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Bagwell CB, Hunsberger B, Hill B, Herbert D, Bray C, Selvanantham T, Li S, Villasboas JC, Pavelko K, Strausbauch M, Rahman A, Kelly G, Asgharzadeh S, Gomez-Cabrero A, Behbehani G, Chang H, Lyberger J, Montgomery R, Zhao Y, Inokuma M, Goldberger O, Stelzer G. Multi-site reproducibility of a human immunophenotyping assay in whole blood and peripheral blood mononuclear cells preparations using CyTOF technology coupled with Maxpar Pathsetter, an automated data analysis system. CYTOMETRY PART B-CLINICAL CYTOMETRY 2019; 98:146-160. [PMID: 31758746 DOI: 10.1002/cyto.b.21858] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Revised: 10/29/2019] [Accepted: 11/05/2019] [Indexed: 12/31/2022]
Abstract
High-dimensional mass cytometry data potentially enable a comprehensive characterization of immune cells. In order to positively affect clinical trials and translational clinical research, this advanced technology needs to demonstrate a high reproducibility of results across multiple sites for both peripheral blood mononuclear cells (PBMC) and whole blood preparations. A dry 30-marker broad immunophenotyping panel and customized automated analysis software were recently engineered and are commercially available as the Fluidigm® Maxpar® Direct™ Immune Profiling Assay™. In this study, seven sites received whole blood and six sites received PBMC samples from single donors over a 2-week interval. Each site labeled replicate samples and acquired data on Helios™ instruments using an assay-specific acquisition template. All acquired sample files were then automatically analyzed by Maxpar Pathsetter™ software. A cleanup step eliminated debris, dead cells, aggregates, and normalization beads. The second step automatically enumerated 37 immune cell populations and performed label intensity assessments on all 30 markers. The inter-site reproducibility of the 37 quantified cell populations had consistent population frequencies, with an average %CV of 14.4% for whole blood and 17.7% for PBMC. The dry reagent coupled with automated data analysis is not only convenient but also provides a high degree of reproducibility within and among multiple test sites resulting in a comprehensive yet practical solution for deep immune phenotyping.
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Affiliation(s)
| | | | - Beth Hill
- Verity Software House, Topsham, Maine
| | | | | | | | - Stephen Li
- Fluidigm Canada Inc., Markham, Ontario, Canada
| | | | | | | | - Adeeb Rahman
- Icahn School of Medicine at Mount Sinai, New York, New York
| | - Gregory Kelly
- Icahn School of Medicine at Mount Sinai, New York, New York
| | | | | | | | | | | | | | - Yujiao Zhao
- Yale School of Medicine, New Haven, Connecticut
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2
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Erez A, Mukherjee R, Altan-Bonnet G. Quantifying the Dynamics of Hematopoiesis by In Vivo IdU Pulse-Chase, Mass Cytometry, and Mathematical Modeling. Cytometry A 2019; 95:1075-1084. [PMID: 31150166 DOI: 10.1002/cyto.a.23799] [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: 02/17/2019] [Revised: 05/06/2019] [Accepted: 05/09/2019] [Indexed: 11/09/2022]
Abstract
We present a new method to directly quantify the dynamics of differentiation of multiple cellular subsets in unperturbed mice. We combine a pulse-chase protocol of 5-iodo-2'-deoxyuridine (IdU) injections with subsequent analysis by mass cytometry (CyTOF) and mathematical modeling of the IdU dynamics. Measurements by CyTOF allow for a wide range of cells to be analyzed at once, due to the availability of a large staining panel without the complication of fluorescence spillover. These are also compatible with direct detection of integrated iodine signal, with minimal impact on immunophenotyping based on the surface markers. Mathematical modeling beyond a binary classification of surface marker abundance allows for a continuum of cellular states as the cells transition from one state to another. Thus, we present a complete and robust method for directly quantifying differentiation at the systemic level, allowing for system-wide comparisons between different mouse strains and/or experimental conditions. Published 2019. This article is a U.S. Government work and is in the public domain in the USA.
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Affiliation(s)
- Amir Erez
- Immunodynamics Group, Cancer and Inflammation Program, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Ratnadeep Mukherjee
- Immunodynamics Group, Cancer and Inflammation Program, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Grégoire Altan-Bonnet
- Immunodynamics Group, Cancer and Inflammation Program, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
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3
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Harms RZ, Lorenzo-Arteaga KM, Ostlund KR, Smith VB, Smith LM, Gottlieb P, Sarvetnick N. Abnormal T Cell Frequencies, Including Cytomegalovirus-Associated Expansions, Distinguish Seroconverted Subjects at Risk for Type 1 Diabetes. Front Immunol 2018; 9:2332. [PMID: 30405601 PMCID: PMC6204396 DOI: 10.3389/fimmu.2018.02332] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Accepted: 09/19/2018] [Indexed: 12/12/2022] Open
Abstract
We analyzed T cell subsets from cryopreserved PBMC obtained from the TrialNet Pathway to Prevention archives. We compared subjects who had previously seroconverted for one or more autoantibodies with non-seroconverted, autoantibody negative individuals. We observed a reduced frequency of MAIT cells among seroconverted subjects. Seroconverted subjects also possessed decreased frequencies of CCR4-expressing CD4 T cells, including a regulatory-like subset. Interestingly, we found an elevation of CD57+, CD28–, CD127–, CD27– CD8 T cells (SLEC) among seroconverted subjects that was most pronounced among those that progressed to disease. The frequency of these SLEC was strongly correlated with CMV IgG abundance among seroconverted subjects, associated with IA-2 levels, and most elevated among CMV+ seroconverted subjects who progressed to disease. Combined, our data indicate discrete, yet profound T cell alterations are associated with islet autoimmunity among at-risk subjects.
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Affiliation(s)
- Robert Z Harms
- Surgery-Transplant, University of Nebraska Medical Center, Omaha, NE, United States
| | | | - Katie R Ostlund
- Surgery-Transplant, University of Nebraska Medical Center, Omaha, NE, United States
| | - Victoria B Smith
- Office of the Vice Chancellor of Research, University of Nebraska Medical Center, Omaha, NE, United States
| | - Lynette M Smith
- Biostatistics, University of Nebraska Medical Center, Omaha, NE, United States
| | - Peter Gottlieb
- Barbara Davis Center for Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Nora Sarvetnick
- Surgery-Transplant, University of Nebraska Medical Center, Omaha, NE, United States.,Mary and Dick Holland Regenerative Medicine Program, University of Nebraska Medical Center, Omaha, NE, United States
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High-Dimensional Modeling for Cytometry: Building Rock Solid Models Using GemStone™ and Verity Cen-se'™ High-Definition t-SNE Mapping. Methods Mol Biol 2018; 1678:11-36. [PMID: 29071673 DOI: 10.1007/978-1-4939-7346-0_2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
This chapter outlines how to approach the complex tasks associated with designing models for high-dimensional cytometry data. Unlike gating approaches, modeling lends itself to automation and accounts for measurement overlap among cellular populations. Designing these models is now easier because of a new technique called high-definition t-SNE mapping. Nontrivial examples are provided that serve as a guide to create models that are consistent with data.
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5
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Strindhall J, Löfgren S, Främsth C, Matussek A, Bengner M, Ernerudh J, Wikby A. CD4/CD8 ratio<1 is associated with lymphocyte subsets, CMV and gender in 71-year old individuals: 5-Year follow-up of the Swedish HEXA Immune Longitudinal Study. Exp Gerontol 2017; 95:82-87. [PMID: 28476585 DOI: 10.1016/j.exger.2017.03.020] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2016] [Revised: 01/27/2017] [Accepted: 03/22/2017] [Indexed: 10/19/2022]
Affiliation(s)
- Jan Strindhall
- School of Health and Welfare, Department of Natural Science and Biomedicine, Jönköping University, Box 1026, SE-551 11 Jönköping, Sweden.
| | - Sture Löfgren
- Division of Medical Diagnostics, Ryhov County Hospital, SE-551 85 Jönköping, Sweden
| | - Caroline Främsth
- Division of Medical Diagnostics, Ryhov County Hospital, SE-551 85 Jönköping, Sweden
| | - Andreas Matussek
- Division of Medical Diagnostics, Ryhov County Hospital, SE-551 85 Jönköping, Sweden; Karolinska University Laboratory, Karolinska University Hospital, Solna, Sweden; Division of Clinical Microbiology, Department of Laboratory Medicine, Karolinska Institutet, Karolinska University Hospital Huddinge, Stockholm, Sweden
| | - Malin Bengner
- Department of Infectious Diseases, Ryhov County Hospital, SE-551 85 Jönköping, Sweden
| | - Jan Ernerudh
- Division of Clinical Immunology, Department of Molecular and Clinical Medicine, Faculty of Health Sciences, Linköping University, Linköping, Sweden
| | - Anders Wikby
- School of Health and Welfare, Department of Natural Science and Biomedicine, Jönköping University, Box 1026, SE-551 11 Jönköping, Sweden
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6
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Bagwell CB, Hill BL, Herbert DJ, Bray CM, Hunsberger BC. Sometimes simpler is better: VLog, a general but easy-to-implement log-like transform for cytometry. Cytometry A 2016; 89:1097-1105. [DOI: 10.1002/cyto.a.23017] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2016] [Revised: 09/08/2016] [Accepted: 10/13/2016] [Indexed: 11/07/2022]
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7
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Sriram U, Hill BL, Cenna JM, Gofman L, Fernandes NC, Haldar B, Potula R. Impaired Subset Progression and Polyfunctionality of T Cells in Mice Exposed to Methamphetamine during Chronic LCMV Infection. PLoS One 2016; 11:e0164966. [PMID: 27760221 PMCID: PMC5070876 DOI: 10.1371/journal.pone.0164966] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2016] [Accepted: 10/04/2016] [Indexed: 01/23/2023] Open
Abstract
Methamphetamine (METH) is a widely used psychostimulant that severely impacts the host’s innate and adaptive immune systems and has profound immunological implications. T cells play a critical role in orchestrating immune responses. We have shown recently how chronic exposure to METH affects T cell activation using a murine model of lymphocytic choriomeningitis virus (LCMV) infection. Using the TriCOM (trinary state combinations) feature of GemStone™ to study the polyfunctionality of T cells, we have analyzed how METH affected the cytokine production pattern over the course of chronic LCMV infection. Furthermore, we have studied in detail the effects of METH on splenic T cell functions, such as cytokine production and degranulation, and how they regulate each other. We used the Probability State Modeling (PSM) program to visualize the differentiation of effector/memory T cell subsets during LCMV infection and analyze the effects of METH on T cell subset progression. We recently demonstrated that METH increased PD-1 expression on T cells during viral infection. In this study, we further analyzed the impact of PD-1 expression on T cell functional markers as well as its expression in the effector/memory subsets. Overall, our study indicates that analyzing polyfunctionality of T cells can provide additional insight into T cell effector functions. Analysis of T cell heterogeneity is important to highlight changes in the evolution of memory/effector functions during chronic viral infections. Our study also highlights the impact of METH on PD-1 expression and its consequences on T cell responses.
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Affiliation(s)
- Uma Sriram
- Department of Pathology and Laboratory Medicine, Lewis Katz School of Medicine, Temple University, Philadelphia, United States of America
| | - Beth L. Hill
- Verity Software House, Topsham, Maine, United States of America
| | - Jonathan M. Cenna
- Department of Pathology and Laboratory Medicine, Lewis Katz School of Medicine, Temple University, Philadelphia, United States of America
| | - Larisa Gofman
- Department of Pathology and Laboratory Medicine, Lewis Katz School of Medicine, Temple University, Philadelphia, United States of America
| | - Nicole C. Fernandes
- Department of Pathology and Laboratory Medicine, Lewis Katz School of Medicine, Temple University, Philadelphia, United States of America
| | - Bijayesh Haldar
- Department of Pathology and Laboratory Medicine, Lewis Katz School of Medicine, Temple University, Philadelphia, United States of America
| | - Raghava Potula
- Department of Pathology and Laboratory Medicine, Lewis Katz School of Medicine, Temple University, Philadelphia, United States of America
- Center for Substance Abuse Research, Lewis Katz School of Medicine, Temple University, Philadelphia, PA, United States of America
- * E-mail:
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8
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Saeys Y, Van Gassen S, Lambrecht BN. Computational flow cytometry: helping to make sense of high-dimensional immunology data. Nat Rev Immunol 2016; 16:449-62. [PMID: 27320317 DOI: 10.1038/nri.2016.56] [Citation(s) in RCA: 305] [Impact Index Per Article: 38.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Recent advances in flow cytometry allow scientists to measure an increasing number of parameters per cell, generating huge and high-dimensional datasets. To analyse, visualize and interpret these data, newly available computational techniques should be adopted, evaluated and improved upon by the immunological community. Computational flow cytometry is emerging as an important new field at the intersection of immunology and computational biology; it allows new biological knowledge to be extracted from high-throughput single-cell data. This Review provides non-experts with a broad and practical overview of the many recent developments in computational flow cytometry.
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Affiliation(s)
- Yvan Saeys
- VIB Inflammation Research Center, Technologiepark 927, Ghent B-9052, Belgium.,Department of Internal Medicine, Ghent University, De Pintelaan 185, Ghent B-9000, Belgium
| | - Sofie Van Gassen
- VIB Inflammation Research Center, Technologiepark 927, Ghent B-9052, Belgium.,Department of Information Technology, Technologiepark 15, Ghent B-9052, Belgium
| | - Bart N Lambrecht
- VIB Inflammation Research Center, Technologiepark 927, Ghent B-9052, Belgium.,Department of Internal Medicine, Ghent University, De Pintelaan 185, Ghent B-9000, Belgium.,Department of Pulmonary Medicine, Erasmus MC Rotterdam, Dr Molewaterplein 50, Rotterdam 3015 GE, The Netherlands
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9
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Kan A, Pavlyshyn D, Markham JF, Dowling MR, Heinzel S, Zhou JHS, Marchingo JM, Hodgkin PD. Stochastic Measurement Models for Quantifying Lymphocyte Responses Using Flow Cytometry. PLoS One 2016; 11:e0146227. [PMID: 26742110 PMCID: PMC4704825 DOI: 10.1371/journal.pone.0146227] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2015] [Accepted: 11/09/2015] [Indexed: 01/22/2023] Open
Abstract
Adaptive immune responses are complex dynamic processes whereby B and T cells undergo division and differentiation triggered by pathogenic stimuli. Deregulation of the response can lead to severe consequences for the host organism ranging from immune deficiencies to autoimmunity. Tracking cell division and differentiation by flow cytometry using fluorescent probes is a major method for measuring progression of lymphocyte responses, both in vitro and in vivo. In turn, mathematical modeling of cell numbers derived from such measurements has led to significant biological discoveries, and plays an increasingly important role in lymphocyte research. Fitting an appropriate parameterized model to such data is the goal of these studies but significant challenges are presented by the variability in measurements. This variation results from the sum of experimental noise and intrinsic probabilistic differences in cells and is difficult to characterize analytically. Current model fitting methods adopt different simplifying assumptions to describe the distribution of such measurements and these assumptions have not been tested directly. To help inform the choice and application of appropriate methods of model fitting to such data we studied the errors associated with flow cytometry measurements from a wide variety of experiments. We found that the mean and variance of the noise were related by a power law with an exponent between 1.3 and 1.8 for different datasets. This violated the assumptions inherent to commonly used least squares, linear variance scaling and log-transformation based methods. As a result of these findings we propose a new measurement model that we justify both theoretically, from the maximum entropy standpoint, and empirically using collected data. Our evaluation suggests that the new model can be reliably used for model fitting across a variety of conditions. Our work provides a foundation for modeling measurements in flow cytometry experiments thus facilitating progress in quantitative studies of lymphocyte responses.
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Affiliation(s)
- Andrey Kan
- Division of Immunology, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, VIC, Australia
| | - Damian Pavlyshyn
- Division of Immunology, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, VIC, Australia
| | - John F. Markham
- Division of Immunology, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, VIC, Australia
| | - Mark R. Dowling
- Division of Immunology, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, VIC, Australia
| | - Susanne Heinzel
- Division of Immunology, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, VIC, Australia
| | - Jie H. S. Zhou
- Division of Immunology, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, VIC, Australia
| | - Julia M. Marchingo
- Division of Immunology, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, VIC, Australia
| | - Philip D. Hodgkin
- Division of Immunology, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, VIC, Australia
- * E-mail:
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10
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Mair F, Hartmann FJ, Mrdjen D, Tosevski V, Krieg C, Becher B. The end of gating? An introduction to automated analysis of high dimensional cytometry data. Eur J Immunol 2015; 46:34-43. [DOI: 10.1002/eji.201545774] [Citation(s) in RCA: 164] [Impact Index Per Article: 18.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2015] [Revised: 10/15/2015] [Accepted: 11/03/2015] [Indexed: 11/06/2022]
Affiliation(s)
- Florian Mair
- Institute of Experimental Immunology; University of Zurich; Zurich Switzerland
| | - Felix J. Hartmann
- Institute of Experimental Immunology; University of Zurich; Zurich Switzerland
| | - Dunja Mrdjen
- Institute of Experimental Immunology; University of Zurich; Zurich Switzerland
| | - Vinko Tosevski
- Institute of Experimental Immunology; University of Zurich; Zurich Switzerland
| | - Carsten Krieg
- Institute of Experimental Immunology; University of Zurich; Zurich Switzerland
| | - Burkhard Becher
- Institute of Experimental Immunology; University of Zurich; Zurich Switzerland
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11
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Chester C, Maecker HT. Algorithmic Tools for Mining High-Dimensional Cytometry Data. THE JOURNAL OF IMMUNOLOGY 2015; 195:773-9. [PMID: 26188071 DOI: 10.4049/jimmunol.1500633] [Citation(s) in RCA: 81] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
The advent of mass cytometry has led to an unprecedented increase in the number of analytes measured in individual cells, thereby increasing the complexity and information content of cytometric data. Although this technology is ideally suited to the detailed examination of the immune system, the applicability of the different methods for analyzing such complex data is less clear. Conventional data analysis by manual gating of cells in biaxial dot plots is often subjective, time consuming, and neglectful of much of the information contained in a highly dimensional cytometric dataset. Algorithmic data mining has the promise to eliminate these concerns, and several such tools have been applied recently to mass cytometry data. We review computational data mining tools that have been used to analyze mass cytometry data, outline their differences, and comment on their strengths and limitations. This review will help immunologists to identify suitable algorithmic tools for their particular projects.
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Affiliation(s)
- Cariad Chester
- The Human Immune Monitoring Center, Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, CA 94305; and Division of Oncology, Department of Medicine, Stanford University, Stanford, CA 94305
| | - Holden T Maecker
- The Human Immune Monitoring Center, Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, CA 94305; and
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12
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Bagwell CB, Hill BL, Wood BL, Wallace PK, Alrazzak M, Kelliher AS, Preffer FI. Human B-cell and progenitor stages as determined by probability state modeling of multidimensional cytometry data. CYTOMETRY. PART B, CLINICAL CYTOMETRY 2015; 88:214-26. [PMID: 25850810 PMCID: PMC5828699 DOI: 10.1002/cyto.b.21243] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2015] [Revised: 04/01/2015] [Accepted: 04/03/2015] [Indexed: 12/15/2022]
Abstract
BACKGROUND Human progenitor and B-cell development is a highly regulated process characterized by the ordered differential expression of numerous cell-surface and intracytoplasmic antigens. This study investigates the underlying coordination of these modulations by examining a series of normal bone marrow samples with the method of probability state modeling or PSM. RESULTS The study is divided into two sections. The first section examines B-cell stages subsequent to CD19 up-regulation. The second section assesses an earlier differentiation stage before and including CD19 up-regulation. POST-CD19 ANTIGENIC UP-REGULATION: Statistical analyses of cytometry data derived from sixteen normal bone marrow specimens revealed that B cells have at least three distinct coordinated changes, forming four stages labeled as B1, B2, B3, and B4. At the end of B1; CD34 antigen expression down-regulates with TdT while CD45, CD81, and CD20 slightly up-regulate. At the end of B2, CD45 and CD20 up-regulate. At the end of B3 and beginning of B4; CD10, CD38, and CD81 down-regulate while CD22 and CD44 up-regulate. PRE-CD19 ANTIGENIC UP-REGULATION: Statistical analysis of ten normal bone marrows revealed that there are at least two measurable coordinated changes with progenitors, forming three stages labeled as P1, P2, and P3. At the end of P1, CD38 up-regulates. At the end of P2; CD19, CD10, CD81, CD22, and CD9 up-regulate while CD44 down-regulates slightly. CONCLUSIONS These objective results yield a clearer immunophenotypic picture of the underlying cellular mechanisms that are operating in these important developmental processes. Also, unambiguously determined stages define what is meant by "normal" B-cell development and may serve as a preliminary step for the development of highly sensitive minimum residual disease detection systems. A companion article is simultaneously being published in Cytometry Part A that will explain in further detail the theory behind PSM. Three short relevant videos are available in the online supporting information for both of these papers.
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Affiliation(s)
| | | | - Brent L Wood
- Department of Laboratory Medicine, University of Washington, Seattle, Washington, 98195
- Department of Pathology, University of Washington, Seattle, Washington, 98195
| | - Paul K Wallace
- Department of Flow and Image Cytometry, Roswell Park Cancer Institute, Buffalo, New York, 14263
| | - Muaz Alrazzak
- Department of Flow and Image Cytometry, Roswell Park Cancer Institute, Buffalo, New York, 14263
| | - Abigail S Kelliher
- Department of Pathology, Massachusetts General Hospital, Boston, Massachusetts, 02114
| | - Frederic I Preffer
- Department of Pathology, Massachusetts General Hospital, Boston, Massachusetts, 02114
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13
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Diggins KE, Ferrell PB, Irish JM. Methods for discovery and characterization of cell subsets in high dimensional mass cytometry data. Methods 2015; 82:55-63. [PMID: 25979346 PMCID: PMC4468028 DOI: 10.1016/j.ymeth.2015.05.008] [Citation(s) in RCA: 109] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2015] [Revised: 04/24/2015] [Accepted: 05/06/2015] [Indexed: 02/03/2023] Open
Abstract
The flood of high-dimensional data resulting from mass cytometry experiments that measure more than 40 features of individual cells has stimulated creation of new single cell computational biology tools. These tools draw on advances in the field of machine learning to capture multi-parametric relationships and reveal cells that are easily overlooked in traditional analysis. Here, we introduce a workflow for high dimensional mass cytometry data that emphasizes unsupervised approaches and visualizes data in both single cell and population level views. This workflow includes three central components that are common across mass cytometry analysis approaches: (1) distinguishing initial populations, (2) revealing cell subsets, and (3) characterizing subset features. In the implementation described here, viSNE, SPADE, and heatmaps were used sequentially to comprehensively characterize and compare healthy and malignant human tissue samples. The use of multiple methods helps provide a comprehensive view of results, and the largely unsupervised workflow facilitates automation and helps researchers avoid missing cell populations with unusual or unexpected phenotypes. Together, these methods develop a framework for future machine learning of cell identity.
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Affiliation(s)
- Kirsten E Diggins
- Cancer Biology, Vanderbilt University School of Medicine, United States
| | - P Brent Ferrell
- Medicine/Division of Hematology-Oncology, Vanderbilt University School of Medicine, United States
| | - Jonathan M Irish
- Cancer Biology, Vanderbilt University School of Medicine, United States; Pathology, Microbiology and Immunology, Vanderbilt University School of Medicine, United States.
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14
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Bagwell CB, Hunsberger BC, Herbert DJ, Munson ME, Hill BL, Bray CM, Preffer FI. Probability state modeling theory. Cytometry A 2015; 87:646-60. [DOI: 10.1002/cyto.a.22687] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2014] [Revised: 04/09/2015] [Accepted: 04/16/2015] [Indexed: 11/11/2022]
Affiliation(s)
| | | | | | | | | | | | - Frederic I. Preffer
- Department of Pathology; Massachusetts General Hospital; Boston Massachusetts 02114
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15
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Wong L, Hill BL, Hunsberger BC, Bagwell CB, Curtis AD, Davis BH. Automated analysis of flow cytometric data for measuring neutrophil CD64 expression using a multi-instrument compatible probability state model. CYTOMETRY PART B-CLINICAL CYTOMETRY 2015; 88:227-35. [PMID: 25529112 DOI: 10.1002/cyto.b.21217] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2014] [Accepted: 12/11/2014] [Indexed: 11/08/2022]
Abstract
BACKGROUND Leuko64™ (Trillium Diagnostics) is a flow cytometric assay that measures neutrophil CD64 expression and serves as an in vitro indicator of infection/sepsis or the presence of a systemic acute inflammatory response. Leuko64 assay currently utilizes QuantiCALC, a semiautomated software that employs cluster algorithms to define cell populations. The software reduces subjective gating decisions, resulting in interanalyst variability of <5%. We evaluated a completely automated approach to measuring neutrophil CD64 expression using GemStone™ (Verity Software House) and probability state modeling (PSM). METHODS Four hundred and fifty-seven human blood samples were processed using the Leuko64 assay. Samples were analyzed on four different flow cytometer models: BD FACSCanto II, BD FACScan, BC Gallios/Navios, and BC FC500. A probability state model was designed to identify calibration beads and three leukocyte subpopulations based on differences in intensity levels of several parameters. PSM automatically calculates CD64 index values for each cell population using equations programmed into the model. GemStone software uses PSM that requires no operator intervention, thus totally automating data analysis and internal quality control flagging. Expert analysis with the predicate method (QuantiCALC) was performed. Interanalyst precision was evaluated for both methods of data analysis. RESULTS PSM with GemStone correlates well with the expert manual analysis, r(2) = 0.99675 for the neutrophil CD64 index values with no intermethod bias detected. The average interanalyst imprecision for the QuantiCALC method was 1.06% (range 0.00-7.94%), which was reduced to 0.00% with the GemStone PSM. The operator-to-operator agreement in GemStone was a perfect correlation, r(2) = 1.000. CONCLUSION Automated quantification of CD64 index values produced results that strongly correlate with expert analysis using a standard gate-based data analysis method. PSM successfully evaluated flow cytometric data generated by multiple instruments across multiple lots of the Leuko64 kit in all 457 cases. The probability-based method provides greater objectivity, higher data analysis speed, and allows for greater precision for in vitro diagnostic flow cytometric assays.
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Affiliation(s)
- Linda Wong
- Trillium Diagnostics, LLC, Brewer, Maine
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