High-throughput interrogation of immune responses using the Human Immune Profiling Pipeline
The current abundance of immunotherapy clinical trials presents an opportunity to learn about the underlying mechanisms and pharmacodynamic effects of novel drugs on the human immune system. Here, we present a protocol to study how these immune responses impact clinical outcomes using large-scale hi...
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| Veröffentlicht in: | STAR protocols Jg. 4; H. 2; S. 102289 |
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16.06.2023
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| Abstract | The current abundance of immunotherapy clinical trials presents an opportunity to learn about the underlying mechanisms and pharmacodynamic effects of novel drugs on the human immune system. Here, we present a protocol to study how these immune responses impact clinical outcomes using large-scale high-throughput immune profiling of clinical cohorts. We describe the Human Immune Profiling Pipeline, which comprises an end-to-end solution from flow cytometry results to computational approaches and unsupervised patient clustering based on lymphocyte landscape.
For complete details on the use and execution of this protocol, please refer to Lyudovyk et al. (2022).1
[Display omitted]
•High-dimensional analysis and dimension reduction using UMAP•Earth Mover’s Distance (EMD) calculations to quantify differences in UMAPs•Unsupervised patient classification by EMD values•Cell type annotations and patient phenotyping on EMD groups
Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.
The current abundance of immunotherapy clinical trials presents an opportunity to learn about the underlying mechanisms and pharmacodynamic effects of novel drugs on the human immune system. Here, we present a protocol to study how these immune responses impact clinical outcomes using large-scale high-throughput immune profiling of clinical cohorts. We describe the Human Immune Profiling Pipeline, which comprises an end-to-end solution from flow cytometry results to computational approaches and unsupervised patient clustering based on lymphocyte landscape. |
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| AbstractList | The current abundance of immunotherapy clinical trials presents an opportunity to learn about the underlying mechanisms and pharmacodynamic effects of novel drugs on the human immune system. Here, we present a protocol to study how these immune responses impact clinical outcomes using large-scale high-throughput immune profiling of clinical cohorts. We describe the Human Immune Profiling Pipeline, which comprises an end-to-end solution from flow cytometry results to computational approaches and unsupervised patient clustering based on lymphocyte landscape. For complete details on the use and execution of this protocol, please refer to Lyudovyk et al. (2022).1
•
High-dimensional analysis and dimension reduction using UMAP
•
Earth Mover’s Distance (EMD) calculations to quantify differences in UMAPs
•
Unsupervised patient classification by EMD values
•
Cell type annotations and patient phenotyping on EMD groups
Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. The current abundance of immunotherapy clinical trials presents an opportunity to learn about the underlying mechanisms and pharmacodynamic effects of novel drugs on the human immune system. Here, we present a protocol to study how these immune responses impact clinical outcomes using large-scale high-throughput immune profiling of clinical cohorts. We describe the Human Immune Profiling Pipeline, which comprises an end-to-end solution from flow cytometry results to computational approaches and unsupervised patient clustering based on lymphocyte landscape. The current abundance of immunotherapy clinical trials presents an opportunity to learn about the underlying mechanisms and pharmacodynamic effects of novel drugs on the human immune system. Here, we present a protocol to study how these immune responses impact clinical outcomes using large-scale high-throughput immune profiling of clinical cohorts. We describe the Human Immune Profiling Pipeline, which comprises an end-to-end solution from flow cytometry results to computational approaches and unsupervised patient clustering based on lymphocyte landscape.For complete details on the use and execution of this protocol, please refer to Lyudovyk et al. (2022).1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. The current abundance of immunotherapy clinical trials presents an opportunity to learn about the underlying mechanisms and pharmacodynamic effects of novel drugs on the human immune system. Here, we present a protocol to study how these immune responses impact clinical outcomes using large-scale high-throughput immune profiling of clinical cohorts. We describe the Human Immune Profiling Pipeline, which comprises an end-to-end solution from flow cytometry results to computational approaches and unsupervised patient clustering based on lymphocyte landscape. For complete details on the use and execution of this protocol, please refer to Lyudovyk et al. (2022).1.The current abundance of immunotherapy clinical trials presents an opportunity to learn about the underlying mechanisms and pharmacodynamic effects of novel drugs on the human immune system. Here, we present a protocol to study how these immune responses impact clinical outcomes using large-scale high-throughput immune profiling of clinical cohorts. We describe the Human Immune Profiling Pipeline, which comprises an end-to-end solution from flow cytometry results to computational approaches and unsupervised patient clustering based on lymphocyte landscape. For complete details on the use and execution of this protocol, please refer to Lyudovyk et al. (2022).1. The current abundance of immunotherapy clinical trials presents an opportunity to learn about the underlying mechanisms and pharmacodynamic effects of novel drugs on the human immune system. Here, we present a protocol to study how these immune responses impact clinical outcomes using large-scale high-throughput immune profiling of clinical cohorts. We describe the Human Immune Profiling Pipeline, which comprises an end-to-end solution from flow cytometry results to computational approaches and unsupervised patient clustering based on lymphocyte landscape. For complete details on the use and execution of this protocol, please refer to Lyudovyk et al. (2022).1 [Display omitted] •High-dimensional analysis and dimension reduction using UMAP•Earth Mover’s Distance (EMD) calculations to quantify differences in UMAPs•Unsupervised patient classification by EMD values•Cell type annotations and patient phenotyping on EMD groups Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. The current abundance of immunotherapy clinical trials presents an opportunity to learn about the underlying mechanisms and pharmacodynamic effects of novel drugs on the human immune system. Here, we present a protocol to study how these immune responses impact clinical outcomes using large-scale high-throughput immune profiling of clinical cohorts. We describe the Human Immune Profiling Pipeline, which comprises an end-to-end solution from flow cytometry results to computational approaches and unsupervised patient clustering based on lymphocyte landscape. The current abundance of immunotherapy clinical trials presents an opportunity to learn about the underlying mechanisms and pharmacodynamic effects of novel drugs on the human immune system. Here, we present a protocol to study how these immune responses impact clinical outcomes using large-scale high-throughput immune profiling of clinical cohorts. We describe the Human Immune Profiling Pipeline, which comprises an end-to-end solution from flow cytometry results to computational approaches and unsupervised patient clustering based on lymphocyte landscape. For complete details on the use and execution of this protocol, please refer to Lyudovyk et al. (2022). . |
| ArticleNumber | 102289 |
| Author | Kim, Justin Y. Huang, Alexander C. Lyudovyk, Olga Elhanati, Yuval Greenbaum, Benjamin Wang, Guanning Greenplate, Allison R. Wherry, E. John Mathew, Divij Herati, Ramin S. Vardhana, Santosha A. Lin, Ya-Hui |
| Author_xml | – sequence: 1 givenname: Guanning surname: Wang fullname: Wang, Guanning organization: Division of Hematology/Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA – sequence: 2 givenname: Olga surname: Lyudovyk fullname: Lyudovyk, Olga organization: Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA – sequence: 3 givenname: Justin Y. surname: Kim fullname: Kim, Justin Y. organization: Lewis Katz School of Medicine, Temple University, Philadelphia, PA, USA – sequence: 4 givenname: Ya-Hui surname: Lin fullname: Lin, Ya-Hui organization: Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA – sequence: 5 givenname: Yuval surname: Elhanati fullname: Elhanati, Yuval organization: Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA – sequence: 6 givenname: Divij surname: Mathew fullname: Mathew, Divij organization: Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA – sequence: 7 givenname: E. John surname: Wherry fullname: Wherry, E. John organization: Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA – sequence: 8 givenname: Ramin S. surname: Herati fullname: Herati, Ramin S. organization: Department of Medicine, New York University School of Medicine, New York, NY, USA – sequence: 9 givenname: Allison R. surname: Greenplate fullname: Greenplate, Allison R. organization: Institute for Immunology and Immune Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA – sequence: 10 givenname: Benjamin surname: Greenbaum fullname: Greenbaum, Benjamin email: greenbab@mskcc.org organization: Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA – sequence: 11 givenname: Santosha A. surname: Vardhana fullname: Vardhana, Santosha A. email: vardhans@mskcc.org organization: Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA – sequence: 12 givenname: Alexander C. surname: Huang fullname: Huang, Alexander C. email: alexander.huang@pennmedicine.upenn.edu organization: Division of Hematology/Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA |
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| Keywords | Flow Cytometry/Mass Cytometry Immunology Health Sciences |
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| License | This is an open access article under the CC BY-NC-ND license. Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
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| References | Wickham, François, Henry, Müller, Vaughan (bib7) 2023 Lyudovyk, Kim, Qualls, Hwee, Lin, Boutemine, Elhanati, Solovyov, Douglas, Chen (bib1) 2022; 40 Brewer (bib9) Hahne, Lemeur, Brinkman, Ellis, Haaland, Sarkar, Spidlen, Strain, Gentleman (bib2) 2009; 10 Simon Urbanek (bib6) Dowle, Srinivasan (bib4) 2023 Galili (bib8) Wickham, Hester, Bryan (bib5) 2023 Ashhurst, Marsh-Wakefield, Putri, Spiteri, Shinko, Read, Smith, King (bib10) 2022; 101 Orlova, Zimmerman, Meehan, Meehan, Waters, Ghosn, Filatenkov, Kolyagin, Gernez, Tsuda (bib11) 2016; 11 Greenplate, Mcclanahan, Oberholtzer, Doxie, Roe, Diggins, Leelatian, Rasmussen, Kelley, Gama (bib12) 2019; 7 Huber, Carey, Gentleman, Anders, Carlson, Carvalho, Bravo, Davis, Gatto, Girke (bib3) 2015; 12 Wickham (10.1016/j.xpro.2023.102289_bib5) 2023 Huber (10.1016/j.xpro.2023.102289_bib3) 2015; 12 Orlova (10.1016/j.xpro.2023.102289_bib11) 2016; 11 Lyudovyk (10.1016/j.xpro.2023.102289_bib1) 2022; 40 Simon Urbanek (10.1016/j.xpro.2023.102289_bib6) Galili (10.1016/j.xpro.2023.102289_bib8) Greenplate (10.1016/j.xpro.2023.102289_bib12) 2019; 7 Brewer (10.1016/j.xpro.2023.102289_bib9) Hahne (10.1016/j.xpro.2023.102289_bib2) 2009; 10 Wickham (10.1016/j.xpro.2023.102289_bib7) 2023 Ashhurst (10.1016/j.xpro.2023.102289_bib10) 2022; 101 Dowle (10.1016/j.xpro.2023.102289_bib4) 2023 |
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| Title | High-throughput interrogation of immune responses using the Human Immune Profiling Pipeline |
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