDs), the most common congenital anomalies, are considered to have a significant genetic component. However, despite considerable efforts to identify pathogenic genes in patients with CHDs, few gene variants have been proven as causal. The complexity of the genetic architecture underlying human CHDs likely contributes to this poor genetic discovery rate. However, several other factors are likely to contribute. For example, the level of patient phenotyping required for clinical care may be insufficient for research studies focused on mechanistic discovery. Although several hundred mouse gene knockouts have been described with CHDs, these are generally not phenotyped and described in the same way as CHDs in patients, and thus are not readily comparable. Moreover, most patients with CHDs carry variants of uncertain significance of crucial cardiac genes, further complicating comparisons between humans and mouse mutants. In spite of major advances in cardiac developmental biology over the past 25 years, these advances have not been well communicated to geneticists and cardiologists. As a consequence, the latest data from developmental biology are not always used in the design and interpretation of studies aimed at discovering the genetic causes of CHDs. In this Special Article, while considering other in vitro and in vivo models, we create a coherent framework for accurately modelling and phenotyping human CHDs in mice, thereby enhancing the translation of genetic and genomic studies into the causes of CHDs in patients.
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Affiliation(s)
- Deborah J. Henderson
- MRC National Mouse Genetics Network, Congenital Anomalies Cluster, Harwell, OX11 0RD, UK
- Biosciences Institute, Newcastle University, Centre for Life, Newcastle upon Tyne NE1 3BZ, UK
| | - Ahlam Alqahtani
- Biosciences Institute, Newcastle University, Centre for Life, Newcastle upon Tyne NE1 3BZ, UK
| | - Bill Chaudhry
- Biosciences Institute, Newcastle University, Centre for Life, Newcastle upon Tyne NE1 3BZ, UK
| | - Andrew Cook
- University College London, Zayed Centre for Research, London WC1N 1DZ, UK
| | - Lorraine Eley
- Biosciences Institute, Newcastle University, Centre for Life, Newcastle upon Tyne NE1 3BZ, UK
| | - Lucile Houyel
- Congenital and Pediatric Cardiology Unit, M3C-Necker, Hôpital Universitaire Necker-Enfants Malades, APHP, Université Paris Cité, 149 Rue de Sèvres, 75015 Paris, France
| | - Marina Hughes
- Cardiology Department, Norfolk and Norwich University Hospital, Norwich NR4 7UY, UK
| | - Bernard Keavney
- Division of Cardiovascular Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester M13 9PT, UK
- NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9PT, UK
| | - José Luis de la Pompa
- Intercellular Signaling in Cardiovascular Development and Disease Laboratory, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Melchor Fernández Almagro 3, 28029 Madrid, Spain
- Ciber de Enfermedades Cardiovasculares, Instituto de Salud Carlos III, Melchor Fernández Almagro 3, 28029 Madrid, Spain
| | - John Sled
- Mouse Imaging Centre, Hospital for Sick Children, Toronto M5G 1XS, Canada. Department of Medical Biophysics, University of Toronto, Toronto M5G 1XS, Canada
| | - Nadine Spielmann
- Institute of Experimental Genetics, German Mouse Clinic, Helmholtz Center Munich (GmbH), German Research Center for Environmental Health, D-85764 Neuherberg, Germany
| | - Lydia Teboul
- Mary Lyon Centre, MRC Harwell, Oxfordshire OX11 0RD, UK
| | - Stephane Zaffran
- Aix Marseille Université, INSERM, Marseille Medical Genetics, U1251, 13005 Marseille, France
| | - Pleasantine Mill
- MRC National Mouse Genetics Network, Congenital Anomalies Cluster, Harwell, OX11 0RD, UK
- MRC Human Genetics Unit, Institute for Genetics and Cancer, The University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Karen J. Liu
- MRC National Mouse Genetics Network, Congenital Anomalies Cluster, Harwell, OX11 0RD, UK
- Centre for Craniofacial and Regenerative Biology, King's College London, London SE1 9RT, UK
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Fu C, Qiu D, Zhou M, Ni S, Jin X. Characterization of ligand-receptor pair in acute myeloid leukemia: a scoring model for prognosis, therapeutic response, and T cell dysfunction. Front Oncol 2024; 14:1473048. [PMID: 39484036 PMCID: PMC11525004 DOI: 10.3389/fonc.2024.1473048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2024] [Accepted: 09/20/2024] [Indexed: 11/03/2024] Open
Abstract
Introduction The significance of ligand-receptor (LR) pair interactions in the progression of acute myeloid leukemia (AML) has been the focus of numerous studies. However, the relationship between LR pairs and the prognosis of AML, as well as their impact on treatment outcomes, is not fully elucidated. Methods Leveraging data from the TCGA-LAML cohort, we mapped out the LR pair interactions and distinguished specific molecular subtypes, with each displaying distinct biological characteristics. These subtypes exhibited varying mutation landscapes, pathway characteristics, and immune infiltration levels. Further insight into the immune microenvironment among these subtypes revealed disparities in immune cell abundance. Results Notably, one subtype showed a higher prevalence of CD8 T cells and plasma cells, suggesting increased adaptive immune activities. Leveraging a multivariate Lasso regression, we formulated an LR pair-based scoring model, termed "LR.score," to classify patients based on prognostic risk. Our findings underscored the association between elevated LR scores and T-cell dysfunction in AML. This connection highlights the LR score's potential as both a prognostic marker and a guide for personalized therapeutic interventions. Moreover, our LR.score revealed substantial survival prediction capacities in an independent AML cohort. We highlighted CLEC11A, ICAM4, ITGA4, and AVP as notably AML-specific. Discussion qRT-PCR analysis on AML versus normal bone marrow samples confirmed the significant downregulation of CLEC11A, ITGA4, ICAM4, and AVP in AML, suggesting their inverse biomarker potential in AML. In summary, this study illuminates the significance of the LR pair network in predicting AML prognosis, offering avenues for more precise treatment strategies tailored to individual patient profiles.
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Affiliation(s)
- Chunlan Fu
- Department of Hematology, Zhuji Affiliated Hospital of Wenzhou Medical University, Zhuji, Zhejiang, China
| | - Di Qiu
- Department of Hematology, Zhuji Affiliated Hospital of Wenzhou Medical University, Zhuji, Zhejiang, China
| | - Mei Zhou
- Department of Hematology, Zhuji Affiliated Hospital of Wenzhou Medical University, Zhuji, Zhejiang, China
| | - Shaobo Ni
- Department of Hematology, Zhuji Affiliated Hospital of Wenzhou Medical University, Zhuji, Zhejiang, China
| | - Xin Jin
- Department of Breast Surgery, Zhuji Affiliated Hospital of Wenzhou Medical University, Zhuji, Zhejiang, China
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Zhu J, Wang Y, Chang WY, Malewska A, Napolitano F, Gahan JC, Unni N, Zhao M, Yuan R, Wu F, Yue L, Guo L, Zhao Z, Chen DZ, Hannan R, Zhang S, Xiao G, Mu P, Hanker AB, Strand D, Arteaga CL, Desai N, Wang X, Xie Y, Wang T. Mapping cellular interactions from spatially resolved transcriptomics data. Nat Methods 2024; 21:1830-1842. [PMID: 39227721 DOI: 10.1038/s41592-024-02408-1] [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: 01/24/2024] [Accepted: 08/02/2024] [Indexed: 09/05/2024]
Abstract
Cell-cell communication (CCC) is essential to how life forms and functions. However, accurate, high-throughput mapping of how expression of all genes in one cell affects expression of all genes in another cell is made possible only recently through the introduction of spatially resolved transcriptomics (SRT) technologies, especially those that achieve single-cell resolution. Nevertheless, substantial challenges remain to analyze such highly complex data properly. Here, we introduce a multiple-instance learning framework, Spacia, to detect CCCs from data generated by SRTs, by uniquely exploiting their spatial modality. We highlight Spacia's power to overcome fundamental limitations of popular analytical tools for inference of CCCs, including losing single-cell resolution, limited to ligand-receptor relationships and prior interaction databases, high false positive rates and, most importantly, the lack of consideration of the multiple-sender-to-one-receiver paradigm. We evaluated the fitness of Spacia for three commercialized single-cell resolution SRT technologies: MERSCOPE/Vizgen, CosMx/NanoString and Xenium/10x. Overall, Spacia represents a notable step in advancing quantitative theories of cellular communications.
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Affiliation(s)
- James Zhu
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Yunguan Wang
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Division of Pediatric Gastroenterology, Hepatology and Nutrition, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati, Cincinnati, OH, USA
| | - Woo Yong Chang
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Alicia Malewska
- Department of Urology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Fabiana Napolitano
- Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Jeffrey C Gahan
- Department of Urology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Nisha Unni
- Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Min Zhao
- Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Rongqing Yuan
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Fangjiang Wu
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Lauren Yue
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Lei Guo
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Zhuo Zhao
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA
| | - Danny Z Chen
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA
| | - Raquibul Hannan
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Siyuan Zhang
- Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Guanghua Xiao
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Ping Mu
- Department of Molecular Biology, UT Southwestern Medical Center, Dallas, TX, USA
- Hamon Center for Regenerative Science and Medicine, UT Southwestern Medical Center, Dallas, TX, USA
| | - Ariella B Hanker
- Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Douglas Strand
- Department of Urology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Carlos L Arteaga
- Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Neil Desai
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Xinlei Wang
- Department of Mathematics, University of Texas at Arlington, Arlington, TX, USA.
- Division of Data Science, College of Science, University of Texas at Arlington, Arlington, TX, USA.
| | - Yang Xie
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA.
- Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA.
| | - Tao Wang
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA.
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35
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Hough RF, Alvira CM, Bastarache JA, Erzurum SC, Kuebler WM, Schmidt EP, Shimoda LA, Abman SH, Alvarez DF, Belvitch P, Bhattacharya J, Birukov KG, Chan SY, Cornfield DN, Dudek SM, Garcia JGN, Harrington EO, Hsia CCW, Islam MN, Jonigk DD, Kalinichenko VV, Kolb TM, Lee JY, Mammoto A, Mehta D, Rounds S, Schupp JC, Shaver CM, Suresh K, Tambe DT, Ventetuolo CE, Yoder MC, Stevens T, Damarla M. Studying the Pulmonary Endothelium in Health and Disease: An Official American Thoracic Society Workshop Report. Am J Respir Cell Mol Biol 2024; 71:388-406. [PMID: 39189891 PMCID: PMC11450313 DOI: 10.1165/rcmb.2024-0330st] [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] [Indexed: 08/28/2024] Open
Abstract
Lung endothelium resides at the interface between the circulation and the underlying tissue, where it senses biochemical and mechanical properties of both the blood as it flows through the vascular circuit and the vessel wall. The endothelium performs the bidirectional signaling between the blood and tissue compartments that is necessary to maintain homeostasis while physically separating both, facilitating a tightly regulated exchange of water, solutes, cells, and signals. Disruption in endothelial function contributes to vascular disease, which can manifest in discrete vascular locations along the artery-to-capillary-to-vein axis. Although our understanding of mechanisms that contribute to endothelial cell injury and repair in acute and chronic vascular disease have advanced, pathophysiological mechanisms that underlie site-specific vascular disease remain incompletely understood. In an effort to improve the translatability of mechanistic studies of the endothelium, the American Thoracic Society convened a workshop to optimize rigor, reproducibility, and translation of discovery to advance our understanding of endothelial cell function in health and disease.
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36
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Xu Y, Lau P, Chen X, Zhao S, He Y, Jiang Z, Chen X, Zhang G, Liu H. Integrated multiomics revealed adenosine signaling predict immunotherapy response and regulate tumor ecosystem of melanoma. Hum Genomics 2024; 18:101. [PMID: 39278925 PMCID: PMC11404024 DOI: 10.1186/s40246-024-00651-3] [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: 03/22/2024] [Accepted: 07/28/2024] [Indexed: 09/18/2024] Open
Abstract
Extracellular adenosine is extensively involved in regulating the tumor microenvironment. Given the disappointing results of adenosine-targeted therapy trials, personalized treatment might be necessary, tailored to the microenvironment status of individual patients. Here, we introduce the adenosine signaling score (ADO-score) model using non-negative matrix fraction identified patient subtypes using publicly available melanoma dataset, which aimed to profile adenosine signaling-related genes and construct a model to predict prognosis. We analyzed 580 malignant melanoma samples and demonstrated its robust value for prognosis. Further investigation in immune checkpoint inhibitor dataset suggests its potential as a stratified factor of immune checkpoint inhibitor efficacy. We validated the power of the ADO-score at the protein level immunofluorescence in a melanoma cohort from Xiangya Hospital. More importantly, single-cell and spatial transcriptomic data highlighted the cell-specific expression patterns of adenosine signaling-related genes and the existence of adenosine signaling-mediated crosstalk between tumor cells and immune cells in melanoma. Our study reveals a robust connection between adenosine signaling and clinical benefits in melanoma patients and proposes a universally applicable adenosine signaling model, the ADO-score, in gene expression profiles and histological sections. This model enables us to more precisely and conveniently select patients who are likely to benefit from immunotherapy.
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Affiliation(s)
- Yantao Xu
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, China
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Changsha, China
- Hunan Key Laboratory of Skin Cancer and Psoriasis, Changsha, China
- Hunan Engineering Research Center of Skin Health and Disease, Changsha, China
- Xiangya Clinical Research Center for Cancer Immunotherapy, Central South University, Changsha, China
| | - Poyee Lau
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, China
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Changsha, China
- Hunan Key Laboratory of Skin Cancer and Psoriasis, Changsha, China
- Hunan Engineering Research Center of Skin Health and Disease, Changsha, China
- Xiangya Clinical Research Center for Cancer Immunotherapy, Central South University, Changsha, China
| | - Xiang Chen
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, China
- Xiangya School of Medicine, Central South University, Changsha, China
- Xiangya Clinical Research Center for Cancer Immunotherapy, Central South University, Changsha, China
| | - Shuang Zhao
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, China
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Changsha, China
- Hunan Key Laboratory of Skin Cancer and Psoriasis, Changsha, China
- Hunan Engineering Research Center of Skin Health and Disease, Changsha, China
- Xiangya Clinical Research Center for Cancer Immunotherapy, Central South University, Changsha, China
| | - Yi He
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, China
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Changsha, China
- Hunan Key Laboratory of Skin Cancer and Psoriasis, Changsha, China
- Hunan Engineering Research Center of Skin Health and Disease, Changsha, China
- Xiangya Clinical Research Center for Cancer Immunotherapy, Central South University, Changsha, China
| | - Zixi Jiang
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, China
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Changsha, China
- Hunan Key Laboratory of Skin Cancer and Psoriasis, Changsha, China
- Hunan Engineering Research Center of Skin Health and Disease, Changsha, China
- Xiangya Clinical Research Center for Cancer Immunotherapy, Central South University, Changsha, China
| | - Xiang Chen
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, China.
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Changsha, China.
- Hunan Key Laboratory of Skin Cancer and Psoriasis, Changsha, China.
- Hunan Engineering Research Center of Skin Health and Disease, Changsha, China.
- Xiangya School of Medicine, Central South University, Changsha, China.
- Xiangya Clinical Research Center for Cancer Immunotherapy, Central South University, Changsha, China.
- Research Center of Molecular Metabolomics, Xiangya Hospital, Central South University, Changsha, China.
| | - Guanxiong Zhang
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, China.
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Changsha, China.
- Hunan Key Laboratory of Skin Cancer and Psoriasis, Changsha, China.
- Hunan Engineering Research Center of Skin Health and Disease, Changsha, China.
| | - Hong Liu
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, China.
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Changsha, China.
- Hunan Key Laboratory of Skin Cancer and Psoriasis, Changsha, China.
- Hunan Engineering Research Center of Skin Health and Disease, Changsha, China.
- Xiangya School of Medicine, Central South University, Changsha, China.
- Xiangya Clinical Research Center for Cancer Immunotherapy, Central South University, Changsha, China.
- Research Center of Molecular Metabolomics, Xiangya Hospital, Central South University, Changsha, China.
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37
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Ding Q, Yang W, Xue G, Liu H, Cai Y, Que J, Jin X, Luo M, Pang F, Yang Y, Lin Y, Liu Y, Sun H, Tan R, Wang P, Xu Z, Jiang Q. Dimension reduction, cell clustering, and cell-cell communication inference for single-cell transcriptomics with DcjComm. Genome Biol 2024; 25:241. [PMID: 39252099 PMCID: PMC11382422 DOI: 10.1186/s13059-024-03385-6] [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: 01/09/2024] [Accepted: 08/30/2024] [Indexed: 09/11/2024] Open
Abstract
Advances in single-cell transcriptomics provide an unprecedented opportunity to explore complex biological processes. However, computational methods for analyzing single-cell transcriptomics still have room for improvement especially in dimension reduction, cell clustering, and cell-cell communication inference. Herein, we propose a versatile method, named DcjComm, for comprehensive analysis of single-cell transcriptomics. DcjComm detects functional modules to explore expression patterns and performs dimension reduction and clustering to discover cellular identities by the non-negative matrix factorization-based joint learning model. DcjComm then infers cell-cell communication by integrating ligand-receptor pairs, transcription factors, and target genes. DcjComm demonstrates superior performance compared to state-of-the-art methods.
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Affiliation(s)
- Qian Ding
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150000, China
| | - Wenyi Yang
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150000, China
| | - Guangfu Xue
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150000, China
| | - Hongxin Liu
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150000, China
| | - Yideng Cai
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150000, China
| | - Jinhao Que
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150000, China
| | - Xiyun Jin
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, 150076, China
| | - Meng Luo
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150000, China
| | - Fenglan Pang
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150000, China
| | - Yuexin Yang
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150000, China
| | - Yi Lin
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, 150076, China
| | - Yusong Liu
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, 150076, China
| | - Haoxiu Sun
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, 150076, China
| | - Renjie Tan
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, 150076, China
| | - Pingping Wang
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, 150076, China.
| | - Zhaochun Xu
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, 150076, China.
| | - Qinghua Jiang
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150000, China.
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, 150076, China.
- State Key Laboratory of Frigid Zone Cardiovascular Diseases (SKLFZCD), Harbin Medical University, Harbin, 150076, China.
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38
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Su J, Song Y, Zhu Z, Huang X, Fan J, Qiao J, Mao F. Cell-cell communication: new insights and clinical implications. Signal Transduct Target Ther 2024; 9:196. [PMID: 39107318 PMCID: PMC11382761 DOI: 10.1038/s41392-024-01888-z] [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: 12/29/2023] [Revised: 05/09/2024] [Accepted: 06/02/2024] [Indexed: 09/11/2024] Open
Abstract
Multicellular organisms are composed of diverse cell types that must coordinate their behaviors through communication. Cell-cell communication (CCC) is essential for growth, development, differentiation, tissue and organ formation, maintenance, and physiological regulation. Cells communicate through direct contact or at a distance using ligand-receptor interactions. So cellular communication encompasses two essential processes: cell signal conduction for generation and intercellular transmission of signals, and cell signal transduction for reception and procession of signals. Deciphering intercellular communication networks is critical for understanding cell differentiation, development, and metabolism. First, we comprehensively review the historical milestones in CCC studies, followed by a detailed description of the mechanisms of signal molecule transmission and the importance of the main signaling pathways they mediate in maintaining biological functions. Then we systematically introduce a series of human diseases caused by abnormalities in cell communication and their progress in clinical applications. Finally, we summarize various methods for monitoring cell interactions, including cell imaging, proximity-based chemical labeling, mechanical force analysis, downstream analysis strategies, and single-cell technologies. These methods aim to illustrate how biological functions depend on these interactions and the complexity of their regulatory signaling pathways to regulate crucial physiological processes, including tissue homeostasis, cell development, and immune responses in diseases. In addition, this review enhances our understanding of the biological processes that occur after cell-cell binding, highlighting its application in discovering new therapeutic targets and biomarkers related to precision medicine. This collective understanding provides a foundation for developing new targeted drugs and personalized treatments.
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Affiliation(s)
- Jimeng Su
- Institute of Medical Innovation and Research, Peking University Third Hospital, Beijing, China
- Cancer Center, Peking University Third Hospital, Beijing, China
- College of Animal Science and Technology, Yangzhou University, Yangzhou, Jiangsu, China
| | - Ying Song
- Institute of Medical Innovation and Research, Peking University Third Hospital, Beijing, China
- Cancer Center, Peking University Third Hospital, Beijing, China
| | - Zhipeng Zhu
- Institute of Medical Innovation and Research, Peking University Third Hospital, Beijing, China
- Cancer Center, Peking University Third Hospital, Beijing, China
| | - Xinyue Huang
- Biomedical Research Institute, Shenzhen Peking University-the Hong Kong University of Science and Technology Medical Center, Shenzhen, China
| | - Jibiao Fan
- College of Animal Science and Technology, Yangzhou University, Yangzhou, Jiangsu, China
| | - Jie Qiao
- State Key Laboratory of Female Fertility Promotion, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, China.
- National Clinical Research Center for Obstetrics and Gynecology (Peking University Third Hospital), Beijing, China.
- Key Laboratory of Assisted Reproduction (Peking University), Ministry of Education, Beijing, China.
- Beijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Beijing, China.
| | - Fengbiao Mao
- Institute of Medical Innovation and Research, Peking University Third Hospital, Beijing, China.
- Cancer Center, Peking University Third Hospital, Beijing, China.
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39
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Zang X, Gu S, Wang W, Shi J, Gan J, Hu Q, Zhou C, Ding Y, He Y, Jiang L, Gu T, Xu Z, Huang S, Yang H, Meng F, Li Z, Cai G, Hong L, Wu Z. Dynamic intrauterine crosstalk promotes porcine embryo implantation during early pregnancy. SCIENCE CHINA. LIFE SCIENCES 2024; 67:1676-1696. [PMID: 38748354 DOI: 10.1007/s11427-023-2557-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Accepted: 02/21/2024] [Indexed: 08/09/2024]
Abstract
Dynamic crosstalk between the embryo and mother is crucial during implantation. Here, we comprehensively profile the single-cell transcriptome of pig peri-implantation embryos and corresponding maternal endometrium, identifying 4 different lineages in embryos and 13 cell types in the endometrium. Cell-specific gene expression characterizes 4 distinct trophectoderm subpopulations, showing development from undifferentiated trophectoderm to polar and mural trophectoderm. Dynamic expression of genes in different types of endometrial cells illustrates their molecular response to embryos during implantation. Then, we developed a novel tool, ExtraCellTalk, generating an overall dynamic map of maternal-foetal crosstalk using uterine luminal proteins as bridges. Through cross-species comparisons, we identified a conserved RBP4/STRA6 pathway in which embryonic-derived RBP4 could target the STRA6 receptor on stromal cells to regulate the interaction with other endometrial cells. These results provide insight into the maternal-foetal crosstalk during embryo implantation and represent a valuable resource for further studies to improve embryo implantation.
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Affiliation(s)
- Xupeng Zang
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, 510642, China
| | - Shengchen Gu
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, 510642, China
| | - Wenjing Wang
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, 510642, China
| | - Junsong Shi
- Yunfu Subcenter of Guangdong Laboratory for Lingnan Modern Agriculture, Yunfu, 527300, China
| | - Jianyu Gan
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, 510642, China
| | - Qun Hu
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, 510642, China
| | - Chen Zhou
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, 510642, China
| | - Yue Ding
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, 510642, China
| | - Yanjuan He
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, 510642, China
| | - Lei Jiang
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Ting Gu
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, 510642, China
- Key Laboratory of South China Modern Biological Seed Industry, Ministry of Agriculture and Rural Affairs, Guangzhou, 510520, China
| | - Zheng Xu
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, 510642, China
- Key Laboratory of South China Modern Biological Seed Industry, Ministry of Agriculture and Rural Affairs, Guangzhou, 510520, China
| | - Sixiu Huang
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, 510642, China
- Key Laboratory of South China Modern Biological Seed Industry, Ministry of Agriculture and Rural Affairs, Guangzhou, 510520, China
| | - Huaqiang Yang
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, 510642, China
- Key Laboratory of South China Modern Biological Seed Industry, Ministry of Agriculture and Rural Affairs, Guangzhou, 510520, China
| | - Fanming Meng
- Guangdong Key Laboratory of Animal Breeding and Nutrition, Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, China
| | - Zicong Li
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, 510642, China
- Key Laboratory of South China Modern Biological Seed Industry, Ministry of Agriculture and Rural Affairs, Guangzhou, 510520, China
| | - Gengyuan Cai
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, 510642, China
- Key Laboratory of South China Modern Biological Seed Industry, Ministry of Agriculture and Rural Affairs, Guangzhou, 510520, China
| | - Linjun Hong
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China.
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, 510642, China.
- Key Laboratory of South China Modern Biological Seed Industry, Ministry of Agriculture and Rural Affairs, Guangzhou, 510520, China.
| | - Zhenfang Wu
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China.
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, 510642, China.
- Yunfu Subcenter of Guangdong Laboratory for Lingnan Modern Agriculture, Yunfu, 527300, China.
- Key Laboratory of South China Modern Biological Seed Industry, Ministry of Agriculture and Rural Affairs, Guangzhou, 510520, China.
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40
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Mathys H, Boix CA, Akay LA, Xia Z, Davila-Velderrain J, Ng AP, Jiang X, Abdelhady G, Galani K, Mantero J, Band N, James BT, Babu S, Galiana-Melendez F, Louderback K, Prokopenko D, Tanzi RE, Bennett DA, Tsai LH, Kellis M. Single-cell multiregion dissection of Alzheimer's disease. Nature 2024; 632:858-868. [PMID: 39048816 PMCID: PMC11338834 DOI: 10.1038/s41586-024-07606-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 05/24/2024] [Indexed: 07/27/2024]
Abstract
Alzheimer's disease is the leading cause of dementia worldwide, but the cellular pathways that underlie its pathological progression across brain regions remain poorly understood1-3. Here we report a single-cell transcriptomic atlas of six different brain regions in the aged human brain, covering 1.3 million cells from 283 post-mortem human brain samples across 48 individuals with and without Alzheimer's disease. We identify 76 cell types, including region-specific subtypes of astrocytes and excitatory neurons and an inhibitory interneuron population unique to the thalamus and distinct from canonical inhibitory subclasses. We identify vulnerable populations of excitatory and inhibitory neurons that are depleted in specific brain regions in Alzheimer's disease, and provide evidence that the Reelin signalling pathway is involved in modulating the vulnerability of these neurons. We develop a scalable method for discovering gene modules, which we use to identify cell-type-specific and region-specific modules that are altered in Alzheimer's disease and to annotate transcriptomic differences associated with diverse pathological variables. We identify an astrocyte program that is associated with cognitive resilience to Alzheimer's disease pathology, tying choline metabolism and polyamine biosynthesis in astrocytes to preserved cognitive function late in life. Together, our study develops a regional atlas of the ageing human brain and provides insights into cellular vulnerability, response and resilience to Alzheimer's disease pathology.
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Affiliation(s)
- Hansruedi Mathys
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- University of Pittsburgh Brain Institute, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Carles A Boix
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Computational and Systems Biology Program, MIT, Cambridge, MA, USA
| | - Leyla Anne Akay
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
| | - Ziting Xia
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- Harvard-MIT Health Sciences and Technology Program, MIT, Cambridge, MA, USA
| | | | - Ayesha P Ng
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
| | - Xueqiao Jiang
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
| | - Ghada Abdelhady
- University of Pittsburgh Brain Institute, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Kyriaki Galani
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Julio Mantero
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Neil Band
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Benjamin T James
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sudhagar Babu
- University of Pittsburgh Brain Institute, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Fabiola Galiana-Melendez
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
| | - Kate Louderback
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
| | - Dmitry Prokopenko
- Genetics and Aging Research Unit, McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Rudolph E Tanzi
- Genetics and Aging Research Unit, McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Li-Huei Tsai
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA.
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Manolis Kellis
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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Li W, Wang H, Zhao J, Xia J, Sun X. scHyper: reconstructing cell-cell communication through hypergraph neural networks. Brief Bioinform 2024; 25:bbae436. [PMID: 39276328 PMCID: PMC11401449 DOI: 10.1093/bib/bbae436] [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: 05/26/2024] [Revised: 07/14/2024] [Accepted: 08/07/2024] [Indexed: 09/16/2024] Open
Abstract
Cell-cell communications is crucial for the regulation of cellular life and the establishment of cellular relationships. Most approaches of inferring intercellular communications from single-cell RNA sequencing (scRNA-seq) data lack a comprehensive global network view of multilayered communications. In this context, we propose scHyper, a new method that can infer intercellular communications from a global network perspective and identify the potential impact of all cells, ligand, and receptor expression on the communication score. scHyper designed a new way to represent tripartite relationships, by extracting a heterogeneous hypergraph that includes the source (ligand expression), the target (receptor expression), and the relevant ligand-receptor (L-R) pairs. scHyper is based on hypergraph representation learning, which measures the degree of match between the intrinsic attributes (static embeddings) of nodes and their observed behaviors (dynamic embeddings) in the context (hyperedges), quantifies the probability of forming hyperedges, and thus reconstructs the cell-cell communication score. Additionally, to effectively mine the key mechanisms of signal transmission, we collect a rich dataset of multisubunit complex L-R pairs and propose a nonparametric test to determine significant intercellular communications. Comparing with other tools indicates that scHyper exhibits superior performance and functionality. Experimental results on the human tumor microenvironment and immune cells demonstrate that scHyper offers reliable and unique capabilities for analyzing intercellular communication networks. Therefore, we introduced an effective strategy that can build high-order interaction patterns, surpassing the limitations of most methods that can only handle low-order interactions, thus more accurately interpreting the complexity of intercellular communications.
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Affiliation(s)
- Wenying Li
- School of Mathematics and System Science, Xinjiang University, No. 777 Huarui Street, Shuimogou District, Urumqi, Xinjiang 830017, China
| | - Haiyun Wang
- School of Mathematics and System Science, Xinjiang University, No. 777 Huarui Street, Shuimogou District, Urumqi, Xinjiang 830017, China
| | - Jianping Zhao
- School of Mathematics and System Science, Xinjiang University, No. 777 Huarui Street, Shuimogou District, Urumqi, Xinjiang 830017, China
| | - Junfeng Xia
- School of Mathematics and System Science, Xinjiang University, No. 777 Huarui Street, Shuimogou District, Urumqi, Xinjiang 830017, China
- Institute of Physical Science and Information Technology, Anhui University, No. 111 Jiulong Road, Shushan District, Hefei, Anhui 230601, China
| | - Xiaoqiang Sun
- School of Mathematics, Sun Yat-sen University, No. 135 Xingang Xi Road, Haizhu District, Guangzhou, Guangdong 510275, China
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42
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Sudhakar M, Vignesh H, Natarajan KN. Crosstalk between tumor and microenvironment: Insights from spatial transcriptomics. Adv Cancer Res 2024; 163:187-222. [PMID: 39271263 DOI: 10.1016/bs.acr.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] [Subscribe] [Scholar Register] [Indexed: 09/15/2024]
Abstract
Cancer is a dynamic disease, and clonal heterogeneity plays a fundamental role in tumor development, progression, and resistance to therapies. Single-cell and spatial multimodal technologies can provide a high-resolution molecular map of underlying genomic, epigenomic, and transcriptomic alterations involved in inter- and intra-tumor heterogeneity and interactions with the microenvironment. In this review, we provide a perspective on factors driving cancer heterogeneity, tumor evolution, and clonal states. We briefly describe spatial transcriptomic technologies and summarize recent literature that sheds light on the dynamical interactions between tumor states, cell-to-cell communication, and remodeling local microenvironment.
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Affiliation(s)
- Malvika Sudhakar
- DTU Bioengineering, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Harie Vignesh
- DTU Bioengineering, Technical University of Denmark, Kongens Lyngby, Denmark
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43
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Zhang Y, Yang Y, Ren L, Zhan M, Sun T, Zou Q, Zhang Y. Predicting intercellular communication based on metabolite-related ligand-receptor interactions with MRCLinkdb. BMC Biol 2024; 22:152. [PMID: 38978014 PMCID: PMC11232326 DOI: 10.1186/s12915-024-01950-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: 03/16/2024] [Accepted: 07/03/2024] [Indexed: 07/10/2024] Open
Abstract
BACKGROUND Metabolite-associated cell communications play critical roles in maintaining human biological function. However, most existing tools and resources focus only on ligand-receptor interaction pairs where both partners are proteinaceous, neglecting other non-protein molecules. To address this gap, we introduce the MRCLinkdb database and algorithm, which aggregates and organizes data related to non-protein L-R interactions in cell-cell communication, providing a valuable resource for predicting intercellular communication based on metabolite-related ligand-receptor interactions. RESULTS Here, we manually curated the metabolite-ligand-receptor (ML-R) interactions from the literature and known databases, ultimately collecting over 790 human and 670 mouse ML-R interactions. Additionally, we compiled information on over 1900 enzymes and 260 transporter entries associated with these metabolites. We developed Metabolite-Receptor based Cell Link Database (MRCLinkdb) to store these ML-R interactions data. Meanwhile, the platform also offers extensive information for presenting ML-R interactions, including fundamental metabolite information and the overall expression landscape of metabolite-associated gene sets (such as receptor, enzymes, and transporter proteins) based on single-cell transcriptomics sequencing (covering 35 human and 26 mouse tissues, 52 human and 44 mouse cell types) and bulk RNA-seq/microarray data (encompassing 62 human and 39 mouse tissues). Furthermore, MRCLinkdb introduces a web server dedicated to the analysis of intercellular communication based on ML-R interactions. MRCLinkdb is freely available at https://www.cellknowledge.com.cn/mrclinkdb/ . CONCLUSIONS In addition to supplementing ligand-receptor databases, MRCLinkdb may provide new perspectives for decoding the intercellular communication and advancing related prediction tools based on ML-R interactions.
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Affiliation(s)
- Yuncong Zhang
- Guangdong Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai Institute of Translational Medicine, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Jinan University, Zhuhai, Guangdong, China
| | - Yu Yang
- Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, China
| | - Liping Ren
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, China
| | - Meixiao Zhan
- Guangdong Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai Institute of Translational Medicine, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Jinan University, Zhuhai, Guangdong, China
| | - Taoping Sun
- Guangdong Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai Institute of Translational Medicine, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Jinan University, Zhuhai, Guangdong, China.
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.
| | - Yang Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.
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44
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Wang Y, Shen Z, Mo S, Zhang H, Chen J, Zhu C, Lv S, Zhang D, Huang X, Gu Y, Yu X, Ding X, Zhang X. Crosstalk among proximal tubular cells, macrophages, and fibroblasts in acute kidney injury: single-cell profiling from the perspective of ferroptosis. Hum Cell 2024; 37:1039-1055. [PMID: 38753279 PMCID: PMC11194220 DOI: 10.1007/s13577-024-01072-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: 10/20/2023] [Accepted: 04/27/2024] [Indexed: 06/24/2024]
Abstract
The link between ferroptosis, a form of cell death mediated by iron and acute kidney injury (AKI) is recently gaining widespread attention. However, the mechanism of the crosstalk between cells in the pathogenesis and progression of acute kidney injury remains unexplored. In our research, we performed a non-negative matrix decomposition (NMF) algorithm on acute kidney injury single-cell RNA sequencing data based specifically focusing in ferroptosis-associated genes. Through a combination with pseudo-time analysis, cell-cell interaction analysis and SCENIC analysis, we discovered that proximal tubular cells, macrophages, and fibroblasts all showed associations with ferroptosis in different pathways and at various time. This involvement influenced cellular functions, enhancing cellular communication and activating multiple transcription factors. In addition, analyzing bulk expression profiles and marker genes of newly defined ferroptosis subtypes of cells, we have identified crucial cell subtypes, including Egr1 + PTC-C1, Jun + PTC-C3, Cxcl2 + Mac-C1 and Egr1 + Fib-C1. All these subtypes which were found in AKI mice kidneys and played significantly distinct roles from those of normal mice. Moreover, we verified the differential expression of Egr1, Jun, and Cxcl2 in the IRI mouse model and acute kidney injury human samples. Finally, our research presented a novel analysis of the crosstalk of proximal tubular cells, macrophages and fibroblasts in acute kidney injury targeting ferroptosis, therefore, contributing to better understanding the acute kidney injury pathogenesis, self-repairment and acute kidney injury-chronic kidney disease (AKI-CKD) progression.
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Affiliation(s)
- Yulin Wang
- Department of Nephrology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Shanghai, 200032, China
- Shanghai Medical Center of Kidney Disease, No. 180 Fenglin Road, Shanghai, 200032, China
- Shanghai Key Laboratory of Kidney and Blood Purification, No. 180 Fenglin Road, Shanghai, 200032, China
| | - Ziyan Shen
- Department of Nephrology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Shanghai, 200032, China
- Shanghai Medical Center of Kidney Disease, No. 180 Fenglin Road, Shanghai, 200032, China
- Shanghai Institute of Kidney and Dialysis, No. 180 Fenglin Road, Shanghai, 200032, China
| | - Shaocong Mo
- Department of Digestive Diseases, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Han Zhang
- Department of Nephrology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Shanghai, 200032, China
- Shanghai Medical Center of Kidney Disease, No. 180 Fenglin Road, Shanghai, 200032, China
- Department of Digestive Diseases, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Jing Chen
- Department of Nephrology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Shanghai, 200032, China
- Shanghai Medical Center of Kidney Disease, No. 180 Fenglin Road, Shanghai, 200032, China
- Department of Digestive Diseases, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Cheng Zhu
- Department of Nephrology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Shanghai, 200032, China
- Shanghai Medical Center of Kidney Disease, No. 180 Fenglin Road, Shanghai, 200032, China
- Department of Digestive Diseases, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Shiqi Lv
- Department of Nephrology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Shanghai, 200032, China
- Shanghai Medical Center of Kidney Disease, No. 180 Fenglin Road, Shanghai, 200032, China
- Shanghai Key Laboratory of Kidney and Blood Purification, No. 180 Fenglin Road, Shanghai, 200032, China
| | - Di Zhang
- Department of Nephrology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Shanghai, 200032, China
- Shanghai Medical Center of Kidney Disease, No. 180 Fenglin Road, Shanghai, 200032, China
- Shanghai Key Laboratory of Kidney and Blood Purification, No. 180 Fenglin Road, Shanghai, 200032, China
| | - Xinhui Huang
- Department of Nephrology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Shanghai, 200032, China
- Shanghai Medical Center of Kidney Disease, No. 180 Fenglin Road, Shanghai, 200032, China
- Shanghai Key Laboratory of Kidney and Blood Purification, No. 180 Fenglin Road, Shanghai, 200032, China
| | - Yulu Gu
- Division of Nephrology, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou, 213100, Jiangsu, China
| | - Xixi Yu
- Department of Nephrology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Xiaoqiang Ding
- Department of Nephrology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Shanghai, 200032, China.
- Shanghai Medical Center of Kidney Disease, No. 180 Fenglin Road, Shanghai, 200032, China.
- Shanghai Key Laboratory of Kidney and Blood Purification, No. 180 Fenglin Road, Shanghai, 200032, China.
- Shanghai Institute of Kidney and Dialysis, No. 180 Fenglin Road, Shanghai, 200032, China.
| | - Xiaoyan Zhang
- Department of Nephrology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Shanghai, 200032, China.
- Shanghai Medical Center of Kidney Disease, No. 180 Fenglin Road, Shanghai, 200032, China.
- Shanghai Key Laboratory of Kidney and Blood Purification, No. 180 Fenglin Road, Shanghai, 200032, China.
- Shanghai Institute of Kidney and Dialysis, No. 180 Fenglin Road, Shanghai, 200032, China.
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45
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Sarkar H, Chitra U, Gold J, Raphael BJ. A count-based model for delineating cell-cell interactions in spatial transcriptomics data. Bioinformatics 2024; 40:i481-i489. [PMID: 38940134 PMCID: PMC11211854 DOI: 10.1093/bioinformatics/btae219] [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] [Indexed: 06/29/2024] Open
Abstract
MOTIVATION Cell-cell interactions (CCIs) consist of cells exchanging signals with themselves and neighboring cells by expressing ligand and receptor molecules and play a key role in cellular development, tissue homeostasis, and other critical biological functions. Since direct measurement of CCIs is challenging, multiple methods have been developed to infer CCIs by quantifying correlations between the gene expression of the ligands and receptors that mediate CCIs, originally from bulk RNA-sequencing data and more recently from single-cell or spatially resolved transcriptomics (SRT) data. SRT has a particular advantage over single-cell approaches, since ligand-receptor correlations can be computed between cells or spots that are physically close in the tissue. However, the transcript counts of individual ligands and receptors in SRT data are generally low, complicating the inference of CCIs from expression correlations. RESULTS We introduce Copulacci, a count-based model for inferring CCIs from SRT data. Copulacci uses a Gaussian copula to model dependencies between the expression of ligands and receptors from nearby spatial locations even when the transcript counts are low. On simulated data, Copulacci outperforms existing CCI inference methods based on the standard Spearman and Pearson correlation coefficients. Using several real SRT datasets, we show that Copulacci discovers biologically meaningful ligand-receptor interactions that are lowly expressed and undiscoverable by existing CCI inference methods. AVAILABILITY AND IMPLEMENTATION Copulacci is implemented in Python and available at https://github.com/raphael-group/copulacci.
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Affiliation(s)
- Hirak Sarkar
- Department of Computer Science, Princeton University, Princeton, NJ, 08540, United States
- Ludwig Cancer Institute, Princeton Branch, Princeton University, Princeton, NJ, 08540, United States
| | - Uthsav Chitra
- Department of Computer Science, Princeton University, Princeton, NJ, 08540, United States
| | - Julian Gold
- Department of Computer Science, Princeton University, Princeton, NJ, 08540, United States
- Center for Statistics and Machine Learning, Princeton University, Princeton, NJ, 08540, United States
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ, 08540, United States
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46
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Zuo C, Xia J, Chen L. Dissecting tumor microenvironment from spatially resolved transcriptomics data by heterogeneous graph learning. Nat Commun 2024; 15:5057. [PMID: 38871687 DOI: 10.1038/s41467-024-49171-7] [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: 06/10/2023] [Accepted: 05/22/2024] [Indexed: 06/15/2024] Open
Abstract
Spatially resolved transcriptomics (SRT) has enabled precise dissection of tumor-microenvironment (TME) by analyzing its intracellular molecular networks and intercellular cell-cell communication (CCC). However, lacking computational exploration of complicated relations between cells, genes, and histological regions, severely limits the ability to interpret the complex structure of TME. Here, we introduce stKeep, a heterogeneous graph (HG) learning method that integrates multimodality and gene-gene interactions, in unraveling TME from SRT data. stKeep leverages HG to learn both cell-modules and gene-modules by incorporating features of diverse nodes including genes, cells, and histological regions, allows for identifying finer cell-states within TME and cell-state-specific gene-gene relations, respectively. Furthermore, stKeep employs HG to infer CCC for each cell, while ensuring that learned CCC patterns are comparable across different cell-states through contrastive learning. In various cancer samples, stKeep outperforms other tools in dissecting TME such as detecting bi-potent basal populations, neoplastic myoepithelial cells, and metastatic cells distributed within the tumor or leading-edge regions. Notably, stKeep identifies key transcription factors, ligands, and receptors relevant to disease progression, which are further validated by the functional and survival analysis of independent clinical data, thereby highlighting its clinical prognostic and immunotherapy applications.
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Affiliation(s)
- Chunman Zuo
- Institute of Artificial Intelligence, Shanghai Engineering Research Center of Industrial Big Data and Intelligent System, Donghua University, Shanghai, 201620, China.
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130022, China.
| | - Junjie Xia
- Institute of Artificial Intelligence, Shanghai Engineering Research Center of Industrial Big Data and Intelligent System, Donghua University, Shanghai, 201620, China
- Department of Applied Mathematics, Donghua University, Shanghai, 201620, China
| | - Luonan Chen
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, 200031, China.
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, 310024, China.
- West China Biomedical Big Data Center, Med-X center for informatics, West China Hospital, Sichuan University, Chengdu, 610041, China.
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47
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Armingol E, Baghdassarian HM, Lewis NE. The diversification of methods for studying cell-cell interactions and communication. Nat Rev Genet 2024; 25:381-400. [PMID: 38238518 PMCID: PMC11139546 DOI: 10.1038/s41576-023-00685-8] [Citation(s) in RCA: 37] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/01/2023] [Indexed: 05/20/2024]
Abstract
No cell lives in a vacuum, and the molecular interactions between cells define most phenotypes. Transcriptomics provides rich information to infer cell-cell interactions and communication, thus accelerating the discovery of the roles of cells within their communities. Such research relies heavily on algorithms that infer which cells are interacting and the ligands and receptors involved. Specific pressures on different research niches are driving the evolution of next-generation computational tools, enabling new conceptual opportunities and technological advances. More sophisticated algorithms now account for the heterogeneity and spatial organization of cells, multiple ligand types and intracellular signalling events, and enable the use of larger and more complex datasets, including single-cell and spatial transcriptomics. Similarly, new high-throughput experimental methods are increasing the number and resolution of interactions that can be analysed simultaneously. Here, we explore recent progress in cell-cell interaction research and highlight the diversification of the next generation of tools, which have yielded a rich ecosystem of tools for different applications and are enabling invaluable discoveries.
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Affiliation(s)
- Erick Armingol
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, USA.
- Department of Paediatrics, University of California, San Diego, La Jolla, CA, USA.
| | - Hratch M Baghdassarian
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, USA
- Department of Paediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Nathan E Lewis
- Department of Paediatrics, University of California, San Diego, La Jolla, CA, USA.
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.
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48
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Deng X, Mo Y, Zhu X. Deciphering Müller cell heterogeneity signatures in diabetic retinopathy across species: an integrative single-cell analysis. Eur J Med Res 2024; 29:265. [PMID: 38698486 PMCID: PMC11067085 DOI: 10.1186/s40001-024-01847-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: 01/13/2024] [Accepted: 04/17/2024] [Indexed: 05/05/2024] Open
Abstract
Diabetic retinopathy (DR), a leading cause of visual impairment, demands a profound comprehension of its cellular mechanisms to formulate effective therapeutic strategies. Our study presentes a comprehensive single-cell analysis elucidating the intricate landscape of Müller cells within DR, emphasizing their nuanced involvement. Utilizing scRNA-seq data from both Sprague-Dawley rat models and human patients, we delineated distinct Müller cell clusters and their corresponding gene expression profiles. These findings were further validated through differential gene expression analysis utilizing human transcriptomic data. Notably, certain Müller cell clusters displayed upregulation of the Rho gene, implying a phagocytic response to damaged photoreceptors within the DR microenvironment. This phenomenon was consistently observed across species. Additionally, the co-expression patterns of RHO and PDE6G within Müller cell clusters provided compelling evidence supporting their potential role in maintaining retinal integrity during DR. Our results offer novel insights into the cellular dynamics of DR and underscore Müller cells as promising therapeutic targets for preserving vision in retinal disorders induced by diabetes.
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Affiliation(s)
- Xiyuan Deng
- Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Ya Mo
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China.
| | - Xiuying Zhu
- Chengdu University of Traditional Chinese Medicine, Chengdu, China
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49
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Ho NCW, Yap JYY, Zhao Z, Wang Y, Fernando K, Li CH, Kwang XL, Quah HS, Arcinas C, Iyer NG, Fong ELS. Bioengineered Hydrogels Recapitulate Fibroblast Heterogeneity in Cancer. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2307129. [PMID: 38493497 PMCID: PMC11132030 DOI: 10.1002/advs.202307129] [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: 09/27/2023] [Revised: 01/30/2024] [Indexed: 03/19/2024]
Abstract
Recently mapped transcriptomic landscapes reveal the extent of heterogeneity in cancer-associated fibroblasts (CAFs) beyond previously established single-gene markers. Functional analyses of individual CAF subsets within the tumor microenvironment are critical to develop more accurate CAF-targeting therapeutic strategies. However, there is a lack of robust preclinical models that reflect this heterogeneity in vitro. In this study, single-cell RNA sequencing datasets acquired from head and neck squamous cell carcinoma tissues to predict microenvironmental and cellular features governing individual CAF subsets are leveraged. Some of these features are then incorporated into a tunable hyaluronan-based hydrogel system to culture patient-derived CAFs. Control over hydrogel degradability and integrin adhesiveness enabled derivation of the predominant myofibroblastic and inflammatory CAF subsets, as shown through changes in cell morphology and transcriptomic profiles. Last, using these hydrogel-cultured CAFs, microtubule dynamics are identified, but not actomyosin contractility, as a key mediator of CAF plasticity. The recapitulation of CAF heterogeneity in vitro using defined hydrogels presents unique opportunities for advancing the understanding of CAF biology and evaluation of CAF-targeting therapeutics.
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Affiliation(s)
- Nicholas Ching Wei Ho
- Translational Tumor Engineering Laboratory, Department of Biomedical EngineeringNational University of SingaporeSingapore119276Singapore
| | - Josephine Yu Yan Yap
- Translational Tumor Engineering Laboratory, Department of Biomedical EngineeringNational University of SingaporeSingapore119276Singapore
| | - Zixuan Zhao
- The N.1 Institute for HealthNational University of SingaporeSingapore117456Singapore
| | - Yunyun Wang
- Translational Tumor Engineering Laboratory, Department of Biomedical EngineeringNational University of SingaporeSingapore119276Singapore
| | - Kanishka Fernando
- Translational Tumor Engineering Laboratory, Department of Biomedical EngineeringNational University of SingaporeSingapore119276Singapore
| | - Constance H Li
- Cancer Therapeutics Research LaboratoryNational Cancer Centre SingaporeSingapore168583Singapore
- Duke‐NUS Medical SchoolNational University of SingaporeSingapore169857Singapore
| | - Xue Lin Kwang
- Cancer Therapeutics Research LaboratoryNational Cancer Centre SingaporeSingapore168583Singapore
| | - Hong Sheng Quah
- Cancer Therapeutics Research LaboratoryNational Cancer Centre SingaporeSingapore168583Singapore
- Duke‐NUS Medical SchoolNational University of SingaporeSingapore169857Singapore
| | - Camille Arcinas
- Duke‐NUS Medical SchoolNational University of SingaporeSingapore169857Singapore
| | - N. Gopalakrishna Iyer
- Cancer Therapeutics Research LaboratoryNational Cancer Centre SingaporeSingapore168583Singapore
- Duke‐NUS Medical SchoolNational University of SingaporeSingapore169857Singapore
| | - Eliza Li Shan Fong
- Translational Tumor Engineering Laboratory, Department of Biomedical EngineeringNational University of SingaporeSingapore119276Singapore
- The N.1 Institute for HealthNational University of SingaporeSingapore117456Singapore
- Cancer Science InstituteNational University of SingaporeSingapore117599Singapore
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50
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Hashimoto M, Kojima Y, Sakamoto T, Ozato Y, Nakano Y, Abe T, Hosoda K, Saito H, Higuchi S, Hisamatsu Y, Toshima T, Yonemura Y, Masuda T, Hata T, Nagayama S, Kagawa K, Goto Y, Utou M, Gamachi A, Imamura K, Kuze Y, Zenkoh J, Suzuki A, Takahashi K, Niida A, Hirose H, Hayashi S, Koseki J, Fukuchi S, Murakami K, Yoshizumi T, Kadomatsu K, Tobo T, Oda Y, Uemura M, Eguchi H, Doki Y, Mori M, Oshima M, Shibata T, Suzuki Y, Shimamura T, Mimori K. Spatial and single-cell colocalisation analysis reveals MDK-mediated immunosuppressive environment with regulatory T cells in colorectal carcinogenesis. EBioMedicine 2024; 103:105102. [PMID: 38614865 PMCID: PMC11121171 DOI: 10.1016/j.ebiom.2024.105102] [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: 10/18/2023] [Revised: 03/19/2024] [Accepted: 03/20/2024] [Indexed: 04/15/2024] Open
Abstract
BACKGROUND Cell-cell interaction factors that facilitate the progression of adenoma to sporadic colorectal cancer (CRC) remain unclear, thereby hindering patient survival. METHODS We performed spatial transcriptomics on five early CRC cases, which included adenoma and carcinoma, and one advanced CRC. To elucidate cell-cell interactions within the tumour microenvironment (TME), we investigated the colocalisation network at single-cell resolution using a deep generative model for colocalisation analysis, combined with a single-cell transcriptome, and assessed the clinical significance in CRC patients. FINDINGS CRC cells colocalised with regulatory T cells (Tregs) at the adenoma-carcinoma interface. At early-stage carcinogenesis, cell-cell interaction inference between colocalised adenoma and cancer epithelial cells and Tregs based on the spatial distribution of single cells highlighted midkine (MDK) as a prominent signalling molecule sent from tumour epithelial cells to Tregs. Interaction between MDK-high CRC cells and SPP1+ macrophages and stromal cells proved to be the mechanism underlying immunosuppression in the TME. Additionally, we identified syndecan4 (SDC4) as a receptor for MDK associated with Treg colocalisation. Finally, clinical analysis using CRC datasets indicated that increased MDK/SDC4 levels correlated with poor overall survival in CRC patients. INTERPRETATION MDK is involved in the immune tolerance shown by Tregs to tumour growth. MDK-mediated formation of the TME could be a potential target for early diagnosis and treatment of CRC. FUNDING Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Science Research; OITA Cancer Research Foundation; AMED under Grant Number; Japan Science and Technology Agency (JST); Takeda Science Foundation; The Princess Takamatsu Cancer Research Fund.
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Affiliation(s)
- Masahiro Hashimoto
- Department of Surgery, Kyushu University Beppu Hospital, Beppu, 874-0838, Japan; Department of Gastroenterological Surgery, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan
| | - Yasuhiro Kojima
- Division of Computational Bioscience, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
| | - Takeharu Sakamoto
- Department of Cancer Biology, Institute of Biomedical Science, Kansai Medical University, Hirakata, 573-1010, Japan.
| | - Yuki Ozato
- Department of Surgery, Kyushu University Beppu Hospital, Beppu, 874-0838, Japan; Department of Gastroenterological Surgery, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan
| | - Yusuke Nakano
- Department of Surgery, Kyushu University Beppu Hospital, Beppu, 874-0838, Japan; Department of Gastroenterological Surgery, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan
| | - Tadashi Abe
- Department of Surgery, Kyushu University Beppu Hospital, Beppu, 874-0838, Japan
| | - Kiyotaka Hosoda
- Department of Surgery, Kyushu University Beppu Hospital, Beppu, 874-0838, Japan
| | - Hideyuki Saito
- Department of Surgery, Kyushu University Beppu Hospital, Beppu, 874-0838, Japan; Department of General Surgical Science, Gastroenterological Surgery, Gunma University Graduate School of Medicine, Maebashi, 371-8511, Japan
| | - Satoshi Higuchi
- Department of Surgery, Kyushu University Beppu Hospital, Beppu, 874-0838, Japan; Department of Gastroenterological Surgery, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan
| | - Yuichi Hisamatsu
- Department of Surgery, Kyushu University Beppu Hospital, Beppu, 874-0838, Japan
| | - Takeo Toshima
- Department of Surgery, Kyushu University Beppu Hospital, Beppu, 874-0838, Japan
| | - Yusuke Yonemura
- Department of Surgery, Kyushu University Beppu Hospital, Beppu, 874-0838, Japan
| | - Takaaki Masuda
- Department of Surgery, Kyushu University Beppu Hospital, Beppu, 874-0838, Japan
| | - Tsuyoshi Hata
- Department of Gastroenterological Surgery, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan
| | - Satoshi Nagayama
- Department of Surgery, Uji-Tokushukai Medical Center, Uji, 611-0041, Japan
| | - Koichi Kagawa
- Department of Gastroenterology, Shin Beppu Hospital, Beppu, 874-8538, Japan
| | - Yasuhiro Goto
- Department of Gastroenterology, Shin Beppu Hospital, Beppu, 874-8538, Japan
| | - Mitsuaki Utou
- Department of Pathology, Kyushu University Beppu Hospital, Beppu, 874-0838, Japan
| | - Ayako Gamachi
- Department of Pathology, Oita Oka Hospital, Oita, 870-0192, Japan
| | - Kiyomi Imamura
- Laboratory of Systems Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, 277-8561, Japan
| | - Yuta Kuze
- Laboratory of Systems Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, 277-8561, Japan
| | - Junko Zenkoh
- Laboratory of Systems Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, 277-8561, Japan
| | - Ayako Suzuki
- Laboratory of Systems Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, 277-8561, Japan
| | - Kazuki Takahashi
- Laboratory of Molecular Medicine, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, 108-8639, Japan
| | - Atsushi Niida
- Laboratory of Molecular Medicine, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, 108-8639, Japan
| | - Haruka Hirose
- Division of Systems Biology, Nagoya University Graduate School of Medicine, Nagoya, 466-8550, Japan
| | - Shuto Hayashi
- Division of Systems Biology, Nagoya University Graduate School of Medicine, Nagoya, 466-8550, Japan
| | - Jun Koseki
- Division of Systems Biology, Nagoya University Graduate School of Medicine, Nagoya, 466-8550, Japan
| | - Satoshi Fukuchi
- Department of Gastroenterological Medicine, Almeida Memorial Hospital, Oita, 870-1195, Japan
| | - Kazunari Murakami
- Department of Gastroenterology, Oita University Hospital, Yufu, 879-5593, Japan
| | - Tomoharu Yoshizumi
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, 812-8582, Japan
| | - Kenji Kadomatsu
- Department of Biochemistry, Nagoya University Graduate School of Medicine, Nagoya, 466-8550, Japan
| | - Taro Tobo
- Department of Pathology, Kyushu University Beppu Hospital, Beppu, 874-0838, Japan
| | - Yoshinao Oda
- Department of Anatomic Pathology, Kyushu University Hospital, Fukuoka, 812-8582, Japan
| | - Mamoru Uemura
- Department of Gastroenterological Surgery, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan
| | - Hidetoshi Eguchi
- Department of Gastroenterological Surgery, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan
| | - Yuichiro Doki
- Department of Gastroenterological Surgery, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan
| | - Masaki Mori
- Tokai University School of Medicine, Isehara, 259-1193, Japan
| | - Masanobu Oshima
- Division of Genetics, Cancer Research Institute, Kanazawa University, Kanazawa, 920-1192, Japan
| | - Tatsuhiro Shibata
- Laboratory of Molecular Medicine, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, 108-8639, Japan
| | - Yutaka Suzuki
- Laboratory of Systems Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, 277-8561, Japan
| | - Teppei Shimamura
- Division of Systems Biology, Nagoya University Graduate School of Medicine, Nagoya, 466-8550, Japan; Department of Computational and Systems Biology, Medical Research Insitute, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo 113-0034, Japan.
| | - Koshi Mimori
- Department of Surgery, Kyushu University Beppu Hospital, Beppu, 874-0838, Japan.
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