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Mahyari E, Boggy GJ, McElfresh GW, Kaza M, Benjamin S, Varco-Merth B, Ojha S, Feltham S, Goodwin W, Nkoy C, Duell D, Selseth A, Bennett T, Barber-Axthelm A, Smedley JV, Labriola CS, Axthelm MK, Reeves RK, Okoye AA, Hansen SG, Picker LJ, Bimber BN. Enhanced interpretation of immune cell phenotype and function through a rhesus macaque single-cell atlas. CELL GENOMICS 2025; 5:100849. [PMID: 40233746 DOI: 10.1016/j.xgen.2025.100849] [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: 10/08/2024] [Revised: 02/17/2025] [Accepted: 03/18/2025] [Indexed: 04/17/2025]
Abstract
Single-cell RNA sequencing (scRNA-seq) allows cell classification using genome-wide transcriptional state; however, high-dimensional transcriptomic profiles, and the unsupervised analyses employed to interpret them, provide a systematically different view of biology than well-established functional/lineage definitions of immunocytes. Understanding these differences and limits is essential for accurate interpretation of these rich data. We present the Rhesus Immune Reference Atlas (RIRA), the first immune-focused macaque single-cell multi-tissue atlas. We contrasted transcriptional profiles against immune lineages, using surface protein and marker genes as ground truth. While the pattern of clustering can align with cell type, this is not always true. Especially within T and natural killer (NK) cells, many functionally distinct subsets lack defining markers, and strong shared expression programs, such as cytotoxicity, result in systematic intermingling by unsupervised clustering. We identified gene programs with high discriminatory/diagnostic value, including multi-gene signatures that model T/NK cell maturation. Directly measuring these diagnostic programs complements unsupervised analyses.
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Affiliation(s)
- Eisa Mahyari
- Oregon National Primate Research Center, Oregon Health and Science University, Beaverton, OR 97006, USA
| | - Gregory J Boggy
- Oregon National Primate Research Center, Oregon Health and Science University, Beaverton, OR 97006, USA
| | - G W McElfresh
- Oregon National Primate Research Center, Oregon Health and Science University, Beaverton, OR 97006, USA
| | - Maanasa Kaza
- Oregon National Primate Research Center, Oregon Health and Science University, Beaverton, OR 97006, USA
| | - Sebastian Benjamin
- Oregon National Primate Research Center, Oregon Health and Science University, Beaverton, OR 97006, USA
| | - Benjamin Varco-Merth
- Oregon National Primate Research Center, Oregon Health and Science University, Beaverton, OR 97006, USA; Vaccine and Gene Therapy Institute, Oregon Health and Science University, Beaverton, OR 97006, USA
| | - Sohita Ojha
- Vaccine and Gene Therapy Institute, Oregon Health and Science University, Beaverton, OR 97006, USA
| | - Shana Feltham
- Vaccine and Gene Therapy Institute, Oregon Health and Science University, Beaverton, OR 97006, USA
| | - William Goodwin
- Oregon National Primate Research Center, Oregon Health and Science University, Beaverton, OR 97006, USA; Vaccine and Gene Therapy Institute, Oregon Health and Science University, Beaverton, OR 97006, USA
| | - Candice Nkoy
- Oregon National Primate Research Center, Oregon Health and Science University, Beaverton, OR 97006, USA; Vaccine and Gene Therapy Institute, Oregon Health and Science University, Beaverton, OR 97006, USA
| | - Derick Duell
- Oregon National Primate Research Center, Oregon Health and Science University, Beaverton, OR 97006, USA; Vaccine and Gene Therapy Institute, Oregon Health and Science University, Beaverton, OR 97006, USA
| | - Andrea Selseth
- Vaccine and Gene Therapy Institute, Oregon Health and Science University, Beaverton, OR 97006, USA
| | - Tyler Bennett
- Vaccine and Gene Therapy Institute, Oregon Health and Science University, Beaverton, OR 97006, USA
| | - Aaron Barber-Axthelm
- Vaccine and Gene Therapy Institute, Oregon Health and Science University, Beaverton, OR 97006, USA
| | - Jeremy V Smedley
- Oregon National Primate Research Center, Oregon Health and Science University, Beaverton, OR 97006, USA; Vaccine and Gene Therapy Institute, Oregon Health and Science University, Beaverton, OR 97006, USA
| | - Caralyn S Labriola
- Oregon National Primate Research Center, Oregon Health and Science University, Beaverton, OR 97006, USA; Vaccine and Gene Therapy Institute, Oregon Health and Science University, Beaverton, OR 97006, USA
| | - Michael K Axthelm
- Oregon National Primate Research Center, Oregon Health and Science University, Beaverton, OR 97006, USA; Vaccine and Gene Therapy Institute, Oregon Health and Science University, Beaverton, OR 97006, USA
| | - R Keith Reeves
- Division of Innate and Comparative Immunology, Center for Human Systems Immunology, Duke University School of Medicine, Durham, NC, USA; Department of Surgery, Duke University School of Medicine, Durham, NC, USA
| | - Afam A Okoye
- Oregon National Primate Research Center, Oregon Health and Science University, Beaverton, OR 97006, USA; Vaccine and Gene Therapy Institute, Oregon Health and Science University, Beaverton, OR 97006, USA
| | - Scott G Hansen
- Oregon National Primate Research Center, Oregon Health and Science University, Beaverton, OR 97006, USA; Vaccine and Gene Therapy Institute, Oregon Health and Science University, Beaverton, OR 97006, USA
| | - Louis J Picker
- Oregon National Primate Research Center, Oregon Health and Science University, Beaverton, OR 97006, USA; Vaccine and Gene Therapy Institute, Oregon Health and Science University, Beaverton, OR 97006, USA
| | - Benjamin N Bimber
- Oregon National Primate Research Center, Oregon Health and Science University, Beaverton, OR 97006, USA; Vaccine and Gene Therapy Institute, Oregon Health and Science University, Beaverton, OR 97006, USA.
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2
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Maurer K, Park CY, Mani S, Borji M, Raths F, Gouin KH, Penter L, Jin Y, Zhang JY, Shin C, Brenner JR, Southard J, Krishna S, Lu W, Lyu H, Abbondanza D, Mangum C, Olsen LR, Lawson MJ, Fabani M, Neuberg DS, Bachireddy P, Glezer EN, Farhi SL, Li S, Livak KJ, Ritz J, Soiffer RJ, Wu CJ, Azizi E. Coordinated immune networks in leukemia bone marrow microenvironments distinguish response to cellular therapy. Sci Immunol 2025; 10:eadr0782. [PMID: 39854478 DOI: 10.1126/sciimmunol.adr0782] [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: 06/13/2024] [Accepted: 12/18/2024] [Indexed: 01/26/2025]
Abstract
Understanding how intratumoral immune populations coordinate antitumor responses after therapy can guide treatment prioritization. We systematically analyzed an established immunotherapy, donor lymphocyte infusion (DLI), by assessing 348,905 single-cell transcriptomes from 74 longitudinal bone marrow samples of 25 patients with relapsed leukemia; a subset was evaluated by both protein- and transcriptome-based spatial analysis. In acute myeloid leukemia (AML) DLI responders, we identified clonally expanded ZNF683+ CD8+ cytotoxic T lymphocytes with in vitro specificity for patient-matched AML. These cells originated primarily from the DLI product and appeared to coordinate antitumor immune responses through interaction with diverse immune cell types within the marrow microenvironment. Nonresponders lacked this cross-talk and had cytotoxic T lymphocytes with elevated TIGIT expression. Our study identifies recipient bone marrow microenvironment differences as a determinant of an effective antileukemia response and opens opportunities to modulate cellular therapy.
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Affiliation(s)
- Katie Maurer
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Harvard Medical School, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Cameron Y Park
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY 10027, USA
- Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA
| | - Shouvik Mani
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY 10027, USA
- Department of Computer Science, Columbia University, New York, NY 10027, USA
| | - Mehdi Borji
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | | | | | - Livius Penter
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Harvard Medical School, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Hematology, Oncology, and Tumorimmunology, Campus Virchow Klinikum, Berlin, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 13353 Berlin, Germany
| | - Yinuo Jin
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY 10027, USA
- Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA
| | - Jia Yi Zhang
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY 10027, USA
- Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA
| | - Crystal Shin
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY 10027, USA
- Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA
| | - James R Brenner
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Jackson Southard
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Translational Immunogenomics Laboratory, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Sachi Krishna
- Spatial Technology Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Wesley Lu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Translational Immunogenomics Laboratory, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Haoxiang Lyu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Translational Immunogenomics Laboratory, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Domenic Abbondanza
- Spatial Technology Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Medicine, Section of Rheumatology, University of Chicago, Chicago, IL 60637, USA
| | - Chanell Mangum
- Spatial Technology Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Lars Rønn Olsen
- Department of Health Technology, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
| | | | | | - Donna S Neuberg
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Pavan Bachireddy
- Department of Hematopoietic Biology & Malignancy, MD Anderson Cancer Center, Houston, TX 77030, USA
| | | | - Samouil L Farhi
- Spatial Technology Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Shuqiang Li
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Translational Immunogenomics Laboratory, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Kenneth J Livak
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Translational Immunogenomics Laboratory, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Jerome Ritz
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Robert J Soiffer
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Catherine J Wu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Harvard Medical School, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Elham Azizi
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY 10027, USA
- Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA
- Department of Computer Science, Columbia University, New York, NY 10027, USA
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Ran R, Trapecar M, Brubaker DK. Systematic analysis of human colorectal cancer scRNA-seq revealed limited pro-tumoral IL-17 production potential in gamma delta T cells. Neoplasia 2024; 58:101072. [PMID: 39454432 PMCID: PMC11539345 DOI: 10.1016/j.neo.2024.101072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2024] [Revised: 10/10/2024] [Accepted: 10/10/2024] [Indexed: 10/28/2024]
Abstract
Gamma delta T cells play a crucial role in anti-tumor immunity due to their cytotoxic properties. However, the role and extent of γδ T cells in production of pro-tumorigenic interleukin-17 (IL-17) within the tumor microenvironment of colorectal cancer (CRC) remains controversial. In this study, we re-analyzed nine published human CRC whole-tissue single-cell RNA sequencing datasets, identifying 18,483 γδ T cells out of 951,785 total cells, in the neoplastic or adjacent normal tissue of 165 human CRC patients. Our results confirm that tumor-infiltrating γδ T cells exhibit high cytotoxicity-related transcription in both tumor and adjacent normal tissues, but critically, none of the γδ T cell clusters showed IL-17 production potential. We also identified various γδ T cell subsets, including poised effector-like T cells, tissue-resident memory T cells, progenitor exhausted-like T cells, and exhausted T cells, and noted an increased expression of cytotoxic molecules in tumor-infiltrating γδ T cells compared to their normal area counterparts. We proposed anti-tumor γδ T effector cells may arise from tissue-resident progenitor cells based on the trajectory analysis. Our work demonstrates that γδ T cells in CRC primarily function as cytotoxic effector cells rather than IL-17 producers, mitigating the concerns about their potential pro-tumorigenic roles in CRC, highlighting the importance of accurately characterizing these cells for cancer immunotherapy research and the unneglectable cross-species discrepancy between the mouse and human immune system in the study of cancer immunology.
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Affiliation(s)
- Ran Ran
- Center for Global Health and Diseases, Department of Pathology, Case Western Reserve University, Cleveland, OH, USA
| | - Martin Trapecar
- Department of Medicine, Johns Hopkins University School of Medicine, Institute for Fundamental Biomedical Research, Johns Hopkins All Children's Hospital, St. Petersburg, FL, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Douglas K Brubaker
- Center for Global Health and Diseases, Department of Pathology, Case Western Reserve University, Cleveland, OH, USA; The Blood, Heart, Lung, and Immunology Research Center, Case Western Reserve University, University Hospitals of Cleveland, Cleveland, OH, USA.
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4
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Mullan KA, Ha M, Valkiers S, de Vrij N, Ogunjimi B, Laukens K, Meysman P. T cell receptor-centric perspective to multimodal single-cell data analysis. SCIENCE ADVANCES 2024; 10:eadr3196. [PMID: 39612336 PMCID: PMC11606500 DOI: 10.1126/sciadv.adr3196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2024] [Accepted: 10/28/2024] [Indexed: 12/01/2024]
Abstract
The T cell receptor (TCR), despite its importance, is underutilized in single-cell analysis, with gene expression features solely driving current strategies. Here, we argue for a TCR-first approach, more suited toward T cell repertoires. To this end, we curated a large T cell atlas from 12 prominent human studies, containing in total 500,000 T cells spanning multiple diseases, including melanoma, head and neck cancer, blood cancer, and lung transplantation. Here, we identified severe limitations in cell-type annotation using unsupervised approaches and propose a more robust standard using a semi-supervised method or the TCR arrangement. We showcase the utility of a TCR-first approach through application of the STEGO.R tool for the identification of treatment-related dynamics and previously unknown public T cell clusters with potential antigen-specific properties. Thus, the paradigm shift to a TCR-first can highlight overlooked key T cell features that have the potential for improvements in immunotherapy and diagnostics.
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Affiliation(s)
- Kerry A. Mullan
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium
- Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), Antwerp, Belgium
- Biomedical Informatics Research Network Antwerp (Biomina), University of Antwerp, Antwerp, Belgium
| | - My Ha
- Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), Antwerp, Belgium
- Antwerp Center for Translational Immunology and Virology (ACTIV), Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
- Centre for Health Economics Research and Modelling Infectious Diseases (CHERMID), Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - Sebastiaan Valkiers
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium
- Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), Antwerp, Belgium
- Biomedical Informatics Research Network Antwerp (Biomina), University of Antwerp, Antwerp, Belgium
| | - Nicky de Vrij
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium
- Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), Antwerp, Belgium
- Biomedical Informatics Research Network Antwerp (Biomina), University of Antwerp, Antwerp, Belgium
- Clinical Immunology Unit, Department of Clinical Sciences, Institute of Tropical Medicine, Antwerp, Belgium
| | - Benson Ogunjimi
- Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), Antwerp, Belgium
- Antwerp Center for Translational Immunology and Virology (ACTIV), Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
- Centre for Health Economics Research and Modelling Infectious Diseases (CHERMID), Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
- Department of Paediatrics, Antwerp University Hospital, Antwerp, Belgium
| | - Kris Laukens
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium
- Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), Antwerp, Belgium
- Biomedical Informatics Research Network Antwerp (Biomina), University of Antwerp, Antwerp, Belgium
| | - Pieter Meysman
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium
- Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), Antwerp, Belgium
- Biomedical Informatics Research Network Antwerp (Biomina), University of Antwerp, Antwerp, Belgium
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Ramarapu R, Wulcan JM, Chang H, Moore PF, Vernau W, Keller SM. Single cell RNA-sequencing of feline peripheral immune cells with V(D)J repertoire and cross species analysis of T lymphocytes. Front Immunol 2024; 15:1438004. [PMID: 39620216 PMCID: PMC11604454 DOI: 10.3389/fimmu.2024.1438004] [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: 09/23/2024] [Indexed: 12/11/2024] Open
Abstract
Introduction The domestic cat (Felis catus) is a valued companion animal and a model for virally induced cancers and immunodeficiencies. However, species-specific limitations such as a scarcity of immune cell markers constrain our ability to resolve immune cell subsets at sufficient detail. The goal of this study was to characterize circulating feline T cells and other leukocytes based on their transcriptomic landscape and T-cell receptor repertoire using single cell RNA-sequencing. Methods Peripheral blood from 4 healthy cats was enriched for T cells by flow cytometry cell sorting using a mouse anti-feline CD5 monoclonal antibody. Libraries for whole transcriptome, αβ T cell receptor transcripts and γδ T cell receptor transcripts were constructed using the 10x Genomics Chromium Next GEM Single Cell 5' reagent kit and the Chromium Single Cell V(D)J Enrichment Kit with custom reverse primers for the feline orthologs. Results Unsupervised clustering of whole transcriptome data revealed 7 major cell populations - T cells, neutrophils, monocytic cells, B cells, plasmacytoid dendritic cells, mast cells and platelets. Sub cluster analysis of T cells resolved naive (CD4+ and CD8+), CD4+ effector T cells, CD8+ cytotoxic T cells and γδ T cells. Cross species analysis revealed a high conservation of T cell subsets along an effector gradient with equitable representation of veterinary species (horse, dog, pig) and humans with the cat. Our V(D)J repertoire analysis identified a subset of CD8+ cytotoxic T cells with skewed TRA and TRB gene usage, conserved TRA and TRB junctional motifs, restricted TRA/TRB pairing and reduced diversity in TRG junctional length. We also identified a public γδ T cell subset with invariant TRD and TRG chains and a CD4+ TEM-like phenotype. Among monocytic cells, we resolved three clusters of classical monocytes with polarization into pro- and anti-inflammatory phenotypes in addition to a cluster of conventional dendritic cells. Lastly, our neutrophil sub clustering revealed a larger mature neutrophil cluster and a smaller exhausted/activated cluster. Discussion Our study is the first to characterize subsets of circulating T cells utilizing an integrative approach of single cell RNA-sequencing, V(D)J repertoire analysis and cross species analysis. In addition, we characterize the transcriptome of several myeloid cell subsets and demonstrate immune cell relatedness across different species.
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MESH Headings
- Animals
- Cats
- Single-Cell Analysis
- Transcriptome
- Species Specificity
- T-Lymphocytes/immunology
- T-Lymphocytes/metabolism
- T-Lymphocyte Subsets/immunology
- T-Lymphocyte Subsets/metabolism
- Dogs
- Sequence Analysis, RNA
- Receptors, Antigen, T-Cell, gamma-delta/genetics
- Receptors, Antigen, T-Cell, gamma-delta/immunology
- Receptors, Antigen, T-Cell, gamma-delta/metabolism
- RNA-Seq
- V(D)J Recombination/genetics
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Affiliation(s)
- Raneesh Ramarapu
- Department of Surgical and Radiological Sciences, School of Veterinary Medicine, University of California, Davis, Davis, CA, United States
- Department of Anatomy, Physiology and Cell Biology, School of Veterinary Medicine, University of California, Davis, Davis, CA, United States
| | - Judit M. Wulcan
- Department of Pathology, Microbiology and Immunology, School of Veterinary Medicine, University of California, Davis, Davis, CA, United States
| | - Haiyang Chang
- Department of Mathematics and Statistics, University of Guelph, Guelph, ON, Canada
| | - Peter F. Moore
- Department of Pathology, Microbiology and Immunology, School of Veterinary Medicine, University of California, Davis, Davis, CA, United States
| | - William Vernau
- Department of Pathology, Microbiology and Immunology, School of Veterinary Medicine, University of California, Davis, Davis, CA, United States
| | - Stefan M. Keller
- Department of Pathology, Microbiology and Immunology, School of Veterinary Medicine, University of California, Davis, Davis, CA, United States
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Nishide M, Shimagami H, Kumanogoh A. Single-cell analysis in rheumatic and allergic diseases: insights for clinical practice. Nat Rev Immunol 2024; 24:781-797. [PMID: 38914790 DOI: 10.1038/s41577-024-01043-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/08/2024] [Indexed: 06/26/2024]
Abstract
Since the advent of single-cell RNA sequencing (scRNA-seq) methodology, single-cell analysis has become a powerful tool for exploration of cellular networks and dysregulated immune responses in disease pathogenesis. Advanced bioinformatics tools have enabled the combined analysis of scRNA-seq data and information on various cell properties, such as cell surface molecular profiles, chromatin accessibility and spatial information, leading to a deeper understanding of pathology. This Review provides an overview of the achievements in single-cell analysis applied to clinical samples of rheumatic and allergic diseases, including rheumatoid arthritis, systemic lupus erythematosus, systemic sclerosis, allergic airway diseases and atopic dermatitis, with an expanded scope beyond peripheral blood cells to include local diseased tissues. Despite the valuable insights that single-cell analysis has provided into disease pathogenesis, challenges remain in translating single-cell findings into clinical practice and developing personalized treatment strategies. Beyond understanding the atlas of cellular diversity, we discuss the application of data obtained in each study to clinical practice, with a focus on identifying biomarkers and therapeutic targets.
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Affiliation(s)
- Masayuki Nishide
- Department of Respiratory Medicine and Clinical Immunology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan.
- Department of Immunopathology, World Premier International Research Center Initiative (WPI), Immunology Frontier Research Center (IFReC), Osaka University, Suita, Osaka, Japan.
- Department of Advanced Clinical and Translational Immunology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan.
| | - Hiroshi Shimagami
- Department of Respiratory Medicine and Clinical Immunology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan
- Department of Immunopathology, World Premier International Research Center Initiative (WPI), Immunology Frontier Research Center (IFReC), Osaka University, Suita, Osaka, Japan
- Department of Advanced Clinical and Translational Immunology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan
| | - Atsushi Kumanogoh
- Department of Respiratory Medicine and Clinical Immunology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan.
- Department of Immunopathology, World Premier International Research Center Initiative (WPI), Immunology Frontier Research Center (IFReC), Osaka University, Suita, Osaka, Japan.
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives (OTRI), Osaka University, Suita, Osaka, Japan.
- Center for Infectious Diseases for Education and Research (CiDER), Osaka University, Suita, Osaka, Japan.
- Center for Advanced Modalities and DDS (CAMaD), Osaka University, Suita, Osaka, Japan.
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7
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Ran R, Trapecar M, Brubaker DK. Systematic Analysis of Human Colorectal Cancer scRNA-seq Revealed Limited Pro-tumoral IL-17 Production Potential in Gamma Delta T Cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.18.604156. [PMID: 39071278 PMCID: PMC11275756 DOI: 10.1101/2024.07.18.604156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Gamma delta (γδ) T cells play a crucial role in anti-tumor immunity due to their cytotoxic properties. However, the role and extent of γδ T cells in production of pro-tumorigenic interleukin- 17 (IL-17) within the tumor microenvironment (TME) of colorectal cancer (CRC) remains controversial. In this study, we re-analyzed nine published human CRC whole-tissue single-cell RNA sequencing (scRNA-seq) datasets, identifying 18,483 γδ T cells out of 951,785 total cells, in the neoplastic or adjacent normal tissue of 165 human CRC patients. Our results confirm that tumor-infiltrating γδ T cells exhibit high cytotoxicity-related transcription in both tumor and adjacent normal tissues, but critically, none of the γδ T cell clusters showed IL-17 production potential. We also identified various γδ T cell subsets, including Teff, TRM, Tpex, and Tex, and noted an increased expression of cytotoxic molecules in tumor-infiltrating γδ T cells compared to their normal area counterparts. Our work demonstrates that γδ T cells in CRC primarily function as cytotoxic effector cells rather than IL-17 producers, mitigating the concerns about their potential pro-tumorigenic roles in CRC, highlighting the importance of accurately characterizing these cells for cancer immunotherapy research and the unneglectable cross-species discrepancy between the mouse and human immune system in the study of cancer immunology.
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Affiliation(s)
- Ran Ran
- Center for Global Health and Diseases, Department of Pathology, Case Western Reserve University, Cleveland, OH
| | - Martin Trapecar
- Department of Medicine, Johns Hopkins University School of Medicine, Institute for Fundamental Biomedical Research, Johns Hopkins All Children’s Hospital, St. Petersburg, FL, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Douglas K. Brubaker
- Center for Global Health and Diseases, Department of Pathology, Case Western Reserve University, Cleveland, OH
- The Blood, Heart, Lung, and Immunology Research Center, Case Western Reserve University, University Hospitals of Cleveland, Cleveland, OH
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8
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Maurer K, Park CY, Mani S, Borji M, Penter L, Jin Y, Zhang JY, Shin C, Brenner JR, Southard J, Krishna S, Lu W, Lyu H, Abbondanza D, Mangum C, Olsen LR, Neuberg DS, Bachireddy P, Farhi SL, Li S, Livak KJ, Ritz J, Soiffer RJ, Wu CJ, Azizi E. Coordinated Immune Cell Networks in the Bone Marrow Microenvironment Define the Graft versus Leukemia Response with Adoptive Cellular Therapy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.09.579677. [PMID: 38405900 PMCID: PMC10888840 DOI: 10.1101/2024.02.09.579677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Understanding how intra-tumoral immune populations coordinate to generate anti-tumor responses following therapy can guide precise treatment prioritization. We performed systematic dissection of an established adoptive cellular therapy, donor lymphocyte infusion (DLI), by analyzing 348,905 single-cell transcriptomes from 74 longitudinal bone-marrow samples of 25 patients with relapsed myeloid leukemia; a subset was evaluated by protein-based spatial analysis. In acute myelogenous leukemia (AML) responders, diverse immune cell types within the bone-marrow microenvironment (BME) were predicted to interact with a clonally expanded population of ZNF683 + GZMB + CD8+ cytotoxic T lymphocytes (CTLs) which demonstrated in vitro specificity for autologous leukemia. This population, originating predominantly from the DLI product, expanded concurrently with NK and B cells. AML nonresponder BME revealed a paucity of crosstalk and elevated TIGIT expression in CD8+ CTLs. Our study highlights recipient BME differences as a key determinant of effective anti-leukemia response and opens new opportunities to modulate cell-based leukemia-directed therapy.
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