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Freeman D, Diefenbach C, Lam L, Le T, Alexandre J, Raphael B, Grossbard M, Kaminetzky D, Ruan J, Chattopadhyay PK. terraFlow, a high-parameter analysis tool, reveals T cell exhaustion and dysfunctional cytokine production in classical Hodgkin's lymphoma. Cell Rep 2024; 43:114605. [PMID: 39128003 DOI: 10.1016/j.celrep.2024.114605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 03/19/2024] [Accepted: 07/24/2024] [Indexed: 08/13/2024] Open
Abstract
Immune cells express an incredible variety of proteins; by measuring combinations of these, cell types influencing disease can be precisely identified. We developed terraFlow, a platform that defines cell subsets exhaustively by combinatorial protein expression. Using high-parameter checkpoint-focused and function-focused panels, we studied classical Hodgkin's lymphoma (cHL), where systemic T cells have not been investigated in detail. terraFlow revealed immune perturbations in patients, including elevated activated, exhausted, and interleukin (IL)-17+ phenotypes, along with diminished early, interferon (IFN)γ+, and tumor necrosis factor (TNF)+ T cells before treatment; many perturbations remained after treatment. terraFlow identified more disease-associated differences than other tools, often with better predictive power, and included a non-gating approach, eliminating time-consuming and subjective manual thresholds. It also reports a method to identify the smallest set of markers distinguishing study groups. Our results provide mechanistic support for past reports of immune deficiency in cHL and demonstrate the value of terraFlow in immunotherapy and biomarker studies.
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Affiliation(s)
- Daniel Freeman
- terraFlow Bioinformatics, Cambridge, MA, USA; Biology and Biomedical Sciences, Harvard Medical School, Boston, MA, USA; Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA
| | | | - Linda Lam
- terraFlow Bioinformatics, Cambridge, MA, USA
| | - Tri Le
- BD Biosciences, San Jose, CA, USA
| | | | - Bruce Raphael
- Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA
| | | | | | - Jia Ruan
- Division of Hematology & Medical Oncology, Weill Cornell Medicine, New York, NY, USA
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2
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Hermansen JU, Yin Y, Rein ID, Skånland SS. Immunophenotyping with (phospho)protein profiling and fluorescent cell barcoding for single-cell signaling analysis and biomarker discovery. NPJ Precis Oncol 2024; 8:107. [PMID: 38769096 PMCID: PMC11106235 DOI: 10.1038/s41698-024-00604-y] [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: 11/03/2023] [Accepted: 05/08/2024] [Indexed: 05/22/2024] Open
Abstract
The microenvironment of hematologic cancers contributes to tumor cell survival and proliferation, as well as treatment resistance. Understanding tumor- and drug-induced changes to the immune cell composition and functionality is therefore critical for implementing optimal treatment strategies and for the development of novel cancer therapies. The liquid nature of peripheral blood makes this organ uniquely suited for single-cell studies by flow cytometry. (Phospho)protein profiles detected by flow cytometry analyses have been shown to correlate with ex vivo drug sensitivity and to predict treatment outcomes in hematologic cancers, demonstrating that this method is suitable for pre-clinical studies. Here, we present a flow cytometry protocol that combines multi-parameter immunophenotyping with single-cell (phospho)protein profiling. The protocol makes use of fluorescent cell barcoding, which means that multiple cell samples, either collected from different donors or exposed to different treatment conditions, can be combined and analyzed as one experiment. This reduces variability between samples, increases the throughput of the experiment, and lowers experimental costs. This protocol may serve as a guide for the use and further development of assays to study immunophenotype and cell signaling at single-cell resolution in normal and malignant cells. The read-outs may provide biological insight into cancer pathogenesis, identify novel drug targets, and ultimately serve as a biomarker to guide clinical decision-making.
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Affiliation(s)
- Johanne U Hermansen
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- K. G. Jebsen Centre for B Cell Malignancies, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Yanping Yin
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- K. G. Jebsen Centre for B Cell Malignancies, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Idun Dale Rein
- Department of Radiation Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Sigrid S Skånland
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.
- K. G. Jebsen Centre for B Cell Malignancies, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
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3
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Chattopadhyay PK. Molecular cytometry for comprehensive immune profiling. Methods Cell Biol 2024; 186:249-270. [PMID: 38705602 DOI: 10.1016/bs.mcb.2024.02.020] [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: 05/07/2024]
Abstract
Molecular cytometry refers to a group of high-parameter technologies for single-cell analysis that share the following traits: (1) combined (multimodal) measurement of protein and transcripts, (2) medium throughput (10-100K cells), and (3) the use of oligonucleotide-tagged antibodies to detect protein expression. The platform can measure over 100 proteins and either hundreds of targeted genes or the whole transcriptome, on a cell-by-cell basis. It is currently one of the most powerful technologies available for immune monitoring. Here, we describe the technology platform (which includes CITE-Seq, REAP-Seq, and AbSeq), provide guidance for its optimization, and discuss advantages and limitations. Finally, we provide some vignettes from studies that demonstrate the application and potential insight that can be gained from molecular cytometry studies.
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4
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Schäfer A, D'Almeida SM, Dorier J, Guex N, Villard J, Garcia M. Comparative assessment of cytometry by time-of-flight and full spectral flow cytometry based on a 33-color antibody panel. J Immunol Methods 2024; 527:113641. [PMID: 38365120 DOI: 10.1016/j.jim.2024.113641] [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: 12/22/2023] [Accepted: 02/12/2024] [Indexed: 02/18/2024]
Abstract
Mass cytometry and full spectrum flow cytometry have recently emerged as new promising single cell proteomic analysis tools that can be exploited to decipher the extensive diversity of immune cell repertoires and their implication in human diseases. In this study, we evaluated the performance of mass cytometry against full spectrum flow cytometry using an identical 33-color antibody panel on four healthy individuals. Our data revealed an overall high concordance in the quantification of major immune cell populations between the two platforms using a semi-automated clustering approach. We further showed a strong correlation of cluster assignment when comparing manual and automated clustering. Both comparisons revealed minor disagreements in the quantification and assignment of rare cell subpopulations. Our study showed that both single cell proteomic technologies generate highly overlapping results and substantiate that the choice of technology is not a primary factor for successful biological assessment of cell profiles but must be considered in a broader design framework of clinical studies.
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Affiliation(s)
- Antonia Schäfer
- Transplantation Immunology Unit and National Reference Laboratory for Histocompatibility, Geneva University Hospitals, Geneva, Switzerland
| | - Sènan Mickael D'Almeida
- Flow Cytometry Core Facility, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Julien Dorier
- Bioinformatics Competence Center, University of Lausanne, Lausanne, Switzerland; Bioinformatics Competence Center, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Nicolas Guex
- Bioinformatics Competence Center, University of Lausanne, Lausanne, Switzerland; Bioinformatics Competence Center, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Jean Villard
- Transplantation Immunology Unit and National Reference Laboratory for Histocompatibility, Geneva University Hospitals, Geneva, Switzerland.
| | - Miguel Garcia
- Flow Cytometry Core Facility, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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5
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Driscoll CL, Keeble AH, Howarth MR. SpyMask enables combinatorial assembly of bispecific binders. Nat Commun 2024; 15:2403. [PMID: 38493197 PMCID: PMC10944524 DOI: 10.1038/s41467-024-46599-9] [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: 09/03/2023] [Accepted: 03/04/2024] [Indexed: 03/18/2024] Open
Abstract
Bispecific antibodies are a successful and expanding therapeutic class. Standard approaches to generate bispecifics are complicated by the need for disulfide reduction/oxidation or specialized formats. Here we present SpyMask, a modular approach to bispecifics using SpyTag/SpyCatcher spontaneous amidation. Two SpyTag-fused antigen-binding modules can be precisely conjugated onto DoubleCatcher, a tandem SpyCatcher where the second SpyCatcher is protease-activatable. We engineer a panel of structurally-distinct DoubleCatchers, from which binders project in different directions. We establish a generalized methodology for one-pot assembly and purification of bispecifics in 96-well plates. A panel of binders recognizing different HER2 epitopes were coupled to DoubleCatcher, revealing unexpected combinations with anti-proliferative or pro-proliferative activity on HER2-addicted cancer cells. Bispecific activity depended sensitively on both binder orientation and DoubleCatcher scaffold geometry. These findings support the need for straightforward assembly in different formats. SpyMask provides a scalable tool to discover synergy in bispecific activity, through modulating receptor organization and geometry.
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Affiliation(s)
- Claudia L Driscoll
- Department of Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, UK
- Department of Pharmacology, University of Cambridge, Tennis Court Road, Cambridge, CB2 1PD, UK
| | - Anthony H Keeble
- Department of Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, UK
- Department of Pharmacology, University of Cambridge, Tennis Court Road, Cambridge, CB2 1PD, UK
| | - Mark R Howarth
- Department of Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, UK.
- Department of Pharmacology, University of Cambridge, Tennis Court Road, Cambridge, CB2 1PD, UK.
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6
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Hu R, Li Y, Yang Y, Liu M. Mass spectrometry-based strategies for single-cell metabolomics. MASS SPECTROMETRY REVIEWS 2023; 42:67-94. [PMID: 34028064 DOI: 10.1002/mas.21704] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 05/05/2021] [Accepted: 05/11/2021] [Indexed: 06/12/2023]
Abstract
Single cell analysis has drawn increasing interest from the research community due to its capability to interrogate cellular heterogeneity, allowing refined tissue classification and facilitating novel biomarker discovery. With the advancement of relevant instruments and techniques, it is now possible to perform multiple omics including genomics, transcriptomics, metabolomics or even proteomics at single cell level. In comparison with other omics studies, single-cell metabolomics (SCM) represents a significant challenge since it involves many types of dynamically changing compounds with a wide range of concentrations. In addition, metabolites cannot be amplified. Although difficult, considerable progress has been made over the past decade in mass spectrometry (MS)-based SCM in terms of processing technologies and biochemical applications. In this review, we will summarize recent progress in the development of promising MS platforms, sample preparation methods and SCM analysis of various cell types (including plant cell, cancer cell, neuron, embryo cell, and yeast cell). Current limitations and future research directions in the field of SCM will also be discussed.
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Affiliation(s)
- Rui Hu
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Ying Li
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yunhuang Yang
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Maili Liu
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
- University of Chinese Academy of Sciences, Beijing, China
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7
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Jaimes MC, Leipold M, Kraker G, Amir E, Maecker H, Lannigan J. Full spectrum flow cytometry and mass cytometry: A 32-marker panel comparison. Cytometry A 2022; 101:942-959. [PMID: 35593221 PMCID: PMC9790709 DOI: 10.1002/cyto.a.24565] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 02/23/2022] [Accepted: 04/25/2022] [Indexed: 01/27/2023]
Abstract
High-dimensional single-cell data has become an important tool in unraveling the complexity of the immune system and its involvement in homeostasis and a large array of pathologies. As technological tools are developed, researchers are adopting them to answer increasingly complex biological questions. Up until recently, mass cytometry (MC) has been the main technology employed in cytometric assays requiring more than 29 markers. Recently, however, with the introduction of full spectrum flow cytometry (FSFC), it has become possible to break the fluorescence barrier and go beyond 29 fluorescent parameters. In this study, in collaboration with the Stanford Human Immune Monitoring Center (HIMC), we compared five patient samples using an established immune panel developed by the HIMC using their MC platform. Using split samples and the same antibody panel, we were able to demonstrate highly comparable results between the two technologies using multiple data analysis approaches. We report here a direct comparison of two technology platforms (MC and FSFC) using a 32-marker flow cytometric immune monitoring panel that can identify all the previously described and anticipated immune subpopulations defined by this panel.
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Affiliation(s)
| | - Michael Leipold
- Department of Microbiology/ImmunologyStanford UniversityStanfordCaliforniaUSA
| | - Geoffrey Kraker
- Technical Applications SupportCytek Biosciences Inc.FremontCaliforniaUSA
| | - El‐ad Amir
- Astrolabe DiagnosticsFort LeeNew JerseyUSA
| | - Holden Maecker
- Department of Microbiology/ImmunologyStanford UniversityStanfordCaliforniaUSA
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8
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Zhu H, Galdos FX, Lee D, Waliany S, Huang YV, Ryan J, Dang K, Neal JW, Wakelee HA, Reddy SA, Srinivas S, Lin LL, Witteles RM, Maecker HT, Davis MM, Nguyen PK, Wu SM. Identification of Pathogenic Immune Cell Subsets Associated With Checkpoint Inhibitor-Induced Myocarditis. Circulation 2022; 146:316-335. [PMID: 35762356 PMCID: PMC9397491 DOI: 10.1161/circulationaha.121.056730] [Citation(s) in RCA: 44] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
BACKGROUND Immune checkpoint inhibitors (ICIs) are monoclonal antibodies used to activate the immune system against tumor cells. Despite therapeutic benefits, ICIs have the potential to cause immune-related adverse events such as myocarditis, a rare but serious side effect with up to 50% mortality in affected patients. Histologically, patients with ICI myocarditis have lymphocytic infiltrates in the heart, implicating T cell-mediated mechanisms. However, the precise pathological immune subsets and molecular changes in ICI myocarditis are unknown. METHODS To identify immune subset(s) associated with ICI myocarditis, we performed time-of-flight mass cytometry on peripheral blood mononuclear cells from 52 individuals: 29 patients with autoimmune adverse events (immune-related adverse events) on ICI, including 8 patients with ICI myocarditis, and 23 healthy control subjects. We also used multiomics single-cell technology to immunophenotype 30 patients/control subjects using single-cell RNA sequencing, single-cell T-cell receptor sequencing, and cellular indexing of transcriptomes and epitopes by sequencing with feature barcoding for surface marker expression confirmation. To correlate between the blood and the heart, we performed single-cell RNA sequencing/T-cell receptor sequencing/cellular indexing of transcriptomes and epitopes by sequencing on MRL/Pdcd1-/- (Murphy Roths large/programmed death-1-deficient) mice with spontaneous myocarditis. RESULTS Using these complementary approaches, we found an expansion of cytotoxic CD8+ T effector cells re-expressing CD45RA (Temra CD8+ cells) in patients with ICI myocarditis compared with control subjects. T-cell receptor sequencing demonstrated that these CD8+ Temra cells were clonally expanded in patients with myocarditis compared with control subjects. Transcriptomic analysis of these Temra CD8+ clones confirmed a highly activated and cytotoxic phenotype. Longitudinal study demonstrated progression of these Temra CD8+ cells into an exhausted phenotype 2 months after treatment with glucocorticoids. Differential expression analysis demonstrated elevated expression levels of proinflammatory chemokines (CCL5/CCL4/CCL4L2) in the clonally expanded Temra CD8+ cells, and ligand receptor analysis demonstrated their interactions with innate immune cells, including monocytes/macrophages, dendritic cells, and neutrophils, as well as the absence of key anti-inflammatory signals. To complement the human study, we performed single-cell RNA sequencing/T-cell receptor sequencing/cellular indexing of transcriptomes and epitopes by sequencing in Pdcd1-/- mice with spontaneous myocarditis and found analogous expansions of cytotoxic clonal effector CD8+ cells in both blood and hearts of such mice compared with controls. CONCLUSIONS Clonal cytotoxic Temra CD8+ cells are significantly increased in the blood of patients with ICI myocarditis, corresponding to an analogous increase in effector cytotoxic CD8+ cells in the blood/hearts of Pdcd1-/- mice with myocarditis. These expanded effector CD8+ cells have unique transcriptional changes, including upregulation of chemokines CCL5/CCL4/CCL4L2, which may serve as attractive diagnostic/therapeutic targets for reducing life-threatening cardiac immune-related adverse events in ICI-treated patients with cancer.
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Affiliation(s)
- Han Zhu
- Department of Medicine, Stanford University; Stanford, California 94305, USA;,Stanford Cardiovascular Institute, Stanford University; Stanford, California 94305, USA,Division of Cardiovascular Medicine, Stanford University School of Medicine; Stanford, California 94305, USA
| | - Francisco X. Galdos
- Stanford Cardiovascular Institute, Stanford University; Stanford, California 94305, USA,Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine; Stanford, California 94305
| | - Daniel Lee
- Stanford Cardiovascular Institute, Stanford University; Stanford, California 94305, USA
| | - Sarah Waliany
- Department of Medicine, Stanford University; Stanford, California 94305, USA
| | | | - Julia Ryan
- Stanford Cardiovascular Institute, Stanford University; Stanford, California 94305, USA
| | - Katherine Dang
- University of California, Santa Barbara, California, 93106
| | - Joel W. Neal
- Department of Medicine, Stanford University; Stanford, California 94305, USA;,Division of Oncology, Stanford University School of Medicine; Stanford, California 94305, USA.,Stanford Cancer Institute, Stanford University; Stanford, California 94305, USA
| | - Heather A. Wakelee
- Department of Medicine, Stanford University; Stanford, California 94305, USA;,Division of Oncology, Stanford University School of Medicine; Stanford, California 94305, USA.,Stanford Cancer Institute, Stanford University; Stanford, California 94305, USA
| | - Sunil A. Reddy
- Department of Medicine, Stanford University; Stanford, California 94305, USA;,Division of Oncology, Stanford University School of Medicine; Stanford, California 94305, USA.,Stanford Cancer Institute, Stanford University; Stanford, California 94305, USA
| | - Sandy Srinivas
- Department of Medicine, Stanford University; Stanford, California 94305, USA;,Division of Oncology, Stanford University School of Medicine; Stanford, California 94305, USA.,Stanford Cancer Institute, Stanford University; Stanford, California 94305, USA
| | - Lih-Ling Lin
- Checkpoint Immunology Cluster, Immunology and Inflammation, Sanofi US, Cambridge, MA, USA
| | - Ronald M. Witteles
- Department of Medicine, Stanford University; Stanford, California 94305, USA;,Division of Cardiovascular Medicine, Stanford University School of Medicine; Stanford, California 94305, USA
| | - Holden T. Maecker
- Department of Microbiology & Immunology, Stanford University School of Medicine; Stanford, California 94305, USA.,Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine; Stanford, California 94305, USA
| | - Mark M. Davis
- Department of Microbiology & Immunology, Stanford University School of Medicine; Stanford, California 94305, USA.,Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine; Stanford, California 94305, USA.,Howard Hughes Medical Institute, Stanford University; Stanford, California 94035
| | - Patricia K. Nguyen
- Department of Medicine, Stanford University; Stanford, California 94305, USA;,Stanford Cardiovascular Institute, Stanford University; Stanford, California 94305, USA,Division of Cardiovascular Medicine, Stanford University School of Medicine; Stanford, California 94305, USA
| | - Sean M. Wu
- Department of Medicine, Stanford University; Stanford, California 94305, USA;,Stanford Cardiovascular Institute, Stanford University; Stanford, California 94305, USA,Division of Cardiovascular Medicine, Stanford University School of Medicine; Stanford, California 94305, USA
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9
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Hu Y, Zhang Y, Liu Y, Gao Y, San T, Li X, Song S, Yan B, Zhao Z. Advances in application of single-cell RNA sequencing in cardiovascular research. Front Cardiovasc Med 2022; 9:905151. [PMID: 35958408 PMCID: PMC9360414 DOI: 10.3389/fcvm.2022.905151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 07/05/2022] [Indexed: 11/13/2022] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) provides high-resolution information on transcriptomic changes at the single-cell level, which is of great significance for distinguishing cell subtypes, identifying stem cell differentiation processes, and identifying targets for disease treatment. In recent years, emerging single-cell RNA sequencing technologies have been used to make breakthroughs regarding decoding developmental trajectories, phenotypic transitions, and cellular interactions in the cardiovascular system, providing new insights into cardiovascular disease. This paper reviews the technical processes of single-cell RNA sequencing and the latest progress based on single-cell RNA sequencing in the field of cardiovascular system research, compares single-cell RNA sequencing with other single-cell technologies, and summarizes the extended applications and advantages and disadvantages of single-cell RNA sequencing. Finally, the prospects for applying single-cell RNA sequencing in the field of cardiovascular research are discussed.
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Affiliation(s)
- Yue Hu
- Department of Cardiology, Jinan Central Hospital, Shandong University, Jinan, China
| | - Ying Zhang
- Department of Cardiology, Central Hospital Affiliated Shandong First Medical University, Jinan, China
| | - Yutong Liu
- Department of Cardiology, Jinan Central Hospital, Shandong University, Jinan, China
| | - Yan Gao
- Department of Research Center of Translational Medicine, Central Hospital Affiliated Shandong First Medical University, Jinan, China
| | - Tiantian San
- Department of Cardiology, Jinan Central Hospital, Shandong University, Jinan, China
| | - Xiaoying Li
- Department of Research Center of Translational Medicine, Central Hospital Affiliated Shandong First Medical University, Jinan, China
- Department of Emergency, Central Hospital Affiliated Shandong First Medical University, Jinan, China
| | - Sensen Song
- Department of Cardiology, Central Hospital Affiliated Shandong First Medical University, Jinan, China
| | - Binglong Yan
- Department of Cardiology, Central Hospital Affiliated Shandong First Medical University, Jinan, China
| | - Zhuo Zhao
- Department of Cardiology, Jinan Central Hospital, Shandong University, Jinan, China
- Department of Cardiology, Central Hospital Affiliated Shandong First Medical University, Jinan, China
- *Correspondence: Zhuo Zhao
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10
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Flow Cytometry Analysis of Mycobacteria and Mycobacteria-Infected Immune Cells. Methods Mol Biol 2021. [PMID: 34235657 DOI: 10.1007/978-1-0716-1460-0_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2023]
Abstract
Flow cytometry enables the measurement of tens of features on individual cells from complex mixtures. Flow cytometry enables high-throughput quantification of cell size, gene and protein expression. In the case of studies of host-pathogen interactions, this tool provides a facile way of identifying cells that have been successfully infected by a pathogen. Several recent technological advances have greatly improved throughput and the number of features that can be simultaneously monitored by this technique. Here, we describe common workflows to study Mycobacterium tuberculosis heterogeneity and host-M. tuberculosis interactions using flow cytometry and related technologies.
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11
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Roca CP, Burton OT, Gergelits V, Prezzemolo T, Whyte CE, Halpert R, Kreft Ł, Collier J, Botzki A, Spidlen J, Humblet-Baron S, Liston A. AutoSpill is a principled framework that simplifies the analysis of multichromatic flow cytometry data. Nat Commun 2021; 12:2890. [PMID: 34001872 PMCID: PMC8129071 DOI: 10.1038/s41467-021-23126-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 04/16/2021] [Indexed: 12/21/2022] Open
Abstract
Compensating in flow cytometry is an unavoidable challenge in the data analysis of fluorescence-based flow cytometry. Even the advent of spectral cytometry cannot circumvent the spillover problem, with spectral unmixing an intrinsic part of such systems. The calculation of spillover coefficients from single-color controls has remained essentially unchanged since its inception, and is increasingly limited in its ability to deal with high-parameter flow cytometry. Here, we present AutoSpill, an alternative method for calculating spillover coefficients. The approach combines automated gating of cells, calculation of an initial spillover matrix based on robust linear regression, and iterative refinement to reduce error. Moreover, autofluorescence can be compensated out, by processing it as an endogenous dye in an unstained control. AutoSpill uses single-color controls and is compatible with common flow cytometry software. AutoSpill allows simpler and more robust workflows, while reducing the magnitude of compensation errors in high-parameter flow cytometry.
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Affiliation(s)
- Carlos P Roca
- VIB Center for Brain and Disease Research, Leuven, Belgium.
- Department of Microbiology and Immunology, KU Leuven - University of Leuven, Leuven, Belgium.
- Laboratory of Lymphocyte Signalling and Development, The Babraham Institute, Cambridge, UK.
| | - Oliver T Burton
- Laboratory of Lymphocyte Signalling and Development, The Babraham Institute, Cambridge, UK
| | - Václav Gergelits
- Laboratory of Lymphocyte Signalling and Development, The Babraham Institute, Cambridge, UK
| | - Teresa Prezzemolo
- VIB Center for Brain and Disease Research, Leuven, Belgium
- Department of Microbiology and Immunology, KU Leuven - University of Leuven, Leuven, Belgium
| | - Carly E Whyte
- Laboratory of Lymphocyte Signalling and Development, The Babraham Institute, Cambridge, UK
| | | | | | | | | | | | - Stéphanie Humblet-Baron
- VIB Center for Brain and Disease Research, Leuven, Belgium
- Department of Microbiology and Immunology, KU Leuven - University of Leuven, Leuven, Belgium
| | - Adrian Liston
- VIB Center for Brain and Disease Research, Leuven, Belgium.
- Department of Microbiology and Immunology, KU Leuven - University of Leuven, Leuven, Belgium.
- Laboratory of Lymphocyte Signalling and Development, The Babraham Institute, Cambridge, UK.
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12
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Wang W, Li H, Zhang L, Jiang W, Shen L, Fan G. Clinical applications of monitoring immune status with 90 immune cell subsets in human whole blood by 10-color flow cytometry. Int J Lab Hematol 2021; 43:1132-1144. [PMID: 33870648 DOI: 10.1111/ijlh.13541] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 02/19/2021] [Accepted: 03/26/2021] [Indexed: 12/17/2022]
Abstract
INTRODUCTION The immune system may involve and predict the different prognosis and therapy consequences. So, it's important to monitor and evaluate the immune status before and after treatments. METHODS Flow cytometry is the best technology to perform immune monitoring, because it can detect immune cells using small amount of sample in a short time. The whole blood is the ideal sample for immune status monitoring, since it includes almost all the immune cells and it's relatively easy to obtain and less invasive than bone marrow or lymph node. RESULTS Here we developed and validated a 10-color panel with only four tubes containing 29 antibodies to monitor 90 immune cell subsets in 2 ml whole blood samples. The major immune cell populations detected by our panel included T cell subsets (CD3+ total T, Th, Tc, Treg, CD8hi , CD8low , αβTCR, γδTCR, naïve, and memory T), T cell activation markers (CD25, CD69, and HLA-DR) and one immune checkpoint PD1, B cell subsets (B1, switched memory, non-switched, naïve B, and CD27- IgD- B cells), neutrophils, basophils, four monocytic cell subsets, dendritic cells (pDCs and mDCs), and four NK cell subsets. These panels of antibodies had been applied to monitor immune status (percentage and absolute number) in total 303 cases with various diseases, such as leukemia (AML, CML, MM, and ALL), lymphoma (B cells and NK/T cells), cancers (colon, lung, prostate, and breast), immune deficiencies, and autoimmune diseases. CONCLUSION We provided proof of feasibility for clinical monitoring immune status and guiding immunotherapy by multicolor flow cytometry testing.
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Affiliation(s)
- Weiwei Wang
- Department of Clinical laboratory, Xinhua hospital, Shanghai Jiaotong University of Medicine School, Shanghai, China
| | - Haibo Li
- Department of Pathology, Oregon Health and Science University, Portland, OR, USA
| | - Lihua Zhang
- Department of Clinical laboratory, Xinhua hospital, Shanghai Jiaotong University of Medicine School, Shanghai, China
| | - Wenli Jiang
- Department of Clinical laboratory, Xinhua hospital, Shanghai Jiaotong University of Medicine School, Shanghai, China
| | - Lisong Shen
- Department of Clinical laboratory, Xinhua hospital, Shanghai Jiaotong University of Medicine School, Shanghai, China
| | - Guang Fan
- Department of Pathology, Oregon Health and Science University, Portland, OR, USA
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13
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Dai Y, Xu A, Li J, Wu L, Yu S, Chen J, Zhao W, Sun XJ, Huang J. CytoTree: an R/Bioconductor package for analysis and visualization of flow and mass cytometry data. BMC Bioinformatics 2021; 22:138. [PMID: 33752602 PMCID: PMC7983272 DOI: 10.1186/s12859-021-04054-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 02/26/2021] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND The rapidly increasing dimensionality and throughput of flow and mass cytometry data necessitate new bioinformatics tools for analysis and interpretation, and the recently emerging single-cell-based algorithms provide a powerful strategy to meet this challenge. RESULTS Here, we present CytoTree, an R/Bioconductor package designed to analyze and interpret multidimensional flow and mass cytometry data. CytoTree provides multiple computational functionalities that integrate most of the commonly used techniques in unsupervised clustering and dimensionality reduction and, more importantly, support the construction of a tree-shaped trajectory based on the minimum spanning tree algorithm. A graph-based algorithm is also implemented to estimate the pseudotime and infer intermediate-state cells. We apply CytoTree to several examples of mass cytometry and time-course flow cytometry data on heterogeneity-based cytology and differentiation/reprogramming experiments to illustrate the practical utility achieved in a fast and convenient manner. CONCLUSIONS CytoTree represents a versatile tool for analyzing multidimensional flow and mass cytometry data and to producing heuristic results for trajectory construction and pseudotime estimation in an integrated workflow.
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Affiliation(s)
- Yuting Dai
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 197 Ruijin Er Road, Shanghai, 200025, China
| | - Aining Xu
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 197 Ruijin Er Road, Shanghai, 200025, China
| | - Jianfeng Li
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 197 Ruijin Er Road, Shanghai, 200025, China
| | - Liang Wu
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 197 Ruijin Er Road, Shanghai, 200025, China
| | - Shanhe Yu
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 197 Ruijin Er Road, Shanghai, 200025, China
| | - Jun Chen
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research and Center for Individualized Medicine, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA
| | - Weili Zhao
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 197 Ruijin Er Road, Shanghai, 200025, China.
| | - Xiao-Jian Sun
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 197 Ruijin Er Road, Shanghai, 200025, China.
| | - Jinyan Huang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 197 Ruijin Er Road, Shanghai, 200025, China.
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14
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Sanmamed MF, Nie X, Desai SS, Villaroel-Espindola F, Badri T, Zhao D, Kim AW, Ji L, Zhang T, Quinlan E, Cheng X, Han X, Vesely MD, Nassar AF, Sun J, Zhang Y, Kim TK, Wang J, Melero I, Herbst RS, Schalper KA, Chen L. A Burned-Out CD8 + T-cell Subset Expands in the Tumor Microenvironment and Curbs Cancer Immunotherapy. Cancer Discov 2021; 11:1700-1715. [PMID: 33658301 DOI: 10.1158/2159-8290.cd-20-0962] [Citation(s) in RCA: 77] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 01/06/2021] [Accepted: 02/26/2021] [Indexed: 11/16/2022]
Abstract
Specific mechanisms by which tumor-infiltrating lymphocytes (TIL) become dysfunctional remain poorly understood. Here, we employed a two-pronged approach using single-cell mass cytometry and tissue imaging technologies to dissect TILs from 25 patients with resectable and 35 patients with advanced non-small cell lung cancer (NSCLC). We identified a burned-out CD8+ TIL subset (Ebo) that specifically accumulated within the tumor microenvironment (TME) but not in adjacent nontumoral tissues. Ebo showed the highest expression of proliferation and activation markers but produced the lowest amount of IFNγ and were the most apoptotic CD8+ TIL subset. Using a humanized patient-derived tumor xenograft model, we demonstrated that Ebo expansion occurred within the TME in a PD-1/B7-H1 pathway-dependent manner. Ebo abundance in baseline tumor tissues was associated with resistance to anti-PD therapy in patients with NSCLC. Our study identifies a dysfunctional TIL subset, with distinct features from previously described exhausted T cells, and implies strategies to overcome immunotherapy resistance. SIGNIFICANCE: We identified a highly proliferative, overactivated, and apoptotic dysfunctional CD8+ tumor-infiltrating subpopulation that is functionally distinct from previously described exhausted T cells. This population is expanded in lung cancer tissues in a PD-1/B7-H1-dependent manner, and its abundance is associated with resistance to cancer immunotherapy, thus becoming a potential tissue biomarker.This article is highlighted in the In This Issue feature, p. 1601.
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Affiliation(s)
- Miguel F Sanmamed
- Department of Immunobiology, Yale University, New Haven, Connecticut.,Division of Immunology and Immunotherapy, CIMA, Universidad de Navarra, Pamplona, Spain
| | - Xinxin Nie
- Department of Immunobiology, Yale University, New Haven, Connecticut
| | - Shruti S Desai
- Department of Pathology, Yale University, New Haven, Connecticut
| | | | - Ti Badri
- Department of Immunobiology, Yale University, New Haven, Connecticut
| | - Dejian Zhao
- Yale Center for Genome Analysis, Department of Genetics, Yale University, New Haven, Connecticut
| | - Anthony W Kim
- Division of Thoracic Surgery, Yale University, New Haven, Connecticut
| | - Lan Ji
- Department of Immunobiology, Yale University, New Haven, Connecticut
| | - Tianxiang Zhang
- Department of Immunobiology, Yale University, New Haven, Connecticut
| | - Edward Quinlan
- Department of Immunobiology, Yale University, New Haven, Connecticut
| | - Xiaoxiao Cheng
- Department of Immunobiology, Yale University, New Haven, Connecticut
| | - Xue Han
- Department of Immunobiology, Yale University, New Haven, Connecticut
| | - Matthew D Vesely
- Department of Immunobiology, Yale University, New Haven, Connecticut.,Department of Dermatology, Yale University, New Haven, Connecticut
| | - Ala F Nassar
- Department of Immunobiology, Yale University, New Haven, Connecticut
| | - Jingwei Sun
- Department of Immunobiology, Yale University, New Haven, Connecticut
| | - Yu Zhang
- Department of Immunobiology, Yale University, New Haven, Connecticut.,Department of Medicine (Medical Oncology), Yale University, New Haven, Connecticut
| | - Tae Kon Kim
- Department of Medicine (Medical Oncology), Yale University, New Haven, Connecticut
| | - Jun Wang
- Department of Immunobiology, Yale University, New Haven, Connecticut
| | - Ignacio Melero
- Division of Immunology and Immunotherapy, CIMA, Universidad de Navarra, Pamplona, Spain
| | - Roy S Herbst
- Department of Medicine (Medical Oncology), Yale University, New Haven, Connecticut
| | - Kurt A Schalper
- Department of Pathology, Yale University, New Haven, Connecticut. .,Department of Medicine (Medical Oncology), Yale University, New Haven, Connecticut
| | - Lieping Chen
- Department of Immunobiology, Yale University, New Haven, Connecticut. .,Department of Dermatology, Yale University, New Haven, Connecticut.,Department of Medicine (Medical Oncology), Yale University, New Haven, Connecticut
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15
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Abstract
Flow cytometry and sorting represents a valuable and mature experimental platform for the analysis of cellular populations. Applications involving higher plants started to emerge around 40 years ago and are now widely employed both to provide unique information regarding basic and applied questions in the biosciences and to advance agricultural productivity in practical ways. Further development of this platform is being actively pursued, and this promises additional progress in our understanding of the interactions of cells within complex tissues and organs. Higher plants offer unique challenges in terms of flow cytometric analysis, first since their organs and tissues are, almost without exception, three-dimensional assemblies of different cell types held together by tough cell walls, and, second, because individual plant cells are generally larger than those of mammals.This chapter, which updates work last reviewed in 2014 [Galbraith DW (2014) Flow cytometry and sorting in Arabidopsis. In: Sanchez Serrano JJ, Salinas J (eds) Arabidopsis Protocols, 3rd ed. Methods in molecular biology, vol 1062. Humana Press, Totowa, pp 509-537], describes the application of techniques of flow cytometry and sorting to the model plant species Arabidopsis thaliana, in particular emphasizing (a) fluorescence labeling in vivo of specific cell types and of subcellular components, (b) analysis using both conventional cytometers and spectral analyzers, (c) fluorescence-activated sorting of protoplasts and nuclei, and (d) transcriptome analyses using sorted protoplasts and nuclei, focusing on population analyses at the level of single protoplasts and nuclei. Since this is an update, details of new experimental methods are emphasized.
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Affiliation(s)
- David W Galbraith
- University of Arizona, School of Plant Sciences and Bio5 Institute, Tucson, AZ, USA. .,Henan University, Institute of Plant Stress Biology, School of Life Sciences, Kaifeng, China.
| | - Guiling Sun
- Henan University, Institute of Plant Stress Biology, School of Life Sciences, Kaifeng, China
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16
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Culos A, Tsai AS, Stanley N, Becker M, Ghaemi MS, McIlwain DR, Fallahzadeh R, Tanada A, Nassar H, Espinosa C, Xenochristou M, Ganio E, Peterson L, Han X, Stelzer IA, Ando K, Gaudilliere D, Phongpreecha T, Marić I, Chang AL, Shaw GM, Stevenson DK, Bendall S, Davis KL, Fantl W, Nolan GP, Hastie T, Tibshirani R, Angst MS, Gaudilliere B, Aghaeepour N. Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions. NAT MACH INTELL 2020; 2:619-628. [PMID: 33294774 PMCID: PMC7720904 DOI: 10.1038/s42256-020-00232-8] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Accepted: 08/26/2020] [Indexed: 12/17/2022]
Abstract
The dense network of interconnected cellular signalling responses that are quantifiable in peripheral immune cells provides a wealth of actionable immunological insights. Although high-throughput single-cell profiling techniques, including polychromatic flow and mass cytometry, have matured to a point that enables detailed immune profiling of patients in numerous clinical settings, the limited cohort size and high dimensionality of data increase the possibility of false-positive discoveries and model overfitting. We introduce a generalizable machine learning platform, the immunological Elastic-Net (iEN), which incorporates immunological knowledge directly into the predictive models. Importantly, the algorithm maintains the exploratory nature of the high-dimensional dataset, allowing for the inclusion of immune features with strong predictive capabilities even if not consistent with prior knowledge. In three independent studies our method demonstrates improved predictions for clinically relevant outcomes from mass cytometry data generated from whole blood, as well as a large simulated dataset. The iEN is available under an open-source licence.
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Affiliation(s)
- Anthony Culos
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
- These authors contributed equally: Anthony Culos, Amy S. Tsai
| | - Amy S Tsai
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- These authors contributed equally: Anthony Culos, Amy S. Tsai
| | - Natalie Stanley
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
| | - Martin Becker
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
| | - Mohammad S Ghaemi
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
- Digital Technologies Research Centre, National Research Council Canada, Toronto, Ontario, Canada
| | - David R McIlwain
- Department of Microbiology and Immunology, Baxter Laboratory in Stem Cell Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Ramin Fallahzadeh
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
| | - Athena Tanada
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
| | - Huda Nassar
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
| | - Camilo Espinosa
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
| | - Maria Xenochristou
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
| | - Edward Ganio
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Laura Peterson
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Xiaoyuan Han
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Ina A Stelzer
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Kazuo Ando
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Dyani Gaudilliere
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Thanaphong Phongpreecha
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Ivana Marić
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Alan L Chang
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
| | - Gary M Shaw
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - David K Stevenson
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Sean Bendall
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Kara L Davis
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Wendy Fantl
- Department of Microbiology and Immunology, Baxter Laboratory in Stem Cell Biology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
| | - Garry P Nolan
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Trevor Hastie
- Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
- Department of Statistics, Stanford University, Stanford, CA, USA
| | - Robert Tibshirani
- Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
- Department of Statistics, Stanford University, Stanford, CA, USA
| | - Martin S Angst
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- These authors jointly supervised this work: Martin S. Angst, Brice Gaudilliere, Nima Aghaeepour
| | - Brice Gaudilliere
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
- These authors jointly supervised this work: Martin S. Angst, Brice Gaudilliere, Nima Aghaeepour
| | - Nima Aghaeepour
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
- These authors jointly supervised this work: Martin S. Angst, Brice Gaudilliere, Nima Aghaeepour
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17
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Melsen JE, van Ostaijen-Ten Dam MM, Lankester AC, Schilham MW, van den Akker EB. A Comprehensive Workflow for Applying Single-Cell Clustering and Pseudotime Analysis to Flow Cytometry Data. THE JOURNAL OF IMMUNOLOGY 2020; 205:864-871. [PMID: 32591399 DOI: 10.4049/jimmunol.1901530] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Accepted: 06/01/2020] [Indexed: 12/24/2022]
Abstract
The introduction of single-cell platforms inspired the development of high-dimensional single-cell analysis tools to comprehensively characterize the underlying cellular heterogeneity. Flow cytometry data are traditionally analyzed by (subjective) gating of subpopulations on two-dimensional plots. However, the increasing number of parameters measured by conventional and spectral flow cytometry reinforces the need to apply many of the recently developed tools for single-cell analysis on flow cytometry data, as well. However, the myriads of analysis options offered by the continuously released novel packages can be overwhelming to the immunologist with limited computational background. In this article, we explain the main concepts of such analyses and provide a detailed workflow to illustrate their implications and additional prerequisites when applied on flow cytometry data. Moreover, we provide readily applicable R code covering transformation, normalization, dimensionality reduction, clustering, and pseudotime analysis that can serve as a template for future analyses. We demonstrate the merit of our workflow by reanalyzing a public human dataset. Compared with standard gating, the results of our workflow provide new insights in cellular subsets, alternative classifications, and hypothetical trajectories. Taken together, we present a well-documented workflow, which utilizes existing high-dimensional single-cell analysis tools to reveal cellular heterogeneity and intercellular relationships in flow cytometry data.
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Affiliation(s)
- Janine E Melsen
- Department of Pediatrics, Leiden University Medical Center, 2333 ZA Leiden, the Netherlands;
| | | | - Arjan C Lankester
- Department of Pediatrics, Leiden University Medical Center, 2333 ZA Leiden, the Netherlands
| | - Marco W Schilham
- Department of Pediatrics, Leiden University Medical Center, 2333 ZA Leiden, the Netherlands
| | - Erik B van den Akker
- Department of Biomedical Data Sciences, Leiden University Medical Center, 2333 ZC Leiden, the Netherlands; and.,Pattern Recognition and Bioinformatics Group, Delft University of Technology, 2628 XE Delft, the Netherlands
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18
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Ding H, Guo W, Su B. Electrochemiluminescence Single‐Cell Analysis: Intensity‐ and Imaging‐Based Methods. Chempluschem 2020; 85:725-733. [DOI: 10.1002/cplu.202000145] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 03/25/2020] [Indexed: 12/21/2022]
Affiliation(s)
- Hao Ding
- Institute of Analytical ChemistryDepartment of ChemistryZhejiang University Hangzhou 310058 P. R. China
| | - Weiliang Guo
- Institute of Analytical ChemistryDepartment of ChemistryZhejiang University Hangzhou 310058 P. R. China
| | - Bin Su
- Institute of Analytical ChemistryDepartment of ChemistryZhejiang University Hangzhou 310058 P. R. China
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19
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Rainbow Kaposi's Sarcoma-Associated Herpesvirus Revealed Heterogenic Replication with Dynamic Gene Expression. J Virol 2020; 94:JVI.01565-19. [PMID: 31969436 PMCID: PMC7108829 DOI: 10.1128/jvi.01565-19] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2019] [Accepted: 01/02/2020] [Indexed: 12/25/2022] Open
Abstract
Molecular mechanisms of Kaposi's sarcoma-associated herpesvirus (KSHV) reactivation have been studied primarily by measuring the total or average activity of an infected cell population, which often consists of a mixture of both nonresponding and reactivating cells that in turn contain KSHVs at various stages of replication. Studies on KSHV gene regulation at the individual cell level would allow us to better understand the basis for this heterogeneity, and new preventive measures could be developed based on findings from nonresponding cells exposed to reactivation stimuli. Here, we generated a recombinant reporter virus, which we named "Rainbow-KSHV," that encodes three fluorescence-tagged KSHV proteins (mBFP2-ORF6, mCardinal-ORF52, and mCherry-LANA). Rainbow-KSHV replicated similarly to a prototype reporter-KSHV, KSHVr.219, and wild-type BAC16 virus. Live imaging revealed unsynchronized initiation of reactivation and KSHV replication with diverse kinetics between individual cells. Cell fractionation revealed temporal gene regulation, in which early lytic gene expression was terminated in late protein-expressing cells. Finally, isolation of fluorescence-positive cells from nonresponders increased dynamic ranges of downstream experiments 10-fold. Thus, this study demonstrates a tool to examine heterogenic responses of KSHV reactivation for a deeper understanding of KSHV replication.IMPORTANCE Sensitivity and resolution of molecular analysis are often compromised by the use of techniques that measure the ensemble average of large cell populations. Having a research tool to nondestructively identify the KSHV replication stage in an infected cell would not only allow us to effectively isolate cells of interest from cell populations but also enable more precise sample selection for advanced single-cell analysis. We prepared a recombinant KSHV that can report on its replication stage in host cells by differential fluorescence emission. Consistent with previous host gene expression studies, our experiments reveal the highly heterogenic nature of KSHV replication/gene expression at individual cell levels. The utilization of a newly developed reporter-KSHV and initial characterization of KSHV replication in single cells are presented.
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20
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Gibellini L, De Biasi S, Porta C, Lo Tartaro D, Depenni R, Pellacani G, Sabbatini R, Cossarizza A. Single-Cell Approaches to Profile the Response to Immune Checkpoint Inhibitors. Front Immunol 2020; 11:490. [PMID: 32265933 PMCID: PMC7100547 DOI: 10.3389/fimmu.2020.00490] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Accepted: 03/03/2020] [Indexed: 12/26/2022] Open
Abstract
Novel treatments based upon the use of immune checkpoint inhibitors have an impressive efficacy in different types of cancer. Unfortunately, most patients do not derive benefit or lasting responses, and the reasons for the lack of therapeutic success are not known. Over the past two decades, a pressing need to deeply profile either the tumor microenvironment or cells responsible for the immune response has led investigators to integrate data obtained from traditional approaches with those obtained with new, more sophisticated, single-cell technologies, including high parameter flow cytometry, single-cell sequencing and high resolution imaging. The introduction and use of these technologies had, and still have a prominent impact in the field of cancer immunotherapy, allowing delving deeper into the molecular and cellular crosstalk between cancer and immune system, and fostering the identification of predictive biomarkers of response. In this review, besides the molecular and cellular cancer-immune system interactions, we are discussing how cutting-edge single-cell approaches are helping to point out the heterogeneity of immune cells in the tumor microenvironment and in blood.
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Affiliation(s)
- Lara Gibellini
- Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, Modena, Italy
| | - Sara De Biasi
- Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, Modena, Italy
| | - Camillo Porta
- Department of Internal Medicine and Therapeutics, Division of Translational Oncology, IRCCS Istituti Clinici Scientifici Maugeri, University of Pavia, Pavia, Italy
| | - Domenico Lo Tartaro
- Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, Modena, Italy
| | - Roberta Depenni
- Department of Oncology, Hematology, University of Modena and Reggio Emilia, Modena, Italy
| | - Giovanni Pellacani
- Department of Surgery, Medicine, Dentistry and Morphological Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Roberto Sabbatini
- Department of Oncology, Hematology, University of Modena and Reggio Emilia, Modena, Italy
| | - Andrea Cossarizza
- Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, Modena, Italy.,Section of Modena, Istituto Nazionale per le Ricerche Cardiovascolari, Bologna, Italy
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21
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EuroFlow Lymphoid Screening Tube (LST) data base for automated identification of blood lymphocyte subsets. J Immunol Methods 2019; 475:112662. [PMID: 31454495 DOI: 10.1016/j.jim.2019.112662] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Revised: 07/31/2019] [Accepted: 08/22/2019] [Indexed: 02/06/2023]
Abstract
In recent years the volume and complexity of flow cytometry data has increased substantially. This has led to a greater number of identifiable cell populations in a single measurement. Consequently, new gating strategies and new approaches for cell population definition are required. Here we describe how the EuroFlow Lymphoid Screening Tube (LST) reference data base for peripheral blood (PB) samples was designed, constructed and validated for automated gating of the distinct lymphoid (and myeloid) subsets in PB of patients with chronic lymphoproliferative disorders (CLPD). A total of 46 healthy/reactive PB samples which fulfilled pre-defined technical requirements, were used to construct the LST-PB reference data base. In addition, another set of 92 PB samples (corresponding to 10 healthy subjects, 51 B-cell CLPD and 31 T/NK-cell CLPD patients), were used to validate the automated gating and cell-population labeling tools with the Infinicyt software. An overall high performance of the LST-PB data base was observed with a median percentage of alarmed cellular events of 0.8% in 10 healthy donor samples and of 44.4% in CLPD data files containing 49.8% (range: 1.3-96%) tumor cells. The higher percent of alarmed cellular events in every CLPD sample was due to aberrant phenotypes (75.6% cases) and/or to abnormally increased cell counts (86.6% samples). All 18 (22%) data files that only displayed numerical alterations, corresponded to T/NK-cell CLPD cases which showed a lower incidence of aberrant phenotypes (41%) vs B-cell CLPD cases (100%). Comparison between automated vs expert-bases manual classification of normal (r2 = 0.96) and tumor cell populations (rho = 0.99) showed a high degree of correlation. In summary, our results show that automated gating of cell populations based on the EuroFlow LST-PB data base provides an innovative, reliable and reproducible tool for fast and simplified identification of normal vs pathological B and T/NK lymphocytes in PB of CLPD patients.
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