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Decoding breast cancer tissue–stroma interactions using species-specific sequencing

INTRODUCTION: Decoding transcriptional effects of experimental tissue–tissue or cell–cell interactions is important; for example, to better understand tumor–stroma interactions after transplantation of human cells into mouse (xenografting). Transcriptome analysis of intermixed human and mouse cells...

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Autores principales: Chivukula, Indira V., Ramsköld, Daniel, Storvall, Helena, Anderberg, Charlotte, Jin, Shaobo, Mamaeva, Veronika, Sahlgren, Cecilia, Pietras, Kristian, Sandberg, Rickard, Lendahl, Urban
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4534116/
https://www.ncbi.nlm.nih.gov/pubmed/26265142
http://dx.doi.org/10.1186/s13058-015-0616-x
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author Chivukula, Indira V.
Ramsköld, Daniel
Storvall, Helena
Anderberg, Charlotte
Jin, Shaobo
Mamaeva, Veronika
Sahlgren, Cecilia
Pietras, Kristian
Sandberg, Rickard
Lendahl, Urban
author_facet Chivukula, Indira V.
Ramsköld, Daniel
Storvall, Helena
Anderberg, Charlotte
Jin, Shaobo
Mamaeva, Veronika
Sahlgren, Cecilia
Pietras, Kristian
Sandberg, Rickard
Lendahl, Urban
author_sort Chivukula, Indira V.
collection PubMed
description INTRODUCTION: Decoding transcriptional effects of experimental tissue–tissue or cell–cell interactions is important; for example, to better understand tumor–stroma interactions after transplantation of human cells into mouse (xenografting). Transcriptome analysis of intermixed human and mouse cells has, however, frequently relied on the need to separate the two cell populations prior to transcriptome analysis, which introduces confounding effects on gene expression. METHODS: To circumvent this problem, we here describe a bioinformatics-based, genome-wide transcriptome analysis technique, which allows the human and mouse transcriptomes to be decoded from a mixed mouse and human cell population. The technique is based on a bioinformatic separation of the mouse and human transcriptomes from the initial mixed-species transcriptome resulting from sequencing an excised tumor/stroma specimen without prior cell sorting. RESULTS: Under stringent separation criteria, i.e., with a read misassignment frequency of 0.2 %, we show that 99 % of the genes can successfully be assigned to be of mouse or human origin, both in silico, in cultured cells and in vivo. We use a new species-specific sequencing technology—referred to as S(3) (“S-cube”)—to provide new insights into the Notch downstream response following Notch ligand-stimulation and to explore transcriptional changes following transplantation of two different breast cancer cell lines (luminal MCF7 and basal-type MDA-MB-231) into mammary fat pad tissue in mice of different immunological status. We find that MCF7 and MDA-MB-231 respond differently to fat pad xenografting and the stromal response to transplantation of MCF7 and MDA-MB-231 cells was also distinct. CONCLUSIONS: In conclusion, the data show that the S(3) technology allows for faithful recording of transcriptomic changes when human and mouse cells are intermixed and that it can be applied to address a broad spectrum of research questions. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13058-015-0616-x) contains supplementary material, which is available to authorized users.
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spelling pubmed-45341162015-08-13 Decoding breast cancer tissue–stroma interactions using species-specific sequencing Chivukula, Indira V. Ramsköld, Daniel Storvall, Helena Anderberg, Charlotte Jin, Shaobo Mamaeva, Veronika Sahlgren, Cecilia Pietras, Kristian Sandberg, Rickard Lendahl, Urban Breast Cancer Res Research Article INTRODUCTION: Decoding transcriptional effects of experimental tissue–tissue or cell–cell interactions is important; for example, to better understand tumor–stroma interactions after transplantation of human cells into mouse (xenografting). Transcriptome analysis of intermixed human and mouse cells has, however, frequently relied on the need to separate the two cell populations prior to transcriptome analysis, which introduces confounding effects on gene expression. METHODS: To circumvent this problem, we here describe a bioinformatics-based, genome-wide transcriptome analysis technique, which allows the human and mouse transcriptomes to be decoded from a mixed mouse and human cell population. The technique is based on a bioinformatic separation of the mouse and human transcriptomes from the initial mixed-species transcriptome resulting from sequencing an excised tumor/stroma specimen without prior cell sorting. RESULTS: Under stringent separation criteria, i.e., with a read misassignment frequency of 0.2 %, we show that 99 % of the genes can successfully be assigned to be of mouse or human origin, both in silico, in cultured cells and in vivo. We use a new species-specific sequencing technology—referred to as S(3) (“S-cube”)—to provide new insights into the Notch downstream response following Notch ligand-stimulation and to explore transcriptional changes following transplantation of two different breast cancer cell lines (luminal MCF7 and basal-type MDA-MB-231) into mammary fat pad tissue in mice of different immunological status. We find that MCF7 and MDA-MB-231 respond differently to fat pad xenografting and the stromal response to transplantation of MCF7 and MDA-MB-231 cells was also distinct. CONCLUSIONS: In conclusion, the data show that the S(3) technology allows for faithful recording of transcriptomic changes when human and mouse cells are intermixed and that it can be applied to address a broad spectrum of research questions. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13058-015-0616-x) contains supplementary material, which is available to authorized users. BioMed Central 2015-08-13 2015 /pmc/articles/PMC4534116/ /pubmed/26265142 http://dx.doi.org/10.1186/s13058-015-0616-x Text en © Chivukula et al. 2015 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Chivukula, Indira V.
Ramsköld, Daniel
Storvall, Helena
Anderberg, Charlotte
Jin, Shaobo
Mamaeva, Veronika
Sahlgren, Cecilia
Pietras, Kristian
Sandberg, Rickard
Lendahl, Urban
Decoding breast cancer tissue–stroma interactions using species-specific sequencing
title Decoding breast cancer tissue–stroma interactions using species-specific sequencing
title_full Decoding breast cancer tissue–stroma interactions using species-specific sequencing
title_fullStr Decoding breast cancer tissue–stroma interactions using species-specific sequencing
title_full_unstemmed Decoding breast cancer tissue–stroma interactions using species-specific sequencing
title_short Decoding breast cancer tissue–stroma interactions using species-specific sequencing
title_sort decoding breast cancer tissue–stroma interactions using species-specific sequencing
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4534116/
https://www.ncbi.nlm.nih.gov/pubmed/26265142
http://dx.doi.org/10.1186/s13058-015-0616-x
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