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A user's guide to multicolor flow cytometry panels for comprehensive immune profiling.
Holmberg-Thyden, Staffan; Grønbæk, Kirsten; Gang, Anne Ortved; El Fassi, Daniel; Hadrup, Sine Reker.
Afiliação
  • Holmberg-Thyden S; Dept. of Hematology, Copenhagen University Hospital, Rigshospitalet, Denmark; T-cells and Cancer, Experimental & Translational Immunology (XTI), Health Technology, Technical University of Denmark, Denmark.
  • Grønbæk K; Dept. of Hematology, Copenhagen University Hospital, Rigshospitalet, Denmark; Dept. of Clinical Medicine, University of Copenhagen, Denmark; Biotech Research and Innovation Centre, BRIC, University of Copenhagen, Denmark.
  • Gang AO; Dept. of Hematology, Copenhagen University Hospital, Rigshospitalet, Denmark; Dept. of Clinical Medicine, University of Copenhagen, Denmark.
  • El Fassi D; Dept. of Hematology, Copenhagen University Hospital, Rigshospitalet, Denmark; Dept. of Clinical Medicine, University of Copenhagen, Denmark.
  • Hadrup SR; T-cells and Cancer, Experimental & Translational Immunology (XTI), Health Technology, Technical University of Denmark, Denmark. Electronic address: sirha@dtu.dk.
Anal Biochem ; 627: 114210, 2021 08 15.
Article em En | MEDLINE | ID: mdl-34033799
ABSTRACT
Multicolor flow cytometry is an essential tool for studying the immune system in health and disease, allowing users to extract longitudinal multiparametric data from patient samples. The process is complicated by substantial variation in performance between each flow cytometry instrument, and analytical errors are therefore common. Here, we present an approach to overcome such limitations by applying a systematic workflow for pairing colors to markers optimized for the equipment intended to run the experiments. The workflow is exemplified by the design of four comprehensive flow cytometry panels for patients with hematological cancer. Methods for quality control, titration of antibodies, compensation, and staining of cells for obtaining optimal results are also addressed. Finally, to handle the large amounts of data generated by multicolor flow cytometry, unsupervised clustering techniques are used to identify significant subpopulations not detected by conventional sequential gating.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Doenças Autoimunes / Coloração e Rotulagem / Citometria de Fluxo / Neoplasias Limite: Humans Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Doenças Autoimunes / Coloração e Rotulagem / Citometria de Fluxo / Neoplasias Limite: Humans Idioma: En Ano de publicação: 2021 Tipo de documento: Article