DOCSLIB.ORG

Mass Cytometry Analysis of the Tumour-Immune Landscape: the Role of Axl Receptor Kinase

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Mass Cytometry Analysis of the Tumour-Immune Landscape: the Role of Axl Receptor Kinase A thesis submitted in partial fulfilment of the requirements for the degree of Master of Science Mass cytometry analysis of the tumour-immune landscape: The role of Axl receptor kinase Sturla Magnus Grøndal Supervisor: Prof. James Bradley Lorens, Dept. of Biomedicine Co-supervisor: Assoc. Prof. Niels Aarsæther, Dept. of Biomedicine Bergen, November, 2018 I Abstract Cancer is one of the leading causes of death in Norway (2016) and worldwide. Despite the advent of new immunotherapies, malignant cancer demonstrates an intrinsic plasticity and is able to evade, adapt and suppress the immune system. An important driver for this malignant phenotype is the epithelial-to-mesenchymal transition (EMT) program, characteristic of stem cells. Previous research showed a link between the AXL receptor tyrosine kinase (Axl) and EMT. The Axl receptor is further involved in immune suppression and could therefore serve as a potential target in immunotherapy and in combination with other cancer treatments. Chemotherapeutic treatment also shows evidence of immune involvement, and the immune system plays a vital role in all forms of cancer treatment. In this study, we evaluated current immunotherapy in combination the Axl kinase inhibitor, bemcentinib. Using single cell mass cytometry we conducted 30 parameter mapping of the immune system in an experimental murine tumour model. The data was analysed using dimensionality reduction and unsupervised clustering. By studying how the immune landscape changes during tumour development and immunotherapy treatment, important insights into how the immune system responds to tumour development and treatment was measured and a new treatment regime was evaluated. II Acknowledgements I would like to thank my family and friends for supporting me through my year of academic research and else in life and for allowing me to have a place outside of the university I can retreat to. In, addition I would like to thank my all my colleagues and supervisors for great input and academic discussions. Of notice, I would like to thank Stacey D’mello and Kjersti Davidsen for their outstanding support, Cara Wogsland whose office was always open whenever I had questions about mass cytometry and Jim, my main supervisor, for giving me the freedom to explore this field without boundaries and supporting me in going abroad to widen my knowledge. III Abbreviations BSA Bovine serum albumin CTL Cytotoxic T lymphocyte CTLA-4 Cytotoxic T-lymphocyte-associated protein 4 CVD Cardiovascular diseases CWB Cell washing buffer CyTOF Cytometry by time-of-flight DNA Deoxyribonucleic acid DOTA 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid DPBS Dulbecco’s PBS DTPA Polymeric pentetic acid EMP Epithelial-to-mesenchymal plasticity EMT Epithelial to mesenchymal transition ICB Immune checkpoint blockade MDSC Myeloid derived suppressor cell MHC Major histocompatibility complex NaAz Sodium Azide NK Natural Killer PBS Phosphate buffered saline PD-1 Programmed cell death protein 1 PP Polypropylene RCF Relative centrifugal force RF Radio frequency RT Room temperature TAM Tumour associated macrophage TCEP tris(2-carboxyethyl)phosphine TCR T cell receptor Th Helper T cell TIL Tumour infiltrating lymphocyte Treg T regulatory IV Glossary Angiogenesis Formation of new blood vessels from existing ones Anoikis Avoiding detachment-induced apoptosis Antigens A molecule that can be bound by an antibody and initiate an immune response. Apoptosis A form of nonimmunogenic controlled cell death. Cell differentiation When a cell goes from one cell type to another and quires different traits. Immunogenic A process capable of initiating an immune response. Immunotherapy The use of the immune system to treat cancer Intercalator A molecule that can be inserted between base pairs in DNA. Leukocytes White blood cells comprising the immune system. Mahalanobis distance A type of distance used in multivariate space to find the distance from the centroid to a given point. The centroid is the point where the means of all variables intersect. MilliQ A type of purified water. Mutation Permanent change in nucleotide sequence, either by insertion, deletion or substitution of a single nucleotide. Neoantigens New antigens. Push A single slice of the gas stream in the TOF chamber. Reading The integrated intensity of several pushes. Torr A unit of pressure that corresponds to 1/760 standard atmospheres. Contents Abstract ....................................................................................................................................... I Acknowledgements .................................................................................................................... II Abbreviations ............................................................................................................................ III Glossary ..................................................................................................................................... IV Contents ..................................................................................................................................... 1 List of Figures .............................................................................................................................. 5 1 Introduction ........................................................................................................................ 1 1.1 Tumours on a population level .................................................................................... 1 1.2 Cancer development ................................................................................................... 2 1.2.1 Malignancy and EMT ............................................................................................ 2 1.2.2 Immunosuppression ............................................................................................. 4 1.3 Treatment of Breast Cancer ........................................................................................ 7 1.3.1 Anti-PD-1 and Anti-CTLA-4 ................................................................................... 8 1.3.2 Bemcentinib ......................................................................................................... 8 1.3.3 Combination of Bemcentinib and Immunotherapy ............................................. 9 1.4 Aims and Objectives .................................................................................................... 9 2 Methodological considerations ........................................................................................ 11 2.1 Mass cytometry ......................................................................................................... 11 2.1.2 Sample Preparation ............................................................................................ 17 2.1.3 Flow Cytometry as an Alternative ...................................................................... 21 2.2 Data analysis .............................................................................................................. 22 2.2.1 Normalisation ..................................................................................................... 22 2.2.2 Debarcoding ....................................................................................................... 22 2.2.3 Gating ................................................................................................................. 23 2.2.4 Dimensionality Reduction .................................................................................. 23 2.2.5 Clustering ............................................................................................................ 24 2.2.6 Cluster Characterisation ..................................................................................... 25 2.3 Reasons for the Approach ......................................................................................... 25 3 Materials and Methods ..................................................................................................... 26 3.1 General Overview ...................................................................................................... 26 3.1.1 Preparation of stock solutions ........................................................................... 29 3.2 Mice ........................................................................................................................... 30 3.2.1 Cell Culture ......................................................................................................... 31 3.2.2 Tumour Implantation ......................................................................................... 32 3.2.3 Preparation of Treatments ................................................................................. 32 3.2.4 Randomisation and Treatments ......................................................................... 33 3.2.5 Tissue Collection ................................................................................................. 34 3.3 Sample Preparation ................................................................................................... 34 3.3.1 Tissue Dissociation ............................................................................................. 34 3.3.2 Antibody Conjugation and Titration ................................................................... 35 3.3.3 Cell Staining ........................................................................................................ 37
Load more

Recommended publications
  • Cytof Workflow: Differential Discovery in High- Throughput High-Dimensional Cytometry Datasets [Version 4; Peer Review: 2 Approved]
    F1000Research 2019, 6:748 Last updated: 04 AUG 2021 METHOD ARTICLE CyTOF workflow: differential discovery in high- throughput high-dimensional cytometry datasets [version 4; peer review: 2 approved] Malgorzata Nowicka1,2, Carsten Krieg3, Helena L. Crowell 1,2, Lukas M. Weber 1,2, Felix J. Hartmann 3, Silvia Guglietta4, Burkhard Becher3, Mitchell P. Levesque5, Mark D. Robinson 1,2 1Institute for Molecular Life Sciences, University of Zurich, Zurich, 8057, Switzerland 2SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, 8057, Switzerland 3Institute of Experimental Immunology, University of Zurich, Zurich, 8057, Switzerland 4Department of Experimental Oncology, European Institute of Oncology, Via Adamello 16, Milan, I-20139, Italy 5Department of Dermatology, University Hospital Zurich, Zurich, CH-8091, Switzerland v4 First published: 26 May 2017, 6:748 Open Peer Review https://doi.org/10.12688/f1000research.11622.1 Second version: 14 Nov 2017, 6:748 https://doi.org/10.12688/f1000research.11622.2 Reviewer Status Third version: 24 May 2019, 6:748 https://doi.org/10.12688/f1000research.11622.3 Invited Reviewers Latest published: 17 Dec 2019, 6:748 https://doi.org/10.12688/f1000research.11622.4 1 2 version 4 Abstract (revision) High-dimensional mass and flow cytometry (HDCyto) experiments 17 Dec 2019 have become a method of choice for high-throughput interrogation and characterization of cell populations. Here, we present an updated version 3 R-based pipeline for differential analyses of HDCyto data, largely (revision) based on Bioconductor packages. We computationally define cell 24 May 2019 populations using FlowSOM clustering, and facilitate an optional but reproducible strategy for manual merging of algorithm-generated version 2 clusters.
    [Show full text]
  • A Deep Profiler's Guide to Cytometry
    TREIMM-937; No. of Pages 10 Review A deep profiler’s guide to cytometry 1 1 2 2 Sean C. Bendall , Garry P. Nolan , Mario Roederer and Pratip K. Chattopadhyay 1 Baxter Laboratory in Stem Cell Biology, Department of Microbiology and Immunology, Stanford University, Stanford, CA 94305, USA 2 ImmunoTechnology Section, Vaccine Research Center, NIAID, NIH, Bethesda, MD, USA In recent years, advances in technology have provided us and data analysis, and have led to state-of-the-art 20-pa- with tools to quantify the expression of multiple genes in rameter flow cytometers. Concomitant with this develop- individual cells. The ability to measure simultaneously ment, our understanding of immunology and stem cell multiple genes in the same cell is necessary to resolve biology has matured tremendously with the discovery of the great diversity of cell subsets, as well as to define scores of functionally diverse cell populations. Here, we their function in the host. Fluorescence-based flow cyto- review the development and highlight applications of poly- metry is the benchmark for this; with it, we can quantify chromatic flow cytometry (PFC, 6+ colors). In addition, we 18 proteins per cell, at >10 000 cells/s. Mass cytometry is review recent advances in a next-generation, ‘post-fluores- a new technology that promises to extend these capa- cence’ single-cell technology termed mass cytometry, which bilities significantly. Immunophenotyping by mass spec- is theoretically capable of measuring 70–100 parameters. trometry provides the ability to measure >36 proteins at Both fluorescence and mass cytometry have unique and a rate of 1000 cells/s.
    [Show full text]
  • Automated Cell Classification of Mass Cytometry Data by Deep
    bioRxiv preprint doi: https://doi.org/10.1101/054411; this version posted May 31, 2016. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. DeepCyTOF: Automated Cell Classification of Mass Cytometry Data by Deep Learning and Domain Adaptation Huamin Li 1∗, Uri Shaham 2∗, Yi Yao 3, Ruth Montgomery 3, and Yuval Kluger 1;4;5y 1Applied Mathematics Program, Yale University, 51 Prospect St., New Haven, CT 06511, USA 2Department of Statistics, Yale University, 24 Hillhouse Ave., New Haven, CT 06511, USA 3Department of Internal Medicine, Yale School of Medicine, 333 Cedar St., New Haven, CT 06520, USA 4Department of Pathology and Yale Cancer Center, Yale University School of Medicine, New Haven, CT, USA 5Interdepartmental Program in Computational Biology and Bioinformatics,Yale University, New Haven, CT, USA Abstract Mass cytometry or CyTOF is an emerging technology for high-dimensional multiparameter sin- gle cell analysis that overcomes many limitations of fluorescence-based flow cytometry. New methods for analyzing CyTOF data attempt to improve automation, scalability, performance, and interpretation of data generated in large studies. However, most current tools are less suitable for routine use where analysis must be standardized, reproducible, interpretable, and comparable. Assigning individual cells into discrete groups of cell types (gating) involves time-consuming se- quential manual steps untenable for larger studies. The subjectivity of manual gating introduces variability into the data and impacts reproducibility and comparability of results, particularly in multi-center studies. The FlowCAP consortium was formed to address these issues and it aims to boost user confidence in the viability of automated gating methods.
    [Show full text]
  • Cytof Supports Efficient Detection of Immune Cell Subsets from Small
    Journal of Immunological Methods 415 (2014) 1–5 Contents lists available at ScienceDirect Journal of Immunological Methods journal homepage: www.elsevier.com/locate/jim CyTOF supports efficient detection of immune cell subsets from small samples Yi Yao a, Rebecca Liu b, Min Sun Shin a,MarkTrentalangea,HeatherAllorea, Ala Nassar a, Insoo Kang a, Jordan S. Pober a,b,c, Ruth R. Montgomery a,c,⁎ a Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, United States b Department of Immunobiology, Yale University School of Medicine, New Haven, CT, United States c Human and Translational Immunology Program, Yale University School of Medicine, New Haven, CT, United States article info abstract Article history: Analysis of immune cell states is paramount to our understanding of the pathogenesis of a broad Received 6 September 2014 range of human diseases. Immunologists rely on fluorescence cytometry for cellular analysis, and Received in revised form 21 October 2014 while detection of 8 markers is now well established, the overlap of fluorescent signals limits Accepted 21 October 2014 efficiency. Mass cytometry or CyTOF (Cytometry by Time-Of-Flight) is a new technology for Available online 4 November 2014 multiparameter single cell analysis that overcomes many limitations of fluorescence-based flow cytometry and can routinely detect as many as 40 markers per sample. This technology provides Keywords: tremendous detail for cellular analysis of multiple cell populations simultaneously and is a powerful Mass cytometry technique for translational investigations. Here we present reproducible detection of immune cell Translational immunology subsets starting with as few as 10,000 cells.
    [Show full text]
  • Uniquely Cytof: Single-Tube Cytometry for Vaccine Development
    Uniquely CyTOF: Single-Tube Cytometry for Vaccine Development CyTOF® technology, which powers mass cytometry and Imaging Mass Cytometry™, is being used by research groups around the world to investigate multiple aspects of vaccine development and testing. The high-parameter data from CyTOF captures the complexity of the immune system, revealing often-unanticipated immune cell interactions. These results define the immune response and disease immunopathology to help refine development of treatments and vaccines for better disease management. CyTOF: Simple yet Powerful CyTOF Advantage for Vaccine Hallmarks of eective Development and Testing immune response The world’s most advanced single-cell cytometric • Define disease immunopathology and technology, CyTOF offers immune response with high-parameter phenotype and functional single-cell analysis. deep profiling of cell • Identify and phenotype antigen-specific phenotype and functional T cells using hundreds of metal-tagged state, all in a single tube. tetramers loaded with epitopes of interest. Mass cytometry is enabling • Perform preclinical vaccination and challenge Single-tube immune studies for information on immunogens, breakthrough discoveries profiling adjuvants and efficacy. with CyTOF in a wide range of immune- • Obtain detailed longitudinal immune profiles mediated diseases including during clinical research trials in order to Clinical Identification research trials, of optimal cancer, autoimmunity, assess efficacy and further refine dose, Phases I and II viral epitopes infectious
    [Show full text]
  • Cytof Mass Cytometry Reveals Phenotypically Distinct Human Blood Neutrophil Populations Differentially Correlated with Melanoma Stage
    Open access Original research J Immunother Cancer: first published as 10.1136/jitc-2019-000473 on 9 September 2020. Downloaded from CyTOF mass cytometry reveals phenotypically distinct human blood neutrophil populations differentially correlated with melanoma stage 1 1 1 Yanfang Peipei Zhu , Tobias Eggert, Daniel J Araujo, 2 3 Pandurangan Vijayanand, Christian Hermann Ottensmeier , Catherine C Hedrick1 To cite: Zhu YP, Eggert T, ABSTRACT populations, each with opposing actions in Araujo DJ, et al. CyTOF Background Understanding neutrophil heterogeneity immune regulation and cancer progression.6 mass cytometry reveals and its relationship to disease progression has become a phenotypically distinct human Marini and coauthors employed flow cytom- recent focus of cancer research. Indeed, several studies + − blood neutrophil populations etry to show that CD10 and CD10 neutro- have identified neutrophil subpopulations associated with differentially correlated with phils represent populations with opposing protumoral or antitumoral functions. However, this work melanoma stage. Journal for effects on T- cell proliferation.7 Pillay and has been hindered by a lack of widely accepted markers ImmunoTherapy of Cancer colleagues identified three neutrophil 2020;8:e000473. doi:10.1136/ with which to define neutrophil subpopulations. jitc-2019-000473 Methods To identify markers of neutrophil heterogeneity subpopulations, based on their differential in cancer, we used single-cell cytometry by time-of- flight expression of CD16 and CD62L, with each ► Additional material is (CyTOF) coupled with high-dimensional analysis on blood exhibiting specific maturation and activa- 8 published online only. To view, samples from treatment- naïve patients with melanoma. tion statuses. CD45RA, CD63, and CD11b please visit the journal online Results Our efforts allowed us to identify seven blood also indicate activation statuses in certain (http:// dx.
    [Show full text]
  • Defining Mucosal Immunity Using Mass Cytometry Following Experimental Human Pneumococcal
    bioRxiv preprint doi: https://doi.org/10.1101/546929; this version posted February 11, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Defining mucosal immunity using mass cytometry following experimental human pneumococcal challenge Simon P. Jochems1,2,11,*, Karin de Ruiter2,11, Carla Solórzano1,11, Astrid Voskamp2,11, Elena Mitsi1, Elissavet Nikolaou1, Beatriz F Carniel1, Sherin Pojar1, Esther L. German1, Jesús Reiné1, Alessandra Soares-Schanoski3, Helen Hill1,4, Rachel Robinson1,4, Angela D. Hyder-Wright1,4, Caroline M. Weight5, Pascal F. Durrenberger6, Robert S. Heyderman5, Stephen B. Gordon1,7, Hermelijn H. Smits2, Britta C. Urban8, Jamie Rylance1, Andrea M. Collins1,4,9, Mark D. Wilkie4, Lepa Lazarova1,4, Samuel C. Leong1,10, Maria Yazdanbakhsh2,12, Daniela M. Ferreira1,12,13* Affiliations: 1 Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, United Kingdom 2 Department of Parasitology, Leiden University Medical Center, Leiden, Netherlands 3 Bacteriology Laboratory, Butantan Institute, Sao Paulo, Brazil 4 Royal Liverpool and Broadgreen University Hospital, Liverpool, United Kingdom 5 Division of Infection and Immunity, University College London, London, United Kingdom 6 Centre for Inflammation and Tissue Repair, University College London, London, United Kingdom 7 Malawi-Liverpool-Wellcome Trust Clinical Research Programme, Blantyre, Malawi 8 Department of Parasitology, Liverpool School of Tropical Medicine, Liverpool, United Kingdom 9 Aintree University Hospital NHS Foundation Trust, Liverpool, United Kingdom 10 Department of Otorhinolaryngology – Head and Neck Surgery, Aintree University Hospital NHS Foundation Trust, Liverpool, United Kingdom 11 These authors contributed equally 12 Joint senior authors *Corresponding authors: Simon Jochems ([email protected]) and Daniela Ferreira ([email protected]).
    [Show full text]
  • From Bivariate to Multivariate Analysis of Cytometric Data: Overview of Computational Methods and Their Application in Vaccination Studies
    Review From Bivariate to Multivariate Analysis of Cytometric Data: Overview of Computational Methods and Their Application in Vaccination Studies Simone Lucchesi 1, Simone Furini 2, Donata Medaglini 1 and Annalisa Ciabattini 1,* 1 Laboratory of Molecular Microbiology and Biotechnology (LA.M.M.B.), Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy; [email protected] (S.L.); [email protected] (D.M.) 2 Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy; [email protected] * Correspondence: [email protected] Received: 27 February 2020; Accepted: 18 March 2020; Published: 20 March 2020 Abstract: Flow and mass cytometry are used to quantify the expression of multiple extracellular or intracellular molecules on single cells, allowing the phenotypic and functional characterization of complex cell populations. Multiparametric flow cytometry is particularly suitable for deep analysis of immune responses after vaccination, as it allows to measure the frequency, the phenotype, and the functional features of antigen-specific cells. When many parameters are investigated simultaneously, it is not feasible to analyze all the possible bi-dimensional combinations of marker expression with classical manual analysis and the adoption of advanced automated tools to process and analyze high-dimensional data sets becomes necessary. In recent years, the development of many tools for the automated analysis of multiparametric cytometry data has been reported, with an increasing record of publications starting from 2014. However, the use of these tools has been preferentially restricted to bioinformaticians, while few of them are routinely employed by the biomedical community. Filling the gap between algorithms developers and final users is fundamental for exploiting the advantages of computational tools in the analysis of cytometry data.
    [Show full text]
  • Cytof for All
    CyTOF for All The Top 8 Reasons to Use Mass Cytometry for Your Project Michelle Poulin, PhD Manager, MC/IMC field applications NA Chief aims of this presentation • Provide information on the ever-increasing use of CyTOF around the world. • Provide the reasons why CyTOF has become so popular Helios™, a CyTOF® system for high-parameter cytometry. • Clear up some misconceptions about the technology. 2 Summary Proven High-parameter capability Performance Array of reagents Simplicity Data quality Easy panels Community 3 Number 1: Mass cytometry is a proven and well-adopted technology Mass cytometry publication ramp Peer-reviewed* publications as of March 2020 900 66 800 Series2New 700 313 Series1Previous 600 500 167 810 400 300 147 497 200 72 330 44 183 100 67 67 111 0 2007– 2015 2016 2017 2018 2019 2020 2014 *Does not include commentaries or reviews Diverse study areas and panel sizes Areas of study Panel size distribution Infectious Transplantation/ disease stem cells Other diseases 41–50, 51–60, 1% 10% 4% 1–20, 8% 14% 15% Autoimmunity/ Method allergy development 12% 16% Data analysis ImmunoImmuno-- 12% 21–30, oncologyoncology 21% 14% 31–40, Immunology 49% Oncology 11% 13% Based on data from 186 peer-reviewed papers published in 2018 and 2019 6 Growing adoption of CyTOF in trials Clinical trials using CyTOF technology As of January 2020: 80 • 67 ongoing clinical trials 1 • 8 completed trials 70 18 60 Number Trial Phase 50 of Trials 17 Phase 1 13 40 74 Phase 2 26 Number 11 Phase 1/2 8 30 56 7 Phase 3 1 20 4 39 2 28 Phase 4 4 6 10 21 4 15 17 9 Other 5 5 23 0 0 (observational) Total 75 Cumulative totalTotal New Trialstrials Source: clinicaltrials.gov, January 2020 For Research Use Only.
    [Show full text]
  • A Streamlined Cytof Workflow to Facilitate Standardized Multi-Site Immune Profiling of COVID-19 Patients
    medRxiv preprint doi: https://doi.org/10.1101/2020.06.26.20141341; this version posted June 28, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. A Streamlined CyTOF Workflow To Facilitate Standardized Multi-Site Immune Profiling of COVID-19 Patients Daniel Geanon1*, Brian Lee1*, Geoffrey Kelly1, Diana Handler1, Bhaskar Upadhyaya1, John Leech1, Manon Herbinet1, Diane Del Valle3, Sacha Gnjatic1,3, Seunghee Kim-Schulze1, Miriam Merad1,3, Adeeb H. Rahman1,4 1. Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, NY, NY, USA 2. Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, NY, NY, USA 3. Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, NY, NY, USA 4. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY, NY, USA *Equal contribution Corresponding author: Adeeb Rahman [email protected] 1 NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. medRxiv preprint doi: https://doi.org/10.1101/2020.06.26.20141341; this version posted June 28, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Abstract Mass cytometry (CyTOF) represents one of the most powerful tools in immune phenotyping, allowing high throughput quantification of over 40 single parameters at single-cell resolution.
    [Show full text]
  • Cytof® Mass Cytometer Discover More
    CyTOF® Mass Cytometer Discover More. It’s Elemental. Cellular analysis on an Familiar workflow, unprecedented scale more data 1 Cell Surface 2 Intracellular The CyTOF® Advantage Sample Preparation Immunostaining Immunostaining Incubate cells with Fix cells 375 pc metal tagged antibodies 375 pc • Simple, highly multiplexed single cell analysis Block non-specific Wash binding groups 375 pc • Robust and reproducible performance 375 pc Fix Cells Stain with metal • Absolute quantitation of target molecules 375 pc tagged antibodies 375 pc • Easy, convenient workflow Incubate with a metallointercalator2 Wash 375 pc 375 pc ® Wash Fix Cells The CyTOF Mass Cytometer introduces the next generation of 375 pc highly multiplexed single cell analyzers which will afford new insights into the biology of complex cellular networks1. Sample Processing Incubate with a metallointercalator Unlike traditional flow cytometry where detection of multiple probes is hindered by the physical limitations of fluorescence emission spectral overlap, the CyTOF instrument uses atomic mass spectrometry to allow simple, independent detection of up to 33 metal labeled antibodies (and two DNA intercalators) based ® on currently available reagents. The instrument has the capacity Introduce individual samples into the CyTOF Mass Cytometer (similar to a flow cytometer) to measure 100 independent signals and new reagents will be 375 pc introduced to take advantage of its full capability in the future. Collect data. 5 The CyTOF system is the first inorganic mass spectrometer (Analyzes 10 cells in four minutes, regardless of the number of tag elements.) specifically built for biologists and single cell applications. The inductively coupled plasma mass spectrometer at its heart Data Analysis has been used extensively by analytical chemists and is proven in the quantification of elements and stable isotopes in a variety of complex matrices.
    [Show full text]
  • Mass Cytometry (Cytof)
    Mass Cytometry (CyTOF): Modern way to analyse >40 markers on hundred thousands of cells on a single cell level using the Helios and Hyperion™ Imaging System Olga Karpus, PhD Field Applications Specialist, BeNeLux & Nordics [email protected] Cytometry the term "cytometry" can apply to any method used to extract quantitative information from individual cells, often antibody-based method. Inductively coupled plasma mass spectrometry (ICP-MS) Mass is an elemental analysis technology capable of Cytometry or detecting most of the periodic table of elements. It is CyTOF used in a variety of industries including, but not limited (Cytometry to, environmental monitoring, geochemical analysis, Time of metallurgy. Flight) Time of flight (TOF) detection in mass spectrometry. Origin of a term • Extremely low detection limits; • A large linear range • Possibilities to detect isotope composition of elements • High sample throughput – speed Using the atomic mass spectrum • 135 channels • >35 metal tags • Additional isotopes possible • Rare Lanthanides: • Nonbiological 13 metals, • Nonradioactive 35 isotopes Each vertical bar is a stable isotope that can be measured. Using the atomic mass spectrum Lanthanides – 35 isotopes for antibody labeling 102Pd 104Pd 195Pt 105 106 Pd Pd 194Pt 198 209Bi 89 Pt Y 108Pd 110Pd 127I CD45 Barcoding IdU – S phase Cisplatin – Live/Dead CD16 103Rh DNA intercalator Metal-Tagged Probes Maxpar IgG Antibodies Cell-ID™ • >600 preconjugates • Barcoding • Human and mouse • Intercalator: Identifies single-cell • Phenotyping and functional events applications • Cisplatin: Dead-cell indicator • Individual or part of panel kits • IdU: S-phase • 35 labeling kits • Conjugation service available Lanthanide labeling of antibodies Maxpar™ IgG antibodies or self- conjugated antibodies with labeling kits fluidigm.com Cell-ID Pd Barcoding Barcoded Samples 1 tube Benefits: = - simplified sample prep - savings of antibodies - robust data - improved data quality by: better cell doublet discrimination vs.
    [Show full text]