Protocol for generating a mouse model of gastric MALT lymphoma and the identification of MALT lymphoma cell populations by immunostaining

Helicobacter suis, a zoonotic infection-related bacterium, induces gastric mucosa-associated lymphoid tissue (MALT) lymphoma in humans and animals. However, a lack of suitable animal models complicates the detailed analysis of this disease. Here, we describe the generation of a gastric MALT lymphoma...

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Detalles Bibliográficos
Autores principales: Yamamoto, Koji, Kondo, Yasuyuki, Sugiyama, Toshiro, Sakamoto, Naoya
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Protocol
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8819470/
https://www.ncbi.nlm.nih.gov/pubmed/35146453
http://dx.doi.org/10.1016/j.xpro.2022.101155
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author Yamamoto, Koji
Kondo, Yasuyuki
Sugiyama, Toshiro
Sakamoto, Naoya
author_facet Yamamoto, Koji
Kondo, Yasuyuki
Sugiyama, Toshiro
Sakamoto, Naoya
author_sort Yamamoto, Koji
collection PubMed
description Helicobacter suis, a zoonotic infection-related bacterium, induces gastric mucosa-associated lymphoid tissue (MALT) lymphoma in humans and animals. However, a lack of suitable animal models complicates the detailed analysis of this disease. Here, we describe the generation of a gastric MALT lymphoma mouse model. We then detail the use of this model combined with an immunostaining protocol to identify the cell populations that constitute gastric MALT lymphoma. This protocol can be used to identify the constituent cells of human MALT lymphoma. For complete details on the use and execution of this profile, please refer to Yamamoto et al. (2021).
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spelling pubmed-88194702022-02-09 Protocol for generating a mouse model of gastric MALT lymphoma and the identification of MALT lymphoma cell populations by immunostaining Yamamoto, Koji Kondo, Yasuyuki Sugiyama, Toshiro Sakamoto, Naoya STAR Protoc Protocol Helicobacter suis, a zoonotic infection-related bacterium, induces gastric mucosa-associated lymphoid tissue (MALT) lymphoma in humans and animals. However, a lack of suitable animal models complicates the detailed analysis of this disease. Here, we describe the generation of a gastric MALT lymphoma mouse model. We then detail the use of this model combined with an immunostaining protocol to identify the cell populations that constitute gastric MALT lymphoma. This protocol can be used to identify the constituent cells of human MALT lymphoma. For complete details on the use and execution of this profile, please refer to Yamamoto et al. (2021). Elsevier 2022-02-03 /pmc/articles/PMC8819470/ /pubmed/35146453 http://dx.doi.org/10.1016/j.xpro.2022.101155 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Protocol
Yamamoto, Koji
Kondo, Yasuyuki
Sugiyama, Toshiro
Sakamoto, Naoya
Protocol for generating a mouse model of gastric MALT lymphoma and the identification of MALT lymphoma cell populations by immunostaining
title Protocol for generating a mouse model of gastric MALT lymphoma and the identification of MALT lymphoma cell populations by immunostaining
title_full Protocol for generating a mouse model of gastric MALT lymphoma and the identification of MALT lymphoma cell populations by immunostaining
title_fullStr Protocol for generating a mouse model of gastric MALT lymphoma and the identification of MALT lymphoma cell populations by immunostaining
title_full_unstemmed Protocol for generating a mouse model of gastric MALT lymphoma and the identification of MALT lymphoma cell populations by immunostaining
title_short Protocol for generating a mouse model of gastric MALT lymphoma and the identification of MALT lymphoma cell populations by immunostaining
title_sort protocol for generating a mouse model of gastric malt lymphoma and the identification of malt lymphoma cell populations by immunostaining
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8819470/
https://www.ncbi.nlm.nih.gov/pubmed/35146453
http://dx.doi.org/10.1016/j.xpro.2022.101155
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