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Mosmann TR, Rebhahn JA, De Rosa SC, Keefer MC, McElrath MJ, Rouphael NG, Pantaleo G, Gilbert PB, Corey L, Kobie JJ, Thakar J. SWIFT clustering analysis of intracellular cytokine staining flow cytometry data of the HVTN 105 vaccine trial reveals high frequencies of HIV-specific CD4+ T cell responses and associations with humoral responses. Front Immunol 2024; 15:1347926. [PMID: 38903517 PMCID: PMC11187089 DOI: 10.3389/fimmu.2024.1347926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 05/20/2024] [Indexed: 06/22/2024] Open
Abstract
Introduction The HVTN 105 vaccine clinical trial tested four combinations of two immunogens - the DNA vaccine DNA-HIV-PT123, and the protein vaccine AIDSVAX B/E. All combinations induced substantial antibody and CD4+ T cell responses in many participants. We have now re-examined the intracellular cytokine staining flow cytometry data using the high-resolution SWIFT clustering algorithm, which is very effective for enumerating rare populations such as antigen-responsive T cells, and also determined correlations between the antibody and T cell responses. Methods Flow cytometry samples across all the analysis batches were registered using the swiftReg registration tool, which reduces batch variation without compromising biological variation. Registered data were clustered using the SWIFT algorithm, and cluster template competition was used to identify clusters of antigen-responsive T cells and to separate these from constitutive cytokine producing cell clusters. Results Registration strongly reduced batch variation among batches analyzed across several months. This in-depth clustering analysis identified a greater proportion of responders than the original analysis. A subset of antigen-responsive clusters producing IL-21 was identified. The cytokine patterns in each vaccine group were related to the type of vaccine - protein antigens tended to induce more cells producing IL-2 but not IFN-γ, whereas DNA vaccines tended to induce more IL-2+ IFN-γ+ CD4 T cells. Several significant correlations were identified between specific antibody responses and antigen-responsive T cell clusters. The best correlations were not necessarily observed with the strongest antibody or T cell responses. Conclusion In the complex HVTN105 dataset, alternative analysis methods increased sensitivity of the detection of antigen-specific T cells; increased the number of identified vaccine responders; identified a small IL-21-producing T cell population; and demonstrated significant correlations between specific T cell populations and serum antibody responses. Multiple analysis strategies may be valuable for extracting the most information from large, complex studies.
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Affiliation(s)
- Tim R. Mosmann
- David H. Smith Center for Vaccine Biology and Immunology, University of Rochester Medical Center, Rochester, NY, United States
| | - Jonathan A. Rebhahn
- David H. Smith Center for Vaccine Biology and Immunology, University of Rochester Medical Center, Rochester, NY, United States
| | - Stephen C. De Rosa
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
| | - Michael C. Keefer
- Department of Medicine, University of Rochester School of Medicine & Dentistry, Rochester, NY, United States
| | - M. Juliana McElrath
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
| | - Nadine G. Rouphael
- Hope Clinic of the Emory Vaccine Center, Division of Infectious Diseases, Emory University, Atlanta, GA, United States
| | - Giuseppe Pantaleo
- Service of Immunology and Allergy, Department of Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Swiss Vaccine Research Institute, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Peter B. Gilbert
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
| | - Lawrence Corey
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
| | - James J. Kobie
- Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Juilee Thakar
- Department of Microbiology and Immunology, University of Rochester Medical Center, Rochester, NY, United States
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Cheung M, Campbell JJ, Whitby L, Thomas RJ, Braybrook J, Petzing J. Current trends in flow cytometry automated data analysis software. Cytometry A 2021; 99:1007-1021. [PMID: 33606354 DOI: 10.1002/cyto.a.24320] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 01/21/2021] [Accepted: 01/28/2021] [Indexed: 12/16/2022]
Abstract
Automated flow cytometry (FC) data analysis tools for cell population identification and characterization are increasingly being used in academic, biotechnology, pharmaceutical, and clinical laboratories. The development of these computational methods is designed to overcome reproducibility and process bottleneck issues in manual gating, however, the take-up of these tools remains (anecdotally) low. Here, we performed a comprehensive literature survey of state-of-the-art computational tools typically published by research, clinical, and biomanufacturing laboratories for automated FC data analysis and identified popular tools based on literature citation counts. Dimensionality reduction methods ranked highly, such as generic t-distributed stochastic neighbor embedding (t-SNE) and its initial Matlab-based implementation for cytometry data viSNE. Software with graphical user interfaces also ranked highly, including PhenoGraph, SPADE1, FlowSOM, and Citrus, with unsupervised learning methods outnumbering supervised learning methods, and algorithm type popularity spread across K-Means, hierarchical, density-based, model-based, and other classes of clustering algorithms. Additionally, to illustrate the actual use typically within clinical spaces alongside frequent citations, a survey issued by UK NEQAS Leucocyte Immunophenotyping to identify software usage trends among clinical laboratories was completed. The survey revealed 53% of laboratories have not yet taken up automated cell population identification methods, though among those that have, Infinicyt software is the most frequently identified. Survey respondents considered data output quality to be the most important factor when using automated FC data analysis software, followed by software speed and level of technical support. This review found differences in software usage between biomedical institutions, with tools for discovery, data exploration, and visualization more popular in academia, whereas automated tools for specialized targeted analysis that apply supervised learning methods were more used in clinical settings.
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Affiliation(s)
- Melissa Cheung
- Centre for Biological Engineering, Loughborough University, Loughborough, Leicestershire, United Kingdom
| | | | - Liam Whitby
- UK NEQAS for Leucocyte Immunophenotyping, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
| | - Robert J Thomas
- Centre for Biological Engineering, Loughborough University, Loughborough, Leicestershire, United Kingdom
| | - Julian Braybrook
- National Measurement Laboratory, LGC, Teddington, United Kingdom
| | - Jon Petzing
- Centre for Biological Engineering, Loughborough University, Loughborough, Leicestershire, United Kingdom
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Cesano A, Cannarile MA, Gnjatic S, Gomes B, Guinney J, Karanikas V, Karkada M, Kirkwood JM, Kotlan B, Masucci GV, Meeusen E, Monette A, Naing A, Thorsson V, Tschernia N, Wang E, Wells DK, Wyant TL, Rutella S. Society for Immunotherapy of Cancer clinical and biomarkers data sharing resource document: Volume II-practical challenges. J Immunother Cancer 2020; 8:e001472. [PMID: 33323463 PMCID: PMC7745522 DOI: 10.1136/jitc-2020-001472] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/06/2020] [Indexed: 01/10/2023] Open
Abstract
The development of strongly predictive validated biomarkers is essential for the field of immuno-oncology (IO) to advance. The highly complex, multifactorial data sets required to develop these biomarkers necessitate effective, responsible data-sharing efforts in order to maximize the scientific knowledge and utility gained from their collection. While the sharing of clinical- and safety-related trial data has already been streamlined to a large extent, the sharing of biomarker-aimed clinical trial derived data and data sets has been met with a number of hurdles that have impaired the progression of biomarkers from hypothesis to clinical use. These hurdles include technical challenges associated with the infrastructure, technology, workforce, and sustainability required for clinical biomarker data sharing. To provide guidance and assist in the navigation of these challenges, the Society for Immunotherapy of Cancer (SITC) Biomarkers Committee convened to outline the challenges that researchers currently face, both at the conceptual level (Volume I) and at the technical level (Volume II). The committee also suggests possible solutions to these problems in the form of professional standards and harmonized requirements for data sharing, assisting in continued progress toward effective, clinically relevant biomarkers in the IO setting.
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Affiliation(s)
| | - Michael A Cannarile
- Roche Pharmaceutical Research and Early Development Oncology, Roche Innovation Center Munich, Penzberg, Germany
| | - Sacha Gnjatic
- Department of Medicine, Tisch Cancer Institute, Icahn School of Medicine, New York, New York, USA
| | - Bruno Gomes
- Roche Pharmaceutical Research and Early Development Oncology, Roche Innovation Center, Basel, Switzerland
| | | | - Vaios Karanikas
- Roche Pharmaceutical Research and Early Development Oncology, Roche Innovation Center, Zürich, Switzerland
| | - Mohan Karkada
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts, USA
| | - John M Kirkwood
- Department of Medicine, Division of Hematology/Oncology, University of Pittsburgh School of Medicine and Melanoma Center at UPMC Hillman Cancer Center, Pittsburgh, Pennsylvania, USA
| | - Beatrix Kotlan
- National Institute of Oncology, Budapest, Budapest, Hungary
| | | | - Els Meeusen
- CancerProbe Pty Ltd, Prahran, Victoria, Australia
| | - Anne Monette
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
| | - Aung Naing
- Department of Investigational Cancer Therapeutics, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | | | - Nicholas Tschernia
- Department of Medicine, Division of Hematology/Oncology, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA
| | - Ena Wang
- Allogene Therapeutics, South San Francisco, California, USA
| | - Daniel K Wells
- Parker Institute for Cancer Immunotherapy, San Francisco, California, USA
| | | | - Sergio Rutella
- John van Geest Cancer Research Centre, Nottingham Trent University, Nottingham, Nottinghamshire, UK
- Centre for Health, Ageing and Understanding Disease (CHAUD), Nottingham Trent University, Nottingham, Nottinghamshire, UK
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SwiftReg cluster registration automatically reduces flow cytometry data variability including batch effects. Commun Biol 2020; 3:218. [PMID: 32382076 PMCID: PMC7205614 DOI: 10.1038/s42003-020-0938-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Accepted: 04/10/2020] [Indexed: 01/29/2023] Open
Abstract
Biological differences of interest in large, high-dimensional flow cytometry datasets are often obscured by undesired variations caused by differences in cytometers, reagents, or operators. Each variation type requires a different correction strategy, and their unknown contributions to overall variability hinder automated correction. We now describe swiftReg, an automated method that reduces undesired sources of variability between samples and particularly between batches. A high-resolution cluster map representing the multidimensional data is generated using the SWIFT algorithm, and shifts in cluster positions between samples are measured. Subpopulations are aligned between samples by displacing cell parameter values according to registration vectors derived from independent or locally-averaged cluster shifts. Batch variation is addressed by registering batch control or consensus samples, and applying the resulting shifts to individual samples. swiftReg selectively reduces batch variation, enhancing detection of biological differences. swiftReg outputs registered datasets as standard .FCS files to facilitate further analysis by other tools. Rebhahn et al. develop swiftReg that automatically corrects undesired sources of variability of flow cytometry data. To identify batch variation, this method registers an internal standard or consensus sample from each batch and applies the resulting registration shifts to individual samples, reducing the batch variation while preserving biological differences.
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Gong Y, Fan N, Yang X, Peng B, Jiang H. New advances in microfluidic flow cytometry. Electrophoresis 2018; 40:1212-1229. [PMID: 30242856 DOI: 10.1002/elps.201800298] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2018] [Revised: 09/07/2018] [Accepted: 09/15/2018] [Indexed: 01/22/2023]
Abstract
In recent years, researchers are paying the increasing attention to the development of portable microfluidic diagnostic devices including microfluidic flow cytometry for the point-of-care testing. Microfluidic flow cytometry, where microfluidics and flow cytometry work together to realize novel functionalities on the microchip, provides a powerful tool for measuring the multiple characteristics of biological samples. The development of a portable, low-cost, and compact flow cytometer can benefit the health care in underserved areas such as Africa or Asia. In this article, we review recent advancements of microfluidics including sample pumping, focusing and sorting, novel detection approaches, and data analysis in the field of flow cytometry. The challenge of microfluidic flow cytometry is also examined briefly.
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Affiliation(s)
- Yanli Gong
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, P. R. China
| | - Na Fan
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, P. R. China
| | - Xu Yang
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, P. R. China
| | - Bei Peng
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, P. R. China
| | - Hai Jiang
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, P. R. China
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Lee AJ, Chang I, Burel JG, Lindestam Arlehamn CS, Mandava A, Weiskopf D, Peters B, Sette A, Scheuermann RH, Qian Y. DAFi: A directed recursive data filtering and clustering approach for improving and interpreting data clustering identification of cell populations from polychromatic flow cytometry data. Cytometry A 2018; 93:597-610. [PMID: 29665244 PMCID: PMC6030426 DOI: 10.1002/cyto.a.23371] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2017] [Revised: 02/05/2018] [Accepted: 03/15/2018] [Indexed: 11/10/2022]
Abstract
Computational methods for identification of cell populations from polychromatic flow cytometry data are changing the paradigm of cytometry bioinformatics. Data clustering is the most common computational approach to unsupervised identification of cell populations from multidimensional cytometry data. However, interpretation of the identified data clusters is labor-intensive. Certain types of user-defined cell populations are also difficult to identify by fully automated data clustering analysis. Both are roadblocks before a cytometry lab can adopt the data clustering approach for cell population identification in routine use. We found that combining recursive data filtering and clustering with constraints converted from the user manual gating strategy can effectively address these two issues. We named this new approach DAFi: Directed Automated Filtering and Identification of cell populations. Design of DAFi preserves the data-driven characteristics of unsupervised clustering for identifying novel cell subsets, but also makes the results interpretable to experimental scientists through mapping and merging the multidimensional data clusters into the user-defined two-dimensional gating hierarchy. The recursive data filtering process in DAFi helped identify small data clusters which are otherwise difficult to resolve by a single run of the data clustering method due to the statistical interference of the irrelevant major clusters. Our experiment results showed that the proportions of the cell populations identified by DAFi, while being consistent with those by expert centralized manual gating, have smaller technical variances across samples than those from individual manual gating analysis and the nonrecursive data clustering analysis. Compared with manual gating segregation, DAFi-identified cell populations avoided the abrupt cut-offs on the boundaries. DAFi has been implemented to be used with multiple data clustering methods including K-means, FLOCK, FlowSOM, and the ClusterR package. For cell population identification, DAFi supports multiple options including clustering, bisecting, slope-based gating, and reversed filtering to meet various autogating needs from different scientific use cases. © 2018 International Society for Advancement of Cytometry.
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Affiliation(s)
| | - Ivan Chang
- J. Craig Venter Institute, La Jolla, California
| | - Julie G. Burel
- La Jolla Institute for Allergy and Immunology, La Jolla, California
| | | | | | - Daniela Weiskopf
- La Jolla Institute for Allergy and Immunology, La Jolla, California
| | - Bjoern Peters
- La Jolla Institute for Allergy and Immunology, La Jolla, California
| | - Alessandro Sette
- La Jolla Institute for Allergy and Immunology, La Jolla, California
- Department of Medicine, University of California, San Diego, California
| | - Richard H. Scheuermann
- J. Craig Venter Institute, La Jolla, California
- Department of Pathology, University of California, San Diego, California
| | - Yu Qian
- J. Craig Venter Institute, La Jolla, California
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