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Hu H, Wang X, Feng S, Xu Z, Liu J, Heidrich-O'Hare E, Chen Y, Yue M, Zeng L, Rong Z, Chen T, Billiar T, Ding Y, Huang H, Duerr RH, Chen W. A unified model-based framework for doublet or multiplet detection in single-cell multiomics data. Nat Commun 2024; 15:5562. [PMID: 38956023 PMCID: PMC11220103 DOI: 10.1038/s41467-024-49448-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 06/03/2024] [Indexed: 07/04/2024] Open
Abstract
Droplet-based single-cell sequencing techniques rely on the fundamental assumption that each droplet encapsulates a single cell, enabling individual cell omics profiling. However, the inevitable issue of multiplets, where two or more cells are encapsulated within a single droplet, can lead to spurious cell type annotations and obscure true biological findings. The issue of multiplets is exacerbated in single-cell multiomics settings, where integrating cross-modality information for clustering can inadvertently promote the aggregation of multiplet clusters and increase the risk of erroneous cell type annotations. Here, we propose a compound Poisson model-based framework for multiplet detection in single-cell multiomics data. Leveraging experimental cell hashing results as the ground truth for multiplet status, we conducted trimodal DOGMA-seq experiments and generated 17 benchmarking datasets from two tissues, involving a total of 280,123 droplets. We demonstrated that the proposed method is an essential tool for integrating cross-modality multiplet signals, effectively eliminating multiplet clusters in single-cell multiomics data-a task at which the benchmarked single-omics methods proved inadequate.
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Affiliation(s)
- Haoran Hu
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Xinjun Wang
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Site Feng
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, 15261, USA
- School of Medicine, Tsinghua University, 100084, Beijing, China
| | - Zhongli Xu
- School of Medicine, Tsinghua University, 100084, Beijing, China
- Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, 15224, USA
| | - Jing Liu
- Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, 15224, USA
| | | | - Yanshuo Chen
- Department of Computer Science, University of Maryland, College Park, MD, 20742, USA
- Center of Bioinformatics and Computational Biology, College Park, MD, 20740, USA
| | - Molin Yue
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Lang Zeng
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Ziqi Rong
- School of Information, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Tianmeng Chen
- Department of Surgery, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Timothy Billiar
- Department of Surgery, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Ying Ding
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Heng Huang
- Department of Computer Science, University of Maryland, College Park, MD, 20742, USA
- Center of Bioinformatics and Computational Biology, College Park, MD, 20740, USA
| | - Richard H Duerr
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, 15261, USA.
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, 15261, USA.
| | - Wei Chen
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
- Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, 15224, USA.
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, 15261, USA.
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Rütgen BC, Wolfesberger B, Baumgartner D, Hammer SE, Groiss S, Hittmair KM, Gradner G, Fuchs-Baumgartinger A, Donovan TA, Schwendenwein I. Flowcytometric data of intermediate-large cell gastrointestinal lymphoma presenting a gross mass in 32 cats - "let them glow in the flow". Front Vet Sci 2024; 11:1378826. [PMID: 38863454 PMCID: PMC11166079 DOI: 10.3389/fvets.2024.1378826] [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: 01/30/2024] [Accepted: 05/02/2024] [Indexed: 06/13/2024] Open
Abstract
Gastrointestinal lymphoma is the most common form of lymphoma in domestic cats. Aggressive phenotypes are much less common but do bear and unfavorable prognosis. Immunophenotyping by flow cytometry (FCM) is not systematically performed in these patients, because of difficulties in the acquisition of suitable sample material from the gastrointestinal tract. A multimodal diagnostic approach is recommended to improve identification of subtypes targeting patient tailored therapeutic strategies. The aim of this prospective study was to present results of multicolor FCM immunophenotyping in surgically removed gastrointestinal mass and relate them with histopathology using the World Health Organization (WHO) classification and clonality PCR testing. Thirty-two patients were included. Eight cats (25%) had gastric, 23 (72%) had intestinal lymphoma and 1 (3%) had gastric/jejunal lymphoma. Intestinal lymphoma sites were represented by 18 small intestinal, 4 ileocaecal, 1 large intestinal. All gastric lymphomas were diffuse large B-cell lymphoma (DLBCL). Small intestinal lymphomas were 10 enteropathy associated T-cell lymphoma type I (EATL I), 2 enteropathy associated T-cell lymphoma type II (EATL II), 2 peripheral T-cell lymphoma (PTCL), 3 DLBCL and one DLBCL+EATL II. The most common small intestinal FCM T-cell phenotype was CD3+CD21- CD4-CD8-CD18+ CD5-CD79- in 7/10 EATL I and one EATL II. The most frequent FCM B-cell phenotype was CD3-CD21+ CD4-CD8-CD18+ CD5-CD79+ in 13/17 DLBCL and the DLBCL+EATL II. Clonality PCR results were positive in 87.5% (28/32) of all cases. No cross-lineage rearrangement was observed. IHC and FCM results agreed in 87.5% (28/32) of all cases. When all 3 methods were combined, consistent results were seen in 75% (24/32). This is the first demonstration of a multicolor FCM approach set in context to the gold standard histopathology and clonality testing results.
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Affiliation(s)
- Barbara C. Rütgen
- Clinical Pathology, Department of Biological Sciences and Pathobiology, University of Veterinary Medicine Vienna, Vienna, Austria
| | - Birgitt Wolfesberger
- Clinical Unit of Internal Medicine Small Animals, Department for Companion Animals and Horses, University of Veterinary Medicine Vienna, Vienna, Austria
| | - Daniel Baumgartner
- Clinical Pathology, Department of Biological Sciences and Pathobiology, University of Veterinary Medicine Vienna, Vienna, Austria
| | - Sabine E. Hammer
- Institute of Immunology, Department of Biological Sciences and Pathobiology, University of Veterinary Medicine Vienna, Vienna, Austria
| | - Sandra Groiss
- Institute of Immunology, Department of Biological Sciences and Pathobiology, University of Veterinary Medicine Vienna, Vienna, Austria
| | - Katharina M. Hittmair
- Diagnostic Imaging, Department for Companion Animals and Horses, University of Veterinary Medicine Vienna, Vienna, Austria
| | - Gabriele Gradner
- Small Animal Hospital Clinic, Department for Companion Animals and Horses, University of Veterinary Medicine Vienna, Vienna, Austria
| | - Andrea Fuchs-Baumgartinger
- Institute of Pathology, Department of Biological Sciences and Pathobiology, University of Veterinary Medicine Vienna, Vienna, Austria
| | - Taryn A. Donovan
- The Schwarzman Animal Medical Center, New York, NY, United States
| | - Ilse Schwendenwein
- Clinical Pathology, Department of Biological Sciences and Pathobiology, University of Veterinary Medicine Vienna, Vienna, Austria
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3
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Herold N, Bruhns M, Babaei S, Spreuer J, Castagna A, Yurttas C, Scheuermann S, Seitz C, Ruf B, Königsrainer A, Jurmeister P, Löffler MW, Claassen M, Wistuba-Hamprecht K. High-dimensional in situ proteomics imaging to assess γδ T cells in spatial biology. J Leukoc Biol 2024; 115:750-759. [PMID: 38285597 DOI: 10.1093/jleuko/qiad167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 12/19/2023] [Accepted: 12/21/2023] [Indexed: 01/31/2024] Open
Abstract
This study presents a high-dimensional immunohistochemistry approach to assess human γδ T cell subsets in their native tissue microenvironments at spatial resolution, a hitherto unmet scientific goal due to the lack of established antibodies and required technology. We report an integrated approach based on multiplexed imaging and bioinformatic analysis to identify γδ T cells, characterize their phenotypes, and analyze the composition of their microenvironment. Twenty-eight γδ T cell microenvironments were identified in tissue samples from fresh frozen human colon and colorectal cancer where interaction partners of the immune system, but also cancer cells were discovered in close proximity to γδ T cells, visualizing their potential contributions to cancer immunosurveillance. While this proof-of-principle study demonstrates the potential of this cutting-edge technology to assess γδ T cell heterogeneity and to investigate their microenvironment, future comprehensive studies are warranted to associate phenotypes and microenvironment profiles with features such as relevant clinical characteristics.
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Affiliation(s)
- Nicola Herold
- Department of Internal Medicine I, University Hospital Tübingen, Otfried-Müller-Straße 10, 72076 Tübingen, Germany
- M3 Research Center for Malignome, Metabolome and Microbiome, Faculty of Medicine, University Tübingen, Otfried-Müller-Straße 37, 72076 Tübingen, Germany
| | - Matthias Bruhns
- Department of Internal Medicine I, University Hospital Tübingen, Otfried-Müller-Straße 10, 72076 Tübingen, Germany
- M3 Research Center for Malignome, Metabolome and Microbiome, Faculty of Medicine, University Tübingen, Otfried-Müller-Straße 37, 72076 Tübingen, Germany
- Department of Computer Science, Eberhard Karls University of Tübingen, Sand 14, 72076 Tübingen, Germany
- Institute of Biomedical Informatics, University Hospital Tübingen, Eberhard Karls University of Tübingen, Sand 14, 72076 Tübingen, Germany
| | - Sepideh Babaei
- Department of Internal Medicine I, University Hospital Tübingen, Otfried-Müller-Straße 10, 72076 Tübingen, Germany
- M3 Research Center for Malignome, Metabolome and Microbiome, Faculty of Medicine, University Tübingen, Otfried-Müller-Straße 37, 72076 Tübingen, Germany
- Department of Computer Science, Eberhard Karls University of Tübingen, Sand 14, 72076 Tübingen, Germany
- Institute of Biomedical Informatics, University Hospital Tübingen, Eberhard Karls University of Tübingen, Sand 14, 72076 Tübingen, Germany
| | - Janine Spreuer
- Department of Internal Medicine I, University Hospital Tübingen, Otfried-Müller-Straße 10, 72076 Tübingen, Germany
- M3 Research Center for Malignome, Metabolome and Microbiome, Faculty of Medicine, University Tübingen, Otfried-Müller-Straße 37, 72076 Tübingen, Germany
| | - Arianna Castagna
- Department of General, Visceral and Transplant Surgery, University Hospital Tübingen, Hoppe-Seyler-Straße 3, 72076 Tübingen, Germany
| | - Can Yurttas
- Department of General, Visceral and Transplant Surgery, University Hospital Tübingen, Hoppe-Seyler-Straße 3, 72076 Tübingen, Germany
| | - Sophia Scheuermann
- Department of Pediatric Hematology and Oncology, University Hospital Tübingen, Hoppe-Seyler-Straße 1, 72076 Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies," Tübingen, Germany
| | - Christian Seitz
- Department of Pediatric Hematology and Oncology, University Hospital Tübingen, Hoppe-Seyler-Straße 1, 72076 Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies," Tübingen, Germany
| | - Benjamin Ruf
- Department of Internal Medicine I, University Hospital Tübingen, Otfried-Müller-Straße 10, 72076 Tübingen, Germany
- M3 Research Center for Malignome, Metabolome and Microbiome, Faculty of Medicine, University Tübingen, Otfried-Müller-Straße 37, 72076 Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies," Tübingen, Germany
| | - Alfred Königsrainer
- Department of General, Visceral and Transplant Surgery, University Hospital Tübingen, Hoppe-Seyler-Straße 3, 72076 Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies," Tübingen, Germany
| | - Philipp Jurmeister
- Institute of Pathology, Ludwig Maximilians University Hospital Munich, Thalkirchner Straße 36, 80337 Munich, Germany
- German Cancer Consortium, Partner Site Munich, and German Cancer Research Center, Heidelberg, Germany
| | - Markus W Löffler
- Department of General, Visceral and Transplant Surgery, University Hospital Tübingen, Hoppe-Seyler-Straße 3, 72076 Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies," Tübingen, Germany
- Department of Immunology, Interfaculty Institute for Cell Biology, Eberhard Karls University Tübingen, Auf der Morgenstelle 15, 72076 Tübingen, Germany
- Institute for Clinical and Experimental Transfusion Medicine, Medical Faculty of Tübingen, University Hospital Tübingen, Otfried-Müller-Straße 4/1, 72076 Tübingen, Germany
- German Cancer Consortium, Partner Site Tübingen, and German Cancer Research Center, Heidelberg, Germany
| | - Manfred Claassen
- Department of Internal Medicine I, University Hospital Tübingen, Otfried-Müller-Straße 10, 72076 Tübingen, Germany
- M3 Research Center for Malignome, Metabolome and Microbiome, Faculty of Medicine, University Tübingen, Otfried-Müller-Straße 37, 72076 Tübingen, Germany
- Department of Computer Science, Eberhard Karls University of Tübingen, Sand 14, 72076 Tübingen, Germany
- Institute of Biomedical Informatics, University Hospital Tübingen, Eberhard Karls University of Tübingen, Sand 14, 72076 Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies," Tübingen, Germany
| | - Kilian Wistuba-Hamprecht
- Department of Internal Medicine I, University Hospital Tübingen, Otfried-Müller-Straße 10, 72076 Tübingen, Germany
- M3 Research Center for Malignome, Metabolome and Microbiome, Faculty of Medicine, University Tübingen, Otfried-Müller-Straße 37, 72076 Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies," Tübingen, Germany
- Department of Immunology, Interfaculty Institute for Cell Biology, Eberhard Karls University Tübingen, Auf der Morgenstelle 15, 72076 Tübingen, Germany
- German Cancer Consortium, Partner Site Tübingen, and German Cancer Research Center, Heidelberg, Germany
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Peng Y, Yang H, Chen Q, Jin H, Xue YH, Du MQ, Liu S, Yao SY. An angel or a devil? Current view on the role of CD8 + T cells in the pathogenesis of myasthenia gravis. J Transl Med 2024; 22:183. [PMID: 38378668 PMCID: PMC10877804 DOI: 10.1186/s12967-024-04965-7] [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: 12/13/2023] [Accepted: 02/07/2024] [Indexed: 02/22/2024] Open
Abstract
BACKGROUND Myasthenia gravis (MG) and the experimental autoimmune MG (EAMG) animal model are characterized by T-cell-induced and B-cell-dominated autoimmune diseases that affect the neuromuscular junction. Several subtypes of CD4+ T cells, including T helper (Th) 17 cells, follicular Th cells, and regulatory T cells (Tregs), contribute to the pathogenesis of MG. However, increasing evidence suggests that CD8+ T cells also play a critical role in the pathogenesis and treatment of MG. MAIN BODY Herein, we review the literature on CD8+ T cells in MG, focusing on their potential effector and regulatory roles, as well as on relevant evidence (peripheral, in situ, cerebrospinal fluid, and under different treatments), T-cell receptor usage, cytokine and chemokine expression, cell marker expression, and Treg, Tc17, CD3+CD8+CD20+ T, and CXCR5+ CD8+ T cells. CONCLUSIONS Further studies on CD8+ T cells in MG are necessary to determine, among others, the real pattern of the Vβ gene usage of autoantigen-specific CD8+ cells in patients with MG, real images of the physiology and function of autoantigen-specific CD8+ cells from MG/EAMG, and the subset of autoantigen-specific CD8+ cells (Tc1, Tc17, and IL-17+IFN-γ+CD8+ T cells). There are many reports of CD20-expressing T (or CD20 + T) and CXCR5+ CD8 T cells on autoimmune diseases, especially on multiple sclerosis and rheumatoid arthritis. Unfortunately, up to now, there has been no report on these T cells on MG, which might be a good direction for future studies.
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Affiliation(s)
- Yong Peng
- Department of Neurology, Affiliated First Hospital of Hunan Traditional Chinese Medical College, Zhuzhou, 412000, Hunan, China.
- Department of Neurology, The Third Affiliated Hospital of Hunan University of Chinese Medicine, Zhuzhou, 412000, Hunan, China.
| | - Huan Yang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
| | - Quan Chen
- Department of Neurology, Affiliated First Hospital of Hunan Traditional Chinese Medical College, Zhuzhou, 412000, Hunan, China
- Department of Neurology, The Third Affiliated Hospital of Hunan University of Chinese Medicine, Zhuzhou, 412000, Hunan, China
| | - Hong Jin
- Department of Neurology, Affiliated First Hospital of Hunan Traditional Chinese Medical College, Zhuzhou, 412000, Hunan, China
- Department of Neurology, The Third Affiliated Hospital of Hunan University of Chinese Medicine, Zhuzhou, 412000, Hunan, China
| | - Ya-Hui Xue
- Department of Neurology, Affiliated First Hospital of Hunan Traditional Chinese Medical College, Zhuzhou, 412000, Hunan, China
- Department of Neurology, The Third Affiliated Hospital of Hunan University of Chinese Medicine, Zhuzhou, 412000, Hunan, China
| | - Miao-Qiao Du
- Department of Neurology, Affiliated First Hospital of Hunan Traditional Chinese Medical College, Zhuzhou, 412000, Hunan, China
- Department of Neurology, The Third Affiliated Hospital of Hunan University of Chinese Medicine, Zhuzhou, 412000, Hunan, China
| | - Shu Liu
- Department of Neurology, Affiliated First Hospital of Hunan Traditional Chinese Medical College, Zhuzhou, 412000, Hunan, China
- Department of Neurology, The Third Affiliated Hospital of Hunan University of Chinese Medicine, Zhuzhou, 412000, Hunan, China
| | - Shun-Yu Yao
- Department of Neurology, Affiliated First Hospital of Hunan Traditional Chinese Medical College, Zhuzhou, 412000, Hunan, China
- Department of Neurology, The Third Affiliated Hospital of Hunan University of Chinese Medicine, Zhuzhou, 412000, Hunan, China
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He Z, Hu S, Chen Y, An S, Zhou J, Liu R, Shi J, Wang J, Dong G, Shi J, Zhao J, Ou-Yang L, Zhu Y, Bo X, Ying X. Mosaic integration and knowledge transfer of single-cell multimodal data with MIDAS. Nat Biotechnol 2024:10.1038/s41587-023-02040-y. [PMID: 38263515 DOI: 10.1038/s41587-023-02040-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 10/23/2023] [Indexed: 01/25/2024]
Abstract
Integrating single-cell datasets produced by multiple omics technologies is essential for defining cellular heterogeneity. Mosaic integration, in which different datasets share only some of the measured modalities, poses major challenges, particularly regarding modality alignment and batch effect removal. Here, we present a deep probabilistic framework for the mosaic integration and knowledge transfer (MIDAS) of single-cell multimodal data. MIDAS simultaneously achieves dimensionality reduction, imputation and batch correction of mosaic data by using self-supervised modality alignment and information-theoretic latent disentanglement. We demonstrate its superiority to 19 other methods and reliability by evaluating its performance in trimodal and mosaic integration tasks. We also constructed a single-cell trimodal atlas of human peripheral blood mononuclear cells and tailored transfer learning and reciprocal reference mapping schemes to enable flexible and accurate knowledge transfer from the atlas to new data. Applications in mosaic integration, pseudotime analysis and cross-tissue knowledge transfer on bone marrow mosaic datasets demonstrate the versatility and superiority of MIDAS. MIDAS is available at https://github.com/labomics/midas .
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Affiliation(s)
- Zhen He
- Center for Computational Biology, Beijing Institute of Basic Medical Sciences, Beijing, China
| | - Shuofeng Hu
- Center for Computational Biology, Beijing Institute of Basic Medical Sciences, Beijing, China
| | - Yaowen Chen
- Center for Computational Biology, Beijing Institute of Basic Medical Sciences, Beijing, China
| | - Sijing An
- Center for Computational Biology, Beijing Institute of Basic Medical Sciences, Beijing, China
| | - Jiahao Zhou
- Center for Computational Biology, Beijing Institute of Basic Medical Sciences, Beijing, China
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China
| | - Runyan Liu
- Center for Computational Biology, Beijing Institute of Basic Medical Sciences, Beijing, China
| | - Junfeng Shi
- School of Automation, China University of Geosciences, Wuhan, China
| | - Jing Wang
- Center for Computational Biology, Beijing Institute of Basic Medical Sciences, Beijing, China
| | - Guohua Dong
- Center for Computational Biology, Beijing Institute of Basic Medical Sciences, Beijing, China
| | - Jinhui Shi
- Center for Computational Biology, Beijing Institute of Basic Medical Sciences, Beijing, China
| | - Jiaxin Zhao
- Center for Computational Biology, Beijing Institute of Basic Medical Sciences, Beijing, China
| | - Le Ou-Yang
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China
| | - Yuan Zhu
- School of Automation, China University of Geosciences, Wuhan, China
| | - Xiaochen Bo
- Institute of Health Service and Transfusion Medicine, Beijing, China.
| | - Xiaomin Ying
- Center for Computational Biology, Beijing Institute of Basic Medical Sciences, Beijing, China.
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Zhang Y, Guo C, Zhou Y, Zhang W, Zhu Z, Wang W, Wan Y. A biphenotypic lymphocyte subset displays both T- and B-cell functionalities. Commun Biol 2024; 7:28. [PMID: 38182721 PMCID: PMC10770049 DOI: 10.1038/s42003-023-05719-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 12/18/2023] [Indexed: 01/07/2024] Open
Abstract
T cell/B cell mixed phenotypic lymphocytes have been observed in different disease contexts, yet their presence and function in physiological conditions remain elusive. Here, we provide evidence for the existence of a lymphocyte subset endogenously expressing both T- and B-cell lineage markers in mice. The majority of these T/B phenotypic lymphocytes (CD3+CD19+) show an origin of pro/pre B cells and distribute widely in mouse bone marrow, lymph nodes, spleen, and peripheral blood. Functional assays show that these biphenotypic lymphocytes can be activated through stimulating TCR or BCR signaling pathways. Moreover, we show that these cells actively participate both the humoral and cellular immune responses elicited by vaccination. Compared to conventional T cells, these biphenotypic lymphocytes can secrete a higher level of IL-2 but a lower level of TNF-α upon antigen specific stimulation. An equivalent lymphocyte subset is found in freshly isolated human PBMCs and exhibits similar functionality, albeit at a lower frequency than in mice.
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Affiliation(s)
- Yifan Zhang
- Clinical Laboratory, The First Affiliated Hospital of Zhengzhou University, Key Laboratory of Laboratory Medicine of Henan Province, Zhengzhou, China
- Department of Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, National Medical Center for Infectious Diseases, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- Department of Laboratory Medicine, Shanghai Public Health Clinical Center, Shanghai, China
| | - Cuiyuan Guo
- Clinical Laboratory, The First Affiliated Hospital of Zhengzhou University, Key Laboratory of Laboratory Medicine of Henan Province, Zhengzhou, China
- Department of Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, National Medical Center for Infectious Diseases, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- Department of Laboratory Medicine, Shanghai Public Health Clinical Center, Shanghai, China
| | - Yigong Zhou
- Life Science Department, Faculty of Agricultural and Environmental Sciences, Macdonald Campus of McGill University, Quebec, Canada
| | - Wenhong Zhang
- Department of Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, National Medical Center for Infectious Diseases, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Zhaoqin Zhu
- Department of Laboratory Medicine, Shanghai Public Health Clinical Center, Shanghai, China.
| | - Wanhai Wang
- Clinical Laboratory, The First Affiliated Hospital of Zhengzhou University, Key Laboratory of Laboratory Medicine of Henan Province, Zhengzhou, China.
| | - Yanmin Wan
- Department of Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, National Medical Center for Infectious Diseases, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
- Shanghai Huashen Institute of Microbes and Infections, Shanghai, China.
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7
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Nguyen HTT, Magez S, Radwanska M. From helping to regulating – A transcriptomic profile of Ifng+ Il10+ Il21+ Cd4+ Th1 cells indicates their role in regulating inflammation during experimental trypanosomosis. FRONTIERS IN TROPICAL DISEASES 2023. [DOI: 10.3389/fitd.2023.1127022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023] Open
Abstract
IntroductionTrypanosoma evansi parasite infections cause a chronic animal wasting disease called Surra, and cases of atypical Human Trypanosomosis (aHT). In experimental models, T. evansi infections are hallmarked by the early onset of excessive inflammation. Therefore, balancing the production of inflammatory cytokines by anti-inflammatory IL-10 is crucial for prolonged survival.MethodsTo improve the understanding of trypanosomosis induced immunopathology, we used scRNA-seq data from an experimental chronic T. evansi infection mouse model, resembling natural infection in terms of disease characteristics. Results and discussionFor the first time, obtained results allowed to assess the transcriptomic profile and heterogeneity of splenic CD4+ T cell subsets, during a trypanosome infection. Here, the predominant subpopulation of T cells was represented by Tbx21(T-bet)+Ccr5+ Id2+ type 1 helper T cells (Th1), followed by Icos+ Cxcr5+Follicular T helper cells (Tfh) and very minor fraction of Il2ra(CD25)+Foxp3+ regulatory T cells (Tregs). Interestingly, the profile of Th1 cells shows that besides Ifng, these cells express high levels of Il10 and Il21, coding for anti-inflammatory and immunoregulatory cytokines. This coincides with the elevated expression of key genes involved in IL-10 and IL-21 secretion pathway such as Stat1 and Stat3, as well as the transcriptional factors Prdm1 (Blimp 1), and Maf (c-Maf). In contrast, there is virtually no IL-10 transcription detected in the Treg population. Finally, differential gene expression and gene ontology analysis of infection-induced Ifng+ Il10+ Il21+ Th1 cells highlights their suppressive function on T cell activation, differentiation and INF-γ production itself. This indicates that during trypanosome infections, the Ifng+ Il10+ Il21+ Th1 cells, rather than Tregs, assume an immune regulatory role that is needed for dampening inflammation.
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Wang Y, Lian B, Zhang H, Zhong Y, He J, Wu F, Reinert K, Shang X, Yang H, Hu J. A multi-view latent variable model reveals cellular heterogeneity in complex tissues for paired multimodal single-cell data. Bioinformatics 2023; 39:btad005. [PMID: 36622018 PMCID: PMC9857983 DOI: 10.1093/bioinformatics/btad005] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 12/27/2022] [Accepted: 01/06/2023] [Indexed: 01/10/2023] Open
Abstract
MOTIVATION Single-cell multimodal assays allow us to simultaneously measure two different molecular features of the same cell, enabling new insights into cellular heterogeneity, cell development and diseases. However, most existing methods suffer from inaccurate dimensionality reduction for the joint-modality data, hindering their discovery of novel or rare cell subpopulations. RESULTS Here, we present VIMCCA, a computational framework based on variational-assisted multi-view canonical correlation analysis to integrate paired multimodal single-cell data. Our statistical model uses a common latent variable to interpret the common source of variances in two different data modalities. Our approach jointly learns an inference model and two modality-specific non-linear models by leveraging variational inference and deep learning. We perform VIMCCA and compare it with 10 existing state-of-the-art algorithms on four paired multi-modal datasets sequenced by different protocols. Results demonstrate that VIMCCA facilitates integrating various types of joint-modality data, thus leading to more reliable and accurate downstream analysis. VIMCCA improves our ability to identify novel or rare cell subtypes compared to existing widely used methods. Besides, it can also facilitate inferring cell lineage based on joint-modality profiles. AVAILABILITY AND IMPLEMENTATION The VIMCCA algorithm has been implemented in our toolkit package scbean (≥0.5.0), and its code has been archived at https://github.com/jhu99/scbean under MIT license. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yuwei Wang
- School of Computer Science, Northwestern Polytechnical University, Shaanxi 710129, China
| | - Bin Lian
- School of Computer Science, Northwestern Polytechnical University, Shaanxi 710129, China
| | - Haohui Zhang
- School of Computer Science, Northwestern Polytechnical University, Shaanxi 710129, China
| | - Yuanke Zhong
- School of Computer Science, Northwestern Polytechnical University, Shaanxi 710129, China
| | - Jie He
- Department of Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China
| | - Fashuai Wu
- Department of Orthopaedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Knut Reinert
- Institut für Informatik, Freie Universität Berlin, 14195 Berlin, Germany
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Shaanxi 710129, China
| | - Hui Yang
- School of Life Science, Northwestern Polytechnical University, Shaanxi 710072, China
| | - Jialu Hu
- School of Computer Science, Northwestern Polytechnical University, Shaanxi 710129, China
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Implementation of Mass Cytometry for Immunoprofiling of Patients with Solid Tumors. J Immunol Res 2019; 2019:6705949. [PMID: 30886872 PMCID: PMC6388349 DOI: 10.1155/2019/6705949] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Revised: 11/12/2018] [Accepted: 11/21/2018] [Indexed: 11/22/2022] Open
Abstract
Monitoring immune responses to solid cancers may be a better prognostic tool than conventional staging criteria, and it can also serve as an important criterion for the selection of individualized therapy. Multiparametric phenotyping by mass cytometry extended possibilities for immunoprofiling. However, careful optimization of each step of such method is necessary for obtaining reliable results. Also, with respect to procedure length and costs, sample preparation, staining, and storage should be optimized. Here, we designed a panel of 31 antibodies which allows for identification of several subpopulations of lymphoid and myeloid cells in a solid tumor and peripheral blood simultaneously. For sample preparation, disaggregation of tumor tissue with two different collagenases combined with DNase I was compared, and removal of dead or tumor cells by magnetic separation was evaluated. Two possible procedures of barcoding for single-tube staining of several samples were examined. While the palladium-based barcoding affected the stability of several antigens, the staining with two differently labeled CD45 antibodies was suitable for cells isolated from a patient's blood and tumor. The storage of samples in the intercalation solution for up to two weeks did not influence results of the analysis, which allowed the measurement of samples collected within this interval on the same day. This procedure optimized on samples from patients with head and neck squamous cell carcinoma enabled identification of various immune cells including rare subpopulations.
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10
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Abstract
The CyTOF system produces single cell protein expression data similar to that from flow cytometry, but with an increased number of features measured. Traditionally, analysis of these data is carried out using manual gating, but with the increased dimensionality, manual gating becomes a suboptimal analysis strategy in some cases. To address this, a number of data analysis tools for tasks such as clustering, differential abundance analysis, and visualization have been developed and made freely available. We here introduce some of the more popular tools for CyTOF analysis and exemplify their utility in a common analysis workflow.
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Affiliation(s)
- Christina B Pedersen
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Lars R Olsen
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark.
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11
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Schuh E, Berer K, Mulazzani M, Feil K, Meinl I, Lahm H, Krane M, Lange R, Pfannes K, Subklewe M, Gürkov R, Bradl M, Hohlfeld R, Kümpfel T, Meinl E, Krumbholz M. Features of Human CD3+CD20+ T Cells. THE JOURNAL OF IMMUNOLOGY 2016; 197:1111-7. [DOI: 10.4049/jimmunol.1600089] [Citation(s) in RCA: 102] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2016] [Accepted: 06/02/2016] [Indexed: 12/17/2022]
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