High-throughput single-cell quantification of hundreds of proteins using conventional flow cytometry and machine learning-Reference-Cited by-同舟云学术

High-throughput single-cell quantification of hundreds of proteins using conventional flow cytometry and machine learning

Published:2021-09-24 Issue:39 Volume:7 Page:
ISSN:2375-2548
Container-title:Science Advances
language:en
Short-container-title:Sci. Adv.

Author:

Becht Etienne¹^ORCID,Tolstrup Daniel²^ORCID,Dutertre Charles-Antoine³⁴⁵,Morawski Peter A.⁶^ORCID,Campbell Daniel J.⁶⁷^ORCID,Ginhoux Florent³⁵⁸^ORCID,Newell Evan W.¹^ORCID,Gottardo Raphael¹^ORCID,Headley Mark B.²⁷^ORCID

Affiliation:

1. Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.

2. Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.

3. Singapore Immunology Network, Agency for Science, Technology and Research, Singapore, Singapore.

4. Program in Emerging Infectious Disease, Duke-NUS Medical School, Singapore, Singapore.

5. Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Center, Singapore 169856, Singapore.

6. Center for Fundamental Immunology, Benaroya Research Institute, Seattle, WA, USA.

7. Department of Immunology, University of Washington School of Medicine, Seattle, WA, USA.

8. Shanghai Institute of Immunology, Shanghai JiaoTong University School of Medicine, 280 South Chongqing Road, Shanghai 200025, China.

Abstract

This study presents a novel method for low-cost cell surface proteomics using flow cytometry and machine learning.

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

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